However, these approaches have only been applied to a few brain regions11 and a comprehensive cell atlas of the whole brain is still missing. Here, we imaged a panel of >1,100 genes in ~8 million cells across the entire adult mouse brain using multiplexed error-robust fluorescence in situ hybridization (MERFISH)12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating MERFISH and single-cell RNA-sequencing (scRNA-seq) data. Using this approach, we generated a comprehensive cell atlas of >5,000 transcriptionally distinct cell clusters, belonging to ~300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of the MERFISH images to the common coordinate framework (CCF) of the mouse brain further allowed systematic quantifications of the cell composition and organization in individual brain regions defined in the CCF. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes in the gene-expression profiles of cells. Finally, this high-resolution spatial map of cells, with a transcriptome-wide expression profile associated with each cell, allowed us to infer cell-type-specific interactions between several hundred pairs of molecularly defined cell types and predict potential molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a valuable resource for future functional investigations of neural circuits and their dysfunction in diseases.In mammalian brains, tens of millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells in the brain have so far hindered our understanding of the molecular and cellular basis of its functions. Recent advances in spatially resolved single-cell transcriptomics have allowed systematic mapping of the spatial organization of molecularly defined cell types in complex tissues Single-cell epigenomic profiling further enables systematic characterizations of gene-regulatory signatures of different cell types19. Indeed, numerous molecularly distinct cell types have been identified in the mammalian brain using scRNA-seq and single-cell epigenomic sequencing . For example, several hundred transcriptionally distinct cell populations have been identified across the entire mouse brain through scRNA-seq of ~500,000 to 700,000 cells26. Despite being a heroic effort at the time, the limited sampling sizes in these studies likely led to an underestimation of the cellular diversity of the brain. Indeed, a recent effort by the BRAIN Initiative Cell Census Network (BICCN) identified ~100 molecularly distinct cell populations in the mouse primary motor cortex16, a small brain region that occupies only a few percent of the total brain volume. It is thus possible that the whole mouse brain contains thousands of molecularly distinct cell populations.Mammalian brain functions are orchestrated by coordinated actions and interactions of many specialized cell types, including several major classes of cells, such as neurons, glial cells, vascular cells, and immune cells, and numerous distinct cell types within each class. The distinct behaviors and functions of different types of cells are, in a large part, determined by their different molecular properties. Hence, single-cell RNA sequencing (scRNA-seq) provides a systematic approach to classify cell types through gene-expression profiling of individual cells44, whereas in subcortical regions, such as thalamus and hypothalamus, neurons often organize into nuclei, which could be structural and functional units for behavior control48. At a finer scale, spatial location is also a major determinant of cell-cell interactions and communications. While synaptic communications can occur between neurons whose cell bodies are far apart, interactions between neurons and non-neuronal cells, as well as among non-neuronal cells, often occur through direct soma contact or paracrine signaling and hence require spatial proximity between cells. In addition, interactions involving local interneurons also tend to occur between spatially proximal neurons. Therefore, a high-resolution, spatially resolved cell atlas of the brain would provide a valuable resource and reference for understanding the molecular and cellular basis of brain function. Recent advances in spatially resolved transcriptomics have enabled gene-expression profiling and cell-type identification while maintaining the spatial information of cells in intact tissues2. These approaches have been used to generate spatial atlases of molecularly defined cell types for a few regions in the mouse and human brain . However, a high-resolution cell atlas of the whole brain is still missing.Moreover, understanding the molecular and cellular mechanisms underlying brain functions requires not only a comprehensive classification of cells and their molecular signatures, but also a detailed characterization of how these cells are spatially organized and how they interact with each other. The brain is made of several major regions, including the olfactory areas, isocortex, hippocampal formation, cortical subplate, striatum, pallidum, thalamus, hypothalamus, midbrain, hindbrain, and cerebellum. Each major region further comprises sub-structures that have distinct cell compositions and perform distinct functions. For example, the cerebral cortex forms layered structures, and information is processed by different cortical layers that contain different cell types12, to generate a molecularly defined and spatially resolved cell atlas of the entire adult mouse brain. By imaging ~8 million cells across the adult mouse brain and integrating the whole-brain MERFISH and scRNA-seq data, we determined the spatial organization of >5,000 transcriptionally distinct cell clusters, belonging to ~300 cell subclasses, across the whole mouse brain. This integration also allowed us to impute a transcriptome-wide expression profile for each cell imaged by MERFISH. We further registered the spatial cell atlas generated by MERFISH to the Allen Mouse Brain Common Coordinate Framework (CCF)49, providing a reference cell atlas that can be broadly used by the community. This CCF registration further allowed us to qualify the cell-type composition and spatial organization of individual brain regions. Finally, using spatial proximity and ligand-receptor co-expression analyses, we predicted interactions or communications between several hundred pairs of cell types (at the subclass level), and determined ligand-receptor pairs, as well as other genes, that were upregulated in spatially proximal cell pairs, providing insights into potential molecular mechanisms and functional implications of these predicted cell-cell interactions.Here, we used a single-cell transcriptome imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)To perform spatially resolved single-cell transcriptomic profiling of the whole mouse brain, we selected a panel of >1,100 genes for MERFISH imaging  based onWe imaged these genes in a series of 10-μm-thick coronal and sagittal sections spanning whole hemispheres of the adult mouse brain, including serial coronal sections at 100-μm intervals  or 200-μm intervals , and serial sagittal sections at 200-μm intervals  . IndividIn total we imaged ~8 million cells across the adult mouse brain, including all 11 major brain regions: olfactory areas (OLF), isocortex (CTX), hippocampal formation (HPF), cortical subplate (CTXsp), striatum (STR), pallidum (PAL), thalamus (TH), hypothalamus (HY), midbrain (MB), hindbrain (HB), and cerebellum (CB).50 and classified each MERFISH cell based on the most frequently appearing cell-type identity among the 100 nearest-neighbour anchor cells in the scRNA-seq dataset in the integrated gene-expression space -based integration methodpace see . The MERpace see , and thepace see . We set pace see . Overall51; for the genes that were not included the MERFISH gene panel, we compared the spatial patterns determined from the imputation results with the Allen Brain Atlas in situ hybridization data51. In both cases, we obtained excellent agreement 49 49  using a .org/)49 . This CC.org/)49 .Pmch, Tfap2b, Prdm12, etc.), and the major neurotransmitter  expressed in the subclass. For example, “LSX Prdm12 do Gaba” stands for the GABAergic neuronal subclass marked by Prdm12 residing in the dorsal (do) part of the lateral septal complex (LSX) in the striatum. Non-neuronal cell subclasses were annotated primarily based on marker genes and named based on prior knowledge  with spatial information being specified only in some cases . For both neurons and non-neuronal cells, the cell clusters were named by the subclass names followed by numerical indices.The spatial location information of the cell subclasses measured by MERFISH were also used for the annotation of the cell subclasses identified by scRNA-seq, as described in the companion manuscript . Briefly, except for some of the previously well-annotated subclasses, each neuronal subclass name has three parts: the brain region in which the subclass primarily resides , one or more major marker genes  subclasses showed a clean separation at the boundaries between isocortex and olfactory areas or hippocampal formation . In thalsubplate , consistFor each of the 11 major regions, we quantified their cell-type composition at the subclass and cluster level . The numSlc17a7, Slc17a6 and/or Slc17a8), GABAergic (expressing Slc32a1), serotonergic (expressing Slc6a4), dopaminergic (expressing Slc6a3), cholinergic (expressing Slc18a3), glycinergic (expressing Slc6a5), noradrenergic (expressing Slc6a2), and histaminergic (expressing Hdc) neurons.Next, we examined the neurotransmitter usage of neurons in different brain regions. Based on the expression of neurotransmitter transporters and genes involved in neurotransmitter biosynthesis, we classified matured neurons into eight partially overlapping groups: glutamatergic  and these neurons were primarily found in non-telencephalic areas of the brain such as the globus pallidus internal segment (GPi), hypothalamic nuclei such as the anterior hypothalamic nucleus (AHN) and supramammillary nucleus (SUM), and some subregions in midbrain and hindbrain, as well as in the outer layer of the main olfactory bulb (MOB)  accounted for only ~2–3% of the total neuronal population , left. Blb (MOB) , both coVglut1 (Slc17a7), Vglut2 (Slc17a6), and Vglut3 (Slc17a8) were differentially distributed in different brain regions , pontine gray (PG), anterior olfactory nucleus (AON), and thalamus , and were often co-expressed with Vglut1 and/or Vglut2  in midbrain and hindbrain , tuberal nucleus (TU), and other neighbouring areas in the ventral hypothalamus  and neighbouring areas in hindbrain 63. Cholinergic neurons were found in many different locations of the brain, including striatum, ventral pallidum, and multiple small subregions such as lateral septal complex (LSX), medial habenula (MH), ARH, pedunculopontine (PPN) and parabigeminal (PBG) nucleus in midbrain, and dorsal motor nucleus of the vagus nerve (DMX) and nucleus of the solitary tract (NTS) in hindbrain  and multiple nuclei in hypothalamus; Tac2 was enriched in BST and multiple nuclei in hypothalamus, striatum, and thalamus. Trh was expressed in RT in the thalamus, hypothalamus, cortical amygdalar area posterior part (COAp) in olfactory areas, and inferior olivary complex (IO) in medulla.Our MERFISH images