Evolution and impact of high content imaging

A comprehensive update on the application of high-throughput fluorescence imaging for novel drug discovery

INTRODUCTION

High-throughput fluorescence imaging (HTFI) is revolutionizing drug discovery by enabling rapid and precise detection of biological targets and cellular processes. Recent advances in fluorescence imaging technologies now provide unprecedented sensitivity, resolution, and throughput. Integration of artificial intelligence (AI) and machine learning (ML) into HTFI workflows significantly enhances data processing, aiding in hit identification, pattern recognition, and mechanistic understanding.

AREAS COVERED

This review outlines recent technological developments, integration strategies, and emerging applications of HTFI. It emphasizes HTFI's role in phenotypic screening, especially for complex diseases such as cancer, neurodegenerative disorders, and viral infections. Additionally, it highlights advances in 3D culture systems, organoids, and organ-on-a-chip technologies, which facilitate physiologically relevant testing, improved predictive accuracy, and translational potential, alongside innovative molecular probes and biosensors.

EXPERT OPINION

Despite its advancements, HTFI faces ongoing challenges, including data standardization, integration with multi-omics approaches, and scalability of advanced models. However, recent progress in organoid and 3D modeling technologies has enhanced the physiological relevance of HTFI assays, complemented by sophisticated AI and ML-driven data analysis techniques.

Cell Painting PLUS: expanding the multiplexing capacity of Cell Painting-based phenotypic profiling using iterative staining-elution cycles

Phenotypic changes in the morphology and internal organization of cells can indicate perturbations in cell functions. Therefore, imaging-based high-throughput phenotypic profiling (HTPP) applications such as Cell Painting (CP) play an important role in basic and translational research, drug discovery, and regulatory toxicology. Here we present the Cell Painting PLUS (CPP) assay, an efficient, robust and broadly applicable approach that further expands the versatility of available HTPP methods and offers additional options for addressing mode-of-action specific research questions. An iterative staining-elution cycle allows multiplexing of at least seven fluorescent dyes that label nine different subcellular compartments and organelles including the plasma membrane, actin cytoskeleton, cytoplasmic RNA, nucleoli, lysosomes, nuclear DNA, endoplasmic reticulum, mitochondria, and Golgi apparatus. In this way, CPP significantly expands the flexibility, customizability, and multiplexing capacity of the original CP method and, importantly, also improves the organelle-specificity and diversity of the phenotypic profiles due to the separate imaging and analysis of single dyes in individual channels.

High-content microscopy and machine learning characterize a cell morphology signature of NF1 genotype in Schwann cells

Neurofibromatosis type 1 (NF1) is a multi-system, autosomal dominant genetic disorder driven by the systemic loss of the NF1 protein neurofibromin. Loss of neurofibromin in Schwann cells is particularly detrimental, as the acquisition of a 'second-hit' (e.g., complete loss of NF1 ) can lead to the development of plexiform neurofibromas (pNF). pNFs are painful, disfiguring tumors with an approximately 1 in 5 chance of sarcoma transition. Selumetinib and mirdametinib are currently the only medicines approved by the U.S. Food and Drug Administration (FDA) for the treatment of pNFs. This motivates the need to develop new therapies, either derived to treat NF1 haploinsufficiency or complete loss of NF1 function. To identify new therapies, we need to understand the impact neurofibromin has on Schwann cells. Here, we aimed to characterize differences in high-content microscopy in neurofibromin-deficient Schwann cells. We applied a fluorescence microscopy assay (called Cell Painting) to an isogenic pair of Schwann cell lines (derived from ipn02.3 2λ), one of wildtype genotype ( NF1 +/+ ) and one of NF1 null genotype ( NF1 -/- ). We modified the canonical Cell Painting assay to mark four organelles/subcellular compartments: nuclei, endoplasmic reticulum, mitochondria, and F-actin. We utilized CellProfiler to perform quality control, illumination correction, segmentation, and cell morphology feature extraction. We segmented 20,680 NF1 wildtype and null cells, measured 894 significant cell morphology features representing various organelle shapes and intensity patterns, and trained a logistic regression machine learning model to predict the NF1 genotype of single Schwann cells. The machine learning model had high performance, with training and testing data yielding a balanced accuracy of 0.85 and 0.80, respectively. However, when applied to a new pair of Schwann cells, the model's balanced accuracy dropped to 0.5, which is no better than random chance. This performance decline appears to result from morphology differences introduced by non-biological factors (cloning procedures, origin of parental cell line, and CRISPR procedures) of the second cell line pair. We plan to improve upon this preliminary model by refining the NF1 morphology signature using a broader panel of Schwann cell lines. Our goal is to apply this enhanced signature in large-scale drug screens of NF1 -deficient cells to identify candidate therapeutic agents that specifically reverse the disease-associated morphology. Ultimately, we aim to identify agents that restore NF1 patient-derived Schwann cells to a phenotype resembling the NF1 wild-type and healthier state.

