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Adult C57BL/6J male mice aged 57–63 days were used in this study. Two mice were used for in situ cell-type identification and spatially mapping by MERFISH; and two animals were used for projection mapping by combining MERFISH with retrograde labelling. Mice were maintained on a 12-h light/12-h dark cycle (14:00 to 02:00 dark period), at a temperature of 22 ± 1 °C, a humidity of 30–70%, with ad libitum access to food and water. Animal care and experiments were carried out in accordance with NIH guidelines and were approved by the Harvard University Institutional Animal Care and Use Committee (IACUC) and the University of South California Institutional Animal Care and Use Committee.

Gene selection for MERFISH

To discriminate transcriptionally distinct cell populations with MERFISH, we designed a panel of 258 genes. Among the 258 genes, 62 were manually picked marker genes including established markers for inhibitory and excitatory neurons, as well as different non-neuronal cell markers for oligodendrocytes, oligodendrocyte precursor cells, astrocytes, microglia, perivascular macrophages, endothelial cells, pericytes, smooth muscle cells and vascular leptomeningeal cells (VLMCs). To further discriminate different neuronal cell types, we combined two approaches to select genes based on clustering results from scRNA-seq and snRNA-seq data. In the first approach, we selected a panel of genes with the highest mutual information as previously reported22. Briefly, we used mutual information to determine the relative amount of information each gene carries in defining the clusters identified by scRNA-seq and snRNA-seq. We used the scRNA-seq 10x v2 A dataset generated by a companion study23 and determined highly variable genes using the Scanpy46 package. We binarized the expression profiles using a gene counts cut-off of zero to simplify the calculation of the mutual information. We selected the top 50 genes with the highest mutual information for excitatory and inhibitory neuronal clusters, respectively, and due to overlap between the two groups, this approach generated a total of 91 top mutual information genes. In the second approach, we selected a panel of 168 genes based on differentially expressed (DE) gene analysis using the scRNA-seq data (scRNA-seq 10x v2 and scRNA-seq SMART data) from the companion study23. We first found DE genes for each neuronal cluster pair (consisting of a foreground cluster and a background cluster) in both directions. The criteria to define DE genes were: the genes have a twofold or more change in expression between the foreground and background clusters and P < 0.05; they are expressed in at least 40% of cells in the foreground cluster, with more than threefold enrichment, in terms of the fraction of cells expressing the gene, relative to the background cluster. P values were calculated using the analysis of variance (ANOVA) test in limma47 on log-transformed data. The top 50 genes that passed all of the tests and ranked by P values in each direction for every cluster pair were pooled together as candidates for scoring for the final marker set. To determine the final marker list, which we required to include at least two genes in each direction for all pairs of clusters, we used a greedy algorithm to find the minimal number of genes that satisfied the requirement. Starting from a manually picked marker gene list as described above, the algorithm checks which pairs already have a sufficient number of DE genes, and works on the remaining pairs of clusters until each pair of clusters has at least two DE genes included in each direction. This approach generated a total of 168 genes.

We note that the mutual information genes tend to be genes that are differentially expressed between groups of cell clusters, whereas the DE genes are differentially expressed between individual pairs of clusters. These two sets have complementary power and, when combined, can give better cluster identification results in our experience. We thus combined the marker gene lists generated by these three different approaches, which partially overlap with each other, resulting in a panel of 258 genes in total. We then screened this gene list to identify genes that are relatively short or have relatively high expression level, which were potentially challenging for highly multiplexed FISH imaging experiments, as previously described21. We found 16 genes that can accommodate fewer than 48 hybridization probes with target sequences that are 30-nucleotides (nt) long, or are expressed at an average of 200 or greater counts per cell in any cell cluster as determined from the scRNA-seq SMART data23. These 16 genes were imaged in a set of eight sequential, two-colour FISH imaging rounds, following the MERFISH run that imaged the remaining 242 genes.

Design and construction of the MERFISH encoding probes

MERFISH encoding probes for the 242 genes were designed as previously described21. We first assigned to each of the 242 genes a unique binary barcode drawn from a 22-bit, Hamming-Distance-4, Hamming-Weight-4 encoding scheme. We included 10 extra barcodes as ‘blank’ barcodes, which were not assigned to any genes, to provide a measure of the false-positive rate in MERFISH as previously described21.

We identified all possible 30-mer targeting regions within each desired gene transcript as previously described48. Each MERFISH encoding probe contains a 30-mer targeting region that is complementary to the RNA of interest, as well as two 20-mer readout sequences that define the specific barcode assigned to each gene. From the set of all possible 30-mer targeting sequences for each gene, we selected 92 30-mer targeting sequences at random. For the transcripts that were not long enough and had fewer than 92 targeting sequences, we allowed these 30-mers to overlap by as much as 20 nt to increase the number of possible encoding probes — because a given transcript is typically bound by less than one-third of the 92 encoding probes49, the encoding probes with overlapping targeting regions do not substantially interfere with each other but partially compensate for reduced binding due to local inaccessible regions on the target RNA or loss of probe during synthesis. We then assigned two readout sequences to each of the encoding probes associated with each gene. For the 22-bit encoding scheme, a total of 22 readout sequences were used, each corresponding to 1 bit, and the collection of encoding probes for each gene contained 4 of the 22 readout sequences that corresponded to the 4 bits that reads ‘1’ in the barcode assigned to that gene.

Encoding probes for the 16 genes imaged in sequential two-colour FISH rounds were produced in the same manner, except that 48 targeting sequences were selected, and one single unique readout sequence was included in each set of the 48 targeting sequences. The readout sequences used here were different from the 22 readout sequences used for the genes detected in the MERFISH run.

