Biological & Soft Matter Seminar: Decoding cellular identities from single-cell data

Abstarct:

Molecular profiling of a cell captures multiple overlapping signals arising from its internal state as well as possible external interventions. Focusing on a single signal (measured or recovered) restricts the ability to unveil the intricacies and diverse attributes of cellular states. To address this, we suggest computational frameworks to decode the rich layers of information encoded in single-cell genomics data. First, we introduce “SiFT” [1], a probabilistic computational framework that utilizes existing prior knowledge to filter known signals uncovering additional underlying biological attributes. To further decouple the different biological signals, we developed “biolord” [2], a deep-learning method for disentangling single-cell genomics data into the multiple facets of cellular identity. We will demonstrate over a diversity of tasks how these methods allow us to uncover and study different facets of cellular identities. For example, when applied to COVID-19 data “SiFT” exposes disease-related dynamics, and “biolord” exposes infection-related signals in an atlas of Plasmodium infection. At last, we will briefly present our optimal transport computational toolbox "moscot"[3], applicable for spatio-temporal era of single-cell genomics, and "moslin"[4] which uses it to map lineage-traced cells across time points to reconstruct precise differentiation trajectories in complex biological systems. 

[1] Z. Piran & M. Nitzan (2024). “SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data.” Nature Communications. https://doi.org/10.1038/s41467-024-44757-7 2. 

[2] Z. Piran, N. Cohen, Y. Hoshen & M. Nitzan (2024). “Disentanglement of single-cell data with biolord.” Nature Biotechnology. https://doi.org/10.1038/s41587-023-02079-x 

[3] D. Klein*, G. Palla*, M. Lange*, M. Klein*, Z. Piran*, M. Gander, L. Meng-Papaxanthos, M. Sterr, A. Bastidas-Ponce, M. Tarquis-Medina, H. Lickert, M. Bakhti, M. Nitzan, M. Cuturi & F. J. Theis (2023). "Mapping cells through time and space with moscot''. bioRxiv (Under review in Nature). *equal contribution. https://doi.org/10.1101/2023.05.11.540374 

[4] M. Lange*, Z. Piran*, M. Klein*, B. Spanjaard*, J. P. Junker, F. J. Theis, & M. Nitzan (2023). "Mapping lineage-traced cells across time points with moslin''. bioRxiv (under review in Genome Biology). *equal contribution. https://doi.org/10.1101/2023.04.14.536867