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      Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain

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          Abstract

          Detailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, and computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus Droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive-site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from the human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for studying complex processes in the brain, such as genetic programs coordinating adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, providing insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.

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          Most cited references25

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          Is Open Access

          Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells

          Summary Understanding human embryonic ventral midbrain is of major interest for Parkinson’s disease. However, the cell types, their gene expression dynamics, and their relationship to commonly used rodent models remain to be defined. We performed single-cell RNA sequencing to examine ventral midbrain development in human and mouse. We found 25 molecularly defined human cell types, including five subtypes of radial glia-like cells and four progenitors. In the mouse, two mature fetal dopaminergic neuron subtypes diversified into five adult classes during postnatal development. Cell types and gene expression were generally conserved across species, but with clear differences in cell proliferation, developmental timing, and dopaminergic neuron development. Additionally, we developed a method to quantitatively assess the fidelity of dopaminergic neurons derived from human pluripotent stem cells, at a single-cell level. Thus, our study provides insight into the molecular programs controlling human midbrain development and provides a foundation for the development of cell replacement therapies.
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            Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.

            Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation of rare neurons from adult tissue is challenging and markers for each phase are limited. Here, we develop Div-Seq, which combines scalable single-nucleus RNA-Seq (sNuc-Seq) with pulse labeling of proliferating cells by 5-ethynyl-2'-deoxyuridine (EdU) to profile individual dividing cells. sNuc-Seq and Div-Seq can sensitively identify closely related hippocampal cell types and track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic niche, respectively. We also apply Div-Seq to identify and profile rare newborn neurons in the adult spinal cord, a noncanonical neurogenic region. sNuc-Seq and Div-Seq open the way for unbiased analysis of diverse complex tissues.
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              Massively multiplex single-cell Hi-C

              We present single-cell combinatorial indexed Hi-C (sciHi-C), which applies the concept of combinatorial cellular indexing to chromosome conformation capture. In this proof-of-concept, we generate and sequence six sciHi-C libraries comprising a total of 10,696 single cells. We use sciHi-C data to separate cells by karytoypic and cell-cycle state differences and identify cell-to-cell heterogeneity in mammalian chromosomal conformation. Our results demonstrate that combinatorial indexing is a generalizable strategy for single-cell genomics.
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                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nat. Biotechnol.
                Nature biotechnology
                1087-0156
                1546-1696
                16 November 2017
                11 December 2017
                January 2018
                11 June 2018
                : 36
                : 1
                : 70-80
                Affiliations
                [1 ]Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
                [2 ]Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
                [3 ]Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
                [4 ]Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA
                [5 ]Department of Pediatric Respiratory Medicine, University of California San Diego, La Jolla, CA, USA
                Author notes
                [* ]Corresponding Authors: Kun Zhang ( kzhang@ 123456bioeng.ucsd.edu ); Peter V. Kharchenko ( Peter_Kharchenko@ 123456hms.harvard.edu ); Jerold Chun ( jchun@ 123456sbpdiscovery.org )
                [†]

                Equally contributed authors.

                Article
                NIHMS920808
                10.1038/nbt.4038
                5951394
                29227469
                926c09b7-fa95-4cc3-ad1a-998a3e9cf2d0

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                Biotechnology
                Biotechnology

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