The spatiotemporal dynamics of microglia across the human lifespan

Abstract

Microglia, the brain’s resident macrophages, shape neural development and are key neuroimmune hubs in the pathological signatures of neurodevelopmental disorders. Despite the importance of microglia, their development has not been carefully examined in the human brain, and most of our knowledge derives from rodents. We aimed to address this gap in knowledge by establishing an extensive collection of 97 post-mortem tissues in order to enable quantitative, sex-matched, detailed analysis of microglia across the human lifespan. We identify the dynamics of these cells in the human telencephalon, describing waves in microglial density across gestation, infancy, and childhood, controlled by a balance of proliferation and apoptosis, which track key neurodevelopmental milestones. These profound changes in microglia are also observed in bulk RNA-seq and single-cell RNA-seq datasets. This study provides a detailed insight into the spatiotemporal dynamics of microglia across the human lifespan and serves as a foundation for elucidating how microglia contribute to shaping neurodevelopment in humans.
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The spatiotemporal dynamics of microglia across the
human lifespan
Graphical abstract
Highlights
dA roadmap of microglial dynamics across the human lifespan
dThe density of microglial cells follows a wave-like pattern
during development
dMicroglial fluctuations are spatiotemporally linked to key
neurodevelopmental hallmarks
Authors
David A. Menassa, Tim A.O. Muntslag,
Maria Martin-Estebane
´, ...,
Zeljka Krsnik, Ivica Kostovic,
Diego Gomez-Nicola
Correspondence
david.menassa@queens.ox.ac.uk
(D.A.M.),
d.gomez-nicola@soton.ac.uk (D.G.-N.)
In brief
Microglial cells are pivotal players in brain
development and function. However,
most of our knowledge about their
development derives from rodents.
Menassa et al. describe the dynamics of
microglia across the human lifespan from
early gestation until old age, identifying
distinct dynamics that are intimately
associated with key neurodevelopmental
hallmarks.
Menassa et al., 2022, Developmental Cell 57, 1–13
September 12, 2022 ª2022 The Author(s). Published by Elsevier Inc.
https://doi.org/10.1016/j.devcel.2022.07.015 ll

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The spatiotemporal dynamics
of microglia across the human lifespan
David A. Menassa,
1,2,
*Tim A.O. Muntslag,
1
Maria Martin-Estebane
´,
1
Liam Barry-Carroll,
1
Mark A. Chapman,
1
Istvan Adorjan,
3
Teadora Tyler,
3
Bethany Turnbull,
1
Matthew J.J. Rose-Zerilli,
4
James A.R. Nicoll,
5
Zeljka Krsnik,
6
Ivica Kostovic,
6
and Diego Gomez-Nicola
1,7,
*
1
School of Biological Sciences, University of Southampton, Southampton, United Kingdom
2
Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
3
Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
4
Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
5
Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
6
Croatian Institute for Brain Research, University of Zagreb Medical School, Zagreb, Croatia
7
Lead contact
*Correspondence: david.menassa@queens.ox.ac.uk (D.A.M.), d.gomez-nicola@soton.ac.uk (D.G.-N.)
https://doi.org/10.1016/j.devcel.2022.07.015
SUMMARY
Microglia, the brain’s resident macrophages, shape neural development and are key neuroimmune hubs in
the pathological signatures of neurodevelopmental disorders. Despite the importance of microglia, their
development has not been carefully examined in the human brain, and most of our knowledge derives
from rodents. We aimed to address this gap in knowledge by establishing an extensive collection of 97
post-mortem tissues in order to enable quantitative, sex-matched, detailed analysis of microglia across
the human lifespan. We identify the dynamics of these cells in the human telencephalon, describing waves
in microglial density across gestation, infancy, and childhood, controlled by a balance of proliferation and
apoptosis, which track key neurodevelopmental milestones. These profound changes in microglia are also
observed in bulk RNA-seq and single-cell RNA-seq datasets. This study provides a detailed insight into
the spatiotemporal dynamics of microglia across the human lifespan and serves as a foundation for eluci-
dating how microglia contribute to shaping neurodevelopment in humans.
INTRODUCTION
Microglia are the main resident immune cells of the brain. During
development, their roles include the phagocytosis of neuronal
precursors to restrict progenitor pool size (Cunningham et al.,
2013), the modulation of forebrain wiring by guiding interneuron
positioning (Squarzoni et al., 2014), the pruning of synapses
(Paolicelli et al., 2011), and the formation and refinement of
axonal tracts (Verney et al., 2010;Pont-Lezica et al., 2014;
Squarzoni et al., 2014). In the adult, microglia retain key homeo-
static functions, suppressing interneuron activation and modi-
fying animal behavior (Badimon et al., 2020), regulating hippo-
campal neurogenesis (Sierra et al., 2010), and driving
inflammation in a disease context (Gomez-Nicola and
Perry, 2015).
However, most of our knowledge of microglial developmental
dynamics is derived from rodent studies. In the mouse, microglia
originate from erythromyeloid progenitors in the extraembryonic
yolk sac (YS) from E7.5 (Ginhoux et al., 2010). These progenitors
begin populating the brain primordium at E9.5 and their pheno-
typic specification into microglia is defined by intrinsic and
brain-specific transcriptional regulators (Lavin et al., 2014;Gos-
selin et al., 2014;Bennett et al., 2018). The entire microglial pop-
ulation is generated by expansion during embryonic life and the
early postnatal developmental stages, followed by a transient
postnatal selection phase (Askew et al., 2017;Re
´u et al.,
2017). Under homeostatic conditions, the population is main-
tained throughout life by cycles of slow self-renewal, estimated
at approximately 0.69% per day (Askew et al., 2017).
Human microglial development has some similarities to
mouse, as macrophages are detectable by 2–3 postconcep-
tional weeks (pcw) in the blood islands of the extraembryonic
YS (Nogales, 1993;Janossy et al., 1986;Popescu et al., 2019;
Park et al., 2020), to later appear in the forebrain from the 3
rd
pcw (Verney et al., 2010;Menassa and Gomez-Nicola, 2018).
A series of descriptive post-mortem studies collectively report
on microglial proliferation in clusters appearing in the ventral
telencephalon and diencephalon from the 4
th
pcw and
continuing throughout fetal development (Monier et al., 2007;
Monier et al., 2006;Verney et al., 2010). Recent single-cell tran-
scriptomic studies suggest that microglial ontogenic pathways
are conserved between human and mouse during embryonic
development (Bian et al., 2020), with microglia progressing
from an undifferentiated state toward a mature adult-like immu-
nocompetent state from 11 pcw in humans (Kracht et al., 2020).
The early arrival of microglia to the brain precedes the onset of
Developmental Cell 57, 1–13, September 12, 2022 ª2022 The Author(s). Published by Elsevier Inc. 1
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pivotal processes of human cortical development, such as neu-
rogenesis, neuronal migration, and gliogenesis (Menassa and
Gomez-Nicola, 2018). In the adult, the population self-renews
at a slow daily turnover rate, which has been estimated at
0.08%–2% (Askew et al., 2017;Re
´u et al., 2017). However, we
lack a deep understanding of microglial spatiotemporal dy-
namics during human development, compared with the mouse,
largely due to the scarcity of developing tissue available for
research.
We established an unprecedented collection of 97 post-mor-
tem tissues (Figure 1), enabling quantitative, sex-matched, and
detailed analysis of microglial dynamics across the human life-
span (3
rd
pcw—75 years), in relation to their immunocompetent
and neurogenetic roles. We identify developmental and post-
natal waves of expansion and refinement, where the population
undergoes marked changes in numbers, and we determine the
contributions of proliferation, apoptosis, and migration to this
process. We validate our identified critical windows in further
analyses of datasets from 251 bulk RNA-seq samples and four
single-cell RNA-seq (scRNA-seq) studies comprising 24,751 mi-
croglial cells spanning the embryonic and fetal ages (3–24 pcw).
This study is pivotal for our understanding of human microglial
biology, providing a granular view of the population dynamics
across the life course and its intertwined nature with key neuro-
developmental processes. These findings serve as a solid basis
for elucidating how microglia shape brain development in hu-
mans, key for understanding neurodevelopmental disorders.
RESULTS
Brain colonization by microglia begins in the 4
th
postconceptional week (CS12)
The onset of embryonic circulation is concomitant with the
development of the cardiovascular system, which in humans be-
comes functional from the late 3
rd
/early 4
th
pcw (somites: 4–12;
CS10 onward) (Tavian and Pe
´ault, 2005;O’Rahilly and M€
uller,
2010). At CS10, we identify early colonization of embryonic or-
gans such as the heart by cells committed to the myeloid
lineage (PU.1
+
;Figure 2A), with the expression of IBA1 not yet
detected (Figure 2A). By CS12 (the end of the 4
th
pcw), organ
colonization by IBA1
+
cells (Figure 2B) is profuse. Proliferation
is highest at CS16 (6
th
pcw) and lowest at CS21 (8
th
pcw) in
the liver (Kruskal-Wallis p < 0.05, multiple comparisons’ adjusted
p values < 0.01) (Figure 2C). Liver macrophage densities are not
Figure 1. Overview of the study
Post-mortem collection of human tissues across the lifespan with 52 prenatal and 45 postnatal cases, which adds up to n = 97. The overall number of cases
collected, sampled, and analyzed was in excess of 130 (see STAR Methods section and Table S2). Temporal windows mapped onto key human neuro-
developmental milestones across the lifespan were defined consistent with existing classifications (Carroll et al., 2021;Silbereis et al., 2016;Kostovi 
c et al., 2002):
the embryonic period (3–8 pcw), the early fetal period (9–15 pcw), the mid-late fetal period (16–25 pcw), the preterm period (26–35 pcw), the term period (36 pcw–
birth), the neonatal period (0–1 month), the infancy period (1–12 months), childhood (1–3 years), and adulthood (>18 years). Gene expression datasets: bulk RNA-
seq of 251 samples from the Wellcome MRC/HDBR resource in 4 anatomical regions between 7 and 17 postconceptional weeks and an integrated dataset of
24,751 microglial cells from 4 single-cell RNA-seq studies (Cao et al., 2020;Kracht et al., 2020;Bian et al., 2020;Fan et al., 2020) between 3 and 24 postconcep-
tional weeks. F, female; M, male; MRC/HDBR, Medical Research Council/Human Developmental Biology Resource; yindicates not known, and *
indicates
brain
banks that are part of the BRAINUK network. Embryonic brain drawings were based on our own samples, and other model brains were redrawn and colored
based on illustrations from Haymaker and Adams (1982).
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significantly different between pcw across the embryonic age
(Kruskal-Wallis p > 0.05, ns) (Figure 2C).
At CS10, no IBA1
+
or PU.1
+
myeloid cells are detected in the
brain rudiment (Figure 2A). By CS12, the first wave of brain colo-
nization begins, with IBA1
+
macrophages seeding the forebrain,
midbrain, and hindbrain (Figure 2B). Microglial proliferation in-
creases sharply from CS12 to CS14 and remains relatively stable
across the embryonic age with no significant differences in mean
proliferation levels between pcw (Kruskal-Wallis test, p > 0.05,
ns) (Figure 2C). The brain’s proliferative index is much higher
than the liver’s (Mann-Whitney U test, p < 0.05) but its IBA1 den-
sities are significantly lower (Mann-Whitney U test, p < 0.001)
(Figure 2D). The first detectable expansion phase of the popula-
tion is observed after a sharp increase in IBA1
+
cell density at
CS23 (9
th
pcw) compared with every pre-CS23 pcw (Kruskal-
Wallis test, p < 0.05, multiple comparisons’ adjusted post hoc
p values < 0.01) (Figure 2C).
In the developing brain, we found no significant differences
between the cumulative distributions of microglia by sex for
both density and proliferation (2-sample Kolmogorov-Smirnov
test, p > 0.05, ns) (Figure 2E, top). When we considered the
mean proliferation and density by sex across the embryonic
age, we also found no differences between males and females
(Mann-Whitney U test, p > 0.05) (Figure 2E, bottom). Early in
development (5
th
pcw), migratory microglia (IBA1
+
) account for
40% of the total population in the dorsal telencephalon, which
at this age is bilaminar with a proliferative zone, the ventricular
zone (VZ), and a plexiform mantle layer (ML) (Figures 2B and
2F, top). Based on a morphometric analysis, most of the migra-
tion in the VZ appears radial, indicating the colonization of
Figure 2. Early colonization of the human embryo
(A) Representative sagittal cross-section through a CS10 (late 3
rd
pcw) human embryo. Immunolabeling was done on consecutive 8-mm sections identifying the
presence of PU.1 cells in organs apart from the brain and the absence of IBA1
+
cells overall. Scale bars: 500 mm (left); 40 mm (right).