In addition, we concatenated to each encoding probe sequence two PCR primers, the first comprising the T7 promoter, and the second being a random 20-mer designed to have no region of homology greater than 15 nt with any of the encoding probe target sequences designed above, as we previously described48.

With the above-described template encoding probe sequences, we constructed the MERFISH probe set as previously described21. The template DNA were synthesized as a complex oligo pool (Twist Biosciences). This pool contained both the encoding probes to the 242 genes measured in the MERFISH run and the 16 genes measured in the sequential two-colour FISH rounds, but different primer sequences for the two sets, which allowed us to amplify these two probe sets separately via PCR followed by the same synthesis and purification procedures. The two probe sets were then mixed during tissue staining.

Design and construction of MERFISH readout probes

For the 258-gene panel used in this study, 38 readout probes were designed, each complementary to one of the 38 readout sequences. Twenty-two of the 38 readout probes correspond to the 22 bits in the barcodes used for MERFISH imaging, and the remaining 16 readout probes each corresponds to one gene that was imaged in the sequential two-colour FISH rounds. Each readout probe was conjugated to one of the two dye molecules (Alexa750 or Cy5) via a disulfide linkage, as previously described48. These readout probes were synthesized and purified by Bio-Synthesis, Inc., resuspended immediately in Tris-EDTA (TE) buffer, pH 8 (Thermo Fisher), to a concentration of 100 μM and stored at −20 °C.

Tissue preparation for MERFISH

Mice aged 57–63 days were euthanized with CO2; their brains were quickly harvested and cut into hemispheres and each hemisphere was frozen immediately on dry ice in optimal cutting temperature compound (Tissue-Tek O.C.T.; 25608-930, VWR), and stored at −80 °C until cutting. Frozen brain hemispheres were sectioned at −18 °C on a cryostat (Leica CM3050 S). Slices were removed and discarded until the MOp region was reached. A continuous set of 300, 10-μm-thick slices were cut from anterior to posterior, and approximately every tenth slice was placed onto coverslips for imaging. Each coverslip contained 4–6 slices. The coverslips were prepared as previously described21,50.

Tissue slices were fixed by treating with 4% PFA in 1× PBS for 15 min and were washed three times with 1× PBS and stored in 70% ethanol at 4 °C for at least 18 h to permeabilize cell membranes. The tissue slices from the same mouse were cut at the same time and distributed to six coverslips, which were store in 70% ethanol at 4 °C for no longer than 2 weeks until all the coverslips were imaged. We observed no degradation in sample quality over this time period.

The tissue slices were stained with the MERFISH probe set as previously described21. Briefly, the samples were removed from the 70% ethanol and washed with 2× saline sodium citrate (SSC) three times. Then, we equilibrated the samples with encoding-probe wash buffer (30% formamide in 2× SSC) for 5 min at room temperature. The wash buffer was then aspirated from a coverslip, and the coverslip was inverted onto a 50-μl droplet of encoding-probe mixture on a parafilm-coated Petri dish. The encoding-probe mixture comprised approximately 1 nM of each encoding probe for the MERFISH run, approximately 5 nM of each encoding probe for the sequential two-colour FISH rounds and 1 μM of a polyA-anchor probe (IDT) in 2× SSC with 30% v/v formamide, 0.1% wt/v yeast tRNA (15401-011, Life Technologies) and 10% v/v dextran sulfate (D8906, Sigma,). We then incubated the sample at 37 °C for 36–48 h. The polyA-anchor probe containing a mixture of DNA and LNA nucleotides (/5Acryd/TTGAGTGGATGGAGTGTAATT+TT+TT+TT+TT+TT+TT+TT+TT+TT+T, where T+ is locked nucleic acid, and /5Acryd/ is 5′ acrydite modification) hybridized to the polyA sequence on the polyadenylated mRNAs and allowed these RNAs to be anchored to a polyacrylamide gel as described below. After hybridization, the samples were washed in encoding-probe wash buffer for 30 min at 47 °C for a total of two times to remove excess encoding probes and polyA-anchor probes. All tissue samples were cleared to remove fluorescence background as we previously described21,50. Briefly, the samples were embedded in a thin polyacrylamide gel and were then treated with a digestion buffer of 2% v/v sodium dodecyl sulfate (SDS; AM9823, Thermo Fisher), 0.5% v/v Triton X-100 (X100, Sigma) and 1% v/v proteinase K (P8107S, New England Biolabs) in 2× SSC for 36–48 h at 37 °C. After digestion, the coverslips were washed in 2× SSC for 30 min for a total of four washes and then stored at 4 °C in 2× SSC supplemented with 1:100 murine RNase inhibitor (M0314S, New England Biolabs) before imaging.

