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Pyramidal neuron subtype diversity governs microglia states in the neocortex

Nature volume 608, pages 750–756 (2022)Cite this article

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Abstract

Microglia are specialized macrophages in the brain parenchyma that exist in multiple transcriptional states and reside within a wide range of neuronal environments1,2,3,4. However, how and where these states are generated remains poorly understood. Here, using the mouse somatosensory cortex, we demonstrate that microglia density and molecular state acquisition are determined by the local composition of pyramidal neuron classes. Using single-cell and spatial transcriptomic profiling, we unveil the molecular signatures and spatial distributions of diverse microglia populations and show that certain states are enriched in specific cortical layers, whereas others are broadly distributed throughout the cortex. Notably, conversion of deep-layer pyramidal neurons to an alternate class identity reconfigures the distribution of local, layer-enriched homeostatic microglia to match the new neuronal niche. Leveraging the transcriptional diversity of pyramidal neurons in the neocortex, we construct a ligand–receptor atlas describing interactions between individual pyramidal neuron subtypes and microglia states, revealing rules of neuron–microglia communication. Our findings uncover a fundamental role for neuronal diversity in instructing the acquisition of microglia states as a potential mechanism for fine-tuning neuroimmune interactions within the cortical local circuitry.

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Fig. 1: PN subtypes locally control microglia density in the cortex.
Fig. 2: Microglia display cortical laminar heterogeneity.
Fig. 3: Cortical microglia display spatial heterogeneity.
Fig. 4: Conversion of PN class identity alters the laminar proportion of homeostatic microglia.
Fig. 5: Ligand–receptor interactome of PN–microglia communication in the neocortex.

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Data availability

Raw FASTQ files and counts matrices from scRNA-seq data of microglia from dissected P14 and P60 Cx3cr1GFP layers, microglia from dissected control and Fezf2-KO/Tcerg1l-creERT2/LSL-tdTomato layers, and control and Fezf2-KO S1 nuclei are available at the Gene Expression Omnibus (GEO) under accession number GSE158096. Raw MERFISH images are available at https://console.cloud.google.com/storage/browser/pyramidal_neurons_microglia (a public access restriction is in place owing to storage cost). The processed MERFISH counts matrix is available under GEO accession number GSE193760. All data are available upon reasonable request from the corresponding author. Source data are provided with this paper.

Code availability

The tool to open raw images can be found at https://github.com/ZhuangLab/storm-analysis/tree/master/imagej_plugins. All source code can be found at https://github.com/kimkh415/MicrogliaLayers.

References

  1. Bennett, F. C. et al. A combination of ontogeny and CNS environment establishes microglial identity. Neuron 98, 1170–1183.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Biase, L. M. D. et al. Local cues establish and maintain region-specific phenotypes of basal ganglia microglia. Neuron 95, 341–356.e6 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271.e6 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Lawson, L. J., Perry, V. H., Dri, P. & Gordon, S. Heterogeneity in the distribution and morphology of microglia in the normal adult mouse brain. Neuroscience 39, 151–170 (1990).

    Article  CAS  PubMed  Google Scholar 

  5. Colonna, M. & Butovsky, O. Microglia function in the central nervous system during health and neurodegeneration. Annu. Rev. Immunol. 35, 441–468 (2016).

    Article  CAS  Google Scholar 

  6. Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Fujita, Y., Nakanishi, T., Ueno, M., Itohara, S. & Yamashita, T. Netrin-G1 regulates microglial accumulation along axons and supports the survival of layer V neurons in the postnatal mouse brain. Cell Rep. 31, 107580 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Bella, D. J. D. et al. Molecular logic of cellular diversification in the mouse cerebral cortex. Nature 595, 554–559 (2021).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  9. Molyneaux, B. J., Arlotta, P., Hirata, T., Hibi, M. & Macklis, J. D. Fezl is required for the birth and specification of corticospinal motor. Neuron 47, 817–831 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Kwan, K. Y., Šestan, N. & Anton, E. S. Transcriptional co-regulation of neuronal migration and laminar identity in the neocortex. Development 139, 1535–1546 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hartfuss, E. et al. Reelin signaling directly affects radial glia morphology and biochemical maturation. Development 130, 4597–4609 (2003).

    Article  CAS  PubMed  Google Scholar 

  12. Jung, S. et al. Analysis of fractalkine receptor CX3CR1 function by targeted deletion and green fluorescent protein reporter gene insertion. Mol. Cell. Biol. 20, 4106–4114 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jordão, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).

    Article  PubMed  CAS  Google Scholar 

  14. Marsh, S. E. et al. Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain. Nat. Neurosci. 25, 306–316 (2022).

    Article  CAS  PubMed  Google Scholar 

  15. Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Askew, K. et al. Coupled proliferation and apoptosis maintain the rapid turnover of microglia in the adult brain. Cell Reports 18, 391–405 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Villa, A. et al. Sex-specific features of microglia from adult mice. Cell Rep. 23, 3501–3511 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mao, W., Zaslavsky, E., Hartmann, B. M., Sealfon, S. C. & Chikina, M. Pathway-level information extractor (PLIER) for gene expression data. Nat. Methods 16, 607–610 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  20. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kongsui, R., Beynon, S. B., Johnson, S. J. & Walker, F. R. Quantitative assessment of microglial morphology and density reveals remarkable consistency in the distribution and morphology of cells within the healthy prefrontal cortex of the rat. J. Neuroinflamm. 11, 182 (2014).

