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      Comparative transcriptomics reveals human-specific cortical features

      1 , 2 , 3 , 4 , 5 , 2 , 3 , 4 , 5 , 6 , 6 , 7 , 1 , 1 , 1 , 1 , 1 , 1 , 8 , 9 , 2 , 3 , 4 , 5 , 1 , 6 , 10 , 1 , 8 , 1 , 1 , 1 , 8 , 1 , 1 , 1 , 11 , 1 , 1 , 1 , 12 , 13 , 14 , 15 , 9 , 16 , 11 , 7 , 17 , 8 , 18 , 19 , 1 , 2 , 3 , 4 , 5 , 6 , 20 , 1 , 1 , 1
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      American Association for the Advancement of Science (AAAS)

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          Abstract

          The cognitive abilities of humans are distinctive among primates, but their molecular and cellular substrates are poorly understood. We used comparative single-nucleus transcriptomics to analyze samples of the middle temporal gyrus (MTG) from adult humans, chimpanzees, gorillas, rhesus macaques, and common marmosets to understand human-specific features of the neocortex. Human, chimpanzee, and gorilla MTG showed highly similar cell-type composition and laminar organization as well as a large shift in proportions of deep-layer intratelencephalic-projecting neurons compared with macaque and marmoset MTG. Microglia, astrocytes, and oligodendrocytes had more-divergent expression across species compared with neurons or oligodendrocyte precursor cells, and neuronal expression diverged more rapidly on the human lineage. Only a few hundred genes showed human-specific patterning, suggesting that relatively few cellular and molecular changes distinctively define adult human cortical structure.

          Abstract

          INTRODUCTION

          The cerebral cortex is involved in complex cognitive functions such as language. Although the diversity and organization of cortical cell types has been extensively studied in several mammalian species, human cortical specializations that may underlie our distinctive cognitive abilities remain poorly understood.

          RATIONALE

          Single-nucleus RNA sequencing (snRNA-seq) offers a relatively unbiased characterization of cellular diversity of brain regions. Comparative transcriptomic analysis enables the identification of molecular and cellular features that are conserved and specialized but is often limited by the number of species analyzed. We applied deep transcriptomic profiling of the cerebral cortex of humans and four nonhuman primate (NHP) species to identify homologous cell types and human specializations.

          RESULTS

          We generated snRNA-seq data from humans, chimpanzees, gorillas, rhesus macaques, and marmosets (more than 570,000 nuclei in total) to build a cellular classification of a language-associated region of the cortex, the middle temporal gyrus (MTG), in each species and a consensus primate taxonomy. Cell-type proportions and distributions across cortical layers are highly conserved among great apes, whereas marmosets have higher proportions of L5/6 IT CAR3 and L5 ET excitatory neurons and Chandelier inhibitory neurons. This strongly points to the possibility that other cellular features drive human-specific cortical evolution. Profiling gorillas enabled discrimination of which human and chimpanzee expression differences are specialized in humans. We discovered that chimpanzee neurons have gene expression profiles that are more similar to those of gorilla neurons than to those of human neurons, despite chimpanzees and humans sharing a more-recent common ancestor. By contrast, glial expression changes were consistent with evolutionary distances and were more rapid than neuronal expression changes in all species. Thus, our data support a faster divergence of neuronal, but not glial, expression on the human lineage. For all primate species, many differentially expressed genes (DEGs) were specific to one or a few cell types and were significantly enriched in molecular pathways related to synaptic connectivity and signaling. Hundreds of genes had human-specific differences in transcript isoform usage, and these genes were largely distinct from DEGs. We leveraged published datasets to link human-specific DEGs to regions of the genome with human-accelerated mutations or deletions (HARs and hCONDELs). This led to the surprising discovery that a large fraction of human-specific DEGs (15 to 40%), and particularly those associated with synaptic connections and signaling, were near these genomic regions that are under adaptive selection.

          CONCLUSION

          Our study found that MTG cell types are largely conserved across approximately 40 million years of primate evolution, and the composition and spatial positioning of cell types are shared among great apes. In each species, hundreds of genes exhibit cell type–specific expression changes, particularly in pathways related to neuronal and glial communication. Human-specific DEGs are enriched near likely adaptive genomic changes and are poised to contribute to human-specialized cortical function.

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          Most cited references86

          • Record: found
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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Is Open Access

            Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

            Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.
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              • Article: not found

              GREAT improves functional interpretation of cis-regulatory regions.

              We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.
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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                October 13 2023
                October 13 2023
                : 382
                : 6667
                Affiliations
                [1 ]Allen Institute for Brain Science, Seattle, WA 98109, USA.
                [2 ]Allen Discovery Center for Human Brain Evolution, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA.
                [3 ]Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA.
                [4 ]Department of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA.
                [5 ]Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA.
                [6 ]Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
                [7 ]Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
                [8 ]LKEB, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.
                [9 ]Computer Graphics and Visualization Group, Delft University of Technology, Delft, Netherlands.
                [10 ]Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
                [11 ]Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
                [12 ]McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
                [13 ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
                [14 ]Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
                [15 ]Keeling Center for Comparative Medicine and Research, University of Texas, MD Anderson Cancer Center, Houston, TX 78602, USA.
                [16 ]Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA.
                [17 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
                [18 ]Pattern Recognition and Bioinformatics Group, Delft University of Technology, Delft, Netherlands.
                [19 ]Department of Anthropology, The George Washington University, Washington, DC 20037, USA.
                [20 ]Department of Physiology, University of Toronto, Toronto, ON, Canada.
                Article
                10.1126/science.ade9516
                37824638
                66646061-4916-4b11-9cc5-3736c55bef59
                © 2023

                Free to read

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