MERFISH imaging

We used a home-built imaging platform in this study as previously described20. To prepare the sample for imaging, we first stained it with a readout hybridization mixture containing the readout probes associated with the first round of imaging in the MERFISH run, as well as a probe complementary to the polyA-anchor probe and conjugated via a disulfide bond to the dye Alexa488 at a concentration of 3 nM. The readout hybridization mixture comprised the readout-probe wash buffer comprised 2× SSC, 10% v/v ethylene carbonate (E26258, Sigma) and 0.1% v/v Triton X-100, supplemented with 3 nM each of the appropriate readout probes. The sample was incubated in this mixture for 15 min at room temperature, and then washed in the readout-probe wash buffer supplemented with 1 μg/ml DAPI for 10 min to stain nuclei within the sample. The sample was then washed briefly in 2× SSC and imaged. Briefly, the sample was loaded into a commercial flow chamber (FCS2, Bioptechs) with a 0.75-mm-thick flow gasket (DIE# F18524; 1907-100, Bioptechs). Imaging buffer comprising 5 mM 3,4-dihydroxybenzoic acid (P5630, Sigma), 2 mM trolox (238813, Sigma), 50 μM trolox quinone, 1:500 recombinant protocatechuate 3,4-dioxygenase (rPCO; OYC Americas), 1:500 murine RNase inhibitor and 5 mM NaOH (to adjust pH to 7.0) in 2× SSC was introduced into the chamber and the sample was imaged with a low-magnification objective (CFI Plan Apo Lambda ×10, Nikon) with 405-nm illumination to produce a low-resolution mosaic of all slices in the DAPI channel. We then used this mosaic image to locate the MOp region in each slice and generated a grid of field-of-view (FOV) positions to cover the MOp region and adjacent areas to be imaged. We then switched to a high-magnification, high-numerical aperture objective (CFI Plan Apo Lambda ×60, Nikon) and imaged each of the FOV positions generated above. In the first round of imaging, we collected images in the 750-nm, 650-nm, 560-nm, 488-nm and 405-nm channels to image the first two readout probes (conjugated to Alexa750 and Cy5, respectively), the orange fiducial beads, the total polyA mRNA stained by the polyA-anchor probe (Alexa488) and the nucleus stained by DAPI (405-nm channel). The latter two channels were used for cell segmentation as described below. We took a single image for the fiducial beads on the surface of the coverslip using the 560-nm illumination channel for each imaging round as a spatial reference to correct for slight misalignments in the stage position over the imaging rounds. To image the entire volume of each 10-μm-thick slice, we collected seven 1.5-μm-thick z-stacks for the other four channels (two readout probes, polyA probe and DAPI) in each FOV.

After the first round of imaging, the dyes were removed by flowing 2.5 ml of cleavage buffer comprising 2× SSC and 50 mM of Tris (2-carboxyethyl) phosphine (TCEP; 646547, Sigma) with 15-min incubation in the flow chamber, to cleave the disulfide bond linking the dyes to the readout probes. The sample was then washed by flowing 1.5 ml 2× SSC.

To perform subsequent rounds of imaging, we flowed 3.5 ml of the readout probe mixture containing the appropriate readout probes across the chamber and incubated the sample in this mixture for a total of 15 min for each round. The sample was then washed by 1.5 ml of readout-probe wash buffer and then 1.5 ml of imaging buffer was introduced into the chamber. For each round, we took images for all FOV locations in the 750-nm, 650-nm and 560-nm channels for the two readout probes and fiducial beads. Two readout probes were imaged in each round, one labelled with Alexa750 and the other with Cy5, and a readout-probe mixture containing 3 nM of appropriate readout probes was used for each round. We repeated the hybridization, wash, imaging and cleavage for all rounds to complete the 22-bit MERFISH imaging and the eight rounds of sequential two-colour FISH. All buffers and readout-probe mixtures were loaded with a home-built, automated fluidics system composed of three 12-port valves (EZ1213-820-4, IDEX) and a peristaltic pump (MP3, Gilson), configured as previously described5. The total MERFISH imaging time was approximately 24–36 h for each experiment, which contained 4–6 coronal slices.

MERFISH image analysis and cell segmentation

All MERFISH image analysis was performed using MERlin51, a Python-based MERFISH analysis pipeline, using algorithms similar to what we have previously described20,21. First, we aligned the images taken during each imaging round based on the fiducial bead images, accounting for X–Y drift in the stage position relative to the first round of imaging. For the MERFISH images, we then high-pass filtered the image stacks for each FOV to remove background, deconvolved them using 20 rounds of Lucy–Richardson deconvolution to tighten RNA spots, and low-pass filtered them to account for small movements in the apparent centroid of RNAs between imaging rounds. Individual RNA molecules were identified by our previously published pixel-based decoding algorithm48. After assigning barcodes to each pixel independently, we aggregated adjacent pixels that were assigned with the same barcodes into putative RNA molecules, and then filtered the list of putative RNA molecules to enrich for correctly identified transcripts as previously described20 for an overall barcode misidentification rate at 5%. We further removed putative RNAs that contained only a single pixel as they are prone to be background of spurious barcodes generated by random fluorescent fluctuations and had a substantially higher misidentification rate than those that contained 2 or more pixels.

We segmented cell boundaries in each FOV using a seeded watershed approach as previously described21. The DAPI images were used as seeds and the polyA signals were used to identify segmentation boundaries. Finally, we assigned individual RNA molecules identified in the MERFISH run to individual cells based on whether they fell within the segmented boundaries of the cells. For the sequential two-colour FISH rounds, we quantified the signal from these imaging rounds by summing the fluorescence intensity of all pixels that fell within the segmentation boundaries of the cells associated with the central z-plane and normalized the signal by the areas of the cells in this z-plane. Then, the normalized signals of the 16 genes from the sequential two-colour FISH rounds were merged with the RNA count matrix from the 242 genes measured in the MERFISH run and used for cell clustering analysis.

