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      Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

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          Abstract

          We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD).

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          Most cited references87

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat Neurosci
                Nature neuroscience
                1097-6256
                1546-1726
                28 April 2021
                08 February 2021
                March 2021
                08 August 2021
                : 24
                : 3
                : 425-436
                Affiliations
                [1. ]Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
                [2. ]Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
                [3. ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
                [4. ]10x Genomics, Pleasanton, CA, 94588, USA.
                [5. ]Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
                [6. ]Department of Genetic Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
                [7. ]Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
                [8. ]Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
                [9. ]Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
                Author notes
                [+ ]Corresponding authors

                Author contribution

                • K.R.M. - Conceptualization, Methodology, Validation, Investigation, Writing, Visualization

                • L.C-T. - Methodology, Software, Formal analysis, Data Curation, Writing, Visualization

                • L.M.W. - Methodology, Software, Formal analysis, Writing, Visualization

                • C.U. - Methodology, Investigation, Resources

                • B.K.B. - Formal analysis, Data Curation, Visualization

                • S.R.W. - Software, Data Curation

                • J.L.C. - Software, Formal analysis, Visualization

                • M.N.T. - Investigation, Formal analysis

                • Z.B. - Software

                • M.T. - Formal analysis, Visualization

                • J.C. - Investigation

                • Y.Y. - Investigation

                • J.E.K. - Resources

                • T.M.H. - Methodology, Resources

                • N.R. - Resources, Supervision, Funding acquisition

                • S.C.H. - Methodology, Software, Formal analysis, Writing, Visualization, Supervision

                • K.M. - Conceptualization, Methodology, Writing, Supervision, Project administration, Funding acquisition

                • A.E.J.- Conceptualization, Methodology, Software, Formal analysis, Writing, Visualization, Supervision, Project administration, Funding acquisition

                Article
                NIHMS1656366
                10.1038/s41593-020-00787-0
                8095368
                33558695
                00c9e047-9107-4488-ae90-6536a844ecbf

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                Neurosciences
                Neurosciences

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