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Molecular logic of cellular diversification in the mouse cerebral cortex

An Author Correction to this article was published on 02 August 2021

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The mammalian cerebral cortex has an unparalleled diversity of cell types, which are generated during development through a series of temporally orchestrated events that are under tight evolutionary constraint and are critical for proper cortical assembly and function1,2. However, the molecular logic that governs the establishment and organization of cortical cell types remains unknown, largely due to the large number of cell classes that undergo dynamic cell-state transitions over extended developmental timelines. Here we generate a comprehensive atlas of the developing mouse neocortex, using single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing. We sampled the neocortex every day throughout embryonic corticogenesis and at early postnatal ages, and complemented the sequencing data with a spatial transcriptomics time course. We computationally reconstruct developmental trajectories across the diversity of cortical cell classes, and infer their spatial organization and the gene regulatory programs that accompany their lineage bifurcation decisions and differentiation trajectories. Finally, we demonstrate how this developmental map pinpoints the origin of lineage-specific developmental abnormalities that are linked to aberrant corticogenesis in mutant mice. The data provide a global picture of the regulatory mechanisms that govern cellular diversification in the neocortex.

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Fig. 1: Comprehensive atlas of mouse cortical development.
Fig. 2: Spatial distribution of cell types in the developing cortex.
Fig. 3: Molecular developmental trajectories of neocortical cell types.
Fig. 4: scATAC–seq landscape of the developing neocortex.
Fig. 5: FEZF2 prevents the acquisition of callosal identity in CFuPN.

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Data availability

The datasets generated during the current study are available in the Gene Expression Omnibus (GEO SuperSeries GSE153164) and at the Single Cell Portal:

Code availability

R markdown scripts enabling the main steps of analysis are available on a GitHub repository

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We thank former and present members of the Arlotta and Regev laboratories for discussions and editing of the manuscript. This work was supported by grants from the Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard  to P.A., grants from the National Institutes of Health (P50MH094271, U19MH114821 and R01NS103758 to P.A., and DP5OD024583 to F.C.), and grants from The Klarman Cell Observatory, HHMI and NHGRI Center for Cell Circuits CEGS to A.R. D.J.D. was supported by the Pew Latin American Postdoctoral Fellowship.

Author information

Authors and Affiliations



P.A., D.J.D., E.H. and A.R. conceived the experiments. D.J.D. and E.H. performed all the experiments and analysed data with help from P.Y. and S.M.Y. D.J.D. performed and analysed in situ hybridizations with help from C.A. R.R.S. performed Slide-seq experiments with help from D.J.D., F.C. and E.Z.M. G.S. and T.B. performed all Tangram analyses. P.A., D.J.D., E.H., J.B. and A.R. wrote the manuscript with contributions from all authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Aviv Regev or Paola Arlotta.

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Competing interests

P.A. is a scientific advisory board member for System 1 Biosciences and Foresite Labs and is a co-founder of Serqet Therapeutics. A.R. is a co-founder of and an equity holder in Celsius Therapeutics, an equity holder in Immunitas, and until 31 July 2020 was a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Asimov and Neogene Therapeutics. Since 1 August 2020, A.R. has been an employee of Genentech/Roche. Since 1 February 2021, T.B. has been an employee of Genentech. Since 1 January 2021, G.S. has been an employee of Roche.

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Peer review information Nature thanks Debra Silver, Fabian Theis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Classification of cell types in scRNA-seq data from individual time points.

Related to Fig. 1. a, Number of replicates, total number of embryos, sex of mice and number of cells analysed per time point. b, Number of genes, number of mRNA molecules (counts) and percentage of mitochondrial counts per cell in each time point. c, Proportion of cells corresponding to the different cell types present in each time point. 85–98% of cells were successfully identified for each time point. The earliest stages were primarily composed of apical and intermediate progenitors: AP + IP = 77% at E10.5, 80% at E11.5, 69% at E12.5, 66% at E13.5. d, Correlation between male (M, Xist expression < 1) and female (F, Xist expression > 1) cells at E12.5 and E18.5 in selected cell types. Pearson correlation coefficients are indicated. Distinct genes include X-chromosome genes Xist and Tsix and Y-chromosome genes Ddx3y and Eif2s3y. Some haemoglobin genes also appear distinct, but, as shown in e they constitute few outlier cells. e, Normalized expression levels of some of distinct genes between male and female cells at E18.5. Only two cell types are shown for clarity. f, UMAP visualization of cells collected at each time point, showing expression levels (normalized) of marker genes for dorsal derivatives (Emx1), apical progenitors (Sox2), intermediate progenitors (Eomes), excitatory neurons (Neurod2, Fezf2, Satb2), inhibitory interneurons (Dlx2) and glial cells (Apoe).

