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Klinik für Innere Medizin IIGastroenterologie, Hepatologie, Endokrinologie und Infektiologie

Single-cell Omics Platform

Single-cell technologies

The cell is the basic functional unit of the body and key to understanding health and disease. Recent technological advances now enable in-depth profiling of single cells required for the multi-dimensional understanding of cellular biology in a high-throughput fashion. In the Freiburg single-cell omics unit, we concentrate interdisciplinary expertise across multiple single-cell techniques to drive systems-level understanding at the single cell level. A major goal is to advance the insights into cellular biology for both basic and translational research questions, refining the single-cell methodology and providing optimized pipelines for thorough analysis, thus ultimately accelerating single-cell multiomics projects in a time and resource efficient way. 

 

Vision

  • Bring together interdisciplinary expertise on cutting-edge single-cell technologies
  • Implement and develop novel single-cell methods across different modalities
  • Coordinate the cost-efficient acquisition of shared reagents
  • Maintain a computational analysis infrastructure to integrate single-cell datasets
  • Establish and maintain best practices for optimal experimental procedures
  • Offer problem-oriented training to members of the participating institutions and
  • Offer assistance in efficiently communicating the findings and methods in publications

Technologies

  • Protein expression profiling at the single-cell and spatial level — CyTOF/IMC
  • Gene expression profiling using single-cell mRNA-Seq and spatial transcriptomics
  • Chromatin accessibility profiling with single-cell ATAC-Seq
  • Concomitant protein and gene expression profiling using CITE-Seq
  • T and B cell receptor repertoire analysis with single-cell TCR/BCR-Seq
  • Computational data analysis and visualization
     
Team
Prof. Dr. Dr. Bertram Bengsch
Dr. Gianni Monaco
Dr. Maike Hofmann
Dr. Roman Sankowski
Dr. Sagar

Participating Institutions

Klinik für Innere Medizin II
Gastroenterologie, Hepatologie, Endokrinologie und Infektiologie
Hugstetter Straße 55, 79106 Freiburg
 
Institut für Neuropathologie
Neurozentrum
Breisacher Straße 64, 79106 Freiburg
 

Contact details

E-Mail: sc_omics@uniklinik-freiburg.de
Twitter: https://twitter.com/scomicsFr

 

Hensel N*, Gu Z*, Sagar*, Wieland D, Jechow K, Kemming J, Llewellyn-Lacey S, Gostick E, Sogukpinar O, Emmerich F, Price DA, Bengsch B, Boettler T, Neumann-Haefelin C, Eils R, Conrad C, Bartenschlager R, Grün D, Ishaque N, Thimme R#, Hofmann M#. Memory-like HCV-specific CD8+ T cells retain a molecular scar after cure of chronic HCV infection. Nature Immunology. 2021 in press. 

Schulien I*, Kemming J*, Oberhardt V*, Wild K*, Seidel LM*, Killmer S*, Sagar, Daul F, Salvat Lago M, Decker A, Luxenburger H, Binder B, Bettinger D, Sogukpinar O, Rieg S, Panning M, Huzly D, Schwemmle M, Kochs G, Waller CF, Nieters A, Duerschmied D, Emmerich F, Mei HE, Schule AR, Llewellyn-Lacey S, Price DA, Boettler T#, Bengsch B#, Thimme R#$, Hofmann M#$, Neumann-Haefelin C#$. Characterization of pre-existing and induced SARS-CoV-2-specific CD8+ T cells. Nature Medicine 2020 doi: 10.1038/s41591-020-01143-2. Online ahead of print. 

Sagar, Pokrovskii M, Herman JS, Naik S, Sock E, Zeis P, Lausch U, Wegner M, Tanriver Y, Littman DR, Grun D (2020) Deciphering the Regulatory Landscape of gd T Cell Development by Single-Cell RNA-Sequencing. EMBO J e104159

Masuda T*, Amann L*, Sankowski R, Staszewski O, Lenz M, D Errico P, Snaidero N, Costa Jordão MJ, Böttcher C, Kierdorf K, Jung S, Priller J, Misgeld T, Vlachos A, Luehmann MM, Knobeloch KP, Prinz M. (2020) Novel Hexb-based tools for studying microglia in the CNS. Nature Immunology. doi: 10.1038/s41590-020-0707-4.

Sagar, Grun D (2020) Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis. Annual Review of Biomedical Data Science, 3, 1-22

Masuda T, Sankowski R, Staszewski O, Prinz M. (2020) Microglia Heterogeneity in the Single-Cell Era. Cell Reports. Feb 4;30(5):1271-1281. doi: 10.1016/j.celrep.2020.01.010.

Mereu E, Lafzi A, Moutinho C, Ziegenhain C, McCarthy DJ, Álvarez-Varela A, Batlle E, Sagar, Grün D, Lau JK, et al. (2020) Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nature Biotechnology

Sagar, Grun D (2019) Lineage Inference and Stem Cell Identity Prediction Using Single-Cell RNA-Sequencing Data. Methods in Molecular Biology 1975: 277-301

Sankowski R, Bottcher C, Masuda T, Geirsdottir L, Sagar, Sindram E, Seredenina T, Muhs A, Scheiwe C, Shah MJ, et al. (2019) Mapping microglia states in the human brain through the integration of high-dimensional techniques. Nature Neuroscience 22: 2098-2110

Masuda T*, Sankowski R*, Staszewski O*, Bottcher C, Amann L, Sagar, Scheiwe C, Nessler S, Kunz P, van Loo G, et al. (2019) Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566: 388-392

Aizarani N, Saviano A*, Sagar*, Mailly L, Durand S, Herman JS, Pessaux P, Baumert TF, Grun D (2019) A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572: 199-204

Jordao MJC, Sankowski R*, Brendecke SM*, Sagar, Locatelli G, Tai YH, Tay TL, Schramm E, Armbruster S, Hagemeyer N, et al. (2019) Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363

Bengsch B, Ohtani T, Khan O, Setty M, Manne S, O'Brien S, Gherardini PF, Herati RS, Huang AC, Chang KM, Newell EW, Bovenschen N, Pe'er D, Albelda SM, Wherry EJ. Epigenomic-Guided Mass Cytometry Profiling Reveals Disease-Specific Features of Exhausted CD8 T Cells. Immunity. 2018 May 15;48(5):1029-1045.e5. 

Sagar, Herman JS, Pospisilik JA, Grun D (2018) High-Throughput Single-Cell RNA Sequencing and Data Analysis. Methods in Molecular Biology 1766: 257-283

Tay TL*, Sagar*, Dautzenberg J, Grun D, Prinz M (2018) Unique microglia recovery population revealed by single-cell RNAseq following neurodegeneration. Acta Neuropathologica Communications 6: 87

Herman JS, Sagar, Grun D (2018) FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nature Methods15: 379-386