Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons

ABSTRACT: This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

ORGANISM(S): Mus musculus

SUBMITTER: Naomi Habib

PROVIDER: E-GEOD-84371 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.

Habib Naomi N Li Yinqing Y Heidenreich Matthias M Swiech Lukasz L Avraham-Davidi Inbal I Trombetta John J JJ Hession Cynthia C Zhang Feng F Regev Aviv A

Science (New York, N.Y.) 20160728 6302

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 ...[more]

PMID: 27471252

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Project description:We developed cell2location, a principled and versatile Bayesian model that is designed to resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. To validate cell2location in real tissue, we applied the model to data from the mouse brain, which features diverse neural cell types organised in a well characterised spatial architecture across brain areas, thus presenting a canonical use case to test spatial genomics. We generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon. To assess the biological and intra-organ technical variation in spatial mapping, we assayed two mouse brains and serial tissue sections from each brain (total of 3 and 2 matched sections from two animals, respectively, and an extra section for snRNA-seq), creating a rich multi-modal and replicated transcriptomic dataset. Tissue processing. Brains of wild-type adult C57BL/6 mice (postnatal day 56, 1 female and 1 male) were dissected, snap frozen, embedded in optimal cutting temperature compound (Tissue-Tek) and stored at -80oC. Brain hemispheres were cryosectioned at -20oC using a cryostat (Leica, CM3050S). To assess tissue quality, RNA was extracted from test tissue sections using the RNeasy Pico Kit (Qiagen) and yielded high RIN values (9.6 and 9.7) on an Agilent Bioanalyser, indicating high RNA quality. For matched single nuclei and Visium RNA-seq experiments, brain hemispheres were cryosectioned to adjacent thick (200 µm) and thin (10 µm) coronal sections, respectively, and processed the same day. In total, four consecutive sets of thick and thin tissue sections were collected from each brain. Five sets of tissue sections yielded both good quality single nuclei and Visium data (three adjacent sections from mouse 1 and two sections from mouse 2) while one additional section from mouse 2 yielded good single nuclei; these were considered for analysis in this study. Single nucleus RNA-sequencing. Thick (200 µm) mouse brain sections were cryosectioned, dissected from OCT and kept in a tube on dry ice until subsequent processing. Nuclei were extracted from each section as described previously. Briefly, nuclei were released from sections via Dounce homogenisation, Hoechst-stained, and isolated via fluorescence-activated cell sorting (FACS). Nuclei were then loaded into the 10X Chromium Single Cell 3′ Kit (v3) to obtain 3,000-7,000 nuclei per well, and library preparation was done per manufacturer’s protocol. Libraries were sequenced on an Illumina NovaSeq S4 system. Sequencing data were processed using 10X CellRanger version 3.0.2, aligned to mouse pre-mRNA genome reference version mm10 and mRNA count matrices were generated by adding intronic and exonic unique molecular identifier (UMI) counts for each gene in each cell. Initially, snRNA-seq counts were processed using standard Seurat V3 workflow without correcting batch effects between 6 individual samples.

Dataset Information

Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons

Publications

Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.

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