Extended Data Fig. 2 Molecular signatures and interneuron heterogeneity in the developing cerebral cortex.

Related to Fig. 1. a, Selective expression (normalized) of marker genes per cell type in the combined scRNA-seq dataset. Cell types are grouped on the basis of their identity and shared marker genes. b, Gene signatures for all cell types identified in the combined time points. Top 20 differentially expressed genes for each cell type are presented. Cells were down-sampled to a maximum of 500 cells per cell type. c, Expression of canonical marker genes for selected cell types in the UMAP visualization of the combined scRNA-seq time course. d, Different subtypes of interneuron integrate into the developing cortex through time. Left, clustering of interneurons collected at all time points, visualized via UMAP. Middle, interneuron UMAP plots show the expression of the inhibitory markers Dlx2 and Gad2, as well as a marker of dorsally-derived cell types (Emx1), not expressed by interneurons. Right, proportion of cells corresponding to each cluster in each time point. e, Expression of genes characteristic of interneurons of different embryonic origins. Medial ganglionic eminence (MGE)-derived interneurons express Npy, Sst, Lhx6 and Nxph1. Interneurons originating in the CGE (caudal ganglionic eminence) are positive for Htr3a, Prox1, Cxcl14 and Sp8. A second population of Htr3a+ interneurons express Meis2, Etv1 and Sp8, putatively from the pallial–subpallial (P–SP) boundary.

Extended Data Fig. 3 Spatial mappings of cell types in the developing cerebral cortex.

Related to Fig. 2. a, Scheme summarizing the approach to map cell types from the scRNA-seq developmental atlas onto matching tissue spatial transcriptomes (Slide-seq v2) using Tangram8. b, Mapping of extended cell types from the scRNA-seq data onto the matching Slide-seq section. Beads are coloured according to the probability of the cell type being mapped in that position. c, Gene expression of characteristic genes validating cell types matched for each time point. d, Cell-type assignment in the deep layers of P1 cortex shows accurate distribution. Beads are coloured according to the cell type with highest mapping probability. Right, summed probabilities across bins parallel to the ventricular surface (normalized to maximum value). e, Mapping probabilities for the deep layer cell types grouped by the cell type assigned (cell type with highest probability) corresponding to d. In box plots the middle line is the median, the lower and upper hinges correspond to the 25% and 75% quantiles, the upper whisker corresponds to the largest value no larger than 1.5 × IQR from the hinge (where IQR is the interquartile range) and the lower whisker corresponds to the smallest value at most 1.5 × IQR of the lower hinge. Total number of beads = 812. f, Gene expression in E15.5 scRNA-seq data of genes associated with the migrating neuron substates identified in Fig. 2b.

Extended Data Fig. 4 Consistent ordering of cells in developmental trajectories and characterization of branching tree of cortical development.

Related to Fig. 3. a, UMAP visualizations of the scRNA-seq data from combined time points, with cells coloured by pseudotime inferred by different methods. Left to right: URD pseudotime, Monocle3 pseudotime47, Latent time from sc-Velo45, Diffusion pseudotime (DPT)46, and Velocity pseudotime45. Purple represents earlier cells in the trajectory, while yellow labels later cells. Grey indicates cells that were excluded from the trajectory. b, Correlation (red, low; white, high) for all cells between URD pseudotime values and pseudotime calculated by the specified method. R coefficient and P value of the Pearson correlation is stated. c, UMAP visualization of the cells used for trajectory building (same as cells used for Fig. 3a and related figures) coloured by cell type (left) and pseudotime (right), on which a developmental trajectory was calculated using Monocle3. A similar branching structure was found. Although it did not allow for finer segregation of the terminal neuronal types, Monocle3 ascribed a unique trajectory going from progenitors to all classes of neurons, with a post-mitotic branching into CPN and CFuPN branches (arrows, similar to URD). d, Gene expression along trajectories calculated with URD (right) or Monocle3 (left). e, URD trajectory branching tree of the developing cortex. Cells are coloured according to their developmental time of collection. f, g, Normalized fraction of cells corresponding to each time point of collection (f) and to each cell type (g) across binned pseudotime, showing that pseudotime is aligned with age and cell type (compare with Fig. 1c).

