Z03: Experimental and computational single-cell sequencing

The focus will be on three sc-seq methods that are particularly informative for this purpose:
(i) Single cell RNA sequencing (scRNA-seq) to evaluate cellular heterogeneity and cell fate trajectories
(ii) single cell ATAC-seq (scATAC-seq) to map the heterogeneity of active regulatory elements and chromatin states as an additional “omics” layer linked to epigenetic differences and functional changes
(iii) a “spatially-resolved transcriptomics” (SRT) approach that links imaging-based information with the scRNA readout from the same single cell in order to reveal how the cellular context affects susceptibility and response towards infection

Prof. Dr. Carl Herrmann

Prof. Dr. Karsten Rippe

Winkler F, Hipp AV, Ramirez C, Martin B, Villa M, Neuwirt E, Gorka O, Aerssens J, Johansson SE, Rana N, Llewellyn-Lacey S, Price DA, Panning M, Gross O, Pearce EL, Herrmann CM, Schumann K, Hannibal L, Neumann-Haefelin C, Boettler T, Knolle P, Hofmann M, Wohlleber D, Thimme R, Bengsch B. Enolase represents a metabolic checkpoint controlling the differential exhaustion programs of hepatitis virus-specific CD8+ T cells. Gut accepted (TP01, TP02, TP04, TP05, TP13, TP20, TP21, Z03)

Poos AM, Prokoph N, Przybilla MJ, Mallm JP, Steiger S, Seufert I, John L, Tirier SM, Bauer K, Baumann A, Rohleder J, Munawar U, Rasche L, Kortum KM, Giesen N, Reichert P, Huhn S, Muller-Tidow C, Goldschmidt H, Stegle O, Raab MS, Rippe K*, Weinhold N* (2023) Resolving therapy resistance mechanisms in multiple myeloma by multi-omics subclone analysis. Blood Jun 30;blood.2023019758. doi: 10.1182/blood.2023019758.

Kotsiliti E, ..., Hofmann M, Rippe K, Zitzlesberger H, Weber A, Malek N, Lüdde T, Vucur M, Augustin HG, Flavell R, Parnas O, Rad R, Pabst O, Henderson NC, Huber S, Macpherson A, Knolle P, Claasen M, Geier A, Trautwein C, Unger K, Elinav E, Waisman A, Abdullah Z, Haller D, Tacke F, Anstee QM, Heikenwalder M. (2023) Intestinal B-cells license metabolic T-cell activation in NASH microbiota/antigen-independently and contribute to fibrosis by IgA-FcR signalling. J Hepatol. 2023 May 9:S0168-8278(23)00325-2. doi: 10.1016/j.jhep.2023.04.037.

Muckenhuber M, Seufert I, Müller-Ott K, Mallm JP, Klett LC, Knotz C, Hechler J, Kepper N, Erdel F, Rippe K. (2023) Epigenetic signals that direct cell type-specific interferon beta response in mouse cells. Life Sci Alliance. 2;6(4):e202201823. doi: 10.26508/lsa.202201823.

Faure-Dupuy S, Riedl T, Rolland M, Hizir Z, Reisinger F, Neuhaus K, Schuehle S, Remouchamps C, Gillet N, Schönung M, Stadler M, Wettengel J, Barnault R, Parent R, Schuster LC, Farhat R, Prokosch S, Leuchtenberger C, Öllinger R, Engleitner T, Rippe K, Rad R, Unger K, Tschaharganeh D, Lipka DB, Protzer U, Durantel D, Lucifora J, Dejardin E, Heikenwälder M. (2021) Control of APOBEC3B induction and cccDNA decay by NF-κB and miR-138-5p. JHEP Rep. 25;3(6):100354. doi: 10.1016/j.jhepr.2021.100354.

Al-Ali R, Bauer K, Park JW, Al Abdulla R, Fermi V, von Deimling A, Herold-Mende C, Mallm JP,

Herrmann C, Wick W, Turcan S. 2019. Single-nucleus chromatin accessibility reveals intratumoral epigenetic heterogeneity in IDH1 mutant gliomas. Acta Neuropathol Commun 7(1):201.

Liu L, Liu C, Quintero A, Wu L, Yuan Y, …, Chen F, Herrmann C, Eils R, Shang Z, Xu X. 2019. Deconvolu-tion of single-cell multi-omics layers reveals regulatory heterogeneity. Nat Comm. 10(1): 470.

Thier MC, Hommerding O, Panten J, Pinna R, García-González D, Berger T, …, Herrmann C, Monyer H, Edenhofer F, Trumpp A. 2019. Identification of Embryonic Neural Plate Border Stem Cells and Their Genera-tion by Direct Reprogramming from Adult Human Blood Cells. Cell Stem Cell. 24(1), 166-182.e13

Mallm JP, Iskar M, Ishaque N, Klett LC, Kugler SJ, …, Stilgenbauer S, Lichter P, Zapatka M, Mertens D, Rippe K. 2019. Linking aberrant chromatin features in chronic lymphocytic leukemia to deregulated tran-scription factor networks. Mol Syst Biol. 15(5): e8339.