also showed spatially heterogeneous distributions of many neuropeptide genes . To name67. The excitatory IMNs, composed of 7 clusters, were found in two distinct locations: cluster 5092 was primarily located in the piriform area (PIR) of olfactory areas, while the other clusters were distributed along the dentate gyrus (DG) in hippocampal formation , one inhibitory and one excitatory , left. Tormation , right cWe also examined the spatial organization of non-neuronal cells, comprising 23 subclasses and 99 clusters , and quaAcross the whole brain, non-neuronal cells were composed of 31% of oligodendrocytes, 6% of oligodendrocyte progenitor cells (OPC), 27% of vascular cells , 24% of astrocytes, 8% of immune cells , and 4% other cell types  .25. In addition, Astro clusters 5116–5120 were located in the olfactory bulb; Astro 5103 was located in the cerebellum; Astro 5111 was located in dentate gyrus, Astro 5109 was enriched in the hypothalamus, Astro 5104 was enriched in the medulla part of hindbrain close to the pia surface; Astro 5114 and 5115 clusters were located along the subventricular zone, extending to the olfactory bulb and were colocalized extensively with the inhibitory immature neurons  to quantify gene expression changes. Moreover, to capture the gradients that spanned multiple subclasses, we examined subclasses that were transcriptionally similar, such as the L2/3, L4/5, L5 and L6 IT neurons, and assessed whether the gradients identified within subclasses extended into transcriptionally similar subclasses. Using this approach, we identified many spatial gradients in different brain regions. Several examples are described below.We thus examined all cell subclasses to identify the spatial gradients of cells, in which the gene expression of cells changed gradually in space. To this end, we first systematically quantified the discreteness of clusters within each subclass see , left. B8. In striatum, the D1 and D2 medium spiny neurons (MSNs) both formed a spatial gradient along the dorsolateral-ventromedial axis , several GABAergic subclasses formed a gradient along the dorsal-ventral axis , which was comparable to the soma size of cells in the mouse brain. We then determined, for each cell-type pair, whether the probability of soma contact or proximity observed between cells from these two cell types was statistically significantly greater than that expected from random chance. We determined the random chance (null distribution of probability) by performing local spatial-coordinate randomizations to disrupt the spatial relationship between neighbouring cells while preserving the local density of each cell type11 , left. S11 , right iWe applied the two approaches described above to all major brain regions and identified several hundred pairs of cell subclasses showing statistically significant interactions by our criteria, with dozens of such interacting cell-type pairs in each of the major brain regions . Most of80. It is thus interesting to surmise that our observed upregulation of laminin ligand-receptor pairs in these interacting cell pairs might play a role in regulating the pool of macrophages in the brain. We also observed significant interactions between microglia and these two vascular cell types  by our criteria , e. Intell types . Interesll types .70. Whether our observed interactions between IMNs and astrocytes in the olfactory bulb is related to the interactions between neuroblasts and astrocytes in the RMS remains an open question. We also observed significant interactions between astrocytes and excitatory IMNs in hippocampal formation   , e. Thes to SMCs , consist to SMCs . Althougory bulb . A recen neurons . Wnt sig neurons  suggest In this work, we generated a spatial atlas of molecularly defined cell types across the whole mouse brain with high molecular and spatial resolution. By imaging ~8 million cells with MERFISH and integrating the MERFISH data with a scRNA-seq dataset containing ~4 million cells, we determined the spatial organization of >5,000 transcriptionally distinct cell clusters, which were grouped into ~300 cell subclasses. Registration of the MERFISH images to the Allen mouse brain CCF allowed us to place the imaged cells in a common coordinate framework with each cell containing high-dimensional information, including spatial coordinates, cell-type identity, and transcriptome-wide gene expression profile . This CCF registration further allowed us to determine the composition and spatial organization of transcriptionally distinct cell types in each individual brain region defined in the CCF. Analysis of the spatial relationship between cell types and correlated gene expression between proximal cells further allowed us to infer hundreds of cell-cell interactions or communications, as well as the potentially molecular basis and functional implications of these interactions.This whole-brain cell atlas provides a comprehensive reference of the molecular diversity and spatial organization of cells in the mouse brain. Our results highlight an extraordinary diversity of neurons, comprising >5,000 transcriptionally distinct neuronal cell clusters belonging to 283 subclasses, which is accompanied by a similarly high level of spatial heterogeneity. Most of the molecularly distinct neuronal cell types exhibit distinct spatial distributions. At the subclass level, individual cell types exhibit strong enrichment, if not locate exclusively, within one of the 11 major brain regions. In the cases when a subclass of cells spans multiple brain regions, these regions are often spatially connected. At the finer scale, transcriptionally distinct neuronal clusters within individual subclasses also tend to adopt different spatial distributions from each other. We also observed different level of diversity and distinct spatial organization in different brain regions. Overall, the telencephalic regions  show lower diversity of cells in each region, whereas the hypothalamus, midbrain and hindbrain exhibit higher cellular diversity with each region containing a substantially higher number of transcriptionally distinct cell types. This is not simply because these regions are made of a greater number of sub-regions with a simple cell-type composition. The neighbourhood of each cell also shows a substantially higher level of local cell-type complexity in these regions than in the telencephalic regions. Moreover, cells in these regions exhibit complex spatial organization with transcriptionally distinct cell types often assume irregularly shaped, partially overlapping spatial distributions. On the other hand, spatial organization of cells shows a higher level of regularity in the telencephalic regions, such as the layer-specific distribution of cortical neurons.The spatial distributions of the transcriptionally distinct neuronal cell types allowed us to divide the brain into molecularly defined brain regions, which we termed spatial modules. Our spatial module delineation shows both similarities and differences to the brain regions defined in the current Allen CCF. The differences are in part because of the higher molecular resolution in our spatial module analysis, which provides a high-resolution refinement to the CCF region annotation in some brain areas. However, we also note that some functionally or connectionally defined brain-region segmentation shown in the CCF are missing in our spatial module delineation. In the meantime, we also observed many spatial gradients in the brain where the cell-type composition and molecular profiles of cells change gradually in space. Such spatial gradients can be found in many brain regions. Many of these gradients span multiple subregions within a major brain region, suggesting that some of the subregion divisions in the CCF, as well as some of the boundaries defined by our spatial-module analysis, may represent somewhat arbitrary divisions on continuous gradients. Interestingly, we also observed a large-scale gradient spanning the hypothalamus, midbrain, and hindbrain regions where the gene expression changes gradually along the rostral-caudal and dorsal-ventral axes.97. Although these additional states are often related to development, aging, and inflammation, a small population of cells in some of these states have been observed in healthy adult mice97.Our data also provide a systematic molecular and spatial characterization of the non-neuronal cells. Non-neuronal cells account for more than half of the cells in the adult mouse brain, and this fraction varies substantially from region to region. We observed a remarkably high diversity of non-neuronal cells, comprising ~100 transcriptionally distinct clusters belonging to 23 subclasses. It is possible that the observed diversity of non-neuronal cells is still an underestimation. For example, the whole-brain scRNA-seq data classified all microglia into a single cluster, whereas multiple different microglial states have been identified previouslyNotably, many non-neuronal cell types also exhibited a highly level of regional specificity. This spatial heterogeneity is particularly pronounced for astrocytes, with the 24 astrocyte clusters each adopting a unique spatial distribution. While such regional-specific molecular profiles of astrocytes likely have a developmental origin, it is possible that the interactions of astrocytes with distinct types of neurons in different brain regions also contribute to the molecular diversity of astrocytes. An interest question arises as to whether the different molecular properties of distinct astrocytic subtypes play an important role in their function to support and modulate the activity of diverse neuronal cell types.Our high-resolution cell atlas further enabled a brain-wide investigation of cell-type-specific cell-cell interactions or communications. Across the whole brain, we predicted interactions or communications between several hundred pairs of cell types at the subclass level. Furthermore, we identified multiple ligand-receptor pairs, as well as many other genes, upregulated in proximal cell pairs within each of these cell-type pairs. The identified ligand-receptor pairs provide potential molecular basis underlying the cell-cell interactions and the upregulated genes further suggest potential functional roles of these cell-cell interactions. These analyses thus generated a rich set of hypotheses on cell-cell communications that await validation by future experiments. It should be noted that our spatial-proximity-based analysis are designed to predict soma interaction, paracrine signaling, and other short-range interactions, and hence is unlikely to uncover long-range synaptic communications between neurons. Indeed, most of the predicted interactions from our analyses are between non-neuronal cells and neurons or among non-neuronal cells, although we also observed interactions between some neuronal cell types.98, which are prone to false positives100. Indeed, to mitigate this problem, such predictions have often relied on validations by imaging experiment to probe whether the cells co-expressing the ligand-receptor pairs are indeed in contact or proximity. Our data inherently provides such spatial information in combination with the gene expression information, and