(B) Representative sagittal sections through whole human embryos from CS12, the age of appearance of IBA1
+
cells across the entire embryo, through to CS21.
Insets show the location of proliferating IBA1
+
cells in both the liver and the dorsal telencephalon. Scale bars: 1 mm (top); 100 mm (bottom).
(C) IBA1 proliferative dynamics and cell densities in the developing brain (n = 14) and the liver (n = 11). Black arrows represent the two most relevant time points for
microglial densities: CS12 (4
th
pcw) for the first colonization of the brain rudiment and CS23 (9
th
pcw), the transition from embryonic to early fetal life.
(D) Mean IBA1 proliferation in the liver (n = 11) and the developing brain (n = 11) across the embryonic period. All data are shown as mean ± SEM.
(E) Cumulative frequency distribution plots of densities and proliferative indices by sex in the developing brain (7F:7M) (top panel); mean differences between
sexes in microglial densities and proliferative indices across the embryonic age in the developing brain (7F:7M) (bottom panel, data are shown as mean ± SEM).
(F) Representative migratory profiles of IBA1
+
cells in the bilaminar telencephalon at 5 pcw (n = 2). Data are shown as mean ± SEM.
(G) IBA1
+
cell distributions in the CS12 embryo showing very few cells in the forebrain and a much higher density of cells in the hindbrain and midbrain. Scale
bars: 500 mm.
(H) Entry routes of IBA1
+
cells into the brain rudiment in the forebrain and the hindbrain. Scale bars: 100 mm. C, cardiac muscle; CP, cortical plate; GE, ganglionic
eminence; H, hindbrain; L, liver; M, meninges; MB, midbrain; Me, mesenchyme; ML, mantle layer; SC, spinal cord; T, telencephalon; VZ, ventricular zone. For all
panels, asterisks represent adjusted p value significance as follows: *p < 0.05, **p < 0.001, ***p < 0.0001, and ns p > 0.05.
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adjacent layers, whereas microglia in the ML are characterized
by a tangential migratory phenotype, suggesting expansion
within a layer (Figure 2F, bottom). Morphologically, microglia in
the bilaminar telencephalon are amoeboid, with or without a pro-
liferative core, or migratory (tangential or radial) (Figure S1A).
Microglial densities and proliferation vary between regions:
cell densities tend to be highest in the hindbrain at CS12,
compared with the forebrain and midbrain (Figures 2G and
S1B), through to CS21, and proliferation is significantly higher
in the midbrain compared with the hindbrain and the forebrain
(Friedman’s test, p < 0.05) (Figure S1B).
In sum, we can date the arrival of microglial progenitors to the
brain at CS12 (4
th
pcw), with the population rapidly engaging in
proliferative and migratory activities.
The density of the microglial population fluctuates by
waves of proliferation followed by cell death in the
developing cortex
The future neocortex is formed from the dorsal telencephalon in
the forebrain, which is where we focused all our subsequent an-
alyses. Between CS12 (4
th
pcw) and CS21 (8
th
pcw), microglial
proliferation is restricted to the ML (Figure 2B) and cells are
seen entering the brain parenchyma from the VZ, the meningeal
compartment, and the ganglionic eminence (GE) (Figure 2H).
The last stage of embryonic life is CS23 (9
th
pcw) (O’Rahilly
and M€
uller, 2010), equivalent to the late 8
th
or early 9
th
pcw, fol-
lowed by fetal life. We observed key proliferation waves charac-
terizing the expansion of the microglial population during embry-
onic and early fetal lives.
This first wave is the most significant and coincides with the
appearance of multiple transient layers in the telencephalon,
including the cortical plate (CP) and the pre-subplate (PSP),
which becomes the subplate (SP) at the 12.5
th
pcw
(Figures 3A, 3B, and S1C). This first wave is at the transition be-
tween embryonic and fetal life (late 8
th
to early 9
th
pcw) (O’Rahilly
and M€
uller, 2010;Kostovi
c et al., 2002), as evaluated with an
excess test for bimodal distributions (B = 100 replicas, modes:
2.04, 22.46; antimode: 16.35, p > 0.05 [Ameijeiras-Alonso
et al., 2019]). Mean proliferation levels are highest in the embry-
onic window (5–8 pcw) compared with early fetal windows (9–12
pcw, 13–16 pcw) (Kruskal-Wallis test p < 0.01, multiple compar-
isons’ adjusted post hoc p values < 0.01) (Figure 3C).
This increase in microglial proliferation (IBA1
+
Ki67
+
) is fol-
lowed by a peak in density, observed subsequently at 10–12
pcw (Figures 3B and 3C) in the frontal telencephalic wall. This
first density wave in early fetal life is the most significant, evi-
denced by an excess test for bimodal distributions (B = 100 rep-
licas, modes: 85.77, 484.16; antimode: 426.84; p > 0.05) (Fig-
ure 3B). Mean density levels are highest in the early fetal
window (9–12 pcw) compared with 5–8 pcw and 13–16 pcw
(Kruskal-Wallis test p < 0.001, multiple comparison adjusted
post hoc p values < 0.001) (Figure 3C, bottom panel).
From 12 pcw, the microglial density drops to baseline levels
(Figure 3B) due to microglial apoptosis that we detect by cleaved
caspase-3 labeling in microglia between 12 and 16 pcw (Fig-
ure 3D). The mean percentage of apoptotic microglia before the
density starts decreasing from 12 pcw was 0.58% ± 0.18% (7–
11 pcw), increasing to 2.94% ± 0.83% after 12 pcw until 16
pcw (Mann-Whitney U test, p = 0.007) (Figure 3D).
Between 9 and 10 pcw we also detect a significant transient
switch in the migratory phenotype of microglia, with most cells
adopting a radial migratory pattern (Figure 3E) across all telence-
phalic layers (Figure S2), suggestive of a local colonization pro-
cess being coupled to cycles of expansion.
We complemented the IBA1 quantifications with an analysis of
TMEM119 expression. This study may pose an opportunity to
assess TMEM119, as there are conflicting reports in the literature
about its validity as a bona fide microglial marker (Bennett et al.,
2016;Vankriekelsvenne et al., 2022). TMEM119 cell density
changes tracked the pattern followed by IBA1
+
cells between 9
and 16 pcw (Figure 3F). However, TMEM119 cell densities
were significantly lower compared with IBA1 cells during this
window (Wilcoxon test, p < 0.01) (Figure 3F). TMEM119 densities
correlated significantly with IBA1 densities (Spearman’s r = 0.84,
p < 0.005, Figure S3A) during development. The ratio of
TMEM119/IBA1 labeled cells was 10% between 10 and 28
pcw (Figure 3G), with a colocalization degree of 90%
(Figures 3H and S3B). Topographically, TMEM119
+
cells were
strongly detected in the MZ and the VZ (Figure 3H). Sporadic
expression of TMEM119 was detected in the lower portion of
the CP but not in any other middle layers, while IBA1 expression
was consistent (Figure 3H). TMEM119
+
cells were not prolifera-
tive (Figure S3C) and co-expressed lysosomal marker CD68
particularly in the MZ (Figure S3D).
The second wave of proliferation was less significant (bimodal
distribution excess test p > 0.05) but will be described because
patterns similar to the first wave were observed. The second pro-
liferation window coincides with the early expansion of the hu-
man SP between 13 and 16 pcw (Kostovi
c, 2020)(Figures 3A
and 3B). The increase in proliferation was followed by an
increase in density, which peaked at 20 pcw (Figure 3B). There-
after, the density drops to baseline levels and closely
matches the mean density values obtained in the adult (mean
N
D
= 84.63 ± 4.41 [mean cells/mm
2
± SEM]). This drop in density
can be explained by microglial apoptosis (cleaved caspase-3
+
)
between 18 and 24 pcw (Figure 3I). We also detect non-micro-
glial apoptosis at various time points during development
(Figure 3J).
The wave-like pattern followed by the microglial population
during development is observed in both females and males
(Figures 3B and 3K). No significant differences in microglial pro-
liferation or density between the sexes were observed (Figure 3K)
(Kolmogorov-Smirnov test, p > 0.05 for distribution plots be-
tween males and females [Figure 3K, top], and Mann-Whitney
U test for mean proliferation and density comparisons between
males and females, p > 0.05 [Figure 3K, bottom]).
Microglial expansion is not uniform across transient zones
of the developing cortex. As development progresses, we
observed a pattern of ‘‘hot spots’’ of local proliferation preceding
an increase in local density in subsequent time points, which is
replicated across layers (Figures 4A–4D). Every layer has a
different timing: whereas densities peak earlier in development
for the MZ, CP, and SP (Figures 4A–4C), they do so much later
for the subventricular zone (SVZ) and the VZ (Figures 4A and
4D). Densities and proliferation are significant in the SP and inter-
mediate zone (IZ) (Figure 4E) (Kruskal-Wallis test, adjusted
p < 0.01). Migration patterns vary within each layer (Figure S2),
perhaps indicating movement of cells between adjacent layers.
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During fetal life (9 pcw–term), microglial morphology is diverse:
amoeboid, migratory (radial and tangential), clustered, ramified,
bipolar, and phagocytic/activated with pouches in the vicinity of
the cell (Figures S1A and S4C–S4F). Some morphologies can be
linked to function, such as proliferative (IBA1
+
Ki67
+
cells),
apoptotic (IBA1
+
Casp3
+
), and homeostatic (TMEM119
+
)
(Figures S1C and S2).
Altogether, post colonization at CS12 (4
th
pcw), the microglial
population follows a wave-like pattern of significant increases in
density preceded by increases in proliferation, later refined by
cell death, with the ultimate consequence of densities stabilizing
at levels observed in the adult after every wave.
Microglia undergo a wave-like pattern of transcriptional
changes
We sought to validate our histological findings with an alternative
approach based on the analysis of the transcriptional profile of
microglia. Using bulk RNA-seq data from whole brain (Gerrelli
Figure 3. Developmental dynamics of microglia in the cortex
(A) Representative laminar structure of the developing cortex with its transient zones observed from CS23 (9
th
pcw) in humans and representative cortical
columns from 10 to 12 pcw showing (1) the development of the pre-subplate to the subplate at 12.5 pcw and the alignment of microglial cells at the CP-PSP/SP
boundary and (2) the distribution of microglial cells across transient zones. Scale bars: 2 mm (left); 100 mm (right).
(B) Corrected microglial densities (against fold change in frontal telencephalic wall thickness with age) and proliferative dynamics in the telencephalon during
development (CS10 [late 3
rd
/early 4
th
pcw]) until term (38 pcw) (n = 50). *n/k, not known. Embryonic and early fetal temporal windows are most significant for
proliferation against other temporal windows, while the early fetal window is the most significant against other temporal windows.
(C) Equally spaced temporal windows for proliferation levels (top) and densities (bottom) around the most significant first wave. Data are representedas
mean ± SEM.
(D) Mean apoptotic index around the first peak of densities (n = 15, 8 cases between 7 and 11 pcw and 7 cases between 12 and 16 pcw) (top panel). Data are
represented as mean ± SEM. Representative microglial cell death photomicrographs observed in wave 1 following the decrease in densities (bottom panel, black
arrows in B). Scale bars: 20 mm.
(E) Migratory profile of microglia and type of migration in representative cases from the telencephalon (n = 12).
(F) Representative profile (top panel) of TMEM119
+
and IBA1
+
cell densities around the first significant density wave (10–16 pcw) (n = 6). Mean TMEM119
+
and
IBA1
+
cell densities across the prenatal period (10–28 pcw, n = 10) (bottom panel). Data are shown as mean ± SEM.
(G) Ratio of labeled TMEM119
+
/IBA1
+
cells during the prenatal period (10–28 pcw, n = 10). Data are presented as mean ±SEM.
(H) Representative confocal images of TMEM119
+
cells in the MZ and the VZ of a 13-pcw case (left) and double labeling of TMEM119/IBA1 in bright field in a
neocortical column at 13 pcw observed in the MZ (right). Scale bars (left): 50 mm; scale bars (right): 100 mm.
(I) Wave 2 microglial cell death at 18 and 23 pcw. Scale bars: 20 mm.
(J) Non-microglial death observed in the GE and in cortical transient zones at CS23 (9
th
pcw) (top, scale bars: 100 mm) and at 24 pcw (bottom, scale bars: 50 mm).
