Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

Decoding phenotypic screening: A comparative analysis of image representations

Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches. We propose an evaluation protocol that assesses their performance on mode of action and property prediction tasks using a popular phenotypic screening dataset. Results show that the self-supervised approach that uses data from multiple consortium partners provides representation that is more robust to batch effects whilst simultaneously achieving performance on par with standard approaches. Together with other conclusions, it provides recommendations on the training strategy of a representation model for HCS images.

Towards generative digital twins in biomedical research

Digital twins in biomedical research, i.e. virtual replicas of biological entities such as cells, organs, or entire organisms, hold great potential to advance personalized healthcare. As all biological processes happen in space, there is a growing interest in modeling biological entities within their native context. Leveraging generative artificial intelligence (AI) and high-volume biomedical data profiled with spatial technologies, researchers can recreate spatially-resolved digital representations of a physical entity with high fidelity. In application to biomedical fields such as computational pathology, oncology, and cardiology, these generative digital twins (GDT) thus enable compelling in silico modeling for simulated interventions, facilitating the exploration of 'what if' causal scenarios for clinical diagnostics and treatments tailored to individual patients. Here, we outline recent advancements in this novel field and discuss the challenges and future research directions.

The present and future of the Cancer Dependency Map

Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.

Developing a Screening Strategy to Identify Hepatotoxicity and Drug Interaction Potential of Botanicals

Botanical supplements, herbal remedies, and plant-derived products are used globally. However, botanical dietary supplements are rarely subjected to robust safety testing unless there are adverse reports in post-market surveillance. Botanicals are complex and difficult to assess using current frameworks designed for single constituent substances (e.g. small molecules or discrete chemicals), making safety assessments costly and time-consuming. The liver is a primary organ of concern for potential botanical-induced hepatotoxicity and botanical-drug interactions as it plays a crucial role in xenobiotic metabolism. The NIH-funded Drug Induced Liver Injury Network noted that the number of botanical-induced liver injuries in 2017 nearly tripled from those observed in 2004-2005. New approach methodologies (NAMs) can aid in the rapid and cost-effective assessment of botanical supplements for potential hepatotoxicity. The Hepatotoxicity Working Group within the Botanical Safety Consortium is working to develop a screening strategy that can help reliably identify potential hepatotoxic botanicals and inform mechanisms of toxicity. This manuscript outlines the Hepatotoxicity Working Group's strategy and describes the assays selected and the rationale for the selection of botanicals used in case studies. The selected NAMs evaluated as a part of this effort are intended to be incorporated into a larger battery of assays to evaluate multiple endpoints related to botanical safety. This work will contribute to a botanical safety toolkit, providing researchers with tools to better understand hepatotoxicity associated with botanicals, prioritize and plan future testing as needed, and gain a deeper insight into the botanicals being tested.

ECBD: European chemical biology database

The European Chemical Biology Database (ECBD, https://ecbd.eu) serves as the central repository for data generated by the EU-OPENSCREEN research infrastructure consortium. It is developed according to FAIR principles, which emphasize findability, accessibility, interoperability and reusability of data. This data is made available to the scientific community following open access principles. The ECBD stores both positive and negative results from the entire chemical biology project pipeline, including data from primary or counter-screening assays. The assays utilize a defined and diverse library of over 107 000 compounds, the annotations of which are continuously enriched by external user supported screening projects and by internal EU-OPENSCREEN bioprofiling efforts. These compounds were screened in 89 currently deposited datasets (assays), with 48 already being publicly accessible, while the remaining will be published after a publication embargo period of up to 3 years. Together these datasets encompass ∼4.3 million experimental data points. All public data within ECBD can be accessed through its user interface, API or by database dump under the CC-BY 4.0 license.

Extrapolating Lessons from Targeted Protein Degradation to Other Proximity-Inducing Drugs

Targeted protein degradation (TPD) is an emerging pharmacologic strategy. It relies on small-molecule "degraders" that induce proximity of a component of an E3 ubiquitin ligase complex and a target protein to induce target ubiquitination and subsequent proteasomal degradation. Essentially, degraders thus expand the function of E3 ligases, allowing them to degrade proteins they would not recognize in the absence of the small molecule. Over the past decade, insights gained from identifying, designing, and characterizing various degraders have significantly enhanced our understanding of TPD mechanisms, precipitating in rational degrader discovery strategies. In this Account, I aim to explore how these insights can be extrapolated to anticipate both opportunities and challenges of utilizing the overarching concept of proximity-inducing pharmacology to manipulate other cellular circuits for the dissection of biological mechanisms and for therapeutic purposes.

Microglia morphological response to mesenchymal stromal cell extracellular vesicles demonstrates EV therapeutic potential for modulating neuroinflammation

BACKGROUND

Mesenchymal stromal cell derived extracellular vesicles (MSC-EVs) are a promising therapeutic for neuroinflammation. MSC-EVs can interact with microglia, the resident immune cells of the brain, to exert their immunomodulatory effects. In response to inflammatory cues, such as cytokines, microglia undergo phenotypic changes indicative of their function e.g. morphology and secretion. However, these changes in response to MSC-EVs are not well understood. Additionally, no disease-relevant screening tools to assess MSC-EV bioactivity exist, which has further impeded clinical translation. Here, we developed a quantitative, high throughput morphological profiling approach to assess the response of microglia to neuroinflammation- relevant signals and whether this morphological response can be used to indicate the bioactivity of MSC-EVs.