hence allows cell-cell interaction predictions with both spatial and molecular analyses, which should help reduce false positives. Nonetheless, a few factors could still cause false positives and false negatives in our analyses. On the false positive side, although we applied local position randomizations of cells to generate null distributions in order to reduce the confounding effect of colocalization of cell types in a brain structure without interactions, and we further imposed the requirement of ligand-receptor upregulation in proximal cell pairs in interaction calling, it is impossible to completely eliminate such confounding effect especially when colocalization occurs within a relatively small brain structure. Decreasing the cellproximity distance threshold and randomization distance range could help reduce such false positives, but could also remove bona fide interactions in the meantime because paracrine signaling may occur over a larger distance. Our requirement of ligand-receptor upregulation in proximal cell pair, as compared to non-proximal cell pairs, for cell-cell interaction calling could also cause false negatives, because the ligand-receptor pairs mediating interactions between two cell types may be expressed at a constant level regardless of whether the cells are in proximity of each other. Interested readers could use our cell atlas as a resource and adjust the parameters and requirements in our cell-cell interaction analysis to generate a more stringent or a more inclusive list of hypotheses. It is important to note that, regardless of the parameter choice, additional experiments are needed to validate these cell-cell interaction hypotheses.The spatial information in our MERFISH data offers unique advantages in predicting cell-cell interactions or communications. Previous large-scale predictions of cell-cell interactions or communications have been based on co-expression of ligand-receptor pairs derived from sequencing dataAs another cautionary note, although our CCF registration of the MERFISH-derived cell atlas allows characterization of cell-type composition and organization in different brain regions. alignment errors inevitably exist in CCF registration due to the differences between individual mouse brains and the average template represented by the Allen CCFv3, as well as the deformation of tissue sections that are not completely corrected for during image alignment. Improvement in CCF-registration accuracy is an active research topic and the CCF reference itself is also actively evolving. Thus, our current CCF registration provides a starting point and future method development in this area will help improve the accuracy of CCF registration. Our high-resolution cell atlas could also serve as a resource for method development in this area.102, can also help reveal the functional roles of neurons. We envision exciting future studies combining spatially resolved transcriptomic analysis with measurements of various other properties, such as epigenomic profiles, morphology, connectivity, and function of cells, as well as with systematic gene perturbation methods, to connect our understanding of the brain’s molecular and cellular architecture with its function and dysfunction in health and diseases.Overall, our data provides a comprehensive, molecularly defined, and spatially resolved cell atlas of the adult mouse brain, featuring the expression profiles and spatial distributions of thousands of transcriptionally distinct cell clusters belonging to hundreds of major cell types. This reference cell atlas provides a basis for future functional studies of these distinct cell populations. Both the molecular signatures and the spatial information in the atlas provide important handles for functional interrogation of specific neuronal cell types through transgenic targeting tools and optogenetic manipulations. In addition, the predicted interactions between non-neuronal cells and neurons and among non-neuronal cells, as well as the observed ligand-receptor pairs and other genes upregulated in the interacting cell pairs, further provide hypotheses and entry points for testing the functional roles of the diverse non-neuronal cell types through genetic perturbations. Furthermore, combination of transcriptomic imaging with neuronal activity imaging under various behavior paradigms, as demonstrated previously in a few brain regionsAdult C57BL/6 male and female mice aged 57–63 days were used in this study. Animals were maintained on a 12 hour:12 hour light/dark cycle (2pm-2am dark period) 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).Estimates of the average RNA expression levels of individual genes in the mouse brain were derived from the bulk RNA-seq data of the whole mouse brain. RNA was extracted and isolated using RNAqueous Micro total RNA isolation kit  following manufacturer’s instructions from three different whole mouse brains aged 56–63 days. RNA quality was assessed using Agilent TapeStation and samples with an RNA integrity score >8 were kept for sequencing. RNA sequencing libraries were constructed using the Kapa mRNA HyperPrep Kits and were sequenced using the Illumina NextSeq500 platform performed by the Bauer Center Sequencing Core at Harvard University.https://nemoarchive.org).Single-cell RNA sequencing data were generated by the Allen Institute . These data are available at the Neuroscience Multi-omics Archive  gene analysis using the scRNA-seq data. Genes differentially expressed between pairs of transcriptionally distinct cell clusters from the scRNA-seq data were selected based on the following criteria: the genes had ≥2-fold change in expression between the two clusters with P-value < 0.01; they were expressed in at least 50% cells in the foreground cluster, with more than 3.3-fold enrichment, in terms of the fraction of cells expressing the gene, relative to the background cluster. Top 50 genes that satisfied the criteria and ranked by P-values in each direction for every cell-cluster pair were pooled together as the DE gene candidates for the final marker gene set. We then trimmed this DE gene pool to remove the genes that were too abundant or too short and thus were potentially challenging for MERFISH imaging experiments. Specifically, we excluded the genes that can accommodate fewer than 40 hybridization probes (MERFISH encoding probes) and thus were approximately < 500 nt in length, or were expressed at an average of 3000 counts in its highest expressing cell cluster as determined by the scRNA-seq data.To form the MERFISH gene panel, we first included 123 subclass markers based on the scRNA-seq clustering results, and 229 genes in the gene list that included transcription factors, neuropeptides, clock genes, and GPCR/interleukin/secreted proteins related genes. We then added DE genes to the panel until there were at least 3 DE genes included for each pair of cell clusters in each direction.Two gene panels were used in the MERFISH experiments. The first contained 1124 genes and was used for Animal 1, and the second contained 1147 genes which was used for all other animals. The two panels had 1122 genes in common. Two relatively high-abundance genes in the first gene panel were not included in the second gene panel, but 25 additional genes were added into the second gene panel, which included manually picked canonical marker genes for non-neuronal cells, as well as additional neurotransmitter related genes and neuropeptide genes.Sst, Vip, Avp, Pmch) that can accommodate fewer than 40 hybridization probes or were expressed at an average of >3000 counts in its highest expressing cell cluster. These genes were imaged in two sequential rounds of two-colour FISH imaging, following the MERFISH run that imaged the 1124-gene or 1147-gene panel.In addition to the MERFISH gene panel, we also imaged 4 other genes  within the range of 66–76°C, and no homology longer than 15-nt to rRNAs or tRNAs. From the set of all possible 30-nt target regions for each gene, we selected 64 target regions randomly to construct encoding probes. For the transcripts that were not long enough to accommodate 64 non-overlapping target regions, we allowed these 30-nt targeting regions to overlap by as much as 20 nucleotides to increase the number of probes. We also allowed the minimum number of probes to be included to reduce to 40, and the target regions to have a GC fraction between 30% and 70%, and a melting temperature (Tm) within the range of 61–81°C. Among the 1147 genes, 7 genes had between 40–64 probes and the remaining genes had 64 probes.Each MERFISH encoding probe contained one 30-nt target sequence that could specifically bind to a target gene and two 20-nt readout sequences. We designed a total of 32 readout sequences, each corresponding to one bit of the 32-bit MERFISH code. The collection of encoding probes designed to bind each gene contained the four readout sequences corresponding to the four bits that read “1” in the barcode of that gene. Each encoding probe contained two of the four 20-nt readout sequences that encode the specific barcode assigned to the gene. To design the target sequences in the encoding probes, we identified all possible 30-nt targeting regions within each desired gene as previously described103.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 nucleotides with any of the encoding probe sequences designed above, as previously described4. The template molecules were synthesized as a complex oligo pool (Twist Biosciences) and amplified as previously described103.With the template encoding probe sequences designed above, we constructed the MERFISH probe set as previously describedEncoding probes for the four genes imaged using two rounds of sequential two-colour FISH were produced in the same manner, except that 48 targeting sequences were selected for each gene if possible, and one single unique readout sequence was concatenated with targeting sequences for each gene. The four readout sequences used here, one for each gene, were different from the 32 readout sequences used for the genes imaged in the MERFISH run. These probes were purchased from Integrated DNA Technologies (IDT).The amplified encoding probes for the MERFISH run and encoding probes for the sequential two-colour FISH rounds were mixed for tissue staining.We used two readout probe schemes for the 32-bit MERFISH imaging plus the two sequential rounds of FISH imaging:8: 36 readout probes were designed, each complementary to one of the 36 readout sequences. Each readout probes were conjugated to one of the two dye molecules  via a disulfide linkage. These readout probes were synthesized and purified by Bio-synthesis, stored in Tris-EDTA (TE) buffer, pH 8 (Thermo Fisher) at a concentration of 1 μM at −20 °C.Direct readout strategy with dye-conjugated readout probes complementary to the readout sequences, as described previously104: i) 36 adaptor probes were designed, each consisting of a sequence complementary to one of the 36 readout sequences, concatenated by two additional common readout sequences, each for one colour channel. These adaptor probes were purchased from IDT, resuspended in TE buffer, pH 8 (Thermo Fisher) to a concentration of 1 mM and stored at −20 °C. ii) Two dye-conjugated