Reproducible image-based profiling with Pycytominer

Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Next, whether by deep learning or classical algorithms, image analysis pipelines commonly produce single-cell features. To process these single cells for downstream applications, we present Pycytominer, a user-friendly, open-source Python package that implements the bioinformatics steps key to image-based profiling. We demonstrate Pycytominer's usefulness in a machine-learning project to predict nuisance compounds that cause undesirable cell injuries.

Absorption of Toxicants from the Ocular Surface: Potential Applications in Toxicology

In relation to the eye, the body can absorb substances from the ocular surface fluid (OSF) in a few ways: directly through the conjunctival sac, through the nasal mucosa as the fluid drains into the nose, or through ingestion. Regardless of the absorption method, fluid from the conjunctival sac should be used as a toxicological matrix, even though only small quantities are needed. Contemporary analytical techniques make it a suitable matrix for toxicological research. Analyzing small quantities of the matrix and nano-quantities of the analyte requires high-cost, sophisticated tools, which is particularly relevant in the high-throughput environment of new drug or cosmetics testing. Environmental toxicology also presents a challenge, as many pollutants can enter the system using the same ocular surface route. A review of the existing literature was conducted to assess potential applications in clinical and forensic toxicology related to the absorption of toxicants from the ocular surface. The selection of the studies used in this review aimed to identify new, more efficient, and cost-effective analytical technology and diagnostic methods.

Novel high-content and open-source image analysis tools for profiling mitochondrial morphology in neurological cell models

Mitochondria undergo dynamic morphological changes depending on cellular cues, stress, genetic factors, or disease. The structural complexity and disease-relevance of mitochondria have stimulated efforts to generate image analysis tools for describing mitochondrial morphology for therapeutic development. Using high-content analysis, we measured multiple morphological parameters and employed unbiased feature clustering to identify the most robust pair of texture metrics that described mitochondrial state. Here, we introduce a novel image analysis pipeline to enable rapid and accurate profiling of mitochondrial morphology in various cell types and pharmacological perturbations. We applied a high-content adapted implementation of our tool, MitoProfilerHC, to quantify mitochondrial morphology changes in i) a mammalian cell dose response study and ii) compartment-specific drug effects in primary neurons. Next, we expanded the usability of our pipeline by using napari, a Python-powered image analysis tool, to build an open-source version of MitoProfiler and validated its performance and applicability. In conclusion, we introduce MitoProfiler as both a high-content-based and an open-source method to accurately quantify mitochondrial morphology in cells, which we anticipate to greatly facilitate mechanistic discoveries in mitochondrial biology and disease.

Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks

Single-cell image analysis is crucial for studying drug effects on cellular morphology and phenotypic changes. Most studies focus on single cell types, overlooking the complexity of cellular interactions. Here, we establish an analysis pipeline to extract phenotypic features of cancer cells cultured with fibroblasts. Using high-content imaging, we analyze an oncology drug library across five cancer and fibroblast cell line co-culture combinations, generating 61,440 images and ∼170 million single-cell objects. Traditional phenotyping with CellProfiler achieves an average enrichment score of 62.6% for mechanisms of action, while pre-trained neural networks (EfficientNetB0 and MobileNetV2) reach 61.0% and 62.0%, respectively. Variability in enrichment scores may reflect the use of multiple drug concentrations since not all induce significant morphological changes, as well as the cellular and genetic context of the treatment. Our study highlights nuanced drug-induced phenotypic variations and underscores the morphological heterogeneity of ovarian cancer cell lines and their response to complex co-culture environments.

