A. E. Carpenter

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

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

From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability

This study introduces BioMorph space, which integrates Cell Painting and Cell Health data to improve the interpretability of morphological features and reveal compound mechanisms of action.

High-content microscopy reveals a morphological signature of bortezomib resistance

The paper identifies a morphological signature of bortezomib resistance using high-content microscopy and shows it predicts treatment response in independent cell lines.

nELISA: A high-throughput, high-plex platform enables quantitative profiling of the inflammatory secretome

nELISA enables high-throughput, high-plex protein profiling of the inflammatory secretome, revealing 447 significant cytokine responses across multiple donors and conditions.

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

FALCON systematically analyzes 61 free fatty acids to identify lipotoxic effects and a novel mediator of lipotoxicity, CMIP, in human pancreatic beta cells.

Assessing the performance of the Cell Painting assay across different imaging systems

The paper evaluates Cell Painting assay performance across different imaging systems, finding compatibility and identifying optimal settings for each.

Reference compounds for characterizing cellular injury in high-content cellular morphology assays

The paper profiles 218 cytotoxic and nuisance compounds in U-2 OS cells to create reference compounds for improving assay optimization and compound prioritization in cellular morphology assays.

Integrating inflammatory biomarker analysis and artificial intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis

The paper identifies potential drug targets for intestinal fibrosis by combining image-based morphology analysis with AI and inflammatory biomarker measurements.

Predicting gene expression from cell morphology in human induced pluripotent stem cells

The paper builds a model to predict gene expression from cell morphology in hiPSCs and identifies two genes well-predicted by morphology.

Cell Painting predicts impact of lung cancer variants

The paper uses Cell Painting to predict the functional impact of lung cancer variants by analyzing cell morphology at the single-cell level.

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic

This paper uses VAEs to predict drug polypharmacology from cell morphology data and gene expression, finding that beta-VAE and MMD-VAE better disentangle morphology signals.

CellProfiler 4: Improvements in Speed, Utility and Usability

CellProfiler 4 improves speed, utility, and usability through interface refinements, new modules, and performance optimizations for automated microscopy image analysis.

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

This paper automates the in vitro micronucleus assay using imaging flow cytometry and deep learning, achieving high classification accuracy across laboratories.

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

This paper develops a deep-learning enabled phenomics platform to identify immune-modulating compounds and applies it to study SARS-CoV-2 infection and cytokine storm, identifying clinically relevant compounds and new drug candidates.