RESEARCH ARTICLE | CELL BIOLOGY OPEN ACCESS The human cell count and size distribution Ian A. Hattona,b,1, Eric D. Galbraithb,c , Nono S. C. Merleaua,d , Teemu P. Miettinene , Benjamin McDonald Smithf,g, and Jeffery A. Shanderh Edited by Jan M. Skotheim, Stanford University, Stanford, CA; received February 22, 2023; accepted July 24, 2023 by Editorial Board Member Rebecca Heald Cell size and cell count are adaptively regulated and intimately linked to growth and function. Yet, despite their widespread relevance, the relation between cell size and Significance count has never been formally examined over the whole human body. Here, we com- pile a comprehensive dataset of cell size and count over all major cell types, with data A consistent and comprehensive drawn from >1,500 published sources. We consider the body of a representative male quantitative framework of the (70 kg), which allows further estimates of a female (60 kg) and 10-­y-­old child (32 kg). cells in the human body could We build a hierarchical interface for the cellular organization of the body, giving easy benefit many areas of biology. We access to data, methods, and sources (https://humancelltreemap.mis.mpg.de/). In total, compile data to estimate cell we estimate total body counts of ≈36 trillion cells in the male, ≈28 trillion in the female, mass, size range, and cell count and ≈17 trillion in the child. These data reveal a surprising inverse relation between cell for some 1,200 cell groups, from size and count, implying a trade-­off between these variables, such that all cells within the smallest red blood cells to the a given logarithmic size class contribute an equal fraction to the body’s total cellular largest muscle fibers, across 60 biomass. We also find that the coefficient of variation is approximately independent of mean cell size, implying the existence of cell-­size regulation across cell types. Our data tissues in a representative male, serve to establish a holistic quantitative framework for the cells of the human body, and female, and 10-­y-­old child. We highlight large-­scale patterns in cell biology. find large-­scale patterns revealing that both cellular biomass in any cell size | cell count | cell biomass | size distribution | size homeostasis given logarithmic cell-­size class and the coefficient of cell-­size Cells can be considered the basic units of life. Each cell type typically has a characteristic size variation are both approximately range (1–4), which is not only maintained in regenerating tissues, but also throughout indi- independent of cell size. These vidual ontogeny from birth to maturity, and is often highly constrained across mammals from Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. patterns are suggestive of a mouse to elephant (5, 6) (SI Appendix, Fig. S1). Because of the uniformity of cell size for any given type, a larger body generally has a proportionately larger cell count (1, 5, 7), rather than whole-­organism trade-­off being comprised of larger sized cells. It has also been shown that the sizes of cells within a between cell size and count and given type are usually well described by a lognormal distribution, often ranging over about imply the existence of cell-­size an order of magnitude (SI Appendix, Fig. S2). But despite the uniformity in size of any given homeostasis across cell types. cell type, cell sizes vary over an enormous seven orders of magnitude from red blood cells to the largest muscle fibers, comparable to the mass ratio of a shrew to a blue whale. This raises the question: What is the size distribution of cells over the entire human body? When viewed Author affiliations: aMax Planck Institute for Mathematics across all the cells of a body, should we expect a lognormal distribution, similar to that of a in the Sciences, Leipzig 04103, Germany; bDepartment of Earth and Planetary Sciences, McGill University, Montreal, single cell type, or some other distribution that favors particular sizes and functions? Quebec H3A 0E8, Canada; cICREA, Barcelona 08010, At one level, it is apparent that the body’s cells are the “right size” to best perform their Spain; dCenter for Scalable Data Analytics and Artificial Intelligence, University of Leipzig, D-­04105 Leipzig, function (2, 3, 7, 8). Irregularities in cell size are often a pathological sign of disease or a Germany; eKoch Institute for Integrative Cancer Research, marker of malignancy (9, 10). Moreover, changes in cell size are associated with changes Massachusetts Institute of Technology, Cambridge, MA 02139; fDepartment of Medicine, McGill University Health in biosynthetic capacity and metabolic function (8, 11–13), especially when cell-­size Centre Research Institute, Montreal, Quebec H4A 3S5, changes are not accompanied by changes in ploidy (14, 15). Cell “types” such as myocytes, Canada; gDepartment of Medicine, Columbia University Medical Center, New York, NY 10032; and hIndependent neurons, and adipocytes vary extensively in size, but each cell has a size specificity suited Researcher and Consultant, Solon, IA 52333 to its function (e.g., muscle and sensory cells are smaller in the face than in the legs). This size homeostasis is thought to be maintained to some degree across all cell types, but the strength of size regulation, which is often measured as the coefficient of variation (CV) Author contributions: I.A.H. and J.A.S. designed research; in size, remains unknown across the full cell-­size range. I.A.H. and J.A.S. performed research; I.A.H. and J.A.S. analyzed data; I.A.H. and N.S.C.M. designed the website; At a higher level of organization, the overall functional capacity of a tissue depends on and I.A.H., E.D.G., N.S.C.M., T.P.M., B.M.S., and J.A.S. its total biomass. Since the cell sizes of any given type are relatively well constrained wrote the paper. (SI Appendix, Fig. S2), a tissue’s functional capacity is determined principally by the total The authors declare no competing interest. number of its cells (1, 5, 7). Two important studies in the past decade have surveyed the This article is a PNAS Direct Submission. J.M.S. is a guest editor invited by the Editorial Board. total number of cells over the largest tissues in the human body, with total counts con- Copyright © 2023 the Author(s). Published by PNAS. verging on values of 30 to 37 trillion human cells (16, 17), with an approximately equal This open access article is distributed under Creative number of bacteria cells (17). Although cell size and number are direct corollaries of Commons Attribution License 4.0 (CC BY). growth and proliferation (18, 19), and among the most fundamental quantitative attributes 1 To whom correspondence may be addressed. Email: i.a.hatton@gmail.com. of life’s basic units, the relation between cell size and number has never been formally This article contains supporting information online at examined at the level of the whole body. https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.