|Z03|

Project Area Z
Title

Integrative analysis of single cell genomics and immunoprofiling

 

Research Area

 

Project Summary

Optimizing the efficacy and safety of engineered lymphocytes requires an in-depth understanding of cellular states and state changes: These phenomena have been investigated in other cellular systems based on a sophisticated zoo of bioinformatical tools, that for example is employed in Human Cell Atlas porjects. We propose to bring together state-of-the-art bioinformatics analysis and mathematical modeling to monitor and improve the in vivo functionality of the engineered lymphocytes.

We will develop novel and tailor existing methods for multimodal single-cell data analysis to (i) identify cell states, (ii) combine multiple types of molecular data of individual cells to reveal a concise representation of cellular identity, (iii) model T cell states and perturbation response as a function of the T cell receptor sequence, (iv) build a comprehensive cell state atlas of
CAR-T perturbations, (v) quantify differentiation, migratory and proliferative behavior, and (vi) compare the developmental trajectories of differently engineered lymphocytes.
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Project-Related Publications

Fischer, D,Wu Y, Schubert, B, Theis FJ, Predicting antigen specificity of single T cells based on TCR CDR 3 regions. Molecular Systems Biology 16 (8), e9416 (2020)
Bergen, V, Lange, M, Peidli, S, Wolf FA°, Theis, FJ°. Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology (2020) (online ahead of print)
Lotfollahi, M, Wolf, FA and Theis, FJ. scGen predicts single-cell perturbation responses Nature Methods 16, 715–721 (2019)
Wolf, F, Angerer, P, Theis, FJ. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biology 19, 15 (2019). (ranked most cited paper that year from Gen Biol)
Haghverdi, L, Buettner, M, Wolf FA , Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods 13, 845–848 (2016)
Do, V. H., Elbassioni, K., and Canzar, S. (2020). Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity. iScience, 23(6), 101126.
Berg, D. A., Su, Y., Jimenez-Cyrus, D., Patel, A., Huang, N., Morizet, D., Lee, S., Shah, R., Ringeling, F. R., Jain, R., Epstein, J. A., Wu, Q. F., Canzar, S., Ming, G. L., Song, H., and Bond, A. M. (2019). A Common Embryonic Origin of Stem Cells Drives Developmental and Adult Neurogenesis. Cell, 177(3), 654–668.e15.
Do, V. H., Blažević, M., Monteagudo, P., Borozan, L., Elbassioni, K., Laue, S., Rojas Ringeling, F., Matijević, D., and Canzar, S. (2019). Dynamic pseudo-time warping of complex single-cell trajectories. RECOMB, LNBI 11467:294-297.
Canzar, S., Neu, K. E., Tang, Q., Wilson, P. C., and Khan, A. A. (2017). BASIC: BCR assembly from single cells. Bioinformatics, 33(3), 425-427.

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