Biology subjects

Carreras-Puigvert, J.

Publications and source records attributed to Carreras-Puigvert, J..

At least 19 recordsLinked to original sources

bioRxiv2026

A chemical inducer of ribophagy limits the toxicity of ALS-related arginine-rich peptides

C9ORF72 intronic repeat expansions are the most frequent mutation found in Amyotrophic Lateral Sclerosis (ALS), producing toxic arginine-rich dipeptides (DPR) that disrupt RNA metabolism and trigger the accumulation of orphan ribosomal proteins (RP). Through a large phenotypic chemical screen, we identified "SALSa", a novel compound that mitigates DPR toxicity. Mechanistically, SALSa acts as a chemical inducer of ribophagy, a specialized form of autophagy that promotes RP clearance. Interestingly, this effect is unrelated to mTOR inhibition, the main regulator of autophagy. In contrast, this is due to an effect of the drug in ribosome biogenesis, which triggers a protective response to clear defective ribosomes. Accordingly, SALSa accumulates in nucleoli and perturbs the final steps of rRNA maturation. SALSa reduces DPR toxicity in differentiated neurons and significantly extends lifespan in a Drosophila melanogaster model of C9ORF72 ALS. These findings suggest that stimulating ribophagy could be beneficial for pathologies associated to dysfunctional ribosome biogenesis, including C9ORF72 ALS.

molecular biology↗

bioRxiv2025

Morphological cell profiling for drug repurposing against SARS-CoV-2 infection

Antiviral drug discovery has traditionally focused on targeting viral proteins, while host-directed strategies remain largely underexplored. Here, present a systematic drug repurposing strategy leveraging morphological profiling to identify host-targeting antivirals. Our image-based approach combines viral protein immunostaining with high-content Cell Painting analysis to simultaneously assess viral replication and provide in-depth analysis of host cell responses. By screening 5,275 repurposable drugs against SARS-CoV-2 infected cells, we identified compounds that reversed the infected cell phenotype, including ones not detected by conventional cytopathicity and antibody-based assays. A counter-screen excluded compounds whose antiviral activity was likely driven by drug-induced phospholipidosis (DIPL). Pathway enrichment analysis of compounds validated by both Cell Painting dose-response and DIPL assays, revealed host processes frequently hijacked by viruses, including innate immune responses and kinases. Among the top hits, both novel candidates, such as serdemetan, and previously reported broad-spectrum antivirals, such as sunitinib, were identified. Our approach constitutes an adaptable and scalable platform suited for diverse viral pathogens and cell systems. We provide a resource of open access screening data, images, and automated analysis pipelines to advance both antiviral discovery and pandemic preparedness.

microbiology↗

bioRxiv2025

Integrating Cell Painting and Thermal Proteome Profiling for Inference of Targets and Mechanism of Action

Understanding the mechanism of action (MoA) of bioactive compounds is a central challenge in drug discovery and chemical biology. In this article, we propose a strategy for integrating morphological data with proteomics to provide deeper insights into the MoA of compounds. We combine the rich phenotypic profiles of Cell Painting (CP) with the unbiased protein target detection of Thermal Proteome Profiling (TPP), and construct protein-protein interaction networks based on potential targets identified using both assays. We evaluate our method on public TPP data sets for the five compounds (+)-JQ1, I-BET151, Vemurafenib, Crizotinib and Panobinostat, together with Cell Painting data for 5270 drugs on U2OS cells. We show that the combined approach could accurately identify known MoAs for four out of five compounds. This work highlights the value of multimodal profiling for chemical biology and opens new avenues for data-driven discovery of therapeutic mechanisms.