RESULTS

Using an immortalized human microglia cell-line, we observed increased size (perimeter, major axis length) and complexity (form factor) upon stimulation with interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Upon treatment with MSC-EVs, the overall morphological score (determined using principal component analysis) shifted towards the unstimulated morphology, indicating that MSC-EVs are bioactive and modulate microglia. The morphological effects of MSC-EVs in TNF-α /IFN-γ stimulated cells were concomitant with reduced secretion of 14 chemokines/cytokines (e.g. CXCL6, CXCL9) and increased secretion of 12 chemokines/cytokines (e.g. CXCL8, CXCL10). Proteomic analysis of cell lysates revealed significant increases in 192 proteins (e.g. HIBADH, MEAK7, LAMC1) and decreases in 257 proteins (e.g. PTEN, TOM1, MFF) with MSC-EV treatment. Of note, many of these proteins are involved in regulation of cell morphology and migration. Gene Set Variation Analysis revealed upregulation of pathways associated with immune response, such as regulation of cytokine production, immune cell infiltration (e.g. T cells, NK cells) and morphological changes (e.g. Semaphorin, RHO/Rac signaling). Additionally, changes in microglia mitochondrial morphology were measured suggesting that MSC-EV modulate mitochondrial metabolism.

CONCLUSION

This study comprehensively demonstrates the effects of MSC-EVs on human microglial morphology, cytokine secretion, cellular proteome, and mitochondrial content. Our high-throughput, rapid, low-cost morphometric approach enables screening of MSC-EV batches and manufacturing conditions to enhance EV function and mitigate EV functional heterogeneity in a disease relevant manner. This approach is highly generalizable and can be further adapted and refined based on selection of the disease-relevant signal, target cell, and therapeutic product.

Low concentration cell painting images enable the identification of highly potent compounds

Image-based models that use features extracted from cell microscopy images can estimate the activity of small molecules in various biological assays. Typically, models are trained on images stained by an optimized protocol (e.g. Cell Painting) after exposure to a fairly high small molecule concentration (referred to as 'image concentration') of 10 μ M or higher. Low concentration images (e.g. 0.16 μM, 0.8 μM, 4 μM) tend to yield models with worse performance. In this work, we nevertheless report a practical use for low image concentration data. We propose the combination of well-performing models trained at higher image concentrations, with lower image concentration for inference to identify more potent compounds. We show that this approach improves on the conventional method (directly training a high-potency model) in 65 % of assays investigated in terms of AUC-ROC, and 75 % of assays in terms of RIPtoP-corrected AUC-PR.

Machine learning in microscopy - insights, opportunities and challenges

Machine learning (ML) is transforming the field of image processing and analysis, from automation of laborious tasks to open-ended exploration of visual patterns. This has striking implications for image-driven life science research, particularly microscopy. In this Review, we focus on the opportunities and challenges associated with applying ML-based pipelines for microscopy datasets from a user point of view. We investigate the significance of different data characteristics - quantity, transferability and content - and how this determines which ML model(s) to use, as well as their output(s). Within the context of cell biological questions and applications, we further discuss ML utility range, namely data curation, exploration, prediction and explanation, and what they entail and translate to in the context of microscopy. Finally, we explore the challenges, common artefacts and risks associated with ML in microscopy. Building on insights from other fields, we propose how these pitfalls might be mitigated for in microscopy.

ToxProfiler: A novel human-based reporter assay for in vitro chemical safety assessment

In vitro chemical safety assessment often relies on simple and general cytotoxicity endpoint measurements and fails to adequately predict human toxicity. To improve the in vitro chemical safety assessment, it is important to understand the underlying mechanisms of toxicity. Here we introduce ToxProfiler, a novel human-based reporter assay that quantifies the chemical-induced stress responses at a single-cell level and reveals the toxicological mode-of-action (MoA) of novel drugs and chemicals. The assay accurately measures the activation of seven major cellular stress response pathways (oxidative stress, cell cycle stress, endoplasmic reticulum stress, ion stress, protein stress, autophagy and inflammation) that play a role in the adaptive responses prior to cellular toxicity. To assess the applicability of the assay in predicting the toxicity MoA of chemicals, we tested a set of 100 chemicals with well-known in vitro and in vivo toxicological profiles. Concentration response modeling and point-of-departure estimation for each reporter protein allowed for chemical potency ranking and revealed the primary toxicological MoA of chemicals. Furthermore, the assay could effectively group chemicals based on their shared toxicity signatures and link them to specific toxicological targets, e.g. mitochondrial toxicity and genotoxicity, and different human pathologies, including liver toxicity and cardiotoxicity. Overall, ToxProfiler is a quantitative in vitro reporter assay that can accurately provide insight into the toxicological MoA of compounds, thereby assisting in the future mechanism-based safety assessment of chemicals.

Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics

The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.

Scalable, compressed phenotypic screening using pooled perturbations

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

Learning Molecular Representation in a Cell

Predicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, current molecular representation learning methods do not provide a comprehensive view of cell states under these perturbations and struggle to remove noise, hindering model generalization. We introduce the Information Alignment (InfoAlign) approach to learn molecular representations through the information bottleneck method in cells. We integrate molecules and cellular response data as nodes into a context graph, connecting them with weighted edges based on chemical, biological, and computational criteria. For each molecule in a training batch, InfoAlign optimizes the encoder's latent representation with a minimality objective to discard redundant structural information. A sufficiency objective decodes the representation to align with different feature spaces from the molecule's neighborhood in the context graph. We demonstrate that the proposed sufficiency objective for alignment is tighter than existing encoder-based contrastive methods. Empirically, we validate representations from InfoAlign in two downstream applications: molecular property prediction against up to 27 baseline methods across four datasets, plus zero-shot molecule-morphology matching.

Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data

The continued scaling of genetic perturbation technologies combined with high-dimensional assays such as cellular microscopy and RNA-sequencing has enabled genome-scale reverse-genetics experiments that go beyond single-endpoint measurements of growth or lethality. Datasets emerging from these experiments can be combined to construct perturbative "maps of biology", in which readouts from various manipulations (e.g., CRISPR-Cas9 knockout, CRISPRi knockdown, compound treatment) are placed in unified, relatable embedding spaces allowing for the generation of genome-scale sets of pairwise comparisons. These maps of biology capture known biological relationships and uncover new associations which can be used for downstream discovery tasks. Construction of these maps involves many technical choices in both experimental and computational protocols, motivating the design of benchmark procedures to evaluate map quality in a systematic, unbiased manner. Here, we (1) establish a standardized terminology for the steps involved in perturbative map building, (2) introduce key classes of benchmarks to assess the quality of such maps, (3) construct 18 maps from four genome-scale datasets employing different cell types, perturbation technologies, and data readout modalities, (4) generate benchmark metrics for the constructed maps and investigate the reasons for performance variations, and (5) demonstrate utility of these maps to discover new biology by suggesting roles for two largely uncharacterized genes.

High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 μM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.

Profiling Cellular Morphological Changes Induced by Dual-Targeting PROTACs of Aurora Kinase and RNA-Binding Protein YTHDF2

Proteolysis targeting chimeras (PROTACs) are new chemical modalities that degrade proteins of interest, including established kinase targets and emerging RNA-binding proteins (RBPs). Whereas diverse sets of biochemical, biophysical and cellular assays are available for the evaluation and optimizations of PROTACs in understanding the involved ubiquitin-proteasome-mediated degradation mechanism and the structure-degradation relationship, a phenotypic method profiling the cellular morphological changes is rarely used. In this study, first, we reported the only examples of PROTACs degrading the mRNA-binding protein YTHDF2 via screening of multikinase PROTACs. Second, we reported the profiling of cellular morphological changes of the dual kinase- and RBP-targeting PROTACs using the unbiased cell painting assay (CPA). The CPA analysis revealed the high biosimilarity with the established aurora kinase cluster and annotated aurora kinase inhibitors, which reflected the association between YTHDF2 and the aurora kinase signaling network. Broadly, the results demonstrated that the cell painting assay can be a straightforward and powerful approach to evaluate PROTACs. Complementary to the existing biochemical, biophysical and cellular assays, CPA provided a new perspective in characterizing PROTACs at the cellular morphology.

OrgaMapper: a robust and easy-to-use workflow for analyzing organelle positioning

BACKGROUND

Eukaryotic cells are highly compartmentalized by a variety of organelles that carry out specific cellular processes. The position of these organelles within the cell is elaborately regulated and vital for their function. For instance, the position of lysosomes relative to the nucleus controls their degradative capacity and is altered in pathophysiological conditions. The molecular components orchestrating the precise localization of organelles remain incompletely understood. A confounding factor in these studies is the fact that organelle positioning is surprisingly non-trivial to address e.g., perturbations that affect the localization of organelles often lead to secondary phenotypes such as changes in cell or organelle size. These phenotypes could potentially mask effects or lead to the identification of false positive hits. To uncover and test potential molecular components at scale, accurate and easy-to-use analysis tools are required that allow robust measurements of organelle positioning.

RESULTS

Here, we present an analysis workflow for the faithful, robust, and quantitative analysis of organelle positioning phenotypes. Our workflow consists of an easy-to-use Fiji plugin and an R Shiny App. These tools enable users without background in image or data analysis to (1) segment single cells and nuclei and to detect organelles, (2) to measure cell size and the distance between detected organelles and the nucleus, (3) to measure intensities in the organelle channel plus one additional channel, (4) to measure radial intensity profiles of organellar markers, and (5) to plot the results in informative graphs. Using simulated data and immunofluorescent images of cells in which the function of known factors for lysosome positioning has been perturbed, we show that the workflow is robust against common problems for the accurate assessment of organelle positioning such as changes of cell shape and size, organelle size and background.

CONCLUSIONS

OrgaMapper is a versatile, robust, and easy-to-use automated image analysis workflow that can be utilized in microscopy-based hypothesis testing and screens. It effectively allows for the mapping of the intracellular space and enables the discovery of novel regulators of organelle positioning.

In Vitro Predictions of Acute Fish Toxicity for Development of Sustainable Crop Protection Products

New agrochemicals must demonstrate safety to numerous ecological systems, including aquatic systems, and aquatic vertebrate toxicity is typically evaluated by using the in vivo acute fish toxicity (AFT) test. Here, we investigated two alternative in vitro assays using a cell line isolated from rainbow trout (Onchorhynchus mykiss) gill tissue: (i) adenosine triphosphate (ATP) luminescence and (ii) cell painting. The former assay measures cytotoxicity, while the latter measures changes in cellular morphology in response to chemical exposure. We assessed how well end points in these two assays predicted acute lethality (i.e., LC50 values) in independent in vivo AFT tests. When compared to results from OECD TG 249 (in vitro), we found that the ATP assay was not as predictive (R2 = 0.53) as the cell painting assay. Similarly, when compared to results from OECD TG 203 (in vivo), the cell painting was much more predictive (R2 = 0.67). Our results show that such in vitro assays are useful for fast and efficient screening alternatives to in vivo fish testing that can aid in the agrochemical discovery phase, where thousands of potential new actives are tested each year.

Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.

Hit screening with multivariate robust outlier detection

Hit screening, which involves the identification of compounds or targets capable of modulating disease-relevant processes, is an important step in drug discovery. Some assays, such as image-based high-content screenings, produce complex multivariate readouts. To fully exploit the richness of such data, advanced analytical methods that go beyond the conventional univariate approaches should be employed. In this work, we tackle the problem of hit identification in multivariate assays. As with univariate assays, a hit from a multivariate assay can be defined as a candidate that yields an assay value sufficiently far away in distance from the mean or central value of inactives. Viewed another way, a hit is an outlier from the distribution of inactives. A method was developed for identifying multivariate hit in high-dimensional data sets based on principal components and robust Mahalanobis distance (the multivariate analogue to the Z- or T-statistic). The proposed method, termed mROUT (multivariate robust outlier detection), demonstrates superior performance over other techniques in the literature in terms of maintaining Type I error, false discovery rate and true discovery rate in simulation studies. The performance of mROUT is also illustrated on a CRISPR knockout data set from in-house phenotypic screening programme.

Advances in the approaches used to repurpose drugs for neuroblastoma

INTRODUCTION

Neuroblastoma (NB) remains a challenging pediatric malignancy with limited treatment options, particularly for high-risk cases. Drug repurposing offers a convenient and cost-effective strategy for treating rare diseases like NB. Using existing drugs with known safety profiles accelerates the availability of new treatments, reduces development costs, and mitigates risks, offering hope for improved patient outcomes in challenging conditions.

AREAS COVERED

This review provides an overview of the advances in approaches used to repurpose drugs for NB therapy. The authors discuss strategies employed in drug repurposing, including computational and experimental methods, and rational drug design, highlighting key examples of repurposed drugs with promising clinical results. Additionally, the authors examine the challenges and opportunities associated with drug repurposing in NB and discuss future directions and potential areas for further research.

EXPERT OPINION

The fact that only one new drug has been approved in the last 30 years for the treatment of neuroblastoma plus a significant proportion of high-risk NB patients that remain uncurable, evidences the need for new fast and cost-effective alternatives. Drug repurposing may accelerate the treatment development process while reducing expenses and risks. This approach can swiftly bring effective NB therapies to market, enhancing survival rates and patient quality of life.

Design and synthesis of large Stokes shift DNA dyes with reduced genotoxicity

Despite intensive search over the past decades, only a few small-molecule DNA fluorescent dyes were found with large Stokes shifts. These molecules, however, are often too toxic for widespread usage. Here, we designed DNA-specific fluorescent dyes rooted in benzimidazole architectures with a hitherto unexplored molecular framework based on thiazole-benzimidazole scaffolding. We further incorporated a pyrazole ring with an extended sidechain to prevent cell penetration. These novel benzimidazole derivatives were predicted by quantum calculations and subsequently validated to have large Stokes shifts ranging from 135 to 143 nm, with their emission colors changed from capri blue for the Hoechst reference compound to iguana green. These readily-synthesized compounds, which displayed improved DNA staining intensity and detection limits along with a complete loss of capability for cellular membrane permeation and negligible mutagenic effects as designed, offer a safer alternative to the existing high-performance small-molecule DNA fluorescent dyes.

Applying cell painting in non-tumorigenic breast cells to understand impacts of common chemical exposures

The general population is exposed to many chemicals which have putative, but incompletely understood, links to breast cancer. Cell Painting is a high-content imaging-based in vitro assay that allows for unbiased measurements of concentration-dependent effects of chemical exposures on cellular morphology. We used Cell Painting to measure effects of 16 human exposure relevant chemicals, along with 21 small molecules with known mechanisms of action, in non-tumorigenic mammary epithelial cells, the MCF10A cell line. Using CellProfiler image analysis software, we quantified 3042 morphological features across approximately 1.2 million cells. We used benchmark concentration modeling to identify features both conserved and different across chemicals. Benchmark concentrations were compared to exposure biomarker concentration measurements from the National Health and Nutrition Examination Survey to assess which chemicals induce morphological alterations at human-relevant concentrations. We found significant feature overlaps between chemicals, including similarities between the organochlorine pesticide DDT metabolite p,p'-DDE and an activator of Wnt signaling CHIR99201. We validated these findings by assaying the activation of Wnt, as reflected by translocation of ꞵ-catenin, following p'-p' DDE exposure. Consistent with Wnt signaling activation, low concentration p',p'-DDE (25 nM) significantly enhanced the nuclear translocation of ꞵ-catenin. Overall, these findings highlight the ability of Cell Painting to enhance mode-of-action studies for toxicants which are common in our environment but incompletely characterized with respect to breast cancer risk.

