Intelligent image-based in situ single-cell isolation

• Published in: Nature communications Vol. 9; no. 1; pp. 226 - 7
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.01.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 45/23; 45/47; 45/77; 45/91; 631/114/1305; 631/114/1564; 631/1647/245/2226; 631/553/2706; Animals; Cancer; Cell Separation - methods; Cells, Cultured; Gene Expression Profiling; Genetic analysis; Humanities and Social Sciences; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted - methods; Learning algorithms; Machine Learning; Microscopy; Microscopy, Confocal - methods; multidisciplinary; Nervous system; Neurosciences; Pyramidal Cells - cytology; Pyramidal Cells - metabolism; Reproducibility of Results; Science; Science (multidisciplinary); Single-Cell Analysis - methods
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-017-02628-4

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. The isolation of single cells while retaining context is important for quantifying cellular heterogeneity but technically challenging. Here, the authors develop a high-throughput, scalable workflow for microscopy-based single cell isolation using machine-learning, high-throughput microscopy and laser capture microdissection.