bioinformatics↗

bioRxiv2025

Label-Free Live-Cell Imaging improves Mode of Action Classification

Morphological profiling is a common approach to investigate the modes of action (MOAs) of compounds. Most methods rely on fixed-cell assays, which provide only a single snapshot at a predefined time point and overlook the dynamic nature of cellular responses. In contrast, live-cell imaging tracks responses over time, offering deeper insight into compound-specific effects and mechanisms; however, time-series analysis of image data remains challenging due to limited analytical tools. We present Live Cell Temporal Profiling (LCTP), a workflow for morphological profiling of label-free live-cell time series data that yields interpretable, biologically relevant results. We showcase LCTP in an MOA classification study using label-free data. The workflow integrates established deep-learning components, cell segmentation, live/dead classification, and single-cell feature extraction, with data-driven models to capture MOA-specific temporal phenotypes and produce time-resolved profiles that can be compared across compounds and cell lines. We assess MOA classification performance using double-blinded cross-validation simulating a real-world screening scenario. LCTP significantly improves MOA classification over single-time point analysis, consistently across both cell lines used in the study. Time-resolved phenotypic modeling reveals transient, sustained, and delayed responses, clarifying compound-specific temporal effects and mechanisms across MOAs. The presented workflow is modular: each step removes irrelevant information, enriching signal, and enabling straightforward updates as technologies evolve and as new technologies become available, while supporting reuse across studies broadly. We believe LCTP adds substantial value to high-throughput compound screening, showing that live-cell imaging combined with this workflow yields informative visualizations of temporal effects and improved MOA classification.

bioinformatics↗

bioRxiv2025

High-content morphological profiling by Cell Painting in 3D spheroids.

Cell Painting is a popular assay for morphological profiling of multi-labeled 2D monolayer cell cultures used in a wide range of applications. Culturing cells in 3D has potential for higher physiological relevance, such as when studying effects of perturbations. Robust and scalable 3D models can be challenging to characterize through imaging - particularly because light has difficulty penetrating cell multilayers. We introduce a scalable method where the Cell Painting assay is combined with tissue-clearing and applied to 3D spheroids generated in a ULA microplate format. Multi-channel images are acquired using confocal microscopy, and cells can be segmented inside those spheroids allowing for relevant morphological features to be extracted. Our end-to-end analysis pipeline comprises cell segmentation, morphological feature extraction, and between-spheroids and within-spheroid normalization. We demonstrate the method using spheroids cultured from two colorectal cancer cell lines and successfully detect distinct phenotypic changes upon compound treatments, on both spheroid-level using maximum intensity projections and on single cell-level. We show that drugs group by mechanism of action, with biologically relevant clusters especially evident with single-cell data. Finally, we contrast our method to results from 2D Cell Painting and discover a different pattern in DNA damaging drugs in HCT116 colorectal cancer cells. This work lays the foundation for multi-channel image-based screening in 3D spheroids.

pharmacology and toxicology↗

bioRxiv2025

Single-cell morphological profiling reveals insights into cell death

Analysis at the single-cell level is a powerful approach to study biological processes and responses to perturbations. However, its application in morphological profiling with phenomics remains underexplored. Here, we use the Cell Painting assay to investigate morphological effects of 53 small molecule compounds, associated with six distinct programmed cell death mechanisms, across six concentrations in MCF7 cells. To compare single-cell and aggregated analysis strategies, we conduct both supervised and unsupervised evaluations aimed at identifying features linked to programmed cell death. We apply an energy distance as a metric to quantify morphological perturbation strength, enabling efficient filtering. Among three tested feature extraction methods, self-supervised DINO embeddings applied to single-cell data captured high-resolution morphological patterns. Focused analyses of apoptosis-inducing compounds revealed biological heterogeneity attributable to specific molecular targets and concentration-dependent effects, which were not apparent in aggregated profiles. In contrast, multi-class classification models for the six programmed cell death mechanisms trained on single-cell features achieved F1 scores of 79.86%, while models trained on aggregated features reached F1 scores of up to 89.97%. Our results highlight the advantages of single-cell data for unsupervised exploration and show that aggregated representations yield more robust and accurate performance in supervised models.

bioinformatics↗

bioRxiv2024

Combined inhibition of MDM2 and PARP lead to a synergistic anti-tumoral response in p53 wild-type rhabdomyosarcoma models

Targeting MDM2-p53 interaction to enhance p53 activity represents a promising antitumoral strategy for p53 wild-type pediatric tumors, including soft-tissue sarcomas. However, results from early-phase clinical trials in hematological and solid cancers have shown limited efficacy of MDM2 inhibitors, underscoring the necessity to explore more effective combinations with other agents. In this study, we provide results from a 22-drug combination screening demonstrating the therapeutic potential of combining siremadlin (MDM2 inhibitor) and olaparib (PARP inhibitor) for treating p53 wild-type rhabdomyosarcoma (p53 WT RMS). Cell survival, cell death, and apoptosis analysis revealed synergistic effects of combining siremadlin plus olaparib in vitro. The combination of both drugs led to a significant increase in p53 activity, as evidenced by p53 accumulation and K382 acetylation, along with an increased expression of bona fide p53 targets. Furthermore, treatment with both drugs resulted in significant reduction of tumor growth and increased mice overall survival when compared to single treatments and control in cell line-derived orthotopic xenograft models. Overall, our study demonstrates, for the first time, the synergistic effect of combining siremadlin plus olaparib in inhibiting p53 WT RMS tumor growth in vitro and in vivo. Our findings support the potential to study the combination of both drugs in clinical trials and warrants further investigation in other tumors with similar molecular features.