Discovery of a novel inhibitor of macropinocytosis with antiviral activity

Several viruses hijack various forms of endocytosis in order to infect host cells. Here, we report the discovery of a molecule with antiviral properties that we named virapinib, which limits viral entry by macropinocytosis. The identification of virapinib derives from a chemical screen using high-throughput microscopy, where we identified chemical entities capable of preventing infection with a pseudotype virus expressing the spike (S) protein from SARS-CoV-2. Subsequent experiments confirmed the capacity of virapinib to inhibit infection by SARS-CoV-2, as well as by additional viruses, such as mpox virus and TBEV. Mechanistic analyses revealed that the compound inhibited macropinocytosis, limiting this entry route for the viruses. Importantly, virapinib has no significant toxicity to host cells. In summary, we present the discovery of a molecule that inhibits macropinocytosis, thereby limiting the infectivity of viruses that use this entry route such as SARS-CoV2.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Deciphering the impact of genomic variation on function

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

GILEA: In silico phenome profiling and editing using GAN Inversion

BACKGROUND

Modeling heterogeneous disease states by data-driven methods has great potential to advance biomedical research. However, a comprehensive analysis of phenotypic heterogeneity is often challenged by the complex nature of biomedical datasets and emerging imaging methodologies.

METHODS

Here, we propose a novel GAN Inversion-enabled Latent Eigenvalue Analysis (GILEA) framework and apply it to in silico phenome profiling and editing.

RESULTS

We show the performance of GILEA using cellular imaging datasets stained with the multiplexed fluorescence Cell Painting protocol. The quantitative results of GILEA can be biologically supported by editing of the latent representations and simulation of dynamic phenotype transitions between physiological and pathological states.

CONCLUSION

In conclusion, GILEA represents a new and broadly applicable approach to the quantitative and interpretable analysis of biomedical image data. The GILEA code and video demos are available at https://github.com/CTPLab/GILEA.

Community-Engaged Research and the Use of Open Access ToxVal/ToxRef In Vivo Databases and New Approach Methodologies (NAM) to Address Human Health Risks From Environmental Contaminants

BACKGROUND

The paper analyzes opportunities for integrating Open access resources (Abstract Sifter, US EPA and NTP Toxicity Value and Toxicity Reference [ToxVal/ToxRefDB]) and New Approach Methodologies (NAM) integration into Community Engaged Research (CEnR).

METHODS

CompTox Chemicals Dashboard and Integrated Chemical Environment with in vivo ToxVal/ToxRef and NAMs (in vitro) databases are presented in three case studies to show how these resources could be used in Pilot Projects involving Community Engaged Research (CEnR) from the University of California, Davis, Environmental Health Sciences Center.

RESULTS

Case #1 developed a novel assay methodology for testing pesticide toxicity. Case #2 involved detection of water contaminants from wildfire ash and Case #3 involved contaminants on Tribal Lands. Abstract Sifter/ToxVal/ToxRefDB regulatory data and NAMs could be used to screen/prioritize risks from exposure to metals, PAHs and PFAS from wildfire ash leached into water and to investigate activities of environmental toxins (e.g., pesticides) on Tribal lands. Open access NAMs and computational tools can apply to detection of sensitive biological activities in potential or known adverse outcome pathways to predict points of departure (POD) for comparison with regulatory values for hazard identification. Open access Systematic Empirical Evaluation of Models or biomonitoring exposures are available for human subpopulations and can be used to determine bioactivity (POD) to exposure ratio to facilitate mitigation.

CONCLUSIONS

These resources help prioritize chemical toxicity and facilitate regulatory decisions and health protective policies that can aid stakeholders in deciding on needed research. Insights into exposure risks can aid environmental justice and health equity advocates.

Predicting DNA damage response in non-small cell lung cancer organoids via simultaneous label-free autofluorescence multiharmonic microscopy

The DNA damage response (DDR) is a fundamental readout for evaluating efficacy of cancer therapeutics, many of which target DNA associated processes. Current techniques to evaluate DDR rely on immunostaining for phosphorylated histone H2AX (γH2AX), which is an indicator of DNA double-strand breaks. While γH2AX immunostaining can provide a snapshot of DDR in fixed cell and tissue samples, this method is technically cumbersome due to temporal monitoring of DDR requiring timepoint replicates, extensive assay development efforts for 3D cell culture samples such as organoids, and time-consuming protocols for γH2AX immunostaining and its evaluation. The goal of this current study is to reduce overall burden on assay duration and development in non-small cell lung cancer (NSCLC) organoids by leveraging label-free multiphoton imaging. In this study, simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy was used to provide rich intracellular information based on endogenous contrasts. SLAM microscopy enables imaging of live samples eliminating the need to generate sacrificial sample replicates and has improved image acquisition in 3D space over conventional confocal microscopy. Predictive modeling between label-free SLAM microscopy and γH2AX immunostained images confirmed strong correlation between SLAM image features and γH2AX signal. Across multiple DNA targeting chemotherapeutics and multiple patient-derived NSCLC organoid lines, the optical redox ratio and third harmonic generation channels were used to robustly predict DDR. Imaging via SLAM microscopy can be used to more rapidly predict DDR in live 3D NSCLC organoids with minimal sample handling and without labeling.

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.

