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  • 1.
    Braeuning, Albert
    et al.
    German Fed Inst Risk Assessment, Dept Food Safety, Berlin, Germany..
    Balaguer, Patrick
    Univ Montpellier, IRCM Inst Rech Cancerol Montpellier, Inserm, U1194,ICM, Montpellier, France..
    Bourguet, William
    Univ Montpellier, CBS Ctr Biol Structurale, CNRS, Inserm, Montpellier, France..
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Feiertag, Katreece
    German Fed Inst Risk Assessment, Dept Pesticides Safety, Berlin, Germany..
    Kamstra, Jorke H.
    Univ Utrecht, Inst Risk Assessment Sci, Fac Vet Med, Dept Populat Hlth Sci, Utrecht, Netherlands..
    Knapen, Dries
    Univ Antwerp, Dept Vet Sci, Zebrafishlab Vet Physiol & Biochem, Antwerp, Belgium..
    Lichtenstein, Dajana
    German Fed Inst Risk Assessment, Dept Food Safety, Berlin, Germany..
    Marx-Stoelting, Philip
    German Fed Inst Risk Assessment, Dept Pesticides Safety, Berlin, Germany..
    Rietdijk, Jonne
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Schubert, Kristin
    UFZ Helmholtz Ctr Environm Res, Dept Mol Syst Biol, Leipzig, Germany..
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Stinckens, Evelyn
    Univ Antwerp, Dept Vet Sci, Zebrafishlab Vet Physiol & Biochem, Antwerp, Belgium..
    Thedieck, Kathrin
    Univ Innsbruck, Inst Biochem, Innsbruck, Austria.;Univ Innsbruck, Ctr Mol Biosci Innsbruck, Innsbruck, Austria..
    van den Boom, Rik
    Univ Antwerp, Dept Vet Sci, Zebrafishlab Vet Physiol & Biochem, Antwerp, Belgium..
    Vergauwen, Lucia
    Univ Antwerp, Dept Vet Sci, Zebrafishlab Vet Physiol & Biochem, Antwerp, Belgium..
    von Bergen, Martin
    UFZ Helmholtz Ctr Environm Res, Dept Mol Syst Biol, Leipzig, Germany..
    Wewer, Neele
    German Fed Inst Risk Assessment, Dept Food Safety, Berlin, Germany..
    Zalko, Daniel
    Univ Toulouse, Univ Paul Sabatier UPS,Inst Natl Rech Pour Agr Ali, Ecole Natl Veterinaire Toulouse ENVT,INP Purpan, Inst Natl Rech Agr,Toxalim Res Ctr Food Toxicol, Toulouse, France..
    Development of new approach methods for the identification and characterization of endocrine metabolic disruptors: a PARC project2023In: Frontiers in Toxicology, E-ISSN 2673-3080, Vol. 5, article id 1212509Article in journal (Refereed)
    Abstract [en]

    In past times, the analysis of endocrine disrupting properties of chemicals has mainly been focused on (anti-)estrogenic or (anti-)androgenic properties, as well as on aspects of steroidogenesis and the modulation of thyroid signaling. More recently, disruption of energy metabolism and related signaling pathways by exogenous substances, so-called metabolism-disrupting chemicals (MDCs) have come into focus. While general effects such as body and organ weight changes are routinely monitored in animal studies, there is a clear lack of mechanistic test systems to determine and characterize the metabolism-disrupting potential of chemicals. In order to contribute to filling this gap, one of the project within EU-funded Partnership for the Assessment of Risks of Chemicals (PARC) aims at developing novel in vitro methods for the detection of endocrine metabolic disruptors. Efforts will comprise projects related to specific signaling pathways, for example, involving mTOR or xenobiotic-sensing nuclear receptors, studies on hepatocytes, adipocytes and pancreatic beta cells covering metabolic and morphological endpoints, as well as metabolism-related zebrafish-based tests as an alternative to classic rodent bioassays. This paper provides an overview of the approaches and methods of these PARC projects and how this will contribute to the improvement of the toxicological toolbox to identify substances with endocrine disrupting properties and to decipher their mechanisms of action.

