This is the submitted version of the article: Sanz Iglesias, Elisenda; Bean, Jonathan C.; Carey, Daniel P.; [et al.]. «RiboTag : Ribosomal tagging strategy to analyze cell type specific mRNA expression in vivo». Current Protocols in Neuroscience, 2019. DOI 10.1002/cpns.77 This version is avaible at https://ddd.uab.cat/record/204710 under the terms of the license Current Protocols RiboTag: Ribosomal tagging strategy to analyze cell type specific mRNA expression in vivo Journal: Current Protocols Manuscript ID CP-19-0078.R2 Wiley - Manuscript type: Protocol Date Submitted by the 24-May-2019 Author: Complete List of Authors: Sanz, Elisenda; Autonomous University of Barcelona, Department of Cell Biology, Physiology, and Immunology and Neuroscience Institute Bean, Jonathan; University of Washington, Pharmacology Carey, Daniel; University of Washington, Pharmacology Quintana, Albert; Autonomous University of Barcelona, Department of Cell Biology, Physiology, and Immunology and Neuroscience Institute McKnight, G Stanley; University of Washington, Pharmacology gene expression, ribosome tagging, neuronal transcripts, polyribosome Keywords: immunoprecipitation Ribosome tagging has become a very useful in vivo approach for analyzing gene expression and mRNA translation in specific cell types that are difficult and time consuming to isolate by conventional methods. The approach is based on selectively expressing an HA-tagged ribosomal protein in a target cell type and then using antibodies against HA to purify the polysomes and associated mRNAs from the target cell. The original approach makes use of a mouse line (RiboTag) harboring a Abstract: modified allele of Rpl22 (Rpl22-HA) that is induced by the action of Cre recombinase. The Rpl22-HA gene can also be introduced into the animal by stereotaxic injections of an AAV-DIO-Rpl22-HA that is then activated in Cre-expressing cells. Both methods for tagging ribosomes facilitate the immunoprecipitation of ribosome bound mRNAs and their analysis by qRT-PCR or RNA-Seq. This protocol will discuss the technical procedures and describe important considerations relevant to the analysis of the data. John Wiley & Sons Page 1 of 56 Current Protocols 1 2 3 4 RiboTag: Ribosomal Tagging Strategy to Analyze Cell Type Specific 5 mRNA Expression In Vivo 6 7 Running Title: RiboTag protocol 8 9 Authors: Elisenda Sanz1, Jonathan C. Bean2, Daniel P. Carey2, Albert Quintana1, and G. 10 Stanley McKnight2, 11 1Department of Cell Biology, Physiology, and Immunology and Neuroscience Institute, 12 Autonomous University of Barcelona, Barcelona, Spain 13 E.Sanz: email: elisenda.sanz@uab.cat, tel:+34 93 581 4968 14 A. Quintana: email: albert.quintana@uab.cat, tel: +34 93 581 4967 15 2Department of Pharmacology, University of Washington, Seattle, WA 98195 16 J. Bean: email: bean79@uw.edu, tel:206-543-0144 17 18 D. Carey: email: careyd5@uw.edu tel: 206-543-0144 19 S. McKnight: email: mcknight@uw.edu, tel:206-616-4237 20 21 Abstract: 22 Ribosome tagging has become a very useful in vivo approach for analyzing gene expression 23 and mRNA translation in specific cell types that are difficult and time consuming to isolate by 24 conventional methods. The approach is based on selectively expressing an HA-tagged 25 ribosomal protein in a target cell type and then using antibodies against HA to purify the 26 polysomes and associated mRNAs from the target cell. The original approach makes use of a 27 mouse line (RiboTag) harboring a modified allele of Rpl22 (Rpl22-HA) that is induced by the 28 action of Cre recombinase. The Rpl22-HA gene can also be introduced into the animal by 29 stereotaxic injections of an AAV-DIO-Rpl22-HA that is then activated in Cre-expressing cells. 30 Both methods for tagging ribosomes facilitate the immunoprecipitation of ribosome bound 31 mRNAs and their analysis by qRT-PCR or RNA-Seq. This protocol will discuss the technical 32 procedures and describe important considerations relevant to the analysis of the data. 33 34 [*Copy Editor – query authors for keywords] 35 36 37 Introduction: 38 39 The analysis of gene expression provides a description of a cell's potential functions and its 40 response to extracellular signals. However, when researchers attempt to analyze gene 41 expression in vivo they are confronted with the problem that all tissues contain a diverse array 42 of different cell types with unique functions. Efforts to isolate a specific cell type for analysis 43 based on sorting techniques can be successful but these additional manipulations are both time 44 consuming and have been shown to alter transcription (Haimon et al., 2018). The brain is a 45 particularly challenging tissue in which to quantitate gene expression and translation. A typical 46 mouse brain is made up of around 75 million neurons, 23 million glial cells, 7 million endothelial 47 cells and several million other miscellaneous cell types (Williams, 2000). Neurons are 48 extremely diverse in terms of their expression of various neurotransmitters, neuropeptides, and 49 receptors and typically these neurons are anatomically intermingled making them very difficult to 50 separate for molecular analysis. In situ analysis of mRNA expression has been very useful in 51 identifying the overall anatomical distribution of neurons expressing specific genes and the Allen 52 Brain Institute has assembled a comprehensive database for both the mouse and human brain 53 which is very useful. However, anatomical localization cannot define specific neuronal cell 54 types. Characterization of the mRNA expression phenotype of specific neurons has progressed 55 with the development of ribosome-tagging approaches, cell sorting based on promoter specific 56 57 58 59 60 John Wiley & Sons Current Protocols Page 2 of 56 1 2 3 expression of fluorescent marker proteins, and single-cell RNA-Seq. Each approach has 4 advantages and disadvantages. In this protocol we describe a ribosome-tagging approach 5 (RiboTag) which our lab developed (Sanz et al., 2009) and discuss the application of this 6 approach to the analysis of polyribosome associated mRNA transcripts in specific populations of 7 neurons. The RiboTag approach takes advantage of the hundreds of specific promoter-driven 8 9 Cre recombinase transgenics currently available (Jackson Laboratories has more than 480 10 lines) and others are being added at a rapid pace. 11 12 There are two basic approaches involving the use of RiboTag as diagrammed in Figure 1 and 13 each has advantages and disadvantages. In the first approach we use a RiboTag mouse line in 14 which the 60S ribosomal gene, Rpl22, was modified so that in the presence of Cre recombinase 15 the last exon of Rpl22 will be replaced with an HA-tagged exon effectively labeling the cell's 16 polyribosomes so that they can be isolated by immunoprecipitation. These RiboTag mice are 17 crossed to a Cre recombinase driver mouse line and the double heterozygotes are used for 18 analysis. Immunoprecipitation of polyribosomes from neuronal tissue purifies only the 19 polysomes that are tagged in the Cre-expressing cells. The mRNA from these polysomes is 20 isolated and the transcripts analyzed by qRT-PCR or RNA-Seq. The second approach utilizes a 21 Rpl22-HA construct in a flex or DIO virus (AAV) flanked by two different sets of loxP sites (Sanz 22 et al., 2015). When the AAV-DIO-RiboTag is injected into a specific anatomical region of the 23 brain of an adult mouse the Rpl22-HA is only expressed in the neurons that are actively 24 expressing Cre recombinase and also infected by the AAV. After waiting several weeks for the 25 Rpl22-HA to be expressed and incorporated into polyribosomes, tissue samples are isolated 26 and used to immunoprecipitate HA-tagged polyribosomes followed by qRT-PCR or RNA-Seq. 27 28 The viral approach requires accurate stereotaxic injections of virus and is more labor intensive 29 but has the advantage that only adult neurons that still express the Cre transgenic will be 30 targeted. The direct cross of RiboTag mice with Cre driver mice is easier to do but has the 31 potential disadvantage that the Rpl22-HA could continue to be expressed in neurons that have 32 differentiated from a progenitor population that expressed Cre recombinase at an earlier stage 33 during development. One can take advantage of this difference by comparing results from the 34 same brain region using the RiboTag mouse and the RiboTag virus using the same Cre driver 35 transgenic. We have used this approach to demonstrate that embryonic Pomc-expressing 36 progenitor neurons differentiate into both Agrp and Kiss1 expressing neurons in the adult 37 arcuate nucleus of the hypothalamus (Sanz et al., 2015). 38 39 [*Copy Editor – place Figure 1 here] 40 41 Basic Protocol 1 outlined below describes our methods in detail using the RiboTag mouse 42 crossed with a specific Cre-recombinase expressing transgenic mouse to isolate polyribosomes 43 by immunoprecipitation from brain homogenates and then further isolate RNA from both the 44 input (original homogenate) and the immunoprecipitated (IP) polysomes. Alternative Protocol 45 1 describes the viral approach using stereotaxic injection of AAV-DIO-RiboTag into adult Cre- 46 recombinase expressing mouse brain to avoid developmental activation of RiboTag expression. 47 48 In Support Protocol 1 we describe the breeding and genotyping of RiboTag mice and in 49 Support Protocol 2 we describe the analysis of captured RNA using qRT-PCR. Basic 50 Protocol 2 discusses the use of RiboTag mice crossed with a specific Cre transgenic line to 51 identify the anatomical localization of RiboTag expression by immunohistochemistry. In Basic 52 Protocol 3 we describe the current approach our lab is using to produce DNA libraries for RNA- 53 Seq. Basic Protocol 4 describes the bioinformatic tools that we are using to analyze the RNA- 54 Seq data. In the Commentary we will provide a discussion of these methods and identify some 55 of the most critical parameters and potential trouble spots. 56 57 58 59 60 John Wiley & Sons Page 3 of 56 Current Protocols 1 2 3 BASIC PROTOCOL 1 4 5 The RiboTag Assay 6 7 This approach has been described and updated in several publications from our laboratory 8 9 (Quintana et al., 2012; Sanz et al., 2013; Sanz et al., 2015; Sanz et al., 2009) and is shown 10 schematically in Figure 1. We have now introduced modifications that improve the efficiency of 11 the immunocapture and therefore increase the RNA yields obtained in the immunoprecipitates. 12 These modifications include using an indirect method for the immunocapture of HA-tagged 13 polysomes (instead of the direct immunoprecipitation procedure described in the original 14 publication) and using Protein A/G beads with a higher binding capacity for IgGs. 15 16 Materials: 17 18 Mice: 19 RiboTag mouse line (JAX stock No. 011029) 20 Cre-driver mouse line of choice 21 22 23 Equipment: 24 Large sharp surgical scissors (FST cat No. 14001-14) 25 Small extra fine scissors (FST cat No. 14084-08) 26 Spatula (FST cat No. 10091-12) 27 28 Harris Uni-Core (Electron Microscopy Sciences cat No. 69036) 29 Razor blades (Electron Microscopy Sciences cat No. 71960) 30 Mouse brain matrix (Electron Microscopy Sciences cat No. 69022) 31 Glass Dounce homogenizers (2ml or 7ml) (Sigma cat. No. 8938 or 9063) 32 Refrigerated centrifuge (Eppendorf; cat. No. 5417R) with rotor for 1.5 ml microcentrifuge tubes. 33 Microcentrifuge tubes (Axygen; MCT-175-C) 34 10 ul ART Barrier pipette tips (Thermo Fisher Sci.; cat. No. 2140-HR) 35 20 ul ART Barrier pipette tips (Thermo Fisher Sci.; cat. No. 2149-HR) 36 200 ul ART Barrier pipette tips (Thermo Fisher Sci.; cat. No. 2069-HR) 37 1000 ul ART Barrier pipette tips (Thermo Fisher Sci.; cat. No. 2179-HR) 38 Microtube magnetic stand (Thermo Fisher Sci.; cat. No. 12320D) 39 Microtube rotator (Thermo Fisher Sci.; cat No. 88881002) 40 Nanodop One (Thermo Fisher Sci.; cat. No. 13-400-518) 41 Fluorescence microplate reader (for Quant-iT Ribogreen RNA assays) 42 2100 Bioanalyzer Instrument (Agilent; cat. No. G2939BA) 43 44 Materials: 45 Sterile water for irrigation (Baxter, cat. No. 2F7114) 46 1M DTT, 10X 0.5ml ampules (Sigma; cat. No. 646563-10X 0.5ml). Aliquot open ampules and 47 48 store at -80ºC 49 Protease inhibitor cocktail (Sigma, cat. No. P8340-5ml). Aliquot and store at -20C 50 RNAsin (40U/ul) (Promega, cat. No. N2115) 51 b-mercaptoethanol (Bio-Rad; cat. No.1610710 52 Pierce protein A/G magnetic beads (Thermo Fisher Sci.; cat. No. 88803) 53 Covance Anti-HA.11 Epitope Tag Antibody (BioLegend; cat. No. MMS-101R). Aliquot and store 54 at -20ºC 55 RNeasy Mini Kit (Qiagen; cat. No. 74104) or RNeasy Micro kit (Qiagen; cat. No. 74004) 56 Quant-iT RiboGreen RNA Assay kit (Thermo Fisher Sci.; cat. No. R11490) 57 58 59 60 John Wiley & Sons Current Protocols Page 4 of 56 1 2 3 RNA 6000 Nano kit or RNA 6000 Pico kit (Agilent; cat. No. G2939BA, 5067-1511 or 5067-1513) 4 Ethanol (200 proof) 5 6 7 Protocol steps: 8 9 10 Harvest tissue 11 12 1. Euthanize mice using IACUC approved procedures such as carbon dioxide inhalation 13 2. Decapitate mice using large sharp surgical scissors 14 3. Make a midline incision in the skin and expose the skull 15 4. Using small scissors and a spatula remove the skull 16 5. Lift the brain out of the skull and use a spatula to section the optic and other cranial 17 nerves 18 6. Place the brain in a cold block or brain matrix for further dissection. Sampling tools such 19 as Harris Uni-cores can be used to punch the area of interest. 20 21 Lysate preparation 22 23 7. Clean Dounce tissue grinder and dissection tools and rinse thoroughly with RNase-free 24 water (sterile water for irrigation can be used at this step). Air dry and place Dounce 25 homogenizer on ice. 26 8. Prepare the homogenization buffer as described in the Reagents and Solutions section 27 28 and keep on ice. 29 9. Dissect the tissue containing the cell type of interest. Specific areas or whole tissue can 30 be used in this approach. 31 A brain matrix and sampling tools such as brain punches or Harris Uni-Cores can be 32 used to precisely dissect the area of interest. Dissected tissue or punches of the area 33 of interest can either be flash-frozen in liquid nitrogen right after dissection and 34 stored at -80ºC for months or processed fresh. 35 10. Place the tissue in the ice-cold Dounce homogenizer vessel containing a defined volume 36 of pre-chilled homogenization buffer and homogenize using 40 strokes of the loose- 37 fitting pestle (type A pestle), followed by 40 more strokes with the tight-fitting pestle (type 38 B pestle). 39 We typically use a 2-5% w/v ratio (for areas with a limited number of cells from the 40 cell type of interest a pool of punches of the same area from different animals can be 41 used to increase RNA yields). For small amounts of tissue (10-50mg tissue) we use 42 the 2 ml Dounce and for 50-300mg we use the 7 ml Dounce. 43 11. After homogenization, transfer lysate to a microtube on ice. 44 12. Clear the lysate at 10,000 x g for 10 min at 4ºC using a refrigerated centrifuge. 45 13. Transfer the post-mitochondrial supernatant to a pre-chilled microcentrifuge tube on ice. 46 Discard resulting pellet. 47 48 14. Take a small aliquot of the cleared lysate (40-80 ul) and store it at -80ºC for subsequent 49 Input analysis. 50 51 Immunoprecipitation 52 15. Add 4 ul of the anti-Hemagglutinin (HA) antibody (Covance Anti-HA.11 Epitope Tag 53 Antibody) to 800 ul of the remaining cleared lysate and incubate for 4 hours at 4ºC in a 54 microtube rotator with gentle mixing (end-over-end). 55 16. Prepare the magnetic protein A/G beads: 56  Thoroughly resuspend the beads by repeated inversion or gentle vortexing. 57 58 59 60 John Wiley & Sons Page 5 of 56 Current Protocols 1 2 3  Place a microtube magnetic stand on ice and transfer 200 ul of the resuspended 4 beads to a clean microcentrifuge tube. 5  Place the tube into the magnetic stand on ice to collect the beads and remove 6 7 and discard the storage buffer. 8  Add 400 ul of homogenization buffer and rotate end-over-end for 5 min to wash 9 the beads. 10  Collect the beads with the magnetic stand, discard buffer. 11 12 17. Add the cleared lysate with the antibody to the beads. Incubate samples overnight at 13 4ºC in a tube rotator with gentle end-over-end mixing. 14 18. Next day, prepare the high salt buffer as described in the Reagents and Solutions 15 section. Keep on ice. 16 a. Place samples into the magnetic stand on ice and remove supernatant 17 (supernatant from the immunoprecipitation can be discarded or saved at -80ºC 18 for subsequent analysis). 19 Leave tubes in the magnet for at least one minute to collect all the beads 20 against the side of the tube. 21 19. With the tubes on the stand, add 800 ul of high salt buffer (Reagents and Solutions) to 22 remove non-specific binding from the immunoprecipitate (IP). Transfer tubes to the 23 24 rotator and wash the IP with gentle end-over-end mixing for 5 min at 4ºC. 25 20. Place samples into the magnetic stand on ice and remove supernatant 26 21. Repeat high salt washes two more times with the same volume of high salt buffer for 5 27 min each, for a total of three washes. 28 22. Resuspend beads in 800 ul of high salt buffer, mix on rotator for 5 min and transfer 29 beads and buffer to a clean tube. 30 This step helps remove any RNA or polyribosomes that might be sticking to the 31 original tube. 32 23. Place tubes on the magnetic stand for at least 1 min, carefully remove all high salt buffer 33 24. Add 350 ul of RLT buffer from the Qiagen’s RNeasy extraction kit (mini or micro) 34 supplemented with -mercaptoethanol (provided with the kit). 35 25. Remove tubes from stand and vortex samples for 30 seconds at RT. 36 At this step samples can be stored at -80ºC for subsequent RNA extraction or 37 processed through the RNA extraction procedure. 38 39 RNA extraction 40 41 42 26. Place tubes containing IPs in RLT buffer (from Qiagen kit) into the magnetic stand for 1 43 min at RT to allow beads to be collected against the side of the tube 44 27. Transfer the clear RLT buffer containing RNA to a new microcentrifuge tube. 45 28. Proceed with RNA isolation with DNAse digestion following Qiagen's RNeasy extraction 46 kit directions. 47 29. To extract RNA from the Inputs (Step 6), bring input sample to 350 ul with RLT buffer 48 supplemented with -mercaptoethanol, vortex extensively and proceed to RNA 49 extraction as described for IPs. 50 RNA extraction using column-based kits should be performed at RT. However, 51 eluted RNA should be kept on ice at all times. For long term storage, RNA is 52 safely kept at -80ºC for years. Whenever working with isolated RNA, always wear 53 gloves to minimize RNase contamination, decontaminate working surfaces and 54 use clean nuclease-free tubes and barrier pipette tips. 55 56 57 58 59 60 John Wiley & Sons Current Protocols Page 6 of 56 1 2 3 30. Quantitate RNA using Quant-iT RiboGreen RNA Reagent (Thermo Fisher) or a 4 Nanodrop-One (Thermo Fischer Sci) following the manufacturer's protocols. 5 If very low levels of RNA are expected (less than 2 ng/ul) the samples can be 6 accurately quantitated by doing qRT-PCR using a house keeping gene such as 7 Actb and comparing the results to known standard RNA solutions prepared with 8 9 input RNA from the same tissue as detailed in Support Protocol 2. 10 31. Analyze RNA integrity on an Agilent 2100 Bioanalyzer system with the associated RNA 11 6000 nano and pico kits (chose nano chips for samples 5-500 ng/ul, for samples with 12 RNA concentrations < 5 ng/ul (down to 50 pg/ul) the pico chips should be used). Follow 13 manufacturer’s directions. 