(K) Proliferative dynamics and densities by sex across human development shown as relative cumulative frequency distribution plots (top panel) and mean values
between the sexes (27M:23F) (bottom panel). Data are shown as mean ± SEM. R, right; L, left; CP, cortical plate; GE, ganglionic eminence; IZ, intermediate zone;
M, meninges; MZ, marginal zone; PSP, pre-subplate; SP, subplate; SVZ, subventricular zone; VZ, ventricular zone. For all panels, asterisks represent adjusted
p value significance as follows: *p < 0.05, **p < 0.001, ***p < 0.0001, and ns p > 0.05.
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et al., 2015;Lindsay et al., 2016), we examined the expression of
genes characteristic of published adult and juvenile microglial
signatures (Figure 5A, top panel; Data S1) across development
(7–17 pcw). We considered a gene to be constitutively expressed
when present in >80% of the samples in every time point at a
TPM value > 2 (Figure 5A). We identified that 24% (212) of
cortical genes from the adult signature were constitutively ex-
pressed in the telencephalon between 8 and 17 pcw ("On"
genes; Figure 5A, bottom panel). These genes are involved in
the regulation of the innate immune and inflammatory responses
as well as cytokine production (Figure 5A, right panel). Heatmap
representation of a set (75) of highly enriched microglial genes
(Data S1) indicated a highly changeable signature with higher
expression in the earlier time points (8 and 9 pcw) compared
with later time points, where only a few genes had high expres-
sion by 13–17 pcw (Figure 5B). The homeostatic marker P2RY12
had a stable expression throughout, while CX3CR1 increased
gradually toward the 13- to 17-pcw temporal window (Figure 5C).
Pro-inflammatory factors, such as IRF5 and IFNGR1, had a sta-
ble expression throughout. The sensome gene CD37 had a
100-fold higher expression compared with other genes in our
samples, declining with age. AIF1, encoding the ubiquitously ex-
pressed IBA1 protein, was expressed at low levels but consis-
tently across the various ages (Figure 5C).
We identified a peak of differentially expressed (DE) genes at 9
pcw in the cortex (Figure 5D), matching the timing of the start of
the first wave of expansion of microglial density (Figure 3). At
9pcw, we detected 81 DE genes: 27 upregulated DE genes
were associated with chemokine and Toll-like receptor signaling,
such as IRF5, SIGLECs (e.g., SIGLEC10), and MHC-II/co-stimu-
latory molecules such as HCG4B (Figure 5D; Data S1), and 54
downregulated DE genes were associated with cytokine-cyto-
kine receptor signaling and interferon-gamma-mediated
signaling such as TGFBR1 (Figure 5D). Collectively, microglial
DE genes are significantly associated with cellular components
of immunological synapses and cytoplasmic, as well as intracel-
lular, vesicles (Figure 5D).
We compared the expression of the microglial signature by
region, testing samples from the cerebellum, the choroid
plexus, the cortex, and the midbrain (Figure 5E). We detected
a substantial increase in the %DE genes in the midbrain and
cerebellum at 7–8 pcw, prior to that seen in the cortex and
choroid plexus between 9 and 10 pcw (Figure 5E). Although
we did not perform deconvolution of cell-specific gene expres-
sion, this would suggest that different regions may follow
different microglial maturation timings. Thereafter, change in
gene expression by region declined substantially with age
(Figure 5E).
Figure 4. Spatiotemporal microglial dynamics within the developing cortex
(A) Representative heatmaps showing microglial dynamics throughout development in the human cortex. Heatmaps were constructed based on the densityof
immunoreactive cells. Density of all IBA1 immunoreactive cells are represented by blue-red spectral heatmaps (left column of each stage), while IBA1
+
/Ki67
+
double-positive cells are represented by purple-yellow spectral heatmaps (right column of each stage). Approximate densities are shown on the color bar
with minimal density being blue/purple, maximum density being red/yellow. All cortical columns are 450-mm wide.
(B–D) (B) Proliferation and densities of microglia in the marginal zone (n = 35), cortical plate (n = 31); (C) the subplate (n = 31) and the intermediate zone (n = 31);
(D) the subventricular (n = 31) and the ventricular zones (n = 35).
(E) Proliferation and densities in the subplate, the intermediate zone, and remaining cortical layers. Data are shown as mean ±SEM. CP, cortical plate; GM, gray
matter; IZ, intermediate zone; MZ, marginal zone; PSP, pre-subplate; SP, subplate; SVZ, subventricular zone; VZ, ventricular zone; WM, white matter. For all
panels, asterisks represent adjusted p value significance as follows: *p < 0.05, **p < 0.001, ***p < 0.0001, and ns p > 0.05.
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To further our analysis, we performed an integration of 4 avail-
able datasets from scRNA-seq of human brain myeloid cells
(Bian et al., 2020;Kracht et al., 2020;Fan et al., 2020;Cao
et al., 2020). We also considered including single-cell data into
our integrated single-cell dataset from (Li et al., 2018), as this
study shows that deconvolution of tissue-level data against
cell-type-enriched markers for microglia follows an inverse
sigmoidal curve. However, given the number of single-cell mi-
croglial transcriptomes available (5%–10% of 1,195 cells are
likely to be microglial), it was unlikely that the pattern of our
data would have changed significantly if we had incorporated
these data. As each of the individual datasets (Bian et al.,
2020;Kracht et al., 2020;Fan et al., 2020;Cao et al., 2020)
covered specific temporal windows, the integration allowed
the analysis of an extended timeline (Table S1), similar to that
analyzed by immunohistochemistry (IHC) (Figure 3) and bulk
RNA-seq (Figures 5A–5E). The initial integration identified the
presence of cells expressing markers of erythrocytes in the orig-
inal datasets (Figure S5A), and we removed these and re-clus-
tered to avoid any effects of contamination with non-myeloid
cells (Figure S5C). In the integrated dataset, we identified 8 clus-
ters of microglia that express typical myeloid markers (see clus-
ter markers in Data S2), among which are actively cycling and
proliferating cells (cluster 5) (Figures 5F and S5). The integration
successfully represented all datasets, regardless of the original
number of cells (Figure S5D). The proliferation cluster also dis-
played a wave-like distribution across ages, first peaking at 9
pcw and then peaking again at 18 pcw (Figures 5F and S5),
tracking the pattern we observed at the histological level. The
alignment of cycling and proliferative microglial cells between
the source data identified 40 conserved markers (Data S2).
Gene ontology and protein-protein interaction enrichment
Figure 5. Microglial gene signature during development
(A) 906 genes chosen from two published microglial-specific cortical adult and juvenile gene lists (Gosselin et al., 2017;Galatro et al., 2017). Bar plot shows the
number of constitutively expressed microglial genes (‘‘On’’), unexpressed genes (‘‘Off’’) and those with sporadic expression in the developing cortex (‘‘Other’’)
with gene ontology analysis of constitutively expressed genes only.
(B) Heatmap of highly enriched microglial genes during development (n = 75).
(C) Transcript reads per million expression levels of a selection of homeostatic and immune modulatory microglial markers during development.
(D) Volcano plot of upregulated and downregulated microglial genes at the peak of differential gene expression between 9 and 10 pcw, with gene ontology
analysis significant for cellular component.
(E) Regional differential expression between time points of the cortical adult microglial genes.
(F) Actively cycling and proliferating microglia in the scRNA-seq dataset were identified across the gestational age. Only ages with a minimum of 50 cells were
selected (7–24 pcw). Cycling and proliferating cell signatures display a bimodal pattern of proliferation consistent with histological findings.
(G) Metascape analysis of conserved cycling and proliferating microglia markers.
(H) Gene ontology and protein-protein interaction enrichment analysis marking key mitotic cell-cycle processes. Protein-protein interaction enrich ment identifies
mitotic spindle checkpoint and amplification of signals from the kinetochores (in red), as well as mitotic chromosome condensation and condensing I complex (in
blue). DE, differentially expressed genes.
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analyses of these genes underscored their association with cell-
cycle processes (Figures 5G and 5H), supporting the correct
identification of these cells as proliferative microglia.
Microglial proliferation is prominent in key
neurodevelopmental structures
The fetal SP is a major site for synaptogenesis and neuronal
maturation, as well as a waiting compartment for cortical affer-
ents (Kostovi
c, 2020). Microglia are enriched in the PSP mono-
layer below the CP from the 10
th
pcw (Figures 3A and 4A). The
SP is fully formed at 12.5 pcw by the gradual merging of the
PSP and the deep, loose portion of the CP in parallel with the
development of the afferent fiber system (Kostovi
c, 2020). Mi-
croglia appear to track these key changes, appearing densely
clustered in the SP, with microglial proliferation highest in the
SP and the IZ (Kruskal-Wallis test, adjusted p < 0.01)
(Figures 4E and S4A–S4F).
By 15–20 pcw, we observed significant clustering of microglia
in the periventricular crossroads of projection and callosal path-
ways in the frontal lobe; these cells co-express MHCII and are
non-proliferative (Figures S4C–S4E). By 20 pcw, microglia are
evenly distributed throughout the expanding SP and only pene-
trate the upper portion of the CP by 25 pcw (Figure S4B). Dense
clusters of microglia can be observed in the VZ from 22 to 28
pcw, colonizing all the cortical layers by 28 pcw (Figure S4B).
From 32 pcw, the 6-layered cortical Grundtypus can be
observed with a resolving SP zone, and gray matter (GM) and
white matter (WM) are clearly identifiable.
In sum, we found qualitative evidence of the intimate associa-
tion of microglia with key neurodevelopmental hallmarks that
characterize brain formation and wiring.
The microglial population undergoes an expansion
phase during infancy and childhood and is refined to
adult levels by cell death
Postnatally, the microglial density increases from birth, and at
around 1–2 years of age we observe a third wave. Though
not statistically significant (bimodal distribution excess test
p > 0.05), we thought it important to characterize, given the chal-
lenge of obtaining samples during the crucial temporal window
of early postnatal life, particularly infancy and childhood. Further-
more, the pattern observed strikingly resembles the develop-
mental pattern. This third wave is characterized by a significant
increase in proliferation during neonatal and infant life compared
with adulthood and childhood (Kruskal-Wallis p < 0.0001,
adjusted p values < 0.001) (Figures 6A and 6B). This is followed
by a 3- to 4-fold increase in densities in childhood in comparison
with neonatal and adult densities (Kruskal-Wallis p < 0.01,
adjusted p values < 0.01) (Figures 6A and 6B). In childhood, pro-
liferation decreases to levels observed in the adult, remaining
constant through life (Figures 6A and 6B) (p > 0.05 between
adulthood and childhood). The mechanism through which micro-
glial densities decrease after 1.5 years may be likened to the se-
lection phase observed in rodents postnatally (Askew et al.,
2017;Nikodemova et al., 2015), and we indeed detect microglial
death (IBA1
+
caspase-3
+
) in the human cortex (Figure 6C). We
found no significant differences between the cumulative distribu-
tions by sex for both density and proliferation (Kolmogorov-Smir-
nov test, p > 0.05) (Figure 6D) or when comparing mean prolifer-
ation and densities by sex during postnatal life (0–75 years)
(Mann-Whitney U test, p = 0.09 for densities, p > 0.2 for prolifer-
ation). Regionally, mean GM and WM proliferative indices are
similar in early postnatal life (neonate–child) (Figures 6E–6G)
and WM densities are higher but not significant statistically (Fig-
ure 6F). WM densities, however, are highest in the child
compared with the infant, neonate, and adult (Figures 6E and
6F) (Kruskal-Wallis p < 0.01, adjusted p < 0.05). The adult density
and proliferation of microglia retain the previously described
(Mittelbronn et al., 2001) higher density in the WM (Figures 6G
and 6H). From 18 years of age, throughout adult life and healthy
aging, the population has a slow turnover of 1.12% ± 0.18% and
the density stabilizes at 84.63 ± 4.42 cells/mm
2
(Figure 6H). The
mean TMEM119 and IBA1 densities are similar during postnatal
life (Figures 6I and 6J) (Wilcoxon test, p > 0.05). Our TMEM119
findings were consistent with mouse studies showing limited/ab-
sent expression during the developmental period and an
increased expression in postnatal life (Bennett et al., 2016).
These analyses collectively identify that infancy and childhood
are important temporal windows for microglial dynamics.
DISCUSSION
The past decade saw an exponential increase in experimental
studies focusing on microglia, in part fueled by the resolution of
a long debate on the origins of these cells in mouse by fate-map-
ping studies (Ginhoux et al., 2010). In humans, microglia enter the
brain primordium prior to the onset of substantial neurogenesis
and neuronal migration (Menassa and Gomez-Nicola, 2018).