Cell-vision fusion: A Swin transformer-based approach for predicting kinase inhibitor mechanism of action from Cell Painting data

Image-based profiling of the cellular response to drug compounds has proven effective at characterizing the morphological changes resulting from perturbation experiments. As data availability increases, however, there are growing demands for novel deep-learning methods. We applied the SwinV2 computer vision architecture to predict the mechanism of action of 10 kinase inhibitor compounds directly from Cell Painting images. This method outperforms the standard approach of using image-based profiles (IBP)-multidimensional feature set representations generated by bioimaging software. Furthermore, our fusion approach-cell-vision fusion, combining three different data modalities, images, IBPs, and chemical structures-achieved 69.79% accuracy and 70.56% F1 score, 4.20% and 5.49% higher, respectively, than the best-performing IBP method. We provide three techniques, specific to Cell Painting images, which enable deep-learning architectures to train effectively and demonstrate approaches to combat the significant batch effects present in large Cell Painting datasets.

Identification of lysosomotropism using explainable machine learning and morphological profiling cell painting data

Lysosomotropism is a phenomenon of diverse pharmaceutical interests because it is a property of compounds with diverse chemical structures and primary targets. While it is primarily reported to be caused by compounds having suitable lipophilicity and basicity values, not all compounds that fulfill such criteria are in fact lysosomotropic. Here, we use morphological profiling by means of the cell painting assay (CPA) as a reliable surrogate to identify lysosomotropism. We noticed that only 35% of the compound subset with matching physicochemical properties show the lysosomotropic phenotype. Based on a matched molecular pair analysis (MMPA), no key substructures driving lysosomotropism could be identified. However, using explainable machine learning (XML), we were able to highlight that higher lipophilicity, basicity, molecular weight, and lower topological polar surface area are among the important properties that induce lysosomotropism in the compounds of this subset.

Identification of readily available pseudo-natural products

Pseudo-natural products (PNPs) combine fragments derived from NPs in ways that are not found in nature, and may lead to the discovery of novel chemotypes for unexpected targets or the identification of unprecedented bioactivities. PNPs have increasingly been explored in recent drug discovery programs, and are strongly enriched in clinical compounds. We describe how a large number of structurally different PNPs can be accessed readily and without the need to execute labor- and time intensive synthesis programs. We employed an improved version of the previously reported natural product fragment combination (NPFC) tool to analyze the full library of 3.5 M synthetic small molecules and screening libraries from Enamine for PNP content, assessed the spatial complexity of Enamine-PNPs using the recently developed normalized spatial score (nSPS) and evaluated the bioactivity of a selected subset of Enamine-PNPs in the unbiased morphological cell painting assay. A major fraction (32%; 1.1 million compounds) of the Enamine library are PNPs which contain a significant number of compounds with unexpected and probably new bioactivity.

Detection of a Mitochondrial Fragmentation and Integrated Stress Response Using the Cell Painting Assay

Mitochondria are cellular powerhouses and are crucial for cell function. However, they are vulnerable to internal and external perturbagens that may impair mitochondrial function and eventually lead to cell death. In particular, small molecules may impact mitochondrial function, and therefore, their influence on mitochondrial homeostasis is at best assessed early on in the characterization of biologically active small molecules and drug discovery. We demonstrate that unbiased morphological profiling by means of the cell painting assay (CPA) can detect mitochondrial stress coupled with the induction of an integrated stress response. This activity is common for compounds addressing different targets, is not shared by direct inhibitors of the electron transport chain, and enables prediction of mitochondrial stress induction for small molecules that are profiled using CPA.

Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities

Artificial intelligence tools promise transformative impacts in drug development. Regulatory agencies face challenges in integrating AI while ensuring reliability and safety in clinical trial approvals, drug marketing authorizations, and post-market surveillance. Incorporating these technologies into the existing regulatory framework and agency practices poses notable challenges, particularly in evaluating the data and models employed for these purposes. Rapid adaptation of regulations and internal processes is essential for agencies to keep pace with innovation, though achieving this requires collective stakeholder collaboration. This article thus delves into the need for adaptations of regulations throughout the drug development lifecycle, as well as the utilization of AI within internal processes of medicine agencies.

Metabolic and phenotypic changes induced by PFAS exposure in two human hepatocyte cell models

PFAS are ubiquitous industrial chemicals with known adverse health effects, particularly on the liver. The liver, being a vital metabolic organ, is susceptible to PFAS-induced metabolic dysregulation, leading to conditions such as hepatotoxicity and metabolic disturbances. In this study, we investigated the phenotypic and metabolic responses of PFAS exposure using two hepatocyte models, HepG2 (male cell line) and HepaRG (female cell line), aiming to define phenotypic alterations, and metabolic disturbances at the metabolite and pathway levels. The PFAS mixture composition was selected based on epidemiological data, covering a broad concentration spectrum observed in diverse human populations. Phenotypic profiling by Cell Painting assay disclosed predominant effects of PFAS exposure on mitochondrial structure and function in both cell models as well as effects on F-actin, Golgi apparatus, and plasma membrane-associated measures. We employed comprehensive metabolic characterization using liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS). We observed dose-dependent changes in the metabolic profiles, particularly in lipid, steroid, amino acid and sugar and carbohydrate metabolism in both cells as well as in cell media, with HepaRG cell line showing a stronger metabolic response. In cells, most of the bile acids, acylcarnitines and free fatty acids showed downregulation, while medium-chain fatty acids and carnosine were upregulated, while the cell media showed different response especially in relation to the bile acids in HepaRG cell media. Importantly, we observed also nonmonotonic response for several phenotypic features and metabolites. On the pathway level, PFAS exposure was also associated with pathways indicating oxidative stress and inflammatory responses. Taken together, our findings on PFAS-induced phenotypic and metabolic disruptions in hepatocytes shed light on potential mechanisms contributing to the broader comprehension of PFAS-related health risks.