​ We compile cell size, abundance, and diversity across tissues to provide a holistic view 2303077120/-­/DCSupplemental. of the body’s basic functional units (1, 2–4, 7, 8, 20). Such a whole-­organism view is Published September 18, 2023. PNAS 2023 Vol. 120 No. 39 e2303077120 https://doi.org/10.1073/pnas.2303077120 1 of 10 particularly timely given the recent plan to build a Human Cell Most of our estimates of cell mass are not direct mass measure- Atlas (21–23). This initiative seeks to create molecular profiles of ments, but derive from microscopy or other inferences of cell all human cell types, enabled by the rapid development of dimensions that are then converted to mass based on specific single-­cell multiomics. However, there are challenges to integrate densities for particular cell classes (e.g., 1.06 g/mL). While this studies focused on tissue samples into a whole-­body framework approach is less accurate than direct mass measurements which that is consistent with what is known from classical cell biology can be taken on certain cell types in solution, these inaccuracies (22). By integrating existing histology and anatomy across broad are likely to be minor relative to the trends that we observe over cell types in all major tissues, we hope that our data might help to several orders of magnitude variation in cell size. Our classification establish a quantitative foundation for all cells of the human body. of some 400 cell types is based on cell morphology and function (Materials and Methods) rather than molecular markers and RNA Approach expression, which are currently resolving classical cell taxonomy into many additional cell types (27, 28). We use a combination We consider cell size and number for some 400 cell types in the of reported count or concentration, total aggregate mass, surface bodies of three reference human anatomical models (24–26) area, and/or DNA concentration to estimate the cell count for (Materials and Methods). These include an adult male of 70 kg, an each cell type in each tissue. Our approach thus provides a con- adult female of 60 kg, and a 10-­y-­old child of 32 kg. We base our sistent methodology applied to all cell types, including those less analysis on the extensive catalogue of tissue masses and composi- amenable to more modern techniques. We cross-­validate our tions compiled for the reference male model by the International results by comparing multiple levels of aggregate mass at tissue Commission on Radiological Protection, ICRP (24–26). We then and organ levels (24–26) (Materials and Methods). use the male model as the basis for estimating cell sizes and abun- dance values for the ICRP female and child models. We use tissue Results mass ratios reported by ICRP (24–26) to maintain correspondence with their models. In most cases, we assumed similar ratios among A Comprehensive Human Cell Dataset. Our compilation of data cell types, and similar cell sizes across the three models, the excep- presents an attempt to constrain cell parameters across all major tions being neurons, adipocytes, and myocytes (Materials and cell types of the human body. A hierarchical interface to these Methods). We thus have greatest confidence in our size and count data, giving methods and sources for each cell type in each tissue, data for the male model, but uncertainty in the conversions from is available at: https://humancelltreemap.mis.mpg.de/. male to female and child are unlikely to be significant relative to For several tissue systems, our compilation provides much other sources of error in the data. greater resolution than was previously available, including muscle Our data and methods build on prior work that estimated cell fiber sizes of all major striated muscle groups (n = 243 cell groups), counts in approximately 20 tissue systems (16, 17). We partition neurons and glial cell groups throughout the central and peripheral the body into 60 well-­studied tissue systems (24–26), each of nervous system (n = 244 cell groups), and the tissue resident blood Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. which is further subdivided into major components (stroma, cell groups (n = 172) from their origin in bone marrow, to their parenchyma, and extracellular matter) and dominant cell types. circulation and sequestration in the major blood organs and other Such partitioning yields 1,264 separate cell groups, comprising tissues. We also focus on tissues of interest for human health, the 400 major cell types across 60 tissues. For each cell group, we including digestive (n = 148 cell groups) and female reproductive compile data from the literature to calculate cell-­size mean and tissues (n = 156 cell groups). While gaps remain (SI Appendix), range, and to estimate their number across the three models. these data present a detailed taxonomy of cell types across all major Fig. 1. Contrasting cell count and bio­ mass distributions by cell type. Voronoi tree maps for all 400 investigated cell types of the reference male anatomical model (area represents relative cell number or biomass). (A) Cell counts are dominated by red blood cells and platelets, which are removed from the cell count tree map. Even after removing nonnucleated blood cells (≈29 trillion), white blood cells (≈3.4 trillion) still dominate the ≈7 trillion nucleated cell count, with 98% of white blood cells as tissue resident, 1% circulating and 1% intravascular marginating. (B) Cell biomass is dominated by skeletal myocytes, comprising about half of all 45 kg of cell biomass in the body, even though they make up <0.002% of the nucleated cell count, which are removed from the biomass tree map. Most of the remaining 23.5 kg of cell biomass are white adipocytes (≈12 kg; though body fat varies widely among subjects). Tree maps for a reference 60-­kg female (≈28 trillion cells) and 32-­kg child (≈17 trillion cells), and more detailed cell groups and organ systems can be explored at https://humancelltreemap.mis.mpg.de/ (SI Appendix, Figs. S7–S9). 2 of 10 https://doi.org/10.1073/pnas.2303077120 pnas.org tissue groups and provide baseline values of cell size and count over its size range, which sometimes spans multiple size classes throughout the body. (gray bars in Fig. 3). Given that a lognormal distribution provides These data emphasize the vast size range of human cells, from a reasonable fit to prior published size distributions for most cell a tiny red blood cell to a myocyte, more than a million-­fold larger. types (SI Appendix, Fig. S2), we assume a lognormal better describe Because of these vast size differences across some 400 major cell each of the 1,264 cell groups in our dataset. We therefore partition types (27, 28), we find dramatically different distributions between cell counts lognormally to different size classes based on their cell count and cell biomass (Fig. 1). Cell counts are completely reported range, and sum all cell counts over all cell types in each dominated by red blood cells, platelets, and tissue resident white size class (Materials and Methods; SI Appendix, Fig. S3). blood cells, while cell biomass is dominated by muscle and fat We find a consistent relation between log total count in each (myocytes and adipocytes). Within particular tissues, on the other log size class extending from red blood cells to the largest myocytes hand, the dominant cell sizes vary much less, and so both count (Fig. 3). The slope of the relation is robust to alternative fitting and biomass distributions are often quite similar (Fig. 2). methods and various binning schemes, and similar to the exponent of the corresponding cumulative distribution function, which Cell Size Is Near Inverse to Count. A holistic view of cell size and avoids binning data into size classes (Materials and Methods). All count can be gained by constructing a cell-­size histogram over the approaches give exponents near the value of −0.97 and within the whole body. This distribution can be expressed in several ways, all 95% CI reported in Fig. 3 (−1.09 to −0.87; SI Appendix, Fig. S4; of which yield similar results (Materials and Methods; SI Appendix, Materials and Methods). A slope near −1 implies a roughly inverse Fig. S4). The size distribution shown in Fig. 3 is built by spreading relation between cell size and count, such that an increase in size mean cell size of each cell type (shown by colored points in Fig. 3) by a given factor is offset by a decrease in cell count by a similar Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. Fig. 2. Cell class distributions across select tissues. Cell count and biomass distributions across 18 broad cell classes (colored) are shown for the 32 most significant tissue systems of the body, representing about half of all 60 investigated tissue systems, including the vast majority of total cell biomass. Numerical values refer to a reference male except for the female breasts, uterus, and ovaries. Most tissue systems are dominated by the ≈140 distinct cell types making up the epithelial cell class. “CNS” and “PNS” refer to central and peripheral nervous systems, respectively. The “Other blood cell” class is dominated by macrophages, but includes monocytes and precursors to red blood cells and platelets. Cell biomass excludes noncellular components of biomass in each tissue, made up of extracellular water, protein, and minerals (~25 kg; see SI Appendix). The blood cells of the blood organs and