Recent studies indicate that microglia show marked heterogene-
ity during development, becoming immunocompetent from the
10
th
pcw (Kracht et al., 2020), with conserved ontogenic path-
ways between mouse and human (Bian et al., 2020). The popula-
tion expands during embryonic life to yield the adult population,
which maintains itself by cycles of self-renewal at a slow daily
turnover rate (Askew et al., 2017;Re
´u et al., 2017;Nikodemova
et al., 2015). Due to the limited availability of healthy human devel-
opmental tissue, most of our knowledge of microglial develop-
ment stems from rodent studies. Such studies have highlighted
the roles that microglia play in shaping the neurodevelopmental
landscape, including neuronal genesis, migration, dendritic
development, axonal pruning, synaptogenesis, and wiring (Pao-
licelli et al., 2011;Squarzoni et al., 2014;Verney et al., 2010).
However, until now we lacked a reference map for the develop-
ment of microglia in humans as an essential framework to contex-
tualize basic studies and target human-relevant mechanisms.
Here, we have characterized the precise spatiotemporal dy-
namics of microglia in the frontal cortex across the human life-
span (3
rd
pcw—75 years). We provide granularity and breadth,
identifying critical temporal windows during development and
postnatal life and defining processes fundamentally different
from the known development of murine microglia, which will in-
fluence how we study the role of these cells in humans. This
was achieved by the collection of post-mortem specimens
from a multitude of tissue resources, enabling us to conduct a
cross-sectional study with an extensive scope. We determine
that microglia enter the telencephalon by the 4
th
pcw, mirroring,
with some delay, the colonization of peripheral organs by other
tissue-resident macrophages. After colonization, microglia
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undergo significant fluctuations in their cell density, character-
ized by newly identified wave-like patterns of proliferation fol-
lowed by apoptosis. This is particularly evident at a key neurode-
velopmental stage, the transition between embryonic and fetal
life (9
th
pcw). This pattern is corroborated by bulk RNA-seq
and scRNA-seq analyses and coincides with the appearance
of the CP and transient zones. Throughout embryonic, fetal,
and postnatal development, the population transits through
different cycles of expansion and apoptosis-driven refinement,
stabilizing during childhood and being maintained by self-
renewal across the adult and older ages.
The elucidation of microglial dynamics during human cortical
development offers new avenues for examining how these cells
contribute to neurodevelopmental disorders. These cells partic-
ipate in brain wiring pre- and postnatally (Menassa and Gomez-
Nicola, 2018;Squarzoni et al., 2014;Paolicelli et al., 2011) and
are involved in the pathophysiology of autism spectrum disorder
(Tetreault et al., 2012;Carroll et al., 2021), schizophrenia (Sekar
et al., 2016), and intellectual disability (Coutinho et al., 2017).
With our identification of critical temporal windows of the popu-
lation’s expansion and refinement, we pave the way for un-
charted territories in the field of neurodevelopmental disorders,
which, with the current tissue resources in place, may begin to
closely characterize in space and time how the population alters
the neurodevelopmental environment. We know that targeting
microglial dynamics may have a therapeutic potential when
these cells go awry, and this is likely to extend to the treatment
of neurodevelopmental disorders (Olmos-Alonso et al., 2016).
One interesting, and somewhat unexpected, finding of
this study was the very limited, and in most cases absent,
Figure 6. Postnatal dynamics of microglia in the cortex
(A) Microglial densities and proliferative dynamics in early postnatal life (n = 24; 14M:10F) and adulthood (n = 21; 14M:7F) and a representative cross-section of
frontal cortex with anatomical histochemistry and photomicrographs of homeostatic and proliferating microglial morphologies in gray and white matters.
Neonatal and infant proliferation is most significant against other postnatal windows, while microglial densities are most significant in the child against other
temporal windows. Scale bars: 2 mm (left); 40 mm (right).
(B) Mean proliferation and mean densities in the neonate, the infant, child, and adult. Data are shown as mean ± SEM.
(C) Microglial cell death in gray and white matters at 2 years (black arrow in A). Scale bars: 15 mm.
(D) Cumulative relative frequency distribution plots of microglial densities and proliferation by sex (top panel) and mean proliferation and densities by sex
(28M:17F) (bottom panel, data are shown as mean ± SEM).
(E) Representative photomicrographs of cell densities in gray and white matters from birth to 2 years. Scale bars: 500 mm.
(F) Regional differences of microglial densities and proliferation in gray (left) and white matters (right) between the neonate, infant, child, and adult (n = 45). Data are
shown as mean ± SEM.
(G) Proliferation rates and densities in early postnatal life in gray and white matters (n = 24). Data are shown as mean ± SEM.
(H) Regional differences in the adult in gray and white matters in proliferation (top) and density (bottom). Data are shown as mean ± SEM.
(I) Mean TMEM119 and IBA1 cell densities postnatally (n = 5) (left) and rati o of TMEM119/IBA1 between early life (3–6 months, n = 2) and adult life (40–70 years,
n = 3). Data are represented as mean ± SEM.
(J) Representative photomicrograph of TMEM119/IBA1 labeling showing colocalization of both markers (black arrows). Scale bars: 25 mm. CC, cingulate cortex;
GM, gray matter; WM, white matter. For all panels, asterisks represent adjusted p value significance as follows: *p < 0.05, **p < 0.001, ***p < 0.0001, and
ns p > 0.05.
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sex-dependent differences in the different variables we analyzed
related to microglial development. This is in contrast to previous
rodent literature, which observed increased microglial density in
males postnatally, as well as sex-dependent morphological
changes lasting until juvenile and adult stages (Schwarz et al.,
2012). Differences between developing microglia in male and fe-
male rodents have also been framed within the microglial devel-
opmental index (MDI), a compound index of transcriptional matu-
ration. This program was found to be delayed in males relative to
females and sensitive to immune challenge (Hanamsagar et al.,
2017). This perhaps underscores the urgent need to pivot to hu-
man or humanized models in order to understand the biology of
microglia and thus refine our knowledge of the contribution of
this myeloid population to human brain function and dysfunction.
Altogether, our findings identify key features of, and relevant
temporal windows for, microglial population expansion and
refinement across the human lifespan. This study provides a
foundational map of the precise microglial spatiotemporal dy-
namics from early development through adulthood to healthy ag-
ing. This resource will inform future research on how microglial
cells participate in the neurodevelopmental landscape in hu-
mans and their relevance for neurodevelopmental disorders, as
they form part of the pathological signature of these conditions
(Velmeshev et al., 2019;Coutinho et al., 2017).
Limitations of the study
Our study was affected by the scarcity of post-mortem brain
samples from key ages. For example, we could not obtain tis-
sues from childhood and adolescence, a period of protracted
intracortical myelination in the frontal and temporal cortices in
humans (Deoni et al., 2015). As we know, microglia follow myeli-
nation very closely (Menassa and Gomez-Nicola, 2018;Verney
et al., 2010); the dynamics of the population have yet to be care-
fully defined during the critical window of intracortical myelina-
tion in humans. Early adolescence is a dynamic period and is
important for the onset of some neuropsychiatric disorders
(Penzes et al., 2011). Additionally, hormonal changes during pu-
berty are likely to influence microglial dynamics and these could
drive sex-specific effects. Another limitation relates to our defini-
tion of migration, which is inferred from morphometric analyses
that may be less accurate in reflecting true microglial movement
and did not allow us to distinguish between peripheral migration
(into the brain) and within-brain migration. Microglia acquire a
migratory phenotype subsequent to a proliferation cycle (Mar-
tı
´n-Estebane
´et al., 2017), and our assessments here are of a
possible migratory phenotype irrespective of contribution. We
observed that within-brain migration may vary according to the
topography of transient layers, defined by the type of neuroge-
netic process. Overall, migration may contribute to the growth
of the population at specific time points during development;
thereafter, and once microglia are in the parenchyma and prolif-
erating, they can acquire a migratory phenotype after each pro-
liferative cycle to tile the anatomical local territory for which they
are destined.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
dKEY RESOURCES TABLE
dRESOURCE AVAILABILITY
BLead contact
BMaterials availability
BData and code availability
dEXPERIMENTAL MODEL AND SUBJECT DETAILS
BHuman tissues
BAnatomy and neuropathology
dMETHOD DETAILS
BImmunohistochemistry
BImmunofluorescence
BConfocal imaging
BImage analysis
BAnalysis of TMEM119/IBA1 cells
BCalculation of the apoptotic index
BMigration analysis
BHistological heatmaps
BBulk RNA-sequencing analysis
BSingle-cell RNA-sequencing analysis
dQUANTIFICATION AND STATISTICAL ANALYSIS
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
devcel.2022.07.015.
ACKNOWLEDGMENTS
The research was funded by the Leverhulme Trust (RPG-2016-311), the
Medical Research Council (MRC) (MR/P024572/1), and the Alfonso Martin
Escudero Foundation (fellowship to M.M.-E.). We woul d like to thank th e
joint MRC/Wellcome Trust Human Developme ntal Biology Resource
(HDBR) (grant # MR/006237/1) (Gerrelli et al., 2015;Lindsay et al.,
2016), the Zagreb Research Brain Collection (Hraba
c et al., 2018), and
BRAIN UK, a collaboration between representatives from the University
of Southampto n, Plymouth Hospitals NHS Tru st, the Univer sity of Bristol ,
and a network of many NHS neuropathology centers across the United
Kingdom. BRA INUK is funded by the MRC and Br ain Tumour Research.
In this study, the following NHS neuropathology centers that are part of
BRAIN UK participated: the South-Central Oxford NHS Trust (the Thomas
Willis Oxford Brain Bank), the South-Central Hampshire NHS Trust
(Southampton Brain Bank), and the Cambridgeshire NHS Trust (Cam-
bridge Brain Bank). We also thank the East Scotland NHS Trust (Edin-
burgh Brain Bank) and the Nor th Somerset and South Bristol NHS Trust
(Southwest Dementia Brain Bank) for providing tissues for this study. We
would like to thank the Research Cooperability Program of the Croatian
Science Foundation funded by the European Union from the European
Social Fund under the Operational Progr am Efficient Human Resources
2014-2020 PSZ-2019-02-4710 (Z.K.). Many thanks to the Scientific
Centre of Excellence for Basic, Clinical, and Translational Neuroscience
(project GA KK01.1.1.01.0007 funded by the European Union through
the European Regional Development Fund). We thank the Institutional
Excellence in Higher Educat ion Grant (FIKP) and Thematic Excellence
Programme (National Research, Development and Innovation Office,
Hungary), wh ich supported the work of T.T. and I.A. We would like to
thank Steven Lisgo, Janet Kerwin, Tabitha Bloom, Carolyn Sloan, Chris-
Anne McKenzie, Laura Palmer, Oliver Green, Poonam Singh, Danica Bu-
dinscak, Ana Bosak, Georgina Dawes, Jenny Dewing, David Chatelet,
Olaf Ansorge, Thomas Jacque s, Janja Kopi 
c, and Douglas Cro ckett for
all their technical support in this project. We acknowledge the IRIDIS
High Performance Computing Facility and associated support services
at the University of Southampton toward the completion of this work.
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AUTHOR CONTRIBUTIONS
D.A.M. and D.G.-N. designed the study. D.G.-N. secured the funding
and supervised the project. D.A.M. contacted the relevant brain banks, orga-
nized the collection, performed all histological experiments, scan ned the
slides, and analyzed the data. M.M.-E. performed the migration analysis.
T.T. and I.A. constructed the heatmaps and provided expertise about case se-
lection. Z.K. and I.K. provided neuroanatomical expertise and neurogenetic
analysis of microglial findings. M.A.C. performed the bulk RNA-seq analysis.
T.A.O.M. performed the scRNA-seq analysis. J.A.R.N. assisted in the assess-
ment of the neuropathology for case selection. L.B.-C. and B.T. assisted with
cell count analysis. D.A.M. and D.G.-N. wrote the manuscript. All authors
contributed to drafting the manuscript.
DECLARATION OF INTERESTS
The authors declare no competing interests.
Received: September 15, 2021
Revised: April 22, 2022
Accepted: July 26, 2022
Published: August 16, 2022
REFERENCES
Ameijeiras-Alonso, J., Crujeiras, R.M., and Rodrı
´guez-Casal, A. (2019). Mode
testing, critical bandwidth and excess mass. Test 28, 900–919.
Anders, S., Pyl, P.T., and Huber, W. (2015). HTSeq—a Python framework to
work with high-throughput sequencing data. Bioinformatics 31, 166–169.
Askew, K., Li, K., Olmos-Alonso, A., Garcia-Moreno, F., Liang, Y., Richardson,
P., Tipton, T., Chapman, M.A., Riecken, K., Beccari, S., et al. (2017). Coupled
proliferation and apoptosis maintain the rapid turnover of microglia in the adult
brain. Cell Rep. 18, 391–405.
