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Brasko, Csilla; Smith, Kevin; Molnar, Csaba; Farago, Nora; Hegedus, Lili; Balind, Arpad; Balassa, Tamas; Szkalisity, Abel; Sukosd, Farkas; Kocsis, Katalin; Balint, Balazs; Paavolainen, Lassi; Enyedi, Marton Z; Nagy, Istvan; Puskas, Laszlo G; Haracska, Lajos; Tamas, Gabor; Horvath, Peter
Nature communications, 01/2018, Letnik: 9, Številka: 1Journal Article
Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.
