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Chlis, N.-K. ; Rausch, L.* ; Brocker, T.* ; Kranich, J.* ; Theis, F.J.

Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.

Nucleic Acids Res. 48, 11335-11346 (2020)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used to estimate gene mutations and to predict the cell-cycle state and the cellular decision making directly from cellular morphology. Thus, high-throughput imaging methodologies, such as imaging flow cytometry can potentially aim beyond simple sorting of cellpopulations. We introduce IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe singlecell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Hematopoietic Stem-cells; T-cells; Cd8
ISSN (print) / ISBN 0305-1048
e-ISSN 1362-4962
Quellenangaben Band: 48, Heft: 20, Seiten: 11335-11346 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Great Clarendon St, Oxford Ox2 6dp, England
Begutachtungsstatus Peer reviewed
Förderungen Deutsche Forschungsgemeinschaft
Chan Zuckerberg Initiative DAF (advised fund of Silicon Valley Community Foundation)
Helmholtz Association (Incubator grant sparse2big)
DFG Fellowship through the Graduate School of Quantitative Biosciences Munich (QBM)