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The cerebral cortex of the mammalian brain controls a wide range of flexible and motivated behaviours and is extensively expanded in species with more advanced cognitive functions (including humans). This brain region has been a prime site for the study of the diverse cell types it contains and how they form functionally specific neural circuits4,5,6. Cell types in the cortex can be defined on the basis of multiple cellular properties, including gene expression, morphology, physiology, connectivity or various combinations thereof6,7,8,9. Over the past decade, single-cell transcriptomics has provided comprehensive and detailed cell-type classifications that define around 100 transcriptomic cell types (T-types) in each cortical area of the adult brain, and these are markedly consistent across areas and across species (for example, from mouse to human)10,11,12,13. These T-types can be hierarchically organized into classes and subclasses that reflect their varied relatedness and are likely to be rooted in the evolutionary and developmental histories of the cell types9. Specifically, in each cortical area, about 28 cell subclasses have been defined: 9 glutamatergic excitatory neuronal subclasses, 8 GABAergic inhibitory neuronal subclasses, 3 glial subclasses, 3 immune subclasses and 5 vascular subclasses. The glutamatergic subclasses are organized on the basis of their layer (L) specificity and long-range projections: L2/3 intratelencephalic projecting (IT), L4/5 IT, L5 IT, L6 IT, L6 Car3, L5 extratelencephalic projecting (ET), L5/6 near-projecting (NP), L6 corticothalamic projecting (CT) and L6b. The GABAergic subclasses—Lamp5, Sncg, Vip, Pvalb, Pvalb chandelier, Sst, Sst Chodl and Lamp5 Lhx6—are organized on the basis of their developmental origins. The glial subclasses comprise astrocytes, oligodendrocytes and oligodendrocyte precursor cells (OPCs), whereas the immune subclasses comprise microglia, border-associated macrophages (BAMs) and lymphoid cells. Finally, the vascular subclasses include vascular leptomeningeal cells (VLMCs), arachnoid barrier cells (ABCs), endothelial cells, pericytes and smooth muscle cells (SMCs)13,14.

Multimodal integrative approaches have been used to align the different levels of transcriptomic cell types to morphology, physiology and connectivity and, in some cases, to refine cell-type definition13,15,16. For example, using Patch-seq, GABAergic neurons in the mouse visual cortex are classified into 28 morphoelectric-transcriptomic types (MET-types), which represent a coarser resolution from the original 61 T-types but with increased cross-modality concordance in each MET-type15. Computational matching of local dendritic and axonal morphology has enabled the assignment of T-type identities to reconstructed neurons in the mouse visual cortex that have long-range projection patterns or synaptic connectivity profiles derived from light or electron microscopy data17,[18](https://www.nature.com/articles/s41586-025-09644-1#ref-CR18 “Sorensen, S. A. et al. Connecting single-cell transcriptomes to projectomes in mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/2023.11.25.568393

(2023).“). Cell-type-targeting genetic tools, barcoded viruses and spatial transcriptomic approaches have also been used to relate transcriptomic identities to connectivity or functional properties19,20,21.

Development of the mammalian cortex has been extensively studied over the years3,22,23,24,25. It is now known that glutamatergic neurons, astrocytes and oligodendrocytes are generated in the dorsal pallium (which subsequently becomes the cortex), whereas GABAergic neurons are generated in the subpallium and undergo long-distance migration into the cortex following specific routes26,27. Immune and vascular cell types originate outside the brain. In both the pallium and the subpallium, progenitors in the ventricular zone (VZ) and the subventricular zone (SVZ) progressively give rise to radial glia (RG), intermediate progenitors (IPs) and immature neurons (IMNs). In the developing cortex, glutamatergic neurons in different layers are thought to be generated sequentially and migrate radially to reach their target layers in an inside–out manner28,29. After neurogenesis, RG switch to gliogenesis and generate astrocytes, OPCs and oligodendrocytes (although some oligodendrocytes also come from the subpallium). Postmitotically, all cell types undergo specific maturation processes. Glutamatergic and GABAergic neurons go through dendritic and axonal arborization, synapse formation and activity-dependent circuit refinement. In particular, the visual cortex goes through experience-independent and experience-dependent circuit development to acquire increasingly refined visual response properties30.

