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      iPSC motor neurons, but not other derived cell types, capture gene expression changes in postmortem sporadic ALS motor neurons

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          SUMMARY

          Motor neuron degeneration, the defining feature of amyotrophic lateral sclerosis (ALS), is a primary example of cell-type specificity in neurodegenerative diseases. Using isogenic pairs of induced pluripotent stem cells (iPSCs) harboring different familial ALS mutations, we assess the capacity of iPSC-derived lower motor neurons, sensory neurons, astrocytes, and superficial cortical neurons to capture disease features including transcriptional and splicing dysregulation observed in human postmortem neurons. At early time points, differentially regulated genes in iPSC-derived lower motor neurons, but not other cell types, overlap with one-third of the differentially regulated genes in laser-dissected motor neurons from ALS compared with control postmortem spinal cords. For genes altered in both the iPSC model and bona fide human lower motor neurons, expression changes correlate between the two populations. In iPSC-derived lower motor neurons, but not other derived cell types, we detect the downregulation of genes affected by TDP-43-dependent splicing. This reduction takes place exclusively within genotypes known to involve TDP-43 pathology.

          In brief

          Amyotrophic lateral sclerosis causes motor neuron degeneration but leaves most other neurons intact. Held et al. find that familial ALS mutations cause distinct RNA profiles in iPSC-derived motor neurons compared with other cell types and that fALS iPSC motor neurons capture gene expression changes in postmortem sporadic ALS motor neurons.

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          Most cited references66

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                25 October 2023
                26 September 2023
                30 August 2023
                02 November 2023
                : 42
                : 9
                : 113046
                Affiliations
                [1 ]Department of Neurology, Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
                [2 ]The Collaborative Center for X-Linked Dystonia-Parkinsonism, Department of Neurology, Massachusetts General Hospital, Charlestown, MA 02129, USA
                [3 ]iPSC Neurodegenerative Research Initiative, Center for Alzheimer’s and Related Dementias, National Institute on Aging, NIH, Bethesda, MD 20892, USA
                [4 ]National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA
                [5 ]Broad Institute of Harvard University and MIT, Cambridge MA 02142, USA
                [6 ]Department of Anesthesiology, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston MA 02114, USA
                [7 ]Harvard Stem Cell Institute, Cambridge MA 02138, USA
                [8 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                A.H., C.M., and B.J.W. conceived the project. A.H., M.A., C.M., C.J.R., A.S.K., A.R.A.A.Q., and I.S.N. performed experiments and analyzed data. E.L., M.W., and C.L.-T. provided key reagents. A.H. and B.J.W. wrote the manuscript. All authors read the manuscript, provided feedback, and approved the final manuscript.

                Article
                NIHMS1933889
                10.1016/j.celrep.2023.113046
                10622181
                37651231
                a3a5457b-9ba9-4b34-94f8-51d0037dcf4e

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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