1. Genetics and Genomics

Sibling chimerism among microglia in marmosets

  1. Ricardo CH del Rosario  Is a corresponding author
  2. Fenna M Krienen
  3. Qiangge Zhang
  4. Melissa Goldman
  5. Curtis Mello
  6. Alyssa Lutservitz
  7. Kiku Ichihara
  8. Alec Wysoker
  9. James Nemesh
  10. Guoping Feng
  11. Steven A McCarroll  Is a corresponding author
  1. Department of Genetics, Harvard Medical School, United States
  2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, United States
  3. McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, United States
  4. Howard Hughes Medical Institute, United States
https://doi.org/10.7554/eLife.93640.3
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Cite this article
  1. Ricardo CH del Rosario
  2. Fenna M Krienen
  3. Qiangge Zhang
  4. Melissa Goldman
  5. Curtis Mello
  6. Alyssa Lutservitz
  7. Kiku Ichihara
  8. Alec Wysoker
  9. James Nemesh
  10. Guoping Feng
  11. Steven A McCarroll
(2026)
Sibling chimerism among microglia in marmosets
eLife 13:RP93640.
https://doi.org/10.7554/eLife.93640.3
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eLife Assessment

This fundamental study substantially advances our understanding of sibling chimerism in marmosets by demonstrating that chimerism is limited to hematopoietic cells. The evidence supporting these findings is compelling, demonstrated through comprehensive analyses, including single-cell RNA-seq data from multiple individuals and tissues. A few minor concerns were successfully addressed in a revision. The work will be of broad interest to many fields of biology.

https://doi.org/10.7554/eLife.93640.3.sa0

Abstract

Chimerism happens rarely among most mammals, but is common in marmosets and tamarins, a result of fraternal twin or triplet birth patterns in which in utero connected circulatory systems (through which stem cells transit) lead to persistent blood chimerism (12–80%) throughout life. The presence of Y-chromosome DNA sequences in organs of female marmosets has long suggested that chimerism might also affect these organs. However, a longstanding question is whether this chimerism is driven by blood-derived cells or involves contributions from other cell types. To address this question, we analyzed single-cell RNA-seq data from blood, liver, kidney, and many brain regions across a number of marmosets, using transcribed single-nucleotide polymorphisms (SNPs) to identify cells with the sibling’s genome in various cell types within these tissues. Sibling-derived chimerism in all tissues arose entirely from cells of hematopoietic origin (i.e., myeloid and lymphoid lineages). In brain tissue this was reflected as sibling-derived chimerism among microglia (20–52%) and macrophages (18–64%) but not among other resident cell types (neurons, glia, or ependymal cells). The percentage of microglia that were sibling-derived showed significant variation across brain regions, even within individual animals, likely reflecting distinct responses by genetic-sibling microglia to local recruitment or proliferation cues or, potentially, distinct clonal expansion histories in different brain areas. In the animals and tissues we analyzed, microglial gene expression profiles bore a much stronger relationship to local/host context than to sibling genetic differences. Naturally occurring marmoset chimerism will provide new ways to recognize the effects of genes, mutations, and brain contexts on microglial biology and to distinguish between effects of microglia and other cell types on brain phenotypes.

Introduction

Chimerism, in which an organism contains cells from genetically distinct animals, happens rarely among mammals. Chimerism is common, however, in the Callitrichidae family that consists of the marmosets (Callithrix) and their close relatives the tamarins (Saguinus): in these primate species, animals usually give birth to dizygotic twins or trizygotic triplets whose blood contains cells from siblings. During development, the siblings share circulation in utero, allowing the exchange of hematopoietic stem cells (Gengozian et al., 1969; Wislocki, 1939). Most marmosets then exhibit blood chimerism throughout life: their blood-derived DNA is a mixture of both twins’ genomes, with the twin’s genome contributing 12–80% of the DNA in the blood (Niblack et al., 1977; The Marmoset Genome Sequencing and Analysis Consortium, 2014). This indicates that the twins’ hematopoietic stem cells establish permanent residency in one another’s bodies and contribute to blood cell populations throughout life.

A longstanding mystery involves whether other tissues and organs also harbor chimerism. Beyond the blood, Y-chromosome DNA has been detected in the brain and other organs of female marmosets with male twins (Ross et al., 2007; Sweeney et al., 2012), eliciting much speculation about how chimerism might have shaped behavior and natural selection in marmosets. However, it is still not known what cell types harbor this sibling DNA; such observations could in principle be explained by the presence of blood cells within these organs.

Here, we analyze chimerism in the common marmoset (Callithrix jacchus) brain, liver, kidney, and blood, with single-nucleus RNA-seq (snRNA-seq), using each cell’s RNA-expression pattern to identify its type and using combinations of transcribed SNPs (visible in the snRNA-seq sequence reads) to determine which marmoset sibling is the source of each cell. This approach makes it possible to determine whether chimerism arises from blood cells, resident immune cells, or other cell types, and to explore what chimerism can teach us about cellular migrations and cellular population dynamics.

Results

Marmoset chimerism can be characterized at single-cell resolution

To identify which individual cells have the genome of the host marmoset, and which have the genome of the host’s birth sibling, we used combinations of many transcribed SNPs that were visible in the snRNA-seq sequencing reads for each nucleus (using the methods described in Wells et al., 2023).

We first determined whether marmosets have sufficient sequence variation to enable the distinction between host and sibling cells. From whole-genome sequences of 123 marmosets, we identified 13 million polymorphic bi-allelic SNPs in the marmoset genome, with individual marmosets harboring 2.3–3.8 million (average 3.4 million) heterozygous sites – comparable to levels of heterozygosity in humans. For sibling comparisons, we detected a large number of sites at which any two siblings’ genomes differed, ranging from 2.0 to 3.7 million sites (average 2.9 million sites) across 96 sibling comparisons (Figure 1A). To determine how many of these sites were visible in snRNA-seq data, we analyzed snRNA-seq data for several marmoset tissues. The results varied by cell type, reflecting that different cell types’ nuclei harbored different quantities of RNA. The hundreds of transcribed variant sites that differed between siblings (median >311 per nucleus) suggested ample power to distinguish between siblings in all cell types (Wells et al., 2023; Figure 1B).

Figure 1
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Sibling chimerism analysis at single-cell resolution.

(A) Numbers of variant sites at which marmoset sibling genomes differ, for 96 sibling-pairs. Each dot is a sibling-pair; birth siblings are colored in pink. (B) Numbers of transcribed single-nucleotide polymorphisms (SNPs; per nucleus) visible in various cell types from blood and brain snRNA-seq datasets of marmoset CJ028. x-axis: snRNA-seq library; y-axis: number of ascertained, transcribed SNPs for which host and sibling have different genotypes; black horizontal lines: median values per cell type. (C) Donor-of-origin assignment of each nucleus in blood snRNA-seq of a marmoset. The host marmoset (CJ028) was born with two siblings; each nucleus was assigned to either the host or to one of the two birth siblings. x-axis: number of unique molecular identifiers (UMI; a measure of transcript abundance) that contains SNPs for which the host and sibling’s genomes differ, in log scale; y-axis: inferred likelihood that the cell has host genome minus likelihood that the cell has sibling genome (log10). (D) Two-dimensional visualization (tSNE plot) of snRNA-seq data from a marmoset’s (CJ026) blood, kidney, and liver. (E) Donor-of-origin assignment in marmoset CJ026’s blood, kidney, and liver. Axes: same as in (C). (F) Levels of chimerism in each cell type ascertained in blood, kidney, and liver snRNA-seq of marmoset CJ026. y-axis: fraction of sibling cells in each cell type; numbers in fraction: number of sibling nuclei over total nuclei in the cell type; percentage in x-axis labels: cell type representation in the tissue; vertical bars: binomial confidence interval (95%); p-values: test of heterogeneity (Chi-square) across immune cell types of a tissue (to test for differences in contribution of sibling across immune cell types).