Cross-Modal Graph Contrastive Learning with Cellular Images

Constructing discriminative representations of molecules lies at the core of a number of domains such as drug discovery, chemistry, and medicine. State-of-the-art methods employ graph neural networks and self-supervised learning (SSL) to learn unlabeled data for structural representations, which can then be fine-tuned for downstream tasks. Albeit powerful, these methods are pre-trained solely on molecular structures and thus often struggle with tasks involved in intricate biological processes. Here, it is proposed to assist the learning of molecular representation by using the perturbed high-content cell microscopy images at the phenotypic level. To incorporate the cross-modal pre-training, a unified framework is constructed to align them through multiple types of contrastive loss functions, which is proven effective in the formulated novel tasks to retrieve the molecules and corresponding images mutually. More importantly, the model can infer functional molecules according to cellular images generated by genetic perturbations. In parallel, the proposed model can transfer non-trivially to molecular property predictions, and has shown great improvement over clinical outcome predictions. These results suggest that such cross-modality learning can bridge molecules and phenotype to play important roles in drug discovery.

In silico labeling in cell biology: Potential and limitations

In silico labeling is the computational cross-modality image translation where the output modality is a subcellular marker that is not specifically encoded in the input image, for example, in silico localization of organelles from transmitted light images. In principle, in silico labeling has the potential to facilitate rapid live imaging of multiple organelles with reduced photobleaching and phototoxicity, a technology enabling a major leap toward understanding the cell as an integrated complex system. However, five years have passed since feasibility was attained, without any demonstration of using in silico labeling to uncover new biological insight. In here, we discuss the current state of in silico labeling, the limitations preventing it from becoming a practical tool, and how we can overcome these limitations to reach its full potential.

Artificial intelligence for high content imaging in drug discovery

Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.

Deep learning large-scale drug discovery and repurposing

Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing.

A bypass flow model to study endothelial cell mechanotransduction across diverse flow environments

Disturbed flow is one of the pathological initiators of endothelial dysfunction in intimal hyperplasia (IH) which is commonly seen in vascular bypass grafts, and arteriovenous fistulas. Various in vitro disease models have been designed to simulate the hemodynamic conditions found in the vasculature. Nonetheless, prior investigations have encountered challenges in establishing a robust disturbed flow model, primarily attributed to the complex bifurcated geometries and distinctive flow dynamics. In the present study, we aim to address this gap by introducing an in vitro bypass flow model capable of inducing disturbed flow and other hemodynamics patterns through a pulsatile flow in the same model. To assess the model's validity, we employed computational fluid dynamics (CFD) to simulate hemodynamics and compared the morphology and functions of human umbilical venous endothelial cells (HUVECs) under disturbed flow conditions to those in physiological flow or stagnant conditions. CFD analysis revealed the generation of disturbed flow within the model, pinpointing the specific location in the channel where the effects of disturbed flow were observed. High-content screening, a single-cell morphological profile assessment, demonstrated that HUVECs in the disturbed flow area exhibited random orientation, and morphological features were significantly distinct compared to cells in the physiological flow or stagnant condition after a two days of flow exposure. Furthermore, HUVECs exposed to disturbed flow underwent extensive remodeling of the adherens junctions and expressed higher levels of endothelial cell activation markers compared to other hemodynamic conditions. In conclusion, our in vitro bypass flow model provides a robust platform for investigating the associations between disturbed flow pattern and vascular diseases.

Cell Painting unravels insecticidal modes of action on Spodoptera frugiperda insect cells

The "Cell Painting" technology utilizes multiplexed fluorescent staining of various cell organelles, to produce high-content microscopy images of cells for multidimensional phenotype assessment. The phenotypic profiles extracted from those images can be analyzed upon perturbations with biologically active molecules to annotate the mode of action or biological activity by comparison with reference profiles of already known mechanisms of action, ultimately enabling the determination of on-target and off-target effects. This approach is already described in various human cell cultures, the most commonly used being the U2OS cell line, yet allows broad applications in additional areas of chemical-biological research. Here we describe for the first time the application and adaptation of Cell Painting to an insect cell line, the Sf9 cells from Spodoptera frugiperda. By adjusting image acquisition and analysis models, specific phenotypic profiles were obtained in a dose-dependent manner for 20 reference compounds, including representatives for the most relevant insecticidal modes of action categories (nerve & muscle, respiration and growth & development). Through a dimensionality-reduction method, both calculations of phenotypic half maximal inhibition concentration (IC50) values as well as similarity analysis of the obtained profiles by hierarchical clustering were performed. By Cell Painting effects on the phenotype could be obtained at higher sensitivity than in other assay formats, such as cytotoxicity assessments. More importantly, these analyses provide insight into mechanistic determinants of biological activity. Compounds with similar modes of action showed a high degree of proximity in a hierarchical clustering analysis while being distinct from actives with an unrelated mode of action. In essence, we provide strong evidence on the impact of Cell Painting mechanistic understanding of insecticides with regards to determinants of efficacy and safety utilizing an insect cell model system.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

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. CytoSummaryNet uses self-supervised contrastive learning in a multiple-instance learning framework, providing an easier-to-apply method for aggregating single-cell feature data than previously published strategies. Interpretability analysis suggests that the model achieves this improvement by downweighting small mitotic cells or those with debris and prioritizing large uncrowded cells. The approach requires only perturbation labels for training, which are readily available in all cell profiling datasets. CytoSummaryNet offers a straightforward post-processing step for single-cell profiles that can significantly boost retrieval performance on image-based profiling datasets.

