期刊
SCIENTIFIC REPORTS
卷 10, 期 1, 页码 -出版社
NATURE RESEARCH
DOI: 10.1038/s41598-020-65483-2
关键词
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资金
- Medical Research Council [MC PC 15075]
- Biotechnology and Biological Sciences Research Council [BB/R02183X/1]
- Carnegie Collaborative Research Grant [50468]
- BBSRC [BB/R02183X/1] Funding Source: UKRI
- MRC [MC_PC_15075] Funding Source: UKRI
The recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable insight into cell function. To support this progress, novel upstream capabilities are required for single cell preparation for analysis. Presented here is a droplet microfluidic, image-based single-cell sorting technique that is flexible and programmable. The automated system performs real-time dualcamera imaging (brightfield & fluorescent), processing, decision making and sorting verification. To demonstrate capabilities, the system was used to overcome the Poisson loading problem by sorting for droplets containing a single red blood cell with 85% purity. Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amongst clusters based on their instantaneous size and circularity. The presented system aspires to replace manual cell handling techniques by translating expert knowledge into cell sorting automation via machine learning algorithms. This powerful technique finds application in the enrichment of single cells based on their micrographs for further downstream processing and analysis.
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