There are substantial gaps in our understanding of the developmental processes and mechanisms involved in the formation of the mammalian cortex. It is still unclear when specific cell-type identities are established, to what extent cell types observed in the adult cortex are established during the embryonic stage and how lineage-bifurcation decisions occur. In the postnatal developmental period, many processes are in play with overlapping time courses. Such processes include intrinsic neuronal activities, influence of external sensory inputs, incoming and outgoing long-range connections, formation of local excitatory and inhibitory circuit motifs, and neuronal and non-neuronal cell–cell interactions. Consequently, cells are undergoing rapid state transitions. Despite the discovery of many genes, proteins and epigenetic signatures involved in these processes, we have little systematic knowledge about the cell-type-specific events and their dynamics, how cell-type-specific circuits are formed and what mechanisms drive cell-type and circuit maturation. To address these questions, it is important to investigate developmental changes at the single-cell level and to link these changes across time with cell-type specificity.

Here we report a comprehensive transcriptomic and epigenomic cell-type atlas of the developing mouse visual cortex with high temporal resolution from embryonic to postnatal development. We systematically identify the precise timing of the onset of all excitatory, inhibitory and non-neuronal cell subclasses and clusters in the visual cortex and demonstrate that there is a pattern of continuous cell-type diversification. We also systematically categorize large numbers of differentially expressed (DE) gene sets and differentially accessible (DA) chromatin peak modules that are concurrently associated with specific cell types and developmental ages. Together, these data provide a real-time dynamic molecular map associated with individual cell types and specific developmental events that will facilitate future investigations of the mechanisms of cell-type and circuit development.

Mouse visual cortex developmental cell-type atlas

We generated two datasets of the developing mouse visual cortex using single-cell RNA sequencing (scRNA-seq) and single-nucleus Multiome (snMultiome, a combination of single-nucleus RNA-seq (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq)). We used the scRNA-seq data to generate a transcriptomic cell-type atlas and developmental trajectory map. We then used the snMultiome data to reconstruct an epigenetic chromatin-accessibility landscape across development (described below).

We first generated 91 scRNA-seq libraries using 10x Genomics Chromium v3 (10xv3), which resulted in a dataset of 913,297 single-cell transcriptomes (Supplementary Table 1). The scRNA-seq dataset densely covered the embryonic and postnatal periods over 34 time points from embryonic day 11.5 (E11.5) to postnatal day 28 (P28) and adult stage P56 (Fig. 1a). We established stringent quality control (QC) metrics (Methods and Supplementary Table 2), similar to our previous studies14, to remove low-quality single-cell transcriptomes. To overcome natural variation over fixed collection times, we assigned a predicted ‘synchronized age’ to each cell to obtain more homogeneous temporal transcriptomic profiles for some analyses (Methods and Extended Data Fig. 2a,d).

Fig. 1: Transcriptomic developmental cell-type atlas of the mouse visual cortex.

a, Schematic timeline of samples collected from scRNA-seq and Multiome in this study along with major developmental events of the isocortex. b, The transcriptomic taxonomy tree of 148 clusters organized in a dendrogram (scRNA-seq, n = 568,654 cells; Multiome, n = 200,061 nuclei). The classes and subclasses are marked on the taxonomy tree. Full cluster names are provided in Supplementary Table 3. Bar plots represent (top to bottom): major neurotransmitter (NT) type, number of scRNA-seq cells, number of Multiome nuclei, age distribution of scRNA-seq cells, age distribution of Multiome nuclei and number of scRNA-seq subclusters for each cluster. c–i, UMAP representations of all cell types coloured by class (c), subclass (d), cluster (e), subcluster (f), age (g), synchronized age (h) and pseudotime (i). j, Constellation plot showing the UMAP centroids of subcluster nodes coloured by cluster.

To build the developmental trajectory of the adult cell types, we first conducted label transfer using our recently established adult mouse whole brain taxonomy14. The Allen Brain Cell–Whole Mouse Brain (ABC–WMB) Atlas served as the reference for cells at the adult stage to assign cell-type identities at the cluster level. Adult cell-type identities were then propagated to younger cells through sequential cell-type label transfer from older to younger synchronized ages for all postnatal ages (Methods and Extended Data Figs. 2a and 3). Overall, to the P20–28 age bin, we transferred labels from 35 out of the 35 P56 glutamatergic clusters, 60 out of the 61 P56 GABAergic clusters and 16 out of the 20 P56 glial clusters derived from the adult ABC–WMB Atlas to capture nearly all the cell-type diversity in the adult mouse visual cortex.