Medicinal polypharmacology-a scientific glossary of terminology and concepts

Medicinal polypharmacology is one answer to the complex reality of multifactorial human diseases that are often unresponsive to single-targeted treatment. It is an admittance that intrinsic feedback mechanisms, crosstalk, and disease networks necessitate drugs with broad modes-of-action and multitarget affinities. Medicinal polypharmacology grew to be an independent research field within the last two decades and stretches from basic drug development to clinical research. It has developed its own terminology embedded in general terms of pharmaceutical drug discovery and development at the intersection of medicinal chemistry, chemical biology, and clinical pharmacology. A clear and precise language of critical terms and a thorough understanding of underlying concepts is imperative; however, no comprehensive work exists to this date that could support researchers in this and adjacent research fields. In order to explore novel options, establish interdisciplinary collaborations, and generate high-quality research outputs, the present work provides a first-in-field glossary to clarify the numerous terms that have originated from various individual disciplines.

Unleashing the potential of cell painting assays for compound activities and hazards prediction

The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modes-of-action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach.

Let's talk about flux: the rising potential of autophagy rate measurements in disease

Macroautophagy/autophagy is increasingly implicated in a variety of diseases, making it an attractive therapeutic target. However, many aspects of autophagy are not fully understood and its impact on many diseases remains debatable and context-specific. The lack of systematic and dynamic measurements in these cases is a key reason for this ambiguity. In recent years, Loos et al. 2014 and Beesabathuni et al. 2022 developed methods to quantitatively measure autophagy holistically. In this commentary, we pose some of the unresolved biological questions regarding autophagy and consider how quantitative measurements may address them. While the applications are ever-expanding, we provide specific use cases in cancer, virus infection, and mechanistic screening. We address how the rate measurements themselves are central to developing cancer therapies and present ways in which these tools can be leveraged to dissect the complexities of virus-autophagy interactions. Screening methods can be combined with rate measurements to mechanistically decipher the labyrinth of autophagy regulation in cancer and virus infection. Taken together, these approaches have the potential to illuminate the underlying mechanisms of various diseases.Abbreviation MAP1LC3/LC3: microtubule-associated protein 1 light chain 3; R1: rate of autophagosome formation; R2: rate of autophagosome-lysosome fusion; R3: rate of autolysosome turnover.

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Intracellular Protein Editing to Enable Incorporation of Non-Canonical Residues into Endogenous Proteins

The ability to study proteins in a cellular context is crucial to our understanding of biology. Here, we report a new technology for "intracellular protein editing", drawing from intein- mediated protein splicing, genetic code expansion, and endogenous protein tagging. This protein editing approach enables us to rapidly and site specifically install residues and chemical handles into a protein of interest. We demonstrate the power of this protein editing platform to edit cellular proteins, inserting epitope peptides, protein-specific sequences, and non-canonical amino acids (ncAAs). Importantly, we employ an endogenous tagging approach to apply our protein editing technology to endogenous proteins with minimal perturbation. We anticipate that the protein editing technology presented here will be applied to a diverse set of problems, enabling novel experiments in live mammalian cells and therefore provide unique biological insights.

Distinct profile of antiviral drugs effects in aortic and pulmonary endothelial cells revealed by high-content microscopy and cell painting assays

Antiretroviral therapy have significantly improved the treatment of viral infections and reduced the associated mortality and morbidity rates. However, highly effective antiretroviral therapy (HAART) may lead to an increased risk of cardiovascular diseases, which could be related to endothelial toxicity. Here, seven antiviral drugs (remdesivir, PF-00835231, ritonavir, lopinavir, efavirenz, zidovudine and abacavir) were characterized against aortic (HAEC) and pulmonary (hLMVEC) endothelial cells, using high-content microscopy. The colourimetric study (MTS test) revealed similar toxicity profiles of all antiviral drugs tested in the concentration range of 1 nM-50 μM in aortic and pulmonary endothelial cells. Conversely, the drugs' effects on morphological parameters were more pronounced in HAECs as compared with hLMVECs. Based on the antiviral drugs' effects on the cytoplasmic and nuclei architecture (analyzed by multiple pre-defined parameters including SER texture and STAR morphology), the studied compounds were classified into five distinct morphological subgroups, each linked to a specific cellular response profile. In relation to morphological subgroup classification, antiviral drugs induced a loss of mitochondrial membrane potential, elevated ROS, changed lipid droplets/lysosomal content, decreased von Willebrand factor expression and micronuclei formation or dysregulated cellular autophagy. In conclusion, based on specific changes in endothelial cytoplasm, nuclei and subcellular morphology, the distinct endothelial response was identified for remdesivir, ritonavir, lopinavir, efavirenz, zidovudine and abacavir treatments. The effects detected in aortic endothelial cells were not detected in pulmonary endothelial cells. Taken together, high-content microscopy has proven to be a robust and informative method for endothelial drug profiling that may prove useful in predicting the organ-specific endothelial toxicity of various drugs.