We next evaluated whether the genome variation visible in snRNA-seq reads was sufficient to distinguish between host and sibling cells. For this, we used Dropulation, which identifies the donors of individual cells (from a set of genome-sequenced candidate donors) by using combinations of the transcribed SNPs visible on the snRNA-seq reads of the individual cells (Wells et al., 2023). We first analyzed the blood cells by snRNA-seq of a marmoset (CJ028) born with two birth siblings and used Dropulation (Wells et al., 2023) to assign individual cells to the correct sibling (Figure 1C). The relative likelihoods of the original source of each cell could be strongly differentiated (relative likelihoods of 103–1023) for >99% of the nuclei (Figure 1C). We found a high level of chimerism: 84% of all nuclei sampled in this marmoset’s blood appeared to contain the genome of one (67% – sibling #1) or the other (17% – sibling #2) of its two birth siblings (Figure 1C), consistent with the wide range of chimerism found in previous studies: 4–82% in marmoset T cells and B cells (Niblack et al., 1977); 13–37% in marmoset whole blood (The Marmoset Genome Sequencing and Analysis Consortium, 2014).

Apparent liver and kidney chimerism arises from infiltrating monocytes

Earlier studies have identified Y-chromosome-derived DNA sequences in the organs of female marmosets with male birth siblings, suggesting that these organs harbor chimerism (Sweeney et al., 2012). However, such observations could also in principle arise from blood or from blood-derived immune cells that are present in those organs (Sweeney et al., 2012).

We performed snRNA-seq analysis of the blood (1741 nuclei), liver (10,877 nuclei), and kidney (9262 nuclei) of a marmoset (CJ026) with one birth sibling (Figure 1D). The snRNA-seq profiles clustered into groups that were readily recognized (based on the RNAs expressed) as the principal cell types of each organ; we determined the identity of each cluster using scType, a cell-type identification tool that uses a database of known marker genes (Ianevski et al., 2022).

In kidney and liver, the only clearly twin-derived cells were cells of hematopoietic origin: the resident macrophages in liver (Kupffer cells), lymphocytes in liver, and lymphocytes in kidney (Figure 1E and F). All non-hematopoietic cell types in liver and kidney appeared to contain only the host marmoset’s own genome. Chimerism levels for the two chimeric liver immune cell types appeared to diverge, with sibling-derived cells accounting for 15% of Kupffer cells and just 4% of lymphocytes (5/122 vs 57/383; Chi-square test p-value = 0.003). In the blood, chimerism levels varied across the various cell types: the most abundant cell types, the Naive B cells and Naive CD8+ T cells, were, respectively, 29% and 32% sibling-derived, while the less-abundant CD8+ NKT-like cells were 15% sibling-derived (Figure 1F, Chi-square test p-value = 0.01).

These results indicate that, in this animal’s liver and kidney, apparent DNA chimerism likely arose from infiltrating immune cells rather than other cell types. These results also indicate that cells with siblings’ genomes can differ in their tendency to acquire specific hematopoietic cell fates and in their tendency to infiltrate into organs.

Marmoset brain microglia and macrophages exhibit abundant chimerism

To characterize chimerism in the marmoset brain, we utilized a large snRNA-seq dataset that was generated for a marmoset brain cell atlas (Krienen et al., 2020; Krienen et al., 2023). Brain snRNA-seq was performed on 11 animals (6 adults, 3 neonates, and 1 six months old; Table 1). All were unrelated except for CJ006 and CJ007, which are birth siblings, and CJ025 and CJ026, which are (non-birth) siblings. All animals come from the three main marmoset colonies that comprise the animals in our facilities: New England Primate Research Center (NEPRC), CLEA Japan, and from a non-clinical contract research organization in Massachusetts. All adult marmosets had no known previous disease and were selected as part of a larger project to create a single-cell atlas of the marmoset brain (Krienen et al., 2020; Krienen et al., 2023). The three neonates died shortly after birth due to unknown reasons and were subsequently selected for snRNA-seq analysis.

Table 1
Marmosets analyzed with snRNA-seq in this study.

Colonies: NEPRC – New England Primate Research Colony; CLEA – Central Institute for Experimental Animals, Japan; Company A: marmosets obtained from a non-clinical contract research organization.

IDTissueSexAgeNo. birth siblings (sex of sibling(s))ColonySingle-cell platform
CJ001BrainFNeonate2 (M, M)NEPRCDrop-seq
CJ006BrainMNeonate2 (F, unknown)CLEA10X
CJ007BrainFNeonate2 (M, unknown)CLEA10X
CJ022BrainF2 years 8 months1 (M)NEPRCDrop-seq, 10X
CJ023BrainF2 years2 (F, M)NEPRCDrop-seq
CJ025BrainM2 years1 (M)NEPRCDrop-seq
CJ026BrainM2 years 8 months1 (F)NEPRCDrop-seq
CJ026Blood10X
CJ026Liver10X
CJ026Kidney10X
CJ027BrainM2 years1 (M)Company A10X
CJ027Blood10X
CJ028BrainF2 years2 (M, F)NEPRC10X
CJ028Blood10X
CJ029BrainM2 years1 (M)CLEA10X
CJ102BrainF6 months1 (M)Company ADrop-seq, 10X

We first analyzed 497,000 single-nucleus RNA-expression profiles from the neocortex, thalamus, striatum, hippocampus, basal forebrain, hypothalamus, and amygdala of an adult marmoset with two birth siblings (marmoset CJ028). We clustered the cell types using gene expression similarities and identified brain cell types as in earlier work (Krienen et al., 2020), identifying neurons, astrocytes, oligodendrocytes, ependymal cells, endothelial cells, microglia, and macrophages (Figure 2A). Microglia (which expressed markers TREM2, LAPTM5, and C3) and macrophages (which expressed LYVE1 and F13A1) were a small fraction of all nuclei analyzed (about 3.6%), but due to the large number of nuclei we profiled (53 brain tissue dissections, 497,000 nuclei), we were able to ascertain sufficient numbers of microglia (18,185 nuclei) and, to a lesser extent, macrophages (172 nuclei) for many downstream analyses. We found microglia and macrophages in snRNA-seq data from 10 additional marmosets with different genetic backgrounds from 3 different colonies. Brain snRNA-seq of all 11 marmosets showed consistently the presence of these two myeloid cell types in the brain (Figure 2—figure supplement 1; number of microglia and macrophages in Appendix 1—table 1).

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Microglia and macrophages, but not neurons and glia, are chimeric in the marmoset brain.

(A) Two-dimensional visualization (tSNE plot) of marmoset CJ028’s snRNA-seq profiles from eight brain regions. (B) Donor assignment of each nucleus to one of three possible donors (host, sibling1, or sibling2) of marmoset CJ028’s brain snRNA-seq data. x-axis: number of unique molecular identifier (UMI) that contains single-nucleotide polymorphisms (SNPs) for which the host and sibling’s genomes differ, in log10 scale; y-axis: inferred likelihood that the cell has host genome minus likelihood that the cell has sibling genome (log10). Data from all of CJ028’s brain regions. (C) Fractions of cells with a sibling’s genome among microglia, macrophages, and other brain cell types (neurons, astrocytes, oligodendrocytes, polydendrocytes, ependyma, and endothelial) from 11 marmosets. CJ001, CJ023, and CJ028 were part of a triplet litter, and the chimerism fraction of each birth sibling is shown in separate panels. y-axis: fraction of sibling cells in the cell type. vertical bars: binomial confidence interval (95%). p-values: Chi-square test from comparison of chimerism fractions in microglia and macrophage; *p-value <0.05. (D) Sibling contributions to microglial (x-axis) and macrophage (y-axis) populations in the same animals. The 14 pairs of animals are from 11 host-sibling1 pairs plus 3 host-sibling2 pairs (three of the 11 host animals were born in a triplet litter; see Table 1). Dots and bars are from (D). Pearson correlation R = 0.32, 95% confidence interval (−0.26 to 0.73), p-value = 0.27.