readout probes were designed, each complementary to one common readout sequence for a colour channel, and each were conjugated to one of the two dye molecules  via a disulfide linkage. These readout probes were synthesized and purified by IDT, stored in TE buffer, pH 8 (Thermo Fisher) at a concentration of 100 μM at −20 °C.Two-step readout strategy with oligonucleotide adaptors, as described previously2, and their brain were quickly harvested and was frozen immediately in optimal cutting temperature compound , and stored at −80 °C until sectioning. Frozen brains were sectioned at −18 °C on a cryostat (Leica CM3050 S). Continuous set of 10-μm-thick slices were collected for imaging. For Animal 1, 10-μm-thick serial coronal sections were collected from the anterior edge to the posterior edge of the brain and every 20th section was kept; for Animal 2, the brain were sectioned similarly as for animal 1, but every 10th coronal section was kept; for Animal 3, 10-μm-thick serial sagittal sections were collected from the midline to the lateral edge of the brain and every 20th section was kept; for Animal 4, the brain were sectioned similarly as for Animal 3, but only the sections corresponding the same medial-lateral positions as the ones that showed broken regions for Animal 3 were imaged. Each coverslip contained 2–4 coronal slices or 1–2 sagittal slices. In total, 67 slices were imaged for Animal 1, 150 slices were imaged for Animal 2, 25 slices were imaged for Animal 3, and 3 slices were imaged for Animal 4. The coverslips were prepared as described previously4.Mice aged 57–63 days were euthanized with COTissue slices were fixed by treating with 4% PFA in 1×PBS for 15 minutes and were washed three times with 1×PBS and stored in 70% ethanol at 4 °C for at least 18 hours to permeabilize cell membranes. The tissue slices from the same animal were sectioned at the same time and were stored in 70% ethanol at 4 °C for no longer than 2 months until all the tissue sections from the same animal were imaged.105. 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 , 0.5% v/v Triton X-100 , and 1% v/v proteinase K  in 2×SSC for 36–48 hours at 37 °C. After digestion, the coverslips were washed in 2×SSC for 30 minutes for a total of four washes and then stored at 4°C in 2×SSC supplemented with 1:100 Murine RNase inhibitor  for no longer than 2 weeks prior to imaging.The tissue slices were then stained with the MERFISH encoding probes. Briefly, the samples were removed from the 70% ethanol and washed with 2× saline sodium citrate (2×SSC) for three times. Then we equilibrated the samples with encoding-probe wash buffer (30% formamide in 2×SSC) for five minutes at room temperature. The wash buffer was then aspirated from the coverslip, and the coverslip was inverted onto a 50 μL droplet of probe mixture on a parafilm coated petri dish. The probe mixture comprised ~0.5 nM of each encoding probe for the MERFISH imaging, ~ 5 nM of each encoding probe for the two sequential rounds of two-colour FISH imaging, and 1 μM of a polyA-anchor probe (IDT) in 2×SSC with 30% v/v formamide, 0.1% wt/v yeast tRNA  and 10% v/v dextran sulfate . We then incubated the sample at 37 °C for 36~48 hours. The polyA-anchor probe  was 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 minutes 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 previously described106. A commercial flow chamber  with a 0.75-mm-thick flow gasket  was used, and imaging buffer comprising 5 mM 3,4-dihydroxybenzoic acid , 50 μM trolox quinone, 1:500 recombinant protocatechuate 3,4-dioxygenase , 1:500 Murine RNase inhibitor, and 5 mM NaOH (to adjust pH to 8.0) in 2×SSC was used for all experiment. For sagittal slices, whole tissue slices were imaged, and for coronal slices, we imaged one hemisphere plus a narrow region near the midline in the other hemisphere. Two imaging schemes were used for the two different readout strategies:We used home-built imaging platforms for MERFISH imaging in this study, as described previouslyFor the direct readout strategy, we first stained the sample with a readout hybridization mixture containing the readout probes associated with the first round of imaging, 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 for imaging total poly-adenylated mRNA. The readout hybridization mixture was composed of the readout-probe-wash buffer containing 2×SSC, 10% v/v ethylene carbonate , and 0.1 % v/v Triton X-100, supplementing with 3 nM each of the appropriate readout probes. The sample was incubated in this mixture for 15 minutes at room temperature, and then washed in the readout-probe-wash buffer supplemented with 1 μg/mL DAPI for 10 minutes to stain nuclei within the sample. The sample was then washed briefly in 2×SSC and was ready for imaging. 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  with 15 min incubation in the flow chamber to cleave the dyes linked to the readout probes through disulfide bond. The sample was then washed by flowing 1.5 mL 2× SSC. To perform the second round 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 15 minutes. Then 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 the two-step adaptor readout strategy, we first stained the sample with an adaptor probe hybridization mixture containing the adaptor probes associated with the first round of imaging. The readout hybridization mixture was composed of the readout-probe-wash buffer containing 2× SSC, 30% v/v formamide , supplementing with 100 nM each of the appropriate adaptor probes. The sample was incubated in this mixture for 15 minutes at room temperature, washed in the readout-probe-wash buffer, and stained with a readout hybridization mixture containing 10 nM each of the two readout probes, as well as the polyA-anchor probe (Alexa488) at a concentration of 3 nM in the readout-probe-wash buffer . The sample was incubated in this mixture for 15 minutes at room temperature, washed again, and was then washed in 2×SSC supplemented with 1 μg/mL DAPI for 10 minutes to stain nuclei. Finally, the sample was washed briefly in 2×SSC and was ready for imaging. After the first round of imaging, the dyes were removed by flowing 2.5 mL of cleavage buffer comprising 2×SSC, 30% formamide and 50 mM TCEP, supplemented with unlabeled common readout probes at 100 nM each to block unoccupied readout sequences on the adaptor probes to prevent crosstalk between rounds of hybridizations. The sample was incubated in this cleavage buffer for 15 min in the flow chamber, then washed by flowing 1.5 mL readout-probe-wash buffer. To perform the second round of imaging, we flowed 3.5 mL of the adaptor probe mixture containing the appropriate adaptor probes across the chamber and incubated the sample in this mixture for 15 minutes, washed by 1.5 mL of readout-probe-wash buffer, and flowed 3.5 mL of the readout probe mixture containing the common readout probes across the chamber and incubated the sample in this mixture for another 15 minutes. Then the sample was washed again by 1.5 mL of readout-probe-wash buffer and then 1.5 mL of imaging buffer was introduced into the chamber.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 , the orange fiducial beads, the total polyA-mRNA signal by the polyA-anchor readout probe (Alexa488), and the nucleus signal by DAPI (405-nm channel). The latter two channels were used for cell segmentation as described below. For the second and all following imaging rounds, we collected images in the 750-nm, 650-nm, and 560-nm channels for the 2 readout probes and fiducial beads. During each imaging round, for the fiducial beads, we took a single image at one z-position for each field of view (FOV) on the surface of the coverslip using the 560-nm illumination channel as a spatial reference to correct for slight drift of the stage position over the course of imaging rounds. For imaging readout probes in the MERFISH rounds, we imaged multiple z-positions in each FOV: For Animal 1, we collected three or six 1.5-μm-thick z-stacks; for all other animals, we collected five 1.5-μm-thick z-stacks. We repeated the hybridization, wash, imaging and cleavage for all rounds to complete the 16 rounds of imaging for 32-bit MERFISH experiments. We then performed two additional rounds of two-color FISH imaging to image the four additional genes, and these images were only acquired from one z-plane per FOV. All buffers and readout probe mixtures were loaded with a home-built, automated fluidics system composed of three, 12-port valves  and a peristaltic pump .107, as described previously106. First, we identified the locations of the fiducial beads in each FOV in each round of imaging and used these locations to determine the X-Y drift in the stage position relative to the first round of imaging and to align images for each FOV across all imaging rounds. We then high-pass filtered the MERFISH image stacks for each FOV to remove background, deconvolved them using 10 rounds of Lucy-Richardson deconvolution to tighten RNA spots, and low-pass filtered them to account for small movements in the centroid of RNAs between imaging rounds. Individual RNA molecules imaged by MERFISH were identified by our previously published pixel-based decoding algorithm using MERlin107. 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 described previously106 for a gross barcode misidentification rate at 5% using MERlin107.All MERFISH image analysis was performed using MERlin108, as described previously11. For each individual z-plane, we segmented cell nuclei with the DAPI stain images with diameter parameter of 100 pixels in the “nuclei” mode. The centroid positions of the cells were then identified in each z-plane, and the centroids within distance of 2 μm in the xy direction across different z- planes were considered to be the same cell and were connected. We also segmented cell soma by the polyA images also using Cellpose with diameter parameter of 200 pixels in a “cytoplasm” mode.We performed cell segmentation using the DAPI and total polyA-mRNA signals and a deep learning-based cell segmentation algorithm (Cellpose)We assigned unique IDs for each segmented cell and assigned individual RNAs to segmentation boundaries of the cells based on whether or not they fell within those boundaries to obtain the cell x gene matrix, i.e., the copy number of RNAs for each gene in each cell.For the two sequential rounds of two-colour FISH imaging, we quantified the signal from these images by summing the fluorescence intensity of all pixels that fell within the segmentation boundaries of the cells associated with the imaged z-plane and normalized the signal by the areas of the cells in the z-plane.3 or > 1500 μm3 for the 3-z-plane measurements, the cells that had a volume of < 80 μm3 or > 2500 μm3 for the 5-z-plane measurements, and cells that had a volume of < 100 μm3 or > 3000 μm3 for the 6-z-plane measurements. (2) To remove the differences in RNA counts due to the different soma volumes captured in the images, we normalized the RNA counts per cell by the imaged volume of each cell. (3) We corrected the mean total RNA counts per cell to a same mean value (250 in this case) for each experiment. (6) We removed the cells that had total RNA counts in top and bottom 1% quantile. (8) We removed