intravascular are shown separately in Fig. 1. PNAS 2023 Vol. 120 No. 39 e2303077120 https://doi.org/10.1073/pnas.2303077120 3 of 10 beginning at red blood cells (4 × 10−11 g). There are also promi- nent biomass peaks comprising adipocytes (near 10−6 g) and myocytes (near 10−4 g), separated by a gap of relatively low bio- mass. Unlike most other cell types, adipocytes and myocytes have uncommonly broad size distributions and vary much more in size than in count, with cell counts remaining relatively fixed through ontogeny, obesity, or muscle atrophy (29–31). This gap is also evident but less prominent in the reference female, and is even less evident in the 10-­y-­old reference child (Fig. 4B and SI Appendix, Fig. S5). We expect that this gap may be even smaller in earlier ontogeny, when the musculature is less devel- oped. With the exception of this gap, all variation in cellular biomass lies within two orders of magnitude while cell size varies over more than seven orders of magnitude. Despite the irregu- larities, these results suggest little systematic tendency for cellular biomass to vary across size classes. Cell-­Size Variation Across Cell Types. Our data also allow us to consider variation in cell size across cell types. For cell groups with sufficient data, we use size range values across 522 different cell groups to estimate their CV (SD divided by mean cell mass; Fig. 5A). These estimates roughly align with the CV of prior published human cell-­size distributions (n = 54; SI Appendix, Fig. S2), as well as ten classic model systems commonly used in cell-­ size research, and for which numerous high quality measurements exist (Fig. 5B; Materials and Methods). These distinct data sources all show that across the full suite of cell sizes, CV is approximately independent of mean cell size (Fig. 5B and SI Appendix, Fig. S6). This implies that cell mass variance should scale with the mean following a power law exponent of 2, and we estimate a value of 2.17 ± 0.03 (Fig. 5C; Materials and Methods). Despite adipocytes Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. and some neuron cell groups displaying higher variability than most other cell types, there is not significant systematic variation in CV across cell types over the full size range. Any constancy in CV may imply that the underlying mechanisms responsible for cell-­size homeostasis are present across cells in the human body (SI Appendix, Fig. S6). Discussion Fig. 3. Human cell size and count are nearly inversely related. Data and Data Uncertainty. The data on which our study relies are often regression are shown for a 70-­kg reference male, though very similar patterns coarse and include several types of error. For example, many of the are found for female and child (SI Appendix, Fig. S5). The total number of cells cell-­size measurements rely on 2D microscopy and approximations in each of 26 logarithmic size classes is regressed against cell mass (g), giving a slope near −0.97, as shown by the black line fit to black circles. The yellow band of cell shape, rather than direct mass measurements, which are shows the 95% CI (±0.1), while the dashed gray line shows a reference slope of available for some cell types that can be isolated. For many −1. Small colored circles (n = 700) include 401 distinct cell types, in addition to cell types, there are limited available literature sources, and single cell types that have large variation in size (e.g., adipocytes, neurons and estimated mean sizes sometimes differ by as much as fourfold myocytes). These points represent mean cell-­size values for each cell group, and are aggregated over their size range into size class sums represented between sources. To express the uncertainty in cell size, we report by black points (Materials and Methods). In some cases, cells with broad size upper and lower bounds on cell size (for 1,020 of 1,264 cell distributions (e.g., adipocytes; light blue) have counts of mean sizes that are groups), which indicates the potential cell-­size errors in the data higher than the size class sum (black points), since the counts of actual sizes are spread lognormally over multiple size classes. The open gray circle (Top (Materials and Methods). Left) is the bacterial microbiome (17). Estimation errors are also evident in cell counts, which can vary as high as 20-­fold between studies, though a factor of twofold to fourfold variation is more common. Examples of large varia- factor (and vice versa). Such an inverse relation implies that a tions in data between studies include the small intestine entero- roughly similar fraction of total body mass is present in each log- cytes and endothelial cells, as well as counts in the heart, adipose arithmic cell-­size class (Fig. 4A). tissue, and lymph nodes (Materials and Methods and SI Appendix). Although the cellular biomass is overall approximately evenly It is not known how these errors might affect the noise observed spread among cell-­size classes (Fig. 4A), there are notable irreg- in our analyses, be it irregularities in Fig. 3, or variability in Fig. 5. ularities, which are more evident when plotted on a linear bio- Nor can we be certain whether these differences reflect biological mass axis (Fig. 4B). Among the smallest cells, there are breaks in differences or measurement error. Nonetheless, we do not expect the continuity of the size distribution around platelets and hap- that the range of estimation error, typically less than a factor of loid sperm cells, near 10−11 g. Our size distribution does not 10, to significantly alter the broad-­scale patterns we observe over include these small cells, and only considers the distribution seven orders of magnitude. 4 of 10 https://doi.org/10.1073/pnas.2303077120 pnas.org Fig. 4. Cellular biomass and cell-­size variation. The number of cells in a group multiplied by cell mass gives total biomass of a cell group. (A) Numerical counts (from Fig. 3) are transformed to total cellular biomass within a size class, and show little systematic variation with log cell mass. Here we show female cell biomass, which is broadly similar to the male and child (as shown in B on linear axes). Horizontal colored bands represent approximate size ranges based on literature values. In some cases, the biomass of a cell type (colored points) may be greater than the size class sum if that cell type is broadly distributed (e.g., adipocyte and myocyte). (B) Cell biomass can also be displayed on linear axes to highlight irregularities, particularly among the largest cell types (adipocytes and myocytes). Male, female, and 10-­y-­old child are broadly similar, though the child displays a slightly more even distribution of biomass (SI Appendix, Fig. S5). Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. Estimates for a number of cell types are prone to significant experiments in particular tissues where cell size and number trade-­ uncertainty. These include the size and counts of neuron and glial off with one another to preserve tissue size. As mentioned, in some cells, and particularly the neurons of the peripheral nervous sys- cell types, a doubling of cell ploidy can approximately double cell tem. Stromal cells in general and vascular cells in particular also size, but the effect this often has is to halve the number of cells. This pose challenges to whole-­body estimation, given their ubiquity size-­count trade-­off maintains overall tissue biomass, as has been throughout the body. Finally, our counts of the nucleated blood observed in salamanders (34), compartments of the Drosophila cells such as lymphocytes, are several fold higher than some prior wing (35), mouse embryos (36), mouse liver (37), and possibly estimates, a difference that should be further researched (Fig. 6). during human liver regeneration (38, 39). A plot of cell size vs. cell A broader discussion of major uncertainties is in Materials and number across different experimental ploidies would thus reveal Methods and SI Appendix. an inverse relation. The trade-­off between cell size and count that maintains a relatively constant tissue size thus bears similarity to Cell DNA Content. We have not investigated the relation between