    Article  Google Scholar 

  23. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Matho, K. S. et al. Genetic dissection of the glutamatergic neuron system in cerebral cortex. Nature 598, 182–187 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Easley-Neal, C., Foreman, O., Sharma, N., Zarrin, A. A. & Weimer, R. M. CSF1R ligands IL-34 and CSF1 are differentially required for microglia development and maintenance in white and gray matter brain regions. Front. Immunol. 10, 2199 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Badimon, A. et al. Negative feedback control of neuronal activity by microglia. Nature 586, 417–423 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Akiyoshi, R. et al. Microglia enhance synapse activity to promote local network synchronization. Eneuro 5, ENEURO.0088–18.2018 (2018).

    Article  Google Scholar 

  30. Cserép, C. et al. Microglia monitor and protect neuronal function through specialized somatic purinergic junctions. Science 367, 528–537 (2020).

    Article  ADS  PubMed  CAS  Google Scholar 

  31. Liu, Y. U. et al. Neuronal network activity controls microglial process surveillance in awake mice via norepinephrine signaling. Nat. Neurosci. 22, 1771–1781 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ye, Z. et al. Instructing perisomatic inhibition by direct lineage reprogramming of neocortical projection. Neuron 88, 475–483 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lodato, S. et al. Excitatory projection neuron subtypes control the distribution of local inhibitory interneurons in the cerebral cortex. Neuron 69, 763–779 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wester, J. C. et al. Neocortical projection neurons instruct inhibitory interneuron circuit development in a lineage-dependent manner. Neuron 102, 960–975.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tomassy, G. S. et al. Distinct profiles of myelin distribution along single axons of pyramidal neurons in the neocortex. Science 344, 319–324 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Favuzzi, E. et al. GABA-receptive microglia selectively sculpt developing inhibitory circuits. Cell 184, 4048–4063.e32 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Velmeshev, D. et al. Single-cell genomics identifies cell type–specific molecular changes in autism. Science 364, 685–689 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Hirata, T. et al. Zinc finger gene Fez‐like functions in the formation of subplate neurons and thalamocortical axons. Dev. Dynam. 230, 546–556 (2004).

    Article  CAS  Google Scholar 

  39. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. He, L. et al. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Commun. Biology 4, 629 (2021).

    Article  CAS  Google Scholar 

  45. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R Package for comparing biological themes among gene clusters. Omics J. Integr. Biology 16, 284–287 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank members of the Arlotta and Levin laboratories for insightful discussions and editing of the manuscript; J. Brown for the dedicated revision work on the manuscript; and S. Simmons for the expert review of the bioinformatic work. This work was supported by grants from the Broad Institute of MIT and Harvard and the US National Institute of Health (P50MH094271 and U19MH114821) to P.A., the Stanley Center for Psychiatric Research to P.A. and J.Z.L., and The Klarman Cell Observatory to J.Z.L. J.A.S. was supported by a career developmental award from HHMI.

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Author notes

  1. These authors contributed equally: Jeffrey A. Stogsdill, Kwanho Kim

Authors and Affiliations

  1. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Jeffrey A. Stogsdill, Kwanho Kim & Paola Arlotta

  2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jeffrey A. Stogsdill, Kwanho Kim, Joshua Z. Levin & Paola Arlotta

  3. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Kwanho Kim, Loïc Binan, Samouil L. Farhi & Joshua Z. Levin

  4. Optical Profiling Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Loïc Binan & Samouil L. Farhi

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  1. Jeffrey A. Stogsdill
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Contributions

J.A.S., K.K. and P.A. conceptualized the project, designed experiments and wrote the manuscript. J.A.S. carried out all cortical micro-dissections, immunohistochemistry experiments and in situ hybridizations. J.A.S. performed all cell and nuclei sorting, with the accompanying library preparations for RNA-seq experiments. J.A.S. and K.K. analysed all scRNA-seq and snRNA-seq datasets. L.B. ran the MERFISH experiments. J.A.S., K.K. and L.B. performed all MERFISH data analyses. J.Z.L. and S.L.F. supervised the computational analyses. P.A. supervised all the experimental work. J.A.S. and P.A. wrote the mansucript with input from all authors, J.A.S., K.K. and P.A. designed manuscript figures. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Paola Arlotta.

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Competing interests

P.A. is a member of the scientific advisory board of System 1 Biosciences and Foresite Labs and a co-founder of FL60. J.A.S. is an employee of Sana Biotechnology as of September 2021.

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Extended data figures and tables

Extended Data Fig. 1 Control experiments associated with Fezf2 and Reln KO and control cortices.