Badimon, A., Strasburger, H.J., Ayata, P., Chen, X., Nair, A., Ikegami, A.,
Hwang, P., Chan, A.T., Graves, S.M., Uweru, J.O., et al. (2020). Negative feed-
back control of neuronal activity by microglia. Nature 586, 417–423.
Bayer, S.A., and Altman, J. (2006). The human brain during the late first
trimester. Atlas of Human Central Nervous System Development v 4
(CRC Press).
Bayer, S.A., and Altman, J. (2008). The human brain during the early first
trimester. Atlas of Human Central Nervous System Development (CRC Press).
Bennett, F.C., Bennett, M.L., Yaqoob, F., Mulinyawe, S.B., Grant, G.A.,
Hayden Gephart, M., Plowey, E.D., and Barres, B.A. (2018). A combination
of ontogeny and CNS environment establishes microglial identity. Neuron
98, 1170–1183.e8.
Bennett, M.L., Bennett, F.C., Liddelow, S.A., Ajami, B., Zamanian, J.L.,
Fernhoff, N.B., Mulinyawe, S.B., Bohlen, C.J., Adil, A., Tucker, A., et al.
(2016). New tools for studying microglia in the mouse and human CNS.
Proc. Natl. Acad. Sci. USA 113, E1738–E1746.
Bian, Z., Gong, Y., Huang, T., Lee, C.Z.W., Bian, L., Bai, Z., Shi, H., Zeng, Y.,
Liu, C., He, J., et al. (2020). Deciphering human macrophage development at
single-cell resolution. Nature 582, 571–576.
Cao, J., O’Day, D.R., Pliner, H.A., Kingsley, P.D., Deng, M., Daza, R.M., Zager,
M.A., Aldinger, K.A., Blecher-Gonen, R., Zhang, F., et al. (2020). A human cell
atlas of fetal gene expression. Science 370, eaba7721.
Carroll, L., Braeutigam, S., Dawes, J.M., Krsnik, Z., Kostovic, I., Coutinho, E.,
Dewing, J.M., Horton, C.A., Gomez-Nicola, D., and Menassa, D.A. (2021).
Autism spectrum disorders: multiple routes to, and multiple consequences
of, abnormal synaptic function and connectivity. Neuroscientist 27, 10–29.
Coutinho, E., Menassa, D.A., Jacobson, L., West, S.J., Domingos, J.,
Moloney, T.C., Lang, B., Harrison, P.J., Bennett, D.L.H., Bannerman, D., and
Vincent, A. (2017). Persistent microglial activation and synaptic loss with
behavioral abnormalities in mouse offspring exposed to CASPR2-antibodies
in utero. Acta Neuropathol. 134, 567–583.
Cuadros, M.A., and Navascue
´s, J. (1998). The origin and differentiation of mi-
croglial cells during development. Prog. Neurobiol. 56, 173–189.
Cunningham, C.L., Martı
´nez-Cerden
˜o, V., and Noctor, S.C. (2013). Microglia
regulate the number of neural precursor cells in the developing cerebral cortex.
J. Neurosci. 33, 4216–4233.
Daniszewski, M., Senabouth, A., Nguyen, Q.H., Crombie, D.E., Lukowski,
S.W., Kulkarni, T., Sluch, V.M., Jabbari, J.S., Chamling, X., Zack, D.J., et al.
(2018). Single cell RNA sequencing of stem cell-derived retinal ganglion cells.
Sci. Data 5, 180013.
Deoni, S.C., Dean, D.C., 3rd, Remer, J., Dirks, H., and O’Muircheartaigh, J.
(2015). Cortical maturation and myelination in healthy toddlers and young chil-
dren. Neuroimage 115, 147–161.
Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut,
P., Chaisson, M., and Gingeras, T.R. (2013). STAR: ultrafast universal RNA-seq
aligner. Bioinformatics 29, 15–21.
Duque, A., Krsnik, Z., Kostovi
c, I., and Rakic, P. (2016). Secondary expansion
of the transient subplate zone in the developing cerebrum of human and
nonhuman primates. Proc. Natl. Acad. Sci. USA 113, 9892–9897.
Engle, W.A., and American Academy of Pediatrics Committee on Fetus and
Newborn. (2004). Age terminology during the perinatal period. Pediatrics
114, 1362–1364.
Fan, X., Fu, Y., Zhou, X., Sun, L., Yang, M., Wang, M., Chen, R., Wu, Q., Yong,
J., Dong, J., et al. (2020). Single-cell transcriptome analysis reveals cell lineage
specification in temporal-spatial patterns in human cortical development. Sci.
Adv. 6, eaaz2978.
Finak, G., Mcdavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A.K., Slic hter,
C.K., Miller, H.W., Mcelrath, M.J., Prlic, M., et al. (2015). MAST: a flexible sta-
tistical framework for assessing transcriptional changes and characterizing
heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278.
Galatro, T.F., Holtman, I.R., Lerario, A.M., Vainchtein, I.D., Brouwer, N., Sola,
P.R., Veras, M.M., Pereira, T.F., Leite, R.E.P., Mo
¨ller, T., et al. (2017).
Transcriptomic analysis of purified human cortical microglia reveals age-asso-
ciated changes. Nat. Neurosci. 20, 1162–1171.
Gallyas, F. (1979). Silver staining of myelin by means of physical development.
Neurol. Res. 1, 203–209.
Gerrelli, D., Lisgo, S., Copp, A.J., and Lindsay, S. (2015). Enabling research
with human embryonic and fetal tissue resources. Development 142,
3073–3076.
Ginhoux, F., Greter, M., Leboeuf, M., Nandi, S., See, P., Gokhan, S., Mehler,
M.F., Conway, S.J., Ng, L.G., Stanley, E.R., et al. (2010). Fate mapping analysis
reveals that adult microglia derive from primitive macrophages. Science 330,
841–845.
Gomez-Nicola, D., and Perry, V.H. (2015). Microglial dynamics and role in the
healthy and diseased brain: a paradigm of functional plasticity. Neuroscientist
21, 169–184.
Gosselin, D., Link, V.M., Romanoski, C.E., Fonseca, G.J., Eichenfield, D.Z.,
Spann, N.J., Stender, J.D., Chun, H.B., Garner, H., Geissmann, F., and
Glass, C.K. (2014). Environment drives selection and function of enhancers
controlling tissue-specific macrophage identities. Cell 159, 1327–1340.
Gosselin, D., Skola, D., Coufal, N.G., Holtman, I.R., Schlachetzki, J.C.M. , Sajti,
E., Jaeger, B.N., O’Connor, C., Fitzpatrick, C., Pasillas, M.P., et al. (2017). An
environment-dependent transcriptional network specifies human microglia
identity. Science 356, eaal3222.
Haas, B.J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P.D., Bowden,
J., Couger, M.B., Eccles, D., Li, B., Lieber, M., et al. (2013). De novo transcript
sequence reconstruction from RNA-seq using the Trinity platform for reference
generation and analysis. Nat. Protoc. 8, 1494–1512.
Hanamsagar, R., Alter, M.D., Block, C.S., Sullivan, H., Bolton, J.L., and Bilbo,
S.D. (2017). Generation of a microglial developmental index in mice and in hu-
mans reveals a sex difference in maturation and immune reactivity. Glia 65,
1504–1520.
Harry, G.J. (2013). Microglia during development and aging. Pharmacol. Ther.
139, 313–326.
Haymaker, W., and Adams, R.D. (1982). Histology and Histopathology of the
Nervous System (Thomas).
ll
OPEN ACCESS
Resource
Developmental Cell 57, 1–13, September 12, 2022 11
Please cite this article in press as: Menassa et al., The spatiotemporal dynamics of microglia across the human lifespan, Developmental Cell (2022),
https://doi.org/10.1016/j.devcel.2022.07.015

Herculano-Houzel, S., Von Bartheld, C.S., Miller, D.J., and Kaas, J.H. (2015).
How to count cells: the advantages and disadvantages of the isotropic frac-
tionator compared with stereology. Cell Tissue Res. 360, 29–42.
Hraba
c, P., Bosak, A., Vuk
si
c, M., Juda
s, M., Kostovi
c, I., and Krsnik,

Z. (2018).
The Zagreb collection of human brains: entering the virtual world. Croat. Med.
J. 59, 283–287.
Janossy, G., Bofill, M., Poulter, L.W., Rawlings, E., Burford, G.D., Navarrete,
C., Ziegler, A., and Kelemen, E. (1986). Separate ontogeny of two macro-
phage-like accessory cell populations in the human fetus. J. Immunol. 136,
4354–4361.
Kostovi
c, I. (2020). The enigmatic fetal subplate compartment forms an early
tangential cortical nexus and provides the framework for construction of
cortical connectivity. Prog. Neurobiol. 194, 101883.
Kostovi
c, I., Judas, M., Rados, M., and Hrabac, P. (2002). Laminar organiza-
tion of the human fetal cerebrum revealed by histochemical markers and mag-
netic resonance imaging. Cereb. Cortex 12, 536–544.
Kracht, L., Borggrewe, M., Eskandar, S., Brouwer, N., Chuva De Sousa Lopes,
S.M., Laman, J.D., Scherjon, S.A., Prins, J.R., Kooistra, S.M., and Eggen,
B.J.L. (2020). Human fetal microglia acquire homeostatic immune-sensing
properties early in development. Science 369, 530–537.
Lavin, Y., Winter, D., Blecher-Gonen, R., David, E., Keren-Shaul, H., Merad,
M., Jung, S., and Amit, I. (2014). Tissue-resident macrophage enhancer land-
scapes are shaped by the local microenvironment. Cell 159, 1312–1326.
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G.,
Abecasis, G., Durbin, R., and 1000 Genome Project Data Processing
Subgroup. (2009). The Sequence Alignment/Map format and SAMtools.
Bioinformatics 25, 2078–2079.
Li, M., Santpere, G., Imamura Kawasawa, Y., Evgrafov, O.V., Gulden, F.O.,
Pochareddy, S., Sunkin, S.M., Li, Z., Shin, Y., Zhu, Y., et al. (2018).
Integrative functional genomic analysis of human brain development and
neuropsychiatric risks. Science 362, eaat7615.
Lindsay, S.J., Xu, Y., Lisgo, S.N., Harkin, L.F., Copp, A.J., Gerrelli, D., Clowry,
G.J., Talbot, A., Keogh, M.J., Coxhead, J., et al. (2016). HDBR expression: a
unique resource for global and individual gene expression studies during early
human brain development. Front. Neuroanat. 10, 86.
Love, S.E., Perry, A.E., Ironside, J.W.E., Budka, H.E., and Greenfield, J.G.
(2015). Greenfield’s Neuropathology (CRC Press).
Marı
´n-Teva, J.L., Almendros, A., Calvente, R., Cuadros, M.A., and Navascue
´s,
J. (1998). Tangential migration of ameboid microglia in the developing quail
retina: mechanism of migration and migratory behavior. Glia 22, 31–52.
Marı
´n-Teva, J.L., Calvente, R., Cuadros, M.A., Almendros, A., and Navascue
´s,
J. (1999). Circumferential migration of ameboid microglia in the margin of the
developing quail retina. Glia 27, 226–238.
Martı
´n-Estebane
´, M., Navascue
´s, J., Sierra-Martı
´n, A., Martı
´n-Guerrero, S.M.,
Cuadros, M.A., Carrasco, M.C., and Marı
´n-Teva, J.L. (2017). Onset of micro-
glial entry into developing quail retina coincides with increased expression of
active caspase-3 and is mediated by extracellular ATP and UDP. PLoS One
12, e0182450.
Menassa, D.A., and Gomez-Nicola, D. (2018). Microglial dynamics during hu-
man brain development. Front. Immunol. 9, 1014.
Mittelbronn, M., Dietz, K., Schluesener, H.J., and Meyermann, R. (2001). Local
distribution of microglia in the normal adult human central nervous system dif-
fers by up to one order of magnitude. Acta Neuropathol. 101, 249–255.
Monier, A., Adle-Biassette, H., Delezoide, A.L., Evrard, P., Gressens, P., and
Verney, C. (2007). Entry and distribution of microglial cells in human embryonic
and fetal cerebral cortex. J. Neuropathol. Exp. Neurol. 66, 372–382.
Monier, A., Evrard, P., Gressens, P., and Verney, C. (2006). Distribution and dif-
ferentiation of microglia in the human encephalon during the first two trimes-
ters of gestation. J. Comp. Neurol. 499, 565–582.