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  • 2.
    Carreras-Puigvert, Jordi
    et al.
    Karolinska Inst, Div Translat Med & Chem Biol, Dept Mol Biochem & Biophys, Sci Life Lab, S-17165 Stockholm, Sweden.
    Zitnik, Marinka
    Univ Ljubljana, Fac Comp & Informat Sci, SI-1000 Ljubljana, Slovenia.; Stanford Univ, Dept Comp Sci, Palo Alto, CA 94305 USA.
    Jemth, Ann-Sofie
    Karolinska Inst, Div Translat Med & Chem Biol, Dept Mol Biochem & Biophys, Sci Life Lab, S-17165 Stockholm, Sweden.
    Carter, Megan
    Stockholm Univ, Dept Biochem & Biophys, S-10691 Stockholm, Sweden.
    Unterlass, Judith E
    Karolinska Inst, Div Translat Med & Chem Biol, Dept Mol Biochem & Biophys, Sci Life Lab, S-17165 Stockholm, Sweden.
    Hallström, Björn
    KTH Royal Inst Technol, Sci Life Lab, Cell Profiling Affin Prote, S-17165 Stockholm, Sweden.
    Loseva, Olga
    Karolinska Inst, Div Translat Med & Chem Biol, Dept Mol Biochem & Biophys, Sci Life Lab, S-17165 Stockholm, Sweden.
    Karem, Zhir
    Karolinska Inst, Div Translat Med & Chem Biol, Dept Mol Biochem & Biophys, Sci Life Lab, S-17165 Stockholm, Sweden.
    Calderón-Montaño, José Manuel
    Karolinska Inst, Div Translat Med & Chem Biol, Dept Mol Biochem & Biophys, Sci Life Lab, S-17165 Stockholm, Sweden.
    Lindskog, Cecilia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Edqvist, Per-Henrik D
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology.
    Matuszewski, Damian J.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Ait Blal, Hammou
    KTH Royal Inst Technol, Sci Life Lab, Cell Profiling Affin Prote, S-17165 Stockholm, Sweden.
    Berntsson, Ronnie P A
    Stockholm Univ, Dept Biochem & Biophys, S-10691 Stockholm, Sweden.
    Häggblad, Maria
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Biochem & Cellular Screening Facil, S-17165 Stockholm, Sweden.
    Martens, Ulf
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Biochem & Cellular Screening Facil, S-17165 Stockholm, Sweden.
    Studham, Matthew
    Stockholm Univ, Dept Biochem & Biophys, Stockholm Bioinformat Ctr, Sci Life Lab, Box 1031, S-17121 Solna, Sweden.
    Lundgren, Bo
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Biochem & Cellular Screening Facil, S-17165 Stockholm, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sonnhammer, Erik L L
    Stockholm Univ, Dept Biochem & Biophys, Stockholm Bioinformat Ctr, Sci Life Lab, Box 1031, S-17121 Solna, Sweden.
    Lundberg, Emma
    KTH Royal Inst Technol, Sci Life Lab, Cell Profiling Affin Prote, S-17165 Stockholm, Sweden.
    Stenmark, Pål
    Stockholm Univ, Dept Biochem & Biophys, S-10691 Stockholm, Sweden.
    Zupan, Blaz
    Univ Ljubljana, Fac Comp & Informat Sci, SI-1000 Ljubljana, Slovenia.; Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA.
    Helleday, Thomas
    Karolinska Inst, Div Translat Med & Chem Biol, Dept Mol Biochem & Biophys, Sci Life Lab, S-17165 Stockholm, Sweden.
    A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family2017In: Nature Communications, E-ISSN 2041-1723, Vol. 8, no 1, article id 1541Article in journal (Refereed)
    Abstract [en]

