An optimized protocol for single nuclei isolation from clinical biopsies for RNA-seq View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-06-14

AUTHORS

Thomas V. Rousselle, Jennifer M. McDaniels, Amol C. Shetty, Elissa Bardhi, Daniel G. Maluf, Valeria R. Mas

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. More... »

PAGES

9851

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-022-14099-9

DOI

http://dx.doi.org/10.1038/s41598-022-14099-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148647415

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35701599


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