For the embryonic time points, we mapped cells in prenatal-enriched global clusters to a developmental mouse brain scRNA-seq reference from a previous study31 to identify broad cell types (Methods). Clusters mapped to RG in that study31 were further classified as neuroepithelial cells (NECs) or RG, with RG arising from NECs. Previous studies have suggested that a transcriptomic continuum exists for the gradual transition from IPs to IMNs, including migrating neurons, to mature cortical excitatory neurons32. Thus, we used a combinatorial set of marker genes to assign clusters to these categories (Methods). We also defined preplate Cajal-Retzius (CR) cells and glioblasts.

After iterative de novo clustering and merging, we conducted further annotation and identified and removed an additional set of ‘noise’ subclusters that had escaped the initial QC process or subclusters that probably originated from outside the cortex. This step resulted in a final set of 568,654 high-quality single-cell transcriptomes that form 714 subclusters (Extended Data Fig. 1a). As part of the annotation, we integrated early developmental ages of our data with three external datasets31,32,33 using scVI34 (Extended Data Fig. 4). Overall, our cell-type assignment was broadly consistent with those from the previous studies at the subclass level while providing finer cell-type and temporal resolutions with additional cluster and subcluster annotations.

We present these complex molecular relationships through a high-resolution transcriptomic cell-type taxonomy for the adult and developing mouse visual cortex, visualized in a dendrogram and a uniform manifold approximation and projection (UMAP) plot (Fig. 1b–j). The taxonomy comprises four nested levels of classification: 15 classes, 40 subclasses, 148 clusters and 714 subclusters (full cluster names are provided in Supplementary Table 3). It includes all known neuronal and non-neuronal cell classes of the developing neocortex from the literature3 and many transitional cell types and subtypes discovered here. We also generated a list of 6,724 DE genes that differentiate among all clusters and subclusters (Supplementary Table 4).

Of the 148 clusters (Supplementary Table 3), 132 clusters (containing 517 subclusters) aligned with the adult ABC–WMB Atlas14, which represent maturing cell types. These clusters belong to 27 of the abovementioned 28 canonical cortical cell subclasses13,14 (without lymphoid cells), and 1 destined to the entorhinal cortex (see below), under a total of 9 classes. We used the labels of these 28 subclasses and 132 clusters from the ABC–WMB Atlas while modifying some of their class labels to be more consistent with the embryonic classes. The remaining 16 clusters (containing 197 subclusters) represent progenitor cells and IMNs in embryonic and perinatal stages and belong to 12 subclasses under 8 classes.

Neuronal cell types and their progenitors constitute a large proportion of the developmental atlas and represent 10 classes: NECs, CR Glut, RG, IPs, IMNs, nonIT Glut, IT Glut, CTX-CGE GABA, CTX-MGE GABA and CNU-MGE GABA (Fig. 1b,c). The 10 classes are further divided into 29 subclasses, 109 clusters and 599 subclusters. The nonIT Glut class consists of four main glutamatergic subclasses—L5 ET, L5 NP, L6 CT and L6b—and a L6b/CT ENT subclass that is mostly present at E17–P3 and belongs to the entorhinal cortex on the basis of our mapping result (Fig. 1b,d). The IT Glut class contains four main subclasses—L2/3 IT, L4/5 IT, L5 IT and L6 IT—and a CLA-EPd-CTX Car3 subclass that consists of a distinct L6 cell type shared with the claustrum and the endopiriform nucleus12,19.