the inverse size-­count pattern over the whole body (Fig. 3). cell ploidy and cell mass, which has long been thought to vary A similar pattern may hold across the bodies of mammal spe- proportionately (32), or possibly as a sublinear power law (20, 33). cies. From mouse to elephant, the mean size of most cell types is There are however, some significant exceptions to these relations. similar (SI Appendix, Fig. S1) (5, 6), while most major tissue For example, some of the largest cells, such as adipocytes and masses are roughly proportional with body mass (40). If we assume neurons are typically diploid, while several smaller cells such that the ratios of different cell types in different tissues are constant as keratinocytes and pancreatic acinar and beta cells may be across species, we should expect a similar inverse cell size-­count polyploid. It is also not yet clear how these relations might extend relation to hold in other mammals. As we have also suggested, to the myonuclear domain. there is reason to expect the same pattern may exist in infants. We can, however, make a rough estimate of the proportion of Taken together, these expectations suggest that predictable pat- polyploid cells throughout the body from ploidy estimates across terns between cell mass and count may be established across mam- major cell types. We estimate only about 1% of all nucleated cells malian developmental programs. are polyploid (see Dataset S1 for details). However, myocytes, each This size distribution appears to be common in nature. The with thousands of diploid nuclei (31), make up about half of all data in Fig. 3, can be expressed as a cumulative distribution func- cell biomass, such that polyploid cells dominate the cell biomass tion, which has an exponent α ≈ −0.95 (SI Appendix, Fig. S4C). in the body. In total, polyploid cells account for about 10% of the A cumulative distribution function is proportional to a rank-­size body’s DNA. relation (Materials and Methods), and highlights a parallel to what is often termed Zipf ’s law in other fields (41, 42). This pattern A Recurrent Pattern. The near-­inverse relationship between cell includes such disparate phenomena as the size distribution of size and count that we report in Fig. 3 is reminiscent of classic cities (41), of asteroids (43) and of bodies in the ocean from PNAS 2023 Vol. 120 No. 39 e2303077120 https://doi.org/10.1073/pnas.2303077120 5 of 10 Fig. S6). Such power law scaling between the mean and variance in the size of a system is widely observed, particularly in the sizes of animal populations, and is known as Taylor’s law in ecology (47, 48). Both Zipf ’s law (Fig. 3) and Taylor’s law (Fig. 5C) are common among populations of individuals (44, 47, 48), includ- ing unicellular systems (45, 49, 50). However, we are not aware of these patterns having been previously reported below the level of the individual, which highlights a recurrence across levels of organization. Origins. To our knowledge, there is no universally recognized process that accounts for these patterns. Nonetheless, many simple random growth processes are capable of giving a stable inverse size distribution (41, 42, 51). In particular, because cell size and count emerge from the interplay of cell growth and division, the balance of these respective rates may prove to be an important factor. For balanced growth, rates of enlargement must match rates of division (52–55). Mismatches between these (possibly exponential) rates have the effect of multiplicatively varying size while, inversely, varying count. A relatively fast division rate results in both smaller cells, and more of them (and vice versa). For example, this distribution might emerge if we assume mismatches in these rates vary in such a way as to preserve mean cell size. If we further assume a lower (reflective) bound on cell size, then, following arguments in ref. 41, we should expect a size distribution with exponent −1. We emphasize that many simple mechanisms are possible, and no doubt the actual process is likely a great deal more complex. Nonetheless, the observed regularity between size and count may reflect fundamental principles that underlie the balanced control of cell growth and proliferation. Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. Applications. Our data may help to place overall bounds on human cell quantities and to identify major areas of uncertainty, as we summarize further in SI Appendix. For example, the total number of lymphocytes, often considered to be near 500 billion (56–59), is of key relevance for understanding immune function. Our assessment of the available data and more up-­to-­date studies of the blood organs and other tissues, suggests that this number may need a fourfold revision upward to approximately 2 trillion (Materials and Methods, SI Appendix). Such a major discrepancy of lymphocytes in the major blood organs could have implications for diagnostics of lymphocyte kinetics in leukemia, HIV, and other lymphocyte-­related illnesses (60). Our compilation of size ranges across cell types are rough, sometimes deriving from a small number of literature values. Nonetheless, they provide some constraint on expected cell size, which is often needed for cell identification in immunohisto- chemistry, cell isolation, or predictions on successful flow in microfluidics. More generally, an integrated framework of the prior literature facilitates contextualization of more focused stud- Fig. 5. Size variation across cell types. (A) Published size distributions of select ies, such as those pertaining to the Human Cell Atlas (21, 22). human cell types (colored; n = 30) and cell-­size model systems (gray; n = 10) For example, our data could provide reference values in cases are normalized and found to be well approximated by a log-­normal distribution where different sequencing methods give different cell propor- (Materials and Methods; SI Appendix, Fig. S2). (B) The CV (SD divided by mean cell mass) shows little tendency to vary systematically with mean size across the full tions over similar tissues (21, 23, 61). We also expect these data range of cell types. (C) The variance in cell mass for each cell type scales as a power may help in understanding protein content and turnover, dosim- law with exponent 2.17 (±0.04) against mean cell mass across all cell groups. etry, development of therapeutics, and as a baseline reference for Small solid points in B and C derive from estimates of SD from size ranges in our cell biology (7). Finally, cell size may offer energetic and dynam- dataset (n = 522). Open circles (n = 54) are from prior published distributions and black shapes (n = 10) are from cell-­size model systems shown in A (see SI Appendix, ical predictions, in the same way that body size predicts rates of Fig. S2 for legend, and Materials and Methods and SI Appendix for sources). growth, metabolism, maturation, and mortality at a much larger scale (62, 63). bacteria to whales (44). Moreover, this general size-­distribution Conclusion. We have attempted to summarize the basic quantities has been previously associated with mean-­ variance scaling of size and count over most major cells of the human body. We (45, 46), similar to what we show in Fig. 5C (SI Appendix, show that cell biomass is roughly independent of cell-­size class. 