potential doublets using Scrublet109 as described previously. The cells with doublet score higher than 0.25 were removed as doublets, which accounted for ~4% of the total cell number.With the cell x gene matrix obtained as described above, we preprocessed the matrix by several steps: (1) The segmentation approach we used generated a small fraction of putative cells with very small total volumes due to spurious segmentation artifacts, as well as some cells that overlapped in the z dimension and were not properly separated. We hence removed the cells that had a volume of < 50 μmWe grouped MERFISH data from all four animals for integration with scRNA-seq data. Hence, only the overlapping 1122 genes between the two MERFISH gene panels used for all four animals were included in the cell x gene matrix for integration of MERFISH and scRNA-seq data and subsequent analyses.50 to integrate the MERFISH dataset and the scRNA-seq dataset. The integration works by co-embedding the two datasets in a common space and finding pairs of cells from the two datasets that are close to each other in the co-embedded space. The identified close pairs are termed anchors, which were used for transferring cell type labels and imputing gene expressions from the scRNA-seq dataset to the MERFISH dataset. We performed co-embedding of the two datasets by a canonical correlation analysis (CCA) based integration method initially developed in the Seurat R package50. In order to integrate more than ten million cells from the two datasets while achieving a fine resolution for >5,000 transcriptionally distinct cell clusters identified in the scRNA-seq data, we performed two rounds of integration.We used the SeuratIntegration class from the ALLCools python package110: normalize the total count of each cell to 1000, log1p transform the counts, and scale the transformed counts to Z-scores. We reduced the dimensionality to 100 principal-component-analysis (PCA) dimensions and calculated the 15 nearest neighbors of each cell in the PCA space. From the nearest neighbor graph, we calculated a connectivity graph of subclasses where each node was a subclass, and the weight of each edge was the number of edges in the nearest neighbor graph that connected cells from the two subclasses. Then we used the direct k-way cuts method from the METIS graph partitioning library111 to divide the 306 subclasses into 48 integration partitions. This method aimed to evenly distribute cells into partitions while minimizing the sum weight of cut edges.First, we divided the cells from both datasets into 48 integration partitions. We used the scRNA-seq dataset to define the partitions. Each integration partition was a group of subclasses that were close in the transcription space. We subset the genes in the scRNA-seq dataset to the genes measured by MERFISH. Then we preprocessed the dataset using the Scanpy pipeline110: normalize the total count of each cell to 1000, log1p transform the counts, and scale the transformed counts to Z-scores. We combined the two datasets and performed PCA to reduce the dimensionality to 100. We ran CCA to co-embed the scRNA-seq cells and MERFISH cells into a 100-dimensional space. In order to co-embed the large number of cells from the two datasets, the CCA was first performed on randomly downsampled scRNA-seq and MERFISH datasets, each containing 100,000 cells. Then the CCA coordinates of the full datasets were calculated by a linear transformation from the gene expression space to the CCA space. We found the five nearest neighbors across the two datasets in the CCA space. We defined all pairs of cells from the two datasets that were mutual nearest neighbors as integration anchors. Then we used the label_transfer function from the SeuratIntegration class to transfer the integration-partition labels from the scRNA-seq dataset to the MERFISH dataset. For each MERFISH cell, the label_transfer function calculated the probability of assigning the MERFISH cell to every integration-partition based on the 100 nearest-neighbour anchor cells from the scRNA-seq dataset in the PCA space. We set the integration-partition label of a MERFISH cell to be the one with the highest probability  and defined this probability as the confidence score of the transferred partition label.In the first round of integration, we transferred the integration-partition labels from the cells in the scRNA-seq dataset to the cells in the MERFISH dataset. We subset the genes in the scRNA-seq dataset to the genes measured by MERFISH. Then we independently preprocessed the scRNA-seq and MERFISH datasets by the Scanpy pipeline110. Only genes with log dispersions greater than zero were kept for integration. Using the same method for the first round of integration, we transferred the subclass and cluster labels from the scRNA-seq dataset to the MERFISH dataset and calculated the confidence scores for label transfer. Because a cell-type label is transferred correctly to a cell only when both the integration-partition label and the cell-type label within the integration partition were transferred correctly, we adjusted the confidence scores of the subclass and cluster label transfer by multiplying them with the integration-partition label-transfer confidence scores.In the second round of integration, we transferred subclass and cluster labels from the scRNA-seq dataset to the MERFISH dataset. We performed this round of integration for each integration-partition separately. We subset the genes in the scRNA-seq dataset to the genes measured by MERFISH, normalized the total count of each cell to 1000, and log1p transformed the counts. We used the genes that were highly variable in each integration partition. To this end, we calculated the dispersions of all the selected genes using the highly_variable_genes function from the Scanpy package50. In short, the imputed expression profile of a MERFISH cell was calculated as the weighted average of the expression profiles of its 30 nearest-neighbour anchor cells in the scRNA-seq dataset in the co-embeded PCA space. The weights were based on the distance between the scRNA-seq cells to the MERFISH cell and were calculated by the find_nearest_anchor function from the SeuratIntegration class using default parameters.Based on the integration of MERFISH and scRNA-seq data, we also imputed the transcriptome-wide gene expression for each cell in the MERFISH images using the method described previously51 for the genes included in the MERFISH gene panel, and with the Allen Brain Atlas in situ hybridization data only for the genes not included in the MERFISH gene panel. We performed two correlation analysis for comparing imputation results with the MERFISH measurement results. First, we calculated for each gene the mean expression level in every cluster from the imputation results and the MERFISH-measurement results. We then quantified for each gene the Pearson correlation coefficient between the imputed cluster means and MERFISH-measured cluster means across all clusters. Second, we calculated for each gene the mean expression levels of every imaged FOVs from the imputation results and the MERFISH-measurement results, and then quantified the Pearson correlation coefficient between the imputed FOV means and MERFISH-measured FOV means across all imaged FOVs. The first comparison evaluated how well the relative expression levels of genes in different clusters were recapitulated by the imputation and the second comparison evaluated how well the spatial dependence of gene expression was recapitulated by the imputation.We evaluated the validity of the imputation results by comparing them with the gene expression measured by MERFISH and with the previously measured spatial expression patterns in Allen Brain Atlas in situ hybridization dataFor the genes not included in the MERFISH, we visually compared the spatial patterns of gene expression determined by imputation with those determined in Allen Brain Atlas in situ hybridization data.112. Each transformation was then applied to the cell positions to find their initial position in CCF space.Registration of MERFISH data to the Allen Mouse Brain Common Coordinate Framework (CCF) version 3 was performed in a two-step process involving the reconstruction of 2D MERFISH tissue slices to a 3D volumetric image through alignment of DAPI signals in the MERFISH images to the Nissl template images in Allen Reference Atlas, followed by a 3D refinement using landmarks based on cell types with known localizations in the CCF. For the initial reconstruction, we used the DAPI channel of in the MERFISH images of individual brain slices and the Nissl template images in the Allen Reference Atlas, which is aligned to the Allen CCF. For each MERFISH sample from the same animal, brain slices were ordered and rotated to match coronal or sagittal orientation of the CCF. Coronal slices were cropped ~200 μm past the midline while sagittal slices were cropped at the posterior end of the cerebellum. In each animal, key slices containing recognizable landmarks were used to identify corresponding CCF planes, and all remaining CCF planes were determined by linear interpolation. To aid the registration process, features in the DAPI image were enhanced by highlighting pixels containing cell types that localized to known brain regions . The corresponding features in the Nissl image were also highlighted using the CCF annotations and/or morphological operations. Finally, each DAPI/Nissl image pair was registered with an affine, and then B-spline transformation using the program ElastixIn the second alignment step to refine the CCF registration, an additional 3D-3D registration was performed using additional selected cell types from the MERFISH data that are known to be localized to certain brain regions in the CCF. In total, 36 suitable cell types were identified along with their corresponding brain region annotations in the CCF, as well as two level-1space modules  that delineated the cells in the isocortex. These selected cell types  were each randomly assigned an intensity label, and a 3D volumetric image was generated using their initial positions in the CCF space from the first reconstruction step. A second target 3D image was generated but using only the CCF annotations: For each selected cell type, the corresponding brain region annotations in the CCF were assigned the intensity label for that cell type or spatial module, and all other annotated regions were removed. As before, for certain cell types, morphological operations on certain annotations were used to denote the midline, tissue surface or hollow ventricles. Finally, these two 3D images were registered using a B-spline transformation and the cell positions were refined.After the MERFISH data was registered to the CCF, each MERFISH cell was assigned a 3-dimensional coordinate, , indicating its spatial location in the CCF space, where ccfx indicates the coordinate value along the rostral-caudal direction, ccfy indicates the coordinate value along the dorsal-ventral direction, and ccfz indicates the coordinate value along the lateral-medial direction. Each MERFISH cell was also assigned a brain region annotation ID as defined in the CCF, indicating its brain region identity.For visualization