Single Cell Sequencing


Biologically important differences between individual cells are averaged out in sequencing approaches from bulk tumor cell populations. This is a fundamental problem in oncology since the clinically highly relevant information on tumor heterogeneity is lost with respect to the initial tumor composition, the cellular response to drug treatment and the relapse of the tumor. To address these current limitations single cell sequencing (sc-seq) based 'omics' methods (genomics, transcriptomics and epigenomics) are emerging. They synergize with imaging and cell sorting technologies as an important new opportunity to dissect cancer cell populations in personalized medicine approaches. Furthermore, they are compementary to chromatin editing methods used in our lab with fluorescence microscopy readouts.

Work of the group

We have established single-cell sequencing (sc-seq) methods to analyze the transcriptome (scRNA-seq) as well as open chromatin loci at enhancer and promoters (scATAC-seq) in different leukemias. The single cell analysis is applied to address the following topics: (i) Identification and characterization of functionally important tumor subpopulations. (ii) Tracing tumor development over time to understand how the tumor cell population changes during disease progression. (iii) Elucidating epigenetic mechanisms that differentiate cells responding to drug treatment from the non-responding ones. To reach these research objectives we are currently establishing and developing technologies sc-seq technologies for the high-throughput analysis of primary cells and nuclei isolated from fresh and frozen tissue as well as from cell line systems. These approaches apply instrumentation for capturing cells in a microfluidics flow-cell (C1-Fluidigm), the drop-seq method (10x Genomics) as well as cell sorting on 384 well plates and automated sequencing library generation with liquid handling systems from Agilent and TTP Labtech. The group is active in the sc-seq@dkfz cross-topic program and contributes technologies developed in our work to the scOpenLab headed by Jan-Philipp Mallm (


Single-cell sequencing workflow. Whole populations are analyzed by Drop-seq and relevant sub­populations are detected. For further analysis, these populations are FACS-sorted and subjected to scRNA-seq and scATAC-seq with the Fluidigm system. In the figure profiles of ~90 cells for RNA-seq after clustering analysis and for scATAC-seq (38 primary leukemic cells with an enlarged 2.9 kb locus) are shown. Cellular sub­groups with distinct expression or open promoter/enhancer signatures can be directly identified from these profiles.


We conduct single cell sequencing analysis of different leukemias in a number of projects including DKFZ-HIPO2, DFG Research Unit 2674 as well as the Human Cell Atlas pilot project "START - Standardization of Single-Cell RNA-seq Protocols for Tumors" funded by the Chan Zuckerberg Initiative.

Selected references

Zirkel A, Nikolic M, Sofiadis K, Mallm JP, Brackley CA, Gothe H, Drechsel O, Becker C, Altmuelle J, Josipovic N, Georgomanolis T, Brant L, Franzen J, Koker M, Gusmao EG, Costa IG, Ullrich RT, Wagner W, Roukos V, Nuernberg P, Marenduzzo D, Rippe K & Papantonis A (2018). HMGB2 loss upon senescenceentry disrupts genomic organization and induces CTCF clustering across cell types. Mol Cell, published online 17 May 2018. doi: 10.1016/j.molcel.2018.03.030 | Abstract | Reprint (7.7 MB) | Article metrics.

Delacher M, Imbusch CD, Weichenhan D, Breiling A, Hotz-Wagenblatt A, Trager U, Hofer AC, Kagebein D, Wang Q, Frauhammer F, Mallm JP, Bauer K, Herrmann C, Lang PA, Brors B, Plass C & Feuerer M (2017). Genome-wide DNA-methylation landscape defines specialization of regulatory T cells in tissues. Nat Immunol 18, 1160-1172. doi: 10.1038/ni.3799 | Abstract | Article metrics

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