6 of 10 https://doi.org/10.1073/pnas.2303077120 pnas.org s Fig. 6. Summary of blood cell distribution across blood organs and other tissues. Blood cell size and count are estimated for each of the intravascular, major blood organs and over all remaining tissues. Numerical values refer to a reference male. These estimates derive from reported total biomass of each major tissue system. The relative biomass fraction for each tissue is shown as a hatched shaded gray bar in the leftmost column. The first row (intravascular) shows the same data as the second, except that nonnucleated red blood cells and platelets are removed (as in Fig. 1A). Total cell biomass (kg) and cell count in each system are partitioned into eight blood cell groups (colored bars) based on a variety of methods. We highlight where our methods and estimates differ with Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. prior published sources. Although cell size is often tightly regulated and closely linked to – Male, (70 kg, 176 cm in height and 20 to 50 y of age). function (1–4, 9, 11–13), this independence suggests that there – Female (60 kg, 163 cm in height and 20 to 50 y of age). is no best size for a cell. Rather, the cells of the body are roughly – Child (32 kg, 138 cm in height and 10 y of age) (24–26). spread across the cell-­size range, through a trade-­off between cell Our main dataset (Dataset S1) compiles cell size and count data from more size and count. While the developmental mechanisms underlying than 1,500 published sources for 400 known cell types across 60 tissue systems this pattern remain unresolved, its similarity to patterns at other in the body, giving 1,264 cell groups. These cell groups are classified according scales and in other fields is intriguing and may offer clues to its to two separate taxonomies; one based on various levels of cell type groupings, origin. Our holistic perspective of cell size and count has identified and one based on the body and tissue system in which different cell types are some major gaps in knowledge (SI Appendix), some of which located. Recent work is further refining cell types into many additional classes may have health implications, such as the total body lymphocyte (22), and we also provide a more resolved cell type classification into 924 more count. More generally, cell size and count are intimately linked specific cell types. Although this may better align cell type taxonomy with more to the dynamics of growth and proliferation (18, 19). A better resolved cell types based on molecular markers or RNA expression, we cannot understanding of the relation between cell size and count may keep pace with the speed with which new cell types are being described. Included thus yield insight into balanced growth as well as abnormalities in Dataset S1 is a worksheet that lists the levels of ploidy in common cell types, in its regulation. from which we estimated the proportions of polyploid cells throughout the body. Supporting data (Dataset S2) include a dataset on cell-­size distributions for 54 cell types compiled from the literature, from which we selected the 30 cell types Materials and Methods shown in Fig. 5 and the 25 cell types shown in SI Appendix, Fig. S2. This dataset Below we provide a high-­level summary of the methods used to build our data- also includes the 10 model systems commonly studied in cell-­size research, sets and our approach for representing these data in the figures. More details are shown as black shapes in Fig. 5 and SI Appendix, Fig. S2. Finally, we include a available in SI Appendix, which includes additional methods, major outstanding dataset of cell size for 33 cell types across mammal species spanning the size uncertainties, the differences with prior whole body cell counts, further notes to range from mouse to elephant shown in SI Appendix, Fig. S1. Figs. 1–6 and SI Appendix, Figs. S1–S9. All data and detailed methods and original sources are available in Dataset S1, Data Compilation. In order to estimate the cell size and number for the different with supporting data in Dataset S2. An interface to Dataset S1 is at https://human- cell groups, we used tissue parameters for the reference male, which comprises celltreemap.mis.mpg.de/. considerably more extensive and detailed data than exist for the female and To estimate the human cell-­size distribution, we compiled measures of cell size child models (24–26). Tissue level data and parameters were drawn from the and count across cell types and tissue groups of the bodies of three reference publications of the ICRP (24–26). The ICRP provides a common unifying base for anatomical models (24–26): which separate tissue masses and associated surface area, hydration level, protein PNAS 2023 Vol. 120 No. 39 e2303077120 https://doi.org/10.1073/pnas.2303077120 7 of 10 content, extracellular mass, and fat mass have been estimated from multiple endothelial apical surface area (600 to 1,350 µm2), to put bounds on endothelial autopsies, and in vivo MRI and CT-­scan data. cell counts for different parts of the body. Given their ubiquity throughout the body, Estimates of cell size and count for a reference female and child are based there remain large uncertainties in endothelial cell sizes and counts. on the more complete reference male model, following several simplifying Stem, germ, and pericyte. Our data include n = 39 stem cell types distributed assumptions. For most cell types, we assumed similar cell sizes and similar ratios across most major body systems (73 stem cell groups). We refer to germ cells as of different cell types in each tissue. This allowed us to scale male cell counts all potentially reproductive cells in the germline, thus including haploid gamete to female and child models on the basis of reported ratios of respective tissue cells. The reference male germ cells refer to the various diploid and haploid stages masses in each model for most of the ≈60 major tissue systems. For cells such as of sperm formation (n = 13 cell groups), while the reference female germ cells myocytes and peripheral nerves, on the other hand, we assumed that cell count refer to the various groups of oocytes (n = 10 cell groups). Our data estimate is constant (29, 30), while cell size was assumed to vary as a system enlarges. pericyte counts in 38 tissues based primarily on location-­specific pericytes to In these cases, we scaled cell size (rather than cell count) accordingly between endothelial cell ratios, the latter of which we have estimated separately. reference models. Finally, we undertook a detailed study of female reproductive Adipocytes. The reference male potassium-­based total cellular mass of adipo- organs, with particular emphasis on the cell groups of the breasts (n = 74), uterus cytes is 13.3 kg (24). This is distributed as hypodermal (≈6.5 kg), yellow bone (n = 34) and ovaries (n = 48). marrow (≈2.2 kg), visceral (≈3.5 kg), and the interstitial adipose tissue in differ- For each tissue group detailed in the ICRP, we determined the relevant cell ent organs (≈840 g), with an additional 300 g of stromal cell mass, partitioned types, their relative proportions, and the mean and range (where possible) proportionately across the four groups. These values differ significantly in female of cell mass. For commonly studied cell types, this was determined by taking and child models, totaling 18.1 kg and 6.9 kg of adipocyte cellular mass, respec- an average and range of values from multiple published studies. For poorly tively (25). We assumed that the relative proportions of different cell masses studied cell types, cell sizes were estimated from closely related cell types, or of adipocytes were constant across all anatomical models. We thus scaled the calculations based on multiple cell components or contextual indicators. For adipocyte counts according to the relative adipose tissue masses (25). There is example, in the absence of published measurements for peripheral neurons some uncertainty in the size distribution of adipocytes over the whole body, as or myocytes we used average estimates of axon or myocyte diameter, based discussed in SI Appendix. on cells in similar locations with similar functions. Estimates of cross-­sectional Neuron and glial. Estimates of neuron and glial cells require several kinds of area were then multiplied by an approximate length, appropriate for the cell’s methods and pose significant challenges. In total, our data list mean cell masses relevant location in the body. In the case of neurons, we added cell body and and cell counts for 57 neuron cell types (159 groups) and 22 glial cell types (81 dendrite mass. We determine the mean and range of cell mass for each cell groups). These are distributed across various locations of the central and periph- type, and we calculate the number of cells based on their aggregate cellular eral nervous systems, as well as other tissue systems, particularly the enteric mass, surface area and/or DNA concentration. Finally, cell masses and counts nervous system. We estimate a total of 88 billion neurons and 87 billion nonneu- are cross-­validated against multiple sources over a nested set of boundaries rons, consisting of ≈53 billion glial cells, ≈4 billion microglial macrophages, and across the tissue to organ hierarchy. 