14 Since most of the RNA isolated in Inputs and IPs is rRNA, the quality of mRNA is 15 indirectly estimated by assessing the integrity of rRNA. A measure of RNA 16 quality, the RNA integrity number (RIN), is also calculated by the software using 17 an algorithm that assesses the electrophoretic traces of each sample. A RIN 18 value of 7-10 indicates high quality RNA but RNA-Seq libraries can still be made 19 and successfully analyzed starting with partially degraded RNA. 20 21 22 23 ALTERNATE PROTOCOL 1 24 25 Messenger RNA profiling using the RiboTag viral vector 26 27 28 To overcome the limitations associated with developmental activation of Cre 29 recombinase transgenes and allow cell type-specific mRNA profiling of adult cells using 30 Cre-Driver lines, we have developed a Cre-dependent RiboTag viral vector based on 31 AAV1 (AAV1-DIO-RiboTag) (Sanz et al., 2015), where RiboTag expression is dependent 32 on the activity of the promoter driving Cre recombinase expression in adult mice. This 33 viral vector was created by inserting the cDNA of the mouse ribosomal protein Rpl22 34 gene fused to three hemagglutinin (HA) sequences (Rpl22 · 3HA), into a double-floxed 35 inverse open reading frame (DIO) AAV construct. For easy visualization of transduced 36 cells, this construct also contains an IRES sequence (IRES2) followed by the YFP 37 coding sequence as shown in Figure 1. 38 39 Materials: 40 41 Mice: 42 Cre-driver mouse line of choice. 43 44 Reagents: 45 RiboTag viral vector (AAV1-DIO-RPL22HA-IRES-YFP). Available upon request from authors 46 47 48 Equipment: 49 Stereotaxic apparatus with microsyringe pump (e.g. from World Precision Instruments) 50 Injection syringe (e.g. 5 ul Hamilton syringe) 51 Surgical tools 52 53 Protocol steps: 54 55 1. Define stereotaxic coordinates by locating the injection target in a mouse brain atlas 56 such as the Paxinos and Franklin’s “the Mouse Brain in Stereotaxic Coordinates” 57 58 59 60 John Wiley & Sons Page 7 of 56 Current Protocols 1 2 3 (Paxinos & Franklin, 2013) or the Allen institute for Brain Science’s “Adult Mouse Brain 4 Reference Atlas” (Dong) 5 2. Inject 0.2 to 0.5 ul of a RiboTag viral vector preparation (7 x 1011 viral genomes/ml) at 6 the target site using the defined coordinates. 7 Follow a stereotaxic injection protocol such as (Gore, Soden, & Zweifel, 2013). 8 9 When targeting bilateral structures, inject each side. Injection volumes can be 10 adjusted to the targeted area, or multiple injections performed to cover larger 11 areas. Injecting different mice using the same coordinates and pooling the 12 targeted areas after dissection helps correct for variability resulting from 13 stereotaxic viral vector injections. 14 3. Allow at least 2 weeks for the viral vector to express Rpl22-HA. For RiboTag assays, 15 good results are obtained when sacrificing animals at least 3 weeks after injections to 16 allow for robust incorporation of Rpl22-HA into polyribosomes. 17 4. Perform RiboTag immunoprecipitations as described in Basic Protocol 1 18 19 All surgical procedures should be performed in accordance with protocols approved by your 20 Institutional Animal Care Use Committee (IACUC). 21 22 23 SUPPORT PROTOCOL 1 Breeding and Genotyping of RiboTag Mice 24 25 Breeding of RiboTag Mice: 26 We maintain a live colony of RiboTag Breeders on a C57BL/6J background by mating 27 28 homozygotes since they have no adverse phenotypes. Experimental mice for RiboTag 29 assays are usually obtained by breeding male Cre recombinase-expressing mice to 30 female homozygous RiboTag mice (Rpl22HA/HA), Therefore, experimental mice are 31 always heterozygous for the RiboTag allele (Rpl22HA/+). 32 33 PCR Genotyping: 34 To genotype the RiboTag mice, a PCR was developed that includes the loxP site 5’ to 35 the wild-type exon 4 of Rpl22; the wild-type PCR product is 242 bp, while the mutant 36 product is about 290 bp. This PCR also distinguishes homozygous RiboTag mice, as the 37 5’ oligo corresponding to the intron between exons 3 and 4 of Rpl22 is not present in the 38 repeated segment of intron preceding the C-terminal Rpl22HA exon 4. Resulting 39 offspring from RiboTag mice bred to Cre recombinase-expressing mouse lines 40 (experimental mice) are genotyped for the presence of the Cre recombinase gene and 41 the RiboTag allele using the PCR oligonucleotides and reaction conditions described 42 below 43 44 Materials: 45 NucleoSpin Tissue DNA extraction kit (Macherey-Nagel cat No. 740952) 46 DreamTaq DNA polymerase (Thermo Fisher Sci. cat No. EP0702 and B71) 47 48 dNTPs 10 mM each (Thermo Fisher Sci. cat No. R0192) 49 (dilute to 2.5 mM for each dNTP) 50 51 Alternative: 52 Phire Tissue Direct PCR Master Mix kit (Thermo Fisher Sci. cat No. F170 53 54 Oligonucleotides for RiboTag genotyping: 55 RiboTag loxP For: 5’-GGGAGGCTTGCTGGATATG-3’ 56 RiboTag loxP Rev: 5’-TTTCCAGACACAGGCTAAGTACAC-3’ 57 58 59 60 John Wiley & Sons Current Protocols Page 8 of 56 1 2 3 4 Oligonucleotides For Cre recombinase genotyping: 5 Cre cDNA For: 5’-GCATTACCGGTCGATGCAACGAGTG-3’ 6 Cre cDNA Rev: 5’-GAACGCTAGAGCCTGTTTTGCACGTTC-3’ 7 8 9 Acrylamide-bis solution 40% (37.5:1) (Merck cat No. 1.00638.1000) 10 Tris-Borate-EDTA (TBE) 10x (Fisher Scientific cat No. BP1333) 11 Ammonium Persulfate (APS; Sigma cat No. A3678). 12 (Prepare a 10% solution. Store at 20ºC.) 13 TEMED (Merck cat No. 1.10732.0100) 14 SYBR Safe DNA gel stain (Thermo Fisher Sci. S33102) 15 100 bp DNA ladder (Thermo Fisher Sci. cat No. SM0241) 16 17 18 Equipment: 19 Thermal cycler (Thermo Fisher Sci. cat No. A24811) 20 Mini protean vertical gel electrophoresis system (Bio-Rad cat No. 1658003) 21 Power supply (Bio-Rad cat No. 1645050) 22 UV Gel imaging system 23 Strip tubes and caps for PCR 24 25 26 Protocol steps: 27 28 29 1. Extract DNA from mouse tail snips (2-5 mm) using a silica membrane column-based 30 kit such as the NucleoSpin Tissue following manufacturer’s directions. 31 We have also successfully used direct PCR kits that do not require prior DNA 32 extraction such as the Phire Tissue Direct PCR Master Mix kit for RiboTag and 33 Cre recombinase genotyping. DNA can also be extracted from tail samples by 34 conventional methods using SDS/Protease K digestion, phenol/chloroform 35 extraction, and ethanol precipitation. 36 2. Prepare the DreamTaq PCR Master mix as follows: (per reaction) 13 µl of water, 2 µl 37 of 10x DreamTaq green buffer, 2 µl of 10mM dNTPs, 1 µl of the forward oligo 38 (10uM), 1 µl of the reverse oligo (10uM), 0.1 µl of DreamTaq polymerase. Mix gently 39 and briefly centrifuge. 40 3. Add 19 µl of the master mix to each well. 41 4. Add 1 µl of tail DNA (~2 ng) to each reaction. 42 5. Mix and briefly centrifuge. 43 6. Place strips in the thermal cycler and run the appropriate program for each reaction. 44 Thermal profile for RiboTag reactions is: 95°C for 2 min followed by 30 cycles of 45 95°C for 20 s, 57°C for 20 s, and 72°C for 30 s, with a final extension at 72°C for 5 46 min. Thermal profile for Cre reactions is 95°C for 2 min followed by 30 cycles of 95°C 47 48 for 10 s, 63°C for 10 s, and 72°C for 40 s, with a final extension at 72°C for 5 min. 49 7. Prepare a 10% acrylamide gel by mixing (for two gels) 14 ml of water, 5 ml of 40% 50 acrylamide-bis, 1ml of TBE 10x, 30 µl of TEMED and 150 µl of 10% APS. Allow gels 51 to polymerize for 10-15 min. 52 8. Place gels into a vertical gel electrophoresis system, add buffer (0.5X TBE), and load 53 DNA ladder and PCR reactions. 54 9. Run the electrophoresis at 175v (constant voltage) for 45 min. 55 10. Stain gels with SYBR safe and analyze PCR products under UV light. 56 57 58 59 60 John Wiley & Sons Page 9 of 56 Current Protocols 1 2 3 Detecting “Ectopic” Cre Recombination in tail DNA 4 5 Mouse lines driving Cre recombinase expression under cell type specific promoters 6 should typically restrict RiboTag activation to the cell type of interest. However, transient 7 expression of Cre recombinase during development is often observed in different Cre 8 9 driver lines and can lead to recombination of the RiboTag allele in unexpected cell types. 10 If this “ectopic” expression takes place in the fertilized egg or during early development, 11 it will be detected during the RiboTag genotyping PCR and a “recombination” band will 12 show up at ~350 bp. If recombination occurs in the fertilized egg the loxP band will be 13 replaced by the “recombination” band. If Cre recombinase is expressed during early 14 development, recombination of the RiboTag allele will only take place in a portion of the 15 cells and both the loxP and the recombination band will show up during PCR analysis. If 16 genotyping heterozygous animals (Rpl22HA/+), the wild-type band will always be present 17 in the PCR assay independently of the potential recombination events that may take 18 place on the floxed allele. 19 20 21 22 SUPPORT PROTOCOL 2 qRT-PCR analysis 23 The success of the RiboTag procedure can be assessed by using qRT-PCR to evaluate 24 whether specific mRNAs have been enriched during the isolation. Transcripts from the gene 25 that is driving the Cre-recombinase in the specific cell types should be enriched in the IP RNA 26 sample compared to the input RNA. We also recommend monitoring mRNAs that are not 27 28 expected to be expressed in the targeted cells to verify that they are de-enriched in the IP. 29 Quantitative RT-PCR may substitute for a more expensive and time-consuming analysis by 30 RNA-Seq when only a small subset of transcripts need to be assessed. 31 32 Materials: 33 Equipment: 34 Real-Time PCR machine 35 36 Reagents: 37 We use the one step Brilliant II or III SYBR green kits from Agilent or RNA-Ct one step 38 kits from Thermo Fisher Sci. 39 40 41 42 43 1. To confirm the specific isolation of transcripts from the cell type of study, we analyze 44 RNA extracted from Inputs and IPs by single-step qRT-PCR. The enriched presence of 45 known cell type-specific transcripts in the IPs compared to inputs indicates that the 46 RiboTag isolation was successful. 47 48 2. Test samples for expression of transcripts specific to cell types in the dissected area 49 other than the one of interest such as a glial marker when using a neuron-specific Cre 50 transgenic. These are expected to appear depleted in the IPs when compared to inputs. 51 3. Perform qRT-PCR analysis for a housekeeping gene such as Actb. Normalizing all data 52 to that standard can help correct for errors in RNA quantitation or differences in RNA 53 quality. Adding equal amounts of RNA to the qRT-PCR reaction for all samples results in 54 little variation in the CTs obtained for the housekeeping gene (less than 1 CT of 55 difference is expected among all samples), which confirms RNA integrity in all samples 56 and suitability of the housekeeping gene for data normalization. 57 58 59 60 John Wiley & Sons Current Protocols Page 10 of 56 1 2 3 4 5 6 7 8 9 BASIC PROTOCOL 2: RiboTag mice as a reporter mouse line for direct visualization of 10 Cre recombination 11 12 Materials: 13 14 Mice: 15 RiboTag mouse line (JAX stock No. 011029) 16 Cre-driver mouse line of choice 17 18 Reagents: 19 Normal Donkey Serum (Jackson ImmunoResearch cat. No. 017-000-121) 20 Zymed’s rabbit anti-HA antibody (Thermo Fisher Sci., cat. No. 71-5500 21 Covance’s Alexa Fluor 488-conjugated mouse anti-HA antibody (BioLegend,cat. No. A488- 22 101L) 23 Alexa Fluor 488 donkey anti-rabbit antibody (Thermo Fisher Sci., cat. No. A21206) 24 SuperFrost plus slides (Thermo Fisher Sci. cat No. J1800AMNT) 25 Coverslips 24 x 60 mm #1 (Menzel-Glaser, available through VWR cat No. 631-1339) 26 Fluoromount G with DAPI (Electron Microscopy Sciences, cat. No. 17984-24) 27 28 29 Equipment: 30 Netwells (Corning CLS3477) 31 Nutator mixer (e.g. Boekel or Clay Adams) 32 Paint brush 33 34 Protocol steps: 35 36 Tissue preparation 37 38 1. Transcardially perfuse mice with ice-cold phosphate-buffered saline (PBS) pH 7.4 39 containing 4% paraformaldehyde (PFA). 40 Perfusion with fixative solution reduces background resulting from endogenous 41 mouse IgG detection when using anti-mouse secondary antibodies. If no primary 42 antibodies raised in mouse are used for staining, brains can by directly fixed after 43 removal by immersion in fixative for 12-24 h. 44 2. Remove brains and postfix overnight at 4ºC in PBS pH 7.4 containing 4% PFA. 45 3. Wash brains twice in ice-cold PBS pH 7.4 46 4. Immerse brains in an ice-cold solution of 30% sucrose in PBS pH 7.4. Specimens will 47 48 float. Keep at 4ºC until they sink (about 24 h) 49 5. Remove brains from the cryoprotective solution and pat dry to remove excess solution 50 6. Freeze in powdered dry ice for 5 min and store at -80 ºC until sectioning. 51 7. Cryo-section brains in 30 um-slices using a freezing microtome. Collect sections from 52 the area of interest for free-floating staining in PBS pH 7.4 with 0.02% sodium azide as 53 preservative. Store sections at 4º. 54 55 Imunofluorescence 56 57 58 59 60 John Wiley & Sons Page 11 of 56 Current Protocols 1 2 3 8. Use a paint brush to transfer sections to wells containing Netwells inserts filled with PBS 4 pH 7.4. Rinse sections once with PBS to remove preservative. 5 9. Block sections in PBS with 0.2% Triton X-100 and 10% normal donkey serum (NDS) for 6 1 h at room temperature with agitation. 7 10. Incubate overnight at 4ºC with a primary antibody solution containing an anti-HA 8 9 antibody such as the Zymed’s rabbit anti-HA (#71-5500 Thermo Fisher Sci; use at 10 1:1000), or with a fluorophore-conjugated anti-HA antibody such as the Covance’s Alexa 11 Fluor 488-conjugated mouse anti-HA antibody (# A488-101L BioLegend, use at 1:500), 12 in PBS pH 7.4 with 1% NDS and 0.2% Triton X-100. 13 Fluorophore-conjugated antibodies allow for a direct visualization of the signal 14 without secondary antibody detection. Using fluorophore-conjugated secondary 15 antibodies will however provide an amplified signal. 16 11. Wash sections three times for 5 minutes in PBS pH 7.4 with 0.2% Triton X-100. If a 17 fluorophore-conjugated anti-HA antibody is used proceed to step 13. 18 12. Incubate with secondary antibody solution containing an anti-rabbit antibody conjugated 19 to the fluorophore of choice (e.g., Alexa Fluor 488; Invitrogen; 1:500 in PBS pH 7.4 with 20 1% NDS and 0.2% Triton X-100) for 1 h at room temperature. 21 13. Wash sections three times for 5 minutes in PBS pH 7.4 with 0.2% Triton X-100 22 14. Mount stained sections onto slides for imaging using Fluoromount G with DAPI. 23 15. Seal the edge with nail polish and allow slides to dry for 1-2 hours before visualizing. 24 Protect from light. 25 16. Slides can be stored at 4ºC protected from light. For best results, slides should be 26 analyzed within 1-2 weeks using the microscope of choice. Staining using anti-HA 27 28 antibodies in Cre-positive RiboTag mice results in a strong perinuclear signal in the cell 29 type of interest. 30 17. Image slides on a fluorescent microscope (standard or confocal) to visualize the Alexa 31 Fluor 488 and DAPI. 32 DAPI will bind DNA and light up the nuclei in all cells and Alexa fluorescence will 33 only be present in the cells that are expressing RiboTag. Expect to see most of 34 the RiboTag labeling in the cytoplasm where polyribosomes are abundant and in 35 the nucleolar structures in the nucleus where ribosomes are being assembled. 36 37 38 39 BASIC PROTOCOL 3: RNA-Seq Library Preparation 40 41 There are many library preparation kits on the market but Trio RNASeq (NuGEN) has worked 42 well in our hands. A detailed protocol is available on NuGEN’s website (NuGEN). Briefly, 500 pg 43 – 50 ng of total RNA in a volume of 10 ul is used as starting material for samples. We 44 recommend using similar quantities of RNA for all samples that are going to be compared with 45 each by RNA-Seq. We have good results starting with ~2 ng of total RNA. 46 47 48 Materials 49 50 Trio RNA-Seq, mouse rRNA, part number 0507-X; (X = 8, 32, or 96), (NuGEN) 51 AMPure XP, reference number A63880, (Beckman Coulter) 52 NEBNext Library Quant Kit for Illumina, catalog number E7630X (X = S, or L), (NEB) 53 NextSeq 500/550 High Output Kit v2.5 (75 Cycles), catalog number 20024906, (Illumina) 54 200 mM NaOH 55 200 mM Tris, pH 7.0 56 57 58 59 60 John Wiley & Sons Current Protocols Page 12 of 56 1 2 3 4 0.2 mL PCR tubes (flat top optically clear) 5 1.5 mL microtubes 6 0.2 mL tube magnetic rack 7 8 9 Equipment: 10 Thermal cycler (PCR machine) 11 Quantitative thermal cycler 12 NextSeq Sequencer (Illumina) 13 14 15 Convert isolated RNA from RiboTag into cDNA libraries 16 17 1. Perform DNase treatment using the Trio RNASeq kit. 18 19 Although DNA digestion is performed on the RNA purification column, the first 20 step of the Trio RNASeq kit is to perform DNase treatment. It is extremely 21 important to eliminate any residual DNA and the second DNase treatment does 22 not result in any loss of sample. 23 24 2. Synthesize cDNA from the total RNA using reverse transcriptase primed with both oligo 25 dT and random hexamers and then use DNA polymerase to produce a double stranded 26 cDNA molecule 27 28 29 These primers include an RNA sequence at the 5’ end that is not complementary 30 and does not hybridize to the sample RNA. Then a second strand is made with a 31 DNA polymerase (Dafforn et al., 2004; Kurn et al., 2005). 32 33 3. Purify double stranded cDNA using silicon coated magnetic beads (AMPure XP, 34 Beckman Coulter). 35 36 These beads are used several times throughout the protocol adding various 37 amounts of a salt containing bead binding solution. By varying the salt 38 concentration in the reaction, one is selecting for different fragment sizes, with 39 more salt attracting almost all fragment sizes and less preferentially selecting for 40 larger fragments (Boom et al., 1990; Melzak, Sherwood, Turner, & Haynes, 41 1996). 42 43 4. Amplify the double-stranded cDNA using single primer isothermal amplification (SPIA). 44 45 RNase H is used to digest the RNA portion of the RNA / DNA heteroduplex at 46 one end of the double stranded cDNA. A 5’ RNA 3’ DNA primer and a strong 47 48 strand displacing DNA polymerase are used to make a single stranded cDNA 49 that is the compliment to the original RNA. The process is continually repeated all 50 at one temperature making many such copies (Dafforn et al., 2004; Kurn et al., 51 2005). 52 53 54 5. Fragment and repair ends of the cDNA 55 56 57 58 59 60 John Wiley & Sons Page 13 of 56 Current Protocols 1 2 3 The kit accomplishes this using an enzymatic process. After fragmentation 4 adapters are ligated to either side of the cDNA with the 3’ side receiving a unique 5 nucleotide barcode that will be used to identify samples after sequencing. 6 7 8 9 6. PCR Amplification of cDNA products. 10 11 The kit uses two PCR amplifications with a targeted step in between designed to 12 deplete any cDNAs that would have been made from ribosomal RNA. Oligo 13 probes are designed to bind to rRNA derived cDNA, extend that product, then 14 restriction enzymes target these cleaving the adaptors off these products 15 essentially eliminating them from the library. 16 17 Quantify cDNA libraries 18 19 Even starting at the low end of the kit’s range 500 pg of total RNA, one ends up with 20 micrograms of cDNA. This needs to be greatly diluted before sequencing. To get the 21 best results from the sequencer the cDNA libraries must be loaded on at a precise 22 quantity. As well, each sample that will be multiplexed together should be loaded in 23 equal quantities. The most accurate way to quantify the cDNA libraries is using qPCR 24 against the adaptors on the cDNA fragments. Again, there are many kits on the market 25 that can accomplish this but NEBNext has worked well in our hands. 26 27 28 Four standard samples come with the kit ranging from 0.01 pM to 10 pM. In order to fall 29 within these one needs to make serial dilutions of the libraries and use a range of those 30 samples to quantify. In our hands 1:100,000 and 1:1,000,000 are the two dilutions that 31 will fall between the given standards. Our libraries are most often between 0.1 and 1 µM. 32 33 A detailed user manual is available on NEB’s website (NEB, 2017). 34 35 Denature and dilute libraries 36 37 The day of the sequencing run, libraries need to be denatured and diluted. A detailed 38 protocol is available on Illumina’s website (Illumina, 2018a). 39 1. Make a 1 nM dilution of each sample to be multiplexed together using a portion of the 40 libraries made earlier. Add an equal amount of each 1 nM sample to a master sample. 