Related to Fig. 1a, Representative micrograph of P60 Fezf2 Control and KO S1 cortex immunolabelled for Iba1 (green) and Satb2 (magenta). Enlarged representative layer images are boxed. Bin divisions for cell counts are indicated. Arrowhead = microglia cell body. Scale = 100 μm. b, Microglia density grouped by bins corresponding to L1-4 and L5-6 for P7, P14 and P60 Control S1 cortices. Data are presented as mean values +/− SEM. n = 3 mice, 4 images/mouse (P7), 4 mice, 4 images/mouse (P14), and 4 mice, 6 images/mouse (P60). Statistics: Linear mixed models. c, t-SNE of snRNA-seq from P14 S1 cortex color-coded by cell type. Oligo = Oligodendrocyte-lineage cell, Fibro = Fibroblast, BAM = Border Associated Macrophage. Data is reproduced from Extended Data Fig. 7g (Fezf2 Control only). d, e, Expression feature plot (d) and violin plot (e) for Fezf2 transcript levels from snRNA-seq data. Fezf2 is excluded from microglia. Cell cluster labels are as in (c). Legend = expression value. Data is from Extended Data Fig. 7g. f, Violin plot of the expression level of Fezf2 among PN subtypes. Clusters are color-coded. Lines = mean. Data are presented as mean values with probability density. Data is from Extended Data Fig. 7n. g, Representative micrograph of RNA fluorescence in situ hybridization images from P7 (left) and P14 (right) S1 cortices marking Fezf2 (red), Fcrls/Tmem119 (microglia, green), and DAPI. Enlarged inserts show that Fezf2 is excluded from all microglia. Arrowheads = microglia. Scale = 100 μm. h, (Left) Schematic of Fezf2-CreERT2/LSL-tdTomato mice injected with 4-Hydroxytamoxifen at embryonic day 16.5 to mark and lineage-trace Fezf2-expressing cells. (Right) Representative widefield tile scan image of a P7 coronal brain section immunolabelled for microglia (Iba1, green), endogenous tdTomato (red) and DAPI. Enlarged inserts show that tdTomato signal is excluded from all microglia. Scale = 100 μm. i, Microglia density grouped by bins corresponding to L1-4 and L5-6 of P60 Fezf2 Control and KO cortices. Data are presented as mean values +/− SEM. n = 4 mice, 6 images/mouse (P60). Statistics: linear mixed models. j, Cartoon schematic of S1 cortices of Reln Control and KO mice. Observed phenotypic variations among Reln-KO cortices are schematized, with PN subytpes color-coded. k, l, Expression feature plot for Reln (k) and violin plots (l) for Reln and the genes that encode the cell surface receptors of Reln, Lrp8 and Vldlr. Reln, Lrp8 and Vldlr expression is excluded from nearly all microglia. Cell cluster labels are as in (c). Legend = expression value. Data are presented as mean values with probability density (l only). Data is from Extended Data Fig. 7g. m, n, Scatter plot of microglia density (y-axis) by CPN density (x-axis) per bin from Reln Control (m) and KO (n) S1 cortices. Dots = bin data points. Data are presented as a linear regression color-coded by bin. R2 is listed for each bin, n = 4 images/mouse, 3 = mice/genotype. o, p, Microglia density (o) and CPN density (p) by cortical bin for Reln Control and KO S1 cortices. Data are presented as mean values +/− SEM. n = 4 images/mouse, 3 mice/genotype. Statistics: linear models.

Source data

Extended Data Fig. 2 Quality control and analysis of scRNA-seq of microglia from Cx3cr1GFP layer dissected cortices.