30 billion other stromal cells (vascular) in the brain of the reference male. This Below, we briefly summarize each major cell class, providing a more detailed is consistent with a 1:1 neuron to nonneuron ratio and a total brain cell count of summary of each cell class in SI Appendix. We focus on the reference male model, 170 ± 13 billion (73, 74). For the reference female and child, we assumed the unless otherwise stated. Note that the ordering by cell class does not reflect the masses of glial cells in both the CNS and PNS were constant across all models, and Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. manner by which the data were originally compiled. Rather, as indicated above, scaled their counts by various tissue mass ratios (25, 75–77). A similar approach the body was segmented into separate tissue groups, with relative proportions was taken for the neuron cells in the CNS, but for those in the PNS, we assumed of all the different cell types in each tissue estimated in each component. We cell counts constant and scaled neuron sizes by tissue mass ratios. then scaled those proportions to the relevant mass across each of the three focal Fibroblasts and osteoid. We have used specific values for cell size and count in anatomical models. different tissue systems, where available. We estimate fibroblast cell sizes and Blood cells. The reference male contains 4.7 kg of blood cellular mass, 55% of counts in 56 tissue systems, which include chondrocytes, tenocytes, keratocytes, which resides in the intravascular, while 45% is extravascular tissue-­resident. reticular cells, etc. Fibroblasts and osteoid cell masses and cell type ratios were Roughly half of the tissue resident cell mass resides in the four blood organs, assumed constant across reference male, female, and child models, while counts distributed as shown in Fig. 6. In Fig. 6, we summarize these data into eight were scaled according to relative cellular mass ratios of the relevant tissues. cell classes across six broad tissue categories. We have classified blood cells A more in-­depth summary of each of these major cell classes is available as into n = 53 blood cell types, and enumerated many of these types across SI Appendix, Supplementary Methods, and specific details for each cell type are some 30 tissue systems. In total, we consider 191 blood cell groups, much of available in Dataset S1 and at https://humancelltreemap.mis.mpg.de/. the data for which derive from refs. 64–67. Our estimates of nucleated blood cell counts are significantly higher than prior estimates (56–59), and deserve Whole-­Body Cell-­Size Distribution. We considered the cell-­size distribution further study (Fig. 6). in multiple ways. First, we simply summed all counts of each cell type whose Myocytes. Myocytes include striated skeletal myocytes and cardiomyocytes, and mean size fell within each log size class (SI Appendix, Fig. S4D). Using only the smooth muscle cells. We compiled separate myocyte mean sizes and counts mean cell size for a cell type, however, can distort the overall distribution if that for 243 skeletal muscle groups in the body addressing 350 muscle pairs. We cell type actually varies over a broad range of size classes, resulting in irregular- assumed that skeletal myocyte counts are fixed across reference models (30), ities that are possibly artifacts. An alternative is to use both the cell-­size mean and scaled myocyte size based on the ratios of the total muscle mass of female (colored points in Figs. 3 and 4A) and range (light horizontal lines in Fig. 4A). To (0.625) or child (0.393) to male. Cardiomyocyte counts are based on published do so, we assumed that the size distribution within any particular cell group can counts of myocytes for specific sections of the heart (68–70), extrapolated to the be described by a lognormal (Figs. 3 and 4 and SI Appendix, Fig. S2; see “Size entire heart. Cardiomyocytes in the reference female and child require scaling Variation across Cell Types,” below) (7, 78). We further assumed that the size range both fiber size and count in both models based on heart mass ratios and patterns represents four SDs, since in many cases our range values derive in this way from of cardiomyocyte maturation and hypertrophy (71, 72). Smooth muscle is distrib- reported SDs in the literature. uted throughout the body (n = 27 cell types and n = 41 cell groups), and made To spread mean cell size across a more realistic size distribution for each cell use of specific studies, where available, or else generic smooth myocyte sizes. type, we obtained lognormal quantiles for the counts, and used these to build Epithelial and endothelial. Epithelial cells make up the majority of the well-­known the whole-­body size distribution in Fig. 3 (outlined in SI Appendix, Fig. S3). We cell types in the body (n = 142 unique cell types and 228 cell groups), and as such verified that partitioning our counts over lognormally distributed sizes in this are difficult to adequately summarize. For many tissues, estimates were made of cell way conserved both the total cell count and total biomass (to within a few grams). count using a generic cell apical surface area and a total internal and external tissue While this method of partitioning counts across size classes makes full use of surface area. Wherever possible, microvascular studies providing capillary densities the data available, it makes little difference to the exponent of the distribution (mm capillary per mm3 of tissue) in different locations were used in conjunction with (SI Appendix, Fig. S4). 8 of 10 https://doi.org/10.1073/pnas.2303077120 pnas.org We report ordinary least squares regression statistics for the fit between the We compiled 54 published cell-­size distributions from the literature for cell log of total count in each size class vs. the log of the geometric mean of the size types in all major cell groups, drawn from 30 published sources (Dataset S2). class. In the case plotted in Fig. 3, bins increase by a multiplicative factor of 2 from Several of these distributions were for the same cell types, in which case we small to large (octave size classes), beginning at the lower bound of erythrocytes, excluded the less complete distribution (fewer size classes), leaving 30 distribu- and giving 26 size classes across the full range of cell sizes. We ignored binning tions, as shown in Fig. 4B and SI Appendix, Fig. S2. Many of these distributions cells smaller than erythrocytes (blood platelets and the smallest haploid gamete reported size in linear or area units, which were converted to mass. This was cells), to avoid fitting the distribution over relatively large gaps. In Fig. 3, we report usually straightforward, but in cases such as with neurons and myocytes, where that the distribution is well approximated by a power law with a slope near −0.97 only a cross-­sectional diameter or area was reported, we assumed that the axon or with a 95% CI of −1.09 to −0.87. fiber length was constant for the section reported and scaled the mass according Several alternative representations and fitting methods of the cell-­size distri- to the variation reported. We set the weighted mean cell size to match the mean bution give exponent values that are typically within our reported CIs (SI Appendix, size obtained from other sources included in our primary dataset (Dataset S1). Fig. S4). Varying size class width or bin number, and the way in which we distribute Although this may introduce an arithmetic and possibly multiplicative bias into counts for each cell type over their size range, all yield exponents typically near the true mass distribution, it should not alter the near lognormal shape of the −1 (SI Appendix, Fig. S4 B and E). As an alternative to binning the data into size size distribution for most cell types. classes, we can represent cell sizes as a complementary cumulative distribution We also compiled size distribution