41 2. Add 20 uL of the master sample to 20 uL of 0.2 M NaOH. Mix by pipetting half of the 42 volume 10 to 20 times. Incubate for 5 minutes. 43 3. Add 20 uL 0.2 M Tris pH 7 to neutralize the NaOH. Mix well by pipetting. 44 4. Add 940 uL of prechilled HT buffer provided with the NextSeq reagents to make a 20 pM 45 dilution. Vortex briefly then spin the contents down. Keep the 20 pM sample on ice until 46 you are ready to make the final dilution. 47 48 5. Add 117 uL of the 20 pM sample to 1183 uL of prechilled HT buffer for a final dilution of 49 1.8 pM in 1.3 mL. Vortex briefly then spin the contents down. Keep this on ice until you 50 are ready to load this on the sequencer. 51 52 Sequencing 53 54 1. Thaw reagents cartridge at 4 degrees overnight. Take the reagents cartridge and flow 55 cell out of the refrigerator 30 minutes to an hour before your sequencing run. Ensure all 56 reagent have thawed before proceeding. 57 58 59 60 John Wiley & Sons Current Protocols Page 14 of 56 1 2 3 2. From the NextSeq500’s software interface select Sequence. 4 3. Following the instructions on screen, load the flow cell. 5 4. Empty the spent reagents container into the appropriate waste container, then reload. 6 Load buffer cartridge. 7 5. Pierce the foil covering reservoir 10 on the reagent cartridge with a pipette tip. Then load 8 9 the full volume of the 1.8 pM sample into reservoir 10. Load the reagent cartridge onto 10 the NextSeq500. 11 6. Specify the run parameters on the setup screen. Assign a name to Run Name, enter 75 12 into Read 1 and 8 into Index 1. 13 14 Single end reads may be sufficient for gene expression quantification and 15 differential gene expression analysis. Running paired end reads will increase 16 alignment confidence for specific isoforms and for novel transcript discovery. In 17 this case you would enter 75 into “Read 2”. NextSeq high output cartridges are 18 rated for 400 million single end or 200 million paired end reads. 19 20 7. Select Next, the system will run through a number of checks. Once it has finished with 21 these select Start. 22 23 A detailed user guide including instructions for loading reagents and sample 24 onto the NextSeq sequencer is available on Illumina’s website (Illumina, 2018b). 25 26 Once reagents and sample have been loaded on the NextSeq and the 27 28 sequencing run has begun, the multiplexed sample is washed over the flow cell 29 where cDNA molecules with adaptors hybridize to an oligo lawn with 30 complementary sequences. A DNA polymerase makes a compliment to the 31 original cDNA molecule primed from the oligo attached to the flow cell, the 32 double stranded cDNA is denatured, and the original cDNA molecule is washed 33 away. The attached compliment binds a second oligo attached to the flow cell, 34 and is copied by a DNA polymerase in a process called bridge amplification. The 35 process is repeated many times over until a cluster of cDNA molecules grows 36 that will be large enough for the sequencer to read. Fluorescently labeled dNTPs 37 are washed over the flow cell where they incorporate into the growing DNA 38 strand primed off the template attached to the flow cell. This process is used to 39 identify the sequence of both the DNA library fragments made from the original 40 samples and the nucleotide barcode ligated to that DNA that identifies which 41 sample it comes from (Illumina, 2016). 42 43 44 45 BASIC PROTOCOL 4: RNA-Seq Bioinformatics 46 47 48 The analysis of RNA-Seq data can be done using a variety of open source or proprietary 49 programs. If you are using a Core Facility to do RNA-Seq they are also likely to be able to 50 analyze the data for you or at least assist you in the basic steps to obtain the aligned datasets. 51 We build the libraries and run the RNA-Seq ourselves and use the methods described below to 52 analyze the data. 53 54 55 Software 56 57 58 59 60 John Wiley & Sons Page 15 of 56 Current Protocols 1 2 3 Software for Linux OS: 4 5 Bcl2fastq2 (Illumina, 2017a) 6 7 Software for Linux OS or Mac OS: 8 9 10 FASTX-Toolkit (Hannon_Lab, 2014) 11 12 TopHat2 (Trapnell et al., 2016) 13 14 Bowtie2 (Langmead, Kim, Charles, Wilks, & Antonescu, 2018) 15 16 Cufflinks (Trapnell et al., 2014) 17 18 Software for Linux OS, Mac OS, or Windows OS: 19 20 R (Gentleman et al., 2018) 21 22 RStudio (RStudio, 2018) 23 24 tidyr (Wickham & Henry, 2018) 25 26 dplyr (Wickham, François, Henry, & Müller, 2018) 27 28 29 ggplot2 (Wickham, Chang, et al., 2018) 30 31 32 Convert BCL files to FASTQ files 33 34 Illumina sequencers generate per cycle base call (BCL) files. However, most informatic 35 programs take FASTQ or FASTA files as input. Bcl2fastq2 software is able to covert the files, as 36 well as, demultiplex samples. A detailed user guide is available on Illumina’s website including 37 system requirements for the software (Illumina, 2017b). 38 39 1. Create a sample sheet with sample names, numbers, and corresponding nucleotide 40 barcodes. This is saved as a .csv in the following format: 41 42 [Data],, 43 Sample_Name,Sample_ID,Index 44 Sample1,1,CGCTACAT 45 Sample2,2,AATCCAGC 46 Sample3,3,CGTCTAAC 47 48 Etc. 49 50 2. Run the blc2fastq2 in Terminal (denoted with $ hereafter) with the following options: 51 52 $ bcl2fastq --minimum-trimmed-read-length 0 --mask-short-adapter-reads 0 --no-lane- 53 splitting -o /output_folder -R run_folder --sample-sheet run_folder/SampleSheet.csv 54 55 Explanation: 56 57 58 59 60 John Wiley & Sons Current Protocols Page 16 of 56 1 2 3 blc2fastq – calls the program. 4 5 minimum-trimmed-read-length – specifies a length to not trim if trimming adapters will 6 result in a read of less than this length. In these cases, the adapter portion will be 7 masked by replacing base calls with Ns. By specifying 0 we are telling the program to 8 9 trim adapters no matter the remaining length of the read. Default is 35. 10 11 mask-short-adapter-reads – specifies a length of a read that if it falls below will be 12 masked by Ns. By specifying 0 we are instructing the program not to do this. Default is 13 22 (must be set equal or lower than minimum-trimmed-read-length). 14 15 no-lane-splitting – this option instructs the program to include all reads for each given 16 sample in one file. Without this option the NextSeq will give four files for each sample, 17 one for each of lanes 1-4. If the researcher is planning to upload FASTQs to Illumina’s 18 basespace.com they should not call this option as basespace will only accept sample 19 files that do have lanes split. 20 21 -o – specifies the output folder. 22 -R – specifies the run folder or where the BCL files are located. 23 --sample-sheet – points to where the sample sheet is located. 24 25 26 Trim and filter FASTQ files 27 28 29 3. Trim the first 5 base calls and filter out low quality reads. FASTX-Toolkit tools can 30 accomplish this with the following command: 31 32 $ for i in sample1 sample2 sample3 etc; do zcat $i_S1_R1_001.fastq.gz | fastx_trimmer 33 -f 6 -v -Q33 | fastq_quality_filter -q 25 -p 80 -v -Q33 -z -o $i.fastq.gz; done 34 35 Explanation: 36 37 Commonly known as a “for loop”. 38 for i in sample1 sample2 sample3 etc – is a list of your samples to iterate over. 39 do X – could be any command. This command will then iterate over your list. 40 done – tells the command to stop. 41 $i – is the placeholder where the list of samples will be substituted in. 42 43 zcat – looks at a compressed file. 44 | – called a pipe, it sends information from first command to the next command. 45 fastx_trimmer -f 6 – this option says trim the first 5 base calls and keep everything from 6 46 onward. 47 48 fastq_quality_filter -q 25 -p 80 – this option says only keep reads with a quality score of 49 25 or higher on at least 80% of the base calls. 50 -z – is the option to compress output file. 51 -o – is the name of file or directory name/name of file. 52 53 54 Align FASTQ files to a reference genome 55 56 57 58 59 60 John Wiley & Sons Page 17 of 56 Current Protocols 1 2 3 We use TopHat2 (Kim et al., 2013; Trapnell, Pachter, & Salzberg, 2009) for alignment against 4 GRCm38/mm10 UCSC. Useful links to reference genomes with prebuilt bowtie indexes and 5 GTF transcript annotation files can be found here: 6 http://ccb.jhu.edu/software/tophat/igenomes.shtml. A detailed protocol for TopHat has been 7 published elsewhere (Trapnell et al., 2012). 8 9 10 4. Use the following command for alignments: 11 12 $ for i in sample1 sample2 sample3 etc; do tophat2 -p 8 -G genes.gtf -o output_folder/$i 13 Bowtie2Index/genome folder_with_fastqs/$i.fastq.gz; done 14 15 explanation: 16 17 tophat2 – is a program that performs alignment. 18 -p 8 – specifies the number of CPU processors to use. Check your system information 19 and set this at or below the total number of logical cores on the computer running the 20 software. 21 -G – points to directory and .gtf file. 22 -o – points to output directory and /$i – is the placeholder for sample. 23 Sequence/Bowtie2Index/genome – points to directory with index files. 24 folder_with_fastqs/$i.fastq.gz – points to directory and fastq files. This is the actual 25 sequencing data. 26 27 28 29 Quantify and compute differential gene expression 30 31 We use cuffdiff of cufflinks (Roberts, Trapnell, Donaghey, Rinn, & Pachter, 2011; Trapnell et al., 32 2013; Trapnell et al., 2010) to compute differential gene expression. A detailed protocol has 33 been published elsewhere (Trapnell et al., 2012). 34 35 5. Use the following command to calculate differential expression: 36 37 $ cuffdiff -p 8 -no-update-check -o output_folder -b Bowtie2Index/genome.fa -u genes.gtf 38 -L condition1,condition2 39 \./tophat_folder/condition1_S1/accepted_hits.bam,./tophat_folder/condition1_S2/accepte 40 d_hits.bam,tophat_folder/condition1_S3/accepted_hits.bam,etc 41 \./tophat_folder/condition2_S1/accepted_hits.bam,./tophat_folder/condition2_S2/accepte 42 d_hits.bam,tophat_folder/condition2_S3/accepted_hits.bam,etc 43 44 explanation: 45 46 cuffdiff – is a program to test for differential gene expression. 47 48 -p 8 – this option specifies how many CPU processors to use. 49 -no-update-check – use this option as the software is no longer being updated. 50 -o – points to folder you want your results saved in. 51 -L – name of your two conditions e.g. control, treatment or input, IP. 52 -b – this option corrects for RNA-seq read coverage bias (Roberts et al., 2011). 53 -u – this option performs an initial estimate to improve accuracy when weighting reads 54 mapping to multiple locations. 55 56 57 58 59 60 John Wiley & Sons Current Protocols Page 18 of 56 1 2 3 We run cuffdiff first on inputs vs IPs within conditions and then IP vs IP across 4 conditions. In some cases, one may only be asking what is unique about these 5 cells within this tissue. In which case one would only compare input to IP. It is 6 important to note that genes that are expressed widely in the total population of 7 neurons will not show up as differentially expressed even if they are expressed in 8 9 the targeted neuron population. The difference between IP and input will only be 10 significant when a transcript is preferentially expressed in the HA-tagged neuron 11 population. 12 13 14 Visualize results 15 16 Use R within RStudio to visualize your results utilizing tidyr, dplyr, and ggplot2 packages 17 (Wickham, 2016). 18 19 6. If necessary, install packages. 20 21 > install.packages(“tidyr”) 22 > install.packages(“dplyr”) 23 > install.packages(“ggplot2”) 24 25 Load packages. 26 27 28 > load(tidyr) 29 > load(dplyr) 30 > load(ggplot2) 31 32 33 7. Import output files “gene_exp.diff” and “genes.read_group_tracking” from cuffdiff and 34 save them as an object. The names of the objects are arbitrary; however, good object 35 names should be short but descriptive. Below the first named object “cdstats_aipvn” 36 describes cuffdiff statistical output for condition A, IP vs input. The example below 37 assumes the three cuffdiff output folders are saved in the working directory the R-project 38 is started in. 39 40 > cdstats_aipvn <- read.delim(“cuffdiff_A_IP_vs_N/gene_exp.diff”) 41 > cdstats_bipvn <- read.delim(“cuffdiff_B_IP_vs_N/gene_exp.diff”) 42 43 > cdstats_avb <- read.delim(“cuffdiff_A_vs_B/gene_exp.diff”) 44 > cdfpkms <- read.delim(“cuffdiff_A_vs_B/genes.read_group_tracking”) 45 46 47 48 49 8. Below are three examples of creating a list of genes expressed at certain thresholds in 50 the IP relative to the input in condition A. 51 52 > A_2fold <- filter(cdstats_aipvn, q_value < 0.05, log2.fold_change. > 1, value_2 > 5) 53 %>% select(“test_id”) 54 55 > A_1fold <- filter(cdstats_aipvn, log2.fold_change. > 0, value_2 > 5) %>% 56 select(“test_id”) 57 58 59 60 John Wiley & Sons Page 19 of 56 Current Protocols 1 2 3 4 > A_half_fold <- anti_join(dr1stats, filter(dr1stats, log2.fold_change. < -1, q_value < 5 0.05), by = "test_id") %>% filter(value_2 > 5) %>% select("test_id") 6 7 8 9 9. Filter condition B the same way, then join the 2 lists to create a list of genes that could 10 be important in either condition A or condition B. 11 12 > AB_2fold <- full_join(A_2fold, B_2fold) 13 14 15 10. Now the new list of genes that could be important in either condition can be used to filter 16 for genes that one would want to compare across conditions. 17 18 > AIP_vs_BIP <- inner_join(AB_2fold, cdstats_avb, by = “test_id”) 19 20 21 Cuffdiff includes a column in the output file gene_exp.diff labeled “significant” that 22 can be useful while graphing data assigning different colors to the binomial 23 values (“yes” or “no”). However, we find it useful to append additional columns 24 that can specify other criteria, for example, both statistically significant and 25 meaningfully different. 26 27 28 11. In the example below, we are using 2-fold again. 29 30 > temp1 <- filter(AIP_vs_BIP, abs(log2.fold_change.) > 1, q_value < 0.05) %>% 31 mutate(DE = “yes”) 32 > temp2 <- anti_join(AIP_vs_BIP, temp1, by = “test_id”) %>% mutate(DE = “no”) 33 > AIP_vs_BIP2 <- full_join(temp1, temp2) 34 35 36 12. A common graph made with RNA-Seq data is a volcano plot. This plots p-value vs fold 37 change, usually both have been log transformed. One can now use the newly created 38 “DE” column to highlight genes that are both 2-fold different and statistically different. 39 40 > volcplot <- ggplot2(AIP_vs_BIP2, aes(log2.fold_change., -log(q_value), color = DE) + 41 geom_point(alpha = 0.5) 42 43 By saving the plot as an object (“volcplot”) it is easy to call up the object and 44 modify various aspects experimenting with different options until one is satisfied 45 with the plot’s appearance. Below we have modified the default colors of the 46 points to black and red, drawn a horizontal line where the corrected p-value is 47 48 0.05, drawn lines indicating where the fold change is 2-fold, and selected a 49 different theme (Figure 2A). We like the way theme_classic and theme_bw look 50 but this is purely a matter of preference. 51 52 > volcplot + scale_color_manual(values=c("black", "red")) + geom_hline(yintercept = - 53 log(0.05)) + geom_vline(xintercept = c(-1,1)) + theme_bw() 54 55 Another common plot one could make is a scatter plot, plotting log transformed values 56 for a list of genes with condition A on one axis and condition B on the other. 57 58 59 60 John Wiley & Sons Current Protocols Page 20 of 56 1 2 3 4 > scatplot <- ggplot(AIP_vs_BIP2, aes(log2(value_1), log2(value_2), color = DE)) + 5 geom_point(alpha = 0.3) + 6 scale_color_manual(values = c("black", "red")) + 7 theme_classic() 8 9 10 One can relabel the axis and give the plot a title if they wish (Figure 2B). 11 12 > scatplot + labs(x = "A log2(FPKM)", y = "B log2(FPKM)", title = "A vs B") + 13 theme(plot.title = element_text(hjust = 0.5)) + 14 theme(text = element_text(size = 20)) 15 16 Finally, one may wish to plot individual genes of interest: 17 18 Boxplot 19 20 > box_geneA <- filter(cdfpkm, tracking_id == "GeneA") %>% 21 ggplot(aes(condition, FPKM, fill = condition)) + 22 geom_boxplot() + 23 theme_bw() + 24 theme(legend.position="none") + 25 scale_fill_manual(values = c("white", "red")) + 26 ylim(0,NA) 27 28 29 Add a title using labs, center justify using hjust, and change the font size with element 30 text (Figure 2C). 31 32 > box_geneA + labs(title = “GeneA”) + 33 theme(plot.title = element_text(hjust = 0.5)) + 34 theme(text = element_text(size = 20)) 35 36 Bar chart 37 38 > bar_geneA <- filter(cdfpkm, tracking_id == "GeneA") %>% 39 ggplot(aes(condition,FPKM)) + 40 stat_summary(fun.data="mean_se", geom="bar", color="black", aes(fill=condition)) + 41 stat_summary(fun.data="mean_se", geom="errorbar", width=0.3, color="black") + 42 theme_bw() + 43 theme(legend.position="none") + 44 scale_fill_manual(values = c("white", "red")) + 45 labs(title = “GeneA”) + 46 theme(plot.title = element_text(hjust = 0.5)) + 47 48 theme(text = element_text(size = 20)) 49 50 Add in individual data points for greater data reporting transparency (Figure 2D). 51 52 > bar_geneA + geom_point(size = 3) 53 54 55 56 [*Copy Editor – Insert Figure 2 here] 57 58 59 60 John Wiley & Sons Page 21 of 56 Current Protocols 1 2 3 REAGENTS AND SOLUTIONS 4 5 6 Tris base (Sigma; cat. No. T1503). 7 8  Prepare a 1.5M pH 7.4 stock solution (pH with HCL) and store at 4ºC 9 10 KCl (Sigma; cat. No. P9541). 11  Prepare a 1M stock solution and store at 4ºC 12 13 MgCl2 (Sigma; cat. No. 63068). 14  Prepare a 1M stock solution and store at 4ºC 15 Nonidet P-40 Substitute, 10% (w/v) solution (Roche, cat. No. 11 332 473 001) 16 Cycloheximide (Sigma, cat. No. C7698). 17  Prepare a 5mg/ml stock solution and store at -20ºC. 18 Heparin (Sigma; cat. No. H3393). 19  Prepare a 100 mg/ml stock solution and store at 4ºC. 20 Phosphate buffered saline (PBS), tablets (Millipore, cat. No. 524650-1EA). 21 22  Prepare at 1x with water. 23 Paraformaldehyde (Sigma cat. No. P6148). 24  Prepare a 4% solution in PBS 1x. Adjust pH to 7.4. Prepare fresh and store at 4ºC. 25 Sucrose (Sigma cat. No. 84097). 26  Prepare a 30% solution in PBS 1x pH 7.4. Store at 4ºC. 27 Sodium Azide (Sigma, cat. No. S2002). 28  Prepare a 0.02% solution in PBS pH 7.4 29 Triton X-100 (Millipore, cat. No. 648466). 30  Prepare a 0.2% solution in PBS pH 7.4. 31 32 33 Homogenization buffer: 34 35  50 mM Tris pH7.5 36  100 mM KCl 37  12 mM MgCl2 38  1% Nonidet P-40 39  1 mM DTT 40  200 U/mL Promega RNasin 41  1 mg/mL heparin 42  100 ug/mL cycloheximide 43  protease inhibitor mixture (1x) 44 45 46 Optional: The addition of 1% sodium deoxycholate (DOC) to the homogenization buffer 47 reduces some of the non-specific background. We have shown that DOC does not 48 significantly reduce the purification of specific polysomes and suggest testing it in your 49 experiments. 50 51 Homogenization buffer is prepared by adding defined volumes of stock solutions for Tris 52 (1.5M pH 7.4), KCl (1M) and MgCl2 (1M) to nuclease free water to achieve the final 53 concentrations described above in the desired volume. Right before homogenization, 54 buffer is supplemented with DTT, protease inhibitors, RNasin, heparin and 55 cycloheximide. 56 57 58 59 60 John Wiley & Sons Current Protocols Page 22 of 56 1 2 3 High salt buffer: 4  50 mM Tris pH 7.5 5  300 mM KCl 6 7  12 mM MgCl2 8  1% Nonidet P-40 9  0.5 mM DTT 10  100 ug/mL cycloheximide 11 12 Prepare using Tris, KCl and MgCl2 stock solutions. Add DTT and Cycloheximide right 13 before using. 14 15 16 17 COMMENTARY 18 19 Background Information 20 It has been a little over 10 years since the first development of cell-type specific 21 22 ribosome tagging strategies that permitted the analysis of a cell's translatome (the mRNAs 23 engaged on polyribosomes) (Heiman et al., 2008). The original technique utilized BAC 24 transgenic mice that were engineered to express a GFP-tagged 60S ribosomal subunit protein 25 (Rpl10a) behind a cell type specific promoter. Antibodies directed against GFP were used to 26 immunoprecipitate the tagged polyribosomes from which mRNA could then be isolated and 27 analyzed by qRT-PCR, microarray, or RNA-Seq. This approach required the generation of novel 28 BAC transgenic mice specific for each cell type and did not take advantage of the many tissue 29 specific Cre recombinase mouse lines that were already developed. 