Related to Fig. 2a, FACS histogram plots of GFP+ cell sorting from P14 (left) and P60 (right) Cx3cr1GFP mice. S1 cortices were dissected and sorted separately for L1-4 (top), L5 (middle) and L6 (bottom). FACS gating for whole cells (left), live cells (Hoechst+, middle), and GFP+ cells (right) are plotted as dashed boxes. Only live GFP+ cells were used for scRNA-seq experiments. b–d, Violin plots of initial quality control (Pre-QC) metrics of the P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Transcripts/cell (b), genes/cell (c), and percent of mitochondrial gene expression/cell (d) are plotted. Data are split by replicate and color-coded by dissected layer. Data are presented as mean values with probability density. e, (Top) Expression feature plots for percent mitochondrial gene expression for the Pre-QC P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Feature plots are split by replicate for each age. Low-quality cells (mitochondrial gene > 10%) removed are circled. Legend = percentage. (Bottom) Violin plot of Pre-QC percent mitochondrial gene expression by initial clustering for the P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Low-quality clusters (mitochondrial gene > 10%) removed are denoted with arrows. Data are presented as mean values with probability density (bottom only). f, g, (Top) Expression feature plots for microglia (f) and BAM (g) cell type identity for the Pre-QC P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Feature plots are split by replicate for each genotype. BAMs removed are circled. (Bottom) Violin plot of Pre-QC microglia (f) and BAM (g) cell type identity by initial clustering for the P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. BAM clusters removed are denoted with arrows. Data are presented as mean values with probability density (bottom only). Cell type scores (legend) are defined as a function of gene expression for core microglia and BAM genes identified in Marsh et al14 and Jordao et al13 (Supplementary Table 1). h, (Top) Expression feature plots for ex vivo activation signature for the Pre-QC P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Feature plots are split by replicate for each genotype. ex vivo-activated cells that were removed are circled. (Bottom) Violin plot of Pre-QC ex vivo activation signature by initial clustering for the P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. ex vivo-activated clusters that were removed are denoted with arrows. Cell scores (legend) are defined as a function of gene expression for core ex vivo activation genes14 (Supplementary Table 1). i–k, Violin plots of final quality control (Post-QC) metrics of the P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets following the removal of BAMs, low-quality cells and ex vivo-activated cells. Transcripts/cell (i), genes/cell (j), and percent of mitochondrial gene expression/cell (k) are plotted. Data are split by replicate and color-coded by layer sample. Data are presented as mean values with probability density. l, m, Violin plots of the core microglia (l) and BAM (m) cell type scores split by replicate and age for the Post-QC P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Cell type scores are defined as a function of gene expression for core microglia14 and BAM13 genes (Supplementary Table 1). The final data strongly identify microglia. Data are presented as mean values with probability density. n, Violin plots of the ex vivo activation state score of all Post-QC cells from the P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Data are displayed as a state score, defined as a function of the core genes that are up-regulated in microglia through aberrant ex vivo activation14 (Supplementary Table 1). Data are split by replicate. Data are presented as mean values with probability density. o, p, Violin plots for canonical microglia genes plotted by color-coded cluster from the P14 (o) and P60 (p) Cx3cr1GFP scRNA-seq experiments. Data are presented as mean values with probability density. q, r, Violin plots for Apoe (q), and Ccr1 (r) expression in P14 (top) and P60 (bottom) microglia Cx3cr1GFP scRNA-seq clusters. Data are presented as mean values with probability density. s, Violin plots depicting expression of the interferon-induced genes Ifi27l2a (top) and Ifitm3 (bottom) in P14 (left) and P60 (right) microglia Cx3cr1GFP scRNA-seq clusters. Data are presented as mean values with probability density. t, Dot plot of the top 10 significant gene ontology terms enriched from the DE genes in the innate immune cluster compared to all other scRNA-seq clusters at P14 (top) and P60 (bottom). Legend: color = adj. P, dot size = number of genes. Statistics: hypergeometric test with BH procedure. u, Violin plots depicting expression of the inflammation-induced genes Cd63 (top) and Abcg1 (bottom) in P14 (left) and P60 (right) microglia Cx3cr1GFP scRNA-seq clusters. Data are presented as mean values with probability density. v, Dot plot of the top 10 significant gene ontology terms enriched from the DE genes in the inflammatory cluster compared to all other scRNA-seq clusters at P14 (top) and P60 (bottom). Legend: color = adj. P, dot size = number of genes. Statistics: hypergeometric test with BH procedure. w, Violin plots for G2M (top) and S-phase (bottom) cell cycle phase scores42 for P14 (top) and P60 (bottom) microglia Cx3cr1GFP scRNA-seq clusters. Data are presented as mean values with probability density.

Extended Data Fig. 3 Identification of cortical non-homeostatic microglia in situ.

Related to Fig. 2a, t-SNE plot highlighting the ApoeHigh cluster (left) and feature plot of Apoe expression (right) in the P14 and P60 Cx3cr1GFP scRNA-seq datasets. Legend = expression value. b, Representative tile scan confocal RNA fluorescence in situ hybridization images from P14 (left) and P60 (right) S1 cortices marking Apoe (red), Fcrls (microglia, green) and DAPI. Enlarged inserts (yellow boxes) show Apoe-positive (arrow) and Apoe-negative (*) microglia at upper- and lower-layers of the cortex. Scale = 100 μm. c, t-SNE plot highlighting the Ccr1High cluster (left) and feature plot of Ccr1 expression (right) in the P14 and P60 Cx3cr1GFP scRNA-seq microglia datasets. Legend = expression value. d, Representative tile scan confocal RNA fluorescence in situ hybridization images from P14 (left) and P60 (right) S1 cortices marking Ccr1 (red), Fcrls (microglia, green) and DAPI. Enlarged inserts (yellow boxes) show Ccr1-positive (arrow) and Ccr1-negative (*) microglia at upper- and lower-layers of the cortex. Scale = 100 μm. e, t-SNE plot highlighting the innate immune cluster (left) and feature plot of Ifi27l2a expression (right) in the P14 and P60 Cx3cr1GFP scRNA-seq microglia datasets. Legend = expression value. f, Representative tile scan confocal RNA fluorescence in situ hybridization images from P14 (left) and P60 (right) S1 cortices marking innate immune microglia with Ifit3 (red), Fcrls (microglia, green) and DAPI. Enlarged inserts (yellow boxes) show Ifit3-positive (arrow) and Ifit3-negative (*) microglia at upper- and lower-layers of the cortex. Scale = 100 μm. g, t-SNE plot highlighting the inflammatory cluster (left) and feature plot of Abcg1 expression (right) in the P14 and P60 Cx3cr1GFP scRNA-seq microglia datasets. Legend = expression value. h, Representative tile scan confocal RNA fluorescence in situ hybridization images from P14 (left) and P60 (right) S1 cortices marking inflammatory microglia with Abcg1 (red), Fcrls or C1qa (microglia, green) and DAPI. Enlarged inserts (yellow boxes) show Abcg1-positive (arrow) and Abcg1-negative (*) microglia at upper- and lower-layers of the cortex. Scale = 100 μm.

Extended Data Fig. 4 Characterization of the transcriptional signature of microglia states.

