| Autor: |
Tala O Khatib, Angelica M Amanso, Christina M Knippler, Brian Pedro, Emily R Summerbell, Najdat M Zohbi, Jessica M Konen, Janna K Mouw, Adam I Marcus |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
PLoS ONE, Vol 18, Iss 10, p e0292554 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0292554&type=printable |
| Popis: |
Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype-such as invasiveness, cell:cell interactions, and changes in spatial positioning-with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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