Donor-of-origin analysis of snRNA-seq data from 2.2 million nuclei sampled from 137 brain tissue samples from these 11 marmosets showed a clear and consistent pattern: microglia and macrophages, but not neurons, glia, or endothelial cells, harbored chimerism (Figure 2B; Figure 2—figure supplement 2). Microglia exhibited abundant chimerism – across the 11 marmosets, the total fraction of cells with the sibling’s genome ranged from 20% to 52% (for triplets, sum of two siblings; Figure 2C). Macrophages exhibited a similarly wide range of sibling fractions across marmosets (18–64%, Figure 2C).

The quantitative extent of microglial chimerism varied across individuals (Figure 2—figure supplement 3A; test of heterogeneity p-value <2.2 × 10–16), as did that of macrophage chimerism (Figure 2—figure supplement 3B; test of heterogeneity p-value = 1 × 10–4). We asked whether microglial and macrophage chimerism were correlated. Intriguingly, only a modest correlation of chimerism levels across 14 host-sibling-pairs was observed between the microglia and macrophages (Figure 2D; Pearson correlation 0.31). We investigated further by performing a statistical test that takes into account the uncertainty in the estimates of the chimeric cell proportion using a binomial framework (Methods); in this analysis, microglia chimerism fraction was not a statistically significant predictor of macrophage chimerism fraction (Methods). This suggests that in addition to the cell’s genome, other factors such as local host environment play a role in differential recruitment, proliferation, or survival of sibling cells. (We note that macrophages often transit the fluid-filled perivascular space, with a substantially different migration history and arrival dynamics than microglia.) Neither of these two myeloid cell types showed consistently higher chimerism than the other cell type did (Figure 2C).

Sibling contributions in blood vs brain

Though microglia (like macrophages) are myeloid cells that derive from hematopoietic stem cells, the ontogenies of microglia and brain macrophages are distinct from those of bone-marrow-derived peripheral blood mononuclear cells (Perdiguero and Geissmann, 2016). As such, differences in the developmental and migration histories of these cell populations could in principle have caused their chimerism fractions to diverge in a systematic way.

We analyzed three marmosets for which snRNA-seq was performed on both blood and brain tissues. Sibling contributions to microglia and brain macrophages were in general quite different from those in blood (Figure 3).

Figure 3
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Sibling contributions to hematopoiesis-derived cells diverge between blood and brain.

(A–E) Chimerism fractions in brain and blood of three animals. CJ028 was part of a triplet litter, and the contribution of each sibling is shown in a separate panel (D, E). Red horizontal lines: twin contribution to blood cells as ascertained from whole-genome sequencing of whole-blood-derived genomic DNA (Census-seq). Blue horizontal lines: total twin contribution to blood cells as estimated from PBMC snRNA-seq (all cells). Vertical bars: binomial confidence interval (95%). Numbers in fraction: number of sibling cells over total cells in the cell type. glia + endothelia: astrocytes, oligodendrocytes, polydendrocytes, ependymal cells, and endothelial cells.

Marmoset CJ028’s chimerism (involving two birth siblings) provided a setting in which cells with three different genomes shared the same environment through development until adulthood (to 2 years of age). Among CJ028’s microglia, the fraction of cells from sibling 1 (35%) was greater than that from sibling 2 (13%) (two-sided test of proportionality p-value <2.2–16), while in blood, the opposite was true (fraction of cells from sibling 1 across all blood cell types was 18%, fraction of cells from sibling 2 across all blood cell types was 67%; two-sided test of proportionality p-value <2.2–16) (Figure 3D, E, Appendix 1—table 2).

Microglia chimerism fraction varies across brain regions

Sibling contributions to the microglial population could in principle be shaped by effects that are local to specific brain areas, including differential response of sibling microglia to local recruitment or proliferation cues, or population bottlenecks such as clonal expansions. To evaluate whether the sibling contribution to the microglial population varied across brain areas within individual marmosets, we performed chimerism analysis for each of the brain regions profiled in the snRNA-seq datasets: neocortex, thalamus, striatum, hippocampus, basal forebrain, hypothalamus, and amygdala (Krienen et al., 2020; Krienen et al., 2023). Within each marmoset, the fraction of microglia with a sibling’s genome diverged across a marmoset’s brain regions (Figure 4). For example, in marmoset CJ025, sibling contributions to microglial populations ranged from 11% (21/193) in the thalamus to 56% (174/310) in the striatum (p-value = 1.1 ×1 0−23, Chi-square test of thalamus vs striatum; p-value = 1.5 × 10–40, Chi-square test across all four brain regions). For marmoset brains profiled with at least 300,000 nuclei, CJ027, CJ028, and CJ029, tests of heterogeneity p-values were even more significant: 8.8 × 10–83, <1 × 10–300, and 6.1 × 10–47, respectively (the error bars are very short in the corresponding panels in Figure 4). We used the binomial generalized linear mixed-model framework and found that all brain regions were statistically significant predictors for microglia chimerism fraction, supporting the conclusion that chimerism varies across brain regions (Methods). Analysis of finer brain substructures showed a similar result (Figure 4—figure supplement 1; the binomial generalized linear mixed-model framework determined that 18 out of 27 brain substructures were statistically significant as predictors for microglia chimerism fraction, Methods). None of the brain regions exhibited consistently higher or lower chimerism levels, suggesting that these divergences did not result from differential physical access of host and sibling microglia to different brain areas (Figure 4 and Figure 4—figure supplement 1).

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Sibling contributions to brain microglial populations vary across an animal’s brain areas.

Contributions of sibling(s) to the microglial populations ascertained in principal brain areas. CJ001, CJ023, and CJ028 were part of a triplet litter, and the chimerism contribution of each twin is shown in a separate panel. CJ102 was profiled in only one brain region and hence was not included in the analysis. Brain regions with missing data were not profiled in that animal. y-axis: fraction of twin cells. Vertical bars: binomial confidence interval (95%); p-values: test of heterogeneity across an animal’s brain regions.

Gene expression comparisons of host- to sibling-derived microglia

Chimerism provides the unusual opportunity to compare cells with different genomes in a shared in vivo biological context. We compared RNA expression between host- and sibling-derived microglia of a female marmoset with two birth siblings. Sex differences among the siblings (the host (CJ028) was a female and one of the two siblings was a male) allowed a natural control: the XIST gene encodes a non-coding RNA involved in silencing one copy of chromosome X in females and thus exhibits sex-specific expression due to cell-autonomous mechanisms. We found that (as expected) XIST transcripts were detected at far higher levels in the snRNA-seq profiles of microglia with the female twin’s genome relative to the male twin’s (Figure 5A, C). By contrast, XIST transcripts were detected at similar levels in two microglial populations with the genomes of female twins (Figure 5B).

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Utilizing natural chimerism to distinguish cell-autonomous from non-cell-autonomous effects on gene expression, and to compare the effects context and genetic variation in shaping gene expression.

