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Sci Rep. 2019 Aug 26;9(1):12367. doi: 10.1038/s41598-019-48771-4.

Pheno-seq - linking visual features and gene expression in 3D cell culture systems.

Tirier SM1,2,3, Park J4,2, Preußer F1,2,5, Amrhein L6,7, Gu Z2,8, Steiger S1,2, Mallm JP1,3,8, Krieger T4,1,2, Waschow M1,2, Eismann B1,2, Gut M9,10, Gut IG9,10, Rippe K1,3, Schlesner M2,11, Theis F6,7, Fuchs C6,7,12, Ball CR13, Glimm H13,14, Eils R4,1,2,8,15, Conrad C16,17,18,19.

Author information

1
Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany.
2
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
Division of Chromatin Networks, German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Digital Health Center, Berlin Institute of Health (BIH)/Charité-Universitätsmedizin Berlin, Berlin, Germany.
5
Max Delbrück Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Berlin, Germany.
6
Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Munich, Neuherberg, Germany.
7
Department of Mathematics, Technische Universität München, Munich, Germany.
8
Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany.
9
CNAG-CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
10
Universitat Pompeu Fabra, Barcelona, Spain.
11
Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
12
Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, Germany.
13
Department of Translational Oncology, NCT Dresden, University Hospital, Carl Gustav Carus, Technische Universität Dresden, Dresden and DKFZ, Heidelberg, Germany.
14
German Cancer Consortium, Heidelberg, Germany.
15
Health Data Science Unit, University Hospital Heidelberg, Heidelberg, Germany.
16
Digital Health Center, Berlin Institute of Health (BIH)/Charité-Universitätsmedizin Berlin, Berlin, Germany. christian.conrad@bihealth.de.
17
Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany. christian.conrad@bihealth.de.
18
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. christian.conrad@bihealth.de.
19
Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany. christian.conrad@bihealth.de.

Abstract

Patient-derived 3D cell culture systems are currently advancing cancer research since they potentiate the molecular analysis of tissue-like properties and drug response under well-defined conditions. However, our understanding of the relationship between the heterogeneity of morphological phenotypes and the underlying transcriptome is still limited. To address this issue, we here introduce "pheno-seq" to directly link visual features of 3D cell culture systems with profiling their transcriptome. As prototypic applications breast and colorectal cancer (CRC) spheroids were analyzed by pheno-seq. We identified characteristic gene expression signatures of epithelial-to-mesenchymal transition that are associated with invasive growth behavior of clonal breast cancer spheroids. Furthermore, we linked long-term proliferative capacity in a patient-derived model of CRC to a lowly abundant PROX1-positive cancer stem cell subtype. We anticipate that the ability to integrate transcriptome analysis and morphological patho-phenotypes of cancer cells will provide novel insight on the molecular origins of intratumor heterogeneity.

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