data for 10 model system cell types function (CCDF) (42). Plots of the CCDF are shown in SI Appendix, Fig. S4 C and commonly used in cell-­size research. These cell types include the following: F. While we have reported CCDF exponent values derived from least squares, we Arabidopsis shoot meristem cells (80) (n = 1,746); human adenocarcinoma have also fit these distributions using maximum likelihood, using methods for cells (81) (HeLa cell line; n = 13,848); fission yeast cells (82) (n = 329); mouse binned data (79), finding that the fitting method does not significantly alter the lymphocytic leukemia cells (81) (L1210 cell line; n = 300); human induced exponent, with best fits within our reported 95% CI. pluripotent stem cells (iPSC) differentiated toward presomitic mesoderm cells The cumulative distribution function (CCDF) is the fraction (or else the total (83) (iPSC-­derived presomitic mesoderm cells; n = 299); haploid and diploid number) of cells with size greater than or equal to x. The CCDF, with exponent α ≈ budding yeast cells (CDC28 mutant); human lung fibroblast cells; immortalized −1, means the probability that the size of a cell is greater than some x is propor- human mammary epithelium cells (HMEC) cells; and, human retinal pigment tional to 1/x; that is, P(size > x) ~ xα. The cumulative distribution is proportional epithelial cells (84). For L1210 cells, human iPSC cells and fission yeast cells, to another common representation, which is the rank of cell size. A rank-­size data were based on direct cell mass measurements based on buoyant mass relation is the way that Zipf’s law is most typically represented (41, 42). If we rank in different density fluids (85). For all other cell types, data were originally the sizes of cells from smallest to largest, then there are n cells with size greater volumetric (Coulter counter) and converted to mass using a conversion density than or equal to that of the nth largest cell. But “the nth largest cell is of size x” of 1.056 g/mL. (rank), is the same as “n cells are of size x or greater” (cumulative distribution). To normalize these distributions to the same reference scale so that area under As such, the exponent obtained from regressing the log count against the log all curves are equal (Fig. 5A), we divided each binned proportion by the mean size class (e.g. α ≈ −0.97, Fig. 3), should give a similar exponent to the CCDF bin width and multiplied by the geometric mean of the respective cell mass (e.g. α ≈ −0.95; SI Appendix, Fig. S4C), or the rank-­size relation. It should also associated with each bin. be noted that the corresponding probability density has an exponent α −1; that Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. is, p(x) ~ xα−1, thus having a value near −2. Data, Materials, and Software Availability. Excel data are available as Size Variation Across Cell Types. We obtained cell-­size range data for 1,020 of SI Appendix “Dataset S1” and “Dataset S2”. 1,264 cell groups in our dataset, though many of these are the same for a given cell type in different tissues. These data were obtained largely from the literature ACKNOWLEDGMENTS. We thank Pierluigi Strippoli and Ron Milo for valua- or calculations from contextual indicators (e.g., body location) or morphological ble discussion, as well as Gregory Fonseca, Alvaro Corral, Ron Sender, Matteo components (e.g., diameter). The latter could include, for example, estimates of Smerlak, Jun Ding, and Joshua Gruber for comments on an earlier version of typical ranges in lengths multiplied by ranges in cross-­sectional area to obtain the manuscript. We thank S. Manalis, F. Chang, O. Pourquie, M. Diaz-­Cuadros, a mass range for a neuron or myocyte group. The cell-­size ranges are shown as E. Zatulovskiy, J. Skotheim, G. Neurohr, and R. Sablowski for sharing data. horizontal lines in Fig. 4A, and are converted to an estimated SD to compute the Funding was provided by the Alexander von Humboldt Foundation in the form CV (SD divided by mean) in Fig. 5B and variance (SD squared) in Fig. 5C. of a Research Fellowship to I.A.H. 1. I. Conlon, M. Raff, Size control in animal development. Cell 96, 235–244 (1999). 16. E. Bianconi et al., An estimation of the number of cells in the human body. Ann. Hum. Biol. 40, 2. M. B. Ginzberg, R. Kafri, M. Kirschner, On being the right (cell) size. Science 348, 1245075 (2015). 463–471 (2013). 3. E. Zatulovskiy, J. M. Skotheim, On the molecular mechanisms regulating animal cell size 17. R. Sender, S. Fuchs, R. Milo, Revised estimates for the number of human and bacteria cells in the homeostasis. Trend Genetics 36, 360–372 (2020). body. PLoS Biol. 14, e1002533 (2016). 4. A. Tzur, R. Kafri, V. S. LeBleu, G. Lahav, M. W. Kirschner, Cell growth and size homeostasis in 18. A. Campbell, Synchronization of cell division. Bacteriol. Rev. 21, 263–272 (1957). proliferating animal cells. Science 325, 167–171 (2009). 19. D. O. Morgan, The cell cycle: Principles of control (New Science Press, 2007). 5. J. C. Lui, J. Baron, Mechanisms limiting body growth in mammals. Endocrine Rev. 32, 422–440 (2011). 20. J. F. Gillooly, A. Hein, R. Damiani, Nuclear DNA content varies with cell size across human cell types. 6. V. M. Savage et al., Scaling of number, size, and metabolic rate of cells with body size in mammals. Cold Spring Harb Perspect Biol. 7, a019091 (2015). Proc. Natl. Acad. Sci. U.S.A. 104, 4718–4723 (2007). 21. O. Rozenblatt-­Rosen, M. J. Stubbington, A. Regev, S. A. Teichmann, The human cell atlas: From vision 7. R. Milo, R. Phillips, Cell Biology by the Numbers (Garland Science, 2015). to reality. Nature 550, 451–453 (2017). 8. J. Lengefeld et al., Cell size is a determinant of stem cell potential during aging. Sci. Adv. 7, 22. D. Osumi-­Sutherland et al., Cell type ontologies of the human cell atlas. Nat. Cell Biol. 23, eabk0271 (2021). 1129–1135 (2021). 9. V. Kumar, A. K. Abbas, N. Fausto, J. C. Aster, Robbins and Cotran pathologic basis of disease, 23. E. Mereu et al., Benchmarking single-­cell RNA-­sequencing protocols for cell atlas projects. Nat. professional edition e-­book (Elsevier Health Sciences, 2014). Biotechnol. 38, 747–755 (2020). 10. C. W. Sandlin et al., Epithelial cell size dysregulation in human lung adenocarcinoma. PLOS One 17, 24. W. S. Snyder et al., ICRP Publication 23: Report of the Task Group on Reference Man: A Report e0274091 (2022). Prepared by a Task Group of Committee 2 of the International Commission on Radiological Protection 11. U. Smith, Effect of cell size on lipid synthesis by human adipose tissue in vitro. J. Lipid Res. 12, (Pergamon Oxford, 1975). 65–70 (1971). 25. J. Valentin, ICRP Publication 89: Basic anatomical and physiological data for use in radiological 12. M. Pende et al., Hypoinsulinaemia, glucose intolerance and diminished β-­cell size in S6K1-­ protection: Reference values (Elsevier Health Sciences, 2003). deficient mice. Nature 408, 994–997 (2000). 26. H.-­G. Menzel, C. Clement, P. DeLuca, ICRP Publication 110. Realistic reference phantoms: An 13. T. P. Miettinen, M. Björklund, Cellular allometry of mitochondrial functionality establishes the ICRP/ICRU joint effort. A report of adult reference computational phantoms. Ann. ICRP 39, 1–164 optimal cell size. Dev. Cell 39, 370–382 (2016). (2009). 14. G. E. Neurohr et al., Excessive cell growth causes cytoplasm dilution and contributes to senescence. 27. B. Alberts et al., Molecular Biology of the Cell (Garland Science, ed. 3, 1994). Cell 176, 1083–1097.e18 (2019). 28. M. K. Vickaryous, B. K. Hall, Human cell type diversity, evolution, development, and classification 15. L. Mu et al., Mass measurements during lymphocytic leukemia cell polyploidization decouple cell with special reference to cells derived from the neural crest. Biol. Rev. Camb Philos. Soc. 81, cycle-­and cell size-­dependent growth. Proc. Natl. Acad. Sci. U.S.A. 117, 15659–15665 (2020). 425–455 (2006). PNAS 2023 Vol. 120 No. 39 e2303077120 https://doi.org/10.1073/pnas.2303077120 9 of 10 29. J. Jo et al., Hypertrophy and/or hyperplasia: Dynamics of adipose tissue growth. PLoS Comput. Biol. 60. B. Asquith, J. A. Borghans, V. V. Ganusov, D. C. Macallan, Lymphocyte kinetics in health and disease. 