30 We developed an alternative approach described in this RiboTag protocol in which an 31 HA-tagged ribosomal protein (Rpl22) is conditionally expressed from the mouse genome by the 32 action of Cre recombinase (Sanz et al., 2009). This approach takes advantage of the constantly 33 expanding diversity of cell type specific Cre recombinase expressing mouse lines. Both 34 approaches suffer from the same limitation: the investigator must have already identified a gene 35 promoter that is specific for the cell type of interest. Although it is unlikely that subtypes of 36 neurons can be uniquely defined by the expression of a single Cre recombinase transgene, the 37 ribosome tagging strategies have facilitated analysis of the mRNAs engaged with 38 polyribosomes in many neuronal cell types and can also be used to study the regulation of 39 specific genes in vivo. 40 41 One alternative approach that has rapidly been adopted by neuroscientists is single cell 42 transcript analysis by direct RNA-Seq. A single anatomical structure such as the arcuate 43 nucleus of the hypothalamus is dissociated into single cells, the transcripts from each cell are 44 uniquely barcoded, and the RNA is analyzed by RNA-Seq. Clustering analysis is used to 45 identify unique cell types including neurons and glial cells, and then the neurons can be further 46 clustered into discreet sub-types based on their transcript phenotype. This unbiased approach 47 is very useful to explore the full range of neurons in a specific anatomical location and will 48 rapidly leading to transcriptome phenotypes for all of the neurons and glial cells in a mouse 49 brain. One limitation of this approach is that relatively non-abundant transcripts escape 50 detection due to the large number of different cell types being sequenced en masse. Another 51 limitation is that the techniques that must be used to obtain single cells, especially starting with 52 the brain, can cause alterations in gene expression (e.g. induction of immediate early genes) 53 (Moffitt et al., 2018; van den Brink et al., 2017) and may also lose transcripts that are 54 sequestered in neuronal processes where they are also likely to be under translational control. 55 56 57 58 59 60 John Wiley & Sons Page 23 of 56 Current Protocols 1 2 3 The ribosome capture approaches like RiboTag will only isolate mRNAs that have ribosomes 4 bound to them and are expected to be actively translated into protein. 5 6 7 Critical Parameters and Troubleshooting 8 9 10 1. Cell type Specificity: The specificity of the RiboTag analysis depends on the 11 specificity of the Cre recombinase transgenic mouse line and every effort should be made to 12 verify that the Cre transgenic drives expression of Rpl22-HA in the appropriate cell type and 13 does not give rise to ectopic expression in other tissues that would interfere with the analysis. It 14 is important to check that recombination of the RiboTag allele is not occurring during early 15 development giving rise to mice with variable expression in many other cell types. This can be 16 done by analyzing expression of Rpl22-HA by immunohistochemistry and comparing it to a 17 marker for the cells of interest as described in Basic Protocol 2. We also routinely screen for 18 RiboTag recombination in tail DNA during genotyping as described in the Support Protocol 1. 19 This will help identify mice that have undergone recombination of the RiboTag allele during early 20 development and they should be removed from the cohort to be studied as adults. 21 22 2. Background and Controls: When we analyze the RNA from a RiboTag experiment 23 we find that the immunoprecipitated polysomes still contain mRNAs from cells that are not 24 expressing Cre recombinase and Rpl22-HA (e.g. glial cells). These transcripts are depleted in 25 the IP (IP/input ratios <0.3) but since some are abundantly expressed, they will still appear at 26 significant levels in the IP. As described in Basic Protocol 4 we advise analyzing both the input 27 28 and IP RNA and then considering whether to filter out background gene transcripts that are 29 depleted. In some of our experiments we are interested in gene transcripts which are enriched in 30 the cell type being studied and help define a unique phenotype for those neurons. In addition to 31 the background of depleted mRNAs seen in the IP we have observed that the IP may also contain 32 enriched transcripts from genes that are clearly not expressed in the cell type of interest. These 33 false positives can confound the data and we suggest doing a control IP experiment starting with 34 equivalent tissue from a mouse that does not express RiboTag. Transcripts that enrich in the 35 absence of Rpl22-HA expression are artifacts of the technique and should be eliminated from the 36 specific transcripts isolated when Rpl22-HA is expressed. As described in the protocol we have 37 recently been able to reduce the capture of these artifactually enriched genes by including an 38 ionic detergent, sodium deoxycholate, in the initial homogenization buffer. 39 40 3. Anticipated Results and Statistical Analysis: Each tissue homogenate yields two 41 RNA-Seq libraries, an input and an IP. We typically run 8-16 RNA-Seq libraries on a NextSeq 42 500/550 flow cell and expect to have 25-50 million aligned reads for each library. Statistical 43 analysis of this data requires at least 3 replicates making these experiments quite expensive in 44 terms of reagents. If one is trying to phenotype the cells the first analysis would be to determine 45 all of the expressed genes comparing IP to input and then to rank those transcripts by the ratio 46 of IP to input. Transcripts that come up as enriched in a control IP from a mouse that does not 47 48 express RiboTag should be removed from this transcript phenotype. If the animals have been 49 treated with pharmacologic agents or exposed to physiological challenges, transcripts that 50 change between samples can be identified and statistically analyzed as described in Basic 51 Protocol 4. It is very helpful to correlate the specific transcripts that are enriched in the Cre- 52 expressing cells with the in situ hybridization results from the Allen Mouse Brain Atlas. 53 Specificity can also be confirmed using RNAscope (Advanced Cell Diagnostics) to colocalize 54 transcripts in the anatomical region of interest. One can also confirm results by qRT-PCR 55 starting with either the original RNA samples or qPCR with the DNA libraries. 56 57 58 59 60 John Wiley & Sons Current Protocols Page 24 of 56 1 2 3 The RiboTag approach is ideally suited to studying the dynamic regulation of genes in a 4 specific neuronal population. Transcripts can be induced within minutes after the addition of 5 hormones and drugs or changes in nutritional or environmental conditions. When an mRNA is 6 specifically induced in the RiboTag targeted cell but constitutively expressed in neighboring 7 cells, the level of transcripts in the IP will increase and the enrichment (IP/input) will also 8 9 increase. If a gene like Fos is induced in all of the cells in the tissue being analyzed, then you 10 will see an increase in IP transcripts for Fos but no change in enrichment. 11 Translational control: RiboTag techniques will only isolate mRNAs that have 12 ribosomes bound and this implies that these mRNAs are being actively translated. However, it 13 is important to consider that RiboTag is not going to be very sensitive to how many ribosomes 14 are engaged on a particular mRNA. For example, if an mRNA is under translational control and 15 only has 2-3 ribosomes in the repressed state and 12 or more ribosomes when induced, the 16 efficiency of immunoprecipitation may not change enough to be a quantitative indicator of 17 translational activity. Ribosomes may also remain attached to mRNAs even when elongation 18 and active synthesis of protein is repressed. 19 20 4. Time Considerations: One of the advantages of the RiboTag procedure is that 21 once the animals are obtained either by mating RiboTag mice with a specific Cre driver 22 transgenic or stereotaxically injecting AAV-RiboTag virus and waiting for RiboTag expression, 23 the immunoprecipitation of specific RNA can be completed in a day. Production of DNA 24 libraries for RNA-Seq and DNA sequencing can be completed in a few more days. The most 25 time-consuming step is the analysis of results and this depends on what questions are being 26 asked and the number of different tissues and conditions being studied. 27 28 29 ACKNOWLEDGEMENT 30 Supported by NIH grants GM032875 and MH086386 (GSM), F32DK116432 (JCB) Marie 31 Sklodowska-Curie Individual Fellowship (H2020-MSCA-IF-2014-658352)(ES), and Ramón y 32 Cajal fellowship (RyC-2012-11873) European Research Council (Starting grant Neuromito, 33 ERC-2014-StG-638106 and Proof of Concept ERC-2018-PoC-825569) 34 Mineco Proyectos I+D de Excelencia (SAF2014-57981P; SAF2017-88108-R) 35 Agaur (2017SGR-323)(AQ) 36 37 38 39 LITERATURE CITED 40 41 Boom, R., Sol, C. J., Salimans, M. M., Jansen, C. L., Wertheim-van Dillen, P. M., & van der 42 Noordaa, J. (1990). Rapid and simple method for purification of nucleic acids. Journal of 43 44 clinical microbiology, 28(3), 495-503. Retrieved from 45 http://www.ncbi.nlm.nih.gov/pubmed/1691208. 46 Dafforn, A., Chen, P., Deng, G., Herrler, M., Iglehart, D., Koritala, S., . . . Kurn, N. (2004). Linear 47 mRNA amplification from as little as 5 ng total RNA for global gene expression analysis. 48 BioTechniques, 37(5), 854-857. Retrieved from 49 http://www.ncbi.nlm.nih.gov/pubmed/15560142. doi:10.2144/04375PF01 50 Dong, H. W. (2019). Allen Mouse Brain Atlas. Retrieved from http://mouse.brain- 51 map.org/static/atlas 52 Gentleman, R., Ihaka, R., Bates, D., Chambers, J., Dalgaard, P., Hornik, K., . . . Murdoch, D. 53 (2018). R (Version 3.5.1) [language and environment for statistical computing]: The R 54 Foundation. Retrieved from https://www.r-project.org/ 55 Gore, B. B., Soden, M. E., & Zweifel, L. S. (2013). Manipulating gene expression in projection- 56 specific neuronal populations using combinatorial viral approaches. Curr Protoc 57 58 59 60 John Wiley & Sons Page 25 of 56 Current Protocols 1 2 3 Neurosci, 65, 4 35 31-20. Retrieved from 4 https://www.ncbi.nlm.nih.gov/pubmed/25429312. doi:10.1002/0471142301.ns0435s65 5 Haimon, Z., Volaski, A., Orthgiess, J., Boura-Halfon, S., Varol, D., Shemer, A., . . . Jung, S. 6 (2018). Re-evaluating microglia expression profiles using RiboTag and cell isolation 7 strategies. Nat Immunol, 19(6), 636-644. doi:10.1038/s41590-018-0110-6 8 9 Hannon_Lab, G. J. (2014). FASTX-Toolkit (Version 0.0.14): Hannon Lab. Retrieved from 10 http://hannonlab.cshl.edu/fastx_toolkit/download.html 11 Heiman, M., Schaefer, A., Gong, S., Peterson, J. D., Day, M., Ramsey, K. E., . . . Heintz, N. 12 (2008). A translational profiling approach for the molecular characterization of CNS cell 13 types. Cell, 135(4), 738-748. Retrieved from 14 https://www.ncbi.nlm.nih.gov/pubmed/19013281. doi:10.1016/j.cell.2008.10.028 15 Illumina. (2016). Introduction to sequencing by synthesis. Retrieved from 16 https://www.youtube.com/watch?v=fCd6B5HRaZ8 17 Illumina. (2017a). bcl2fastq2 (Version 2.2): Illumina. Retrieved from 18 https://support.illumina.com/downloads/bcl2fastq-conversion-software-v2-20.html 19 Illumina. (2017b). bcl2fastq Conversion v2.19. 2. Retrieved from 20 https://support.illumina.com/content/dam/illumina- 21 support/documents/documentation/software_documentation/bcl2fastq/bcl2fastq2_guide_ 22 15051736_v2.pdf 23 Illumina. (2018a). Denature and dilute libraries guide. NextSeq system. 6. Retrieved from 24 https://support.illumina.com/content/dam/illumina- 25 support/documents/documentation/system_documentation/nextseq/nextseq-denature- 26 dilute-libraries-guide-15048776-06.pdf 27 28 Illumina. (2018b). NextSeq 500: System guide. 4. Retrieved from 29 https://support.illumina.com/content/dam/illumina- 30 support/documents/documentation/system_documentation/nextseq/nextseq-500- 31 system-guide-15046563-04.pdf 32 Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013). TopHat2: 33 accurate alignment of transcriptomes in the presence of insertions, deletions and gene 34 fusions. Genome biology, 14(4), R36. Retrieved from 35 http://www.ncbi.nlm.nih.gov/pubmed/23618408. doi:10.1186/gb-2013-14-4-r36 36 Kurn, N., Chen, P., Heath, J. D., Kopf-Sill, A., Stephens, K. M., & Wang, S. (2005). Novel 37 isothermal, linear nucleic acid amplification systems for highly multiplexed applications. 38 Clinical chemistry, 51(10), 1973-1981. Retrieved from 39 http://www.ncbi.nlm.nih.gov/pubmed/16123149. doi:10.1373/clinchem.2005.053694 40 Langmead, B., Kim, D., Charles, R., Wilks, C., & Antonescu, V. (2018). Bowtie2 (Version 41 2.3.4.3) [Sequencer aligner]. Retrieved from http://bowtie- 42 bio.sourceforge.net/bowtie2/index.shtml 43 Melzak, K. A., Sherwood, C. S., Turner, R. F. B., & Haynes, C. A. (1996). Driving forces for DNA 44 adsorption to silica in perchlorate solutions. Journal of Colloid and Interface Science, 45 181(2), 635-644. Retrieved from ://A1996UZ53600031. doi:DOI 46 10.1006/jcis.1996.0421 47 48 Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., . . . 49 Zhuang, X. (2018). Molecular, spatial, and functional single-cell profiling of the 50 hypothalamic preoptic region. Science, 362(6416). Retrieved from 51 https://www.ncbi.nlm.nih.gov/pubmed/30385464 52 http://science.sciencemag.org/content/sci/362/6416/eaau5324.full.pdf. 53 doi:10.1126/science.aau5324 54 NEB. (2017). NEBNext: Library quant kit for illumina. 1.2. Retrieved from https://www.neb.com/- 55 /media/catalog/datacards-or-manuals/manuale7630.pdf 56 57 58 59 60 John Wiley & Sons Current Protocols Page 26 of 56 1 2 3 NuGEN. (2017). User guide: Trio RNA-seq. 2. Retrieved from 4 https://www.nugen.com/sites/default/files/M01440v2_User_Guide%3A_Trio_RNA- 5 Seq_4270.pdf 6 Paxinos, G., & Franklin, K. B. J. (2013). The Mouse Brain in Stereotaxic Coordinates. San 7 Diego, CA: Academic Press. 8 9 Quintana, A., Sanz, E., Wang, W., Storey, G. P., Guler, A. D., Wanat, M. J., . . . Palmiter, R. D. 10 (2012). Lack of GPR88 enhances medium spiny neuron activity and alters motor- and 11 cue-dependent behaviors. Nat Neurosci, 15(11), 1547-1555. Retrieved from 12 https://www.ncbi.nlm.nih.gov/pubmed/23064379. doi:10.1038/nn.3239 13 Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L., & Pachter, L. (2011). Improving RNA-Seq 14 expression estimates by correcting for fragment bias. Genome biology, 12(3), R22. 15 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21410973. doi:10.1186/gb-2011-12- 16 3-r22 17 RStudio. (2018). RStudio (Version 1.1.463) [integrated development environment (IDE)]: 18 RStudio. Retrieved from https://www.rstudio.com/products/rstudio/download/#download 19 Sanz, E., Evanoff, R., Quintana, A., Evans, E., Miller, J. A., Ko, C., . . . McKnight, G. S. (2013). 20 RiboTag analysis of actively translated mRNAs in Sertoli and Leydig cells in vivo. PLoS 21 One, 8(6), e66179. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/23776628. 22 doi:10.1371/journal.pone.0066179 23 Sanz, E., Quintana, A., Deem, J. D., Steiner, R. A., Palmiter, R. D., & McKnight, G. S. (2015). 24 Fertility-regulating Kiss1 neurons arise from hypothalamic POMC-expressing 25 progenitors. J Neurosci, 35(14), 5549-5556. Retrieved from 26 https://www.ncbi.nlm.nih.gov/pubmed/25855171. doi:10.1523/JNEUROSCI.3614- 27 28 14.2015 29 Sanz, E., Yang, L., Su, T., Morris, D. R., McKnight, G. S., & Amieux, P. S. (2009). Cell-type- 30 specific isolation of ribosome-associated mRNA from complex tissues. Proc Natl Acad 31 Sci U S A, 106(33), 13939-13944. Retrieved from 32 https://www.ncbi.nlm.nih.gov/pubmed/19666516. doi:10.1073/pnas.0907143106 33 Trapnell, C., Hendrickson, D., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2014). 34 Cufflinks (Version 2.2.1) [Transcriptome assembly and differential expression analysis 35 for RNA-Seq]. Retrieved from http://cole-trapnell-lab.github.io/cufflinks/install/ 36 Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013). 37 Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature 38 biotechnology, 31(1), 46-+. Retrieved from ://000313563600021. 39 doi:10.1038/nbt.2450 40 Trapnell, C., Kim, D., Pertea, G., Pimentel, H., Kelley, R., Pachter, L., & Salzberg, S. (2016). 41 TopHat 2 (Version 2.1.1) [Splice junction mapper]. Retrieved from 42 http://ccb.jhu.edu/software/tophat/index.shtml 43 Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with 44 RNA-Seq. Bioinformatics, 25(9), 1105-1111. Retrieved from 45 http://www.ncbi.nlm.nih.gov/pubmed/19289445. doi:10.1093/bioinformatics/btp120 46 Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., . . . Pachter, L. (2012). 47 48 Differential gene and transcript expression analysis of RNA-seq experiments with 49 TopHat and Cufflinks. Nature protocols, 7(3), 562-578. Retrieved from 50 http://www.ncbi.nlm.nih.gov/pubmed/22383036. doi:10.1038/nprot.2012.016 51 Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., . . . Pachter, 52 L. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated 53 transcripts and isoform switching during cell differentiation. Nature biotechnology, 28(5), 54 511-515. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/20436464. 55 doi:10.1038/nbt.1621 56 57 58 59 60 John Wiley & Sons Page 27 of 56 Current Protocols 1 2 3 van den Brink, S. C., Sage, F., Vertesy, A., Spanjaard, B., Peterson-Maduro, J., Baron, C. S., . . 4 . van Oudenaarden, A. (2017). Single-cell sequencing reveals dissociation-induced gene 5 expression in tissue subpopulations. Nat Methods, 14(10), 935-936. Retrieved from 6 https://www.ncbi.nlm.nih.gov/pubmed/28960196. doi:10.1038/nmeth.4437 7 Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York,NY: Springer- 8 9 Verlag New York. 10 Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., . . . Yutani, H. 11 (2018). ggplot2 (Version 3.1.0). Retrieved from https://ggplot2.tidyverse.org/ 12 Wickham, H., François, R., Henry, L., & Müller, K. (2018). dplyr (Version 0.7.8): RStudio. 13 Retrieved from https://dplyr.tidyverse.org/ 14 Wickham, H., & Henry, L. (2018). tidyr (Version 0.8.2.9000): RStudio. Retrieved from 15 https://tidyr.tidyverse.org/ 16 Williams, R. W. (2000). Mapping genes that modulate mouse brain development: a quantitative 17 genetic approach (A. F. Goffinet & P. Rakic Eds.). New York: Springer. 18 19 20 21 FIGURE LEGENDS 22 23 Figure 1. Cartoon comparing the RiboTag mouse and AAV-DIO-RiboTag approaches 24 25 On the left side, the Rpl22 locus was modified by homologous recombination to insert LoxP 26 sites on either end of the last exon (Ex4) of the Rpl22 gene and a duplicated Ex4 with an HA tag 27 was inserted downstream. The CRE-Driver mouse removes the WT Ex4 sequence in specific 28 cells and the transcript now splices in Ex4-HA (shown in green). Polyribosomes from cells 29 expressing the Ex4-HA are recovered from a lysate by immunoprecipitation (IP) with an 30 antibody directed against HA and magnetic beads coated with an artificial Protein A/G peptide. 31 RNA is isolated from the IP polyribosomes and RNA is also isolated from the lysate. Gene 32 expression analysis is done by RNA-Seq. On the right side the DIO-RiboTag viral vector is 33 shown schematically. In the presence of Cre-recombinase the Rpl22-HA-IRES-YFP cassette is 34 flipped and locked in the ON configuration so that the eIF1a promoter now drives expression of 35 Rpl22-HA in specific cells. RNA is isolated from the IP and compared with the input. 