cancer biology↗

bioRxiv2024

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 Paintings 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.

cell biology↗

bioRxiv2023

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

Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features are based on image statistics, and are, therefore, often not readily biologically interpretable. In this study, we introduce an approach that maps specific Cell Painting features into the BioMorph space using readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. In summary, BioMorph space offers a more biologically relevant way to interpret cell morphological features from the Cell Painting assays and to generate hypotheses for experimental validation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/549031v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1a6169forg.highwire.dtl.DTLVardef@11757eforg.highwire.dtl.DTLVardef@18e3fc2org.highwire.dtl.DTLVardef@1fc0a90_HPS_FORMAT_FIGEXP M_FIG C_FIG IN BRIEFSeal et al. used machine learning models and feature selection approaches to group cell morphological features from Cell Painting assays and to describe the shared role of these morphological features in various cell health phenotypes. The resulting BioMorph space improves the ability to understand the mechanism of action and toxicity of compounds and to generate hypotheses to guide future experiments. HIGHLIGHTSO_LICombining Cell Painting and Cell Health imaging data defines the BioMorph space. C_LIO_LIBioMorph space allows detecting less common mechanisms for bioactive compounds. C_LIO_LIBioMorph space can generate MOA hypotheses to guide experimental validation. C_LIO_LIBioMorph space is more biologically relevant and interpretable than Cell Painting features. C_LI

cell biology↗

bioRxiv2023

The anti-leprosy drug clofazimine reduces polyQ toxicity through activation of PPARγ

PolyQ diseases are autosomal dominant neurodegenerative disorders caused by the expansion of CAG repeats. While of slow progression, these diseases are ultimately fatal and lack effective therapies. Here, we present our results from a High-Throughput chemical screen oriented to find drugs that lower the toxicity of a protein containing the first exon from the Huntingtons disease protein huntingtin (HTT) harboring 94 glutamines (Htt-Q94). Our screening identified the anti-leprosy drug clofazimine as a hit, which was subsequently validated in several in vitro models as well as in a zebrafish model of polyQ toxicity. Computational analyses of transcriptional signatures, together with molecular modeling and biochemical assays revealed that clofazimine is an agonist of the peroxisome proliferator activated receptor gamma (PPAR{gamma}), previously suggested as a potential therapy for HD by stimulating mitochondrial biogenesis. Accordingly, clofazimine rescued the mitochondrial dysfunction triggered by Htt-Q94 expression. Together, our results support the potential of clofazimine repurposing for the treatment of PolyQ diseases.

neuroscience↗

bioRxiv2022

Is brightfield all you need for mechanism of action prediction?

Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, and labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments.

bioinformatics↗

bioRxiv2022

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models by Leveraging Similarity to Training Data

The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painting annotations were available). We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrated that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and further expanded the applicability domain by better extrapolating to new structural and morphology spaces. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY O_FLOATNOFigure:C_FLOATNO For TOC Only C_FIG_DISPLAY

bioinformatics↗

bioRxiv2022

Designing microplate layouts using artificial intelligence

Microplates are indispensable in large-scale biomedical experiments but the physical location of samples and controls on the microplate can significantly affect the resulting data and quality metric values. We introduce a new method based on constraint programming for designing microplate layouts that reduces unwanted bias and limits the impact of batch effects after error correction and normalisation. We demonstrate that our method applied to dose-response experiments leads to more accurate regression curves and lower errors when estimating IC50/EC50, and for drug screening leads to increased sensitivity, when compared to random layouts. It also reduces the risk of inflated scores from common microplate quality assessment metrics such as Z factor and SSMD. We make our method available via a suite of tools (PLAID) including a reference constraint model, a web application, and Python notebooks to evaluate and compare designs when planning microplate experiments.

