Biology subjects

Neal, J. T.

Publications and source records attributed to Neal, J. T..

4 recordsLinked to original sources

bioRxiv2023

A genome-wide atlas of human cell morphology

A key challenge of the modern genomics era is developing data-driven representations of gene function. Here, we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-scale genotype-phenotype maps comprising >20,000 single-gene CRISPR-Cas9-based knockout experiments in >30 million cells. Our optical pooled cell profiling approach (PERISCOPE) combines a de-stainable high-dimensional phenotyping panel (based on Cell Painting1,2) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This approach provides high-dimensional phenotypic profiles of individual cells, while simultaneously enabling interrogation of subcellular processes. Our atlas reconstructs known pathways and protein-protein interaction networks, identifies culture media-specific responses to gene knockout, and clusters thousands of human genes by phenotypic similarity. Using this atlas, we identify the poorly-characterized disease-associated transmembrane protein TMEM251/LYSET as a Golgi-resident protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes, showing the power of these representations. In sum, our atlas and screening technology represent a rich and accessible resource for connecting genes to cellular functions at scale.

genomics↗

bioRxiv2020

Massively parallel phenotyping of variant impact in cancer with Perturb-seq reveals a shift in the spectrum of cell states induced by somatic mutations

Genome sequencing studies have identified millions of somatic variants in cancer, but their phenotypic impact remains challenging to predict. Current experimental approaches to distinguish between functionally impactful and neutral variants require customized phenotypic assays that often report on average effects, and are not easily scaled. Here, we develop a generalizable, high-dimensional, and scalable approach to functionally assess variant impact in single cells by pooled Perturb-seq. Specifically, we assessed the impact of 200 TP53 and KRAS variants in >300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison to orthogonal assays. Surprisingly, KRAS variants did not merely fit into discrete functional categories, but rather spanned a continuum of gain-of-function phenotypes driven by quantitative shifts in cell composition at the single cell level. We further discovered novel gain-of-function KRAS variants whose impact could not have been predicted solely by their occurrence in patient samples. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, which can be applied in cancer and other diseases driven by somatic or germline coding mutations.

genomics↗

bioRxiv2020

Using protein interaction networks to identify cancer dependencies from tumor genome data

Genes required for tumor proliferation and survival (dependencies) are challenging to predict from cancer genome data, but are of high therapeutic value. We developed an algorithm (network purifying selection [NPS]) that aggregates weak signals of purifying selection across a genes first order protein-protein interaction network. We applied NPS to 4,742 tumor genomes to show that a genes NPS score is predictive of whether it is a dependency and validated 58 NPS-predicted dependencies in six cancer cell lines. Importantly, we demonstrate that leveraging NPS predictions to execute targeted CRISPR screens is a powerful, highly cost-efficient approach for identifying and validating dependencies quickly, because it eliminates the substantial experimental overhead required for whole-genome screening.

cancer biology↗

bioRxiv2020

Massively parallel assessment of human variants with base editor screens

Understanding the functional consequences of single-nucleotide variants is critical to uncovering the genetic underpinnings of diseases, but technologies to characterize variants are limiting. Here we leverage CRISPR-Cas9 cytosine base editors in pooled screens to scalably assay variants at endogenous loci in mammalian cells. We benchmark the performance of base editors in positive and negative selection screens and identify known loss-of-function mutations in BRCA1 and BRCA2 with high precision. To demonstrate the utility of base editor screens to probe small molecule-protein interactions, we conduct screens with BH3 mimetics and PARP inhibitors and identify point mutations that confer drug sensitivity or resistance. Finally, we create a library of 52,034 clinically-observed variants in 3,584 genes and conduct screens in the presence of cellular stressors, identifying loss-of-function variants in numerous DNA damage repair genes. We anticipate that this screening approach will be broadly useful to readily and scalably functionalize genetic variants.

genetics↗