    The NUDIX enzymes are involved in cellular metabolism and homeostasis, as well as mRNA processing. Although highly conserved throughout all organisms, their biological roles and biochemical redundancies remain largely unclear. To address this, we globally resolve their individual properties and inter-relationships. We purify 18 of the human NUDIX proteins and screen 52 substrates, providing a substrate redundancy map. Using crystal structures, we generate sequence alignment analyses revealing four major structural classes. To a certain extent, their substrate preference redundancies correlate with structural classes, thus linking structure and activity relationships. To elucidate interdependence among the NUDIX hydrolases, we pairwise deplete them generating an epistatic interaction map, evaluate cell cycle perturbations upon knockdown in normal and cancer cells, and analyse their protein and mRNA expression in normal and cancer tissues. Using a novel FUSION algorithm, we integrate all data creating a comprehensive NUDIX enzyme profile map, which will prove fundamental to understanding their biological functionality.

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    fulltext
  • 3.
    Colicchia, Valeria
    et al.
    Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Häggblad, Maria
    Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Sirozh, Oleksandra
    Genomic Instability Group Spanish National Cancer Research Centre (CNIO) Madrid Spain.
    Porebski, Bartlomiej
    Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Balan, Mirela
    Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Li, Xuexin
    Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Lidemalm, Louise
    Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Hühn, Daniela
    Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Fernandez‐Capetillo, Oscar
    Science for Life Laboratory, Division of Genome Biology, Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden;Genomic Instability Group Spanish National Cancer Research Centre (CNIO) Madrid Spain.
    New regulators of the tetracycline‐inducible gene expression system identified by chemical and genetic screens2022In: FEBS Open Bio, E-ISSN 2211-5463, Vol. 12, no 10, p. 1896-1908Article in journal (Refereed)
    Abstract [en]

    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 the 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 the doxycycline-dependent 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 and 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. In summary, we have identified new chemical and genetic regulators of the tetR system, thereby raising awareness of the limitations of this approach to conduct chemical or genetic screening in mammalian cells.

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  • 4. Corman, Alba
    et al.
    Kanellis, Dimitris C.
    Michalska, Patrycja
    Häggblad, Maria
    Lafarga, Vanesa
    Bartek, Jiri
    Carreras-Puigvert, Jordi
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Fernandez-Capetillo, Oscar
    A chemical screen for modulators of mRNA translation identifies a distinct mechanism of toxicity for sphingosine kinase inhibitors2021In: PLoS biology, ISSN 1544-9173, E-ISSN 1545-7885, Vol. 19, no 5, article id e3001263Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 5.
    Dahlö, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Larsson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Rosén, Dan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Francisco Rodríguez, María Andreína
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Rietdijk, Jonne
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Georgiev, Polina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyström, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    AROS: Open Source Lab Automation Enables Fully Automated CellPaintingManuscript (preprint) (Other academic)
  • 6.
    Francisco Rodríguez, María Andreína
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Designing microplate layouts using artificial intelligence2023In: Artificial Intelligence in the Life Sciences, ISSN 2667-3185, Vol. 3, article id 100073Article in journal (Refereed)
    Abstract [en]

    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 precision, 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.

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  • 7.
    Garcia-Gomez, Pedro
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Golan, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Dadras, Mahsa Shahidi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Mezheyeuski, Artur
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Bellomo, Claudia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Tzavlaki, Kalliopi
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Morén, Anita
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Caja, Laia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    NOX4 regulates TGF beta-induced proliferation and self-renewal in glioblastoma stem cells2022In: Molecular Oncology, ISSN 1574-7891, E-ISSN 1878-0261, Vol. 16, no 9, p. 1891-1912Article in journal (Refereed)
    Abstract [en]

