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      Single cell analysis reveals T cell infiltration in old neurogenic niches


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          The mammalian brain contains neurogenic niches comprising neural stem cells (NSCs) and other cell types. Neurogenic niches become less functional with age, but how they change during aging remains unclear. Here we perform single cell RNA-sequencing of young and old neurogenic niches in mice. Analysis of 14,685 single cell transcriptomes reveals a decrease in activated NSCs, changes in endothelial cells and microglia, and infiltration of T cells in old neurogenic niches. Surprisingly, T cells in old brains are clonally expanded and generally distinct from those in old blood, suggesting they may experience specific antigens. T cells from old brains express interferon γ, and the subset of NSCs with a high interferon response shows decreased proliferation in vivo. Interestingly, T cells can inhibit NSC proliferation in co-cultures and in vivo, in part by secreting interferon. Our study reveals an interaction between T cells and NSCs in old brains, opening potential avenues to counter age-related decline in brain function.

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          Most cited references41

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          An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation

          Gene set enrichment analysis is a widely used tool for analyzing gene expression data. However, current implementations are slow due to a large number of required samples for the analysis to have a good statistical power. In this paper we present a novel algorithm, that efficiently reuses one sample multiple times and thus speeds up the analysis. We show that it is possible to make hundreds of thousands permutations in a few minutes, which leads to very accurate p-values. This, in turn, allows applying standard FDR correction procedures, which are more accurate than the ones currently used. The method is implemented in a form of an R package and is freely available at \url{https://github.com/ctlab/fgsea}.
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            Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.

            Somatic stem cells contribute to tissue ontogenesis, homeostasis, and regeneration through sequential processes. Systematic molecular analysis of stem cell behavior is challenging because classic approaches cannot resolve cellular heterogeneity or capture developmental dynamics. Here we provide a comprehensive resource of single-cell transcriptomes of adult hippocampal quiescent neural stem cells (qNSCs) and their immediate progeny. We further developed Waterfall, a bioinformatic pipeline, to statistically quantify singe-cell gene expression along a de novo reconstructed continuous developmental trajectory. Our study reveals molecular signatures of adult qNSCs, characterized by active niche signaling integration and low protein translation capacity. Our analyses further delineate molecular cascades underlying qNSC activation and neurogenesis initiation, exemplified by decreased extrinsic signaling capacity, primed translational machinery, and regulatory switches in transcription factors, metabolism, and energy sources. Our study reveals the molecular continuum underlying adult neurogenesis and illustrates how Waterfall can be used for single-cell omics analyses of various continuous biological processes.
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              Is Open Access

              Sequence-specific error profile of Illumina sequencers

              We identified the sequence-specific starting positions of consecutive miscalls in the mapping of reads obtained from the Illumina Genome Analyser (GA). Detailed analysis of the miscall pattern indicated that the underlying mechanism involves sequence-specific interference of the base elongation process during sequencing. The two major sequence patterns that trigger this sequence-specific error (SSE) are: (i) inverted repeats and (ii) GGC sequences. We speculate that these sequences favor dephasing by inhibiting single-base elongation, by: (i) folding single-stranded DNA and (ii) altering enzyme preference. This phenomenon is a major cause of sequence coverage variability and of the unfavorable bias observed for population-targeted methods such as RNA-seq and ChIP-seq. Moreover, SSE is a potential cause of false single-nucleotide polymorphism (SNP) calls and also significantly hinders de novo assembly. This article highlights the importance of recognizing SSE and its underlying mechanisms in the hope of enhancing the potential usefulness of the Illumina sequencers.

                Author and article information

                29 June 2019
                03 July 2019
                July 2019
                01 April 2020
                : 571
                : 7764
                : 205-210
                [1 ]Department of Genetics, Stanford University, Stanford CA 94305
                [2 ]Stanford Medical Scientist Training Program, Stanford University, Stanford CA 94305
                [3 ]Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford CA 94305
                [4 ]Department of Immunology and Microbiology, Stanford CA 94305
                [5 ]Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford CA 94305
                [6 ]Department of Pathology, Stanford University School of Medicine, Stanford CA 94305
                [7 ]Cancer Biology Program, Stanford University, Stanford CA 94305
                [8 ]Fluidigm Corporation, South San Francisco, CA 94080
                [9 ]Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305
                [10 ]Howard Hughes Medical Institute
                [11 ]Glenn Laboratories for the Biology of Aging at Stanford University
                Author notes
                [12 ]Corresponding author: abrunet1@ 123456stanford.edu

                Author Contributions

                B.W.D. and A.B. planned the study. B.W.D. performed and analyzed most experiments, except those indicated below. M.T.B. performed one 10x Genomics and Smart-seq v4 replicate and analyzed the combined data. P.N.N. designed and analyzed human brain experiments and performed and analyzed immunocytochemistry in vivo and in culture. N.S. performed TCR sequencing by nested PCR and MOG injection under the supervision of M.M.D.. R.C. and H.V. provided human brain tissues and helped with design. S.C.B. helped with Fluidigm C1 libraries. D.S.L. helped with the NSC FACS protocol. K.H. helped with statistical analysis. B.M.G., J.V.P, T.W.-C., I.L.W., and M.M.D. provided intellectual contribution. B.W.D. and A.B. wrote the initial manuscript, and M.T.B. and P.N.N. wrote the revised version.


                These authors contributed equally to this work


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