Cortical GABAergic neurons are born in three subpallial progenitor zones: the caudal ganglionic eminence (CGE), the medial ganglionic eminence (MGE) and the preoptic area (POA)24. Neural progenitor cells in these regions35 generate IMNs, and the IMNs migrate to the cortex, where the IMNs mature36,37. Our data showed that MGE GABA IMNs differentiate into four subclasses—Sst Gaba, Pvalb Gaba, Pvalb chandelier Gaba and Lamp5 Lhx6 Gaba—in the CTX-MGE class and one subclass, Sst Chodl Gaba, in the CNU-MGE class14,[38](https://www.nature.com/articles/s41586-025-09644-1#ref-CR38 “van Velthoven, C. T. J. et al. Transcriptomic and spatial organization of telencephalic GABAergic neurons. Nature https://doi.org/10.1038/s41586-025-09296-1

(2025).“) (Fig. 1b,d). The CGE GABA IMNs gradually differentiate into Vip Gaba, Sncg Gaba and Lamp5 Gaba subclasses.

All non-neuronal cell types are classified into 5 classes—glioblast, OPC-Oligo, Astro-Epen, immune and vascular—that are further divided into 11 subclasses (Fig. 1b–d). Glioblasts are one main type of progenitor for the OPC-Oligo and Astro-Epen classes39. The OPC-Oligo class contains two subclasses: OPCs (expressing Olig1, Olig2 and Pdgfra) and oligodendrocytes (Oligo; St18 and Opalin). The Astro-Epen class contains one subclass of telencephalic astrocytes: Astro-TE (Apoe, Aqp4, Aldh1l1 and Slc1a3). The immune class consists of two subclasses: microglia (Siglech, Sall1 and Ifitm10) and BAMs (F13a1, Pf4 and Mrc1). The vascular class consists of five subclasses: ABCs (Slc47a1), VLMCs (Col1a1, Col1a2, Apod and Slc6a13), pericytes (Kcnj8), SMCs (Acta2 and Myh11) and endothelial cells (Endo; Ly6c1 and Slco1a4).

Building cell-type development trajectories

Trajectory analysis is an essential tool for modelling the dynamic process of cellular development and differentiation. Given the cell-type identities at the adult stage and the dense temporal sampling, we were able to progressively propagate cell-type identities between two adjacent ages (see above). Thus, we defined edge weights of the trajectory tree based on k-nearest neighbours (k-NN) in the integrated space across synchronized ages for the postnatal trajectory, whereas the k-NN approach with Monocle3-based40 pseudotime was used for the embryonic trajectory (Methods and Extended Data Fig. 2a–c). To demonstrate the robustness of our taxonomy and trajectory map, we used scVI to integrate data between adjacent age bins. The results using this method closely matched those from Seurat (Methods and Supplementary Fig. 1).

Overall, we retained all edges between a cluster and its potential antecedents that have edge weights of >0.2 (Supplementary Table 5). To simplify visualization and conceptualization of the developmental process, we chose the edge with the maximal weight between a cluster and one antecedent to build the developmental trajectory map across the entire timeline from E11.5 to P56 (Figs. 2a and 3). Of note, the total 987 chosen edges with maximal weights to build all the trajectories have an average weight of 0.71 (and more than 85% with weights of >0.5), whereas the 321 non-chosen edges have weights of <0.5 with an average of 0.29 (Supplementary Table 5). This finding indicates that there is a relatively unambiguous trajectory pattern. We then computed the global pseudotime based on the entire developmental trajectory map (Figs. 1i and 3a–d and Methods).

Fig. 2: Developmental trajectories of visual cortex cell subclasses.

a, Transcriptomic trajectories of visual cortex cell subclasses with estimated timings of onset and major branching nodes. b, Relative proportions of cells corresponding to the different subclasses at each age. Note that relative proportions between neuronal and non-neuronal cells do not reflect the actual situation owing to the variable FACS methods used for different scRNA-seq libraries (Methods, Extended Data Fig. 1d and Supplementary Table 1). c, UMAP representations of early developmental cell types coloured by subclass, cluster, age and expression of key marker genes separating different trajectories. d, Dot plot showing the expression of DE genes across embryonic ages and P0 in NEC and RG populations. The numbers of NECs and RG at each age point are shown at the bottom. e, Representative MERFISH sections at P0 and P56 with specific cell types labelled. an, anterior section that includes the somatosensory cortex; po, posterior section that includes the visual cortex; scl, subcluster.

