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Sarah Urata, Blue Lake, Dinh Diep, Masato Hoshi, Sanjay Jain, Kun Zhang 2019. 10X Genomics Single-Nucleus RNA-Sequencing for Transcriptomic Profiling of Adult Human Tissues. protocols.io https://dx.doi.org/10.17504/protocols.io.86khzcwCopy Citation Copied
URL: https://dx.doi.org/DOI:10.17504/protocols.io.86khzcw
Authors: Sarah Urata, Blue Lake, Dinh Diep, Masato Hoshi, Sanjay Jain, Kun Zhang
Group: KPMP, Human BioMolecular Atlas Program (HuBMAP) Method Development Community
Summary: 10X Genomics Single Cell 3' (v3) RNA sequencing is a microdroplet-based method that permits the effective capture and sequencing of the mRNA and pre-mRNA molecules from single nuclei [1]. RNA molecules are transcribed and processed within the nucleus before exporting to ER for translation into proteins. As such, nuclear RNA is a mixture of nascent transcripts, partially or fully processed mRNA, and various non-coding RNA molecules. The total RNA content within the nucleus is roughly 10% of the RNA content in a whole cell, but has been found to accurately represent whole cell expression values in adult human tissues [2,3] including the kidney [4]. Nuclei can be readily isolated from frozen tissues with a combination of chemical and physical treatments that can effectively circumvent the non-uniform or incomplete dissociation of solid tissues into single cells, as well as RNA degradation or artefacts (such as stress response) during dissociation. Here we present a modified version of the published 10X protocol [1] that we have adapted for the processing of adult human kidney nuclei.References 1. Chromium Single Cell 3' Reagent Kits v3 User Guide (Rev A) CG000183, support.10xgenomics.com. 2. Lake et al. (2016). Science, doi:10.1126/science.aaf1204. 3. Lake et al. (2018). Nature Biotechnology, doi:10.1038/nbt.4038. 4. Lake et al. (2019). Nature Communications, doi:10.1038/s41467-019-10861-2.
Affiliations: University of California, San Diego, University of California, San Diego, University of California, San Diego, Washington University, Saint Louis, Washington University School of Medicine, Saint Louis, University of California, San Diego
External URL: http://genome-tech.ucsd.edu/ZhangLab/
Version: 3
Publication Date: 2019
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