36 37 38 Figure 2. Example plots of hypothetical RNA Seq data. A, Volcano plot of data with log 39 base 2 transformed fold change (condition A vs condition B) plotted on the x axis and negative 40 natural log transformed corrected p-value (q-value) plotted on the y axis. The solid horizontal 41 line denotes where q-value is equal to 0.05. The two solid vertical lines denote where fold 42 change is equal to 2 (or -2). Data points are colored red if they are both 2-fold different and have 43 44 a q-value less than 0.05; otherwise they are black. B, Scatter plot of data with log2 transformed 45 fragments per kilobase million (FPKM) plotted on the x axis for condition A and the y axis for 46 condition B. As in A data points are colored red if they are both 2-fold different and have a q- 47 value less than 0.05; otherwise they are black. C, Box plots for Gene A for condition A and B. 48 FPKM are plotted on the y axis with the two conditions represented side by side on the x axis. 49 D, Bar graphs denoting mean plus or minus standard error of the mean (SEM) for Gene A for 50 condition A and B. FPKM are plotted on the y axis with the two conditions represented side by 51 side on the x axis. Individual data points are superimposed on top of the bars for greater data 52 reporting transparency. DE, differentially expressed. 53 54 55 56 57 58 59 60 John Wiley & Sons Current Protocols Page 28 of 56 1 2 3 4 RiboTag: Ribosomal Tagging Strategy to Analyze Cell Type Specific 5 mRNA Expression In Vivo 6 7 Running Title: RiboTag protocol 8 9 Authors: Elisenda Sanz1, Jonathan C. Bean2, Daniel P. Carey2, Albert Quintana1, and G. 10 Stanley McKnight2, 11 1Department of Cell Biology, Physiology, and Immunology and Neuroscience Institute, 12 Autonomous University of Barcelona, Barcelona, Spain 13 E.Sanz: email: elisenda.sanz@uab.cat, tel:+34 93 581 4968 14 A. Quintana: email: albert.quintana@uab.cat, tel: +34 93 581 4967 15 2Department of Pharmacology, University of Washington, Seattle, WA 98195 16 J. Bean: email: bean79@uw.edu, tel:206-543-0144 17 18 D. Carey: email: careyd5@uw.edu tel: 206-543-0144 19 S. McKnight: email: mcknight@uw.edu, tel:206-616-4237 20 21 Abstract: 22 Ribosome tagging has become a very useful in vivo approach for analyzing gene expression 23 and mRNA translation in specific cell types that are difficult and time consuming to isolate by 24 conventional methods. The approach is based on selectively expressing an HA-tagged 25 ribosomal protein in a target cell type and then using antibodies against HA to purify the 26 polysomes and associated mRNAs from the target cell. The original approach makes use of a 27 mouse line (RiboTag) harboring a modified allele of Rpl22 (Rpl22-HA) that is induced by the 28 action of Cre recombinase. The Rpl22-HA gene can also be introduced into the animal by 29 stereotaxic injections of an AAV-DIO-Rpl22-HA that is then activated in Cre-expressing cells. 30 Both methods for tagging ribosomes facilitate the immunoprecipitation of ribosome bound 31 mRNAs and their analysis by qRT-PCR or RNA-Seq. This protocol will discuss the technical 32 procedures and describe important considerations relevant to the analysis of the data. 33 34 Introduction: 35 36 37 The analysis of gene expression provides a description of a cell's potential functions and its 38 response to extracellular signals. However, when researchers attempt to analyze gene 39 expression in vivo they are confronted with the problem that all tissues contain a diverse array 40 of different cell types with unique functions. Efforts to isolate a specific cell type for analysis 41 based on sorting techniques can be successful but these additional manipulations are both time 42 consuming and have been shown to alter transcription (Haimon et al., 2018). The brain is a 43 particularly challenging tissue in which to quantitate gene expression and translation. A typical 44 mouse brain is made up of around 75 million neurons, 23 million glial cells, 7 million endothelial 45 cells and several million other miscellaneous cell types (Williams, 2000). Neurons are 46 extremely diverse in terms of their expression of various neurotransmitters, neuropeptides, and 47 receptors and typically these neurons are anatomically intermingled making them very difficult to 48 separate for molecular analysis. In situ analysis of mRNA expression has been very useful in 49 identifying the overall anatomical distribution of neurons expressing specific genes and the Allen 50 Brain Institute has assembled a comprehensive database for both the mouse and human brain 51 which is very useful. However, anatomical localization cannot define specific neuronal cell 52 types. Characterization of the mRNA expression phenotype of specific neurons has progressed 53 with the development of ribosome-tagging approaches, cell sorting based on promoter specific 54 expression of fluorescent marker proteins, and single-cell RNA-Seq. Each approach has 55 advantages and disadvantages. In this protocol we describe a ribosome-tagging approach 56 57 58 59 60 John Wiley & Sons Page 29 of 56 Current Protocols 1 2 3 (RiboTag) which our lab developed (Sanz et al., 2009) and discuss the application of this 4 approach to the analysis of polyribosome associated mRNA transcripts in specific populations of 5 neurons. The RiboTag approach takes advantage of the hundreds of specific promoter-driven 6 Cre recombinase transgenics currently available (Jackson Laboratories has more than 480 7 lines) and others are being added at a rapid pace. 8 9 10 There are two basic approaches involving the use of RiboTag as diagrammed in Figure 1 and 11 each has advantages and disadvantages. In the first approach we use a RiboTag mouse line in 12 which the 60S ribosomal gene, Rpl22, was modified so that in the presence of Cre recombinase 13 the last exon of Rpl22 will be replaced with an HA-tagged exon effectively labeling the cell's 14 polyribosomes so that they can be isolated by immunoprecipitation. These RiboTag mice are 15 crossed to a Cre recombinase driver mouse line and the double heterozygotes are used for 16 analysis. Immunoprecipitation of polyribosomes from neuronal tissue purifies only the 17 polysomes that are tagged in the Cre-expressing cells. The mRNA from these polysomes is 18 isolated and the transcripts analyzed by qRT-PCR or RNA-Seq. The second approach utilizes a 19 Rpl22-HA construct in a flex or DIO virus (AAV) flanked by two different sets of loxP sites (Sanz 20 et al., 2015). When the AAV-DIO-RiboTag is injected into a specific anatomical region of the 21 brain of an adult mouse the Rpl22-HA is only expressed in the neurons that are actively 22 expressing Cre recombinase and also infected by the AAV. After waiting several weeks for the 23 Rpl22-HA to be expressed and incorporated into polyribosomes, tissue samples are isolated 24 and used to immunoprecipitate HA-tagged polyribosomes followed by qRT-PCR or RNA-Seq. 25 The viral approach requires accurate stereotaxic injections of virus and is more labor intensive 26 but has the advantage that only adult neurons that still express the Cre transgenic will be 27 28 targeted. The direct cross of RiboTag mice with Cre driver mice is easier to do but has the 29 potential disadvantage that the Rpl22-HA could continue to be expressed in neurons that have 30 differentiated from a progenitor population that expressed Cre recombinase at an earlier stage 31 during development. One can take advantage of this difference by comparing results from the 32 same brain region using the RiboTag mouse and the RiboTag virus using the same Cre driver 33 transgenic. We have used this approach to demonstrate that embryonic Pomc-expressing 34 progenitor neurons differentiate into both Agrp and Kiss1 expressing neurons in the adult 35 arcuate nucleus of the hypothalamus (Sanz et al., 2015). 36 37 [*Copy Editor – place Figure 1 here] 38 39 Basic Protocol 1 outlined below describes our methods in detail using the RiboTag mouse 40 crossed with a specific Cre-recombinase expressing transgenic mouse to isolate polyribosomes 41 by immunoprecipitation from brain homogenates and then further isolate RNA from both the 42 input (original homogenate) and the immunoprecipitated (IP) polysomes. Alternative Protocol 43 1 describes the viral approach using stereotaxic injection of AAV-DIO-RiboTag into adult Cre- 44 recombinase expressing mouse brain to avoid developmental activation of RiboTag expression. 45 In Support Protocol 1 we describe the breeding and genotyping of RiboTag mice and in 46 Support Protocol 2 we describe the analysis of captured RNA using qRT-PCR. Basic 47 48 Protocol 2 discusses the use of RiboTag mice crossed with a specific Cre transgenic line to 49 identify the anatomical localization of RiboTag expression by immunohistochemistry. In Basic 50 Protocol 3 we describe the current approach our lab is using to produce DNA libraries for RNA- 51 Seq. Basic Protocol 4 describes the bioinformatic tools that we are using to analyze the RNA- 52 Seq data. In the Commentary we will provide a discussion of these methods and identify some 53 of the most critical parameters and potential trouble spots. 54 55 BASIC PROTOCOL 1 56 57 58 59 60 John Wiley & Sons Current Protocols Page 30 of 56 1 2 3 The RiboTag Assay 4 5 This approach has been described and updated in several publications from our laboratory 6 (Quintana et al., 2012; Sanz et al., 2013; Sanz et al., 2015; Sanz et al., 2009) and is shown 7 schematically in Figure 1. We have now introduced modifications that improve the efficiency of 8 9 the immunocapture and therefore increase the RNA yields obtained in the immunoprecipitates. 10 These modifications include using an indirect method for the immunocapture of HA-tagged 11 polysomes (instead of the direct immunoprecipitation procedure described in the original 12 publication) and using Protein A/G beads with a higher binding capacity for IgGs. 13 14 Materials: 15 16 Mice: 17 RiboTag mouse line (JAX stock No. 011029) 18 Cre-driver mouse line of choice 19 20 21 Equipment: 22 Large sharp surgical scissors (FST cat No. 14001-14) 23 Small extra fine scissors (FST cat No. 14084-08) 24 Spatula (FST cat No. 10091-12) 25 Harris Uni-Core (Electron Microscopy Sciences cat No. 69036) 26 Razor blades (Electron Microscopy Sciences cat No. 71960) 27 28 Mouse brain matrix (Electron Microscopy Sciences cat No. 69022) 29 Glass Dounce homogenizers (2ml or 7ml) (Sigma cat. No. 8938 or 9063) 30 Refrigerated centrifuge (Eppendorf; cat. No. 5417R) with rotor for 1.5 ml microcentrifuge tubes. 31 Microcentrifuge tubes (Axygen; MCT-175-C) 32 10 ul ART Barrier pipette tips (Thermo Fisher Sci.; cat. No. 2140-HR) 33 20 ul ART Barrier pipette tips (Thermo Fisher Sci.; cat. No. 2149-HR) 34 200 ul ART Barrier pipette tips (Thermo Fisher Sci.; cat. No. 2069-HR) 35 1000 ul ART Barrier pipette tips (Thermo Fisher Sci.; cat. No. 2179-HR) 36 Microtube magnetic stand (Thermo Fisher Sci.; cat. No. 12320D) 37 Microtube rotator (Thermo Fisher Sci.; cat No. 88881002) 38 Nanodop One (Thermo Fisher Sci.; cat. No. 13-400-518) 39 Fluorescence microplate reader (for Quant-iT Ribogreen RNA assays) 40 2100 Bioanalyzer Instrument (Agilent; cat. No. G2939BA) 41 42 Materials: 43 Sterile water for irrigation (Baxter, cat. No. 2F7114) 44 1M DTT, 10X 0.5ml ampules (Sigma; cat. No. 646563-10X 0.5ml). Aliquot open ampules and 45 store at -80ºC 46 Protease inhibitor cocktail (Sigma, cat. No. P8340-5ml). Aliquot and store at -20C 47 48 RNAsin (40U/ul) (Promega, cat. No. N2115) 49 b-mercaptoethanol (Bio-Rad; cat. No.1610710 50 Pierce protein A/G magnetic beads (Thermo Fisher Sci.; cat. No. 88803) 51 Covance Anti-HA.11 Epitope Tag Antibody (BioLegend; cat. No. MMS-101R). Aliquot and store 52 at -20ºC 53 RNeasy Mini Kit (Qiagen; cat. No. 74104) or RNeasy Micro kit (Qiagen; cat. No. 74004) 54 Quant-iT RiboGreen RNA Assay kit (Thermo Fisher Sci.; cat. No. R11490) 55 RNA 6000 Nano kit or RNA 6000 Pico kit (Agilent; cat. No. G2939BA, 5067-1511 or 5067-1513) 56 Ethanol (200 proof) 57 58 59 60 John Wiley & Sons Page 31 of 56 Current Protocols 1 2 3 4 5 Protocol steps: 6 7 Harvest tissue 8 9 10 1. Euthanize mice using IACUC approved procedures such as carbon dioxide inhalation 11 2. Decapitate mice using large sharp surgical scissors 12 3. Make a midline incision in the skin and expose the skull 13 4. Using small scissors and a spatula remove the skull 14 5. Lift the brain out of the skull and use a spatula to section the optic and other cranial 15 nerves 16 6. Place the brain in a cold block or brain matrix for further dissection. Sampling tools such 17 as Harris Uni-cores can be used to punch the area of interest. 18 19 Lysate preparation 20 21 7. Clean Dounce tissue grinder and dissection tools and rinse thoroughly with RNase-free 22 water (sterile water for irrigation can be used at this step). Air dry and place Dounce 23 homogenizer on ice. 24 8. Prepare the homogenization buffer as described in the Reagents and Solutions section 25 and keep on ice. 26 9. Dissect the tissue containing the cell type of interest. Specific areas or whole tissue can 27 28 be used in this approach. 29 A brain matrix and sampling tools such as brain punches or Harris Uni-Cores can be 30 used to precisely dissect the area of interest. Dissected tissue or punches of the area 31 of interest can either be flash-frozen in liquid nitrogen right after dissection and 32 stored at -80ºC for months or processed fresh. 33 10. Place the tissue in the ice-cold Dounce homogenizer vessel containing a defined volume 34 of pre-chilled homogenization buffer and homogenize using 40 strokes of the loose- 35 fitting pestle (type A pestle), followed by 40 more strokes with the tight-fitting pestle (type 36 B pestle). 37 We typically use a 2-5% w/v ratio (for areas with a limited number of cells from the 38 cell type of interest a pool of punches of the same area from different animals can be 39 used to increase RNA yields). For small amounts of tissue (10-50mg tissue) we use 40 the 2 ml Dounce and for 50-300mg we use the 7 ml Dounce. 41 11. After homogenization, transfer lysate to a microtube on ice. 42 12. Clear the lysate at 10,000 x g for 10 min at 4ºC using a refrigerated centrifuge. 43 13. Transfer the post-mitochondrial supernatant to a pre-chilled microcentrifuge tube on ice. 44 Discard resulting pellet. 45 14. Take a small aliquot of the cleared lysate (40-80 ul) and store it at -80ºC for subsequent 46 Input analysis. 47 48 49 Immunoprecipitation 50 15. Add 4 ul of the anti-Hemagglutinin (HA) antibody (Covance Anti-HA.11 Epitope Tag 51 Antibody) to 800 ul of the remaining cleared lysate and incubate for 4 hours at 4ºC in a 52 microtube rotator with gentle mixing (end-over-end). 53 16. Prepare the magnetic protein A/G beads: 54  Thoroughly resuspend the beads by repeated inversion or gentle vortexing. 55  Place a microtube magnetic stand on ice and transfer 200 ul of the resuspended 56 beads to a clean microcentrifuge tube. 57 58 59 60 John Wiley & Sons Current Protocols Page 32 of 56 1 2 3  Place the tube into the magnetic stand on ice to collect the beads and remove 4 and discard the storage buffer. 5  Add 400 ul of homogenization buffer and rotate end-over-end for 5 min to wash 6 7 the beads. 8  Collect the beads with the magnetic stand, discard buffer. 9 10 17. Add the cleared lysate with the antibody to the beads. Incubate samples overnight at 11 4ºC in a tube rotator with gentle end-over-end mixing. 12 18. Next day, prepare the high salt buffer as described in the Reagents and Solutions 13 section. Keep on ice. 14 a. Place samples into the magnetic stand on ice and remove supernatant 15 (supernatant from the immunoprecipitation can be discarded or saved at -80ºC 16 for subsequent analysis). 17 Leave tubes in the magnet for at least one minute to collect all the beads 18 against the side of the tube. 19 19. With the tubes on the stand, add 800 ul of high salt buffer (Reagents and Solutions) to 20 remove non-specific binding from the immunoprecipitate (IP). Transfer tubes to the 21 rotator and wash the IP with gentle end-over-end mixing for 5 min at 4ºC. 22 20. Place samples into the magnetic stand on ice and remove supernatant 23 21. Repeat high salt washes two more times with the same volume of high salt buffer for 5 24 25 min each, for a total of three washes. 26 22. Resuspend beads in 800 ul of high salt buffer, mix on rotator for 5 min and transfer 27 beads and buffer to a clean tube. 28 This step helps remove any RNA or polyribosomes that might be sticking to the 29 original tube. 30 23. Place tubes on the magnetic stand for at least 1 min, carefully remove all high salt buffer 31 24. Add 350 ul of RLT buffer from the Qiagen’s RNeasy extraction kit (mini or micro) 32 supplemented with -mercaptoethanol (provided with the kit). 33 25. Remove tubes from stand and vortex samples for 30 seconds at RT. 34 At this step samples can be stored at -80ºC for subsequent RNA extraction or 35 processed through the RNA extraction procedure. 36 37 RNA extraction 38 39 26. Place tubes containing IPs in RLT buffer (from Qiagen kit) into the magnetic stand for 1 40 min at RT to allow beads to be collected against the side of the tube 41 27. Transfer the clear RLT buffer containing RNA to a new microcentrifuge tube. 42 43 28. Proceed with RNA isolation with DNAse digestion following Qiagen's RNeasy extraction 44 kit directions. 45 29. To extract RNA from the Inputs (Step 6), bring input sample to 350 ul with RLT buffer 46 supplemented with -mercaptoethanol, vortex extensively and proceed to RNA 47 extraction as described for IPs. 48 RNA extraction using column-based kits should be performed at RT. However, 49 eluted RNA should be kept on ice at all times. For long term storage, RNA is 50 safely kept at -80ºC for years. Whenever working with isolated RNA, always wear 51 gloves to minimize RNase contamination, decontaminate working surfaces and 52 use clean nuclease-free tubes and barrier pipette tips. 53 30. Quantitate RNA using Quant-iT RiboGreen RNA Reagent (Thermo Fisher) or a 54 Nanodrop-One (Thermo Fischer Sci) following the manufacturer's protocols. 55 56 57 58 59 60 John Wiley & Sons Page 33 of 56 Current Protocols 1 2 3 If very low levels of RNA are expected (less than 2 ng/ul) the samples can be 4 accurately quantitated by doing qRT-PCR using a house keeping gene such as 5 Actb and comparing the results to known standard RNA solutions prepared with 6 input RNA from the same tissue as detailed in Support Protocol 2. 7 31. Analyze RNA integrity on an Agilent 2100 Bioanalyzer system with the associated RNA 8 9 6000 nano and pico kits (chose nano chips for samples 5-500 ng/ul, for samples with 10 RNA concentrations < 5 ng/ul (down to 50 pg/ul) the pico chips should be used). Follow 11 manufacturer’s directions. 12 Since most of the RNA isolated in Inputs and IPs is rRNA, the quality of mRNA is 13 indirectly estimated by assessing the integrity of rRNA. A measure of RNA 14 quality, the RNA integrity number (RIN), is also calculated by the software using 15 an algorithm that assesses the electrophoretic traces of each sample. A RIN 16 value of 7-10 indicates high quality RNA but RNA-Seq libraries can still be made 17 and successfully analyzed starting with partially degraded RNA. 18 19 20 21 ALTERNATE PROTOCOL 1 22 23 Messenger RNA profiling using the RiboTag viral vector 24 25 To overcome the limitations associated with developmental activation of Cre 26 recombinase transgenes and allow cell type-specific mRNA profiling of adult cells using 27 28 Cre-Driver lines, we have developed a Cre-dependent RiboTag viral vector based on 29 AAV1 (AAV1-DIO-RiboTag) (Sanz et al., 2015), where RiboTag expression is dependent 30 on the activity of the promoter driving Cre recombinase expression in adult mice. This 31 viral vector was created by inserting the cDNA of the mouse ribosomal protein Rpl22 32 gene fused to three hemagglutinin (HA) sequences (Rpl22 · 3HA), into a double-floxed 33 inverse open reading frame (DIO) AAV construct. For easy visualization of transduced 34 cells, this construct also contains an IRES sequence (IRES2) followed by the YFP 35 coding sequence as shown in Figure 1. 36 37 Materials: 38 39 Mice: 40 Cre-driver mouse line of choice. 