(A–C) Comparisons of RNA expression between microglial populations within host animal CJ028, who had two birth siblings. Comparisons of gene expression between microglia with the genomes of (A) the female host and male sibling, (B) the female host and female sibling, and (C) the two siblings (male and female). In (A–C), each point represents a gene; its location on the plot represents the level of expression of that gene among microglia with two different genomes in the same animal. x- and y-axes: normalized gene expression levels (number of transcripts per 100,000 transcripts). FC: fold-change of gene expression, female/male for XIST. Fold-change and p-values were calculated using the binomTest method from the edgeR package (Robinson et al., 2010). Differentially expressed genes (black dots) were defined as: FDR Q-value <0.05 and fold-change >1.5 (in either direction) and the gene must be expressed in at least 10% of at least one of the two sets of microglia being compared. (D–I) Higher effect of context than genetic differences in shaping gene expression. (D) In the brain of marmoset CJ027, the neocortex and striatum are two contexts where two sets of microglia with different genomes reside. (E) Effect of genetic differences. x-axis: log2-fold-change of cortical microglia gene expression between host and sibling cells; y-axis: log2-fold-change of striatal microglia gene expression between host and sibling cells. (F) Effect of context. x-axis: log2-fold-change of the sibling’s gene expression between the two brain regions; y-axis: log2-fold-change of the host’s gene expression between the two brain regions. (G–I) The brains (cortex, striatum, and hippocampus) of two birth siblings provide biological contexts in which populations of microglia with two sibling genomes reside. The effect of genetic differences (H) and effect of animal context (I) are compared, for the same brain areas (combined data from cortex, striatum, and hippocampus). x- and y-axes: log2-fold-changes of the gene expression between two sets of microglia (the sets being compared are indicated in the axis labels). R: Spearman correlation.

Gene-expression differences between host- and sibling-derived microglia in the same brain could in principle arise from asymmetries in their developmental histories (which would be shared across host animals) or from genomic differences (which would vary from host animal to host animal). In all eleven individual marmosets, analysis identified genes whose differential expression distinguished microglia with the two sibling genomes (hundreds of genes in total), documenting a substantial effect of sibling genetic differences on microglial gene expression. However, we did not find any gene whose expression level recurrently distinguished ‘host’ microglia (microglia with the same genome as neural cell types) from ‘guest’ microglia (microglia with the sibling genome), aside from the XIST gene (a proxy for sibling sex differences, which were of course common) (Figure 5—figure supplement 1, Figure 5A–C). In other words, although there were always gene-expression differences between sibling microglia, none of them consistently distinguished between host and guest microglia, suggesting that they were instead due to sibling genetic differences. We note that both analyses are power-limited, as the number of microglia in most animals, especially guest microglia, were modest (Figure 5—figure supplement 1); thus, we cannot rule out the possibility that there may be one or more genes whose expression levels reflect developmental histories (host vs guest origin), just as there are likely far more genes (than the hundreds we identified) that can have sibling expression differences due, for example to genetic differences between siblings. We sought to increase power (beyond single-gene analysis) by using latent factor analysis (Ling et al., 2024) to identify and quantify the expression of microglial gene-expression programs; however, even this analysis did not find any gene expression programs that exhibited consistent host-twin differences in expression levels (Methods).

Brain context vs genetic differences as determinants of microglial gene expression

Chimerism presents interesting opportunities to distinguish between cell-autonomous and contextual effects on a cell’s biology, and to compare the magnitudes of such effects.

We first considered the difference in contexts provided by pairs of brain areas by analyzing snRNA-seq data from the neocortex and striatum of marmoset CJ027; the resident microglial populations with different genomes make it possible to compare contextual to genetic effects on microglial gene expression (Figure 5D). Genetic effects appeared to elicit very many small-magnitude gene-expression differences; these differences were shared between cortical and striatal microglia (Figure 5E). Brain-area context elicited much larger-magnitude gene-expression differences, which were experienced in common by microglia with both genotypes (Figure 5F). We obtained similar results for all pairs of brain areas analyzed (52 context vs genetic effect from brain snRNA-seq of 6 marmosets with at least 60 cells available for analysis in each context; Appendix 1—table 3; Figure 5—figure supplement 2).

We next considered the difference in contexts provided by the same brain area in different marmosets. Two of the marmosets profiled, CJ006 and CJ007, were birth siblings who passed away as neonates (the only birth siblings in our dataset), and thus provided the additional opportunity to distinguish genetic from contextual effects by analyzing the two sibling microglial populations in the cortex, striatum, and hippocampus of both marmosets (Figure 5G). The effects of context (host marmoset) in microglia from all three brain areas appeared to be far larger than the cell-autonomous effects of genetic differences (Figure 5H, I).

Discussion

A longstanding debate concerns the extent of chimerism in marmosets and tamarins. Chimerism in these species has been detected in diverse organs but arises from unknown cell types (Ross et al., 2007; Sweeney et al., 2012). Here, we found that chimerism in the brain, liver, and kidney is present but appears to arise entirely from cells of the myeloid and lymphoid lineages, including infiltrating macrophages, monocytes, and microglia.

Cells of the myeloid and lymphoid lineages derive developmentally from hematopoietic stem cells. We found no strong evidence of chimerism among 2.2 million non-hematopoietic cells in the liver (from one marmoset), kidney (from one marmoset), or brain (from 11 marmosets). Thus, while marmosets share a circulation in utero, we found no evidence that other kinds of stem cells or progenitors (beyond those of hematopoietic lineage) had been shared via this route in any appreciable number. However, we found that in the marmoset brain, the microglia and macrophages, which also derive from this lineage, routinely harbor abundant chimerism, with 10–50% of a marmoset’s microglia containing the genome(s) of birth sibling(s).

Organs in the same marmoset (liver, kidney, and brain) differed markedly in the sibling contribution to resident macrophage and monocyte populations, with microglial chimerism fraction (the fraction of cells contributed by siblings) varying by as much as 40 percentage points across a marmoset’s brain areas. This phenomenon has more than one potential explanation. First, cells from the host and sibling could in principle respond differently to recruitment or proliferation cues that vary spatially; if this is the case, marmoset chimerism could provide a model for studying the effects of mutations and natural sequence variation on cell migration and recruitment. (Although we found that genetic effects were smaller than contextual effects in shaping microglial gene expression at any moment in time, genetic effects were clear (Figure 5E), and even small effects on proliferation rates would tend to have effects that increase exponentially over time.) Second, beyond such recruitment effects, it is also possible that these differences suggest a substantial role of clonal expansions and population bottlenecks in shaping local microglial and macrophage populations.

We found that the cellular contribution of birth siblings to myeloid cell populations was significantly different in blood than in brain in the modest number of marmosets analyzed (Figure 3). Unlike the differences among brain areas, the blood–brain differences tended to be directional, with more-modest sibling contributions in the brain than in the blood. Though this would need to be confirmed in many more marmosets to be definitive on its own, it is plausibly connected to this aspect of marmoset fetal development: sharing of a blood circulation between the two fetuses occurs during a window that is more temporally extended than the waves of colonization of the brain by microglia, potentially allowing for greater exchange in the centers of blood hematopoiesis (the liver and then the bone marrow). In microglia and macrophages, due to the defined waves of hematopoiesis in the yolk sac and migration patterns of microglia and macrophages to the developing brain, the opportunity for a progenitor cell from a twin to colonize a host’s brain may need to occur during a more restricted temporal window.