Related to Fig. 2a, Bar plot of P60 Cx3cr1GFP scRNA-seq microglia cluster proportions normalized by dissected layer. Color coding indicates the layer from which the microglia were isolated. Data are presented as normalized mean values. n = 2 independent biological replicates. b, t-SNE of P14 (top) and P60 (bottom) Cx3cr1GFP scRNA-seq color-coded microglia state clusters split by dissected layer of origin and replicate. c, Heatmap of the top DE genes by microglia cluster in the P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Each row is a gene. Color-coded clusters are grouped. Legend = scaled expression value. d, Volcano plot of the DE genes between homeostatic1 versus homeostatic2 clusters in the P60 Cx3cr1GFP scRNA-seq dataset. Genes are color-coded by enrichment (log2 Fold Change > 0.15 and -log10 BH adj. P < 0.05). Homeostatic1-enriched (gold), homeostatic2-enriched (red), not significant (grey). Several homeostatic state signature genes for each state are annotated. Vertical and horizontal dotted lines represent enrichment cut-offs. Statistics: generalized linear model with BH correction (MAST). e, Violin plots of homeostatic1-enriched genes expressed in homeostatic1 and homeostatic2 clusters from P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Data are presented as mean values with probability density.; Statistics: generalized linear model with BH correction (MAST). f, Dot plot of the top 10 significant gene ontology terms enriched from the DE genes of the homeostatic1 cluster compared to all other clusters in the P14 Cx3cr1GFP scRNA-seq dataset. Legend: color = BH adj. P, dot size = number of genes. Statistics: hypergeometric test with BH procedure. g, Violin plots of homeostatic2-enriched microglia genes expressed in homeostatic1 and homeostatic2 clusters from P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq dataset. Data are presented as mean values with probability density. Statistics: generalized linear model with BH correction (MAST). h, Dot plot of the top 10 significant gene ontology terms enriched from the DE genes of homeostatic2 cluster compared to all other clusters in the P14 Cx3cr1GFP scRNA-seq dataset. Legend: color = BH adj. P, dot size = number of genes. Statistics: hypergeometric test with BH procedure. i, Violin plots of canonical microglia genes expressed in homeostatic1 and homeostatic2 clusters from the P14 (left) and P60 (right) Cx3cr1GFP scRNA-seq datasets. Data are presented as mean values with probability density. Statistics: generalized linear model with BH correction (MAST). j, Dot plot of the DE genes between male and female microglia from the P14 (top) and P60 (bottom) Cx3cr1GFP scRNA-seq datasets. The data represent analysis from the combined dissected layers. Legend: color = scaled average expression, dot size = percent of the cluster cells expressing the gene. Most sex-specific DE genes do not match the list of DE gene between homeostatic state clusters. Statistics: generalized linear model with BH correction (MAST). k–l, homeostatic1 (k) and homeostatic2 (l) microglia signature state scoring of P60 Cx3cr1GFP scRNA-seq data: (Left) Violin plot of state scores for microglia in clusters homeostatic1 and homeostatic2. (Middle) Expression feature plot of state scores in t-SNE space. Legend: score value. (Right) Violin plot of state scores of homeostatic microglia split by dissected layer. Violin plot data are presented as mean values with probability density. Statistics: linear mixed models. Genes pertaining to the homeostatic1 signature are listed in Supplementary Table 5. m, Violin plots of the homeostatic1 (top row) and homeostatic2 (bottom row) state scores split by all clusters from P14 (left) and P60 (bottom) Cx3cr1GFP scRNA-seq datasets. Data are presented as mean values with probability density. State scores are defined as a function of gene expression pertaining to the genes enriched in the homeostatic signatures listed in Supplementary Table 5. n–p, Violin plots of gene module scores for PLIER latent variable (LV) 1 (n), LV 4 (o), and LV 11 (p) applied to homeostatic1 and 2 microglia from the P14 scRNA-seq data. The data are split by state (left) and by dissected layer (right). Data are presented as mean values with probability density. Statistics: linear mixed models. Genes pertaining to the LV modules are listed in Supplementary Table 6.

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Extended Data Fig. 5 Quality control metrics for the MERFISH datasets.

Related to Fig. 3a, b, Violin plots of cell-type scores for MERFISH gene modules targeting cell types (a) and PN subtypes (b). The data are from P14 Fezf2 control snRNA-seq in Extended Data Fig. 7. Data are presented as mean values with probability density. State scores are defined as a function of gene expression for the genes grouped by cell type or PN subtype listed in Supplementary Table 7. c, Cartoon schematic of the MERFISH pipeline to map cell types and microglia states in the P14 S1 cortex. d, Representative micrographs depicting the automated segmentation of cells for MERFISH analysis. (Left) All nuclei were automatically segmented (white outlines) based on DAPI signal. (Right) Microglia were separately segmented based on Tmem119 (red) and Fcrls (green) RNA puncta with a small dilation (blue outline) in order to maximize RNA detection and counts. See Methods for full cell segmentation details. e, Violin plots of the size of segmented cells split by the cell types identified in the MERFISH dataset. Data are presented as mean values with probability density. f, g, Violin plots representing the number of RNA transcripts/cell (f) and genes/cell (g) for the MERFISH dataset split by region of interest (ROI) samples. Data are presented as mean values with probability density. h, Spatial distribution MERFISH maps of cell types (top row), PN subtypes (middle row), and microglia states (bottom row) for each ROI from all imaged samples. Cropped images displayed are the analyzed regions of interest (S1 cortex) for each sample. Cell types and states are color-coded as indicated in the legend.