41 42 Reagents: 43 RiboTag viral vector (AAV1-DIO-RPL22HA-IRES-YFP). Available upon request from authors 44 45 Equipment: 46 Stereotaxic apparatus with microsyringe pump (e.g. from World Precision Instruments) 47 48 Injection syringe (e.g. 5 ul Hamilton syringe) 49 Surgical tools 50 51 Protocol steps: 52 53 1. Define stereotaxic coordinates by locating the injection target in a mouse brain atlas 54 such as the Paxinos and Franklin’s “the Mouse Brain in Stereotaxic Coordinates” 55 (Paxinos & Franklin, 2013) or the Allen institute for Brain Science’s “Adult Mouse Brain 56 Reference Atlas” (Dong) 57 58 59 60 John Wiley & Sons Current Protocols Page 34 of 56 1 2 3 2. Inject 0.2 to 0.5 ul of a RiboTag viral vector preparation (7 x 1011 viral genomes/ml) at 4 the target site using the defined coordinates. 5 Follow a stereotaxic injection protocol such as (Gore, Soden, & Zweifel, 2013). 6 When targeting bilateral structures, inject each side. Injection volumes can be 7 adjusted to the targeted area, or multiple injections performed to cover larger 8 9 areas. Injecting different mice using the same coordinates and pooling the 10 targeted areas after dissection helps correct for variability resulting from 11 stereotaxic viral vector injections. 12 3. Allow at least 2 weeks for the viral vector to express Rpl22-HA. For RiboTag assays, 13 good results are obtained when sacrificing animals at least 3 weeks after injections to 14 allow for robust incorporation of Rpl22-HA into polyribosomes. 15 4. Perform RiboTag immunoprecipitations as described in Basic Protocol 1 16 17 All surgical procedures should be performed in accordance with protocols approved by your 18 Institutional Animal Care Use Committee (IACUC). 19 20 21 SUPPORT PROTOCOL 1 Breeding and Genotyping of RiboTag Mice 22 23 Breeding of RiboTag Mice: 24 We maintain a live colony of RiboTag Breeders on a C57BL/6J background by mating 25 homozygotes since they have no adverse phenotypes. Experimental mice for RiboTag 26 assays are usually obtained by breeding male Cre recombinase-expressing mice to 27 28 female homozygous RiboTag mice (Rpl22HA/HA), Therefore, experimental mice are 29 always heterozygous for the RiboTag allele (Rpl22HA/+). 30 31 PCR Genotyping: 32 To genotype the RiboTag mice, a PCR was developed that includes the loxP site 5’ to 33 the wild-type exon 4 of Rpl22; the wild-type PCR product is 242 bp, while the mutant 34 product is about 290 bp. This PCR also distinguishes homozygous RiboTag mice, as the 35 5’ oligo corresponding to the intron between exons 3 and 4 of Rpl22 is not present in the 36 repeated segment of intron preceding the C-terminal Rpl22HA exon 4. Resulting 37 offspring from RiboTag mice bred to Cre recombinase-expressing mouse lines 38 (experimental mice) are genotyped for the presence of the Cre recombinase gene and 39 the RiboTag allele using the PCR oligonucleotides and reaction conditions described 40 below 41 42 Materials: 43 NucleoSpin Tissue DNA extraction kit (Macherey-Nagel cat No. 740952) 44 DreamTaq DNA polymerase (Thermo Fisher Sci. cat No. EP0702 and B71) 45 dNTPs 10 mM each (Thermo Fisher Sci. cat No. R0192) 46 (dilute to 2.5 mM for each dNTP) 47 48 49 Alternative: 50 Phire Tissue Direct PCR Master Mix kit (Thermo Fisher Sci. cat No. F170 51 52 Oligonucleotides for RiboTag genotyping: 53 RiboTag loxP For: 5’-GGGAGGCTTGCTGGATATG-3’ 54 RiboTag loxP Rev: 5’-TTTCCAGACACAGGCTAAGTACAC-3’ 55 56 Oligonucleotides For Cre recombinase genotyping: 57 58 59 60 John Wiley & Sons Page 35 of 56 Current Protocols 1 2 3 Cre cDNA For: 5’-GCATTACCGGTCGATGCAACGAGTG-3’ 4 Cre cDNA Rev: 5’-GAACGCTAGAGCCTGTTTTGCACGTTC-3’ 5 6 Acrylamide-bis solution 40% (37.5:1) (Merck cat No. 1.00638.1000) 7 Tris-Borate-EDTA (TBE) 10x (Fisher Scientific cat No. BP1333) 8 9 Ammonium Persulfate (APS; Sigma cat No. A3678). 10 (Prepare a 10% solution. Store at 20ºC.) 11 TEMED (Merck cat No. 1.10732.0100) 12 SYBR Safe DNA gel stain (Thermo Fisher Sci. S33102) 13 100 bp DNA ladder (Thermo Fisher Sci. cat No. SM0241) 14 15 16 Equipment: 17 Thermal cycler (Thermo Fisher Sci. cat No. A24811) 18 Mini protean vertical gel electrophoresis system (Bio-Rad cat No. 1658003) 19 Power supply (Bio-Rad cat No. 1645050) 20 UV Gel imaging system 21 Strip tubes and caps for PCR 22 23 24 Protocol steps: 25 26 1. Extract DNA from mouse tail snips (2-5 mm) using a silica membrane column-based 27 28 kit such as the NucleoSpin Tissue following manufacturer’s directions. 29 We have also successfully used direct PCR kits that do not require prior DNA 30 extraction such as the Phire Tissue Direct PCR Master Mix kit for RiboTag and 31 Cre recombinase genotyping. DNA can also be extracted from tail samples by 32 conventional methods using SDS/Protease K digestion, phenol/chloroform 33 extraction, and ethanol precipitation. 34 2. Prepare the DreamTaq PCR Master mix as follows: (per reaction) 13 µl of water, 2 µl 35 of 10x DreamTaq green buffer, 2 µl of 10mM dNTPs, 1 µl of the forward oligo 36 (10uM), 1 µl of the reverse oligo (10uM), 0.1 µl of DreamTaq polymerase. Mix gently 37 and briefly centrifuge. 38 3. Add 19 µl of the master mix to each well. 39 4. Add 1 µl of tail DNA (~2 ng) to each reaction. 40 5. Mix and briefly centrifuge. 41 6. Place strips in the thermal cycler and run the appropriate program for each reaction. 42 Thermal profile for RiboTag reactions is: 95°C for 2 min followed by 30 cycles of 43 95°C for 20 s, 57°C for 20 s, and 72°C for 30 s, with a final extension at 72°C for 5 44 min. Thermal profile for Cre reactions is 95°C for 2 min followed by 30 cycles of 95°C 45 for 10 s, 63°C for 10 s, and 72°C for 40 s, with a final extension at 72°C for 5 min. 46 7. Prepare a 10% acrylamide gel by mixing (for two gels) 14 ml of water, 5 ml of 40% 47 48 acrylamide-bis, 1ml of TBE 10x, 30 µl of TEMED and 150 µl of 10% APS. Allow gels 49 to polymerize for 10-15 min. 50 8. Place gels into a vertical gel electrophoresis system, add buffer (0.5X TBE), and load 51 DNA ladder and PCR reactions. 52 9. Run the electrophoresis at 175v (constant voltage) for 45 min. 53 10. Stain gels with SYBR safe and analyze PCR products under UV light. 54 55 Detecting “Ectopic” Cre Recombination in tail DNA 56 57 58 59 60 John Wiley & Sons Current Protocols Page 36 of 56 1 2 3 Mouse lines driving Cre recombinase expression under cell type specific promoters 4 should typically restrict RiboTag activation to the cell type of interest. However, transient 5 expression of Cre recombinase during development is often observed in different Cre 6 driver lines and can lead to recombination of the RiboTag allele in unexpected cell types. 7 If this “ectopic” expression takes place in the fertilized egg or during early development, 8 9 it will be detected during the RiboTag genotyping PCR and a “recombination” band will 10 show up at ~350 bp. If recombination occurs in the fertilized egg the loxP band will be 11 replaced by the “recombination” band. If Cre recombinase is expressed during early 12 development, recombination of the RiboTag allele will only take place in a portion of the 13 cells and both the loxP and the recombination band will show up during PCR analysis. If 14 genotyping heterozygous animals (Rpl22HA/+), the wild-type band will always be present 15 in the PCR assay independently of the potential recombination events that may take 16 place on the floxed allele. 17 18 19 20 SUPPORT PROTOCOL 2 qRT-PCR analysis 21 The success of the RiboTag procedure can be assessed by using qRT-PCR to evaluate 22 whether specific mRNAs have been enriched during the isolation. Transcripts from the gene 23 that is driving the Cre-recombinase in the specific cell types should be enriched in the IP RNA 24 sample compared to the input RNA. We also recommend monitoring mRNAs that are not 25 expected to be expressed in the targeted cells to verify that they are de-enriched in the IP. 26 Quantitative RT-PCR may substitute for a more expensive and time-consuming analysis by 27 28 RNA-Seq when only a small subset of transcripts need to be assessed. 29 30 Materials: 31 Equipment: 32 Real-Time PCR machine 33 34 Reagents: 35 We use the one step Brilliant II or III SYBR green kits from Agilent or RNA-Ct one step 36 kits from Thermo Fisher Sci. 37 38 39 40 41 1. To confirm the specific isolation of transcripts from the cell type of study, we analyze 42 RNA extracted from Inputs and IPs by single-step qRT-PCR. The enriched presence of 43 known cell type-specific transcripts in the IPs compared to inputs indicates that the 44 RiboTag isolation was successful. 45 2. Test samples for expression of transcripts specific to cell types in the dissected area 46 other than the one of interest such as a glial marker when using a neuron-specific Cre 47 48 transgenic. These are expected to appear depleted in the IPs when compared to inputs. 49 3. Perform qRT-PCR analysis for a housekeeping gene such as Actb. Normalizing all data 50 to that standard can help correct for errors in RNA quantitation or differences in RNA 51 quality. Adding equal amounts of RNA to the qRT-PCR reaction for all samples results in 52 little variation in the CTs obtained for the housekeeping gene (less than 1 CT of 53 difference is expected among all samples), which confirms RNA integrity in all samples 54 and suitability of the housekeeping gene for data normalization. 55 56 57 58 59 60 John Wiley & Sons Page 37 of 56 Current Protocols 1 2 3 4 5 6 BASIC PROTOCOL 2: RiboTag mice as a reporter mouse line for direct visualization of 7 Cre recombination 8 9 10 Materials: 11 12 Mice: 13 RiboTag mouse line (JAX stock No. 011029) 14 Cre-driver mouse line of choice 15 16 Reagents: 17 Normal Donkey Serum (Jackson ImmunoResearch cat. No. 017-000-121) 18 Zymed’s rabbit anti-HA antibody (Thermo Fisher Sci., cat. No. 71-5500 19 Covance’s Alexa Fluor 488-conjugated mouse anti-HA antibody (BioLegend,cat. No. A488- 20 101L) 21 Alexa Fluor 488 donkey anti-rabbit antibody (Thermo Fisher Sci., cat. No. A21206) 22 SuperFrost plus slides (Thermo Fisher Sci. cat No. J1800AMNT) 23 Coverslips 24 x 60 mm #1 (Menzel-Glaser, available through VWR cat No. 631-1339) 24 Fluoromount G with DAPI (Electron Microscopy Sciences, cat. No. 17984-24) 25 26 Equipment: 27 28 Netwells (Corning CLS3477) 29 Nutator mixer (e.g. Boekel or Clay Adams) 30 Paint brush 31 32 Protocol steps: 33 34 Tissue preparation 35 36 1. Transcardially perfuse mice with ice-cold phosphate-buffered saline (PBS) pH 7.4 37 containing 4% paraformaldehyde (PFA). 38 Perfusion with fixative solution reduces background resulting from endogenous 39 mouse IgG detection when using anti-mouse secondary antibodies. If no primary 40 antibodies raised in mouse are used for staining, brains can by directly fixed after 41 removal by immersion in fixative for 12-24 h. 42 2. Remove brains and postfix overnight at 4ºC in PBS pH 7.4 containing 4% PFA. 43 3. Wash brains twice in ice-cold PBS pH 7.4 44 4. Immerse brains in an ice-cold solution of 30% sucrose in PBS pH 7.4. Specimens will 45 float. Keep at 4ºC until they sink (about 24 h) 46 5. Remove brains from the cryoprotective solution and pat dry to remove excess solution 47 48 6. Freeze in powdered dry ice for 5 min and store at -80 ºC until sectioning. 49 7. Cryo-section brains in 30 um-slices using a freezing microtome. Collect sections from 50 the area of interest for free-floating staining in PBS pH 7.4 with 0.02% sodium azide as 51 preservative. Store sections at 4º. 52 53 Imunofluorescence 54 55 8. Use a paint brush to transfer sections to wells containing Netwells inserts filled with PBS 56 pH 7.4. Rinse sections once with PBS to remove preservative. 57 58 59 60 John Wiley & Sons Current Protocols Page 38 of 56 1 2 3 9. Block sections in PBS with 0.2% Triton X-100 and 10% normal donkey serum (NDS) for 4 1 h at room temperature with agitation. 5 10. Incubate overnight at 4ºC with a primary antibody solution containing an anti-HA 6 antibody such as the Zymed’s rabbit anti-HA (#71-5500 Thermo Fisher Sci; use at 7 1:1000), or with a fluorophore-conjugated anti-HA antibody such as the Covance’s Alexa 8 9 Fluor 488-conjugated mouse anti-HA antibody (# A488-101L BioLegend, use at 1:500), 10 in PBS pH 7.4 with 1% NDS and 0.2% Triton X-100. 11 Fluorophore-conjugated antibodies allow for a direct visualization of the signal 12 without secondary antibody detection. Using fluorophore-conjugated secondary 13 antibodies will however provide an amplified signal. 14 11. Wash sections three times for 5 minutes in PBS pH 7.4 with 0.2% Triton X-100. If a 15 fluorophore-conjugated anti-HA antibody is used proceed to step 13. 16 12. Incubate with secondary antibody solution containing an anti-rabbit antibody conjugated 17 to the fluorophore of choice (e.g., Alexa Fluor 488; Invitrogen; 1:500 in PBS pH 7.4 with 18 1% NDS and 0.2% Triton X-100) for 1 h at room temperature. 19 13. Wash sections three times for 5 minutes in PBS pH 7.4 with 0.2% Triton X-100 20 14. Mount stained sections onto slides for imaging using Fluoromount G with DAPI. 21 15. Seal the edge with nail polish and allow slides to dry for 1-2 hours before visualizing. 22 Protect from light. 23 16. Slides can be stored at 4ºC protected from light. For best results, slides should be 24 analyzed within 1-2 weeks using the microscope of choice. Staining using anti-HA 25 antibodies in Cre-positive RiboTag mice results in a strong perinuclear signal in the cell 26 type of interest. 27 28 17. Image slides on a fluorescent microscope (standard or confocal) to visualize the Alexa 29 Fluor 488 and DAPI. 30 DAPI will bind DNA and light up the nuclei in all cells and Alexa fluorescence will 31 only be present in the cells that are expressing RiboTag. Expect to see most of 32 the RiboTag labeling in the cytoplasm where polyribosomes are abundant and in 33 the nucleolar structures in the nucleus where ribosomes are being assembled. 34 35 36 37 BASIC PROTOCOL 3: RNA-Seq Library Preparation 38 39 There are many library preparation kits on the market but Trio RNASeq (NuGEN) has worked 40 well in our hands. A detailed protocol is available on NuGEN’s website (NuGEN). Briefly, 500 pg 41 – 50 ng of total RNA in a volume of 10 ul is used as starting material for samples. We 42 recommend using similar quantities of RNA for all samples that are going to be compared with 43 each by RNA-Seq. We have good results starting with ~2 ng of total RNA. 44 45 Materials 46 47 48 Trio RNA-Seq, mouse rRNA, part number 0507-X; (X = 8, 32, or 96), (NuGEN) 49 AMPure XP, reference number A63880, (Beckman Coulter) 50 NEBNext Library Quant Kit for Illumina, catalog number E7630X (X = S, or L), (NEB) 51 NextSeq 500/550 High Output Kit v2.5 (75 Cycles), catalog number 20024906, (Illumina) 52 200 mM NaOH 53 200 mM Tris, pH 7.0 54 55 56 0.2 mL PCR tubes (flat top optically clear) 57 58 59 60 John Wiley & Sons Page 39 of 56 Current Protocols 1 2 3 1.5 mL microtubes 4 0.2 mL tube magnetic rack 5 6 Equipment: 7 Thermal cycler (PCR machine) 8 9 Quantitative thermal cycler 10 NextSeq Sequencer (Illumina) 11 12 13 Convert isolated RNA from RiboTag into cDNA libraries 14 15 1. Perform DNase treatment using the Trio RNASeq kit. 16 17 Although DNA digestion is performed on the RNA purification column, the first 18 step of the Trio RNASeq kit is to perform DNase treatment. It is extremely 19 important to eliminate any residual DNA and the second DNase treatment does 20 not result in any loss of sample. 21 22 2. Synthesize cDNA from the total RNA using reverse transcriptase primed with both oligo 23 dT and random hexamers and then use DNA polymerase to produce a double stranded 24 cDNA molecule 25 26 These primers include an RNA sequence at the 5’ end that is not complementary 27 28 and does not hybridize to the sample RNA. Then a second strand is made with a 29 DNA polymerase (Dafforn et al., 2004; Kurn et al., 2005). 30 31 3. Purify double stranded cDNA using silicon coated magnetic beads (AMPure XP, 32 Beckman Coulter). 33 34 These beads are used several times throughout the protocol adding various 35 amounts of a salt containing bead binding solution. By varying the salt 36 concentration in the reaction, one is selecting for different fragment sizes, with 37 more salt attracting almost all fragment sizes and less preferentially selecting for 38 larger fragments (Boom et al., 1990; Melzak, Sherwood, Turner, & Haynes, 39 1996). 40 41 4. Amplify the double-stranded cDNA using single primer isothermal amplification (SPIA). 42 43 RNase H is used to digest the RNA portion of the RNA / DNA heteroduplex at 44 one end of the double stranded cDNA. A 5’ RNA 3’ DNA primer and a strong 45 strand displacing DNA polymerase are used to make a single stranded cDNA 46 that is the compliment to the original RNA. The process is continually repeated all 47 48 at one temperature making many such copies (Dafforn et al., 2004; Kurn et al., 49 2005). 50 51 52 5. Fragment and repair ends of the cDNA 53 54 The kit accomplishes this using an enzymatic process. After fragmentation 55 adapters are ligated to either side of the cDNA with the 3’ side receiving a unique 56 nucleotide barcode that will be used to identify samples after sequencing. 57 58 59 60 John Wiley & Sons Current Protocols Page 40 of 56 1 2 3 4 5 6. PCR Amplification of cDNA products. 6 7 The kit uses two PCR amplifications with a targeted step in between designed to 8 9 deplete any cDNAs that would have been made from ribosomal RNA. Oligo 10 probes are designed to bind to rRNA derived cDNA, extend that product, then 11 restriction enzymes target these cleaving the adaptors off these products 12 essentially eliminating them from the library. 13 14 Quantify cDNA libraries 15 16 Even starting at the low end of the kit’s range 500 pg of total RNA, one ends up with 17 micrograms of cDNA. This needs to be greatly diluted before sequencing. To get the 18 best results from the sequencer the cDNA libraries must be loaded on at a precise 19 quantity. As well, each sample that will be multiplexed together should be loaded in 20 equal quantities. The most accurate way to quantify the cDNA libraries is using qPCR 21 against the adaptors on the cDNA fragments. Again, there are many kits on the market 22 that can accomplish this but NEBNext has worked well in our hands. 23 24 Four standard samples come with the kit ranging from 0.01 pM to 10 pM. In order to fall 25 within these one needs to make serial dilutions of the libraries and use a range of those 26 samples to quantify. In our hands 1:100,000 and 1:1,000,000 are the two dilutions that 27 28 will fall between the given standards. Our libraries are most often between 0.1 and 1 µM. 29 30 A detailed user manual is available on NEB’s website (NEB, 2017). 31 32 Denature and dilute libraries 33 34 The day of the sequencing run, libraries need to be denatured and diluted. A detailed 35 protocol is available on Illumina’s website (Illumina, 2018a). 36 1. Make a 1 nM dilution of each sample to be multiplexed together using a portion of the 37 libraries made earlier. Add an equal amount of each 1 nM sample to a master sample. 38 2. Add 20 uL of the master sample to 20 uL of 0.2 M NaOH. Mix by pipetting half of the 39 volume 10 to 20 times. Incubate for 5 minutes. 40 3. Add 20 uL 0.2 M Tris pH 7 to neutralize the NaOH. Mix well by pipetting. 41 4. Add 940 uL of prechilled HT buffer provided with the NextSeq reagents to make a 20 pM 42 dilution. Vortex briefly then spin the contents down. Keep the 20 pM sample on ice until 43 you are ready to make the final dilution. 44 5. Add 117 uL of the 20 pM sample to 1183 uL of prechilled HT buffer for a final dilution of 45 1.8 pM in 1.3 mL. Vortex briefly then spin the contents down. Keep this on ice until you 46 are ready to load this on the sequencer. 47 48 49 Sequencing 50 51 1. Thaw reagents cartridge at 4 degrees overnight. Take the reagents cartridge and flow 52 cell out of the refrigerator 30 minutes to an hour before your sequencing run. Ensure all 53 reagent have thawed before proceeding. 54 2. From the NextSeq500’s software interface select Sequence. 55 3. Following the instructions on screen, load the flow cell. 56 57 58 59 60 John Wiley & Sons Page 41 of 56 Current Protocols 1 2 3 4. Empty the spent reagents container into the appropriate waste container, then reload. 4 Load buffer cartridge. 