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Last updated: 2023-12-21
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Knit directory: 1_heteroAstrocytes/
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Molecularly stratified hypothalamic astrocytes are cellular foci for obesity
This repository contains code and output of the analysis of single-nuclei/cells RNA sequencing data for the article Tretiakov E et al., 2023.
Please see file CITATION in the root of your Git repo
that contains the citation information.
To see the results of the analysis please visit the analysis website.
Purpose
This research aimed to develop a novel computational approach, termed AstroTRAP, to uncover molecular heterogeneity among astrocytes in distinct anatomical regions of the mouse hypothalamus. Astrocytes are an important yet understudied class of glial cells that support neuronal function, but their diversity has been difficult to decipher using standard bioinformatic techniques designed for neurons. The hypothalamus was chosen as a model system due to its complex cytoarchitecture and involvement in many vital physiological processes. The researchers hypothesized that improving astrocyte classification would provide new insights into their specialized roles in modulating hypothalamic circuits and associated behaviors.
The study had three main goals:
Generate a consolidated dataset of single-cell RNA sequencing profiles for astrocytes across all hypothalamic nuclei, applying rigorous quality control.
Develop AstroTRAP, a semi-supervised machine learning pipeline tailored to uncover stable molecular signatures of astrocytes despite their dynamic gene expression patterns.
Validate AstroTRAP’s predictions of region-specific astrocyte subclasses by histological analysis and functional experiments on neuron-astrocyte interactions.
Overall, this work demonstrates the power of specialized computational tools to reveal biological complexity hidden within large-scale transcriptomic data. The astrocyte diversity uncovered has important implications for understanding hypothalamic development, homeostasis, and disease.
Results
The researchers assembled and processed published scRNA-seq data on over 73,000 astrocytes from multiple hypothalamic nuclei. Standard unsupervised clustering methods, including principal component analysis, RNA velocity analysis, and differential gene expression, failed to identify stable molecular signatures that could delineate distinct astrocyte subclasses. The cells were grouped primarily by dynamic functional states rather than anatomical localization.
Figure 1:
Figure 1. Cellular states of
hypothalamic astrocytes revealed by single-cell RNA-seq.
(a) Schema to suggest that unsupervised learning
approaches, commonly used to analyze single-cell RNA-seq data, recognize
functional states in astrocytes. (b,c)
Single-cell-resolved rPCA-integration of data on hypothalamic astrocytes
collated from 18 datasets6,7,37,40–42,44–52,62,64,66,
including feature plots for select genes (c).
(d,e) RNA velocity revealed a limited number of
terminal states and the corresponding marker genes for hypothalamic
astrocytes. (f) Reconstruction of pseudotime cellular
trajectories for arcuate astrocytes by CellRank. Data from
control and high-fat diet-fed conditions37
were shown. This approach distinguished subsets of cells with prominent
gene expression changes (blue) vs. unchanged
trajectories (green). Dimed colors label the 95% confidence
interval. (g-j) In situ hybridization
confirmed the expression of select markers in astrocytes (Gja1
being a stable mark) in hypothalamic subregions. Other cell types were
labeled by Snap25 (neurons) and Olig1
(oligodendrocytes) to show complementarity with Gja1.
(k) UMAP plot, an outcome of semi-supervised
representation learning, yielded higher group segregation with genes for
receptors, signaling molecules, and ligands resolved in patterns amongst
astrocytes yet without regional segregation. (l) In
situ hybridization validated select markers from (k). Solid
arrows point to Gja1+ astrocytes co-expressing
the markers indicated. Conversely, open arrows denote the lack
of co-existence. (m) Expression of genes encoding
neuropeptide receptors in hypothalamic astrocytes. (n)
Schema identifies the unresolved challenge of the spatial assignment of
astrocytes that cannot succeed when using classical algorithms.
Scale bars = 10 mm (g-j,l).
To overcome this hurdle, the team developed AstroTRAP, a semi-supervised feature selection pipeline tailored to astrocyte heterogeneity. The key features included:
- Iterative integration of regional datasets with whole hypothalamus references to subtract out variable functional markers
- Use of original region-specific sample labels to guide selection of location-specific features with cross-validation to optimize model hyperparameters
- The trained AstroTRAP model has the ability to stratify a heterogeneous mixture of hypothalamic astrocytes by predicting their anatomical origin region.
