Conor Klamann

Antisense oligonucleotides (ASOs) are a versatile therapeutic modality for inborn genetic diseases. ASOs can induce skipping of “dispensable” exons containing disease-causing variants to rescue protein amount and function, but this approach has been studied for only a small number of genes. We developed a high-throughput in silico tool for assessing exon dispensability and designing exon-skipping ASO sequences. Parameters were optimized using known dispensable and in-frame indispensable exons. Across 72,644 exons of 5,057 disease genes, we identified thousands of new targets for exon-skipping ASOs (3.4% of exons with most stringent filters, 24.5% with less stringent filters) that collectively include 0.97%-15.6% of disease-causing variants in large-scale databases. To facilitate recognition of DNA variants potentially amenable to exon skipping as a therapeutic strategy, we established the HAWK-EYE database as a repository of exon dispensability predictions and corresponding in silico -optimized ASO sequences, available as an open-access web application ( https://hawk-eye.research.sickkids.ca/ ). To illustrate translational utility, we experimentally validated a subset of the in silico -optimized ASO sequences that were generated for all Dispensable exons in the HAWK-EYE database, and showed that skipping a Dispensable exon in SOX5 preserves protein function using in vivo and in vitro assays. This scalable platform approach to exon-skipping ASOs will accelerate identification and testing of amenable genetic variants.

Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors. To evaluate performance, we develop a comprehensive benchmarking workflow by generating highly multiplexed imaging data of cell line pellet standards with controlled cell content and marker expression and additionally established a score to quantify the biological plausibility of discovered cellular phenotypes on patient-derived tissue sections. Moreover, we generate spatial expression data of the human tonsil—a densely packed tissue prone to segmentation errors—and demonstrate cellular states captured by STARLING identify known cell types not visible with other methods and enable quantification of intra- and inter- individual heterogeneity.

Background: As single-cell sequencing technologies continue to advance, the growing volume and complexity of the ensuing data present new analytical challenges. Large cellular populations from single-cell atlases are more difficult to visualize and require extensive processing to identify biologically relevant subpopulations. Managing these workflows is also laborious for technical users and unintuitive for nontechnical users. Results: We present TooManyCellsInteractive (TMCI), a browser-based JavaScript application for interactive exploration of cell populations. TMCI provides an intuitive interface to visualize and manipulate a radial tree representation of hierarchical cell subpopulations and allows users to easily overlay, filter, and compare biological features at multiple resolutions. Here we describe the software architecture and demonstrate how we used TMCI in a pan-cancer analysis to identify unique survival pathways among drug-tolerant persister cells. Conclusions: TMCI will facilitate exploration and visualization of large-scale sequencing data in a user-friendly way. TMCI is freely available at https://github.com/schwartzlab-methods/too-many-cells-interactive. An example tree from data within this article is available at https://tmci.schwartzlab.ca/.