Nikodemova, M., Kimyon, R.S., De, I., Small, A.L., Collier, L.S., and Watters,
J.J. (2015). Microglial numbers attain adult levels after undergoing a rapid
decrease in cell number in the third postnatal week. J. Neuroimmunol. 278,
280–288.
Nogales, F.F. (1993). The Human Yolk Sac and Yolk Sac Tumors (Springer).
Olmos-Alonso, A., Schetters, S.T., Sri, S., Askew, K., Mancuso, R., Vargas-
Caballero, M., Holscher, C., Perry, V.H., and Gomez-Nicola, D. (2016).
Pharmacological targeting of CSF1R inhibits microglial proliferation and pre-
vents the progression of Alzheimer’s-like pathology. Brain 139, 891–907.
O’Rahilly, R., and M€
uller, F. (2010). Developmental stages in human embryos:
revised and new measurements. Cells Tissues Organs 192, 73–84.
Paolicelli, R.C., Bolasco, G., Pagani, F., Maggi, L., Scianni, M., Panzanelli, P.,
Giustetto, M., Ferreira, T.A., Guiducci, E., Dumas, L., et al. (2011). Synaptic
pruning by microglia is necessary for normal brain development. Science
333, 1456–1458.
Park, J.E., Jardine, L., Gottgens, B., Teichmann, S.A., and Haniffa, M. (2020).
Prenatal development of human immunity. Science 368, 600–603.
Penzes, P., Cahill, M.E., Jones, K.A., Vanleeuwen, J.E., and Woolfrey, K.M.
(2011). Dendritic spine pathology in neuropsychiatric disorders. Nat.
Neurosci. 14, 285–293.
Pont-Lezica, L., Beumer, W., Colasse, S., Drexhage, H., Versnel, M., and
Bessis, A. (2014). Microglia shape corpus callosum axon tract fasciculation:
functional impact of prenatal inflammation. Eur. J. Neurosci. 39, 1551–1557.
Popescu, D.M., Botting, R.A., Stephenson, E., Green, K., Webb, S., Jardine, L.,
Calderbank, E.F., Polanski, K., Goh, I., Efremova, M., et al. (2019). Decoding
human fetal liver haematopoiesis. Nature 574, 365–371.
Re
´u, P., Khosravi, A., Bernard, S., Mold, J.E., Salehpour, M., Alkass, K., Perl,
S., Tisdale, J., Possnert, G., Druid, H., et al. (2017). The lifespan and turnover of
microglia in the human brain. Cell Rep. 20, 779–784.
Rezaie, P., and Male, D. (1999). Colonisation of the developing human brain
and spinal cord by microglia: a review. Microsc. Res. Tech. 45, 359–382.
Robinson, M.D., Mccarthy, D.J., and Smyth, G.K. (2010). edgeR: a
Bioconductor package for differential expression analysis of digital gene
expression data. Bioinformatics 26, 139–140.
Sa
´nchez-Lo
´pez, A., Cuadros, M.A., Calvente, R., Tassi, M., Marı
´n-Teva, J.L.,
and Navascue
´s, J. (2004). Radial migration of developing microglial cells in
quail retina: a confocal microscopy study. Glia 46, 261–273.
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M.,
Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al.
(2012). Fiji: an open-source platform for biological-image analysis. Nat.
Methods 9, 676–682.
Schwarz, J.M., Sholar, P.W., and Bilbo, S.D. (2012). Sex differences in
microglial colonization of the developing rat brain. J. Neurochem. 120,
948–963.
Sekar, A., Bialas, A.R., de Rivera, H., Davis, A., Hammond, T.R., Kamitaki, N.,
Tooley, K., Presumey, J., Baum, M., Van Doren, V., et al. (2016). Schizophrenia
risk from complex variation of complement component 4. Nature 530,
177–183.
Sierra, A., Abiega, O., Shahraz, A., and Neumann, H. (2013). Janus-faced mi-
croglia: beneficial and detrimental consequences of microglial phagocytosis.
Front. Cell. Neurosci. 7,6.
Sierra, A., Encinas, J.M., Deudero, J.J., Chancey, J.H., Enikolopov, G.,
Overstreet-Wadiche, L.S., Tsirka, S.E., and Maletic-Savatic, M. (2010).
Microglia shape adult hippocampal neurogenesis through apoptosis-coupled
phagocytosis. Cell Stem Cell 7, 483–495.
Silbereis, J.C., Pochareddy, S., Zhu, Y., Li, M., and Sestan, N. (2016). The
cellular and molecular landscapes of the developing human central nervous
system. Neuron 89, 248–268.
Soneson, C., and Robinson, M.D. (2018). Bias, robustness and scalability in
single-cell differential expression analysis. Nat. Methods 15, 255–261.
Squarzoni, P., Oller, G., Hoeffel, G., Pont-Lezica, L., Rostaing, P., Low, D.,
Bessis, A., Ginhoux, F., and Garel, S. (2014). Microglia modulate wiring of
the embryonic forebrain. Cell Rep. 8, 1271–1279.
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W.M.,
3rd, Hao, Y., Stoeckius, M., Smibert, P., and Satija, R. (2019). Comprehensive
integration of single-cell data. Cell 177, 1888–1902.e21.
Swinnen, N., Smolders, S., Avila, A., Notelaers, K., Paesen, R., Ameloot, M.,
Bro
ˆne, B., Legendre, P., and Rigo, J.M. (2013). Complex invasion pattern of
ll
OPEN ACCESS Resource
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Please cite this article in press as: Menassa et al., The spatiotemporal dynamics of microglia across the human lifespan, Developmental Cell (2022),
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the cerebral cortex bymicroglial cells during development of the mouse em-
bryo. Glia 61, 150–163.
Tavian, M., and Pe
´ault, B. (2005). Embryonic development of the human he-
matopoietic system. Int. J. Dev. Biol. 49, 243–250.
Tetreault, N.A., Hakeem, A.Y., Jiang, S., Williams, B.A., Allman, E., Wold, B.J.,
and Allman, J.M. (2012). Microglia in the cerebral cortex in autism. J. Autism
Dev. Disord. 42, 2569–2584.
Vankriekelsvenne, E., Chrzanowski, U., Manzhula, K., Greiner, T., Wree, A.,
Hawlitschka, A., Llovera, G., Zhan, J., Joost, S., Schmitz, C., et al. (2022).
Transmembrane protein 119 is neither a specific nor a reliable marker for mi-
croglia. Glia 70, 1170–1190.
Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S.,
Bhaduri, A., Goyal, N., Rowitch, D.H., and Kriegstein, A.R. (2019). Single-cell
genomics identifies cell type-specific molecular changes in autism. Science
364, 685–689.
Verney, C., Monier, A., Fallet-Bianco, C., and Gressens, P. (2010). Early micro-
glial colonization of the human forebrain and possible involvement in periven-
tricular white-matter injury of preterm infants. J. Anat. 217, 436–448.
Waise, S., Parker, R., Rose-Zerilli, M.J.J., Layfield, D.M., Wood, O., West, J.,
Ottensmeier, C.H., Thomas, G.J., and Hanley, C.J. (2019). An optimised tissue
disaggregation and data processing pipeline for characterising fibroblast phe-
notypes using single-cell RNA sequencing. Sci. Rep. 9, 9580.
ll
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STAR+METHODS
KEY RESOURCES TABLE
REAGENT OR RESOURCE SOURCE IDENTIFIER
Antibodies
Rabbit anti-IBA1 Wako Chemicals Cat# 019-19741
Goat anti-IBA1 Abcam Cat# ab5076
Mouse anti-IBA1 Abcam Cat# ab283319
Mouse anti-MHC-II/HLA-DP/DQ/DR Dako, Agilent Cat# M077501-2
Rabbit anti-PU.1 Cell Signalling Cat# 2258S
Biotinylated anti-RCA1 Vector Labs Cat# B10855
Rabbit anti-TMEM119 Abcam Cat# ab185333
Mouse anti-CD68 Dako, Agilent Cat# GA609
Mouse anti-SOX2 Santa Cruz Cat# sc365823
Mouse anti-Ki67 Dako, Agilent Cat# M724029-2
Rabbit anti-Caspase3 (cleaved) Cell Signalling Cat# 9664S
Goat anti-rabbit Alexafluor 488 Invitrogen Cat# A32731
Goat anti-mouse Alexafluor 568 Invitrogen Cat# A11031
Horse anti-rabbit HRP (DAB, brown) Vector Labs Cat# SK4103
Horse anti-mouse HRP (DAB, brown) Vector Labs Cat# MP7724
Horse anti-rabbit HRP (DAB + Nickel, black) Vector Labs Cat# SK100
Horse anti-rabbit AP (magenta) Vector Labs Cat# MP7724
Horse anti-rabbit AP (blue) Vector Labs Cat# SK5400
Biological samples
Human post-mortem developmental tissues Wellcome/MRC HDBR N/A
Human post-mortem developmental tissues Zagreb Brain Collection N/A
Human post-mortem developmental tissues Thomas Willis Oxford Brain Bank N/A
Human post-mortem developmental tissues Southampton Brain Bank N/A
Human post-mortem postnatal tissues Southampton Brain Bank N/A
Human post-mortem postnatal tissues Edinburgh Brain Bank N/A
Human post-mortem postnatal tissues Cambridge Brain Bank N/A
Human post-mortem postnatal tissues Southwest Dementia Brain Bank N/A
Chemicals, peptides, and recombinant proteins
Immpress Duet Kit Vector Labs Cat# MP7724
ImmPACT DAB-EqV substrate kit Vector Labs Cat# SK4103
BCIP/NBT substrate kit Vector Labs Cat# SK5400
Vectastain Elite ABC kit Vector Labs Cat# PK6100
Haematoxylin QS Vector Labs Cat# H3404-100
Methyl Green Vector Labs Cat# H3042
Dual enzyme block Dako, Agilent Cat# S2003
Periodic Acid Sigma-Aldrich (Merck) Cat# P7875
Schiff’s Reagent Sigma-Aldrich (Merck) Cat# 3952016
Alcian blue Sigma-Aldrich (Merck) Cat# A5268
Cresyl Violet acetate Sigma-Aldrich (Merck) Cat# C5042
Citric acid Sigma-Aldrich (Merck) Cat# 251275
Sodium borohydride Sigma-Aldrich (Merck) Cat# 213462
Vectashield antifade mounting medium Vector Labs Cat #H1000-10
DPX mounting medium Sigma-Aldrich (Merck) Cat # 06522
Trueblack autofluorescence quencher Biotium Cat# 23007
(Continued on next page)
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e1 Developmental Cell 57, 1–13.e1–e6, September 12, 2022
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https://doi.org/10.1016/j.devcel.2022.07.015

RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Diego
Gomez-Nicola (d.gomez-nicola@soton.ac.uk).
Materials availability
This study did not generate new unique reagents.
Data and code availability
dThe integrated human single-cell RNA-seq dataset, and the developmental layer isolation macro are publicly available in [Syn-
apse]: [syn33055573].
dCode is available in [Synapse]: [syn33055573].
dRaw data is available upon request to the Lead Contact.
Continued
REAGENT OR RESOURCE SOURCE IDENTIFIER
Deposited data
Bulk human RNAseq data This paper Gerrelli et al., 2015 &Lindsay et al., 2016
Human adult microglial juvenile RNAseq data This paper Galatro et al., 2017
Human adult microglia adult RNAseq data This paper Gosselin et al., 2017
Human development single-cell RNA seq This paper Bian et al., 2020
Human development single-cell RNA seq This paper Kracht et al., 2020
Human development single-cell RNA seq This paper Fan et al., 2020
Human development single-cell RNA seq This paper Cao et al., 2020
Software and algorithms
Prism 9 Graphpad https://www.graphpad.com/ scientific-
software/prism/
Fiji Schindelin et al., 2012 https://imagej.net/software/fiji/
R studio R Studio Development Team https://www.rstudio.com/
QGIS (v2.18.3) QGIS Development Team https://www.qgis.org/en/site/
Seurat (v3.2.2) Stuart et al., 2019 https://satijalab.org/seurat/
Fluoview Olympus Olympus https://www.olympus-lifescience.com/en/laser-
scanning/fv3000/
Imagescope Aperio (v12.4.3) Leica biosystems https://www.leicabiosystems.com/en-gb/
digital-pathology/manage/aperio-imagescope/
VS110 Desktop (v4) Olympus https://www.olympus-lifescience.com/en/
solutions-based-systems/vs200/
NDP.view2 Hamamatsu Photonics https://www.hamamatsu.com/jp/en/product/
life-science-and-medical-systems/digital-
slide-scanner.html
Gimp (v2.10.22) Gimp Development Team https://docs.gimp.org/2.10/en/
Samtools (v1.1) Li et al., 2009 http://samtools.sourceforge.net/
EdgeR Robinson et al., 2010;Soneson and
Robinson, 2018
https://bioconductor.org/packages/release/
bioc/html/edgeR.html
HTSeq (v0.6.1) Anders et al., 2015 https://academic.oup.com/bioinformatics/
article/31/2/166/2366196
Trinity (v2.4.0) Haas et al., 2013 https://www.nature.com/articles/nprot.2013.084
Metascape Metascape Development Team https://metascape.org/gp/index.html#/main/step1
STAR v2.5.2b Dobin et al., 2013 https://academic.oup.com/bioinformatics/article/
29/1/15/272537?login=false
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EXPERIMENTAL MODEL AND SUBJECT DETAILS
Human tissues
Human developmental tissues were obtained through the joint Medical Research Council (MRC)/Wellcome Trust Human Develop-
mental Biology Resource (HDBR) (Gerrelli et al., 2015), the Zagreb Research Brain Collection (Hraba
c et al., 2018) and BRAINUK
neuropathology centres (Figure 1;Table S2 for demographics of cases and acknowledgements for participating BRAINUK centres).
