Automated interpretation of single-cell RNA-seq data using reference atlases
Channel: SIB - Swiss Institute of Bioinformatics
Date: 2021-07-07

What you will discover: a powerful tool to interpret immune cell states, ProjecTILs

You are: a life-scientist using single-cell transcriptomics to study immunology-related problems 

Speaker: Santiago Carmona, Group Leader at SIB and University of Lausanne

Click to access specific sections of the talk: 
- 00:28 Promises and challenges of single-cell transcriptomics 
- 01:47 Typical vs reference atlas-based approach to define cell states
- 02:51 Creating the atlas
- 04:22 Real life example on tumor-infiltrating T cells using ProjecTILs
- 06:39 Application of ProjecTILs to mining public data
- 06:53 Summary

Reference paper and resource
- Andreatta M et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases, Nature Communications 2021. https://doi.org/10.1038/s41467-021-23324-4 
- https://gitlab.unil.ch/carmona/ProjecTILs

Any question about this talk? Contact Santiago at santiago.carmona[at]unil.ch

More about the speaker:
Santiago Carmona is a group leader at the Department of Oncology of the University of Lausanne and the SIB Swiss Institute of Bioinformatics. His group focuses on understanding how the immune system interacts with cancer.  Single-cell omics data of cancer patients have the potential to reveal mechanisms of therapy resistance and novel therapeutic targets. However, as we gain resolution to discriminate cell type heterogeneity, our ability to interpret these data is becoming a bottleneck. Carmona’s group develops computational methods to translate single-cell omics data into biological insight, with a focus on cancer immunology.

Other paper mentioned in the talk:
- Sandu et al. Landscape of Exhausted Virus-Specific CD8 T Cells in Chronic LCMV Infection, Cell Reports 2020. https://doi.org/10.1016/j.celrep.2020.108078
- Yost et al. Clonal replacement of tumor-specific T cells following PD-1 blockade, Nature Medicine 2019. https://doi.org/10.1038/s41591-019-0522-3

Read the news on our website: https://www.sib.swiss/about-sib/news/10858-fast-and-automated-interpretation-of-single-cell-rna-seq-data

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