Comparisons of gene expression between microglial cells with host and sibling genomes in a shared brain context may provide many future opportunities to distinguish the cell-autonomous from non-cell-autonomous genetic effects of genetic differences and engineered mutations. Such analyses could become especially useful scientifically as genome editing increasingly enables the utilization of marmosets as a model organism in translational neuroscience (Aida and Feng, 2020; Feng et al., 2020). Our pilot analysis of host-sibling microglial gene expression differences in the brains of two co-twins revealed a large role of animal context (relative to genetic differences) in shaping microglial gene expression. This result points to an important principle: the ability to isolate the effects of a mutation will be greatly strengthened by the ability to make within-animal (rather than just between-animal) comparisons of cells with different genotypes.

A long history of innovation in genetics involves elaborate ways to create mosaics in mice, C. elegans, and other laboratory organisms in order to distinguish cell-autonomous from non-cell-autonomous genetic effects. Natural chimerism in marmosets may enable many straightforward ways to pursue such kinds of studies. Natural chimerism may also make it possible to determine when microglia or macrophages, as opposed to other cell types, mediate the effect of a mutation on an animal’s phenotype.

Chimerism could also enable interesting future analyses of whether there are adaptive benefits of chimerism in marmoset immune cells, among whom chimerism could in principle allow presentation of a wider variety of antigens for adaptive immunity. In a recent outbreak of yellow fever in Brazil in 2016–2018, marmosets were found to be less susceptible than other primates that lack immune system chimerism, including the howler monkeys (Alouatta), robust capuchins (Sapajus), and titi monkeys (Callicebus) (de Azevedo Fernandes et al., 2021). In studying future outbreaks in marmosets, one could use single-cell RNA-seq and the methods described here to study how genetically distinct immune cells (in the same animal) have differentially migrated to affected tissues and/or assumed ‘activated’ immune cell states. Recent innovations in spatial transcriptomics with sequencing readouts (that detect SNP alleles) may also make it possible to identify any differential recruitment of genetically distinct immune cells to focal infection sites.

Microglia perform essential roles in the development and regulation of the central nervous system, including by sculpting or ‘pruning’ neuronal circuits (Hammond et al., 2018; Schafer et al., 2012; Schafer and Stevens, 2015), and are implicated in or hypothesized to contribute to a wide range of brain disorders and diseases, including Alzheimer’s disease, Parkinson’s disease, autism spectrum disorder, and schizophrenia. Marmoset microglial chimerism will enable many new ways of studying microglia and the effects of genes and alleles upon brain biology.

Methods

Ethical compliance

Marmoset experiments were approved by and in accordance with Massachusetts Institute of Technology IACUC protocol number 051705020.

Nucleus Drop-seq library preparation and sequencing

Nucleus suspensions were prepared from frozen tissue and used for nucleus Drop-seq following the protocol we have described at https://doi.org/10.17504/protocols.io.2srged6. Drop-seq libraries were prepared as previously described (Macosko et al., 2015), with modifications, quantification, and quality control as described in a previous study (Saunders et al., 2018), as well as the following modifications optimized for nuclei: in the Drop-seq lysis buffer, 8 M guanidine hydrochloride (pH 8.5) was substituted for water, nuclei were loaded into the syringe at a concentration of 176 nuclei/μl, and cDNA amplification was performed using around 6000 beads per reaction, 15 PCR cycles. Raw sequencing reads were aligned to the calJac3 marmoset reference genome assembly and reads that mapped to exons or introns of each assembly were assigned to annotated genes (https://github.com/broadinstitute/Drop-seq, Broad Institute, 2026). Drop-seq libraries are indicated in Table 1.

Nucleus 10X Chromium library preparation and sequencing

Single-nucleus suspensions from frozen tissue were generated as for Drop-seq; GEM generation and library preparation followed the manufacturer’s protocol (protocol versions #CG00052 Chromium Single Cell 3′ v2 and #CG000183 Chromium Single Cell3′ v3 UG_Rev-A). Raw sequencing reads were processed and aligned using the same method for aligning Drop-seq reads. 10X Chromium libraries are indicated in Table 1.

Clustering of cells using independent component analysis

Nuclei from intact cells were identified and clustered into cell types using a method that we have previously described (Krienen et al., 2020; Saunders et al., 2018). Briefly, nuclei with less than 400 detected genes were not used in the analysis. A digital gene expression matrix was created for a set of libraries from the same animal that were to be co-analyzed (Appendix 1—table 4), and independent component analysis using the fastICA package in R was used after normalization and variable gene selection as previously described (Krienen et al., 2020; Saunders et al., 2018). A Louvain-based clustering algorithm was performed on the top 60 independent components. Due to the large number of nuclei profiled in some marmosets (CJ027, CJ028, and CJ029), memory requirements exceeded machine limits and for these marmosets, we divided the clustering analysis into two or three batches (Appendix 1—table 4). The brain of marmoset CJ022 was profiled using both Drop-seq and 10X and a separate clustering was done for each snRNA-seq method (Appendix 1—table 4). We ran the clustering algorithm 12 times using three nearest neighbor parameters (10, 20, and 30) and four resolution parameters (0.3, 0.5, 0.1, and 1.0). Markers for each cluster were identified using differential gene expression analysis (Krienen et al., 2020; Saunders et al., 2018). We inspected each clustering result and chose the one which yielded separate clusters for microglia and macrophages (Appendix 1—table 4).

Identification of cell types

For the brain datasets, the microglia and macrophage clusters were identified by the markers TREM2, C3, and LAPTM5 for microglia and F13A1 and LYVE1 for macrophages. The other brain cell types were identified using cell type markers for neurons, astrocytes, oligodendrocytes, polydendrocytes, and endothelial cells that we used as before (Krienen et al., 2020). Cell types in blood, liver, and kidney were identified using the ScType method (Ianevski et al., 2022).

Donor-of-origin analysis and detection of host-sibling doublets (Dropulation)

We used the Dropulation suite to calculate a donor likelihood for each cell (Wells et al., 2023) (software available at https://github.com/broadinstitute/Drop-seq, Broad Institute, 2026). The host and birth sibling genotypes were provided as input to Dropulation’s AssignCellsToSamples tool, together with the snRNA-seq BAM file and a list of cell barcodes that were identified to be intact cells. To generate chimerism-free reference genotypes, we cultured fibroblasts and performed whole-genome sequencing (WGS) on the resulting DNA. We found that the difference in likelihoods between host and sibling increases with the number of unique molecular identifier (UMI) of the cell, and hence we imposed a minimum number of UMI for each marmoset’s cells (Appendix 1—table 4). We also performed doublet detection using Dropulation’s DetectDoublets to obtain a likelihood of a cell having a mix of transcripts from the host and sibling(s). Doublets lie between the host and sibling curves (Figure 2—figure supplement 4), and for each marmoset, we empirically obtained a threshold for the Dropulation test statistic to identify them and were discarded in all analyses (Figure 2—figure supplement 4; Appendix 1—table 4). The likelihoods plotted in Figures 1C, E, 2B, Figure 2—figure supplement 2 are from cells that have been filtered for minimum UMI and doublets.

Marmosets CJ006 and CJ007 were born in a triplet litter (tri-zygotic) that all died shortly after birth. We did not have access to any tissue from the third sibling and were not able to perform WGS on it. For Dropulation analysis of CJ006 and CJ007’s brains, we provided only the genotypes of marmosets CJ006 and CJ007. Nuclei that contain the genome of the third unknown sibling will mostly be identified as doublets, which were discarded in our analysis.