Cellular Activity of CQWW Nullomer-Derived Peptides

Analysis of observed protein sequences across all species within the UniProtKB/Swiss-Prot data set reveals CQWW as the shortest absent stretch of amino acids. While DNA can be found encoding the CQWW sequence, it has never been observed to be translated or included in manually curated sets of proteins, existing only in predicted, tentative sequences and in a single mature antibody sequence. We have synthesized this "nullomer" peptide, along with 13 derivatives, reversed, truncated, stereoisomers, and alanine-scanning peptides, conjugated to polyarginine stretches to increase cellular uptake. We observed their impact against a healthy neuronal line and six patient-derived glioblastoma cell lines spanning three clinical subtypes. Results reveal IC50 values averaging 4.9 μM for inhibition of cell survival across tested oncogenic cell lines. High-content phenotypic analysis of cellular features and reverse-phase protein arrays failed to discern a clear mode of action for the nullomer peptide but suggests mitochondrial impairment through the inhibition of GSK3 and isoforms, supported by observations of reduced mitochondrial stain intensities. With a recent increase in interest in nullomer peptides, we see the results in this study as a starting point for further investigation into this potentially therapeutic peptide class.

Single-cell morphological tracking of cell states to identify small-molecule modulators of liver differentiation

We have developed a single-cell assay that combines Cell Painting-a morphological profiling assay-with trajectory inference analysis. We have applied this morphological trajectory inference to the bi-potent HepaRG liver progenitor cell line allowing us to track liver cell fate and map small-molecule-induced changes using a morphological atlas of liver cell differentiation. Our overarching goal is to demonstrate the potential of Cell Painting to study biological processes as continuous trajectories at the single-cell level, enhancing resolution and biological understanding. This work has identified small-molecule Src family kinase inhibitors that promote the differentiation of HepaRG cells toward a hepatocyte-like lineage as well as primary human hepatic progenitor cells toward a hepatocyte-like phenotype in vitro. These findings could significantly advance research on liver cell regeneration mechanisms and facilitate the development of cell-based and small-molecule therapies.

Imaging Flow Cytometry in HIV Infection Research: Advantages and Opportunities

The human immunodeficiency virus (HIV) is a type of retrovirus that infects humans and belongs to the Lentivirus group. Despite the availability of effective treatments, HIV infections are still increasing in some parts of the world, according to the World Health Organization (WHO). Another major challenge is the growing problem of HIV becoming resistant to drugs. This highlights the importance of ongoing research to better understand HIV and find new ways to stop the virus from spreading in the body. Scientists use a variety of methods to study HIV, including techniques from molecular and cellular biology. Many of these methods rely on fluorescent dyes to help visualize specific parts of the virus or infected cells. This article focuses on a technique called imaging flow cytometry, which is particularly useful for studying HIV. Imaging flow cytometry is unique because it not only measures fluorescence (light emitted by the dyes) but also captures images of each cell being analyzed. This allows researchers to see where the fluorescence is located within the cell and to study the cell's shape and structure in detail. Additionally, this method can be combined with machine learning to analyze large amounts of data more efficiently.

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. Cell Painting's ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.

Dually Labeled Neurotensin NTS1R Ligands for Probing Radiochemical and Fluorescence-Based Binding Assays

The determination of ligand-receptor binding affinities plays a key role in the development process of pharmaceuticals. While the classical radiochemical binding assay uses radioligands, fluorescence-based binding assays require fluorescent probes. Usually, radio- and fluorescence-labeled ligands are dissimilar in terms of structure and bioactivity, and can be used in either radiochemical or fluorescence-based assays. Aiming for a close comparison of both assay types, we synthesized tritiated fluorescent neurotensin receptor ligands ([3H]13, [3H]18) and their nontritiated analogues (13, 18). The labeled probes were studied in radiochemical and fluorescence-based (high-content imaging, flow cytometry, fluorescence anisotropy) binding assays. Equilibrium saturation binding yielded well-comparable ligand-receptor affinities, indicating that all these setups can be used for the screening of new drugs. In contrast, discrepancies were found in the kinetic behavior of the probes, which can be attributed to technical differences of the methods and require further studies with respect to the elucidation of the underlying mechanisms.