in individual figures, we presented the MERFISH-imaged cells in the experimental coordinates, but reverse transformed the brain region boundaries defined in the CCF into the experimental coordinates by reversing the above-described MERFISH image-to-CCF transformation.Slc17a7, Slc17a6 and Slc17a8 were used for glutamatergic neurons, Slc32a1 for GABAergic neurons, Slc6a4 for serotonergic neurons, Slc6a3 for dopaminergic neurons, Slc18a3 for cholinergic neurons, Slc6a5 for glycinergic neurons, and Slc6a2 for noradrenergic neurons. In addition, Hdc, which is involved in histamine synthesis, was used to mark the histaminergic neurons. For all these genes, we used an expression threshold of RNA counts per cell n ≥ 2, determined by MERFISH, to assign neurotransmitter identity to individual neurons.We assigned neurotransmitter identity to the neurons based on their expression of canonical neurotransmitter transporter genes. Specifically, We did two rounds of spatial-module analysis to delineate molecularly defined brain regions based on local cell-type composition.For the first round of spatial module analysis, we defined a local cell-type-composition vector for each cell to characterize its neighbourhood composition of cell types at the subclass level. We began by finding the 50 spatially nearest neighbours for each cell. Because vascular and immune cells are usually randomly distributed across most brain regions, we excluded them from the spatial-module analysis. Then we assigned a weight to each neighbor cell j of a cell i as:th nearest spatial neighbour. Then we defined the local cell-type-composition vector of a cell from its neighbour cell types and weights. Each element of a local cell-type-composition vector corresponds to a cell type, and the value is the sum of the weights of the spatial neighbours that belong to this cell type.Where We generated the first level of spatial modules by clustering cells based on their local cell-type-composition vectors at the subclass level. We normalized the local cell-type-composition vectors by their L2 norms and ran the Leiden clustering method to cluster the cells. We manually curated the clusters by merging the clusters that did not form clear spatial boundaries and annotated the clusters based on the major brain regions that they corresponded to. This round of analysis gave level-1 spatial modulesth nearest spatial neighbour. Then we used the same method described for the first round of spatial module analysis to cluster cells based on their local cell-type-composition vectors to generate level-2 spatial modules.We then generated the level-2 spatial modules for each level-1 spatial module separately. Because the spatial heterogeneity of cell types within individual major brain regions are mainly due to neurons, we only considered neurons for the second round of spatial module analysis. We calculated the local cell-type-composition vectors using the same method described for the first round of spatial-module analysis with two modifications. The first modification is that we considered both subclasses and clusters to define the local cell-type-composition vectors – the subclass-based vector was concatenated with the cluster-based vector to form the overall vector. The second modification is that we used a shorter distance scaling factor To define the degree of how discrete or how well separated individual clusters were within each subclass, for each cell we calculated its “neighbourhood purity” defined by the fraction of cells that had the same cell-cluster label as the center cell among its 50 nearest neighbours in the gene-expression space. The discreteness of a cell cluster was defined by the mean value of the neighbourhood purities of all cells within the cluster. We then determined the median cluster discreteness of a subclass as a measure of how discrete individual clusters were within the subclass.8. Next, spatial gradients were visualized by representing gene expression profiles of the cells using either the first PC (PC1) or the pseudotime value of individual cells on the spatial maps. In addition, correlation of the PC1 or pseudotime values and the spatial coordinate of the cells were plotted. For the IT neurons in the isocortex, cortical depth was used as the spatial coordinate, and was calculated for individual neurons as previously described8 for coronal slices in the region between Bregma ~−0.8 and ~+1.7 where the L6b CTX cells formed clear thin layer at the bottom border of isocortex. For the D1 and D2 MSNs, locations along the dorsolateral-ventromedial axis were used as spatial coordinate values and were calculated using the ccfy  and ccfz  locations of individual cells. For LSX neurons and tanycytes, locations along the dorsal-lateral axis (ccfy) were used as spatial coordinate values.To visualize the spatial gradient of the subclasses or groups of transcriptionally similar subclasses, PCA was used to reduce dimensionality of the normalized expression data, and to calculate a ‘pseudotime’ value for each cell as previously describedWe performed cell-cell interaction analysis at the subclass level. All cells with a subclass label-transfer confidence score greater than 0.8 were used in this analysis. We divided cells into major brain regions based on their CCF coordinates. Due to the high complexity of cell type compositions of hypothalamus, midbrain, and hindbrain, we further divided these regions each into two regions: hypothalamus was divided into anterior and posterior hypothalamus; midbrain was divided into anterior and posterior midbrain; hindbrain was divided into pons and medulla. For hypothalamus and midbrain, the region was divided based on the cell locations along the rostral-caudal axis (ccfx), specifically, the mean value of minimum and maximum ccfx value for all the cells within the region was used to divide the region into anterior and posterior part. We only considered the subclasses that were either enriched or had a sufficient abundance in each brain region for the cell-cell interaction analysis. For neuronal subclasses, we used the enrichment score as described in 11. Two cells were considered in contact or proximity if the distance between the cell centroid positions was within a distance threshold . We first defined Rproximal to be 15 μm, which is comparable to the soma size of the cells in the mouse brain. To generate the null distribution by randomly shifting spatial positions of the cells locally, for each round of randomization, we shifted the spatial location of each cell to a random position within 100 μm from its original location. We did 1000 rounds of randomization. After each round, we calculated the number of cell pairs that were in contact or proximity between every pair of subclasses. For each pair of subclasses, we fitted the distribution of the number of contact/proximal cell pairs generated by 1000 randomizations to a normal distribution to generate the null distribution. We then compared the observed contact/proximal cell-pair number with the null distribution to determine the enrichment fold change and the p-value of the enrichment. Then we used the Benjamini-Hochberg multiple-hypothesis testing correction method to adjust the p-values. We used the adjusted p-value threshold of 0.05 and the number of observed proximal pair threshold of 20 to select pair of subclasses that showed significant probability to be in contact or proximity and called these subclass pairs as interacting cell-type pairs.For each subclass pair within each region, we determined the number of cell pairs (one from each subclass) that were in contact or proximity and compared the number of contact or proximal cell pairs to a null distribution generated by randomly shifting spatial positions of the cells locallyproximal = 15 μm, may eliminate some cell-type pairs that communicate through paracrine signaling, we also relaxed this distance threshold to a greater value , but for cell-type pairs identified with this relaxed distance threshold, we further required that at least one ligand-receptor pair was upregulated in the proximal cell pairs as compared to non-proximal cell pairs (see below) in order to call these cell types as interacting cell-type pairs.Since the stringent distance threshold, R113 to define the ligand-receptor pairs. For a ligand-receptor pair k, we defined the ligand-receptor expression score for a pair of cells i and j as:We performed the ligand-receptor analysis at the subclass level. All cells with subclass label-transfer confidence score greater than 0.8 were used in this analysis. We used the CellChat databaseth component of the ligand of the ligand-receptor pair k in the cell i; th component of the receptor of the ligand-receptor pair k in the cell j. The expression levels used here were the imputed gene expression results as described in the “Where proximal = 30 μm. For a pair of cell types and a ligand-receptor pair, we calculated the distributions of ligand-receptor expression scores for all proximal cell pairs, i.e., cell pairs with soma centroid distance smaller than Rproximal, from this cell-type pair (one cell from each cell type). Then we randomly selected the same number of cell pairs from this cell-type pair with soma centroid distance greater than Rproximal. We calculated the distributions of ligand-receptor expression scores for the non-proximal cell pairs. We used one-sided Welch’s t-test to test if the mean ligand-receptor expression scores were significantly higher in proximal cell pairs than the scores in the non-proximal cell pairs. Then we used the Benjamini-Hochberg multiple-hypothesis testing correction method to adjust the p-values. We selected significant ligand-receptor pairs that satisfied the following three criteria: the mean of ligand-receptor expression score was at least 2-fold higher in the proximal cell pairs than those in the non-proximal cell pairs; the adjusted p-value was < 0.01; the ligand-receptor expression scores were greater than zero in at least 40% of the proximal cell pairs. Using this approach, we determined the ligand-receptor pairs that were statistically significantly upregulated in the proximal cell pairs as compared to the non-proximal cell pairs in each cell-type pair that showed statistically significant proximity using Rproximal = 30.We performed ligand-receptor pair analysis for the cell-type pairs that showed statistically significant proximity compared to the null distribution as described in the “proximal of any type-B cells. We calculated for each gene the expression distributions in the two groups respectively and used one-sided Welch’s t-test to test if the mean expression was significantly higher in the first group than that in the second group. We used the Benjamini-Hochberg multiple-hypothesis testing correction method to adjust the p-values. We selected significantly upregulated genes using the following criteria: the mean expression level was at least 2-fold higher in the proximal cell pairs than those in the non-proximal cell pairs; the adjusted p-values were < 0.01.We then used a similar approach to determine other genes that were upregulated in the proximal cell pairs as compared to non-proximal cell pairs in each cell-type pair. We first determined the highly variable genes for each cell type. Only highly variable genes were considered for this gene upregulation analysis. For each cell-type A that showed significant proximity with another cell type B as compared to the null distribution, we divided the type-A cells into two groups based on whether they were within RSupplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 6