Extended Data Fig. 5 Neuronal cell types diverge post-mitotically.

Related to Fig. 3. a, Branching trees showing the expression of marker genes of apical progenitors (Sox2, Hes5), intermediate progenitors (Eomes) and excitatory neurons (Neurod2), as well as genes characteristic of the dorsally-derived cortical cell types, including callosal neurons (Satb2, Cux2), layer 4 stellate neurons (Rorb), corticofugal neurons (Fezf2, Tle4, Pcp4, Tcerg1l), putative near-projecting neurons (Tshz2), astrocytes (Slc1a3, Aqp4, Aldh1l1), and ependymocytes (Foxj1). There is a sequential progression of apical progenitors, intermediate progenitors and excitatory neurons, followed by neuronal subtypes, astrocytes and ependymocytes. b, c, Force-directed layout embedding representation of the developmental branching tree, showing the initial part of the tree. Cells are coloured according to their pseudotime value (left), age of collection (middle), or cell type (right). Differentially expressed genes between AP in each branch are highlighted and their expression levels are shown in c (see also Supplementary Table 2). AP corresponding to the astrocytic and neuronal branches form a continuum of cells. d, Tangram mapping probabilities of E13.5 AP from each branch onto matching Slide-seq section show that both states coexist in the ventricular zone. Arrowheads and arrows in the inset show probabilities in individual beads. e, Top, apical progenitors from different ages form a continuum of cells and do not segregate into distinct clusters. AP from all time points were sub-clustered separately, coloured by age (left) and clusters identified by Seurat (right). Bottom, a similar effect is observed when both apical and intermediate progenitors were sub-clustered, cells first separate mostly by cell type (right), and then continuously by time point (left). f, Expression of CPN markers (Satb2, Pou3f3 and Cux1, left), and CFuPN markers (Fezf2, Tle4 and Bcl11b, right) in both early (E12.5) and late (E15.5) AP, as well as in the combined AP populations (all time points), when AP were co-embedded using the top 100 differentially expressed genes between CFuPN and CPN as input for principal component analysis and downstream clustering and visualization. Cell-type marker genes are expressed in progenitors but do not drive clustering of the cells. g, Separation in different classes of neurons occurs post-mitotically. Branching tree and UMAP representation of the full developmental atlas coloured by cell-cycle phase, as predicted by gene expression. h, Tangram mapping of layer 5 and 6 CPN on P1 Slide-seq section. P1 cells allocated to each of the two terminal branches broadly labelled as layer 5&6 CPN were mapped onto the Slide-seq P1 section to find their distribution in the developing cortex. Mapping probabilities (top) indicated that cells from branch 1 were more likely to be mapped to layer 5, while cells from branch 2 mapped with enrichment to layer 6. Genes differentially expressed between both populations, layer 5- (Rorb, Fam19a2) and layer 6-CPN markers (Cdh13, Igsf21, Gnb4) show matching distribution (bottom).

Extended Data Fig. 6 Novel expression pattern of selected genes and NMF gene modules.

