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      Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq

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          Abstract

          Direct lineage reprogramming represents a remarkable conversion of cellular and transcriptome states 13 . However, the intermediates through which individual cells progress are largely undefined. Here we used single-cell RNA-seq 47 at multiple time points to dissect direct reprogramming from mouse embryonic fibroblasts (MEFs) to induced neuronal (iN) cells. By deconstructing heterogeneity at each time point and ordering cells by transcriptome similarity, we find that the molecular reprogramming path is remarkably continuous. Overexpression of the proneural pioneer factor Ascl1 results in a well-defined initialization, causing cells to exit the cell cycle and re-focus gene expression through distinct neural transcription factors. The initial transcriptional response is relatively homogeneous among fibroblasts suggesting the early steps are not limiting for productive reprogramming. Instead, the later emergence of a competing myogenic program and variable transgene dynamics over time appear to be the major efficiency limits of direct reprogramming. Moreover, a transcriptional state, distinct from donor and target cell programs, is transiently induced in cells undergoing productive reprogramming. Our data provide a high-resolution approach for understanding transcriptome states during lineage differentiation.

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          R: A language and environment for statistical computing

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            Direct conversion of fibroblasts to functional neurons by defined factors

            Cellular differentiation and lineage commitment are considered robust and irreversible processes during development. Recent work has shown that mouse and human fibroblasts can be reprogrammed to a pluripotent state with a combination of four transcription factors. This raised the question of whether transcription factors could directly induce other defined somatic cell fates, and not only an undifferentiated state. We hypothesized that combinatorial expression of neural lineage-specific transcription factors could directly convert fibroblasts into neurons. Starting from a pool of nineteen candidate genes, we identified a combination of only three factors, Ascl1, Brn2, and Myt1l, that suffice to rapidly and efficiently convert mouse embryonic and postnatal fibroblasts into functional neurons in vitro. These induced neuronal (iN) cells express multiple neuron-specific proteins, generate action potentials, and form functional synapses. Generation of iN cells from non-neural lineages could have important implications for studies of neural development, neurological disease modeling, and regenerative medicine.
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              Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

              Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                7 June 2016
                08 June 2016
                16 June 2016
                08 December 2016
                : 534
                : 7607
                : 391-395
                Affiliations
                [1 ]Department of Bioengineering, Stanford University, Stanford, CA94305, USA
                [2 ]School of Medicine, Stanford University, Stanford, CA94305, USA
                [3 ]Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, 04103, Germany
                [4 ]Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA
                [5 ]Department of Pathology, Stanford University, Stanford, CA 94305, USA
                [6 ]Department of Biology, Stanford University, Stanford, CA 94305, USA
                [7 ]Department of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA
                [8 ]Department of Developmental Biology, Stanford University, Stanford, CA 94305, USA
                [9 ]Howard Hughes Medical Institute, Stanford, CA 94305 USA
                [10 ]Department of Applied Physics, Stanford University, Stanford, CA94305, USA
                Author notes
                Correspondence: S.R. Quake ( quake@ 123456stanford.edu ) and M. Wernig ( wernig@ 123456stanford.edu )
                [*#]

                These authors contributed equally to the work

                Article
                NIHMS788092
                10.1038/nature18323
                4928860
                27281220
                5158b6c6-70f9-44c6-ade0-7811a1155ee5

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