Webinar: Virtual Inference of Protein Activity From Single Cell Multiomics

While single cell RNA sequencing coupled with cell surface protein profiling provides a new window into physiologic and pathologic tissue biology and heterogeneity, it can suffer from low signal-to-noise ratio and a high dropout rate at the individual gene level, thus challenging quantitative analyses. To address this problem, Aleksandar Obradovic of Columbia University and his team, introduce an integrated analytical framework for the protein activity–based analysis of single cell subpopulations. This approach was used to build a single cell atlas of the immune microenvironment in clear cell renal carcinoma and paired adjacent non-tumor tissue, identifying a population of recurrence-associated tumor-specific macrophages and its distinct phenotypic markers.