A New Image for Cell Sorting Introduced

Cells can also be sorted on the basis of signals from extrinsic probes.

Fluorescence-activated cell sorting (FACS) is the most popular means of separating a population of cells into subsets according to the total amount of key biomarkers that are expressed by each cell. These biomarkers are typically detected with fluorochrome-labeled antibodies.

Flow cytometry specialists from Newcastle University (Newcastle upon Tyne, UK) and the Broad Institute (Cambridge, MA, USA) have reviewed the latest advances in the use of FACS. The prototype instrument for this new means of sorting is an adaptation of the BD FACSMelody cell sorter (BD Biosciences, San Jose, CA, USA), which is considered the workhorse of FACS.

The new method, called BD CellView Image Technology, combined ultrafast microscopy and image analysis with a flow cytometric cell sorter to unlock spatial phenotypes for high-throughput sorting applications. Although this prototype instrument has only a few fluorescence channels (complex FACS systems have many more), it sorts cells according to the spatial pattern of fluorescence within each cell. The system relies on fast fluorescence imaging that uses radiofrequency-tagged emission and specialized low-latency signal processing and sorting electronics, a clever approach that provides spatial information for each signal.

The sorter physically separated the cells that had nuclear expression of RelA after nine hours of run time at a sorting rate of 14 million cells per hour. The investigators could then identify specific loci, the ablation of which blocked the nuclear translocation of RelA. They did this by sequencing unique guide RNAs that served as barcodes in the affected cells Overall, it appears that this system can aptly sort cells on the basis of prespecified complex morphologic characteristics.

Image-based cell-sorting technologies have myriad applications. Bar-coded genetic reagents that are present in image-sorted cell populations can be sequenced, which permits powerful genomewide knockdown or overexpression screens. This application expands the number of phenotypes that are amenable to screens to identify genes that underlie disease-related cell phenotypes, a development that can uncover potential therapeutic targets.

Image-based cell sorting can be used to explore the relationship between the sorted, visible cell phenotypes and other characteristics. Cell subpopulations sorted according to morphologic characteristics might subsequently be analyzed for their genome, transcriptome, epigenome, proteome, or additional morphologic properties. In addition, sorted populations can be compared according to the response to perturbations such as drug treatments.

Sorting technologies require the classification of each cell in real time, and classifying cells with complex phenotypes requires machine learning. Therefore, whether cells with complex phenotypes might be successfully sorted remains to be seen. Nevertheless, together these advancements in the process point to a future in which precision cell subpopulations might be rapidly purified and put to good use. The study was published on May 5, 2022 in the journal The New England Journal of Medicine.

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Newcastle University 
Broad Institute 
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