bioinformatics↗

bioRxiv2022

Chemical and genetic screens identify new regulators of tetracycline-inducible gene expression system in mammalian cells

The tetracycline repressor (tetR)-regulated system is a widely used tool to specifically control gene expression in mammalian cells. Based on this system, we generated a human osteosarcoma cell line which allows for inducible expression of an EGFP-fusion of the TAR DNA-binding protein 43 (TDP-43), which has been linked to neurodegenerative diseases. Consistent with previous findings, TDP-43 overexpression led to the accumulation of aggregates and limited the viability of U2OS. Using this inducible system, we conducted a chemical screen with a library that included FDA-approved drugs. While the primary screen identified several compounds that prevented TDP-43 toxicity, further experiments revealed that these chemicals abrogated doxycyclinedependent TDP-43 expression. This antagonistic effect was observed with both doxycycline and tetracycline, and in several Tet-On cell lines expressing different genes, confirming the general effect of these compounds as inhibitors of the tetR system. Using the same cell line, a genome-wide CRISPR/Cas9 screen identified epigenetic regulators such as the G9a methyltransferase or TRIM28 as potential modifiers of TDP-43 toxicity. Yet again, further experiments revealed that G9a inhibition or TRIM28 loss prevented doxycycline-dependent expression of TDP-43. Together, these results suggest that none of the medically approved drugs significantly mitigates TDP-43 toxicity, identify new chemical and genetic regulators of the tetR system, and raise awareness on the limitations of this approach to conduct chemical or genetic screenings in mammalian cells.

molecular biology↗

bioRxiv2022

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

Environmental chemicals are commonly studied one at a time, and there is a need to advance our understanding of the effect of exposure to their combinations. Here we apply high-content microscopy imaging of cells stained with multiplexed dyes (Cell Painting) to profile the effects of Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), and Dibutyltin dilaurate (DBTDL) exposure on four human cell lines; both individually and in all combinations. We show that morphological features can be used with multivariate data analysis to discern between exposures from individual compounds, concentrations, and combinations. CTAB and DBTDL induced concentration-dependent morphological changes across the four cell lines, and BPA exacerbated morphological effects when combined with CTAB and DBTDL. Combined exposure to CTAB and BPA induced changes on the ER, Golgi apparatus, nucleoli and cytoplasmic RNA in one of the cell lines. Different responses between cell lines indicate that multiple cell types are needed when assessing combination effects. The rapid and relatively low-cost experiments combined with high information content makes Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare. HighlightsO_LIAssessment of combination effects of BPA, CTAB and DBTDL on four human cell lines C_LIO_LIMorphological profiling/Cell Painting captures dose and combination dependent effects C_LIO_LIBPA exacerbated morphological effects when combined with CTAB and DBTDL. C_LIO_LICell models of diverse origin are needed when profiling environmental chemicals C_LI

pharmacology and toxicology↗

bioRxiv2022

Integrating Cell Morphology with Gene Expression and Chemical Structure to Aid Mitochondrial Toxicity Detection

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/475326v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@f29341org.highwire.dtl.DTLVardef@1a1970borg.highwire.dtl.DTLVardef@1c3581dorg.highwire.dtl.DTLVardef@1ce00d4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

bioinformatics↗

bioRxiv2021

An Open-Source Modular Framework for Automated Pipetting and Imaging Applications

The number of samples in biological experiments are continuously increasing, but complex protocols and human experimentation in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments and due to lack of open APIs they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, we demonstrate automated, high-throughput experiments for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools Openflexure, Opentrons, ImJoy and UC2. Our automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on analysis of previously acquired images, enables the realization of smart microscopy experiments, featuring completely autonomously performed experiments. All documents and source-files are publicly available (https://beniroquai.github.io/Hi2) to prove the concept of smart lab automation using inexpensive, open tools. We believe this democratizes access to the power and repeatability of automated experiments.

cell biology↗

bioRxiv2021

A rapid phenomics workflow for the in vitro identification of antiviral drugs

BackgroundThe current COVID-19 pandemic has highlighted the need for new and fast methods to identify novel or repurposed therapeutic drugs. Here we present a method for untargeted phenotypic drug screening of virus-infected cells, combining Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. ResultsWe show that the methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with Human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state. ConclusionsThe method can be used in drug discovery for morphological profiling of novel antiviral compounds on both infected and non-infected cells.

pharmacology and toxicology↗