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
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Olivier Elemento, PhD

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

TitleExtracting 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

TitleHow 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.