Project description:Genome sequencing studies have identified millions of somatic variants in cancer, but it remains challenging to predict the phenotypic impact of most. Experimental approaches to distinguish impactful variants often use phenotypic assays that report on predefined gene-specific functional effects in bulk cell populations. Here, we develop an approach to functionally assess variant impact in single cells by pooled Perturb-seq. We measured the impact of 200 TP53 and KRAS variants on RNA profiles in over 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 with orthogonal assays. We discovered that KRAS variants did not merely fit into discrete functional categories, but spanned a continuum of gain-of-function phenotypes, and that their functional impact could not have been predicted solely by their frequency in patient cohorts. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, with potential applications in multiple disease settings.
Project description:Leveraging AAV's versatile tropism and labeling capacity, we expanded the scale of in vivo CRISPR screen with single-cell transcriptomic phenotyping across embryonic to adult brains and peripheral nervous systems. Through extensive tests of 86 AAV serotypes, combined with transposon systems, we substantially amplified labeling and accelerated in vivo gene delivery from weeks to days. Our proof-of-principle in utero screen identified the pleiotropic effects of Foxg1, highlighting its tight regulation of distinct networks essential for cell fate specification of Layer 6 corticothalamic neurons. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% by lentivirus, to achieve analysis of over 30,000 cells in one experiment and enable massively parallel in vivo Perturb-seq. Compatible with various phenotypic measurements (single-cell or spatial multi-omics), it presents a flexible approach to interrogate gene function across cell types in vivo, translating gene variants to their causal function.
Project description:It is estimated that 10-30% of disease-associated genetic variants affect splicing. Splicing variants may generate deleteriously altered gene product and are potential therapeutic targets. However, experimental diagnosis for splicing variants is time-consuming and reliable computational prediction tools have not been established, especially for the 3’ end of introns. The major challenge lies in the redundant and ill-defined branch site motif therein. Here, we carried out unbiased massively parallel splicing assays on 5,307 disease-associated variants overlapped with branch sites. We observed that 11.0% (455 out of 4,154 valid comparisons) of candidate variants showed a consistent pattern of altered splicing across four experimental replicates, among which 244 candidates (6.1%) presented more than two-fold changes in the use of noncanonical splice sites and these are named high-confidence (HC) significant candidates.
Project description:This SuperSeries is composed of the following subset Series: GSE12019: Fine-scale mapping of copy-number alterations with massively parallel sequencing GSE13372: High-resolution mapping of copy-number alterations with massively parallel sequencing Refer to individual Series
Project description:We performed a massively parallel screen in human HAP1 cells to identify loss-of-function missense variants in the key DNA mismatch repair factor MSH2. Resulting variant loss-of-function (LOF) scores are strongly concordant with previous functional evidence and available variant classification.