Cell clustering analysis of MERFISH data

With the cell-by-gene matrix obtained as described above (each row representing a cell and each column representing a gene, and each element representing the expression level a specific gene in a specific cell), we preprocessed the matrix by the following steps. (1) The segmentation approach that we used generated a small fraction of putative ‘cells’ with very small total volumes due to spurious segmentation artefacts, as well as some cells that overlapped in the 3D and were not properly separated. We hence removed the segmented ‘cells’ that had a volume that was either less than 100 μm3 or larger than three times of the median volume of all cells. (2) A fraction of cells did not have the whole soma included in a 10-μm-thick tissue slice and was thus not imaged completely. To remove the differences in RNA counts due to the incompleteness of the imaged soma volume, we normalized the RNA counts per cell by the imaged volume of each cell. (3) We observed a modest batch effect between MERFISH experiments accounting for approximately 30% variation of the mean total number of RNAs per cell. We normalized the mean total RNA counts per cell to a same mean value (250 in this case) for each experiment to remove the influence of these batch effects. (4) Since the 16 genes that were imaged in the sequential FISH rounds contained many specific marker genes that should not co-express in individual cell types, and no cells should express a majority of these 16 genes, we considered the segmented ‘cells’ that had a normalized fluorescence signal that was higher than the 90% quantile in 12 out of the total 16 sequential FISH channels as caused by spurious fluorescence background and removed them. (5) Since the fluorescence background in the 650-nm and 750-nm channels was different, we subtracted the background for each cell by taking the minimum of the signal for each cell across all sequential FISH rounds as the background, for 650-nm and 750-nm channels separately. (6) We removed the cells that had total RNA counts lower than 2% quantile or higher than 98% quantile. (7) We removed potential doublets using Scrublet52. Briefly, principal component analysis (PCA) was used to train a k-nearest neighbour (kNN) classifier to predict a doublet score for each cell. Since we recorded the DAPI-stained nucleus image of each cell, we were able to visually inspect a random subset of potential doublets picked by Scrublet and fine-tuned the doublet score threshold to remove connected cells more accurately. Finally, the cells with a doublet score higher than 0.18 were removed, which accounted for approximately 12% of the total cell number. (8) We also found that 4 out of the 16 genes imaged in the sequential FISH rounds — Cd52, Rprml, Mup5 and Igfbp6 — were not stained well in all experiments and failed to yield high-quality signals. These four genes were removed for subsequent analysis.

After the above preprocessing steps, we normalized the total RNA counts for each cell to the median total RNA counts of all cells and log-transformed the cell-by-gene matrix. We then normalized their expression profiles by computing the z-score for each gene. We performed dimensionality reduction of the matrix using PCA, and used the first 35 principal components. To determine the number of principal components to keep, we randomly shuffled the values in each column of the cell-by-gene matrix and calculated the eigenvalue of the first principal component for the randomly shuffled matrix. The random shuffling was repeated 20 times and the mean eigenvalue of the first principal component across 20 iterations was obtained, and we kept all of the principal components that had an eigenvalue greater than this mean value. We then performed graph-based Louvain community detection53 in the 35 principal components space using Scanpy46 for a range of nearest neighbourhood size k values with a bootstrap analysis to both identify stable clusters and select the optimal k value (k = 10) as previously described21. We further identified six small clusters that expressed mixtures of markers for multiple distinct cell classes, for example, Slc17a7, which marks excitatory neurons, and Sox10, which marks the oligodendrocytes, and that did not correspond to any of the major subclasses defined by the scRNA-seq and snRNA-seq data23 (based on classifier analysis, which is described below), as potential doublets, which were excluded from subsequent analysis.

From the first round of clustering, we identified 16 excitatory neuronal clusters, 8 inhibitory neuronal clusters and 14 other clusters. To further refine our detection of transcriptionally distinct populations, we separated all of the cells into five groups: IT-projecting neurons (marked by the excitatory neuronal marker Slc17a7 and the pan-IT marker Slc30a3), non-IT neurons (marked by the excitatory neuronal marker Slc17a7 but not Slc30a3), caudal ganglionic eminence (CGE)-derived inhibitory neurons (marked by Gad1, Gad2 and Lamp5/Sncg/Vip), medial ganglionic eminence (MGE)-derived inhibitory neurons (marked by Gad1, Gad2 and Sst/Pvalb) and non-neuronal cells. We then repeated the procedure of dimensionality reduction and clustering, as described above, for these five cell groups separately. In addition, we sampled a range of resolution parameter r (r = 1, 2, 3), a parameter value defined in Scanpy46 that controls the coarseness of the clustering, to search for optimal granularity that represents the diversity of the transcriptomic profiles. We kept k = 40 and r = 2 for IT and non-IT excitatory neurons, k = 15 and r = 2 for CGE-derived and MGE-derived inhibitory neurons, and k = 20, r = 1 for the non-neuronal cells.

After the second round of clustering, we further removed a small fraction of cells as potential doublets as described above. We also found four unique clusters that did not correspond to any subclass in the MOp region defined by the scRNA-seq and snRNA-seq data23 (using the classifier approach described below). We located the cells that belonged to these clusters and found that two clusters were in the striatum, and the other two clusters were probably ependymal cells located in the lateral ventricle. We removed these clusters from subsequent analysis.

After the clustering was done, the cell clusters were first each assigned into a subclass based on their marker gene expression as described in the main text. The IT neurons were further divided into L2/3, L4/5, L5 and L6 subclasses based on the expression of layer-specific makers (Cux2, Otof, Rorb, Rspo1, Sulf2, Fezf2 and Osr1). Since these markers showed gradual changes between individual IT clusters, the subclass identification at the border of layers can be ambiguous, in which case, we identify the parent subclass for the cluster by judging both its marker gene expression and its strongest corresponding cluster in the scRNA-seq and snRNA-seq data. For example, L4/5 IT 5 expressed both Rorb and Fezf2, and corresponded to the L4/5 IT 2 cluster in the scRNA-seq and snRNA-seq data, and was thus classified as a L4/5 IT cluster. After the subclass identity was assigned, within each subclass, a numerical index was added following the subclass name to form the cluster name (for example, L5 IT 1, astrocyte 2, and so on).