Fig. 3: Developmental trajectories of visual cortex cell types.

a–h, Transcriptomic trajectory trees (a–d) and constellation plots (e–h) of glutamatergic (a,e), neuroglia (b,f), MGE (c,g) and CGE (d,h) clusters, which are grouped into subclasses. Each branch represents a cluster, for which the name is labelled in the same colour in the trajectory tree. In a, for glutamatergic clusters, the root is NECs and the tips are E12.5 terminal CR Glut cluster and 35 P56 terminal nonIT and IT cell clusters. In b, for neuroglia, the root is RG and the tips are 18 P56 terminal OPC-Oligo and Astro-TE clusters. In c, for MGE GABAergic neurons, the root is MGE GABA RG and the tips are 32 P56 terminal CTX-MGE and CNU-MGE clusters. In d, for CGE GABAergic neurons, the root is CGE GABA and the tips are 29 P56 terminal CTX-CGE clusters. Marker genes for each branch point are shown along each branch. Branch lengths represent pseudotime, measured from the origin of each trajectory. Each internal node represents a cluster composed of cells from one synchronized age bin and is coloured by that synchronized age bin. In the constellation trajectory plots, subclass names and P56 cluster identifiers are labelled.

We constructed a branched trajectory tree for the neuronal and glial subclasses in the visual cortex (Fig. 2a). The tree was largely consistent with previous studies41,42 and supported by the progression of relative proportions of different subclasses with time (Fig. 2b) and by key marker genes for each branching node (Fig. 2c and Extended Data Fig. 5), with a UMAP focused on early developmental cell types (Fig. 2c). The trajectory tree revealed that the earliest cell type emerging from NECs is IMN CR, which probably arises from NECs in the cortical hem43 before E11.5 and gradually matures into CR cells. Then RG molecular identity emerges at E13, followed immediately by the emergence of IP nonIT and IMN nonIT molecular identities at E13.5. Molecular identities of IP IT cells and glioblasts, as well as IMN IT deep-layer cells, appear around E15.5. IP nonIT cells give rise to more IMN nonIT cells. IMN nonIT cells turn into three subclasses of nonIT neurons (L5 ET, L6 CT and L6b) at E17, whereas the fourth subclass (L5 NP) emerges at E18.5. IP IT cells give rise to more IMN IT cells. IMN IT deep-layer cells turn into L6 IT and L5 IT neurons at E17, and IMN IT upper-layer cells turn into L4/5 IT and L2/3 IT neurons at E18.5. Meanwhile, glioblasts give rise to astrocytes and OPCs around E17. Separately, for GABAergic neuron classes, MGE RG and MGE IMNs appear before E11.5, and MGE IMNs differentiate into Sst and Pvalb neurons at E14.5. CGE IMNs appear in the cortex later around E15.5, and they differentiate into Vip, Sncg and Lamp5 neurons at E18–P2.

It should be noted that in our data, the identity of a cell is defined by its real-time transcriptional profile (molecular identity) and not by its birth date. At the earliest stage (E11.5–E12.5), cells originating from the pallium are mainly composed of NECs (expressing Hmga2), IMN CR cells and the early-born CR cells (expressing Ebf1, Ebf2, Ebf3, Reln, Calb2, Crabp2 and Trp73) (branching node 1; Fig. 2a–c and Extended Data Fig. 5). The IMN CR cells are antecedents of CR cells, and the expression of Eomes, Neurog2, Neurod1 and Neurod2 decreases as CR cells mature.

Beginning at E13, RG molecular identity (Sox2, Pax6, Hes1 and Hes5) emerges, and RG simultaneously give rise to IPs (Eomes, Btg2, Neurog2 and Gadd45g) and IMNs (Dcx, Neurod1, Neurod2, Neurod6, Tubb3 and Tbr1), consistent with the co-existence of direct and indirect neurogenesis42. Most IPs generated between E13.5 and E16.5 are IP nonIT cells and transition into IMN nonIT cells, whereas most IPs between E17 and P0 are IP IT cells and transition into IMN IT cells (Fig. 2b). IMN IT deep-layer cells are observed at later times than IMN nonIT cells (E15.5–P1 compared with E13.5–E17; Fig. 2a–c), even though they colocalize in deep layers.

