| Autor: |
Devin Harrison, David Wu, Jun Huang, Yun Fang |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
STAR Protocols, Vol 2, Iss 3, Pp 100807- (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2666-1667 |
| DOI: |
10.1016/j.xpro.2021.100807 |
| Popis: |
Summary: Heterogeneous metabolism supports critical single-cell functions. Here, we describe deep-learning-enabled image analyses of a genetically encoded lactate-sensing probe which can accurately quantify metabolite levels and glycolytic rates at the single-cell level. Multiple strategies and test data have been included to obviate possible obstacles including successful sensor expression and accurate segmentation. This protocol reliably discriminates between metabolically diverse subpopulations which can be used to directly link metabolism to functional phenotypes by integrating spatiotemporal information, genetic or pharmacological perturbations, and real-time metabolic states.For complete details on the use and execution of this protocol, please refer to Wu et al. (2021a). |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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