Additional filtering for microglia and macrophage clusters

We performed additional filtering of microglia and macrophage cells. When we compared the gene expression of host microglia and sibling microglia using cell-types from first-round clustering (and with UMI and doublet filtering), we found an abundance of genes that have higher expression in host than in the sibling (Figure 2—figure supplement 5, see panels B, C, G, I, and K). The asymmetry could arise from neuronal cells misclassified as microglia or macrophages. To filter out these misclassified cells, we subclustered the microglia and macrophage cell types of each marmoset using the same fastICA and Louvain-based clustering used in the first round of clustering. We found that some subclusters were not chimeric, indicating that they were not cells of hematopoietic origin, and that discarding these cells improved the symmetry between host and sibling gene expression (Figure 2—figure supplement 5).

Whole-genome sequencing

Illumina libraries from fibroblast, blood, brain, and buccal cells (Appendix 1—table 5) were created as follows. An aliquot of genomic DNA (150 ng in 50 μl) is used as the input into DNA fragmentation (aka shearing). Shearing is performed acoustically using a Covaris focused-ultrasonicator, targeting 385 bp fragments. Following fragmentation, additional size selection is performed using a SPRI cleanup. Library preparation is performed using a commercially available kit provided by KAPA Biosystems (KAPA Hyper Prep with Library Amplification Primer Mix, product KK8504), and with palindromic forked adapters using unique 8-base index sequences embedded within the adapter (purchased from Roche). The libraries are then amplified by 10 cycles of PCR. Following sample preparation, libraries are quantified using quantitative PCR (kit purchased from KAPA Biosystems) with probes specific to the ends of the adapters. This assay is automated using Agilent’s Bravo liquid handling platform. Based on qPCR quantification, libraries are normalized to 2.2 nM and pooled into 24-plexes. Sample pools are combined with NovaSeq Cluster Amp Reagents DPX1, DPX2, and DPX3 and loaded into single lanes of a NovaSeq 6000 S4 flowcell cell using the Hamilton Starlet Liquid Handling system. Cluster amplification and sequencing occur on NovaSeq 6000 Instruments utilizing sequencing-by-synthesis kits to produce 151 bp paired-end reads. Output from Illumina software is processed by the Picard data-processing pipeline to yield CRAM or BAM files containing demultiplexed, aggregated aligned reads. All sample information tracking is performed by automated LIMS messaging. All samples were sequenced to 30X coverage.

Variant site detection and genotyping from WGS

Illumina paired-end reads were aligned to the calJac3 reference marmoset genome assembly using bwa (Li and Durbin, 2010) with command ‘bwa mem’. Duplicate reads were marked using Picard Markduplicates, and for each chromosome, the GATK Haplotype Caller (McKenna et al., 2010) was run in genotype discovery GVCF mode. For each chromosome, the GVCFs of all samples analyzed in this study (from fibroblasts, blood, buccal cells, skin, brain, and hair) were combined into a single GVCF file using GATK CombineGVCFs. To obtain the highest sensitivity in calling SNPs, we included in the GVCF additional fibroblasts whole-genome sequences from the colony, yielding a total of 113 marmosets for multi-sample variant calling. The GVCF of each chromosome was genotyped using GATK GenotypeGVCFs. Only bi-allelic SNPs were used in the analysis, and the following filters were used: QD <4.0 | FS >60.0 | MQ <40.0 | MQRankSum <−12.5 | ReadPosRankSum <−8.0 | MAF <0.01 | QUAL <500. SNP calls from all chromosomes were combined into one VCF file, and additional filtering was performed to discard heterozygous sites that exhibited extreme allelic imbalance, that is, the fraction of non-reference allele (from all samples) is less than 0.2 or greater than 0.8, and furthermore, sites in copy number variant regions were discarded (copy number variant regions were obtained by running Genome STRiP Handsaker et al., 2015 on WGS data from 113 fibroblast samples).

Dropulation analysis using sibling genotypes from WGS of buccal cells

For four marmosets in our dataset (CJ022, CJ025, CJ026, and CJ102; all born with one sibling and the siblings are CJ106, CJ104, CJ105, and CJ103, respectively), only the buccal cells (from cheek swabs) of their siblings were available for WGS. Using a method that quantifies chimerism from WGS data (Census-seq; software available at https://github.com/broadinstitute/Drop-seq, Broad Institute, 2026; Mitchell et al., 2020), we estimated the chimerism fraction in buccal cells as follows: CJ106: 10%, CJ104: 24%, CJ105: 24%, and CJ103: 9%. Thus, the genotypes we obtained for these marmosets will include errors, and those genotyping errors could subsequently affect the Dropulation (donor-of-origin) analysis that was used to estimate chimerism. To empirically estimate how sibling genotypes obtained from a chimeric tissue affect Dropulation analysis, we selected a host-sibling pair whose genome sequencing was both obtained from fibroblast cultures: CJ027 and its birth sibling CJ140. To simulate DNA contamination, we fixed the sequencing coverage of CJ140 to 40X, and replaced between 1% to 60% of the reads from CJ027’s sequencing reads (random subsampling using ‘samtools view -s’). We genotyped CJ140’s ‘chimeric’ bam files using GATK’s ‘genotype given alleles’ mode and compared the genotypes with CJ140’s true genotypes from its pure fibroblast WGS. We found that the sensitivity at heterozygous sites remains constant with different contamination levels, while the false positive rate increases. The false positive calls at heterozygous sites come from homozygous sites incorrectly genotyped as heterozygous (Figure 2—figure supplement 6A–F). Next, we re-analyzed donor-of-origin on brain snRNA-seq of CJ027 sibling (CJ140) genotypes from simulated contaminated DNA (300,000 nuclei; CJ027 genotypes from pure fibroblast WGS, CJ140 genotypes from WGS with various contamination levels). We found that chimerism in CJ140’s WGS resulted in doublets being assigned to the twin (Figure 2—figure supplement 6G–L), which subsequently causes a slight increase in chimerism estimates (Figure 2—figure supplement 6M–U). The contamination levels in buccal cells were from 9% to 24%, which we estimate will result in an overestimation in microglia chimerism of up to 3.5 and 4.5 percentage points in macrophage chimerism. Our results will not be affected by this overestimation of chimerism in 4 marmosets since the conclusions were made from the analysis of all 11 marmosets (including 7 marmosets whose siblings were genotyped from fibroblast cultures).

Gene expression analysis of host and sibling meta cells

For each cell type, the host and sibling ‘meta cells’ were calculated from the sum of UMI counts per gene across cells and were scaled to counts per 100,000 transcripts. The fold-changes and p-values of differentially expressed genes were identified using the binomTest method from the edgeR package (Robinson et al., 2010). The filters we used to identify statistically significant differentially expressed genes are: FDR Q-value <0.05 and fold-change >1.5 (in either direction) and the gene must be expressed in at least 10% of at least one of the two sets of microglia being compared.

Binomial generalized linear mixed-effects model analysis

To perform an analysis of Figure 2D that takes into account the uncertainty in the estimate of the chimeric cell proportion, we performed a binomial generalized linear mixed-effects model analysis in R using the command glmer(y ~ (1|indiv) + chimerism_micro, family = binomial), where y is a vector (of length 1333) containing the genomic identity of each macrophage (either host or twin), 1|indiv models a random effect for the identity of each animal, and chimerism_micro is the microglia chimerism of the animal’s brain. The fixed effects probability of chimerism_micro was 0.795 indicating that microglial chimerism fraction was not statistically significant as a predictor for macrophage chimerism fraction. The estimate for the intercept was –0.8115 and estimate for chimerism_micro was 0.3106, which indicates that the probability of a cell is a macrophage given the microglia chimerism fraction was only 0.57 (plogis(–0.8115 + 0.3106)).