Related to Fig. 3. a–d, New expression patterns emerging from the inferred tree. Expression levels overlaid on the tree (left), UMAP of full scRNA-seq developmental data (middle), and Slide-seq counts on an E15.5 or P1 section of cortex (right) for each gene. a, Rorb is expressed in developing CFuPN, astrocytes and layer 4 stellate neurons and present in the deep cortical plate (CP). b, Pcp4 is expressed in migrating and immature neurons that contribute to both CPN and CFuPN, as well as in SCPN, layer 6b, NP and Cajal–Retzius cells (CR), and is found in the intermediate zone (IZ) and CP. c, Npy is expressed in CFuPN and highly in CPN of layers 5 and 6. A positive Npy signal is evident in the deep CP through Slide-seq. d, Cck was also detected in CFuPN and at higher levels in CPN of layers 5 and 6. Low levels of expression in the CP were detected via Slide-seq. VZ, ventricular zone. e, Validation of expression of novel cell-type-specific genes emerging from the cascade analysis. Expression levels overlaid on the tree (left), time course expression on purified subtypes of PN from DeCoN transcriptomic resource24,52 (middle), and in situ hybridization from the Allen Developing Mouse Brain Atlas23,53 (right, age indicated in figure). f, Complete set of gene programs of connected modules found by NMF. Each circular node represents a module. Modules are horizontally aligned to the developmental stage that the module was computed from, and coloured by the annotated function (see also Supplementary Table 3). g, Scaled expression overlaid on branching tree of modules corresponding to broad neuronal differentiation programs, coloured according to program identity. h, Selected NMF modules expression from scRNA-seq data mapped onto time-matched Slide-seq section using Tangram (Methods).

Extended Data Fig. 7 Genetic cascades accompanying the development of cortical cell types.

Related to Fig. 3. Gene cascades for projection neuron subtypes, astrocytes and ependymocytes differentiaton. The x axis represents pseudotime across the tree. Each row is a gene in which gene expression is scaled to the maximum observed expression and then smoothened. Genes are ordered by the pseudotime value at which they enter and then leave ‘peak’ expression (expression 50% higher than minimum value), and start and then leave ‘expression’ (expression 20% higher than minimum value), in that order. Smoothening of expression values was performed using spline fitting from URD for expression dynamics (Methods). Known marker genes for the cell type are labelled; see Supplementary Table 3 for the full list of genes.

Extended Data Fig. 8 Extended analysis of genes distinguishing between branches in URD tree.

Related to Fig. 3. a, Feature importance (0.5 power transformed, dot size) and average expression of genes predicted to be involved in cell-type divergence (row-scaled, colour). Top 10 genes per branch, ranked by their Friedman MSE score (importance) for distinguishing between cells in one branch versus cells in sibling and parent branch. The colour bar at the top indicates branch-points marked on the tree to the left. Genes in red correspond to TFs. Expression in parent branch not shown. b, Gene Ontology analysis showing molecular function enrichment among genes involved in branch-points as determined in a. c, Simplified URD branching trees on which average gene expression within a segment and a pseudotime bin is overlaid on the tree structure, showing restricted expression patterns of genes identified in a.

Extended Data Fig. 9 Characterization of scATAC–seq atlas and developmental trajectories of accessible elements through cortical development.

Related to Fig. 4. a, scATAC–seq data per time point. UMAP visualization of the single cells coloured by their predicted identity from integration with scRNA-seq datasets (left). Gene accessibility of selected markers for main cell types present in each time point (middle). Maximum prediction score for each cell based on labels transferred from scRNA-seq data (right). b, URD chromatin accessibility trajectories during cortical development. Cells are coloured according to their age of collection. c, ATAC trees highlighting the accessibility of marker genes characteristic of the different cortical cell types, including apical and intermediate progenitors, astrocytes, callosal and corticofugal neurons. d, RNA-based tree generated from only the E13.5, E15.5 and E18.5 time points, corresponding to the scATAC–seq data. Trees are coloured by cell type (left) and time of collection (right). e, Chromatin accessibility and gene expression cascades for layers 2&3 CPN and SCPN. Same genes are plotted for both modalities, in the same order. f, Chromatin accessibility and gene expression across pseudotime for illustrative genes from the SCPN cascade, CPN markers, or general neuronal markers plotted on the SCPN cascade. In many cases accessibility rises before gene expression.

Extended Data Fig. 10 Transcription factors with binding sites enriched along cortical development.

