Label-free imaging flow cytometry for analysis and sorting of enzymatically dissociated tissues

Research output: Contribution to journalResearch articleContributedpeer-review



Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the cells' properties and should be avoided, especially when purifying cells for clinical applications. A promising alternative is the label-free identification of cells based on physical properties. Sorting real-time deformability cytometry (soRT-DC) is a microfluidic technique for label-free analysis and sorting of single cells. In soRT-FDC, bright-field images of cells are analyzed by a deep neural net (DNN) to obtain a sorting decision, but sorting was so far only demonstrated for blood cells which show clear morphological differences and are naturally in suspension. Most cells, however, grow in tissues, requiring dissociation before cell sorting which is associated with challenges including changes in morphology, or presence of aggregates. Here, we introduce methods to improve robustness of analysis and sorting of single cells from nervous tissue and provide DNNs which can distinguish visually similar cells. We employ the DNN for image-based sorting to enrich photoreceptor cells from dissociated retina for transplantation into the mouse eye.


Original languageEnglish
Article number963
JournalScientific reports
Issue number1
Publication statusPublished - Dec 2022

External IDs

PubMedCentral PMC8770577
Scopus 85123063900
unpaywall 10.1038/s41598-022-05007-2


Research priority areas of TU Dresden


  • Animals, Cell Aggregation, Flow Cytometry/instrumentation, Mice, Microfluidic Analytical Techniques, Neural Networks, Computer, Photoreceptor Cells, Vertebrate/transplantation, Software