Figure 2:
Figure 2. Implementation of ‘AstroTRAP’
for the spatial assignment of hypothalamic astrocytes.
(a) Supervised UMAPs for integrated nucleus-specific
hypothalamic datasets (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50)
color-coded with their true labels (left), and as predicted by
AstroTRAP (right). Top: Optimized outcome when using
an XGBoost-based model with the highest reliability scores
(MCC, F1). (b,c) Altered gene expression in astrocytes
from the ARC (left)37
and PVN47
(right) upon exposure to high-fat diet (HFD) and chronic social
defeat stress, respectively. Genes that had undergone significant
changes (that is, inducible) were shown in (b).
Unchanged gene sets (considered non-inducible) were marked in
(c). (d) Hexagonal bin (hexbin) plots
depicting genetic marks specific to hypothalamic subregions identified
by AstroTRAP. UMAP space was partitioned by using the color codes from
(a).
Applying AstroTRAP to the curated astrocyte dataset uncovered distinct molecular profiles for cells in the paraventricular nucleus (PVN), arcuate nucleus/median eminence (ARC/ME), suprachiasmatic nucleus (SCN), ventromedial hypothalamus (VMH), ventroposterior hypothalamus (VPH), lateral hypothalamus (LH), preoptic area (POA), and median preoptic nucleus (MnPO). The signatures were enriched in transcription factors, cell surface receptors, and signaling components.
Figure 3:
Figure 3. Implementation of ‘AstroTRAP’
for the regional assignment of genes and gene regulatory networks
(GRNs). (a) Spatial assignment of astrocytes
extracted from 44
to hypothalamic areas (color-coded as in (b)), as well
as the analysis of GRNs using the SCENIC workflow. Transcription factors
are highlighted as specific marks of regionality. (b)
Schema of how to extract spatial information from unsorted datasets
using AstroTRAP. (c) AstroTRAP-derived regional
enrichment in hypothalamus datasets that lacked spatial information.
Feature plots for n = 4 selected genes are shown. Based on data
from 4 ‘whole’ hypothalamus datasets7,43–45,
a consensus existed on their spatial assignments, which was also
consistent with their gene expression data in the integrated dataset
used to benchmark spatial information (see: Fig.
2d).
Notably, AstroTRAP retained information on dynamic functional markers like GFAP and FOS, while extracting stable location-specific features. Spatial segregation of astrocyte subclasses was confirmed by histological analysis of selected marker genes.
Figure 4:
Figure 4. Benchmarking canonical glial
markers and inducible genes in the hypothalamus.
(a) Hexagonal bin plots for Slc1a3, S100b, Aqp4,
Gfap, and Fos, superimposed on a supervised UMAP, shows
area-specific gene enrichment. UMAP space was partitioned as in Fig. 2a.
Gfap and Fos, being inducible genes, had unequal
distribution across hypothalamic areas. (b) Lineage
tracing of Slc1a3+ cells in the postnatal
hypothalamus, with tamoxifen injected on P21. Although only ARC and VMH
were shown, labeled cells morphologically resembling astrocytes were
indiscriminately found in the hypothalamic nuclei. (c)
S100b, AQP4, GGFAP (astrocyte markers), and NeuN were localized in ARC.
(d) In situ hybridization for Slit2,
another inducible gene in astrocytes, showed indiscriminate distribution
of Slit2+/Gja1+ cells in hypothalamic
areas under control conditions. (e) In TRAP2 mice
induced with 4OH-tamoxifen, cFOS+/GFAP+ astrocytes
concentrated in the SCN, and showed diurnal fluctuations.
Arrows point to star-shaped protoplasmic astrocytes.
Quantification is from n = 3 male mice per condition.
(f) TRAP mice induced with tamoxifen and exposed to
acute stress harbored significantly more
Fos+astrocytes (solid arrows) that
co-expressed glutamate-ammonia ligase (GLUL) and GFP in the PVN
vs. controls. Open arrows indicate neuron-like cells.
Quantification is from n = 7 male mice/condition, p =
0.017 of two-tailed t-test with Welch’s correction. The PVN was
demarcated by its higher cell density than surrounding areas. Scale
bars = 50 mm (b,c), 40 mm (e,f),
10 mm (d).