5 5. Pierce the foil covering reservoir 10 on the reagent cartridge with a pipette tip. Then load 6 the full volume of the 1.8 pM sample into reservoir 10. Load the reagent cartridge onto 7 the NextSeq500. 8 9 6. Specify the run parameters on the setup screen. Assign a name to Run Name, enter 75 10 into Read 1 and 8 into Index 1. 11 12 Single end reads may be sufficient for gene expression quantification and 13 differential gene expression analysis. Running paired end reads will increase 14 alignment confidence for specific isoforms and for novel transcript discovery. In 15 this case you would enter 75 into “Read 2”. NextSeq high output cartridges are 16 rated for 400 million single end or 200 million paired end reads. 17 18 7. Select Next, the system will run through a number of checks. Once it has finished with 19 these select Start. 20 21 A detailed user guide including instructions for loading reagents and sample 22 onto the NextSeq sequencer is available on Illumina’s website (Illumina, 2018b). 23 24 Once reagents and sample have been loaded on the NextSeq and the 25 sequencing run has begun, the multiplexed sample is washed over the flow cell 26 where cDNA molecules with adaptors hybridize to an oligo lawn with 27 28 complementary sequences. A DNA polymerase makes a compliment to the 29 original cDNA molecule primed from the oligo attached to the flow cell, the 30 double stranded cDNA is denatured, and the original cDNA molecule is washed 31 away. The attached compliment binds a second oligo attached to the flow cell, 32 and is copied by a DNA polymerase in a process called bridge amplification. The 33 process is repeated many times over until a cluster of cDNA molecules grows 34 that will be large enough for the sequencer to read. Fluorescently labeled dNTPs 35 are washed over the flow cell where they incorporate into the growing DNA 36 strand primed off the template attached to the flow cell. This process is used to 37 identify the sequence of both the DNA library fragments made from the original 38 samples and the nucleotide barcode ligated to that DNA that identifies which 39 sample it comes from (Illumina, 2016). 40 41 42 43 BASIC PROTOCOL 4: RNA-Seq Bioinformatics 44 45 The analysis of RNA-Seq data can be done using a variety of open source or proprietary 46 programs. If you are using a Core Facility to do RNA-Seq they are also likely to be able to 47 48 analyze the data for you or at least assist you in the basic steps to obtain the aligned datasets. 49 We build the libraries and run the RNA-Seq ourselves and use the methods described below to 50 analyze the data. 51 52 53 Software 54 55 Software for Linux OS: 56 57 58 59 60 John Wiley & Sons Current Protocols Page 42 of 56 1 2 3 Bcl2fastq2 (Illumina, 2017a) 4 5 Software for Linux OS or Mac OS: 6 7 FASTX-Toolkit (Hannon_Lab, 2014) 8 9 10 TopHat2 (Trapnell et al., 2016) 11 12 Bowtie2 (Langmead, Kim, Charles, Wilks, & Antonescu, 2018) 13 14 Cufflinks (Trapnell et al., 2014) 15 16 Software for Linux OS, Mac OS, or Windows OS: 17 18 R (Gentleman et al., 2018) 19 20 RStudio (RStudio, 2018) 21 22 tidyr (Wickham & Henry, 2018) 23 24 dplyr (Wickham, François, Henry, & Müller, 2018) 25 26 ggplot2 (Wickham, Chang, et al., 2018) 27 28 29 30 Convert BCL files to FASTQ files 31 32 Illumina sequencers generate per cycle base call (BCL) files. However, most informatic 33 programs take FASTQ or FASTA files as input. Bcl2fastq2 software is able to covert the files, as 34 well as, demultiplex samples. A detailed user guide is available on Illumina’s website including 35 system requirements for the software (Illumina, 2017b). 36 37 1. Create a sample sheet with sample names, numbers, and corresponding nucleotide 38 barcodes. This is saved as a .csv in the following format: 39 40 [Data],, 41 Sample_Name,Sample_ID,Index 42 Sample1,1,CGCTACAT 43 Sample2,2,AATCCAGC 44 Sample3,3,CGTCTAAC 45 Etc. 46 47 48 2. Run the blc2fastq2 in Terminal (denoted with $ hereafter) with the following options: 49 50 $ bcl2fastq --minimum-trimmed-read-length 0 --mask-short-adapter-reads 0 --no-lane- 51 splitting -o /output_folder -R run_folder --sample-sheet run_folder/SampleSheet.csv 52 53 Explanation: 54 55 blc2fastq – calls the program. 56 57 58 59 60 John Wiley & Sons Page 43 of 56 Current Protocols 1 2 3 minimum-trimmed-read-length – specifies a length to not trim if trimming adapters will 4 result in a read of less than this length. In these cases, the adapter portion will be 5 masked by replacing base calls with Ns. By specifying 0 we are telling the program to 6 trim adapters no matter the remaining length of the read. Default is 35. 7 8 9 mask-short-adapter-reads – specifies a length of a read that if it falls below will be 10 masked by Ns. By specifying 0 we are instructing the program not to do this. Default is 11 22 (must be set equal or lower than minimum-trimmed-read-length). 12 13 no-lane-splitting – this option instructs the program to include all reads for each given 14 sample in one file. Without this option the NextSeq will give four files for each sample, 15 one for each of lanes 1-4. If the researcher is planning to upload FASTQs to Illumina’s 16 basespace.com they should not call this option as basespace will only accept sample 17 files that do have lanes split. 18 19 -o – specifies the output folder. 20 -R – specifies the run folder or where the BCL files are located. 21 --sample-sheet – points to where the sample sheet is located. 22 23 24 Trim and filter FASTQ files 25 26 3. Trim the first 5 base calls and filter out low quality reads. FASTX-Toolkit tools can 27 28 accomplish this with the following command: 29 30 $ for i in sample1 sample2 sample3 etc; do zcat $i_S1_R1_001.fastq.gz | fastx_trimmer 31 -f 6 -v -Q33 | fastq_quality_filter -q 25 -p 80 -v -Q33 -z -o $i.fastq.gz; done 32 33 Explanation: 34 35 Commonly known as a “for loop”. 36 for i in sample1 sample2 sample3 etc – is a list of your samples to iterate over. 37 do X – could be any command. This command will then iterate over your list. 38 done – tells the command to stop. 39 $i – is the placeholder where the list of samples will be substituted in. 40 41 zcat – looks at a compressed file. 42 | – called a pipe, it sends information from first command to the next command. 43 fastx_trimmer -f 6 – this option says trim the first 5 base calls and keep everything from 6 44 onward. 45 fastq_quality_filter -q 25 -p 80 – this option says only keep reads with a quality score of 46 25 or higher on at least 80% of the base calls. 47 48 -z – is the option to compress output file. 49 -o – is the name of file or directory name/name of file. 50 51 52 Align FASTQ files to a reference genome 53 54 We use TopHat2 (Kim et al., 2013; Trapnell, Pachter, & Salzberg, 2009) for alignment against 55 GRCm38/mm10 UCSC. Useful links to reference genomes with prebuilt bowtie indexes and 56 GTF transcript annotation files can be found here: 57 58 59 60 John Wiley & Sons Current Protocols Page 44 of 56 1 2 3 http://ccb.jhu.edu/software/tophat/igenomes.shtml. A detailed protocol for TopHat has been 4 published elsewhere (Trapnell et al., 2012). 5 6 4. Use the following command for alignments: 7 8 9 $ for i in sample1 sample2 sample3 etc; do tophat2 -p 8 -G genes.gtf -o output_folder/$i 10 Bowtie2Index/genome folder_with_fastqs/$i.fastq.gz; done 11 12 explanation: 13 14 tophat2 – is a program that performs alignment. 15 -p 8 – specifies the number of CPU processors to use. Check your system information 16 and set this at or below the total number of logical cores on the computer running the 17 software. 18 -G – points to directory and .gtf file. 19 -o – points to output directory and /$i – is the placeholder for sample. 20 Sequence/Bowtie2Index/genome – points to directory with index files. 21 folder_with_fastqs/$i.fastq.gz – points to directory and fastq files. This is the actual 22 sequencing data. 23 24 25 Quantify and compute differential gene expression 26 27 28 We use cuffdiff of cufflinks (Roberts, Trapnell, Donaghey, Rinn, & Pachter, 2011; Trapnell et al., 29 2013; Trapnell et al., 2010) to compute differential gene expression. A detailed protocol has 30 been published elsewhere (Trapnell et al., 2012). 31 32 5. Use the following command to calculate differential expression: 33 34 $ cuffdiff -p 8 -no-update-check -o output_folder -b Bowtie2Index/genome.fa -u genes.gtf 35 -L condition1,condition2 36 \./tophat_folder/condition1_S1/accepted_hits.bam,./tophat_folder/condition1_S2/accepte 37 d_hits.bam,tophat_folder/condition1_S3/accepted_hits.bam,etc 38 \./tophat_folder/condition2_S1/accepted_hits.bam,./tophat_folder/condition2_S2/accepte 39 d_hits.bam,tophat_folder/condition2_S3/accepted_hits.bam,etc 40 41 explanation: 42 43 cuffdiff – is a program to test for differential gene expression. 44 -p 8 – this option specifies how many CPU processors to use. 45 -no-update-check – use this option as the software is no longer being updated. 46 -o – points to folder you want your results saved in. 47 48 -L – name of your two conditions e.g. control, treatment or input, IP. 49 -b – this option corrects for RNA-seq read coverage bias (Roberts et al., 2011). 50 -u – this option performs an initial estimate to improve accuracy when weighting reads 51 mapping to multiple locations. 52 53 We run cuffdiff first on inputs vs IPs within conditions and then IP vs IP across 54 conditions. In some cases, one may only be asking what is unique about these 55 cells within this tissue. In which case one would only compare input to IP. It is 56 important to note that genes that are expressed widely in the total population of 57 58 59 60 John Wiley & Sons Page 45 of 56 Current Protocols 1 2 3 neurons will not show up as differentially expressed even if they are expressed in 4 the targeted neuron population. The difference between IP and input will only be 5 significant when a transcript is preferentially expressed in the HA-tagged neuron 6 population. 7 8 9 10 Visualize results 11 12 Use R within RStudio to visualize your results utilizing tidyr, dplyr, and ggplot2 packages 13 (Wickham, 2016). 14 15 6. If necessary, install packages. 16 17 > install.packages(“tidyr”) 18 > install.packages(“dplyr”) 19 > install.packages(“ggplot2”) 20 21 Load packages. 22 23 > load(tidyr) 24 > load(dplyr) 25 > load(ggplot2) 26 27 28 29 7. Import output files “gene_exp.diff” and “genes.read_group_tracking” from cuffdiff and 30 save them as an object. The names of the objects are arbitrary; however, good object 31 names should be short but descriptive. Below the first named object “cdstats_aipvn” 32 describes cuffdiff statistical output for condition A, IP vs input. The example below 33 assumes the three cuffdiff output folders are saved in the working directory the R-project 34 is started in. 35 36 > cdstats_aipvn <- read.delim(“cuffdiff_A_IP_vs_N/gene_exp.diff”) 37 > cdstats_bipvn <- read.delim(“cuffdiff_B_IP_vs_N/gene_exp.diff”) 38 39 > cdstats_avb <- read.delim(“cuffdiff_A_vs_B/gene_exp.diff”) 40 > cdfpkms <- read.delim(“cuffdiff_A_vs_B/genes.read_group_tracking”) 41 42 43 44 8. Below are three examples of creating a list of genes expressed at certain thresholds in 45 the IP relative to the input in condition A. 46 47 48 > A_2fold <- filter(cdstats_aipvn, q_value < 0.05, log2.fold_change. > 1, value_2 > 5) 49 %>% select(“test_id”) 50 51 > A_1fold <- filter(cdstats_aipvn, log2.fold_change. > 0, value_2 > 5) %>% 52 select(“test_id”) 53 54 > A_half_fold <- anti_join(dr1stats, filter(dr1stats, log2.fold_change. < -1, q_value < 55 0.05), by = "test_id") %>% filter(value_2 > 5) %>% select("test_id") 56 57 58 59 60 John Wiley & Sons Current Protocols Page 46 of 56 1 2 3 4 9. Filter condition B the same way, then join the 2 lists to create a list of genes that could 5 be important in either condition A or condition B. 6 7 > AB_2fold <- full_join(A_2fold, B_2fold) 8 9 10 11 10. Now the new list of genes that could be important in either condition can be used to filter 12 for genes that one would want to compare across conditions. 13 14 > AIP_vs_BIP <- inner_join(AB_2fold, cdstats_avb, by = “test_id”) 15 16 17 Cuffdiff includes a column in the output file gene_exp.diff labeled “significant” that 18 can be useful while graphing data assigning different colors to the binomial 19 values (“yes” or “no”). However, we find it useful to append additional columns 20 that can specify other criteria, for example, both statistically significant and 21 meaningfully different. 22 23 11. In the example below, we are using 2-fold again. 24 25 > temp1 <- filter(AIP_vs_BIP, abs(log2.fold_change.) > 1, q_value < 0.05) %>% 26 mutate(DE = “yes”) 27 28 > temp2 <- anti_join(AIP_vs_BIP, temp1, by = “test_id”) %>% mutate(DE = “no”) 29 > AIP_vs_BIP2 <- full_join(temp1, temp2) 30 31 32 12. A common graph made with RNA-Seq data is a volcano plot. This plots p-value vs fold 33 change, usually both have been log transformed. One can now use the newly created 34 “DE” column to highlight genes that are both 2-fold different and statistically different. 35 36 > volcplot <- ggplot2(AIP_vs_BIP2, aes(log2.fold_change., -log(q_value), color = DE) + 37 geom_point(alpha = 0.5) 38 39 By saving the plot as an object (“volcplot”) it is easy to call up the object and 40 modify various aspects experimenting with different options until one is satisfied 41 with the plot’s appearance. Below we have modified the default colors of the 42 points to black and red, drawn a horizontal line where the corrected p-value is 43 0.05, drawn lines indicating where the fold change is 2-fold, and selected a 44 different theme (Figure 2A). We like the way theme_classic and theme_bw look 45 but this is purely a matter of preference. 46 47 48 > volcplot + scale_color_manual(values=c("black", "red")) + geom_hline(yintercept = - 49 log(0.05)) + geom_vline(xintercept = c(-1,1)) + theme_bw() 50 51 Another common plot one could make is a scatter plot, plotting log transformed values 52 for a list of genes with condition A on one axis and condition B on the other. 53 54 > scatplot <- ggplot(AIP_vs_BIP2, aes(log2(value_1), log2(value_2), color = DE)) + 55 geom_point(alpha = 0.3) + 56 scale_color_manual(values = c("black", "red")) + 57 58 59 60 John Wiley & Sons Page 47 of 56 Current Protocols 1 2 3 theme_classic() 4 5 One can relabel the axis and give the plot a title if they wish (Figure 2B). 6 7 > scatplot + labs(x = "A log2(FPKM)", y = "B log2(FPKM)", title = "A vs B") + 8 9 theme(plot.title = element_text(hjust = 0.5)) + 10 theme(text = element_text(size = 20)) 11 12 Finally, one may wish to plot individual genes of interest: 13 14 Boxplot 15 16 > box_geneA <- filter(cdfpkm, tracking_id == "GeneA") %>% 17 ggplot(aes(condition, FPKM, fill = condition)) + 18 geom_boxplot() + 19 theme_bw() + 20 theme(legend.position="none") + 21 scale_fill_manual(values = c("white", "red")) + 22 ylim(0,NA) 23 24 Add a title using labs, center justify using hjust, and change the font size with element 25 text (Figure 2C). 26 27 28 > box_geneA + labs(title = “GeneA”) + 29 theme(plot.title = element_text(hjust = 0.5)) + 30 theme(text = element_text(size = 20)) 31 32 Bar chart 33 34 > bar_geneA <- filter(cdfpkm, tracking_id == "GeneA") %>% 35 ggplot(aes(condition,FPKM)) + 36 stat_summary(fun.data="mean_se", geom="bar", color="black", aes(fill=condition)) + 37 stat_summary(fun.data="mean_se", geom="errorbar", width=0.3, color="black") + 38 theme_bw() + 39 theme(legend.position="none") + 40 scale_fill_manual(values = c("white", "red")) + 41 labs(title = “GeneA”) + 42 theme(plot.title = element_text(hjust = 0.5)) + 43 theme(text = element_text(size = 20)) 44 45 Add in individual data points for greater data reporting transparency (Figure 2D). 46 47 48 > bar_geneA + geom_point(size = 3) 49 50 51 52 [*Copy Editor – Insert Figure 2 here] 53 REAGENTS AND SOLUTIONS 54 55 56 Tris base (Sigma; cat. No. T1503). 57 58 59 60 John Wiley & Sons Current Protocols Page 48 of 56 1 2 3  Prepare a 1.5M pH 7.4 stock solution (pH with HCL) and store at 4ºC 4 5 KCl (Sigma; cat. No. P9541). 6 7  Prepare a 1M stock solution and store at 4ºC 8 9 MgCl2 (Sigma; cat. No. 63068). 10  Prepare a 1M stock solution and store at 4ºC 11 Nonidet P-40 Substitute, 10% (w/v) solution (Roche, cat. No. 11 332 473 001) 12 Cycloheximide (Sigma, cat. No. C7698). 13  Prepare a 5mg/ml stock solution and store at -20ºC. 14 Heparin (Sigma; cat. No. H3393). 15  Prepare a 100 mg/ml stock solution and store at 4ºC. 16 Phosphate buffered saline (PBS), tablets (Millipore, cat. No. 524650-1EA). 17  Prepare at 1x with water. 18 Paraformaldehyde (Sigma cat. No. P6148). 19  Prepare a 4% solution in PBS 1x. Adjust pH to 7.4. Prepare fresh and store at 4ºC. 20 21 Sucrose (Sigma cat. No. 84097). 22  Prepare a 30% solution in PBS 1x pH 7.4. Store at 4ºC. 23 Sodium Azide (Sigma, cat. No. S2002). 24  Prepare a 0.02% solution in PBS pH 7.4 25 Triton X-100 (Millipore, cat. No. 648466). 26  Prepare a 0.2% solution in PBS pH 7.4. 27 28 29 Homogenization buffer: 30  50 mM Tris pH7.5 31  100 mM KCl 32 33  12 mM MgCl2 34  1% Nonidet P-40 35  1 mM DTT 36  200 U/mL Promega RNasin 37  1 mg/mL heparin 38  100 ug/mL cycloheximide 39  protease inhibitor mixture (1x) 40 41 Optional: The addition of 1% sodium deoxycholate (DOC) to the homogenization buffer 42 reduces some of the non-specific background. We have shown that DOC does not 43 significantly reduce the purification of specific polysomes and suggest testing it in your 44 experiments. 45 46 47 Homogenization buffer is prepared by adding defined volumes of stock solutions for Tris 48 (1.5M pH 7.4), KCl (1M) and MgCl2 (1M) to nuclease free water to achieve the final 49 concentrations described above in the desired volume. Right before homogenization, 50 buffer is supplemented with DTT, protease inhibitors, RNasin, heparin and 51 cycloheximide. 52 53 High salt buffer: 54  50 mM Tris pH 7.5 55  300 mM KCl 56 57 58 59 60 John Wiley & Sons Page 49 of 56 Current Protocols 1 2 3  12 mM MgCl2 4  1% Nonidet P-40 5 6  0.5 mM DTT 7  100 ug/mL cycloheximide 8 9 Prepare using Tris, KCl and MgCl2 stock solutions. Add DTT and Cycloheximide right 10 before using. 11 12 13 14 COMMENTARY 15 16 Background Information 17 It has been a little over 10 years since the first development of cell-type specific 18 ribosome tagging strategies that permitted the analysis of a cell's translatome (the mRNAs 19 engaged on polyribosomes) (Heiman et al., 2008). The original technique utilized BAC 20 transgenic mice that were engineered to express a GFP-tagged 60S ribosomal subunit protein 21 (Rpl10a) behind a cell type specific promoter. Antibodies directed against GFP were used to 22 immunoprecipitate the tagged polyribosomes from which mRNA could then be isolated and 23 24 analyzed by qRT-PCR, microarray, or RNA-Seq. This approach required the generation of novel 25 BAC transgenic mice specific for each cell type and did not take advantage of the many tissue 26 specific Cre recombinase mouse lines that were already developed. 27 We developed an alternative approach described in this RiboTag protocol in which an 28 HA-tagged ribosomal protein (Rpl22) is conditionally expressed from the mouse genome by the 29 action of Cre recombinase (Sanz et al., 2009). This approach takes advantage of the constantly 30 expanding diversity of cell type specific Cre recombinase expressing mouse lines. Both 31 approaches suffer from the same limitation: the investigator must have already identified a gene 32 promoter that is specific for the cell type of interest. Although it is unlikely that subtypes of 33 neurons can be uniquely defined by the expression of a single Cre recombinase transgene, the 34 ribosome tagging strategies have facilitated analysis of the mRNAs engaged with 35 polyribosomes in many neuronal cell types and can also be used to study the regulation of 36 specific genes in vivo. 37 One alternative approach that has rapidly been adopted by neuroscientists is single cell 38 transcript analysis by direct RNA-Seq. A single anatomical structure such as the arcuate 39 nucleus of the hypothalamus is dissociated into single cells, the transcripts from each cell are 40 uniquely barcoded, and the RNA is analyzed by RNA-Seq. Clustering analysis is used to 41 identify unique cell types including neurons and glial cells, and then the neurons can be further 42 43 clustered into discreet sub-types based on their transcript phenotype. This unbiased approach 44 is very useful to explore the full range of neurons in a specific anatomical location and will 45 rapidly leading to transcriptome phenotypes for all of the neurons and glial cells in a mouse 46 brain. One limitation of this approach is that relatively non-abundant transcripts escape 47 detection due to the large number of different cell types being sequenced en masse. Another 48 limitation is that the techniques that must be used to obtain single cells, especially starting with 49 the brain, can cause alterations in gene expression (e.g. induction of immediate early genes) 50 (Moffitt et al., 2018; van den Brink et al., 2017) and may also lose transcripts that are 51 sequestered in neuronal processes where they are also likely to be under translational control. 