Extended Data Fig. 6 Identification of cell type and microglia state from MERFISH data.

Related to Fig. 3a, Spatial distribution feature (top row) and violin plots (bottom row) for the genes targeting cell types, via MERFISH. The spatial distribution feature plots are from a single representative image (ROI 1). Violin plots are split by cell type cluster. Spatial distribution feature plot legend = expression value. Violin plot legend = cell type. b, Heatmap of gene expression specific to cell types as captured by MERFISH. Cell type clusters are color coded (subset of 200 cells/cluster). Legend: scaled expression value. c, Mean cell type cluster proportion across MERFISH ROIs. Cell types are color coded. Data are presented as mean values +/- SEM. Dots = individual ROI frequencies. n = 10 ROI from 3 mice. d, Spatial distribution feature (top row) and violin plots (bottom row) for the genes targeting PN subtypes by MERFISH. Spatial distribution feature plots shown are a single representative image (ROI 1). Violin plots are split by PN subtype cluster. Spatial distribution feature plot legend = expression value. Violin plot legend = cell type. e, Heatmap of gene expression specific to PN subtypes as captured by MERFISH. PN subtype clusters are color coded (subset of 200 cells/cluster). Legend: scaled expression value. f, Mean PN subtype cluster proportion across MERFISH ROIs. PN subtypes are color-coded. Data are normalized to the total PN count. Dots = individual ROI proportions. Data are presented as mean values +/− SEM. n = 10 ROI from 3 mice. g, Mean layer proportion for each PN subtype identified by MERFISH. Data are normalized to the total count for each PN subtype. See methods for the calculation strategy for layer distinctions. PN subtypes are color-coded. Dots = individual sample proportions for each PN subtype by cortical layer. Data are presented as mean values +/− SEM. n = 10 ROI from 3 mice. h, Violin plots for representative genes targeting different microglia states by MERFISH. Microglia states are color-coded and split by state. Data are presented as mean values with probability density. i, Box and whisker plots for MERFISH microglia state scoring. Microglia state assignments are color coded and labelled at the left. See Methods for full details on MERFISH microglia state scoring. Microglia state-enriched genes used for scoring are listed in Supplementary Table 7. Plots indicate median, 1st and 3rd quartiles, 1.5 inter-quartile range plus outlying points. n = all Tmem119+/Fcrls+ microglia from 10 ROI of 3 mice. j, Heatmap of gene expression enriched in microglia states captured by MERFISH. Microglia state assignments are color coded (subset of 200 cells/cluster). Legend: scaled expression value. k, Mean microglia state assignment proportions across MERFISH ROIs. Microglia state assignments are color coded. Dots = individual ROI proportions. Data are presented as mean values +/− SEM. n = 10 ROI from 3 mice.

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Extended Data Fig. 7 Molecular profiles of cell types and PN subtypes in Fezf2 Control and KO cortices by snRNA-seq.

Related to Fig. 4a, Cartoon of S1 cortex dissection, nuclei extraction, and purification by FACS from P14 Fezf2 Control and KO mice. n = 2 biological replicates for each genotype (1 male and 1 female for each genotype). b, c, FACS scatter plots sorting nuclei from P14 Fezf2 Control (b) and KO (c) S1 cortices. FACS gating for whole nuclei (left) and Hoechst+ nuclei (right) are plotted as dashed boxes. Only Hoechst+ nuclei were used for snRNA-seq experiments. d, Violin plots of initial quality control (Pre-QC) metrics for the Fezf2 Control and KO snRNA-seq dataset. Data are split by replicate and color-coded by genotype. Transcripts/cell (left), genes/cell (middle), and percent of mitochondrial gene expression/cell (right) are plotted. Data are presented as mean values with probability density. e, Expression feature plots for Pre-QC metrics: transcripts/cell, genes/cell, percent mitochondrial gene expression/cell, and percent ribosomal gene expression/cell for the Pre-QC P14 Fezf2 Control and KO snRNA-seq data sets. Low-quality nuclei (circled) were those expressing very low genes/nucleus and did not return DE genes specific to known cell types. Legend: number (transcripts/nucleus and genes/nucleus), percent expression (mitochondrial and ribosomal gene %). f, Violin plots of quality control (Post-QC) metrics of the P14 Fezf2 Control and KO snRNA-seq datasets following the removal of low-quality nuclei. Transcripts/nucleus (left), genes/nucleus (middle), and percent of mitochondrial gene expression/nucleus (right) are plotted, split by replicate and genotype. Data are presented as mean values with probability density. g, t-SNE of Post-QC P14 Fezf2 Control (left) and KO (right) snRNA-seq datasets color-coded by cell type cluster. Oligo = Oligodendrocyte-lineage cells, Fibro = Fibroblast, BAM = Border Associated Macrophage. The number of sequenced nuclei is annotated for each genotype is noted. h, Violin plot of the number of genes/nuclei split by cell type cluster and genotype for the P14 Fezf2 Control and KO snRNA-seq dataset. Data are presented as mean values with probability density. i, Heatmap of gene expression for the top DE genes by cell type cluster from the P14 Fezf2 Control and KO snRNA-seq dataset. Each row is a gene and grouped by cell type cluster (color-coded). Legend = scaled expression value. j, Violin plots depicting the expression level of marker genes for each cell type cluster. Cell type clusters are color coded. Data are presented as mean values with probability density. k, Bar plot of mean cluster proportions by genotype for the P14 Fezf2 Control and KO snRNA-seq dataset. Data are normalized to the total cell count for each genotype. Color-coding indicates the genotype. n = 2 biological replicates for each genotype (1 male and 1 female for each genotype). l, t-SNE of PN subtype clusters from the P14 Fezf2 Control and KO snRNA-seq dataset split by genotype. PNs were subsetted Slc17a7+/Neurod2+ nuclei from (g). PN subtypes are color-coded and labelled by their cortical layer localization. The number of sequenced nuclei for each genotype is annotated. m, Heatmap of gene expression for the top 10 DE genes for each PN subtype cluster in the P14 Fezf2 Control and KO snRNA-seq datasets. Each row is a gene and grouped by PN subtype cluster (color-coded). Legend = scaled expression value. n, Violin plots depicting the expression of select markers genes (split by cluster): PN class-specific (top) and PN subtype-specific (bottom). Clusters are color-coded. Data are presented as mean values with probability density. Dashed line splits upper- and lower- cortical layer PN subtypes, as well as Fezf2-KO mismatch PNs. o, Bar plot of the mean PN subtype cluster proportion by genotype from P14 Fezf2 Control and KO snRNA-seq datasets. Data are normalized to the total PN count for each genotype. Color coding indicates the genotype. n = 2 biological replicates for each genotype (1 male and 1 female for each genotype). p, Stacked bar plots depicting the prediction results of the Fezf2-KO PN subtype clusters using Azimuth24. PNs of the manually assigned subtype clusters (rows) from the P14 Fezf2-KO snRNA-seq data were re-classified based on the PN subtypes defined from the control snRNA-seq data (color-coded). The re-classified data clusters are shown as proportions of the manual cluster size. q, Violin plots of the Azimuth prediction scores for the P14 Fezf2-KO re-classified clusters. Re-classified clusters are color-coded. Data are presented as mean values with probability density.