As a functional case study, the researchers focused on arcuate nucleus astrocytes expressing insulin receptors and the neuropeptides-processing protease inhibitor latexin. Computational modeling predicted preferential latexin release from these cells would modulate processing of the satiety peptide POMC in local neurons. Experiments in mice verified that disrupting mitochondrion-shaping protein Mfn2 specifically in arcuate astrocytes led to POMC neuron changes and increased food intake resulting in diet-induced obesity.
Figure 5:
Figure 5. Modes of communication between
astrocytes and Agrp+ or Pomc+
neurons in ARC. (a) Illustration of possible
ligand-receptor interactions between astrocytes and neurons.
(b,c) CellChat applied to single-cell RNA-seq
data reconstructed ligand-receptor interactions between subgroups of
astrocytes, subset by being either Insr+ or
Insr-, and Pomc+ vs.
Agrp+ neurons. Interactions exceeding ± 15% were
indicated. (d) Shema of possible metabolic interactions
between astrocytes and neurons driven by differential enzyme expression.
(e) Relative densities of Ins+,
Lxn+ and
Ins+/Lxn+ astrocytes
(normalized) in n = 8 hypothalamic areas, based on single-cell
RNA-seq data (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50).
(f) Feature plot showing the expression of Lxn
in astrocytes from all areas computed in (e). (g)
Left: In situ hybridization of Lxn in VMH, ARC, PVN,
SCN. Gja1 and Apoe marked astrocytes. Right:
Immunohistochemistry for LXN and GFAP in VMH and ARC.
(h) The end-feet of LXN+/GFAP+
astrocytes preferentially enwrapped POMC+ neurons.
(i) Computational comparison of potential interactions
between Lxn+ astrocytes (residing in ARC or PVN) and
POMC+ neurons. Left: Calculation by Liana.
Right: Data from CellChat. Data were filtered for
signaling pathways with ligands specific to Lxn+
astrocytes (all those also found in PVN were excluded) and receptors in
POMC+ neurons. Local astrocytes in ARC had many more matches
than those from PVN. Pathways that were specific to the ARC were labeled
to the right. (j) Signaling interactions reconstructed
with Liana between Lxn+ astrocytes and
either Pomc+ or Agrp+ neurons in
ARC. Sizes of the labels reflect the level of ‘specificity’ across the
groups labeled. (k) Feature plots for Ntn1 and
Efna5 for astrocytes in ARC. (l) Extracellular
LXN released from astrocytes under control conditions and when
stimulated by insulin in vitro. Scale bars = 100 mm
(g,h), 10 mm (high power micrographs).
Figure 6:
Figure 6. Mfn2 expression and
function in astrocytes implicated in the control of bodyweight.
(a) CellChat-based cell-cell interactions.
Left: The number of putative interactions between
Lxn+ or Lxn- astrocytes and
neurons (Pomc+ or Agrp+) in
control (normal chow) and after exposure to a high-fat hypercaloric diet
(HFD)37.
Right: Reconstruction of efferent (‘outbound’) and afferent
(‘inbound’) signaling patterns across the four cell groups.
(b) Changes in gene expression for mitochondrial genes
upon exposure to HFD. (c) Correlated expression for
Lxn/Mfn2/Gfap in astrocytes.
(d) Illustration of astrocyte contributions to the
control of synaptic communication. (e) GFP+
astrocytes after viral transduction (AAV-Gfap-Gfp-Cre) of the
ARC in Mfn2f/f mice. (f)
Representative ultrastructural details of mitochondria in astrocytes
under control conditions and the virus-mediated ablation of
Mfn2. (g-h) Quantitative electron microscopy
of morphological parameters in control vs. site-directed
astrocyte-specific Mfn2 loss-of-function.
(i,j) Metabolic parameters in control vs.
Mfn2f/f::GfapCre mice under
normal-fed vs. HFD conditions; experiments were performed in
adult male mice at the age of 6-25 weeks. Data were expressed as means ±
s.e.m.; sample size was at least 4 animals per condition; two-tailed
t-test with Welch’s correction was used for statistical analysis: *P
< 0.05, **P < 0.01 (h-j). Scale bars =
100 mm (e), 500 nm (f).