Disruptions in essential health services during the COVID-19 pandemic have been reported in several countries. Yet, patterns in health service disruption according to country responses remain unclear. In this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries in 2020.Using routine health information systems and administrative data from 10 countries  we estimated health service disruptions for the period of April to December 2020 by dividing monthly service provision at national levels by the average service provision in the 15 months pre-COVID (January 2019-March 2020). We used the Oxford COVID-19 Government Response Tracker (OxCGRT) index and multi-level linear regression analyses to assess associations between the stringency of restrictions and health service disruptions over nine months. We extended the analysis by examining associations between 11 individual containment or closure policies and health service disruptions. Models were adjusted for COVID caseload, health service category and country GDP and included robust standard errors.Chronic disease care was among the most affected services. Regression analyses revealed that a 10% increase in the mean stringency index was associated with a 3.3 percentage-point  reduction in relative service volumes. Among individual policies, curfews, and the presence of a state of emergency, had the largest coefficients and were associated with 14.1  and 10.7  percentage-point lower relative service volumes, respectively. In contrast, number of COVID-19 cases in 2020 was not associated with health service disruptions in any model.Although containment policies were crucial in reducing COVID-19 mortality in many contexts, it is important to consider the indirect effects of these restrictions. Strategies to improve the resilience of health systems should be designed to ensure that populations can continue accessing essential health care despite the presence of containment policies during future infectious disease outbreaks.The online version contains supplementary material available at 10.1186/s12913-023-09363-1. In response to the COVID-19 pandemic and in an effort to reduce the spread of infections, countries imposed a series of non-pharmaceutical interventions, such as stay-at-home orders, curfews and quarantines. These measures and policies were known as “lockdowns”, or COVID-19 containment or closure policies. By the end of March 2020 more than 100 countries had instituted either a full or partial lockdown [An increasing breadth of literature has shown disruptions in health services during the initial phase of the COVID-19 pandemic with heterogeneity in the degree and duration of these disruptions –16. NoneFew studies have sought to understand the relation between the stringency of COVID-19 containment policies and the level of health service disruptions. In 18 low- and middle-income countries, a study found a significant relationship between the stringency of COVID-19 mobility restrictions and disruptions in outpatient consultations, child vaccinations, and the fourth antenatal care visit, whereby a standard deviation in the mobility restrictions stringency was associated with a 3.9 percent reduction in outpatient consultation volume on average . In 11 sIn this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries. We further extend this analysis by examining the relative impact of 11 individual containment or closure policies on health service disruptions. This study was undertaken as a part of the QuEST Network, a global partnership of researchers and policy makers conducting health systems research. Participating countries were selected based on prior collaborations. Colleagues from 10 countries with different income levels, health system types, severity of COVID and government responses to COVID joined the effort.Data on the volume of varying health services provided from January 2019 to December 2020 (24 months) were extracted from administrative sources and Routine Health Information Systems (RHIS) in Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, South Korea, and Thailand. In Ethiopia, Ghana, Haiti, Lao PDR, Nepal and South Africa, RHIS data were extracted from the DHIS2 platform. The DHIS2 platform is the world’s largest health management information system software used by more than 73 countries . In ChilIn Ghana, Haiti, Nepal, and South Korea the datasets included all health facilities in the country reporting to the DHIS2 [We used a series of data cleaning and validation procedures in countries with disaggregated data . First we identified positive outliers (greater than 3.5 standard deviations from the mean trend) and set any outliers as missing . We did Data on COVID-19 containment policies were obtained from the Oxford COVID-19 Government Response Tracker (OxCGRT) dataset . Gross drelative service volumes by dividing monthly service volumes during COVID by the average service volume pre-COVID. The pre-COVID period included January 2019 to March 2020. The resulting outcome of interest represents the monthly service provision relative to the average before the pandemic. A value of 1 on any given month during COVID would indicate that the same number of patients were seen that month compared to the average pre-COVID, while a relative service volume value of 0.1 would indicate that the numbers of patients seen that month is only 10% of that on average pre-COVID.We obtained data on a total of 31 health services covering a broad range of health needs  linear regression model to estimate the association between the OxCGRT stringency index and health service disruptions across 10 countries.We used variance inflation factors with a threshold of 4 to examine the presence of multicollinearity (correlation amongst the independent variables) . The modi during month t. Stringency is the mean stringency index for country i during month t. COVID-19 cases is the total number of new COVID-19 cases in country i during month t. GDPi is GDP per capita in country i. Service type 2 to 5 are the health service category dummies . Where To assess the robustness of the effect of stringency on relative service volumes we repeated the analyses using other forms of the main exposure variable: 1) the median stringency index per month (instead of the mean) and 2) using the maximum stringency index observed in a month. These models were fitted using the same fixed and random effects structure as described above. The within-country correlation was estimated using adjusted and unadjusted intracluster correlation.As a sub-analysis we assessed associations between 11 individual COVID-19 containment policies and the relative service volumes. We included the nine response indicators used in the OxCGRT stringency index and two additional indicators: presence of curfews and state of emergency in place. The nine response indicators from OxCGRT stringency index were recoded as binary variables for ease of interpretation  were associated with significantly lower relative service volumes (worst disruptions) compared to reproductive and maternal services. Associations between the stringency index and relative service volumes was robust to alternate specifications including using the monthly median stringency index and the monthly maximum stringency index instead of the mean , this finding has policy relevance and demonstrates a lack of health system resilience. Any reduction in essential health service use could have important consequences on health, particularly in lower income countries where health care utilization had low baseline levels. In future pandemics, policy makers must ensure that the population can continue accessing essential care while policies are in place to contain outbreaks.Our study highlights that utilization of essential health services during a pandemic is affected by the types of policies implemented by governments to respond to the crisis. There are many mechanisms by which COVID-19 containment and closure policies could affect people’s willingness or ability to seek health services. Although essential services were permitted during stay-at-home orders, people might still be deterred from seeking care if restrictions were rigidly enforced. In South Africa, people reported fearing being arrested or fined as a reason for not visiting healthcare facilities in April 2020 . In NepaIt is important to note that COVID caseloads during this period were not associated with health service disruptions. A study in 18 low- and middle-income countries also found no significant relationship between COVID-19-related deaths and the magnitude of change in any health service . During It is apparent that COVID-19 containment policies have had important indirect effects beyond infection control. COVID-19 restrictions had deleterious economic effects on people globally, especially women , 36. RepOur study shows that the stringency of containment policies was also linked to a reduction in health service utilization. Our findings are consistent with those of other studies that have assessed lockdowns and health service utilization , 15. To Nonetheless, our study has limitations. Although a range of countries were included, the association between the stringency of COVID containment policies and service volumes might not be generalizable to all countries or contexts. Second, the associations we describe cannot be interpreted as causal. Models were adjusted for GDP per capita, monthly number of new COVID-19 cases, and type of health service. Nonetheless, it is possible that other variables  may confound the association between restrictions and health services. Third, the administrative datasets used in Chile, Mexico, South Korea and Thailand may differ from the DHIS2 data used in the other six countries, However, co-authors ensured that indicator definitions were comparable across countries. In addition, in some countries, routine health data excluded private facilities, telemedicine visits or some regions in the country [Our study contributes to understanding the effect of pandemic responses on health service utilization , 16. AltRecently, the WHO revealed that an estimated 14.9 million excess deaths occurred globally between January 2020 and December 2021 of which only 5.42 million were reported COVID deaths, calling for more resilient health systems globally . Health Additional file 1: Supplemental Table 1. Health services by service type category in 10 countries. Supplemental Table 2. Definition of containment policies and dichotomous recoding. Supplemental Table 3. Results from multi-level linear regression model for the association between the OxCGRT stringency index and relative service volumes (median stringency index). Supplemental Table 4. Results from multi-level linear regression model for the association between the OxCGRT stringency index and relative service volumes (max stringency index).