We used the same framework to further analyze Figure 4. We included brain region as a covariate in the binomial framework: glmer(y ~ (1|indiv)+brain_reg + assay, family = binomial), where y is a vector of length 48,439 containing the genomic identity of each microglia (either host or twin) and assay is either ‘Drop-seq’ or ‘10X’. The brain regions assayed in Figure 4 are the cortex, hippocampus, hypothalamus, striatum, thalamus, and basal forebrain. All these brain regions were statistically significant as predictors for microglia chimerism fractions (all p-values <2 × 10–16), supporting the conclusion that chimerism varies across brain regions. We also re-analyzed Figure 4—figure supplement 1. using the same framework and found that 18 out of 27 brain substructures were statistically significant as predictors for microglia chimerism fraction.

Latent factor analysis

Following the method described in Ling et al., 2024, we performed latent factor analysis using the probabilistic estimation of expression residuals (PEER, Stegle et al., 2010) on the gene-by-donor matrix expression of microglia. We started by creating a gene-by-cell matrix of microglia gene expression from all animals and we normalized the matrix using SCT transform version 2 (Choudhary and Satija, 2022) with 3000 variable features. We obtained the Pearson residuals from SCT normalization and summed up the residual across cells with the same genome to obtain a gene-by-donor matrix of expression measurements of microglia. We used this matrix as input to PEER and ran the tool with a provided number of factors from 9 to 12. For each gene expression latent factor, to evaluate whether host/sibling identity has a consistent effect on expression levels, we performed a linear regression with host/sibling identity using glm(peer_factor_k~host_or_twin). For all factors, the p-values for the effect of host_or_twin were all insignificant (greater than 0.1), indicating that no PEER factor associated with host-vs-twin identity. Thus our results found no large-scale gene expression program that was consistently expressed differently between hosts and twins.

Software availability

All software used in the analysis are publicly available. Drop-seq (analysis of snRNA-seq data, clustering, marker genes), Census-seq (estimation of chimerism in WGS data), and Dropulation analysis (estimation of chimerism in snRNA-seq data): https://github.com/broadinstitute/Drop-seq, Broad Institute, 2026; alignment and variant detection of Illumina WGS data: bwa (https://github.com/lh3/bwa, Li, 2026), GATK (https://gatk.broadinstitute.org), BCFtools (https://github.com/samtools/bcftools, Danecek, 2026), samtools (http://www.htslib.org/download), Picard Tools (https://broadinstitute.github.io/picard); R environment (https://www.rstudio.com/products/rstudio/download and https://www.r-project.org); single-cell analysis in R: Seurat (https://github.com/satijalab/seurat, Butler, 2026), cell type identification: scType (https://github.com/IanevskiAleksandr/sc-type, Aleksandr, 2024), SCT transform (https://github.com/satijalab/sctransform/, Hafemeister, 2026); PEER latent factor analysis (https://github.com/PMBio/peer, PMBio, 2012).

Appendix 1

Appendix 1—table 1
Number of microglia and macrophage cells identified in brain datasets and number of nuclei profiled in blood, liver, and kidney.
IDTissueMicrogliaMacrophageOther cell typesTotal nucleiPercent microglia (%)Percent macrophage (%)
CJ001Brain10631689,59990,6781.170.02
CJ006Brain632259165,272166,1630.380.16
CJ007Brain1155139105,407106,7011.080.13
CJ022Brain3659228217,312221,1991.650.10
CJ023Brain5813879,69280,3110.720.05
CJ025Brain1366131210,628212,1250.640.06
CJ026Brain6932662,74363,4621.090.04
CJ027Brain8398167342,419350,9842.390.05
CJ028Brain18,185172478,765497,3333.660.03
CJ029Brain11,874285320,000332,1593.570.09
CJ102Brain11244356,60257,7691.950.07
CJ026BloodNANA17411741NANA
CJ026LiverNANA10,87710,877NANA
CJ026KidneyNANA92629262NANA
CJ027BloodNANA25292529NANA
CJ028BloodNANA10,04210,042NANA
Appendix 1—table 2
Comparison of chimerism between CJ028’s two birth siblings, in blood and in brain myeloid cells (microglia and macrophage).

p-values are from a two-sided test of proportions between chimerism fractions of sibling 1 and sibling 2 using the prop.test function in R.

Cell typeTotal nucleiSibling 1 nucleiSibling 2 nucleiSibling 1 fractionSibling 2 fractionSibling 1 + sibling 2 fractionp-value
Microglia and macrophage19,701692226890.350.140.49<2.2 × 10–16
Microglia only19,447687326210.350.130.49<2.2 × 10–16
Macrophage only25449680.200.270.465.8 × 10–2
Blood10,042175867390.180.670.85<2.2 × 10–16
Appendix 1—table 3
Summary of context vs genetic effects analysis, with two brain regions of an animal as two contexts.

The analysis described in Figure 5D–F was repeated across all animals and brain regions with at least 60 cells that are available for analysis in each context, and the summary of the correlations are tabulated here. The correlations are plotted in Figure 5—figure supplement 2. Abbreviations: STR: striatum; Thal: thalamus; Hippo: hippocampus; Hyp: hypothalamus; BF: basal forebrain.

Host IDSibling IDBrain region 1Brain region 2Genetic effects Spearman correlationContext effects Spearman correlation
CJ022CJ106CortexThal0.10.73
CJ025CJ104CortexSTR0.030.2
CJ027CJ140CortexThal0.130.64
CJ027CJ140CortexSTR0.140.69
CJ027CJ140CortexHippo0.080.46
CJ027CJ140CortexHyp0.030.68
CJ027CJ140ThalHippo0.040.56
CJ027CJ140ThalHyp0.090.66
CJ027CJ140STRHippo0.080.43
CJ027CJ140STRHyp0.080.59
CJ027CJ140HippoHyp0.040.51
CJ028CJ141CortexThal0.10.66
CJ028CJ142CortexThal0.030.55
CJ141CJ142CortexThal0.070.59
CJ028CJ141CortexSTR0.130.68
CJ028CJ142CortexSTR0.10.58
CJ141CJ142CortexSTR0.090.61
CJ028CJ141CortexHippo0.060.39
CJ028CJ142CortexHippo00.33
CJ141CJ142CortexHippo0.040.38
CJ028CJ141CortexBF0.130.73
CJ028CJ142CortexBF0.070.6
CJ141CJ142CortexBF0.090.6
CJ028CJ141CortexHyp0.060.78
CJ028CJ142CortexHyp0.070.72
CJ141CJ142CortexHyp0.080.75
CJ028CJ141CortexAmygdala0.140.65
CJ028CJ142CortexAmygdala0.10.53
CJ141CJ142CortexAmygdala0.120.57
CJ028CJ141STRHippo0.060.63
CJ028CJ142STRHippo0.010.56
CJ141CJ142STRHippo0.030.61
CJ028CJ141STRBF0.140.63
CJ028CJ142STRBF0.060.5
CJ141CJ142STRBF0.050.52
CJ028CJ141HypAmygdala0.140.68
CJ028CJ142HypAmygdala0.210.65
CJ141CJ142HypAmygdala0.070.68
CJ029CJ143CortexThal0.090.61
CJ029CJ143CortexSTR0.060.5
CJ029CJ143CortexHippo0.050.62
CJ029CJ143CortexBF0.090.79
CJ029CJ143CortexHyp0.070.78
CJ029CJ143CortexAmygdala0.070.32
CJ029CJ143ThalSTR0.090.48
CJ029CJ143ThalHippo0.060.71
CJ029CJ143ThalBF0.140.75
CJ029CJ143ThalHyp0.130.8
CJ029CJ143ThalAmygdala0.050.52
CJ029CJ143HippoBF0.060.77
CJ029CJ143HippoHyp0.030.78
CJ102CJ103CortexSTR0.070.32
Appendix 1—table 4
Clustering parameters used to identify microglia and macrophage cell types, and thresholds for identifying host-sibling doublets.

