Friday, January 28, 2022
11:00 am - 12:00pm
Zoom Link: click here
Livnat Jerby, PhD
Assistant Professor of Genetics
School of Medicine, Stanford University
Title: Reprogramming multicellular circuits to unleash targeted immune responses
Friday, February 18, 2022
11:00am - 12:00pm
Zoom Link: click here
Olivier Elemento, PhD
Director, Englander Institute for Precision Medicine
Associate Director of the Institute for Computational Biomedicine
Associate Program Director, Clinical & Translational Science Center
Weill Cornell Medicine
Title: Towards AI-driven Cancer Precision Medicine
Friday, March 18, 2022
11:00am - 12:00pm
Zoom Recording
Dana Pe'er, PhD
Chair, Computational and Systems Biology Program
Scientific Director, Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center
Sloan Kettering Institute
Title: Cellular Plasticity in Cancer
Friday, April 15, 2022
11:00am - 12:00pm
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Menna Clatworthy, FMedSci
NIHR Research Professor and Professor of Translational Immunology, University of Cambridge
Honorary Consultant Nephrologists, Cambridge University Hospitals NHS Foundation Trust
Associate Faculty Cellular Genetics, Wellcome Sanger Institute
Title: Resolving the Immune Landscape of Human Prostate at a Single Cell Level in Health and Cancer
Friday, May 20, 2022
11:00am - 12:00pm
Zoom Recording
Oliver Stegle, PhD
Associate Faculty in the Cellular Genetics Programme
Head, Division of Computational Genomics and Systems Genetics
German Cancer Research Center, Heidelberg
Wellcome Sanger Institute, UK
Title: Extracting knowledge from multi-modal and spatio-temporal single-cell omics
Friday, June 17, 2022
11:00am - 12:00pm
Zoom Recording
Darryl Shibata, MD
Professor of Pathology
Keck School of Medicine
University of Southern California
Title: How to barcode and analyze human cells without trying very hard
Friday, November 18, 2022
1:00 - 2:00pm
Clark Bldg,, 3rd Floor, Rm. S360
Zoom Recording
Shyam Prabhakar, PhD
Associate Director, Spatial and Single Cell Systems
Senior Group Leader, Systems Biology and Data Analytics
Genome Institute of Singapore
Title: Spatial and Single Cell Analysis: Colorectal Cancer and Human Diversity
We used single cell RNA-seq data from 63 patients across three countries (Singapore, S Korea, Belgium) to update the Consensus Molecular Subtype (CMS) classification of colorectal cancer (CRC). We identified a pervasive transcriptomic and genetic dichotomy among malignant cells, based on which we defined two major CRC subtypes: iCMS2 and iCMS3. These two subtypes showed substantially different pathway activation, mutational profiles and anatomical location, based on re-analysis of bulk tumour transcriptomes from 3,614 patients. Notably, microsatellite-stable and -unstable malignant cells within the iCMS3 class were highly similar, and both populations showed hundreds of differentially expressed genes relative to iCMS2 cells. The previously defined CMS4 (mesenchymal) CRC subtype included both iCMS2 and iCMS3 tumours, with the latter showing lower relapse-free survival.
Human diversity is a frontier of omics research – little is known about the effects of age, sex and ancestry on cell proportions and cell states. Within the Human Cell Atlas consortium, the Asian Immune Diversity Atlas (AIDA) team is mapping the landscape of immune cell variation within and across 19 Asian populations. Initial results are based on Phase 1 single cell RNA-seq data from >500 healthy individuals from 5 ancestry groups across three countries (Japan, Singapore, South Korea). AIDA data reveal systematic differences between demographic groups, suggesting that tailored diagnostic and therapeutic tests and strategies may be needed to fulfil the promise of Precision Medicine across the globe.
Moving into spatial omics, we developed BANKSY, an algorithm that unifies cell type clustering and tissue domain segmentation by constructing a product space of cell and neighbourhood transcriptomes, representing cell state and microenvironment, respectively. BANKSY’s spatial kernel-based feature augmentation strategy improves performance and scalability on both tasks when tested on FISH-based and sequencing-based spatial omics data. Uniquely, BANKSY identified hitherto undetected niche-dependent cell states in two mouse brain regions. Importantly, BANKSY is over an order of magnitude more scalable than most existing spatial clustering algorithms, and thus well equipped to handle modern spatial omics datasets at the million-cell scale and beyond.