Related to Fig. 4. a, Total number of accessible sites identified per time point and fraction that is dynamic across cell types (that is, is enriched in at least one cell type). b, Left, schematic of the approach used to identify candidate cell-type-specific enhancers. Differential expression analysis identified cell-type-specific genes, for which we calculated co-accessibility (correlation higher than 25%) between distal elements (within a 100 kb region) and target gene promoters using Cicero, within each cell type. c, Distal elements co-accessible with the Pcp4 promoter region in E18.5 SCPN and migrating neurons. Cicero co-accessibility is shown in blue curves, detected peaks in each cell type are shown as coloured bars. Black bars correspond to promoter peak, blue bars are peaks selectively co-accessible in CFuPN, and purple bars are peaks only co-accessible in migrating neurons. Boxes indicate TFs for which motifs are present in indicated peaks. Peaks are aligned to coverage plots (bottom) showing combined ATAC reads for the indicated cell types. Chromosome coordinates and genes are indicated at bottom. d, TF binding sites enrichment on accessible sites of cells in the CPN vs CFuPN branch-point (see Fig. 3d) shows significant enrichment of some of the TF detected in Fig. 3d, suggesting an actual role in this step. e, Left, in situ hybridization against Eomes (IP marker), Ube2c (mitotic marker) and Dmrta2 showing expression of the latter in the dorsal ventricular zone (VZ) of E12.5 developing cortex. Right, in situ hybridization against Satb2 and Myt1l showing expression of the latter in newborn neurons, co-expressed with Satb2. Slide-seq gene expression at the indicated ages show similar expression patterns. Scale bars, 30 μm. Representative images from in situ hybridizations repeated in 2 different embryos. ML and DV indicate dorso-ventral and medio-lateral orientations. f, Slide-seq gene expression of several TFs whose binding sites were found to be enriched within the accessible regions of the indicated trajectories (or portion of). Confirmation of gene expression in target cell type supports TF activity.

Extended Data Fig. 11 Use of developmental atlas to investigate Fezf2-knockout cortex.

Related to Fig. 5. a, Violin plots of number of genes (left), number of mRNA molecules (counts; middle), and percentage of mitochondrial counts (right) per cell in control (Het) and Fezf2 knockout, and UMAP visualizations of merged scRNA-seq datasets at E15.5 (top) and P1 (bottom). UMAP visualizations are coloured by genotype or assigned cell type. b, UMAP visualization of single-cell transcriptomes from the excitatory lineage of control and KO cortices at P1 (as shown in Fig. 5c for E15.5), coloured by genotype (left) and cell type (right). Proportion of cells of each cell type by genotype (bottom). c, Heat map showing the overlapping scores between NMF modules identified in the E15.5 Fezf2 datasets and the original E15.5 wild-type modules. All modules were identified with an overlapping score of 40% or higher. d, Left, scaled module expression of significant modules in all cells (two-sided Wilcoxon Sum Rank test, Bonferroni correction). Right, average expression of the top 30 genes from selected modules, in apical and intermediate progenitors, and excitatory neurons, by genotype. Differential expression between control (Fezf2 Het) and knockout neurons, at the single-cell level (two-sided Wilcoxon rank-sum test, Bonferroni correction). e, Gene Ontology terms enriched in the Fezf2-knockout-specific module. f, Confusion matrix for random forest classifier calculated using 1,000 cells per cluster of the wild-type developmental atlas. The remaining held-out cells were used to test accuracy. g, Classification of control (Fezf2 Het) and Fezf2 knockout excitatory neurons by the classifier presented in f, for P1 (left) or E15.5 (right) datasets. Cells are grouped according to their manually assigned identity on the basis of the expression of marker genes. Box plots to the right show the corresponding classification scores in which the middle line is the median, the lower and upper hinges correspond to the 25% and 75% quantiles, the upper whisker corresponds to the largest value no larger than 1.5 × IQR from the hinge and the lower whisker corresponds to the smallest value at most 1.5 × IQR of the lower hinge. Lines in magenta, cyan and green indicate 1, 0.5 and 0 values, respectively. Total number of cells: Fezf2 Het E15.5 = 6,092, Fezf2 knockout E15.5 = 6,110, Fezf2 Het P1 = 5,101, Fezf2 knockout P1 = 4,235.

