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    An optimized protocol for single nuclei isolation from clinical biopsies for RNA-seq.

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    Author
    Rousselle, Thomas V
    McDaniels, Jennifer M
    Shetty, Amol C
    Bardhi, Elissa
    Maluf, Daniel G
    Mas, Valeria R
    Date
    2022-06-14
    Journal
    Scientific Reports
    Publisher
    Springer Nature
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1038/s41598-022-14099-9
    Abstract
    Single nuclei RNA sequencing (snRNA-seq) has evolved as a powerful tool to study complex human diseases. Single cell resolution enables the study of novel cell types, biological processes, cell trajectories, and cell–cell signaling pathways. snRNA-seq largely relies on the dissociation of intact nuclei from human tissues. However, the study of complex tissues using small core biopsies presents many technical challenges. Here, an optimized protocol for single nuclei isolation is presented for frozen and RNAlater preserved human kidney biopsies. The described protocol is fast, low cost, and time effective due to the elimination of cell sorting and ultra-centrifugation. Samples can be processed in 90 min or less. This method is effective for obtaining normal nuclei morphology without signs of structural damage. Using snRNA-seq, 16 distinct kidney cell clusters were recovered from normal and peri-transplant acute kidney injury allograft samples, including immune cell clusters. Quality control measurements demonstrated that these optimizations eliminated cellular debris and allowed for a high yield of high-quality nuclei and RNA for library preparation and sequencing. Cellular disassociation did not induce cellular stress responses, which recapitulated transcriptional patterns associated with standardized methods of nuclei isolation. Future applications of this protocol will allow for thorough investigations of small biobank biopsies, identifying cell-specific injury pathways and driving the discovery of novel diagnostics and therapeutic targets.
    Data Availibility
    The raw sequence data generated in this study were deposited in the Gene Expression Omnibus (GEO) under the accession number GSE195719. The data can be accessed by visiting https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195719.
    Data / Code Location
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195719.
    Rights/Terms
    © 2022. The Author(s).
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/19211
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41598-022-14099-9
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