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Extended Data Fig. 8 Cell sorting and quality control analysis for scRNA-seq of microglia from Fezf2 control and KO layer dissected cortices.

Related to Fig. 4a, Cartoon schematic of the compound transgenic mouse line used to fluorescently label cortical L5b of Fezf2 control and KO mice. 4-Hydroxytamoxifen (4-OHT, 50 mg/kg) was administered to pups on P2. Pups were sacrificed at P14 for dissection of the S1 cortex. n = 2 biological replicates (pooled 1 male and 1 female). b, Representative FACS scatter plots sorting CD45low/CX3CR1+/CD11B+ microglia from P14 Fezf2 control or KO/Tcerg1l-CreERT2/LSL-tdTomato mice. The S1 cortex was dissected and sorted separately for L1-4 (top), L5 (middle) and L6 (bottom). FACS gating for whole cell (far left), live cells (Hoechst+ middle left), CD45low/CX3CR1+ (middle right) and CD11B+ (far right) is plotted as dashed boxes. Only CD45low/CX3CR1+/CD11B+ microglia were used for scRNA-seq experiments. c–e, Violin plots of initial quality control (Pre-QC) metrics for the P14 Fezf2 control (left) and KO (right) scRNA-seq datasets. Data are split by replicate and genotype, and color coded by dissected layer. Transcripts/cell (c), genes/cell (d), and percent of mitochondrial gene expression/cell (e) are plotted. Data are presented as mean values with probability density. f, (Top) Expression feature plots for percent mitochondrial gene expression for the Pre-QC P14 Fezf2 control and KO scRNA-seq dataset. Feature plots are split by replicate for each genotype. Low-quality cells (mitochondrial gene > 10%) that were removed are circled. Legend = percentage. (Bottom) Violin plot of Pre-QC percent mitochondrial gene expression by initial clustering for the P14 Fezf2 control and KO scRNA-seq dataset. Low-quality clusters (mitochondrial gene > 10%) removed are denoted with arrows. Violing plot data are presented as mean values with probability density. g, h, (Top) Expression feature plots for BAM cell type identity (g) and ex vivo activation signature (h) from the Pre-QC P14 Fezf2 Control and KO scRNA-seq dataset. Feature plots are split by replicate for each genotype. BAMs and ex vivo activated cells that were removed are circled. (Bottom) Violin plot of Pre-QC BAM (g) and ex vivo activated (h) signature scores by initial clustering for the P14 Fezf2 control and KO scRNA-seq dataset. BAM and ex vivo activated clusters that were removed are denoted with arrows. Violin plot data are presented as mean values with probability density. Scores are calculated as a function of gene expression for core BAM13 and ex vivo activated14 genes, respectively (Supplementary Table 1). i–k, Violin plots of quality control (Post-QC) metrics of the P14 Fezf2 control and KO scRNA-seq dataset following the removal of BAMs, low-quality, and ex vivo activated cells. Data are split by replicate, genotype, and dissected layer. Transcripts/cell (i), genes/cell (j), and percent of mitochondrial gene expression/cell (k) are plotted. Data are presented as mean values with probability density. l–m, Violin plots of the core microglia (l) and BAM (m) cell type scores from the post-QC Fezf2 control and KO scRNA-seq dataset. Data are split by replicate and genotype, and color coded by dissected layer. The final data strongly identify microglia. Cell type scores are defined as a function of gene expression for core microglia14 and BAM13 genes (Supplementary Table 1). Data are presented as mean values with probability density.