Extended Data Fig. 12 CFuPN acquire CThPN-like and layers 5&6 CPN-like identities in the absence of Fezf2.

Related to Fig. 5. af, Two subtypes of deep-layers knockout cells were identified at E15.5. Sub-clustering of deep-layers knockout-exclusive cells alone at E15.5 (a) shows a Satb2low, Bcl11bhigh cluster (cluster 0), and a Satb2high cluster expressing also CPN markers Cux1 and Pou3f2 (cluster 1), as indicated in the violin plots (b). Differential expression analysis between both subtypes indicates enrichment of CFuPN genes in cluster 0 and CPN genes in cluster 1 (c). d, Comparison to neurons in E15.5 wild-type data showing overlap between differentially expressed genes and markers from E15.5 neuronal subtypes. Bars indicate the number of overlapping genes and are coloured by the adjusted P value calculated by hypergeometric test for significant enrichment. e, Classification of cells from both E15.5 knockout-specific clusters according to random forest classifier shows good agreement between both annotations. f, NMF module expression (as in Extended Data Fig. 11d) in the knockout-specific cells, grouped according to the cell type assigned by the random forest classifier. g, h, Sub-clustering (g) and differential expression analysis (h) of deep-layers knockout-exclusive cells alone at P1 reveals two subpopulations that correspond to CThPN-like and layers 5&6 CPN-like populations. i, Classification of cells from both P1 knockout-specific clusters according to random forest classifier shows good agreement between both annotations. j, Differential expression analysis of the aberrant layer 5&6 CPN-like cells from the knockout-exclusive populations at P1 compared to layers 5&6 CPN (left) or SCPN (right) populations in the control. k, Differential expression analysis of the aberrant CThPN-like cells from the knockout-exclusive populations at P1 compared to CThPN in the control. l, m, In situ hybridization against Bcl11b and Lpl (l) or Ptn (m), in P1 control (wild type) and Fezf2 knockout coronal sections, showing higher levels of expression of Lpl and Ptn on layers 5 and 6 and reduced Bcl11b in layer 5 (insets to the right correspond to boxes in left panels). Note cells expressing both Bcl11b and Lpl in magnification from layer 6, reflecting an aberrant CThPN identity. Number of positive speckles per 104 μm2. Quantification was calculated with a modified pipeline from CellProfiler from an area of ~200 by 150 μm or ~200 by 100 μm centred in layers 6 or 5, respectively. Data correspond to mean ± s.e.m., from n = 3 mice, > 3 sections each. Unpaired t-test, exact P values indicated. Scale bars, 30 μm; except in higher magnification in l, 15 μm. n, Summary of phenotypes in the Fezf2 KO. CThPN acquire an aberrant identity and upregulate CPN genes, while SCPN convert to layers 5&6 CPN-like neurons, which project through the anterior commissure (ac) to the contralateral cortex. th: thalamus, sc: spinal cord, bs: brain stem. o, Violin plots of number of genes (left) and mRNA molecules (counts; middle), and percentage of mitochondrial counts (right) per cell in control and KO Fezf2 E13.5 single cell transcriptomes, and UMAP visualizations of combined control and KO complete datasets, coloured by genotype or assigned cell type. p, Dorsally-derived cells in Fezf2 control and KO E13.5 scRNA-seq, visualized via UMAP and coloured by genotype (left) or cell types (right). Proportion of cells in each cell type, according to their genotype. q, Differential expression analysis between control and KO migrating or immature neurons shows upregulation of a subset of CPN marker genes and downregulation of CFuPN-specific genes.

Supplementary information

Reporting Summary

Supplementary Table 1

Marker genes used for mapping scRNA-seq data onto Slide-seq using Tangram.

Supplementary Table 2

Differentially expressed genes between apical progenitors of all ages in the neuronal vs. glial branch of the inferred tree.

Supplementary Table 3

Genes that constitute the cascades plotted in Extended Data Fig. 7, in the same order as plotted.

Supplementary Table 4

Genes and gene scores in NMF modules calculated at each time point of the developmental wild-type atlas.

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

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