Z03: Experimental and computational single-cell analysis with high spatial resolution

The goal of this project is to support all members of TRR179 in the planning, execution, and analysis of single-cell projects. For this purpose, we provide the consortium with a wide range of methods based on single cell sequencing (scRNA-seq, scATAC-seq, scMultiome-seq) to address various scientific questions. In addition, we provide support for the analysis of the data, using analysis pipelines that we have developed over the last years. In addition, a spatially resolved multiomics cell atlas of the HBV infected liver will be created, in close collaboration with Z02. Here, different data sets will be integrated (proteome data, histology, metabolome a.o.) and made available as a resource to the network.

Prof. Dr. Carl Herrmann

Prof. Dr. Karsten Rippe

Sant P, Rippe K, Mallm JP. (2023) Approaches for single-cell RNA sequencing across tissues and cell types. Transcription. 16:1-19. doi:10.1080/21541264.2023.2200721.

Liu L, Liu C, Quintero A, Wu L, Yuan Y, Wang M, Cheng M, Leng L, Xu L, Dong G, Li R, Liu Y, Wei X, Xu J, Chen X, Lu H, Chen D, Wang Q, Zhou Q, Lin X, Li G, Liu S, Wang Q, Wang H, Fink JL, ..., Herrmann C, …, Xu X. (2019) Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat Commun. 10(1):470. doi: 10.1038/s41467-018-08205-7.

Muckenhuber M, Seufert I, Muller-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. 6(4):e202201823. doi: 10.26508/lsa.202201823.

Seufert I, Sant P, Bauer K, Syed AP, Rippe K, Mallm J-P. (2023) Enhancing sensitivity and versatility of Tn5 based single cell omics. Front Epigenet Epigenom., in press. doi: 110.3389/freae.2023.1245879.

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. 30:blood.2023019758. doi: 10.1182/blood.2023019758.

Ghasemi DR, Okonechnikov K, Rademacher A, Tirier S, Maass KK, Schumacher H, Sundheimer J, Statz B, Rifaioglu AS, Bauer K, Schumacher S, Bortolomeazzi M, Giangaspero F, Ernst KJ, Saez-Rodriguez J, Jones DTW, Kawauchi D, Mallm J-P, Rippe K, Korshunov A, Pfister SM, Pajtler KW. (2022) Compartments in medulloblastoma with extensive nodularity are connected through differentiation along the granular precursor lineage. bioRxiv. 2022.09.02.506321. doi: 10.1101/2022.09.02.506321.

Doncevic D, Herrmann C. (2023) Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations. Bioinformatics. 39(6) doi: 10.1093/bioinformatics/btad387.

Winkler F, Herrmann C, Neumann-Haefelin C, Boettler T, Knolle P, Hofmann M, Wohlleber D, Thimme R, Bengsch B. (2023) Enolase represents a metabolic checkpoint controlling the differential exhaustion programs of hepatitis virus-specific CD8+ T cells. Gut. 72(10):1971-1984. doi: 10.1136/gutjnl-2022-328734.

Ramirez Alvarez C, Kee C, Sharma AK, Thomas L, Schmidt FI, Stanifer ML, Boulant S, Herrmann C. (2021) The endogenous cellular protease inhibitor SPINT2 controls SARS-CoV-2 viral infection and is associated to disease severity. PLoS Pathog. 17(6):e1009687. doi: 10.1371/journal.ppat.1009687.

Mallm J-P, Bundschuh C, Kim H, Weidner N, Steiger S, Lander I, Börner K, Bauer K, Hübschmann D, Benes V, Rausch T, de Azevedo NTD, Telzerow A, Jost KL, Parthé S, Schnitzler P, Boutros M, Müller B, Bartenschlager R, Kräusslich H-G, Rippe K. (2021) Local emergence and decline of a SARS-CoV-2 variant with mutations L452R and N501Y in the spike protein. medRxiv.2021.04.27.21254849. doi: 10.1101/2021.04.27.21254849.

Project in detail +

We will continue to offer support for scRNA-seq, scATAC-seq and CITE-seq experiments and their computational analysis as well as training within TRR179. In addition to these activities, we will establish spatial transcriptomics (ST) by hybridization with subcellular resolution as an important technology provided to TRR179 groups. ST approaches allow us to acquire molecular transcriptome readouts of single cells while at the same time evaluating the spatial relations between individual cells and the tissue microanatomy. The ST analysis will be applied to liver tissue samples from TRR179 projects to reveal molecular features on the susceptibility and response towards infection with the following approach: We will first establish the experimental and data analysis workflow to conduct ST with FFPE liver tissue sections based on the preparatory work described below. In close collaboration with Z02, we will then conduct the experimental and data analysis work to create a spatially resolved transcriptomics map to be used for the multiomics cell atlas of the hepatitis virus infected liver (see Z02). To this end, a core activity in Z03 will be the ST analysis of 20 liver tissue sections from patients infected with HBV and provided by Z02. These data will be integrated with the histological and antibody-based spatial proteomics maps of these liver samples that are generated in Z02. For the high-throughput acquisition of ST data in this part, we will employ either the Merscope (Vizgen) or Xenium (10x Genomics) system with a 500 or 480 gene probe set designed according to the needs of the TRR179 projects. The probes will include markers of distinct liver cell types, immune cells, innate immune response genes, as well as hepatitis virus markers with a focus on HBV. Selection of marker genes will be defined in close collaboration with the individual projects within TRR179. For the flexible acquisition of additional ST data, e.g., for mouse models, we will use redesigned smaller sets of 100 probes and resort to the Molecular Cartography system from Resolve Biosciences used previously for the analysis of human cancer tissue.
For data analyses, computational pipelines will be made available to all TRR179 members via docker container running on cloud architectures. Pipelines 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 Z03 project will focus on the integration of single-cell multiomics data acquisition and analysis, e.g., scRNA-seq and scATAC-seq, dimensional reduction and visualization approaches, and the analysis and integration of ST data (in collaboration with the mass cytometry and spatial omics studies conducted in Z02). Extraction of host cell response signatures will be based on statistical methods inspired by image analysis to identify subpopulations and intermediate cellular phenotypes. 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, by coupling of the different molecular layers and applying predictive modeling approaches, we will develop single-cell level regulatory networks that provide experimentally testable predictions that inform about the underlying molecular mechanisms.