Academic Journal
A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing
| Title: | A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing |
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| Authors: | Stefan Scheuermann, Sarah Hücker, Annika Engel, Nicole Ludwig, Philipp Lebhardt, Jens Langejürgen, Stefan Kirsch |
| Source: | SLAS Technology, Vol 29, Iss 3, Pp 100133- (2024) |
| Publisher Information: | Elsevier, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Biotechnology LCC:Medical technology |
| Subject Terms: | Personalized medicine, Molecular characterization, Micro RNAs (miRNA) sequencing, Single-cell analysis, Enzyme-free tissue dissociation, Biotechnology, TP248.13-248.65, Medical technology, R855-855.5 |
| More Details: | Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2472-6303 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2472630324000153; https://doaj.org/toc/2472-6303 |
| DOI: | 10.1016/j.slast.2024.100133 |
| Access URL: | https://doaj.org/article/df933405b14b4dcbafda52c7bffe90c5 |
| Accession Number: | edsdoj.f933405b14b4dcbafda52c7bffe90c5 |
| Database: | Directory of Open Access Journals |
| ISSN: | 24726303 |
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| DOI: | 10.1016/j.slast.2024.100133 |
| Published in: | SLAS Technology |
| Language: | English |