Extended Data Fig. 9 Microglia transcriptional signatures in the Fezf2-KO cortex.

Related to Fig. 4a, b, Violin plots for canonical microglia (a) and state-enriched (b) genes plotted by cluster and split by genotype from the P14 Fezf2 control and KO layer dissected scRNA-seq dataset. Clusters are color coded. Data are presented as mean values with probability density. c, Violin plots for G2M (top) and S-phase (bottom) scores for the P14 Fezf2 control and KO scRNA-seq data split by cluster. Cells are scored by cell cycle phase42. Data are presented as mean values with probability density. d, Heatmap of gene expression for the top DE genes for each microglia state cluster in the P14 Fezf2 control (left) and KO (right) scRNA-seq dataset. Each column is a gene and grouped by microglia state cluster (color-coded). Legend = scaled expression value. e, Violin plots of the homeostatic1 (top row) and homeostatic2 (bottom row) state scores split by all clusters from the P14 Fezf2 control and KO scRNA-seq dataset. Clusters are color-coded. Data are presented as mean values with probability density. State scores are defined as a function of gene expression pertaining to the genes enriched in the homeostatic signatures listed in Supplementary Table 5. f, Stacked bar plots depicting microglia state prediction results using Azimuth24. Microglia of the manually assigned clusters (rows) from the P14 Fezf2 control (left) and KO (right) scRNA-seq dataset were re-classified based on the cell types defined in the P14 Cx3cr1GFP scRNA-seq dataset (color coded). The re-classified data are shown as proportions of the total cell count per manually assigned cluster. g, Violin plots of the Azimuth prediction scores for the P14 Fezf2 control (top) and KO (bottom) re-classified clusters. Re-classified clusters are color coded. Data are presented as mean values with probability density.

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Extended Data Fig. 10 Extended data for the ligand-receptor, PN-microglia interactome of the neocortex.

Related to Fig. 5a, Cartoon schematic of the ligand-receptor analysis to uncover novel PN-microglia subtype interactions using CellPhoneDB26. The interactome predicts interactions between PN subtypes (snRNA-seq data, Extended Data Fig. 7) and homeostatic microglia (scRNA-seq data, Fig. 4) from P14 Fezf2 control and KO mice. b, Heatmap of the number of significant ligand-receptor (L-R) interaction pairs between each PN subtype (column) and microglia state (row) cluster. Legend: PN subtype (colored boxes) and homeostatic microglia state (color-coded circles for the Fezf2 control microglia, stars for the Fezf2-KO microglia). Scale = number of interactions. c, Dot plot for L-R expression between PN subtypes and homeostatic microglia states in Fezf2 control (left) and KO (right) cortices. Legend: PN subtype (colored boxes) and layered homeostatic microglia (color-coded circles for the Fezf2 control microglia, stars for the Fezf2-KO microglia) used in each pairwise comparison. -log10 adj. P values are indicated by circle size. Dot color indicates the average expression level of the interacting molecules from PN subtypes and microglia state. Receptor genes are bolded. Dotted vertical line divides upper- and lower-layer PN subtypes. Statistics: permutation-based BH adj. P. d, e, Expression feature (d) and violin plots (e) of Il34 levels in PN subtypes from the P14 Fezf2 control and KO snRNA-seq dataset. Data originate from Extended Data Fig. 7l. Legend = expression (d) and color-coded PN-subtypes (e). Violin plot data are presented as mean values with probability density. f, Expression feature plot of Csf1r levels in microglia from the P14 Fezf2 control (left) and KO (right) scRNA-seq datasets. Data originate from Fig. 4. Legend = Expression value. g, h, Expression feature (g) and violin plots (h) of Nectin3 levels in PN subtypes from the P14 Fezf2 control and KO snRNA-seq dataset. Data originate from Extended Data Fig. 7l. Legend = expression (g) and color-coded PN subtypes (h). Violin plot data are presented as mean values with probability density. i, Expression feature plot of Nectin2 levels in microglia from the P14 Fezf2 control (left) and KO (right) scRNA-seq dataset. Data originate from Fig. 4. Legend = Expression value. j, k, Expression feature (j) and violin plots (k) of Gas6 levels in PN subtypes from the P14 Fezf2 control and KO snRNA-seq dataset. Data originate in Extended Data Fig. 7l. Legend = expression (j) and color-coded PN-subtypes (k). Violin plot data are presented as mean values with probability density. l, Violin plot of Mertk levels in microglia from the P14 Fezf2 control and KO scRNA-seq dataset. Data denote the differential expression of Mertk between homeostatic1 and homeostatic2 and between genotypes. Data are presented as mean values with probability density. Statistics: generalized linear model with BH correction (MAST).

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Stogsdill, J.A., Kim, K., Binan, L. et al. Pyramidal neuron subtype diversity governs microglia states in the neocortex. Nature 608, 750–756 (2022). https://doi.org/10.1038/s41586-022-05056-7

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