We invite you to join our seminar at UCSF Mission Bay campus with Shyam Prabhakar from A*STAR Genome Institute.
The initial promise of single cell omics was that it could revolutionize our understanding of intra-sample cellular diversity. From a Precision Medicine perspective, however, we want to understand inter-sample diversity, i.e. differences between patients. Using examples from colorectal cancer (CRC), chronic myeloid leukaemia (CML) and type 1 diabetes (T1D), I will show how single cell RNA-seq (scRNA-seq) can help us achieve both objectives.
We can also investigate the diversity of healthy humans by the same methods. To understand how ethnicity and genetic ancestry influenced human physiology we profiled 1,265,624 peripheral blood mononuclear cells from 619 individuals from 7 population groups in 5 Asian countries. We uncovered profound molecular and cellular differences between these 7 groups, including cell populations and genes with diagnostic and prognostic relevance. Our results suggest that ethnicity-specific or ancestry-specific diagnostic and therapeutic strategies may be needed to fulfil the promise of Precision Medicine across the globe.
With the availability of robust commercial solutions for subcellular-resolution spatial RNA profiling, spatial technologies are taking over the space hitherto occupied by single cell omics. Data analysis is now the major bottleneck. We developed BANKSY, a spatial clustering algorithm that uses a cell’s physical neighbourhood as a guide to its own identity. BANKSY is an order of magnitude more scalable than competing methods. Independent benchmarking studies have shown that it is also the most accurate. We also developed MEDOC, a spatial omics algorithm that optimally clusters cells by morphology. We used these two methods in conjunction with large-scale MERFISH and Xenium spatial RNA profiling to identify markers of tumour budding in CRC and nuclear morphology in chronic myelomonocytic leukaemia (CMML).