Introduction to Harnessing Single Cell Resolution Data

From transcriptomics to proteomics, investigating biological questions at single cell resolution has fundamentally changed our understanding of cell identity and tissue heterogeneity across organisms. Of the ~20,000 genes (eukaryotes) that encode for proteins, different cell types express diverse suites of genes depending on their function, location, stimulus and time. Whether performing whole transcriptome sequencing on a suspension of dissociated cells or applying algorithms to microscopy images for protein characterization across a tissue section, these single cell techniques can provide a plethora of data per cell, enabling the analysis of thousands to millions of cells simultaneously. Although these ‘big data’ technologies significantly enrich the amount of information obtained per experiment, there can be several challenges in using the data to gain biologically relevant insight. This webinar will provide a primer for how to get started handling single cell resolution data and discuss how powerful ‘big data’ techniques can be for discovery. Learning Objectives • Review common techniques used to generate data at single cell resolution • Discover strategies for using single cell RNA sequencing and microscopy to gain biologically meaningful insight • Investigate resources for working with large single cell resolution data