For presentation, UMAP54 was used to embed the cells in two dimensions using the same principal components that were used for clustering.

Correspondence between clusters identified by MERFISH and single-cell sequencing-based measurements

Correspondence between cell clusters identified by MERFISH and by scRNA-seq and snRNA-seq in Extended Data Fig. 2c, d was assessed by running a neural-net classifier55, which was trained on the z-scored single-cell expression profiles measured by MERFISH. The snRNA-seq 10x v3 B data in the companion paper23 were used for comparison because it is the largest dataset among the seven scRNA-seq and snRNA-seq datasets included in this companion study and contained the largest number of non-neuronal cells, while all of the other six datasets were collected by fluorescence-activated cell sorting (FACS) to enrich for neurons. The snRNA-seq 10x v3 B data were z-scored, and then the subset of genes measured in the MERFISH data was used together with the trained model to predict a MERFISH cluster label for each cell in the snRNA-seq dataset. From this, each snRNA-seq cell had both a predicted MERFISH cluster label and a cluster label determined from the consensus clustering results for the seven scRNA-seq and snRNA-seq datasets23. Cells were grouped based on their consensus scRNA-seq and snRNA-seq cluster identity, and then the fraction of cells from a given consensus scRNA-seq and snRNA-seq cluster that were predicted to have each MERFISH cluster label was then determined (Extended Data Fig. 2d). The same classifier approach was also used to produce Extended Data Fig. 2c, but in this case, the subclass labels defined by MERFISH and by the seven scRNA-seq and snRNA-seq datasets for each cell was used instead of cluster labels. Likewise, the same classifier approach was used to produce Extended Data Figs. 6a, b, 7a, b, but in Extended Data Figs. 6b, 7b, the cluster labels defined by the integrated analysis of the seven scRNA-seq and snRNA-seq datasets, a snATAC-seq dataset and a snmC-seq dataset were used instead of the cluster labels derived from the scRNA-seq and snRNA-seq datasets alone.

Registration to the Allen Reference Atlas and the common coordinate framework

For each coronal section that we performed, high-resolution MERFISH/DAPI/polyA imaging of the MOp and adjacent areas, we also performed lower-resolution DAPI imaging of the entire hemisphere. The low-resolution DAPI image of each hemisphere coronal section was manually paired with the closest matching coronal section of the Allen Reference Atlas (ARA)27 based on cytoarchitectural features. Once paired, landmark cytoarchitectural features were used to calculate a deformable or affine transformation from our DAPI image to the Nissl template of the matching ARA coronal section. Segmented cell boundaries from high-resolution MERFISH imaging were then aligned to the corresponding low-resolution DAPI image by aligning the high-resolution and low-resolution DAPI images. The overall transformation from both steps then allowed registration of the MERFISH images to the ARA. Out of the 64 coronal slices imaged, 61 slices were registered to the ARA, whereas the remaining three slices did not have a sufficient number of landmarks to be registered.

To define the boundaries of the MOp in the MERFISH images, each ARA template was further scaled and aligned via translation and rotation to the corresponding 2D coronal image in the Allen common coordinate framework (CCF) v326, which in turn allowed the MERFISH images to be registered to the Allen CCF v3.

To estimate the errors in image registration, we determined for each slice the average displacement between the cells on the cortical surface and the top surface of the cortex in the CCF image, as well as the average displacement between the L6b cells and the bottom surface of the cortex in the CCF image, and calculated these displacements as a percentage of the cortical thickness in that slice. For the 61 registered slices, the alignment error was on average 2.5% when calculated using the mean of the absolute values of the top and bottom surface displacements. To further reduce the effect of the alignment error in delineating cells within the MOp, we removed the slices that had an alignment error that was approximately 7% or greater for either the top or bottom surface, or approximately 5% or greater for their mean. In total, eight slices were removed from subsequent analyses that involved MOp delineation, and the remaining slices on average had an alignment error of 2.0% when calculated using the mean of the absolute values of the top and bottom surface displacements.

Registration of the MERFISH images to the Allen CCF v3 allowed us to place the imaged and profiled cells in the CCF, delineating cells in different brain regions. This version of CCF was chosen by the BICCN consortium for multiple modalities of measurements of the MOp to provide consistency among these measurements. While the brain areal boundaries may not be perfectly determined in the CCF v3 and efforts in the community will continue to improve the accuracy of these boundaries, the MERFISH results reported here will continue to serve as a resource as these areal boundaries are improved over time.

Soma depth determination

From the MERFISH images, we segmented the cells and determined the centroid coordinates of all the cells. For each cell, the soma depth was determined as the shortest distance of its centroid position to the cortical surface line, which is marked by the very thin layer of VLMCs. Hence, the soma depths of individual cells were determined along the direction perpendicular to the cortical surface line in each coronal slice. To compensate the variation in cortical thickness from slice to slice, we measured the cortical thickness in each coronal slice, which was defined as the median soma depths of the L6b cells in the slice, and the soma depth of each cell was normalized by the cortical thickness of the slice. Cortical depth distribution analyses were performed for the region between Bregma −0.8 and +1.7 because MERFISH images of slices at Bregma +1.8 or greater did not show L6b cells forming a thin layer, which made normalization of the soma depth by the cortical thickness challenging for these anterior-most slices (Bregma between +1.8 and +2.5).

