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Linking the genotypes and phenotypes of cancer cells in heterogenous populations via real-time optical tagging and image analysis


Linking single-cell genomic or transcriptomic profiles to functional cellular characteristics, in particular time-varying phenotypic changes, could help unravel molecular mechanisms driving the growth of tumour-cell subpopulations. Here we show that a custom-built optical microscope with an ultrawide field of view, fast automated image analysis and a dye activatable by visible light enables the screening and selective photolabelling of cells of interest in large heterogeneous cell populations on the basis of specific functional cellular dynamics, such as fast migration, morphological variation, small-molecule uptake or cell division. Combining such functional single-cell selection with single-cell RNA sequencing allowed us to (1) functionally annotate the transcriptomic profiles of fast-migrating and spindle-shaped MCF10A cells, of fast-migrating MDA-MB-231 cells and of patient-derived head-and-neck squamous carcinoma cells, and (2) identify critical genes and pathways driving aggressive migration and mesenchymal-like morphology in these cells. Functional single-cell selection upstream of single-cell sequencing does not depend on molecular biomarkers, allows for the enrichment of sparse subpopulations of cells, and can facilitate the identification and understanding of the molecular mechanisms underlying functional phenotypes.

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Fig. 1: fSCS consists of UFO miscroscopy, fast and accurate cell tracking, and target cell identification and photoselection.
Fig. 2: fSCS allows cell selection based on dynamically changing phenotypes.
Fig. 3: fSCS differentiates MCF10A-H2B-GFP cells displaying fast migration and mesenchymal-like morphology.
Fig. 4: Functionally annotated single-cell transcriptomic profiling (FUNseq) of MCF10A-H2B-GFP cells identifies pathways underlying fast migration and mesenchymal-like morphology.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. Data generated in this study, including source data and the data used to make the figures, are available from Open Science Framework at The bulk RNA-sequencing data are available at the NCBI BioProject database with the BioProject ID PRJNA751073.

Code availability

The image-analysis programme used in the study is available at


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M.-P.C. acknowledges support from the Oncode Institute, Cancer (CGC), NWO (the Netherlands Organization for Scientific Research) Veni Grant, and Erasmus MC grant. M.-P.C. appreciates Josephine Nefkens Stichting’s support on the UFO microscope. D.B. acknowledges support by an NWO Start-up Grant (740.018.018) and ERC Starting Grant (850818 - MULTIVIsion). P.-R.S. acknowledges support from Ministry of Science and Technology (MOST) in Taiwan (Dragon Gate program: 107-2911-I-002-577 and Columbus Program: 108-2636-M-002-008- &109-2636-M-002-005-). We thank R. Agami and M. Paul for the kind gift of MCF10A-H2B-GFP and U2OS-H2B-mMaple3 cell lines, respectively; A. Theil and T. W. Kan for technical assistance with FACS sorting; J. Kraan and J. Martens for the use of their cell separation machine; the Erasmus MC Center for Biomics for the bulk-cell transcriptomic sequencing; K. T. Chen for the phototagging purification; and P. Keller for discussions about TGMM.

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Authors and Affiliations



L.Y. scripted the mTGMM cell tracking algorithm and analysed most of the image data. P.-R.S. designed and conducted experiments and performed image analysis. M.B. and M.-P.C. scripted code for analysis of the single-cell sequencing data and performed analysis. R.G.R. conducted the HNSCC experiments. R.G.R. and T.-C.C. conducted experiments on HNSCC samples provided by J.A.U.H. and R.B.d.J., and T.-C.C. performed image analysis. C.B. contributed to all the cell culture preparations for the experiments, including the gene knockdown experiments. E.v.O., F.L. and T.-C.C. contributed to the morphology detection algorithm. P.G. conducted the 3D phototagging experiment. M.M. advised on some of the single-cell experiment and sequencing analysis. D.v.S. conducted part of the cell migration and sorting experiment. D.B. and M.-P.C. set up data-acquisition hardware and software with contributions from R.v.T., S.F. and P.G. M.-P.C., D.B., L.Y., M.B. and P.-R.S. wrote the paper with input from all authors. M.-P.C. and D.B. initiated, contributed to and supervised all aspects of the project.

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Correspondence to Daan Brinks or Miao-Ping Chien.

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Nature Biomedical Engineering thanks Konstantinos Konstantopoulos, Luca Magnani and Wei Li for their contribution to the peer review of this work.

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You, L., Su, PR., Betjes, M. et al. Linking the genotypes and phenotypes of cancer cells in heterogenous populations via real-time optical tagging and image analysis. Nat. Biomed. Eng 6, 667–675 (2022).

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