    Y Glioblastoma (GBM) is the most aggressive and common glioma subtype, with a median survival of 15 months after diagnosis. Current treatments have limited therapeutic efficacy; thus, more effective approaches are needed. The glioblastoma tumoural mass is characterised by a small cellular subpopulation - glioblastoma stem cells (GSCs) - that has been held responsible for glioblastoma initiation, cell invasion, proliferation, relapse and resistance to chemo- and radiotherapy. Targeted therapies against GSCs are crucial, as is understanding the molecular mechanisms that govern the GSCs. Transforming growth factor beta (TGF beta) signalling and reactive oxygen species (ROS) production are known to govern and regulate cancer stem cell biology. Among the differentially expressed genes regulated by TGF beta in a transcriptomic analysis of two different patient-derived GSCs, we found NADPH oxidase 4 (NOX4) as one of the top upregulated genes. Interestingly, when patient tissues were analysed, NOX4 expression was found to be higher in GSCs versus differentiated cells. A functional analysis of the role of NOX4 downstream of TGF beta in several patient-derived GSCs showed that TGF beta does indeed induce NOX4 expression and increases ROS production in a NOX4-dependent manner. NOX4 downstream of TGF beta regulates GSC proliferation, and NOX4 expression is necessary for TGF beta-induced expression of stem cell markers and of the transcription factor nuclear factor erythroid 2-related factor 2 (NRF2), which in turn controls the cell's antioxidant and metabolic responses. Interestingly, overexpression of NOX4 recapitulates the effects induced by TGF beta in GSCs: enhanced proliferation, stemness and NRF2 expression. In conclusion, this work functionally establishes NOX4 as a key mediator of GSC biology.

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  • 8.
    Gupta, Ankit
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Harrison, Philip J
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Rietdijk, Jonne
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Georgiev, Polina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Is brightfield all you need for MoA prediction?2022Conference paper (Refereed)
    Abstract [en]

    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.

  • 9.
    Hansel, Catherine S.
    et al.
    Karolinska Institutet/SciLifeLab.
    Yousefian, Schayan
    German Cancer Research Centre (DKFZ).
    Klemm, Anna H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    A workflow for high-throughput screening, data analysis, processing, and hit identification2020Report (Other (popular science, discussion, etc.))
  • 10.
    Harrison, Philip J.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Rietdijk, Jonne
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Holmberg, David
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Exploring the evolution of cellular morphological changes after drug administration based on brightfield image dataManuscript (preprint) (Other academic)
    Abstract [en]

    Most image based studies of the morphological effects of compound treatments on cells, such as those for elucidating a compound's mechanism of action, use fixed-cell based approaches whereby the cells are fixated, stained, and imaged with fluorescence microscopy some time after compound administration. This snapshot data, however, cannot uncover any information on the temporal dynamics of the induced morphological changes. For instance regarding the rate at which these changes occur following compound perturbation. Live-cell compatible dyes can be used although are limited by technical difficulties, cytotoxicity and photobleaching. A simpler, cheaper and less harmful option is to use brightfield microscopy. Although brightfield images have less contrast than fluorescence images and cannot separate out the different cellular compartments, we here show that for compounds inducing morphological changes on cells, brightfield data, together with convolutional neural networks and feature projection techniques, can be used to extract such temporal information from time-lapse experiments.

  • 11.
    Harrison, Philip John
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gupta, Ankit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Rietdijk, Jonne
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Georgiev, Polina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Evaluating the utility of brightfield image data for mechanism of action prediction2023In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 19, no 7, article id e1011323Article in journal (Refereed)
    Abstract [en]

    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, 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 largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.