Becker H, Greve G, Kataoka K, Mallm JP, Duque-Afonso J, …, Cleary ML, Schüler J, Rippe K, Ogawa S, Lübbert M. 2019. Identification of enhancer of mRNA decapping 4 as a novel fusion partner of MLL in acute myeloid leukemia. Blood Adv. 3(5):761-765

Zirkel A, Nikolic M, Sofiadis K, Mallm JP, Brackley CA, …, Roukos V, Nürnberg P, Marenduzzo D, Rippe K, Papantonis A. 2018. HMGB2 loss upon senescence entry disrupts genomic organization and induces CTCF clustering across cell types. Mol Cell 70(4): 730-744.

Molitor J, Mallm JP, Rippe K*, Erdel F*. 2017. Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions. Biophys J. 112: 473-490.

Delacher M, Imbusch CD, Weichenhan D, Breiling A, Hotz-Wagenblatt A, Träger U, Hofer AC, Kägebein 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 Immu-nol. 18(10):1160-1172.

Bauer T, Trump S, Ishaque N, Thürmann L, Gu L, …, Rippe K, Mizuguchi J, Herrmann C*, Eils R*, Lehmann I*. 2016. Environment-induced epigenetic reprogramming in genomic regulatory elements in smoking moth-ers and their children. Mol Syst Biol. 12(3):861.

Teif VB, Beshnova DA, Vainshtein Y, Marth C, Mallm JP, Höfer T, Rippe K. 2014. Nucleosome repositioning links DNA (de)methylation and differential CTCF binding during stem cell development. Genome Res. 24: 1285-1295

Project in detail +

Single cell sequencing (sc-seq) technologies provide unprecedented possibilities to dissect molecular de-tails of cellular heterogeneity, differences in a cell’s response to external signals (including viral infections or cytokine exposure) and cross-talk between cells. During the first funding period, within project Z02 methods were established that allowed the transcriptional profiling of low numbers of immune cells isolated from hepatitis C virus (HCV) infected patients (e.g.1, 2). Obtained results were highly informative and in the light of the emerging field of sc-seq and its rapid technological development we decided to establish the new cen-tral project Z03NN that is dedicated to sc-seq technologies to be applied to the study of immune cells and cell infection by hepatitis viruses. The focus will be on three sc-seq methods that are particularly informative for this purpose: (i) Single cell RNA sequencing (scRNA-seq) to evaluate cellular heterogeneity and cell fate trajectories (Becker et al., 2019; Delacher et al., 2019; Liu et al., 2019; Thier et al., 2019; Zirkel et al., 2018); (ii) single cell ATAC-seq (scATAC-seq) to map the heterogeneity of active regulatory elements and chroma-tin states as an additional “omics” layer linked to epigenetic differences and functional changes (Liu et al., 2019; Mallm et al., 2019; Al-Ali et al., 2019); (iii) a “spatially-resolved transcriptomics” (SRT) approach that links imaging-based information with the scRNA readout from the same single cell in order to reveal how the cellular context affects susceptibility and response towards infection3. In addition, we will, in tight integration with TRR179 members, develop and advance methods in two areas. One area relates to the set-up of exper-imental workflows and a sc-seq reference in-drop platform suited to conduct work under BSL3 conditions (necessary for work with HBV and HCV) at the Department of Infectious Diseases, Molecular Virology in Heidelberg. This part will also include protocols for preparing fixed samples suited for scRNA-seq analysis of infectious samples to allow sample analysis outside of a BSL3 environment. The other area relates to the identification of virus infected cells in scRNA-seq experiments for viruses like HCV that lack polyadenylated RNAs and thus, would not be captured in the Chromium drop-seq approach (this only selects for polyA+ RNA). Via amplification of viral marker RNAs as well as the simultaneous analysis of the single-cell tran-scriptome and viral protein cell-surface markers in a Cellular Indexing of Transcriptomes and Epitopes (CITE)-seq approach, virus infected cells will be identified. Work in Z03NN will also continue with low-input/sc-seq protocols launched as part of project Z02 during the first funding period in order to develop and disseminate sc-seq expertise to all partners of the network and to expand the scope of technologies that are necessary to meet emerging requirements within TRR179. For data analyses, computational pipe-lines will be made available to all participants through docker container running on cloud architectures. Pipe-lines for single-omics (scRNA-seq and scATAC-seq) will be tailored to the Chromium platform from 10x Genomics. In addition, the data analysis work in the Z03NN project will focus on the integration of single-cell multi-omics data acquisition and analysis, e.g., scRNA-seq and scATAC-seq (Liu et al., 2019) as well as dimensional reduction and visualization approaches (in collaboration with the mass cytometry studies con-ducted in Z02). Extraction of host cell response signatures will be based on statistical methods inspired by image analysis to identify subpopulations and intermediate cellular phenotypes4. Analysis of multiple data layers from comparable cells will enable the identification of specific response mechanisms, e.g. through chromatin remodeling combined with transcriptional response. Finally, via coupling of the various molecular layers we will develop single-cell level regulatory networks that provide experimentally testable predictions that are informative on the underlying molecular mechanisms (Mallm et al., 2019).