With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients’ characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal adopts the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method. One of the difficulties in dealing with observational data is the high-dimensionality of the covariates. There have been various methods developed to estimate the optimal individualized treatment rule. For regression-based approaches, Q-learning methods  pose a f methods  pose a m methods , 2018 ad methods , policy  methods , value-s methods  estimato methods . EspeciaStatistical inference for the optimal or estimated individualized treatment rule is particularly challenging in the presence of high-dimensional covariates. Confounding and selection bias presented in large observational data such as EHR data add one more layer of complexity. Another importance and related topic is the inference of the optimal value. The inference of the optimal value has been shown to be challenging at exceptional laws (non-regular case) where there exists a subgroup of patients for which treatment effect vanishes . To achiIn this work, we propose a novel penalized doubly robust approach, termed as penalized efficient augmentation and relaxation learning, to estimate the optimal individualized treatment rule in observational studies with high-dimensional covariates. We construct the decision rules by optimizing a convex relaxation of the augmented inverse probability weighted estimator of the value with penalties, which generalizes the method proposed in 2.In this section, we propose the penalized efficient augmentation and relaxation learning and then introduce the proposed inference procedure.2.1Let outcomes , 2005. Dsumption ; 2) the In this paper, due to the high-dimensional nature of the data we work with, we focus on deriving a linear decision rule of the form To avoid negative 2.2We defineBefore we propose our inference procedure for ion of x . In LemmDefine two subspaces depending on Lemma 1If thehas a linear form, andinthenif the following conditions are satisfied: (a) The contrast function , and the main effectthere exists a p-dimensional vector .The subspaces Condition (b) on the design matrix terature , 2019. Ition (b) .Remark 2Alternatively, instead of assuming the conditions in Lemma 1, the desired relationshipmay still hold under some parametric assumptions on . For example, if the outcomes are non-negative and the following conditions are satisfiedwe still have . Condition (poses a parametric assumption on for the details). This ratio measures the relative change of the potential outcomes. Under Condition  Each covariate (C2) There exists some constants (C3) (C4) The smallest eigenvalue of (C5) Suppose that for some Condition (C1) on the joint distribution of terature . Insteadterature . Conditithan 5pπ . Then n− formula  in Conditerature  due to tterature  of CondiTheorem 3Assume that Conditions (C1)–(C5) hold. By choosing , we have .Theorem 3 assumes that both the outcome and propensity score models are correctly specified, Theorems 4 and 5 provide the uniform validity of the testing procedures in Theorem 4Assume that Conditions (C1)–(C5) hold. Forunder the null hypothesis , by choosing , we haveand , whereis given inandTheorem 5Assume that Conditions (C1)–(C5) hold. The pooled one-step estimator satisfiesRemark 6Theorems 4 and 5 assume that both the propensity and the outcome models are correctly specified and estimated. Nonetheless, when the propensity score is known by the design of the experiment, the conclusions in Theorems 4 and 5 still hold even if the outcome model is misspecified. In contrast, Q-learning requires correctly specified outcome models even when the propensity is known. In practice, an individualized treatment rule can still be linear even if the contrast function is non-linear. As such, our modeling framework is more flexible. The advantages of our methods extend to the high-dimensional setting. The outcome weighted learning approach does not involve modeling outcomes. However, the corresponding penalized estimator in the outcome weighted learning approach may have a slower convergence rate than the proposed estimator in Theorem 3 when the propensity score is estimated with a slow rate. Therefore, the de-correlated score or the one-step estimator based on the outcome weighted learning approach cannot achieve a limiting distribution with .To derive the asymptotic property of the inference procedure for the value, we further introduce the following conditions:(C6) There exists an increasing function (C7) There exist constants Theorem 7Assume thatis bounded and denote the sample size of andas . In addition to the conditions in Theorem 3, we further assume ; 2) Condition (C7) holds with , then we havewhere .Condition (C6) implicitly assumes that 4.In this section, we test our estimation and inference procedure under various simulation scenarios. Let Under these settings, the magnitude of the treatment effect ITRInference (see https://github.com/muxuanliang/ITRInference.git) is coded to implement the proposed method and Q-learning approach. For the proposed method, the user can specify the method or select from a list of candidates to estimate nuisance parameters. In our implementation, we choose to estimate We compare the pooled estimator with Q-learning, a regression-based method . With hirocedure . We thenIn all scenarios, the sample size 5.MICE package in R), which is based on the estimated conditional distributions of each covariate given other covariates  and a testing data set (20% of the entire data set). The proposed method and Q-learning are fitted on the training data set using the same strategies as described in simulation studies. To evaluate these estimated decision rules, we calculate the value function by Next, we conduct the inference procedure to identify driving factors of the optimal individualized treatment rule as well as to provide an interval estimation using the entire data set. Results are presented in 6.In this paper, we consider a single-stage problem and assume a high-dimensional linear decision rule. In practice, especially in managing chronic diseases, dynamic treatment regimes are widely adopted, where sequential decision rules for individual patients adapt overtime to the evolving disease. One future direction is to develop inferential methods in the multi-decision setup. We can also extend the linear decision rule to a single index decision rule In this work, we adopt the de-correlated score to infer the high-dimensional linear decision rule. It is also possible to use other high-dimensional influential tools developed recently. Partial penalized tests proposed in In addition, in this work, we assume that both nuisance parameters are correctly specified and estimated by nonparametric methods after the variable screening. Some recent literature in estimating the average treatment effect assumes that only one of the nuisance parameters is correctly specified . In thesIn this work, we approximate the indicator function by a smooth convex surrogate. In addition to a smooth surrogate, many other non-smooth or non-convex loss functions such as the hinge loss , the raming loss  can be cAnother future work is to extend the proposed approach to multiple treatment options setup. There are several possible directions. The first direction is to transform the multiple treatment problem into multiple binary decision problems. We can consider a sequential decision-making strategy  by conduAppendix A and Appendix B


JCI, White, Wu, and coauthors elucidate a source of nonsuppressible viremia (NSV) in treatment-adherent patients — clonally expanded T cells harboring HIV-1 proviruses with small deletions or mutations in the 5′-leader, the UTR that includes the major splice donor site of viral RNA. These mutations altered viral RNA-splicing efficiency and RNA dimerization and packaging, yet still allowed production of detectable levels of noninfectious virus particles. These particles lacked the HIV-1 Env surface protein required for cell entry and failed to form the mature capsid cone required for infectivity. These studies improve our understanding of NSV and the regulation of viral functions in the 5′-leader with implications for rationalized care in individuals with NSV.HIV-1 replication can be suppressed with antiretroviral therapy (ART), but individuals who stop taking ART soon become viremic again. Some people experience extended times of detectable viremia despite optimal adherence to ART. In this issue of the  Suppressive antiretroviral therapy (ART) reduces the amount of viral RNA in the blood to below the limit of detection with clinical tests, i.e., less than 20 or 40 copies/ml, depending on the test. Despite the use of potent ART, the vast majority of PLWH still have a low level of viremia (LLV) that remains below the limit of detection of the clinical test, but can often be detected by highly sensitive assays , 4 despi the LLV . Our curJCI, White, Wu, and coauthors (In this issue of the oauthors  report ooauthors , althougoauthors , 13.The paper by White, Wu, and coauthors  starts oThe 5′-leader of the full-length HIV-1 transcript is about 790 nucleotides of the 9,800 nucleotide RNA genome and contains numerous structural and sequence-based control elements required for viral replication . EfficieThe 5′-leader can fold into multiple secondary structure conformations that determine whether the viral RNA transcript remains as a full-length viral RNA or is spliced and translated as a subgenomic mRNA . SecondaWhen White, Wu, and coauthors  looked aWe now know that it is possible for proviruses to produce virus particles with viral RNA even when the MSD site is gone. There are reported examples where the virus particles in elevated NSV are infectious, although not replicating given the presence of antiviral drugs , 13. WhiThe authors estimate that NSV occurs at a frequency of about 1 in 250 cases of people on suppressive therapy . SequencSeveral mysteries remain about how these defective particles are made. First, it is unclear what is happening with splicing when the MSD site is missing and why transcripts that initiate splicing are skewed toward complete splicing, causing a reduction in the partially spliced Env mRNA. Second, why are the particles in the immature form? The viral Gag-Pro-Pol polyprotein precursor is made as an alternative translation product of Gag translation and thus independent of splicing. One would assume that if Gag is made, then Gag-Pro-Pol is also produced. Importantly, the Pro in Gag-Pro-Pol is the viral protease tasked with cleaving the Gag and Gag-Pro-Pol polyprotein precursors. White, Wu, and coauthors  showed tWhat limits the rate for generating NSV? Viral DNA synthesis makes one mistake per three proviruses generated . The virIt is also possible that the relatively infrequent detection of persistent NSV only occurs in people with inefficient immune surveillance of virus-producing cells that would normally limit the size of such a clone. This would be the opposite outcome of what is seen in elite controllers who can control viremia even in the absence of ART. Recent analysis  of viral