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  • 12.
    Hühn, Daniela
    et al.
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Martí‐Rodrigo, Pablo
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Mouron, Silvana
    Breast Cancer Clinical Research Unit Spanish National Cancer Research Centre (CNIO) Madrid Spain.
    Hansel, Catherine
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Tschapalda, Kirsten
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Porebski, Bartlomiej
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Häggblad, Maria
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Lidemalm, Louise
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Quintela‐Fandino, Miguel
    Breast Cancer Clinical Research Unit Spanish National Cancer Research Centre (CNIO) Madrid Spain.
    Carreras-Puigvert, Jordi
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden.
    Fernandez‐Capetillo, Oscar
    Science for Life Laboratory Division of Genome Biology Department of Medical Biochemistry and Biophysics Karolinska Institute Stockholm Sweden;Genomic Instability Group Spanish National Cancer Research Centre (CNIO) Madrid Spain.
    Prolonged estrogen deprivation triggers a broad immunosuppressive phenotype in breast cancer cells2021In: Molecular Oncology, ISSN 1574-7891, E-ISSN 1878-0261, Vol. 16, no 1, p. 148-165Article in journal (Refereed)
    Abstract [en]

    Among others, expression levels of programmed cell death 1 ligand 1 (PD-L1) have been explored as biomarkers of the response to immune checkpoint inhibitors in cancer therapy. Here, we present the results of a chemical screen that interrogated how medically approved drugs influence PD-L1 expression. As expected, corticosteroids and inhibitors of Janus kinases were among the top PD-L1 downregulators. In addition, we identified that PD-L1 expression is induced by antiestrogenic compounds. Transcriptomic analyses indicate that chronic estrogen receptor alpha (ER alpha) inhibition triggers a broad immunosuppressive program in ER-positive breast cancer cells, which is subsequent to their growth arrest and involves the activation of multiple immune checkpoints together with the silencing of the antigen-presenting machinery. Accordingly, estrogen-deprived MCF7 cells are resistant to T-cell-mediated cell killing, in a manner that is independent of PD-L1, but which is reverted by estradiol. Our study reveals that while antiestrogen therapies efficiently limit the growth of ER-positive breast cancer cells, they concomitantly trigger a transcriptional program that favors their immune evasion.

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  • 13.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carreras-Puigvert, Jordi
    Karolinska Inst, Dept Med Biochem & Biophys, Div Translat Med & Chem Biol, Stockholm, Sweden; ] Sci Life Lab, Stockholm, Sweden .
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    PopulationProfiler: A Tool for Population Analysis and Visualization of Image-Based Cell Screening Data2016In: PLOS ONE, E-ISSN 1932-6203, Vol. 11, no 3, article id e0151554Article in journal (Refereed)
    Abstract [en]

    Image-based screening typically produces quantitative measurements of cell appearance. Large-scale screens involving tens of thousands of images, each containing hundreds of cells described by hundreds of measurements, result in overwhelming amounts of data. Reducing per-cell measurements to the averages across the image(s) for each treatment leads to loss of potentially valuable information on population variability. We present PopulationProfiler-a new software tool that reduces per-cell measurements to population statistics. The software imports measurements from a simple text file, visualizes population distributions in a compact and comprehensive way, and can create gates for subpopulation classes based on control samples. We validate the tool by showing how PopulationProfiler can be used to analyze the effect of drugs that disturb the cell cycle, and compare the results to those obtained with flow cytometry.

  • 14. Ouyang, Wei
    et al.
    Bowman, Richard W
    Wang, Haoran
    Bumke, Kaspar E
    Collins, Joel T
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Diederich, Benedict
    An Open-Source Modular Framework for Automated Pipetting and Imaging Applications2022In: Advanced biology, ISSN 2701-0198, Vol. 6, no 4, article id 2101063Article in journal (Refereed)
    Abstract [en]

    The number of samples in biological experiments is continuously increasing, but complex protocols and human error 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 the lack of open application programming interfaces (APIs), they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, automated, high-throughput experiments are demonstrated 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. This 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 the analysis of previously acquired images, enables the realization of fully autonomous "smart" microscopy experiments. All documents and source files are publicly available to prove the concept of smart lab automation using inexpensive, open tools. It is believed this democratizes access to the power and repeatability of automated experiments.