5, e1000324 (2009). Trend Immunol. 30, 182–189 (2009). 30. A. C. Paul, N. Rosenthal, Different modes of hypertrophy in skeletal muscle fibers. J. Cell Biol. 156, 61. D. Lambrechts et al., Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. 751–760 (2002). Med. 24, 1277–1289 (2018). 31. J.-­X. Liu et al., Myonuclear domain size and myosin isoform expression in muscle fibres from 62. R. H. Peters, The Ecological Implications of Body Size (Cambridge University Press, ed. 1, 1983). mammals representing a 100 000-­fold difference in body size. Exp. Physiol. 94, 117–129 (2009). 63. I. A. Hatton, A. P. Dobson, D. Storch, E. D. Galbraith, M. Loreau, Linking scaling laws across eukaryotes. 32. W. F. Marshall et al., What determines cell size? BMC Biol. 10, 1–22 (2012). Proc. Natl. Acad. Sci. U.S.A. 116, 21616–21622 (2019). 33. Y. Arata, H. Takagi, Y. Sako, H. Sawa, Power law relationship between cell cycle duration and cell volume 64. J. P. Greer et al., Wintrobe’s Clinical Hematology (Lippincott Williams & Wilkins, ed. 11, 2004). in the early embryonic development of Caenorhabditis elegans. Front. Physiol. 5, 529 (2015). 65. K. Kaushansky, M. A. Lichtman, E. Beutler, Williams Hematology (McGraw-­Hill Medical, ed. 8, 34. G. Fankhauser, The effects of changes in chromosome number on amphibian development. Q. Rev. 2010). Biol. 20, 20–78 (1945). 66. G. P. Downey et al., Retention of leukocytes in capillaries: Role of cell size and deformability. J. Appl. 35. T. P. Neufeld, A. F. A. de la Cruz, L. A. Johnston, B. A. Edgar, Coordination of growth and cell division in Physiol. 69, 1767–1778 (1990). the Drosophila wing. Cell 93, 1183–1193 (1998). 67. M. Di Mascio et al., Noninvasive in vivo imaging of CD4 cells in simian-­human immunodeficiency 36. C. C. Henery, M. H. Kaufman, Relationship between cell size and nuclear volume in nucleated red virus (SHIV)–infected nonhuman primates. Blood 114, 328–337 (2009). blood cells of developmentally matched diploid and tetraploid mouse embryos. J. Exp. Zool. 261, 68. H. W. Vliegen, A. Van der Laarse, C. J. Cornelisse, F. Eulderink, Myocardial changes in pressure 472–478 (1992). overload-­induced left ventricular hypertrophy: A study on tissue composition, polyploidization and 37. M. K. Diril et al., Cyclin-­dependent kinase 1 (Cdk1) is essential for cell division and suppression of multinucleation. Eur. Heart J. 12, 488–494 (1991). DNA re-­replication but not for liver regeneration. Proc. Natl. Acad. Sci. U.S.A. 109, 3826–3831 (2012). 69. C.-­P. Adler, H. Friedburg, G. W. Herget, M. Neuburger, H. Schwalb, Variability of cardiomyocyte DNA 38. Y. Miyaoka et al., Hypertrophy and unconventional cell division of hepatocytes underlie liver content, ploidy level and nuclear number in mammalian hearts. Virchows Archiv. 429, 159–164 regeneration. Curr. Biol. 22, 1166–1175 (2012). (1996). 39. G. K. Michalopoulos, Hepatostat: Liver regeneration and normal liver tissue maintenance. 70. M. Mollova et al., Cardiomyocyte proliferation contributes to heart growth in young humans. Proc. Hepatology 65, 1384–1392 (2017). Natl. Acad. Sci. U.S.A. 110, 1446–1451 (2013). 40. J. W. Prothero, The Design of Mammals (Cambridge University Press, 2015). 71. Y. Guo, W. T. Pu, Cardiomyocyte maturation: New phase in development. Circulation Res. 126, 41. X. Gabaix, Zipf’s law for cities: An explanation. Q. J. Econ. 114, 739–767 (1999). 1086–1106 (2020). 42. M. Newman, Power laws, Pareto distributions and Zipf’s law. Contemporary Phys. 46, 323–351 (2005). 72. O. Bergmann et al., Dynamics of cell generation and turnover in the human heart. Cell 161, 43. D. W. Hughes, N. W. Harris, The distribution of asteroid sizes and its significance. Planet. Space Sci. 1566–1575 (2015). 42, 291–295 (1994). 73. F. A. C. Azevedo et al., Equal numbers of neuronal and nonneuronal cells make the human brain an 44. I. A. Hatton, R. F. Heneghan, Y. M. Bar-­On, E. D. Galbraith, The global ocean size spectrum from isometrically scaled-­up primate brain. J. Comp. Neurol. 513, 532–541 (2009). bacteria to whales. Sci. Adv. 7, eabh3732 (2021). 74. J. Bahney, C. S. von Bartheld, The cellular composition and glia–neuron ratio in the spinal cord of a 45. A. Giometto, F. Altermatt, F. Carrara, A. Maritan, A. Rinaldo, Scaling body size fluctuations. Proc. Natl. human and a nonhuman primate: Comparison with other species and brain regions. Anat. Record Acad. Sci. U.S.A. 110, 4646–4650 (2013). 301, 697–710 (2018). 46. C. James, S. Azaele, A. Maritan, F. Simini, Zipf’s and Taylor’s laws. Phys. Rev. E 98, 032408 (2018). 75. H. H. Mitchell, T. S. Hamilton, F. R. Steggerda, H. W. Bean, The chemical composition of the 47. L. R. Taylor, Aggregation, variance and the mean. Nature 189, 732–735 (1961). adult human body and its bearing on the biochemistry of growth. J. Biol. Chem. 158, 625–637 48. Z. Eisler, I. Bartos, J. Kertesz, Fluctuation scaling in complex systems: Taylor’s law and beyond. Adv. (1945). Phys. 57, 89–142 (2008). 76. R. Mlcoo, A. R. Forbes, H. H. Cooper, Mitchell, The composition of the adult human body as 49. R. B. Azevedo, A. M. Leroi, A power law for cells. Proc. Natl. Acad. Sci. U.S.A. 98, 5699–5704 (2001). determined by chemical analysis. J. Biol. Chem. 203, 359–366 (1953). 50. J. Ramsayer, S. Fellous, J. E. Cohen, M. E. Hochberg, Taylor’s law holds in experimental bacterial 77. R. M. Forbes, H. H. Mitchell, A. R. Cooper, Further studies on the gross composition and mineral populations but competition does not influence the slope. Biol. Lett. 8, 316–319 (2012). elements of the adult human body. J. Biol. Chem. 223, 969–975 (1956). 51. H. A. Simon, On a class of skew distribution functions. Biometrika 42, 425–440 (1955). 78. K. Hosoda, T. Matsuura, H. Suzuki, T. Yomo, Origin of lognormal-­like distributions with a common 52. G. I. Bell, E. C. Anderson, Cell growth and division: I. a mathematical model with applications to cell width in a growth and division process. Phys. Rev. E 83, 031118 (2011). volume distributions in mammalian suspension cultures. Biophys. J. 7, 329–351 (1967). 79. R. Hanel, B. Corominas-­Murtra, B. Liu, S. Thurner, Fitting power-­laws in empirical data with 53. A. A. Amodeo, J. M. Skotheim, Cell-­size control. Cold Spring Harbor Perspect. Biol. 8, a019083 (2016). estimators that work for all exponents. PloS One 12, e0170920 (2017). 54. D. Huang, T. Lo, H. Merrikh, P. A. Wiggins, Characterizing stochastic cell-­cycle dynamics in 80. A. Serrano-­Mislata, K. Schiessl, R. Sablowski, Active control of cell size generates spatial detail Downloaded from https://www.pnas.org by 83.40.235.168 on November 8, 2023 from IP address 83.40.235.168. exponential growth. Phys. Rev. E 105, 014420 (2022). during plant organogenesis. Curr. Biol. 25, 2991–2996 (2015). 55. S. Iyer-­Biswas et al., Scaling laws governing stochastic growth and division of single bacterial cells. 81. J. H. Kang et al., Noninvasive monitoring of single-­cell mechanics by acoustic scattering. Nat. Proc. Natl. Acad. Sci. U.S.A. 111, 15912–15917 (2014). Methods 16, 263–269 (2019). 56. F. Trepel, Number and distribution of lymphocytes in man. A critical analysis. Klin Wochenschr 52, 82. P. D. Odermatt et al., Variations of intracellular density during the cell cycle arise from tip-­growth 511–515 (1974). regulation in fission yeast. ELife 10, e64901 (2021). 57. J. Westermann, R. Pabst, Distribution of lymphocyte subsets and natural killer cells in the human 83. M. Diaz-­Cuadros et al. Metabolic regulation of species-­specific developmental rates. bioRxiv body. Clin. Invest. 70, 539–544 (1992). [Preprint] (2021). https://doi.org/10.1101/2021.08.27.457974 (Accessed 30 August 2021). 58. K. S. Blum, R. Pabst, Lymphocyte numbers and subsets in the human blood. Do they mirror the 84. M. C. Lanz et al., Increasing cell size remodels the proteome and promotes senescence. Mol. Cell 82, situation in all organs? Immunol. Lett. 108, 45–51 (2007). 3255–3269.e8 (2022). 59. V. V. Ganusov, R. J. De Boer, Do most lymphocytes in humans really reside in the gut? Trend 85. W. H. Grover et al., Measuring single-­cell density. Proc. Natl. Acad. Sci. U.S.A. 108, 10992–10996 Immunol. 28, 514–518 (2007). (2011). 10 of 10 https://doi.org/10.1073/pnas.2303077120 pnas.org