Tissues were collected with appropriate maternal consent and approval for use in research. Ethical approval was obtained from the
relevant ethics committees (Newcastle and North Tyneside NHS Health Authority Research Ethics Committee, Fulham NHS Health
Authority Research Ethics Committee, South-Central Oxford C NHS Health Authority Research Ethics Committee and the South-
Central Hampshire B NHS Health Authority Ethics Committee). Embryonic ages were estimated according to the Carnegie classifi-
cation (CS) provided by the HDBR, CS23 being the last Carnegie stage equivalent to the 9
th
pcw. Gestational age corresponds to the
time elapsed between the first day of the last menstrual period and the day of delivery (Engle et al., 2004). We used here postconcep-
tional age consistent with HDBR guidelines which is estimated at 2 weeks earlier than gestational age (Table S2). For fetal develop-
mental stages (age>9 pcw until term), cases were aged by a neuropathologist according to clinical notes. When possible, develop-
mental cases were sex-matched by timepoint. 63 developmental cases were initially sampled but only 52 developmental cases were
included in the final study (n = 15 embryonic tissues; n = 37 fetal tissues; Figure 1 and Table S2) between the late 3
rd
pcw (CS10) and
38 pcw (term). We excluded cases due to poor immunoreactivity (poor antigenicity) or evidence of hypoxic injury identified in the clin-
ical notes (Figure S6A; Table S2). Where possible, maternal data were provided (Table S3). Exclusion criteria, assessed against in-
dividual medical histories, included brain injury due to hypoxia-ischaemia or trauma, infection, or genetic mutations affecting brain
structures. 71 postnatal cases were collected between 0 and 75 years of age with 45 of these cases in the final study. Early postnatal
brain tissues (n = 24) aged between 0-2 years were obtained through BRAINUK. Adult brain tissues (n = 21) aged between 18-75
years were obtained with informed written consent and material approved for use for research purposes through the Zagreb Brain
Collection, Edinburgh Brain Bank, the Cambridge Brain Bank and the Southwest Dementia Brain Bank. Ethical approvals were from
the East Scotland NHS Research Ethics Service, the Cambridgeshire NHS Health Authority Research Ethics Committee and the
North Somerset and South Bristol NHS Health Authority Research Ethics Committee, respectively. It was not possible to get sex-
matching in postnatal cases (n = 45) and additional exclusion criteria included any neurological co-morbidity or a diagnosis of a con-
nectivity disorder (e.g., autism spectrum disorder) (Table S4). We could not obtain samples from children aged between 3 and
17 years as these were scarce and, when present, the absence of consent prohibited the usage of these tissues for research.
All tissues were received as paraffin-embedded slices previously fixed in formalin phosphate buffer saline and sometimes with an
added secondary fixative, methacarn (HDBR samples). Mid-sagittal embryonic sections of 8 mm thickness were obtained through the
whole embryo (up to CS21) allowing the visualisation of the brain rudiment and organs such as the heart, liver and spleen. All other
sections, from CS23 until the late postnatal ages were processed coronally through the frontal axis of the brain at a thickness of 8 mm.
Anatomy and neuropathology
Embryonic samples along the midsagittal axis included the telencephalic wall with its dorsal and ventral components. The frontal
cortex (dorsolateral prefrontal, anterior cingulate) develops from the dorsal telencephalon (Bayer and Altman, 2008;Bayer and Alt-
man, 2006). After CS21, the medial GE in the ventral telencephalon becomes more prominent. From CS23 onwards, developmental
sections were all along the coronal axis of the frontal lobe with a prominent medial GE, an expanding telencephalic wall featuring
transitional zones: the MZ, CP, PSP, SP, IZ, SVZ, VZ; Figure S1C). By 32 pcw, frontal cortex grey and the underlying white matters
are developed, transitional zones have largely resolved, and brain structures begin to resemble those from adulthood. All sections
were histochemically labelled with haematoxylin and eosin according to standard methods, assessed by a neuropathologist for
any signs of tissue pathology prior to analysis such as local or generalised hypoxia, haemorrhage, gliosis or neuronal death. Devel-
opmental tissue sections were histochemically labelled with filtered cresyl violet for the Nissl substance to visualise transitional zones
for anatomical delineation as specified elsewhere (Duque et al., 2016;Kostovi
c et al., 2002). The SP was visualised by histochemical
labelling of the extracellular matrix using the Periodic Acid Schiff Reagent-Alcian Blue method as specified elsewhere (Kostovi
c,
2020;Kostovi
c et al., 2002). White matter was visualised using the Gallyas silver labelling method by means of physical development
(Gallyas, 1979).
METHOD DETAILS
Immunohistochemistry
For immunostaining, sections were placed in an oven at 60C for 45 min, dewaxed in 100% xylene solution, rehydrated in decreasing
graded absolute ethanol (in distilled water) solutions and washed in water and 0.1% TWEEN 20-phosphate buffer saline (PBS-T) so-
lution. Heat-mediated antigen retrieval in 10 mM citrate buffer (pH=6.2) was subsequently performed on the sections in a microwave
for 25 min. For early timepoints (CS10-CS13), 30 min antigen retrieval was performed using 0.5% sodium borohydride (213462,
Sigma-Aldrich, UK). Then, sections were washed and/or cooled in cold tap water. Endogenous peroxidase and phosphatase activity
in the tissues was quenched with a dual enzyme block from Dako (S2003, Agilent, UK). Sections were washed in 0.1% PBS-T and
blocked with the relevant serum (goat, horse or rabbit) and 5% bovine serum albumin (BSA) in 0.2% PBS-T for one hour.
Microglia were probed with a 24h incubation at 4ºC with either a rabbit anti-human IBA1 antibody diluted in blocking solution
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(1:200, 019-19741, Wako chemicals, USA) or a goat anti-human IBA1 antibody (1:200, ab5076, Abcam, UK) or a mouse anti-human
IBA1 antibody (1:200, ab283319, Abcam, UK). We used additional microglial markers including mouse anti-MHC-II/HLA-DP/DQ/DR
(1:250, M077501-2, Agilent, UK), rabbit anti-PU.1 (1:200, 2258S, Cell Signalling, UK), biotinylated RCA1 (1:1000, B10855, Vector
Labs, UK), rabbit TMEM119 (1:500, ab185333, Abcam, UK). We also used mouse an anti-human CD68 as a lysosomal marker
(1:400, GA609, Dako, Agilent, UK) and a mouse anti-human SOX2 antibody (1:200, sc365823, Santa Cruz Biotechnology, USA)
as a neuronal precursor cell marker. Mouse anti-Ki67 (1:400, M724029-2, Agilent, UK) was used to label proliferative cells and rabbit
anti-cleaved-Caspase3 (1:40, 9664S, Cell Signalling, UK) was used to label apoptotic cells. Following incubation with the primary
antibody, sections were washed with 0.1% PBS-T and incubated with the appropriate secondary antibodies. Secondary antibody
double labelling was through an ImmPress Duet double staining anti-mouse HRP (brown)/anti-rabbit AP (magenta) kit (MP7724, Vec-
tor Laboratories, UK) according to the manufacturer’s instructions; horse anti-rabbit HRP using DAB chromogen with Nickel (SK100,
Vector Laboratories, UK); horse anti-rabbit AP (blue) using a BCIP/NBT substrate kit (SK5400, Vector Laboratories, UK); rabbit anti-
mouse secondary using the ImmPACT DAB-EqV HRP (brown) substrate kit (SK4103, Vector Laboratories, UK); signal amplification
after species-specific secondary biotinylated antibody incubation of two hours was done using an avidin/biotin-based peroxidase
system (PK6100, ABC Vectastain Elite kit, Vector Laboratories, UK). Chromogen development was sequential with double labelling.
For triple labelling in brightfield, an additional DAB chromogen development was used, sections were re-quenched, re-blocked and
probed with the relevant primary and secondary antibodies (bound to HRP) then developed with DAB + Nickel (SK4100, Vector Lab-
oratories, UK). Chromogen development reactions were halted with distilled water, sections were washed with 0.1%PBS-T for 15 min
then counterstained with methyl green (H3042, Vector Laboratories, UK) for 10 min or haematoxylin QS (H3404-100, Vector Labo-
ratories, UK) for 30 seconds diluted 1:3 in distilled water. Sections were subsequently washed in distilled water for 5 min, then dehy-
drated in increasing gradients of absolute ethanol, cleared in xylene for 15 min, coverslipped with permanent mounting medium
(DPX), dried for 24 hours then cleaned for scanning.
Immunofluorescence
For immunofluorescence, we followed the same protocol as our brightfield immunohistochemistry except for the following steps: no
blocking needed against endogenous peroxidases and phosphatases. Secondary incubation against primary targets was done with
the relevant secondary antibodies conjuguated to fluorophores (Goat-anti rabbit Alexafluor 488, 1:500 (Invitrogen, UK) and goat anti-
mouse Alexafluor 568, 1:500 (Invitrogen, UK)) for 2 hours at room temperature. Slides were subsequently washed with PBS-T(0.1%)
and incubated to quench autofluorescence with Trueblack lipofuscin autofluorescence quencher (23007, Biotium, USA) at 1:100 in
70% ethanol for 1 minute. Slides were washed with PBS-T(0.1%) and incubated for 5 min in 1:50000 DAPI in PBS then washed and
coverslipped in Vectashield antifade mounting medium (H1000-10, Vector Laboratories, UK) in preparation for confocal imaging.
Confocal imaging
Tissues labelled with fluorophores were scanned using an Olympus FV3000 confocal microscope (Olympus, Europe) with a 20x
objective (UPlanSApo, NA 0.75, Olympus). Z-stacks were taken every 5 um for a total of 10-20 optical sections then Z-projected
in imaFV31S-SW Fluoview software to reveal morphology. Pixel resolution was 0.45 um x 0.45 um in (x, y). All images were imaged
in three channels using the 488, 568 and 350 nm laser lines.
Image analysis
Slides were digitised using a VS110 high-throughput virtual microscopy system (Olympus, Japan), an Aperio Scanscope AT Turbo
system (Leica Biosystems, UK) or a 2.0 RS Nanozoomer high-throughput system (Hamamatsu Photonics, Japan). Pixel resolution
was 172.35 nm/pixel in x, y and z. From the scans, regions of interest were extracted, and brain/layer thicknesses were measured
in the relevant slide viewers (VS110-Desktop (v4), Aperio ImageScope (v12.4.3), NDP.view2). Images were processed using Fiji
(Schindelin et al., 2012). The analysis pipeline included re-scaling the images, deconvolving the signal and calculating a cell density
(in cells/mm
2
) based on absolute counts until CS23. Beyond that age, between 500-1000 cells/case were sampled. While stereologi-
cal methods could not be used due to the limited tissue available as well as the thickness of sections obtained, this approach was
deemed sufficient to provide a representative value of brain IBA1 densities consistent with unbiased sampling methods (Herculano-
Houzel et al., 2015). The proliferative index -I- of microglia was calculated as follows: I = (number of double positive (IBA1/Ki67) cells/
total number of IBA1 cells) x 100 (see also (Askew et al., 2017)). Double positives were confirmed by deconvolution (Figure S6B). Pre-
natally, we calculated fold change values between subsequent ages based on overall cortical wall or individual layer thickness mea-
surements to account for brain/layer growth rates during development. Postnatally, we collected brain weight data (when available)
from the clinical notes of analysed cases and calculated the fold change increase in brain weight between subsequent ages and
applied this correction to our density data. Proliferation index was independent of the changes in brain growth rates. Individual layers
were manually traced in Fiji using a macro (Synapse ID: syn33055573) and a cell density was calculated as N
D
= (Number of cells/
area (mm
2
)).