Conclusions
This study puts forth AstroTRAP as an effective computational strategy for dissecting molecular heterogeneity of dynamic cell populations from scRNA-seq data. It revealed that hypothalamic astrocytes have specialized, location-dependent transcriptional identities that likely coordinate their interactions with regional neuronal circuits.
The gene expression maps generated serve as an important reference resource for the field. The functional analyses demonstrate that astrocytes are not passive elements but active modulators of hypothalamic control over metabolism and other homeostatic programs with distinct agenda. More broadly, delineating astrocyte diversity across brain regions will provide crucial insight into their roles in development, neural coding, and disease susceptibility.
By integrating cutting-edge bioinformatics with biological validation, this work significantly advances understanding of astrocyte biology and establishes a paradigm for unlocking complex cell populations masked within heterogeneous single-cell omics data.
Extended Data Figures
| ED Figure
1. Unsupervised learning on consolidated astrocyte
datasets is limited to reveal cell-state changes
(a) UMAP embedding prior to integration of astrocyte
datasets by reciprocal PCA to control for batch effects.
(b) Density plots for the expression of genes of
metabolic-state markers on UMAP embedding, after integration of
astrocyte datasets by reciprocal PCA to control for batch effects.
(c) Conventional clustering separates astrocytes by
their cellular states as shown by RNA velocity on a ‘whole’ hypothalamus
dataset43.
(d-f) RNA-velocity of an ARC dataset extracted from
Ref37.
UMAP embedding visualized cellular state transitions. Grid arrows in
(d,e) were colored by the confidence of dynamic cell
states (d) and conditions (e). Gene
trends aligned along two lineages (f).
(g,h) UMAP embedding with cellular activation states
similarly redistributed astrocytes in the PVN in response to chronic
social defeat47.
Color coding as in (g,h).
| ED Figure
2. Histochemical controls. In situ
hybridization in the hypothalamic areas indicated was used to validate
select markers. Solid arrows point to Gja1+
astrocytes co-expressing the markers tested. Scale bars = 20
mm.
| ED Figure 3.
Data integration on reference datasets and its histochemical
validation. (a)Scatter plots of PaCMAP
embeddings of paired integration performed by combining ‘whole’
hypothalamus7,43–45
and region-specific datasets (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50)
highlighted consistent region-specific deviations of the intersection
area. (b) In situ hybridization across
hypothalamic areas validated Npy1r expression in astrocytes.
Solid arrows pinpoint Gja1+ astrocytes co-expressing
Npy1r. Scale bars = 20 mm.
| ED Figure 4.
Block scheme of ‘AstroTRAP’, a spatially-resolved supervised machine
learning algorithm.
| ED Figure 5.
AstroTRAP for the regional assignment of astrocytes.
(a) Supervised densMAP embedding of integrated
regional hypothalamic datasets (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50)
color-coded by anatomical domains (nuclei, left), and as
predicted by s logit-based SVC model of AstroTRAP
(right). The latter was cross-validated for MCC and
F-measure performance metrics. (b) Area
contours of the original regions labeled for supervised densMAP
embedding of integrated regional hypothalamic datasets (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50)
classified by the XGBoost-based SVC model of AstroTRAP.
(c) Hexbin plots of transcription factors on the
supervised densMAP embedding of integrated regional
hypothalamic datasets (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50)
that were classified by the XGBoost-based SVC model of
AstroTRAP.
| ED Figure
6. Region-specific marker expression.
(a)Supervised densMAP embedding of integrated
regional hypothalamic datasets (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50)
classified by the XGBoost-based SVC model of AstroTRAP,
particularly for neuropeptide receptors. (b-g) In
situ hybridization in hypothalamic areas validated select markers.
Solid arrows point to Gja1+ astrocytes
co-expressing the markers tested. Scale bars = 20 mm.
| ED Figure 7.