Layer boundary assessment

The layer boundaries along the normalized cortical depth axis were determined as follows: (1) the cortical surface was defined by the positions of surface VLMCs; (2) we calculated the normalized median cortical depth of all cell clusters and used the median depth of the most superficial L2/3 IT cluster, L2/3 IT 1, as the upper boundary of L2/3; (3) the median depth of the most superficial L4/5 IT cluster, L4/5 IT 1, was used as the upper boundary of L4; (4) the median depth of the most superficial cluster among the L6 IT and CT clusters, L6 IT 1, was used as the upper boundary of L6; (5) the median depth of the L6b cells was set to 1 (as the soma depth of all cells were normalized by the median soma depths of the L6b cells) and the upper and lower boundaries of L6b were determined by the width of the L6b cell distribution; and (6) we also used the median depth of the most superficial cluster among the clusters residing in L5 to mark the upper boundary of L5 (that is, the boundary between L4 and L5); however, this boundary has some uncertainty because some of the L4/5 IT clusters may belong to L5, as we discuss below.

To examine which of the L4/5 IT clusters might also belong to L5, we examined the spatial overlap of the IT clusters with the L4 marker gene Rspo1 and the L5 marker gene Fezf2. To this end, we first determined the spatial profile of each of the two marker genes by binning all imaged cells into 100 equal-sized bins based on the normalized cortical depth and determining the mean expression level per cell for each bin. For each IT cluster, its spatial overlap with these marker genes was then determined as the fraction of the cells in the cluster that fell within the cortical depth range where the binned median expression of the marker gene was above half maximum. We observed that the spatial overlap with the L4 marker Rspo1 took a rather precipitous fall at the L4/5 IT 5 cluster, with the overlap between L4/5 IT 5 and Rspo1 being substantially lower than those between L4/5 IT 1–4 clusters and Rspo1. In addition, the spatial overlap of the L4/5 IT 5 cluster with the L5 marker Fezf2 was substantially higher than those of the L4/5 IT 1–4 clusters and comparable to those of several L5 IT clusters (Extended Data Fig. 10a, b). Hence, the L4/5 IT 5 cluster probably resided in (or partially resided in) L5, and we thus considered the region between the median cortical depth of L4/5 IT 5 and the median cortical depth of L5 IT 1 as the uncertainty region for the upper boundary of L5 as shown by the grey area in Fig. 1c–e and Extended Data Fig. 3.

The L5 can be divided into a superficial L5a sublayer devoid of ET cells and a deeper L5b sublayer occupied by ET cells56. We also examined the spatial overlap between L4/5 IT and L5 IT clusters with the L5 ET neurons to assess which clusters may belong to L5a. The spatial overlap between a cell cluster and the L5 ET cells was defined as the overlapping area of the cell density distributions of the cell cluster and the L5 ET cell subclass. We observed that the L4/5 IT 5 cluster showed minimal spatial overlap with L5 ET (Extended Data Fig. 10c, d) and hence may reside in L5a. L5 IT 1 partially overlaps with L5 ET, but the spatial overlap of L5 IT 1 with L5 ET cells was substantially lower than those of the other L5 IT clusters (Extended Data Fig. 10c), suggesting that the L5 IT 1 cluster may partially reside in L5a.

Connectivity and pseudotime analyses of IT neurons

To visualize the degree of similarity (connectivity) in the gene expression profiles of the IT clusters, we employed a recently developed graph abstraction technique called PAGA38 to gain a quantitative understanding of how extensively different IT clusters occupied overlapping gene expression space. To this end, we first took the 19 IT clusters in the L2/3 IT, L4/5 IT, L5 IT and L6 IT subclasses and normalized their expression profiles by computing the z-score for each gene. Cells from the L6 IT Car3 were not included in this analysis as it formed a cluster that was well-separated in gene expression from the other IT cell clusters. PCA was used to reduce dimensionality of the normalized expression data to the first 19 principal components. In selecting the number of principal components to include, we performed the same random shuffling procedure used when setting a PC threshold for cell clustering analysis as described in the ‘Cell clustering analysis of MERFISH data’ section. We then constructed a kNN graph based on the principal components, identifying the 12 nearest neighbours of each cell. Using the kNN graph and the cluster label of each cell, we used Scanpy46 to calculate the frequency that edges from cells with a given cluster label were connected to cells from a different cluster label and then normalized this frequency to that expected by chance. The resulting values represent the connectivity between the clusters in the kNN graph, and are visualized in a graph where each cluster is a node and the edges between nodes indicate the connectivity between those clusters.

Next, we constructed an ordering of the IT cells based on their expression profiles, yielding a ‘pseudotime’ value for each cell. This calculation is most often performed to order cells within a dynamic system, in which case the ordering reflects the ‘time’ relative to some reference cell. Our pseudotime calculation performed on the IT cells is not intended to represent the trajectory from L2/3 to L6 as part of a dynamic process, but rather to obtain an expression-derived measure of where along the trajectory each cell falls. To calculate the pseudotime of the IT cells, we used Scanpy to construct a diffusion map based on the above-described kNN graph, assigned a neuron from the L2/3 IT 1 cluster as the root cell of the trajectory, and then computed the diffusion-based pseudotime57. The resulting value assigned to each cell reflects how far from the root cell its expression profile places it, and since each cell falls along a single trajectory with the L2/3 IT root cell at one end, this value orders the cells relative to one another along this path.

To identify genes that vary as a function of the cortical depths of the IT cells, the expression profiles of the IT cells were normalized by computing the z-score for each gene. The IT cells were split evenly into 50 bins based on their normalized cortical depths, and the mean normalized expression was calculated for each gene across all the bins. Any gene for which the difference in mean normalized expression between any two bins exceeded 0.5 was selected as a gene differentially expressed across cortical depth. To plot these genes in a heatmap, the genes were ordered according to the normalized cortical depths at which they exhibit their maximum expression and the cells were ordered based on their normalized cortical depths. To determine the cortical depth at which each gene exhibits its maximum expression, a rolling average was calculated across the 50 bins, using a window size of 10 bins, and the window at which the maximum expression value occurred was determined.

