ASHG 2023

Systematic reconstruction of molecular pathway signatures using massively scalable single cell pooled CRISPR screens

Thursday, November 2 | 12:30-1:30 PM | Room 144A – Lunch will be provided for the first 50 attendees.

Rahul Satija, PhD Associate Professor New York Genome Center

Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities across the central dogma. However, the challenge now lies in transitioning from correlative insights to causal understanding. In this session, we describe the potential of genome engineering tools in tandem with single cell RNA-seq readout (i.e. Perturb-seq) to systematically identify the targets of signaling regulators in diverse biological contexts. Specifically, we demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial barcoding (to reduce library preparation costs) as well as a new sequencing-by-synthesis platform (to reduce sequencing costs), sequencing 4,000,000 cells in total.

We divide these profiles across 48 different biological contexts (six cell lines and eight signaling pathways) to explore both conserved and cell type-specific responses to each signaling regulator. We introduce an improved computational framework (Mixscape2) to address cellular variation in perturbation efficiency that is inherent to Perturb-seq experiments, alongside optimized statistical methods to learn differentially expressed gene lists and broader signaling programs. Finally, we demonstrate how our data-driven inference of molecular signatures can be used to precisely infer changes in signaling pathway activation for in-vivo samples, including for immune, metabolic, and intestinal disorders. Our work enhances our understanding of signaling regulators and their targets, and lays a computational framework towards the systematic construction of an ‘atlas’ of perturbation signatures.

Expanding Capabilities in Single Cell Sequencing

Thursday, November 2 | 3:00-3:30 PM | Theater 1

Charlie Roco, PhD CTO and Cofounder Parse Biosciences

Single cell experiments are becoming increasingly larger as researchers push the limits on the number of cells and samples profiled in any given experiment. Conventional single cell approaches struggle to keep pace as the path to scalability usually requires an investment in newer hardware or a sacrifice in data quality. The Parse Biosciences combinatorial barcoding platform — Evercode – overcomes these limitations by focusing on chemistry rather than dedicated hardware, providing unprecedented scale and high-quality results. Attend this CoLab to learn more about Evercode, Parse’s entire single cell portfolio, and how the platform compares to other leading single cell technologies.