The final number of microglia and macrophages after the second round of clustering are in Appendix 1—table 2. A cell is assigned as a doublet if the Dropulation tool DetectDoublets assigned the highest likelihood for the cell as a doublet and if the log10 of the best likelihood minus the log10 of the second-best likelihood (lrt_test_stat, calculated by DetectDoublets) is greater than the doublet detection threshold (last column).

Marmoset IDTissueResolution parameterNearest neighbor parameterUMI threshold (min log-UMI per cell)Doublet detection threshold (lrt_test_stat)
CJ001Brain0.8101.20.0
CJ006Brain0.5101.25.0
CJ007Brain0.5201.210.0
CJ022Brain Drop-seq0.5201.22.0
CJ022Brain 10X0.5201.52.0
CJ023Brain0.5201.23.0
CJ025Brain0.5201.00.5
CJ026Brain0.5201.02.0
CJ026Blood0.5201.44.0
CJ026Liver0.5201.98.0
CJ026Kidney0.5201.98.0
CJ027Brain Cortex A0.5201.520.0
CJ027Brain Cortex B0.5201.520.0
CJ027Brain others0.5201.520.0
CJ027Blood0.5201.51.0
CJ028Brain Cortex A0.5201.525.0
CJ028Brain Cortex B0.8101.525.0
CJ028Brain others0.5201.525.0
CJ028Blood0.5201.51.0
CJ029Brain Cortex0.5201.520.0
CJ029Brain others0.5201.520.0
CJ102Brain, Drop-seq0.8101.23.0
CJ102Brain, 10X0.5201.25.0
Appendix 1—table 5
Whole-genome sequencing datasets used in (1) donor-of-origin assignment from snRNA-seq (Dropulation), and (2) estimating chimerism from blood whole-genome sequencing (Census-seq).
IDTissue/DNA sourceSequencing coverage
CJ001Fibroblast culture36.4X
CJ119 (CJ001’s sibling 1)Fibroblast culture39.7X
CJ120 (CJ001’s sibling 2)Fibroblast culture36.7X
CJ006 (CJ007’s sibling)Fibroblast culture45.9X
CJ007 (CJ006’s sibling)Fibroblast culture55.8X
CJ022Fibroblast culture38.4X
CJ106 (CJ022’s sibling)Buccal swab36.0X
CJ023Fibroblast culture34.9X
CJ131 (CJ023’s sibling 1)Fibroblast culture39.5X
CJ116 (CJ023’s sibling 2)Fibroblast culture28.3X
CJ025Fibroblast culture72.7X
CJ104 (CJ025’s sibling)Buccal swab58.0X
CJ026Fibroblast culture74.1X
CJ105 (CJ026’s sibling)Buccal swab43.7X
CJ027Fibroblast culture47.5X
CJ027Blood46.9X
CJ140 (CJ027’s sibling)Fibroblast culture40.2X
CJ028Fibroblast culture44.6X
CJ028Blood52.6X
CJ141 (CJ028’s sibling 1)Fibroblast culture43.6X
CJ142 (CJ028’s sibling 2)Fibroblast culture41.2X
CJ029Fibroblast culture49.2X
CJ143 (CJ029’s sibling)Fibroblast culture40.8X
CJ102Brain32.4X
CJ103 (CJ102’s sibling)Buccal swab38.5X

Data availability

Brain snRNA-seq of 6 marmosets (CJ022, CJ023, CJ025, CJ026, CJ027, CJ028) were generated as part of the NIH's Brain Initiative Cell Census Network (BICCN) project, while brain snRNA-seq of 5 marmosets (CJ001, CJ006, CJ007, CJ023, CJ102), and all blood, liver, and kidney snRNA-seq were generated for this project. All snRNA-seq datasets are available in the BICCN NeMO portal (https://assets.nemoarchive.org/dat-hsgdsgu and https://assets.nemoarchive.org/dat-1je0mn3). The raw whole-genome sequencing datasets are available from the NIH Sequence Read Archive, under accession number BioProject PRJNA1068102.

The following data sets were generated
    1. del Rosario R
    (2023) NeMO
    ID nemo:dat-hsgdsgu. Marmosets Have their Birth Sibling's Microglia.
    https://assets.nemoarchive.org/dat-hsgdsgu
The following previously published data sets were used
    1. Krienen F
    (2023) NeMO
    ID nemo:dat-1je0mn3. A marmoset brain cell census reveals persistent influence of developmental origin on neurons.
    https://assets.nemoarchive.org/dat-1je0mn3
    1. del Rosario R
    (2023) NCBI Sequence Read Archive
    ID SRX32815946. WGS of Marmoset Colony at Broad Institute by Stanley Center for Psychiatric Research.
    https://www.ncbi.nlm.nih.gov/sra/SRX32815946

References

    1. Niblack GD
    2. Kateley JR
    3. Gengozian N
    (1977)
    T-and B-lymphocyte chimerism in the marmoset
    Immunology 32:257–263.

Article and author information

Author details

  1. Ricardo CH del Rosario

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    rcdelros@broadinstitute.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7038-9598

  2. Fenna M Krienen

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Present address
    Princeton Neuroscience Institute, Princeton, United States
    Contribution
    Resources, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1400-6820

  3. Qiangge Zhang

    1. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    2. McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared

  4. Melissa Goldman

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared

  5. Curtis Mello

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared

  6. Alyssa Lutservitz

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared

  7. Kiku Ichihara

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Resources, Funding acquisition, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0000-5860-0488

  8. Alec Wysoker

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Resources, Software
    Competing interests
    No competing interests declared

  9. James Nemesh

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Resources, Software
    Competing interests
    No competing interests declared

  10. Guoping Feng

    1. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    2. McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
    Contribution
    Resources, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8021-277X

  11. Steven A McCarroll

    1. Department of Genetics, Harvard Medical School, Boston, United States
    2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    3. Howard Hughes Medical Institute, Boston, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    smccarro@broadinstitute.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6954-8184

Funding

NIH BRAIN Initiative (U01MH114819)

  • Steven A McCarroll

Stanley Center for Psychiatric Research, Broad Institute

  • Guoping Feng
  • Steven A McCarroll

James and Patricia Poitras Center for Psychiatric Disorders Research at MIT

  • Guoping Feng

Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT

  • Guoping Feng

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the NIH BRAIN Initiative U01MH114819, the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard, the James and Patricia Poitras Center for Psychiatric Disorders Research at MIT, and the Hock E Tan and K Lisa Yang Center for Autism Research at MIT.

Ethics

Marmoset experiments were approved by and in accordance with Massachusetts Institute of Technology IACUC protocol number 051705020.

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Copyright

© 2024, del Rosario et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Ricardo CH del Rosario
  2. Fenna M Krienen
  3. Qiangge Zhang
  4. Melissa Goldman
  5. Curtis Mello
  6. Alyssa Lutservitz
  7. Kiku Ichihara
  8. Alec Wysoker
  9. James Nemesh
  10. Guoping Feng
  11. Steven A McCarroll
(2026)
Sibling chimerism among microglia in marmosets
eLife 13:RP93640.
https://doi.org/10.7554/eLife.93640.3

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https://doi.org/10.7554/eLife.93640

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