Analysis of TMEM119/IBA1 cells
In a subset of cases (n = 15, 10 prenatal and 5 postnatal) and along the frontal neocortical wall, we calculated the density of
TMEM119
+
and IBA1
+
cells and the ratio between TMEM119
+
/IBA1
+
densities to determine the percentage labelled by each marker.
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We also performed colocalisation analyses using two methods in brightfield: the colocalisation algorithm in Aperio imagescope (Leica
Biosystems, UK) and using the deconvolution plugin in Fiji software (Figure S3B).
Calculation of the apoptotic index
We quantified IBA1
+
cells positive for active cleaved Caspase 3 as a marker of cell death. Apoptotic cells undergo nuclear and cyto-
plasmic degradation that can be visible under haematoxylin and eosin histochemistry too. Cells show eosinophilic changes in the
cytoplasm early in the process with a pyknotic nucleus. Further on, the nuclear envelope disintegrates, and the eosinophilic cyto-
plasm shrinks before the cell undergoes karyorrhexis or the breakdown of nuclear material altogether (Sierra et al., 2013;Love
et al., 2015). We calculated the apoptotic index for a selection of cases around the main peak of the most significant increase in mi-
croglial densities (n=15; 8 pre-peak between 7-11 pcw and 7 post-peak between 12-16 pcw). The index was calculated as a percent-
age of double positive cells for cleaved caspase 3 and IBA1 against the total number of IBA1
+
cells. Overall, if we approximated the
total number of cells lost per day per square mm based on our mean density measurements after the peak, we could only account for
a fraction of the lost cells per week (approximately 9 cells per square mm). Given the rapid tagging of a cell with active caspase 3
(5 min) ((Sierra et al., 2013), which may be further compounded by sensitive antigenicity in formalin-fixed paraffin-embedded tis-
sues, it is possible that the index is an underestimation.
Migration analysis
The migratory phenotype of microglia (IBA1
+
cells) was inferred using morphometric analysis in Fiji (Schindelin et al., 2012). While
dynamic processes such as migration cannot be easily assessed in static images, we used morphological determinants to determine
the possible migratory status of microglia based on studies that have linked microglial phenotype to their migratory state in different
stages of development (Harry, 2013;Sa
´nchez-Lo
´pez et al., 2004;Martı
´n-Estebane
´et al., 2017;Marı
´n-Teva et al., 1999;Marı
´n-Teva
et al., 1998;Cuadros and Navascue
´s, 1998;Rezaie and Male, 1999;Swinnen et al., 2013). Microglia in adult brain are not polarised
since they are already differentiated into their final and ramified morphology (thin and long branches). However, during development,
microglia are amoeboid and they show short but thick pseudopodia which they use to migrate along fibres of radial glia, white matter
systems and blood vessels. These two morphologies are very different, and they can be found in different developmental stages. In
quail, these have been described alongside the movement of microglia using pseudopodia, which is a similar phenotype to the one
we observed in the human samples: cells with thick processes that are very polarised in parallel to fibre layers or radial glia along the
layers. The circularity parameter was used to select out the migratory phenotype in each layer: if circularity<0.3, a cell would be
considered migratory and if circularity>0.3, it would be non-migratory (Figure S6C). We also assessed the type of migration (radial
or tangential) by measuring the angle formed between the major axis of the cell and the corresponding layer plane (Figure S6C),
considering an angle<45 degrees as tangential migration and an angle>45 degrees as radial migration. Developmental timepoints
considered were selected based on the cell density profile whereby an increase could not be explained by proliferation alone. We
studied migration between 5 and 26 pcw (n=2/timepoint).
Histological heatmaps
For the visualization of cell densities as heatmaps, cortical columns per stage were manually annotated in Aperio ImageScope soft-
ware (v12.4.3), LeicaBiosystems, IL, USA) (Figure S6D). At least 2 cases by timepoint were analysed and the most representative
column was used for visualisation in the final spatiotemporal profile. IBA1
+
and IBA1
+
/Ki67
+
double-positive cells were analysed
separately. Coordinates of the annotated cells were exported into Quantum Geographic Information System QGIS (v2.18.3, Hann-
over, Germany), a spatial analysis software. The heatmap module in QGIS with metric projection EOV23700 was used. We used a
sampling radius of 150 mm, with a maximum value of 4, which meant that on the red area of a computed heatmap in a (0,15)
2
*p
mm
2
area of a circle, at least 4 cells could be located. Subsequently, proportionally extrapolated values were used to estimate
the density in cells/mm
2
. Heatmaps were exported and superimposed onto their respective immunohistochemically labelled spatial
maps using Gimp 2.10.22 annotating the anatomical boundaries.
Bulk RNA-sequencing analysis
We sourced published lists of genes from adult and adolescent human cortical microglia (Galatro et al., 2017;Gosselin et al., 2017).
906 microglia genes were identified (Data S1) and were mapped onto an available bulk-RNAseq dataset from the HDBR. We utilised
251 samples of tissues ranging in age from 7 to 17 pcw. Four anatomical regions were considered: telencephalon (n = 94), cerebellum
(n = 79), choroid plexus (n = 28) and midbrain (n = 50). Full details of the source, collection, preparation and sequencing of human fetal
RNA samples have been described previously (Gerrelli et al., 2015;Lindsay et al., 2016)(https://www.hdbr.org/expression/). Data
were downloaded from SRA as FASTQ files. Quality control was carried out using Trimmomatic to remove poor quality bases, reads
with too many poor-quality bases and short reads using the following settings:ILLUMINACLIP:/local/software/trimmomatic/0.32/
adapters/TruSeq3-PE-2.fa:2:30:10 LEADING:5 TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:72. After this, unpaired reads were
excluded from further analysis. STAR v2.5.2b (Dobin et al., 2013) was used to map reads back to the GRCh38 genome using settings
–outFilterMismatchNmax 10 –outFilterMismatchNoverReadLmax 0.05 and the result outputted as .sam files, which were sorted us-
ing Samtools (v1.1, (Li et al., 2009)). Read counts were calculated per gene using HTSeq count (in the HTSeq v0.6.1 package; (Anders
et al., 2015)) and the GRCh38 general feature format file. Differential gene expression analysis was carried out using EdgeR (Robinson
et al., 2010;Soneson and Robinson, 2018) in Trinity (v2.4.0) (Haas et al., 2013) on raw read counts. Heatmaps were generated using
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analyze_diff_expr.pl in Trinity (Haas et al., 2013) and the TMM-normalised counts. Gene Ontology (GO) terms over-represented in the
list of DE genes were identified using the plug-in available at the Gene Ontology resource.
Single-cell RNA-sequencing analysis
To validate our histological and bulk-RNAseq findings, we analysed 4 datasets from recently published developmental single-cell
RNA-sequencing studies of human brain cells (Cao et al., 2020;Fan et al., 2020;Bian et al., 2020;Kracht et al., 2020) and generated
an integrated dataset spanning 3 to 24 pcw (Bian et al., 2020;Kracht et al., 2020). We accessed the gene-cell count matrix and cell
annotation matrix data and used Seurat (v3.2.2) for all analyses (Stuart et al., 2019). Guided by the original authors’ annotations, we
enriched for microglial cells by selecting for ‘‘Immune’’, ‘‘Mac_1’’, ‘‘Mac_2’’, ‘‘Mac_3’’, ‘‘Mac_4’’, and ‘‘Microglia’’-annotated clusters.
We then utilized the 3 x Mean Absolute Deviation (MAD) for outlier cut-off across 4 parameters where available: nCount_RNA,
nFeature_RNA, percent.mt, percent.rb (Kracht et al., 2020;Daniszewski et al., 2018;Waise et al., 2019). After quality control, the final
integrated dataset used for our analyses contained 24,751 transcriptomes, 8,117 of which were nuclei. Standard Seurat SCTrans-
form integration was performed, selecting 3,000 features for anchor identification and integration and regressing for nCount_RNA,
nFeature_RNA, percent.mt, and/or percent.rb (Figure S5). We detected immediate-early gene expression suggestive of dissociation-
induced artefacts. However, a discussion of these effects in human tissue was beyond the scope of the study and were not selected
for regression. We also identified a cluster enriched for erythrocyte (ERY) markers (e.g. HBG2, HBB) (Figure S5). The single-nuclei
dataset by (Cao et al., 2020) contributed most of the cells of this ERY cluster, suggesting that this cluster is a technical, method-spe-
cific artefact, albeit all datasets showed some degree of ERY (Figure S5). We considered regressing for such effects, however, doing
so risks distorting transcriptional heterogeneity. To minimize these effects in our analysis, we removed the ERY cluster and re-clus-
tered prior to our analysis of actively cycling and proliferating cells. 8 principal components were selected for dimensionality reduc-
tion and combined with a resolution of 0.5, to visualize transcriptional heterogeneity across human development spanning 3 to 24
pcw. Cell cycle phase was determined with ‘CellCycleScoring’ to identify actively proliferating cells. Only ages with more than 50
cells were selected for the proliferation wave signature. Data were visualized using DimPlot, FeaturePlot, VlnPlot and DoHeatmap
functions, and differential expression analysis was performed with MAST (Finak et al., 2015). Alignment of actively cycling and prolif-
erating cells between source data was done utilizing the ‘FindConservedMarkers’ function of the Seurat package and gene ontology
and protein-protein interaction enrichment analyses was performed with Metascape.
QUANTIFICATION AND STATISTICAL ANALYSIS
Statistical analysis and visualisations were done in RStudio and GraphPad Prism. We have used non-parametric techniques in our an-
alyses and therefore, made no assumptions about data distributions or homoscedasticity. All information about statistical details per
experiment can be found in the figure captions and the results section. In brief here, non-parametric correlations using Spearman’s
tested associations between brain weight, layer thicknesses, age and densities with different markers. Sex differences were assessed
using cumulative distribution plots with the 2-sample Kolmogorov-Smirnov test and means’ differences were tested using the Mann-
Witney U test. Friedman’s test was used to test for differences between layers within matched data. Wilcoxon test was used to test dif-
ferences between matched data (liver and brain from the same cases for example). We also fitted non-parametric regression lines to
density and proliferation data using the following parameters: a Loess regression function with a medium number of 10 points followed
by a smoothing spline function with 6-8 knots as smoothing factors, which were both recommended by Graphpad Prism. We tested
centred polynomial models of up to the maximum recommended order (6
th
order) to model our data but these could explain at most
60% of the variance. Therefore, thesewere not suitable and we opted for presenting all datapoints instead and applying non-parametric
functions that follow the data trend with no assumptions about a model that could fit all data. Increasing the order of polynomial models
may have improved variance but would have resulted in overfitting. Furthermore, biological fluctuations that we report in the microglial
population underlie the difficulty of finding one model that fits all data. To test multimodality of data distributions, we used a non-para-
metric bootstrapapproach with B=100 replicasand (Ameijeiras-Alonso et al.,2019) excess test which if significantwould accept the null-
hypothesis that our data distribution for proliferation or density had a number of modes that was greater than 1. This test was run on the
entire dataset (3 pcw – 75 years of age, n=95) and we have reported the modes and the antimodes. To test for significance between
temporal windows for density and proliferation, we compared mean ranks in temporal windows around the fitted regression lines using
a non-parametric Kruskal-Wallis test corrected for multiple comparisons using Graphpad’s recommended tests (Dunn’s, Benjamini-
Krieger) and report throughout the adjusted p-values. Temporal windows were either equally-spaced and data were grouped accord-
ingly. To place these temporal windows within relevant human milestones, we also grouped data according to existing nomenclature as
follows: embryonic (3-8 pcw), early fetal (9-15 pcw), mid-late fetal (16-25 pcw), preterm (26-35 pcw), term (36 pcw-birth), neonatal
(0-1 month), infant (1 month-12 months), child (1 year-2 years, only in our study because we did not have samples between 2-12 years),
and adult (>18 years) (Silbereis et al., 2016;Kostovi
c et al., 2002;Carroll et al.,2021). For postnatal stages, we used the Mann-Whitney U
non-parametric test to report significant differences betweenmeans. For RNA-seq data (single cell and bulk), p-values, including those
related to analysis of DE genes, were corrected for FDR in EdgeR usingthe Benjamini-Hochbergcorrection. Volcano plotsand heatmaps
were plotted in RStudio. Hierarchical clustering was carried out in Trinity (Haas et al., 2013). Histological heatmaps were plotted using
QGIS spatial analysis software as elaborated upon in the relevant STAR Methods section.
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