Kirit Singh

Spot is a tool which simplifies the plumbing that takes us from a perturb-seq dataset like the CD4 perturb-seq published by Zhu et al., and immediately gives us potential insights into novel pathways/therapeutic angles that could apply in disease-relevant contexts. I work in clinical trials that seek to evaluate biological changes over time (particularly in the context of glioblastoma, an aggressive primary brain cancer), and this dataset stood out to me given it measured various perturbations at different timepoints. Cancers like glioblastoma are immunologically cold, containing few T cells. Of those T cells present, many are either exhausted or suppressive in nature (i.e. regulatory T cells or Tregs). One particular focus of making immunotherapy successful in glioblastoma is reinvigorating the tumor micro-environment and local T cells by limiting or restricting the development of Tregs. Understanding what genes and gene ontology pathways cause T cells to go from activated or immunostimulatory Th1s to Tregs is well studied, but what Spot does is to directly tie this to what existing drugs could impact those pathways (and thus be re-purposed). Spot also accounts for our unique environment in the brain, where we need to consider not just the biological effect, but whether a drug has properties that favor adequate exposure (pharmacokinetics (PK) including blood-brain barrier penetrance, half-life). Fortunately, there are frameworks that can help us to consistently characterize T cell programs and PK parameters. Publications by Masopust et al. (https://doi.org/10.1038/s41577-025-01238-2) provide a framework for characterizing T cell programs. These can be flexibly applied by Spot across any immune profiling RNAseq dataset and allows us to uniformly identify populations. Additionally, work by Grossman et al. provides frameworks to determine if a drug has favorable brain permeability parameters (https://doi.org/10.1093/neuonc/noag051). Spot takes this dataset, and via both programmatic and headless Claude Science calls evaluates between two populations the following: 1) genes of interest (ENSEMBL), 2) relevant gene ontology pathways (GO-BP), 3) drugs that can modulate these pathways (UniProt + ChEMBL) and 4) whether said drugs have a favorable pharmacokinetic/safety profile (RxNorm, openFDA etc) making it potentially suitable for exploring as a re-purposed agent. All this work can be done laboriously one step at a time, or accelerated by spot to give us instant hits for drugs we could test in vitro, in vivo and ultimately possibly in clinical trials. While my personal focus is on glioblastoma, Spot is designed to be flexible. This tool allows us to explore changes in T cell populations, whether it is naïve to Treg, Th1 to Th2, naïve at rest to naïve at 48hrs and so on. This means that researchers interested in any context could use this to identify drugs that might be relevant to their work, in the exact same fashion I have used it to identify drugs for repurposing in glioblastoma. Each step is accompanied by a methods & provenance slider which shows both Claude Sciences reasoning, relevant sources and methods, and all references/datasets/links that were used to formulate the conclusions/analysis (with hashes for validity). This project will seek to continue and grow, incorporating more datasets (with verified versions/analyzed being uploaded to huggingface – akin to https://huggingface.co/datasets/KiritSingh/spot-CD4-Marson). I hope you find it interesting!