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  • 15.
    Rietdijk, Jonne
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Aggarwal, Tanya
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Georgiev, Polina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects2022In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 832, article id 155058Article in journal (Refereed)
    Abstract [en]

    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 mor-phological 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 in the ER, Golgi apparatus, nucleoli and cy-toplasmic 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 infor-mation content make Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare.

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  • 16.
    Rietdijk, Jonne
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Tampere, Marianna
    Karolinska Inst, Dept Oncol & Pathol, SE-17176 Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, SE-17176 Stockholm, Sweden.;Natl Vet Inst, SE-75651 Uppsala, Sweden..
    Pettke, Aleksandra
    Karolinska Inst, Dept Oncol & Pathol, SE-17176 Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, SE-17176 Stockholm, Sweden..
    Georgiev, Polina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Warpman-Berglund, Ulrika
    Karolinska Inst, Dept Oncol & Pathol, SE-17176 Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, SE-17176 Stockholm, Sweden..
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Puumalainen, Marjo-Riitta
    Karolinska Inst, Dept Oncol & Pathol, SE-17176 Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, SE-17176 Stockholm, Sweden..
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    A phenomics approach for antiviral drug discovery2021In: BMC Biology, E-ISSN 1741-7007, Vol. 19, article id 156Article in journal (Refereed)
    Abstract [en]

    Background: The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections.

    Results: We combine 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. We show that this 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.

    Conclusions: The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.

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  • 17.
    Seal, Srijit
    et al.
    Broad Inst MIT & Harvard, Imaging Platform, Cambridge, MA 02142 USA.;Univ Cambridge, Yusuf Hamied Dept Chem, Cambridge CB2 1EW, England..
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Singh, Shantanu
    Broad Inst MIT & Harvard, Imaging Platform, Cambridge, MA 02142 USA..
    Carpenter, Anne E.
    Broad Inst MIT & Harvard, Imaging Platform, Cambridge, MA 02142 USA..
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Bender, Andreas
    Univ Cambridge, Yusuf Hamied Dept Chem, Cambridge CB2 1EW, England..
    From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability2024In: Molecular Biology of the Cell, ISSN 1059-1524, E-ISSN 1939-4586, Vol. 35, no 3, article id mr2Article in journal (Refereed)
    Abstract [en]

    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 extracted from classical software such as CellProfiler are based on statistical calculations and often not readily biologically interpretable. In this study, we propose a new feature space, which we call BioMorph, that maps these Cell Painting features with 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. Overall, BioMorph space offers a biologically relevant way to interpret the cell morphological features derived using software such as CellProfiler and to generate hypotheses for experimental validation.

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  • 18.
    Seal, Srijit
    et al.
    Univ Cambridge, Yusuf Hamied Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England..
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Trapotsi, Maria-Anna
    Univ Cambridge, Yusuf Hamied Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England..
    Yang, Hongbin
    Univ Cambridge, Yusuf Hamied Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England..
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Bender, Andreas
    Univ Cambridge, Yusuf Hamied Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England..
    Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection2022In: Communications Biology, E-ISSN 2399-3642, Vol. 5, article id 858Article in journal (Refereed)
    Abstract [en]

    Cell Painting, gene expression, and chemical structural data are used to examine the differences between mitochondrial toxicants and non-toxicants and enhance the detection of mitotoxic compounds for future drug discovery. 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.

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  • 19.
    Seal, Srijit
    et al.
    Univ Cambridge, Yusuf Hamied Dept Chem, Cambridge, England..
    Yang, Hongbin
    Univ Cambridge, Yusuf Hamied Dept Chem, Cambridge, England..
    Trapotsi, Maria-Anna
    Univ Cambridge, Yusuf Hamied Dept Chem, Cambridge, England..
    Singh, Satvik
    Univ Cambridge, Dept Appl Math & Theoret Phys DAMTP, Cambridge, England..
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Bender, Andreas
    Univ Cambridge, Yusuf Hamied Dept Chem, Cambridge, England..
    Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data2023In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 15, no 1, article id 56Article in journal (Refereed)
    Abstract [en]

    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.

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