52 The ribosome capture approaches like RiboTag will only isolate mRNAs that have ribosomes 53 bound to them and are expected to be actively translated into protein. 54 55 56 57 58 59 60 John Wiley & Sons Current Protocols Page 50 of 56 1 2 3 Critical Parameters and Troubleshooting 4 5 1. Cell type Specificity: The specificity of the RiboTag analysis depends on the 6 specificity of the Cre recombinase transgenic mouse line and every effort should be made to 7 verify that the Cre transgenic drives expression of Rpl22-HA in the appropriate cell type and 8 9 does not give rise to ectopic expression in other tissues that would interfere with the analysis. It 10 is important to check that recombination of the RiboTag allele is not occurring during early 11 development giving rise to mice with variable expression in many other cell types. This can be 12 done by analyzing expression of Rpl22-HA by immunohistochemistry and comparing it to a 13 marker for the cells of interest as described in Basic Protocol 2. We also routinely screen for 14 RiboTag recombination in tail DNA during genotyping as described in the Support Protocol 1. 15 This will help identify mice that have undergone recombination of the RiboTag allele during early 16 development and they should be removed from the cohort to be studied as adults. 17 18 2. Background and Controls: When we analyze the RNA from a RiboTag experiment 19 we find that the immunoprecipitated polysomes still contain mRNAs from cells that are not 20 expressing Cre recombinase and Rpl22-HA (e.g. glial cells). These transcripts are depleted in 21 the IP (IP/input ratios <0.3) but since some are abundantly expressed, they will still appear at 22 significant levels in the IP. As described in Basic Protocol 4 we advise analyzing both the input 23 and IP RNA and then considering whether to filter out background gene transcripts that are 24 depleted. In some of our experiments we are interested in gene transcripts which are enriched in 25 the cell type being studied and help define a unique phenotype for those neurons. In addition to 26 the background of depleted mRNAs seen in the IP we have observed that the IP may also contain 27 28 enriched transcripts from genes that are clearly not expressed in the cell type of interest. These 29 false positives can confound the data and we suggest doing a control IP experiment starting with 30 equivalent tissue from a mouse that does not express RiboTag. Transcripts that enrich in the 31 absence of Rpl22-HA expression are artifacts of the technique and should be eliminated from the 32 specific transcripts isolated when Rpl22-HA is expressed. As described in the protocol we have 33 recently been able to reduce the capture of these artifactually enriched genes by including an 34 ionic detergent, sodium deoxycholate, in the initial homogenization buffer. 35 36 3. Anticipated Results and Statistical Analysis: Each tissue homogenate yields two 37 RNA-Seq libraries, an input and an IP. We typically run 8-16 RNA-Seq libraries on a NextSeq 38 500/550 flow cell and expect to have 25-50 million aligned reads for each library. Statistical 39 analysis of this data requires at least 3 replicates making these experiments quite expensive in 40 terms of reagents. If one is trying to phenotype the cells the first analysis would be to determine 41 all of the expressed genes comparing IP to input and then to rank those transcripts by the ratio 42 of IP to input. Transcripts that come up as enriched in a control IP from a mouse that does not 43 express RiboTag should be removed from this transcript phenotype. If the animals have been 44 treated with pharmacologic agents or exposed to physiological challenges, transcripts that 45 change between samples can be identified and statistically analyzed as described in Basic 46 Protocol 4. It is very helpful to correlate the specific transcripts that are enriched in the Cre- 47 48 expressing cells with the in situ hybridization results from the Allen Mouse Brain Atlas. 49 Specificity can also be confirmed using RNAscope (Advanced Cell Diagnostics) to colocalize 50 transcripts in the anatomical region of interest. One can also confirm results by qRT-PCR 51 starting with either the original RNA samples or qPCR with the DNA libraries. 52 The RiboTag approach is ideally suited to studying the dynamic regulation of genes in a 53 specific neuronal population. Transcripts can be induced within minutes after the addition of 54 hormones and drugs or changes in nutritional or environmental conditions. When an mRNA is 55 specifically induced in the RiboTag targeted cell but constitutively expressed in neighboring 56 cells, the level of transcripts in the IP will increase and the enrichment (IP/input) will also 57 58 59 60 John Wiley & Sons Page 51 of 56 Current Protocols 1 2 3 increase. If a gene like Fos is induced in all of the cells in the tissue being analyzed, then you 4 will see an increase in IP transcripts for Fos but no change in enrichment. 5 Translational control: RiboTag techniques will only isolate mRNAs that have 6 ribosomes bound and this implies that these mRNAs are being actively translated. However, it 7 is important to consider that RiboTag is not going to be very sensitive to how many ribosomes 8 9 are engaged on a particular mRNA. For example, if an mRNA is under translational control and 10 only has 2-3 ribosomes in the repressed state and 12 or more ribosomes when induced, the 11 efficiency of immunoprecipitation may not change enough to be a quantitative indicator of 12 translational activity. Ribosomes may also remain attached to mRNAs even when elongation 13 and active synthesis of protein is repressed. 14 15 4. Time Considerations: One of the advantages of the RiboTag procedure is that 16 once the animals are obtained either by mating RiboTag mice with a specific Cre driver 17 transgenic or stereotaxically injecting AAV-RiboTag virus and waiting for RiboTag expression, 18 the immunoprecipitation of specific RNA can be completed in a day. Production of DNA 19 libraries for RNA-Seq and DNA sequencing can be completed in a few more days. The most 20 time-consuming step is the analysis of results and this depends on what questions are being 21 asked and the number of different tissues and conditions being studied. 22 23 ACKNOWLEDGEMENT 24 Supported by NIH grants GM032875 and MH086386 (GSM), F32DK116432 (JCB) Marie 25 Sklodowska-Curie Individual Fellowship (H2020-MSCA-IF-2014-658352)(ES), and Ramón y 26 Cajal fellowship (RyC-2012-11873) European Research Council (Starting grant Neuromito, 27 28 ERC-2014-StG-638106 and Proof of Concept ERC-2018-PoC-825569) 29 Mineco Proyectos I+D de Excelencia (SAF2014-57981P; SAF2017-88108-R) 30 Agaur (2017SGR-323)(AQ) 31 32 33 34 LITERATURE CITED 35 36 Boom, R., Sol, C. J., Salimans, M. M., Jansen, C. L., Wertheim-van Dillen, P. M., & van der 37 Noordaa, J. (1990). Rapid and simple method for purification of nucleic acids. Journal of 38 clinical microbiology, 28(3), 495-503. Retrieved from 39 http://www.ncbi.nlm.nih.gov/pubmed/1691208. 40 Dafforn, A., Chen, P., Deng, G., Herrler, M., Iglehart, D., Koritala, S., . . . Kurn, N. (2004). Linear 41 mRNA amplification from as little as 5 ng total RNA for global gene expression analysis. 42 BioTechniques, 37(5), 854-857. Retrieved from 43 44 http://www.ncbi.nlm.nih.gov/pubmed/15560142. doi:10.2144/04375PF01 45 Dong, H. W. (2019). Allen Mouse Brain Atlas. Retrieved from http://mouse.brain- 46 map.org/static/atlas 47 Gentleman, R., Ihaka, R., Bates, D., Chambers, J., Dalgaard, P., Hornik, K., . . . Murdoch, D. 48 (2018). R (Version 3.5.1) [language and environment for statistical computing]: The R 49 Foundation. Retrieved from https://www.r-project.org/ 50 Gore, B. B., Soden, M. E., & Zweifel, L. S. (2013). Manipulating gene expression in projection- 51 specific neuronal populations using combinatorial viral approaches. Curr Protoc 52 Neurosci, 65, 4 35 31-20. Retrieved from 53 https://www.ncbi.nlm.nih.gov/pubmed/25429312. doi:10.1002/0471142301.ns0435s65 54 Haimon, Z., Volaski, A., Orthgiess, J., Boura-Halfon, S., Varol, D., Shemer, A., . . . Jung, S. 55 (2018). Re-evaluating microglia expression profiles using RiboTag and cell isolation 56 strategies. Nat Immunol, 19(6), 636-644. doi:10.1038/s41590-018-0110-6 57 58 59 60 John Wiley & Sons Current Protocols Page 52 of 56 1 2 3 Hannon_Lab, G. J. (2014). FASTX-Toolkit (Version 0.0.14): Hannon Lab. Retrieved from 4 http://hannonlab.cshl.edu/fastx_toolkit/download.html 5 Heiman, M., Schaefer, A., Gong, S., Peterson, J. D., Day, M., Ramsey, K. E., . . . Heintz, N. 6 (2008). A translational profiling approach for the molecular characterization of CNS cell 7 types. Cell, 135(4), 738-748. Retrieved from 8 9 https://www.ncbi.nlm.nih.gov/pubmed/19013281. doi:10.1016/j.cell.2008.10.028 10 Illumina. (2016). Introduction to sequencing by synthesis. Retrieved from 11 https://www.youtube.com/watch?v=fCd6B5HRaZ8 12 Illumina. (2017a). bcl2fastq2 (Version 2.2): Illumina. Retrieved from 13 https://support.illumina.com/downloads/bcl2fastq-conversion-software-v2-20.html 14 Illumina. (2017b). bcl2fastq Conversion v2.19. 2. Retrieved from 15 https://support.illumina.com/content/dam/illumina- 16 support/documents/documentation/software_documentation/bcl2fastq/bcl2fastq2_guide_ 17 15051736_v2.pdf 18 Illumina. (2018a). Denature and dilute libraries guide. NextSeq system. 6. Retrieved from 19 https://support.illumina.com/content/dam/illumina- 20 support/documents/documentation/system_documentation/nextseq/nextseq-denature- 21 dilute-libraries-guide-15048776-06.pdf 22 Illumina. (2018b). NextSeq 500: System guide. 4. Retrieved from 23 https://support.illumina.com/content/dam/illumina- 24 support/documents/documentation/system_documentation/nextseq/nextseq-500- 25 system-guide-15046563-04.pdf 26 Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013). TopHat2: 27 28 accurate alignment of transcriptomes in the presence of insertions, deletions and gene 29 fusions. Genome biology, 14(4), R36. Retrieved from 30 http://www.ncbi.nlm.nih.gov/pubmed/23618408. doi:10.1186/gb-2013-14-4-r36 31 Kurn, N., Chen, P., Heath, J. D., Kopf-Sill, A., Stephens, K. M., & Wang, S. (2005). Novel 32 isothermal, linear nucleic acid amplification systems for highly multiplexed applications. 33 Clinical chemistry, 51(10), 1973-1981. Retrieved from 34 http://www.ncbi.nlm.nih.gov/pubmed/16123149. doi:10.1373/clinchem.2005.053694 35 Langmead, B., Kim, D., Charles, R., Wilks, C., & Antonescu, V. (2018). Bowtie2 (Version 36 2.3.4.3) [Sequencer aligner]. Retrieved from http://bowtie- 37 bio.sourceforge.net/bowtie2/index.shtml 38 Melzak, K. A., Sherwood, C. S., Turner, R. F. B., & Haynes, C. A. (1996). Driving forces for DNA 39 adsorption to silica in perchlorate solutions. Journal of Colloid and Interface Science, 40 181(2), 635-644. Retrieved from ://A1996UZ53600031. doi:DOI 41 10.1006/jcis.1996.0421 42 Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., . . . 43 Zhuang, X. (2018). Molecular, spatial, and functional single-cell profiling of the 44 hypothalamic preoptic region. Science, 362(6416). Retrieved from 45 https://www.ncbi.nlm.nih.gov/pubmed/30385464 46 http://science.sciencemag.org/content/sci/362/6416/eaau5324.full.pdf. 47 48 doi:10.1126/science.aau5324 49 NEB. (2017). NEBNext: Library quant kit for illumina. 1.2. Retrieved from https://www.neb.com/- 50 /media/catalog/datacards-or-manuals/manuale7630.pdf 51 NuGEN. (2017). User guide: Trio RNA-seq. 2. Retrieved from 52 https://www.nugen.com/sites/default/files/M01440v2_User_Guide%3A_Trio_RNA- 53 Seq_4270.pdf 54 Paxinos, G., & Franklin, K. B. J. (2013). The Mouse Brain in Stereotaxic Coordinates. San 55 Diego, CA: Academic Press. 56 57 58 59 60 John Wiley & Sons Page 53 of 56 Current Protocols 1 2 3 Quintana, A., Sanz, E., Wang, W., Storey, G. P., Guler, A. D., Wanat, M. J., . . . Palmiter, R. D. 4 (2012). Lack of GPR88 enhances medium spiny neuron activity and alters motor- and 5 cue-dependent behaviors. Nat Neurosci, 15(11), 1547-1555. Retrieved from 6 https://www.ncbi.nlm.nih.gov/pubmed/23064379. doi:10.1038/nn.3239 7 Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L., & Pachter, L. (2011). Improving RNA-Seq 8 9 expression estimates by correcting for fragment bias. Genome biology, 12(3), R22. 10 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21410973. doi:10.1186/gb-2011-12- 11 3-r22 12 RStudio. (2018). RStudio (Version 1.1.463) [integrated development environment (IDE)]: 13 RStudio. Retrieved from https://www.rstudio.com/products/rstudio/download/#download 14 Sanz, E., Evanoff, R., Quintana, A., Evans, E., Miller, J. A., Ko, C., . . . McKnight, G. S. (2013). 15 RiboTag analysis of actively translated mRNAs in Sertoli and Leydig cells in vivo. PLoS 16 One, 8(6), e66179. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/23776628. 17 doi:10.1371/journal.pone.0066179 18 Sanz, E., Quintana, A., Deem, J. D., Steiner, R. A., Palmiter, R. D., & McKnight, G. S. (2015). 19 Fertility-regulating Kiss1 neurons arise from hypothalamic POMC-expressing 20 progenitors. J Neurosci, 35(14), 5549-5556. Retrieved from 21 https://www.ncbi.nlm.nih.gov/pubmed/25855171. doi:10.1523/JNEUROSCI.3614- 22 14.2015 23 Sanz, E., Yang, L., Su, T., Morris, D. R., McKnight, G. S., & Amieux, P. S. (2009). Cell-type- 24 specific isolation of ribosome-associated mRNA from complex tissues. Proc Natl Acad 25 Sci U S A, 106(33), 13939-13944. Retrieved from 26 https://www.ncbi.nlm.nih.gov/pubmed/19666516. doi:10.1073/pnas.0907143106 27 28 Trapnell, C., Hendrickson, D., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2014). 29 Cufflinks (Version 2.2.1) [Transcriptome assembly and differential expression analysis 30 for RNA-Seq]. Retrieved from http://cole-trapnell-lab.github.io/cufflinks/install/ 31 Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013). 32 Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature 33 biotechnology, 31(1), 46-+. Retrieved from ://000313563600021. 34 doi:10.1038/nbt.2450 35 Trapnell, C., Kim, D., Pertea, G., Pimentel, H., Kelley, R., Pachter, L., & Salzberg, S. (2016). 36 TopHat 2 (Version 2.1.1) [Splice junction mapper]. Retrieved from 37 http://ccb.jhu.edu/software/tophat/index.shtml 38 Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with 39 RNA-Seq. Bioinformatics, 25(9), 1105-1111. Retrieved from 40 http://www.ncbi.nlm.nih.gov/pubmed/19289445. doi:10.1093/bioinformatics/btp120 41 Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., . . . Pachter, L. (2012). 42 Differential gene and transcript expression analysis of RNA-seq experiments with 43 TopHat and Cufflinks. Nature protocols, 7(3), 562-578. Retrieved from 44 http://www.ncbi.nlm.nih.gov/pubmed/22383036. doi:10.1038/nprot.2012.016 45 Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., . . . Pachter, 46 L. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated 47 48 transcripts and isoform switching during cell differentiation. Nature biotechnology, 28(5), 49 511-515. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/20436464. 50 doi:10.1038/nbt.1621 51 van den Brink, S. C., Sage, F., Vertesy, A., Spanjaard, B., Peterson-Maduro, J., Baron, C. S., . . 52 . van Oudenaarden, A. (2017). Single-cell sequencing reveals dissociation-induced gene 53 expression in tissue subpopulations. Nat Methods, 14(10), 935-936. Retrieved from 54 https://www.ncbi.nlm.nih.gov/pubmed/28960196. doi:10.1038/nmeth.4437 55 Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. New York,NY: Springer- 56 Verlag New York. 57 58 59 60 John Wiley & Sons Current Protocols Page 54 of 56 1 2 3 Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., . . . Yutani, H. 4 (2018). ggplot2 (Version 3.1.0). Retrieved from https://ggplot2.tidyverse.org/ 5 Wickham, H., François, R., Henry, L., & Müller, K. (2018). dplyr (Version 0.7.8): RStudio. 6 Retrieved from https://dplyr.tidyverse.org/ 7 Wickham, H., & Henry, L. (2018). tidyr (Version 0.8.2.9000): RStudio. Retrieved from 8 9 https://tidyr.tidyverse.org/ 10 Williams, R. W. (2000). Mapping genes that modulate mouse brain development: a quantitative 11 genetic approach (A. F. Goffinet & P. Rakic Eds.). New York: Springer. 12 13 14 15 FIGURE LEGENDS 16 17 Figure 1. Cartoon comparing the RiboTag mouse and AAV-DIO-RiboTag approaches 18 On the left side, the Rpl22 locus was modified by homologous recombination to insert LoxP 19 sites on either end of the last exon (Ex4) of the Rpl22 gene and a duplicated Ex4 with an HA tag 20 was inserted downstream. The CRE-Driver mouse removes the WT Ex4 sequence in specific 21 cells and the transcript now splices in Ex4-HA (shown in green). Polyribosomes from cells 22 expressing the Ex4-HA are recovered from a lysate by immunoprecipitation (IP) with an 23 antibody directed against HA and magnetic beads coated with an artificial Protein A/G peptide. 24 25 RNA is isolated from the IP polyribosomes and RNA is also isolated from the lysate. Gene 26 expression analysis is done by RNA-Seq. On the right side the DIO-RiboTag viral vector is 27 shown schematically. In the presence of Cre-recombinase the Rpl22-HA-IRES-YFP cassette is 28 flipped and locked in the ON configuration so that the eIF1a promoter now drives expression of 29 Rpl22-HA in specific cells. RNA is isolated from the IP and compared with the input. 30 31 32 Figure 2. Example plots of hypothetical RNA Seq data. A, Volcano plot of data with log 33 base 2 transformed fold change (condition A vs condition B) plotted on the x axis and negative 34 natural log transformed corrected p-value (q-value) plotted on the y axis. The solid horizontal 35 line denotes where q-value is equal to 0.05. The two solid vertical lines denote where fold 36 change is equal to 2 (or -2). Data points are colored red if they are both 2-fold different and have 37 a q-value less than 0.05; otherwise they are black. B, Scatter plot of data with log2 transformed 38 fragments per kilobase million (FPKM) plotted on the x axis for condition A and the y axis for 39 condition B. As in A data points are colored red if they are both 2-fold different and have a q- 40 value less than 0.05; otherwise they are black. C, Box plots for Gene A for condition A and B. 41 FPKM are plotted on the y axis with the two conditions represented side by side on the x axis. 42 D, Bar graphs denoting mean plus or minus standard error of the mean (SEM) for Gene A for 43 44 condition A and B. FPKM are plotted on the y axis with the two conditions represented side by 45 side on the x axis. Individual data points are superimposed on top of the bars for greater data 46 reporting transparency. DE, differentially expressed. 47 48 49 50 51 52 53 54 55 56 57 58 59 60 John Wiley & Sons Page 55 of 56 Current Protocols 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 John Wiley & Sons Current Protocols Page 56 of 56 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 124x91mm (300 x 300 DPI) 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 John Wiley & Sons