Shantanu Singh

Cell Painting in primary human hepatocytes exposed to 1,085 compounds detects bioactivity at lower concentrations than cytotoxicity assays and predicts cell-based, but not cell-free, ToxCast endpoints across feature extractors.

The paper develops a scalable workflow to reclassify variants of uncertain significance using experimental and predictive evidence, resolving 75% of VUS in 40 genes.

The paper identifies a mitochondrial localization phenotype in psychosis patients and uses a cell-based assay to screen for compounds and genes affecting this phenotype.

This study evaluates alternative dyes for image-based profiling assays, finding that some substitutions maintain performance while enabling live-cell imaging and expanded feature space.

The paper presents a comprehensive genetic perturbation dataset using Cell Painting to analyze gene expression effects in human cells, revealing novel gene clusters and functional relationships.

The paper introduces a statistical framework using mean average precision (mAP) to evaluate phenotypic activity and consistency in high-dimensional profiling data across various modalities and perturbation types.

The paper introduces Live Cell Painting (LCP), an image-based assay using Acridine Orange to assess cell health and detect treatment effects in live cells, showing improved sensitivity over traditional methods.

The paper shows that cell counting can predict compound bioactivity as well as Cell Painting or gene expression, but Cell Painting adds unique value in protein-target-specific benchmarks.

The paper presents a genome-wide perturbation atlas using CRISPR-Cas9 knockouts to link genes to cellular phenotypes, identifying TMEM251/LYSET as a Golgi-resident protein involved in lysosomal enzyme trafficking.

The paper introduces NeuroPainting, a morphological phenotyping method, and combines it with transcriptomics to identify cell-type-specific effects of the 22q11.2 deletion on mitochondrial structure and gene expression in astrocytes.

The paper introduces CytoSummaryNet, a contrastive learning method for aggregating single-cell features in image-based profiling, improving mechanism of action prediction by 30-68%.

The paper develops a high-throughput imaging platform to assess the functional impact of missense variants on protein localization, finding that mislocalization is common in pathogenic variants.

The paper benchmarks seven scRNA-seq batch correction methods on a Cell Painting dataset, finding Harmony performs best across multiple scenarios.

This study evaluates k-fold n-step forward cross-validation for improving out-of-distribution bioactivity predictions in drug discovery and recommends its use with specific metrics.

The paper improves drug target prediction by combining gene and compound morphological profiles using a transformer model, showing better performance than direct matching methods.

This paper uses conditional neural processes to predict adverse drug reactions from sparse data and finds they are well-calibrated and effective across organ systems.

This paper reviews methodological advancements in Cell Painting over a decade, highlighting improvements in protocols, feature extraction, and machine learning applications for biological insights.

The paper introduces a dataset of cell morphology images treated with matched chemical and genetic perturbations and evaluates methods for identifying perturbation similarities.

The paper develops a weakly supervised learning method to learn treatment effect representations from imaging data, improving downstream analysis performance.

This study identifies genetic variants associated with cellular morphology using high-content imaging and genomic data from iPSCs.

This study develops public classifiers for predicting drug-induced cardiotoxicity using FDA DICTrank data, identifying key chemical and biological features that distinguish toxic from non-toxic compounds.

The paper updates the Cell Painting assay protocol to improve morphological profiling, finding that simplified steps and reduced stain concentrations maintain robust outputs.

The paper predicts compound activity using chemical structures, images, and gene-expression profiles, showing that combining these modalities can improve prediction accuracy in drug discovery.

Cell Painting images and profiles for 115,796 compounds in U2OS cells from 12 laboratories; 15.2% of analyzed compounds are phenotypically active, and cross-laboratory replicates cut reference-compound activity by 34%.

The paper provides four datasets combining gene expression and morphology profiles from over 28,000 genetic and chemical perturbations for multi-modal analysis.

The paper compares morphology and gene expression data to determine their complementary roles in mapping cell states across drug perturbations.

The paper identifies small molecule regulators of biological pathways by matching compound effects to known pathway phenotypes in public cell image data.

The paper develops a method to predict cell health phenotypes from Cell Painting images using machine learning, validated across multiple perturbations and assays.

The paper uses multiplexed fluorescence imaging and machine learning to identify synaptic subtypes and examine their changes under activity suppression.

The paper optimizes biomaterial surface topographies using evolutionary algorithms inspired by natural selection to enhance osteogenic marker expression.