We observed clear molecular signatures that distinguish nonIT and IT trajectories at the IP and IMN stages. The nonIT IPs and IMNs express Neurog1, Lhx9, Fezf2, St18, Rmst, Nhlh1, Nhlh2 and Kif26a, whereas the IT IPs and IMNs express Pou3f1, Pou3f2, Pou3f3, Kif26b, Lama2 and Slco1c1 (node 3; Fig. 2a,c and Extended Data Fig. 5). The canonical deep-layer neuron markers Fezf2, Bcl11b, Foxp2 and Tle4 all have selective expression in nonIT cells but exhibit varying temporal dynamics, with Bcl11b and Fezf2 emerging in IP, whereas Foxp2 and Tle4 appear at the late IMN stage.

The transcriptomic difference between IT and nonIT trajectories is not only present in IPs and IMNs but already in RG at different ages, with the nonIT cell marker Rmst present in early RG and the IP marker Pou3f2 in later RG (Fig. 2d). Recent studies have suggested that the transcriptional profile of cortical RG changes as they generate nonIT neurons, IT neurons and glial cells33,41. In our data, the divergence of progenitors for glutamatergic neurons (nonIT Glut and IT Glut) and glia (OPC-Oligo and Astro) may start as early as E15.5 (node 2; Fig. 2a–c), and the RG subclass shows a continuum of cells among different transcriptomic states (Fig. 2d). First, earlier-stage RG are enriched for Neurog2 and Tenm4 (refs. 44,45), which may represent a committed neurogenic state, whereas expression of Tnc is seen in later-stage RG, which may represent a committed gliogenic state. Second, glioblasts emerge at E15.5 and express higher levels of Fabp7, Lipg, Slco1c1, Tnc, Qk and Slc1a3 than RG, which indicate their transition towards the glial cell trajectory. Our data suggest that RG already show complex temporal gene expression changes, and they exit the RG states at different ages with these temporal signatures to become IPs and IMNs or glioblasts that are committed to distinct neuronal (nonIT or IT) or glial trajectories. These results are consistent with and may explain the observed heterogeneity in previous lineage tracing and transcriptomic profiling studies33,46,47,48.

We used a MERFISH dataset we recently generated that covers the entire mouse brain at P0 to identify the relative abundance and spatial location of developmental clusters at P0, a critical transitioning time point (Fig. 2e and Methods). The P0 MERFISH data revealed the spatial organization of both embryonic cell types and the emerging subclasses that persist into adulthood. At this time, IP nonIT, IMN nonIT and IMN IT deep-layer cells are scarce, whereas there is still a prominent IP IT population localized in the SVZ and a large number of IMN IT upper-layer cells spread across the cortical depth. These results are consistent with our scRNA-seq findings (Fig. 2a–c). Notably, subclusters in the IMN IT upper-layer cluster can be placed into three groups with distinct layer distribution patterns indicative of continued radial migration. Subclusters 1–2 are concentrated in or near the SVZ, whereas subclusters 6–11 are located near the surface of the cortex. Subclusters 3–5 are distributed across the cortical depth between the other two groups. These spatial patterns correspond to the relative locations of the subclusters in UMAP as they progress from more immature to more mature states.

Developmental trajectories of glutamatergic types

Our analysis indicated that the postmitotic IMNs (IMN nonIT and IMN IT) progressively diversify into more distinct cell subclasses and types (Figs. 1c–f, 2a and 3a,e). In the nonIT trajectory, the IMN molecular identity (Fezf2, Bcl11b and Neurod2) emerges at E13.5, with increasing expression of Foxp2, Tle4 and Crym at the late IMN stage. This trajectory splits around E17 into L6 CT, L5 ET and L6b (node 4; Fig. 2a–c and Extended Data Fig. 5). The gene expression profile of the late IMN nonIT cells closely resembles that of L6 CT, the most prevalent subclass in the nonIT group. In the L5 ET subclass, Foxp2 and Tle4 are downregulated, whereas Pou3f1 and Bhlhe22 are upregulated.

