Nature Communications (Jan 2018)
Intelligent image-based in situ single-cell isolation
- Csilla Brasko,
- Kevin Smith,
- Csaba Molnar,
- Nora Farago,
- Lili Hegedus,
- Arpad Balind,
- Tamas Balassa,
- Abel Szkalisity,
- Farkas Sukosd,
- Katalin Kocsis,
- Balazs Balint,
- Lassi Paavolainen,
- Marton Z. Enyedi,
- Istvan Nagy,
- Laszlo G. Puskas,
- Lajos Haracska,
- Gabor Tamas,
- Peter Horvath
Affiliations
- Csilla Brasko
- University of Szeged, Szeged
- Kevin Smith
- School of Computer Science and Communication, KTH Royal Institute of Technology
- Csaba Molnar
- Biological Research Centre of the Hungarian Academy of Sciences
- Nora Farago
- University of Szeged, Szeged
- Lili Hegedus
- Biological Research Centre of the Hungarian Academy of Sciences
- Arpad Balind
- Biological Research Centre of the Hungarian Academy of Sciences
- Tamas Balassa
- Biological Research Centre of the Hungarian Academy of Sciences
- Abel Szkalisity
- Biological Research Centre of the Hungarian Academy of Sciences
- Farkas Sukosd
- University of Szeged, Szeged
- Katalin Kocsis
- University of Szeged, Szeged
- Balazs Balint
- SeqOmics Biotechnology Ltd
- Lassi Paavolainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki
- Marton Z. Enyedi
- Biological Research Centre of the Hungarian Academy of Sciences
- Istvan Nagy
- Biological Research Centre of the Hungarian Academy of Sciences
- Laszlo G. Puskas
- Biological Research Centre of the Hungarian Academy of Sciences
- Lajos Haracska
- Biological Research Centre of the Hungarian Academy of Sciences
- Gabor Tamas
- University of Szeged, Szeged
- Peter Horvath
- Biological Research Centre of the Hungarian Academy of Sciences
- DOI
- https://doi.org/10.1038/s41467-017-02628-4
- Journal volume & issue
-
Vol. 9,
no. 1
pp. 1 – 7
Abstract
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.
