Beth A. Cimini

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 identifies O-mannosylation and protein maturation as therapeutic targets in BRAF fusion oncogenesis by showing their necessity for fusion-induced transformation.

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.

This study uses a mouse model of TB infection to investigate fibrosis mechanisms, finding persistent inflammatory macrophages and elevated matrix remodeling enzymes during treatment.

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 identifies rare missense variants in LAR that destabilize the extracellular domain, disrupt LAR:NGL-3 interactions, and impair synaptic adhesion and synaptogenesis.

This paper validates Cellpose as a reliable method for segmenting single cells in autofluorescence microscopy images, showing high accuracy compared to manual segmentation.

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.

MorphoDiff predicts high-resolution cell morphology changes under different interventions using diffusion models and perturbation embeddings.

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.

This paper describes a postdoctoral training program in bioimage analysis, highlighting its components, outcomes, and implications for training experts in professional skills.

Piximi provides a no-programming web tool for biological image analysis using deep learning, enabling rapid insights without installation or coding.

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 paper describes OME-Zarr, a cloud-optimized bioimaging file format developed through community collaboration to improve scalability and data access.

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 introduces LipocyteProfiler, a high-throughput microscopy assay to profile lipid-storing cells and identifies cellular programs linked to metabolic traits like insulin resistance and lipodystrophy.

The paper develops an automated high-content synaptic phenotyping platform in human neurons and astrocytes, identifying BET inhibitors as key regulators of synapse assembly.

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 summarizes a survey of 484 bioimage analysis community members to identify needs for tools, training, and resources.

This study identifies Sp7 and its target gene osteocrin as critical for osteocyte dendrite formation and shows their role in human skeletal diseases.

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