Stereotaxic injection of retrograde tracers

To retrogradely label MOs-projecting, SSp-projecting and TEa–ECT–PERI-projecting MOp neurons, each region was injected in the same mouse in the right hemisphere with 100 nl of fluorescently conjugated CTb (CTb-AlexaFluor488, CTb-AlexaFluor555 or CTb-AlexaFluor647, respectively; 0.5%; C22841, C22843, and C34778, Thermo Fisher) using the following coordinates relative to Bregma: MOs (anterior–posterior (AP) +2.4 mm, medial–lateral (ML) +1.0 mm, dorsal–ventral (DV) +0.4 mm below the cortical surface), SSp (AP −0.5 mm, ML +2.4 mm, DV +0.5 mm below the cortical surface) and TEa–ECT–PERI (AP −1.7 mm, ML +4.5 mm, DV +2.5 mm below the cortical surface). Injection procedures were performed in adult male C57BL/6J mice (Jackson Laboratories) aged 2–4 months. Briefly, mice were anaesthetized initially in an induction chamber containing 5% isoflurane mixed with oxygen and then transferred to a stereotaxic frame equipped with a heating pad. Anaesthesia was maintained throughout the procedure using continuous delivery of 2% isoflurane through a nose cone at a rate of 1.5 l/min. The scalp was shaved, and a small incision was made along the midline to expose the skull. After levelling the head relative to the stereotaxic frame, the specified injection coordinates were used to mark the locations on the skull directly above each target area and a small hole (0.5 mm diameter) was drilled for each. CTb was delivered through pulled glass micropipettes (inner diameter of tip of approximately 20 μm) using a pressure injection via a micropump (World Precision Instruments). After completing the last injection, the scalp was sutured and mice were administered ketofen (5 mg/kg) to minimize inflammation and discomfort. Mice were recovered from anaesthesia on a heating pad and then returned to their home cage. Mice were euthanized 7 days following injection to allow time for tracer transport, and fresh brain tissue was immediately extracted, embedded in Tissue-Tek O.C.T. Compound (4583, Sakura) and frozen at −80 °C for later cryostat sectioning.

Images of the CTb signal in the injected regions showed that, in the TEa–ECT–PERI injections, the CTb signal covered all cortical layers, whereas in the MOs and SSp injections, the CTb signal appeared relatively weak in L1 and part of L6. Hence, neurons projecting to L1 and L6 of MOs and SSp could be under-represented. In addition, it is known that retrograde tracers such as dye-labelled CTb may not label all neurons projecting to the injected region, and this under-labelling effect could lead to an under-representation of projecting neurons, in particular the double-projecting neurons.

Depending on the location of the injection site, retrograde labelling of TEa–ECT–PERI-projecting neurons in the MOp may display variable patterns40. When injection sites are placed in the middle range of the TEa, retrograde labelling in the MOp exhibits a three-layer pattern staining upper L2/3, upper L5 and L6, whereas injections in the more rostral TEa area leads to less or no L6 labelling. In this work, injection sites were placed in the middle range of TEa that gave the three-layer labelling pattern in the MOp.

Imaging for CTb-injected tissue

The frozen CTb-injected mouse brain was sectioned the same as described in the ‘Tissue preparation for MERFISH’ section. A continuous set of 10-μm-thick slices in the region between Bregma approximately 0 and approximately +1.0) was sectioned with approximately every other slice kept and placed onto coverslips for imaging. We used a much higher sampling frequency for CTb-injected samples due to a higher failure rate of this experiment caused by removing the coverslip from the flow chamber after CTb imaging. Tissue slices were immediately fixed by treating with 4% PFA in 1× PBS for 15 min, washed three times with 1× PBS, stained with DAPI and proceed for imaging. As described in the ‘MERFISH imaging’ section, we used the same imaging buffer, and the sample was first imaged with a low-magnification objective (CFI Plan Apo Lambda ×10, Nikon) for DAPI in a 405-nm channel to produce a low-resolution mosaic of all slices. Next, to align each cell in the tissue with the same tissue slice that would be imaged with the MERFISH probe set later, we picked 10 cells in each coronal slice and recorded the location of the right-side edge for each cell. We then used the mosaic image, created as described above, to locate the MOp region in each slice and generated a grid of FOV positions to cover the MOp region to be imaged. We then switched to the high-magnification objective (CFI Plan Apo Lambda ×60, Nikon) and collected images in the 650-nm channel for CTb-AlexaFluor647, the 560-nm channel for CTb-AlexaFluor555, the 488-nm channel for CTb-AlexaFluor488 and the 405-nm channel for DAPI. We took a single image for each of these channels at the central z-plane.

