Identifying heterogeneity in cell culture through machine learning-enabled lens-free microscopy

Martin Alice; Florian Lemarchand; Kiran Padmanabhan; Lionel Hervé; Olivier Cioni; Chiara Paviolo

doi:10.1117/12.3021685

18 June 2024 Identifying heterogeneity in cell culture through machine learning-enabled lens-free microscopy

Martin Alice, Florian Lemarchand, Kiran Padmanabhan, Lionel Hervé, Olivier Cioni, Chiara Paviolo

Proceedings Volume PC12996, Unconventional Optical Imaging IV; PC129960Y (2024) https://doi.org/10.1117/12.3021685
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Cellular heterogeneity is the hallmark of many cancers, referring to the co-existence of different phenotypes with very distinct biological behaviours in single isolates. Automatically detecting single-cell heterogeneity is therefore critical, and can provide important information on cancer initiation. We present a clustering algorithm that allows identifying heterogeneity in cell culture from time-lapses of lensless microscopic images. A preliminary segmentation and tracking pipeline extract quantitative features (morphology, motility and reproduction cycle) for each cell. An unsupervised learning algorithm then clusters the time-series of the cell tracks measurements, in two steps. We validate our approach on co-cultures of mixed cells lines, and on murine fibroblasts isolated from genetically modified mice, where the modified genome promotes the establishment of cancers and heterogeneous cell morphologies and behaviours

Conference Presentation

Citation Download Citation

Martin Alice, Florian Lemarchand, Kiran Padmanabhan, Lionel Hervé, Olivier Cioni, and Chiara Paviolo "Identifying heterogeneity in cell culture through machine learning-enabled lens-free microscopy", Proc. SPIE PC12996, Unconventional Optical Imaging IV, PC129960Y (18 June 2024); https://doi.org/10.1117/12.3021685

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