Cell Painting: a decade of discovery and innovation in cellular imaging.

Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling.

Combining NeuroPainting with transcriptomics reveals cell-type-specific morphological and molecular signatures of the 22q11.2 deletion.

Neuropsychiatric conditions pose substantial challenges for therapeutic development due to their complex and poorly understood underlying mechanisms. High-throughput, unbiased phenotypic assays present a promising path for advancing therapeutic discovery, especially within disease-relevant neural tissues. Here, we introduce NeuroPainting, a novel adaptation of the Cell Painting assay, optimized for high-dimensional morphological phenotyping of neural cell types, including neurons, neuronal progenitor cells, and astrocytes derived from human stem cells.

Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30-68% in mean average precision compared to average profiling on a public dataset.

Live Cell Painting: image-based profiling in live cells using Acridine Orange.

Image-based profiling has been used to analyze cell health, drug mechanism of action, CRISPR-edited cells, and overall cytotoxicity. Cell Painting is a broadly used image-based assay that uses morphological features to capture how cells respond to treatments. However, this method requires cell fixation for staining, which prevents examining live cells.

Implementing machine learning to predict survival outcomes in patients with resected pulmonary large cell neuroendocrine carcinoma.

Background: The post-surgical prognosis for Pulmonary Large Cell Neuroendocrine Carcinoma (PLCNEC) patients remains largely unexplored. Developing a precise prognostic model is vital to assist clinicians in patient counseling and creating effective treatment strategies.

Research Design And Methods: This retrospective study utilized the Surveillance, Epidemiology, and End Results database from 2000 to 2018 to identify key prognostic features for Overall Survival (OS) in PLCNEC using Boruta analysis.

Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30-68% in mean average precision compared to average profiling on a public dataset.

Evaluating batch correction methods for image-based cell profiling.

High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects severely limit community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment.

Step Forward Cross Validation for Bioactivity Prediction: Out of Distribution Validation in Drug Discovery.

Recent advances in machine learning methods for materials science have significantly enhanced accurate predictions of the properties of novel materials. Here, we explore whether these advances can be adapted to drug discovery by addressing the problem of prospective validation - the assessment of the performance of a method on out-of-distribution data. First, we tested whether k-fold n-step forward cross-validation could improve the accuracy of out-of-distribution small molecule bioactivity predictions.

Learning Molecular Representation in a Cell.

Predicting drug efficacy and safety requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations.

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

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction.

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

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction.

A versatile information retrieval framework for evaluating profile strength and similarity.

In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise.

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations.

The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset.

Giant Modulation of Refractive Index from Picoscale Atomic Displacements.

It is shown that structural disorder-in the form of anisotropic, picoscale atomic displacements-modulates the refractive index tensor and results in the giant optical anisotropy observed in BaTiS, a quasi-1D hexagonal chalcogenide. Single-crystal X-ray diffraction studies reveal the presence of antipolar displacements of Ti atoms within adjacent TiS chains along the c-axis, and threefold degenerate Ti displacements in the a-b plane. Ti solid-state NMR provides additional evidence for those Ti displacements in the form of a three-horned NMR lineshape resulting from a low symmetry local environment around Ti atoms.

The Hydrogen Evolution Activity of BaZrS, BaTiS, and BaVS Chalcogenide Perovskites.

Chalcogenide perovskites are a class of materials with electronic and optoelectronic properties desirable for solar cells, infrared optics, and computing. The oxide counterparts of these chalcogenides have been studied extensively for their electrocatalytic and photoelectrochemical properties. As chalcogenide perovskites are more covalent, conductive, and stable, we hypothesize that they are more viable as electrocatalysts than oxide perovskites.

Fuel forensics: Recent advancements in profiling of adulterated fuels by ATR-FTIR spectroscopy and chemometric approaches.

Gasoline and diesel are the main petroleum products used for road transportation in India. Due to this reason, adulteration can be done by fraudsters using different miscible substances such as kerosene, turpentine, thinner, ethanol etc. In this work, Fourier transform infrared spectroscopy (FTIR) coupled with principal component analysis (PCA) and partial least square regression (PLSR) were used to investigate adulteration in petroleum products and to design an adulterant profiling.

Learning representations for image-based profiling of perturbations.

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation.

A Hybrid Pulsed Laser Deposition Approach to Grow Thin Films of Chalcogenides.

Vapor-pressure mismatched materials such as transition metal chalcogenides have emerged as electronic, photonic, and quantum materials with scientific and technological importance. However, epitaxial growth of vapor-pressure mismatched materials are challenging due to differences in the reactivity, sticking coefficient, and surface adatom mobility of the mismatched species constituting the material, especially sulfur containing compounds. Here, a novel approach is reported to grow chalcogenides-hybrid pulsed laser deposition-wherein an organosulfur precursor is used as a sulfur source in conjunction with pulsed laser deposition to regulate the stoichiometry of the deposited films.

Bringing computation to biology by bridging the last mile.

Advances in technology dramatically accelerate biology research, with computation being a standout example. Typically, adapting a new technology follows stages from method creation, via proof-of-concept application to biology, to the development of usable tools. Creating user-friendly software to bridge computer science and biology is a crucial step, yielding high returns on investment and driving biological discoveries.