Subgroups of astrocytes in the VMH. (a) Glial
cells expressing Tbx3 and Grpr aggregated into a
condensed cluster in the VMH. In contrast, we found many
Gja1+ cells co-expressing Tbx3 and
Grpr around and ventral from the 3rd ventricle (see:
ED Fig. 6f,g). (b)Zonal markers (e.g.,
Col25a1, Col23a1) confirmed that parts of the
ventricular and subventricular areas situated proximal to the median
eminence were sequenced. (c) Contamination by tanycytes
and ependymocytes were filtered out before integrating datasets. The
cells that passed the filtering criteria (left on the volcano
plot) differed from ciliary cells (right: Hydin, Kif6,
Ccdc30/162/170). (d) Feature plot showing that the
cells that passed our filtering criteria have progenitor potential
(Hes1, Sox9/11), and resemble glial progenitors described
earlier101.
| ED Figure
8. Feature plots of gene expression for
receptors (for neurotransmitters and modulators) and signaling
components generated through supervised densMAP embedding of
integrated regional hypothalamic datasets (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50),
classified by the XGBoost-based SVC model of AstroTRAP.
| ED Figure 9.
RNA-FISH to test the regional segregation of master transcription
factors (TFs). (a-c) In situ
hybridization in the hypothalamic areas indicated validated the
distribution of select TF markers. Solid arrows point to
Gja1+ astrocytes co-expressing the markers tested.
Scale bars = 20 mm.
| ED Figure 10.
Feature plots for transcription factors (TFs).
(a) Feature plot of Otp expression on
supervised densMAP embedding of integrated subregional
hypothalamic datasets (ARC37,40,
LH51,
MnPO46,
POA52,
PVN47,
SCN48,49,
VMH41,42,
and VPH50),
classified by the XGBoost-based SVC model of AstroTRAP.
(b) Feature plots for Otp lack coverage on all
four ‘whole’ hypothalamus datasets7,43–45.
(c) In situ hybridization in distinct
hypothalamic nuclei validated the distribution of this TF predicated by
AstroTRAP. Solid arrows indicated Gja1+
cells co-expressing Otp mRNA. Scale bars = 20 mm.
| ED Figure 11.
Insr and Lxn in ARC.Pie charts, which
visualized the statistical reliability of a fit test, were used to show
the proportionality between Insr and Lxn expression in
astrocytes.
| ED Figure
12. Astrocyte-neuron communication in the ARC.
Signaling analysis from Lxn+/- astrocytes ->
neurons in the ARC. Top: Gardner-Altman estimation of
multi-group data Bootstrap-coupled Estimation (DABEST)88
of cell-cell interactions. Bottom: Dot-plots demonstrating the
specificity and magnitude of ligand-receptor pairs quantified as
aggregated values.
| ED Figure
13. Analysis of intercellular signaling from neurons
-> Lxn+/- astrocytes. Top:
Gardner-Altman estimation of multi-group data Bootstrap-coupled
Estimation (DABEST)88
of cell-cell interactions. Bottom: Dot-plots demonstrating the
specificity and magnitude of ligand-receptor pairs quantified as
aggregated values.
| ED Figure 14.
Latexin (LXN) release from astrocytes is inhibited by insulin in
vitro. (a) Purified primary astrocytes
after overnight starvation (left), or stimulation (30 min) with
0.05 µM (middle) or 1 µM insulin (right).
(b) Assessment of cell viability (MTT assay) showed no
detrimental effect of insulin on astrocyte survival in vitro.
(c-e) Western analysis of culture media after
stimulating hypothalamic astrocytes with ascending concentrations of
insulin for 30 min, as indicated. Cell lysates were also compared to
controls (e). Experiments were minimally performed in
triplicates per condition. (f-g) Quantification of LXN
level in the media and cell pellets of cultured astrocytes treated with
insulin, relative to controls. Data were expressed as means ± s.e.m.
Abbreviations: LI/HI: low/high insulin; Ctrl: control.
| ED Figure 15.
In vivo experiments in Mfn2f/f
mice. (a) Schematic drawing of the stereotaxic
delivery of AAVs into the ARC. (b) Representative
images from the hypothalamus of Mfn2f/f mice
injected with AAV8-Gfap-Gfp-Cre particles (green) in
the ARC. Note that Immunosignal was restricted to the ARC.
(c) Representative images from the hypothalamus of
Mfn2f/f mice injected with
AAV8-Gfap-Gfp-Cre particles (green) in the ARC, and
counterstained using an anti-GFAP antibody (red). Note the
exclusive co-localization between GFP and GFAP. (d)
Experimental timeline. Mfn2f/f mice were inoculated
at 6-7 weeks of age. After a recovery period of 10 days, mice were
exposed to a high-fat hypercaloric diet (HFD). Scale bars = 50
mm (b), 10 mm (c).