Subclass L6b (Nxph4 and Pappa2) is thought to derive from the subplate49 with shared markers Cplx3, Lpar1, Nr4a2 and Ccn2. There is a distinct population of L6b-like cells that is more abundant than L6b at E17–P3 (Fig. 2b), and it maps to the adult L6b/CT ENT subclass with confirmed localization to the entorhinal cortex at P0 and P56 (Fig. 2e). The L5 NP subclass (Ptprt and Tshz2) emerges later than the other three nonIT subclasses at around E18.5 (node 5; Fig. 2a–c and Extended Data Fig. 5). It seems to derive from early L6 CT cells, but how it emerges remains unclear, with few transition cells connecting to the closest antecedent type.

The IMN IT subclass is divided into deep-layer and upper-layer IMN clusters (node 6; Fig. 2a–c and Extended Data Fig. 5). More markers emerge that split deep-layer IT and upper-layer IT populations after the IMN stage, including Il1rapl2 and Hs3st2 enriched in L5 IT and L6 IT subclasses and Cux1 and Cux2 in L2/3 IT and L4/5 IT subclasses. The IMN IT deep-layer cluster differentiates into L5 IT (Fezf2) and L6 IT (Fosl2) around E17 (node 7, Fig. 2a–c and Extended Data Fig. 5). Nfia and Sox5, which show strong enrichment in the nonIT trajectory, are also enriched in L6 IT. In the upper-layer IT population, L2/3 IT (Mdag1 and Klhl1) and L4/5 IT (Rorb, Rora and Tox) separate around E18.5 (node 8; Fig. 2a–c and Extended Data Fig. 5).

In the P0 MERFISH data, we observed a divergence in laminar distribution between IT and nonIT subclasses at this age (Fig. 2e). The IT subclasses present a layered profile analogous to that seen in adult brains, but with less clear segregation. By contrast, the nonIT subclasses display a distinct separation into layers. Notably, L5 NP neurons are situated at a deeper cortical depth than L5 ET neurons at P0, whereas they become more intermingled at P56.

In each glutamatergic subclass, cells continue to differentiate and diversify, giving rise to new cell clusters. We derived a cluster trajectory tree of all cell types and conducted DE gene analysis at each branching point (Fig. 3a,e and Extended Data Figs. 6 and 7). In the nonIT class, most of the L5 ET, L5 NP, L6 CT and L6b clusters begin to diverge by P3, except for the L5 ET clusters 371–373 (Chrna6), which represent the most distinct subset10,12,[18](https://www.nature.com/articles/s41586-025-09644-1#ref-CR18 “Sorensen, S. A. et al. Connecting single-cell transcriptomes to projectomes in mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/2023.11.25.568393

(2023).“) and diverge at the onset of critical period (Fig. 3a,e and Extended Data Fig. 6).

In the IT trajectory, many clusters that split off early have a distinct layer distribution (Fig. 3a,e and Extended Data Fig. 7), and many genes with a distinct layer distribution show a specific expression pattern at early stages of IT cell-type divergence. This finding indicates that the cortex has more refined sublayer gradients that are specified by early postnatal age. More clusters arise in later stages of development after eye opening, and these newer clusters have less distinct spatial distributions from sibling clusters. For example, L2/3 IT cluster 109 diverges from cluster 110 at around P11, with increased expression of Bdnf and decreased expression of Adamts2. By contrast, cluster 118 further diverges from cluster 109 at P21, with increased expression of Baz1a and Tnfaip6. Spatially in L2/3, clusters 118 and 110 are located more superficially than cluster 109 at P56. Recent studies have shown functional and developmental distinctions of these L2/3 IT clusters in the somatosensory cortex50 and the visual cortex51. We also observed late divergence of L4/5 IT, L5 IT and L6 IT clusters. In L4/5 IT, cluster 100 is the dominant cluster and is L4-specific, whereas clusters 101 and 82 diverge from cluster 100 at P14 and P20, respectively.

Overall, most nonIT clusters already exist before eye opening, except for a few Chrna6+ L5 ET clusters. By contrast, IT clusters continue to emerge from P11, around the time of eye opening, to as late as P21, at the onset of critical period (Fig. 3a,e). This result suggests that IT cells become molecularly distinct at the embryonic stage and continue to diversify throughout the postnatal period.

Developmental trajectories of glial cell types

RG transition into gliogenesis starting at E15.5, as indicated by the increasing expression of *Tn