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. To address this limitation, here we present Live Cell Painting (LCP), a high-content method based on Acridine orange, a metachromatic dye that labels different organelles and cellular structures. We began by showing that LCP can be applied to follow acidic vesicle redistribution of cells exposed to acidic vesicles inhibitors. Next, we show that LCP can identify subtle changes in cells exposed to silver nanoparticles that are not detected by techniques such as MTT assay. In drug treatments, LCP was helpful in assessing the dose-response relationship and creating profiles that allow clustering of drugs that cause liver injury. Here, we present an affordable and easy-to-use image-based assay capable of assessing overall cell health and showing promise for use in various applications such as assessing drugs and nanoparticles. We envisage the use of Live Cell Painting as an initial screening of overall cell health while providing insights into new biological questions.

Senescent cell heterogeneity and responses to senolytic treatment are related to cell cycle status during cell growth arrest

Cellular senescence has been strongly linked to aging and age-related diseases. It is well established that the phenotype of senescent cells is highly heterogeneous and influenced by their cell type and senescence-inducing stimulus. Recent single-cell RNA-sequencing studies identified heterogeneity within senescent cell populations. However, proof of functional differences between such subpopulations is lacking. To identify functionally distinct senescent cell subpopulations, we employed high-content image analysis to measure senescence marker expression in primary human endothelial cells and fibroblasts. We found that G2-arrested senescent cells feature higher senescence marker expression than G1-arrested senescent cells. To investigate functional differences, we compared IL-6 secretion and response to ABT263 senolytic treatment in G1 and G2 senescent cells. We determined that G2-arrested senescent cells secrete more IL-6 and are more sensitive to ABT263 than G1-arrested cells. We hypothesize that cell cycle dependent DNA content is a key contributor to the heterogeneity within senescent cell populations. This study demonstrates the existence of functionally distinct senescent subpopulations even in culture. This data provides the first evidence of selective cell response to senolytic treatment among senescent cell subpopulations. Overall, this study emphasizes the importance of considering the senescent cell heterogeneity in the development of future senolytic therapies.

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. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

Mesenchymal Stem Cells Target Gastric Cancer and Deliver Epirubicin via Tunneling Nanotubes for Enhanced Chemotherapy

BACKGROUND

A reduced effective local concentration significantly contributes to the unsatisfactory therapeutic results of epirubicin in gastric cancer. Mesenchymal stem cells exhibit targeted chemotaxis towards solid tumors and form tunneling nanotubes with tumor cells, facilitating the delivery of various substances. This study demonstrates the novelty of mesenchymal stem cells in releasing epirubicin into gastric cancer cells through tunneling nanotubes.

OBJECTIVE

Epirubicin delivery to gastric cancer cells using mesenchymal stem cells.

METHODS

In vitro transwell migration assays, live cell tracking, and in vivo targeting assays were used to demonstrate the chemotaxis of mesenchymal stem cells towards gastric cancer. We verified the targeted chemotaxis of mesenchymal stem cells towards gastric cancer cells and the epirubicin loading ability using a high-content imaging system (Equipment type:Operetta CLS). Additionally, tunneling nanotube formation and the targeted release of epirubicin-loaded mesenchymal stem cells co-cultured with gastric cancer cells through mesenchymal stem cell-tunneling nanotubes into gastric cancer cells was observed using Operetta CLS.

RESULTS

Mesenchymal stem cells demonstrated targeted chemotaxis towards gastric cancer, with effective epirubicin loading and tolerance. Co-culturing induced tunneling nanotube formation between these cells. Epirubicin-loaded mesenchymal stem cells were released into gastric cancer cells through tunneling nanotubes, significantly increasing their non-viability compared to the negative control group (p < 0.05).

CONCLUSIONS

We identified a novel approach for precisely targeting epirubicin release in gastric cancer cells. Therefore, mesenchymal stem cell-tunneling nanotubes could serve as a potential tool for targeted delivery of drugs, enhancing their chemotherapeutic effects in cancer cells.