After the CTb signals were imaged, the sample was removed from the imaging chamber and washed three times by 2× SSC and then permeabilized by 70% ethanol at 4 °C for at least 18 h. The tissue slices were then stained with the same MERFISH probe set, followed by the same MERFISH sample preparation and imaging procedures as described in the ‘Tissue preparation for MERFISH’ and ‘MERFISH imaging’ sections. During MERFISH imaging, we first imaged DAPI again with a low-magnification objective, and then located the same 10 cells in each coronal slice that we selected earlier during CTb imaging, and recorded the new location of the right-side edge for each cell. Using the old and new locations of the 10 cells for each slice, we determined the rotation and translation to align the CTb and MERFISH images. Then, MERFISH imaging was performed and the MERFISH images were decoded and segmented as described in the ‘MERFISH image analysis and cell segmentation’ section. We assigned each cell a projection identity by thresholding the normalized CTb dye intensity for each CTb channel and labelled each cell ‘on’ or ‘off’ for each channel. The CTb labelling of the cells were mostly binary (on or off) but still the labelling level varied between cells, therefore the threshold was tuned by manually examining a random subset of the images and was set to a fairly stringent level such that weakly labelled cells were labelled ‘off’. The cell-type identities of the CTb-injected samples were determined by training the MERFISH dataset with the MERFISH cell cluster identities without CTb injections using the classifier as described in the ‘Correspondence between clusters identified by MERFISH and single-cell sequencing-based measurements’ section and predicting on the CTb-injected samples. Each cell in the CTb-injected samples was hence assigned with both a cell-type identity and a projecting-target identity.

Statistics and reproducibility

Two replicate mice were imaged under each condition. From the two replicate mice imaged for the identification and spatial mapping of cell types, a total of approximately 300,000 cells were imaged, which generated a sufficient number of single-cell profiles and gave sufficient statistics for the effect sizes of interest. From the two replicate mice imaged for projection target mapping, a total of approximately 190,000 cells were imaged, which gave sufficient statistics for the effect sizes of interest. No statistical methods were used to predetermine sample size. The mice were randomly chosen. For each mouse, the imaging experiments were definitive and no randomization was necessary for this study, hence the experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment because all images were taken under the same condition, and the results were quantitative, which did not require subjective judgement.

The sample sizes for the violin plots in Fig. 1c–e and Extended Data Fig. 3 are listed as follows: Fig. 1c: from left to right, n = 5,585, 6,624, 7,993, 8,373, 5,686, 4,634, 5,431, 2,590, 8,083, 1,830, 2,303, 4,841, 6,570, 1,618, 4,265, 4,267, 5,183, 2,180, 6,699, 1,510, 1,590, 852, 1,417, 538, 2,624, 1,489, 1,810, 4,544, 4,350, 4,189, 3,654, 3,534, 2,009, 1,052, 690, 260, 2,105, 1,244 and 87 cells. Figure 1d: from left to right, n = 504, 161, 475, 480, 403, 259, 146, 154, 150, 124, 137, 391, 343, 241, 257, 123, 96, 222, 299, 154, 125, 200, 379, 648, 555, 462, 338, 414, 137, 152, 1,297, 868, 967, 1,019, 654, 346, 656, 237, 271, 158, 95 and 48 cells. Figure 1e: from left to right, n = 16,013, 2,993, 547, 5,160, 13,223, 5,948, 946, 17,117, 5,435, 3,524, 6,145, 6,888, 295 and 4,339 cells. Extended Data Fig. 3a: upper panel from left to right, n = 2,903, 2,702, 2,873, 4,505, 1,727, 708, 193, 1,258, 3,723, 559, 1,367, 1,648, 3,381, 716, 2,531, 1,134, 3,102, 737, 2,615, 399, 655, 450, 674, 88, 1,233, 606, 808, 1,709, 2,523, 1,715, 1,648, 403, 1,089, 341, 16, 115, 884, 424 and 27 cells; bottom panel from left to right, n = 228, 70, 199, 220, 183, 113, 40, 73, 72, 65, 68, 170, 158, 85, 108, 63, 45, 100, 124, 69, 58, 60, 150, 287, 261, 209, 137, 184, 67, 71, 550, 393, 440, 271, 256, 143, 284, 95, 68, 51 and 47 cells. Extended Data Fig. 3b: upper panel from left to right, n = 405, 1,550, 1,373, 1,793, 863, 235, 135, 140, 1,872, 40, 750, 651, 1,537, 37, 1,253, 637, 1,170, 387, 989, 245, 189, 305, 6, 538, 255, 328, 827, 1,113, 827, 658, 260, 487, 104, 7, 55, 440, 290 and 13 cells; bottom panel from left to right, n = 90, 39, 87, 94, 81, 46, 20, 22, 33, 22, 32, 77, 66, 36, 46, 25, 18, 42, 52, 30, 25, 28, 58, 99, 100, 87, 58, 71, 25, 21, 203, 166, 168, 105, 105, 59, 112, 41, 27, 22 and 24 cells. Extended Data Fig. 3c: upper panel from left to right (ML position = 1/ML position = 6), n = 339/662, 542/365, 352/531, 569/745, 244/315, 39/266, NA/112, 42/221, 537/602, 26/112, 240/108, 161/363, 409/424, 19/123, 251/403, 62/319, 415/254, 39/147, 232/345, NA/69, 82/100, 57/68, 54/103, NA/19, 169/133, 86/105, 111/90, 303/128, 309/228, 173/214, 187/197, 11/193, 122/111, 35/24, 6/NA, 16/13, 135/50 and 32/81 cells; bottom panel from left to right (ML position = 1/ML position = 6), n = 41/100, 314/205, 147/292, 231/339, 107/165, 7/123, NA/88, 15/38, 243/323, 11/NA, 143/54, 43/185, 166/232, 115/199, 37/207, 173/111, 18/90, 72/193, 39/36, 33/35, 29/62, 80/66, 41/47, 48/40, 142/77, 141/132, 73/126, 78/100, 7/135, 47/70, 16/NA, 8/8, 67/38 and 20/63 cells. Violin plots with cell numbers of five or fewer are not shown and the sample size numbers are listed as ‘NA’ in these cases.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

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