Multi-omics Prediction from High-content Cellular Imaging with Deep Learning

High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function. However, the biological determinants through which changes in multi-omics measurements influence cellular morphology have not yet been systematically explored, and the degree to which cell imaging could potentially enable the prediction of multi-omics directly from cell imaging data is therefore currently unclear. Here, we address the question of whether it is possible to predict bulk multi-omics measurements directly from cell images using Image2Omics -- a deep learning approach that predicts multi-omics in a cell population directly from high-content images stained with multiplexed fluorescent dyes. We perform an experimental evaluation in gene-edited macrophages derived from human induced pluripotent stem cell (hiPSC) under multiple stimulation conditions and demonstrate that Image2Omics achieves significantly better performance in predicting transcriptomics and proteomics measurements directly from cell images than predictors based on the mean observed training set abundance. We observed significant predictability of abundances for 5903 (22.43%; 95% CI: 8.77%, 38.88%) and 5819 (22.11%; 95% CI: 10.40%, 38.08%) transcripts out of 26137 in M1 and M2-stimulated macrophages respectively and for 1933 (38.77%; 95% CI: 36.94%, 39.85%) and 2055 (41.22%; 95% CI: 39.31%, 42.42%) proteins out of 4986 in M1 and M2-stimulated macrophages respectively. Our results show that some transcript and protein abundances are predictable from cell imaging and that cell imaging may potentially, in some settings and depending on the mechanisms of interest and desired performance threshold, even be a scalable and resource-efficient substitute for multi-omics measurements

Integrating metabolome dynamics and process data to guide cell line selection in biopharmaceutical process development

The successful development of mammalian cell culture for the production of therapeutic antibodies is a resource-intensive and multistage process which requires the selection of high performing and stable cell lines at different scale-up stages. Accordingly, science-based approaches exploiting biological information, such as metabolomics, can support and accelerate the selection of promising cell lines to progress. In fact, the integration of dynamic biological information with process data can provide valuable insights on the cell physiological changes as a consequence of the cultivation process. This work studies the industrial development of monoclonal antibodies at micro-bioreactor scale (Ambr®15) and aims at accelerating the selection of the better performing cell lines. To that end, we apply a machine learning approach to integrate time-varying process and biological information (i.e., metabolomics), explicitly exploiting their dynamics. Strikingly, cell line performance during the cultivation can be predicted from early process timepoints by exploiting the gradual temporal evolution of metabolic phenotypes. Furthermore, product titer is estimated with good accuracy at late process timepoints, providing insights into its relationship with underlying metabolic mechanisms and enabling the identification of biomarkers to be further investigated. The biological insights obtained through the proposed machine learning approach provide data-driven metabolic understanding allowing early identification of high performing cell lines. Additionally, this analysis offers the opportunity to identify key metabolites which could be used as biomarkers for industrially relevant phenotypes and onward fit into our commercial manufacturing platforms

Structural characterization of in vitro metabolites of the new anticancer agent EAPB0503 by liquid chromatography–tandem mass spectrometry

International audienceEAPB0503, belonging to the imidazo[1,2-a]quinoxaline series, is an anticancer drug with antitumoral activity against a variety of tumors. Previous studies have shown that this drug undergoes demethylation and oxygenation reactions. In this paper, liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) was employed to assess the structures of unknown oxygenated metabolites of EAPB0503. EAPB0503 and its identified demethylated metabolites, EAPB0502 and EAPB0603, were incubated with human, rat, dog and mouse liver microsomes, as well as human, rat and dog hepatocytes. After separation on a C8 analytical column with a gradient elution of acetonitrile-formate buffer, positive ESI-MS/MS experiments were performed. To facilitate metabolite identification, the detailed fragmentation pathways of the parent compounds were first studied using high-resolution MS/MS. Additional hydrogen/deuterium exchange LC-MS/MS experiments were used to support the identification and structural characterization of metabolites. Four hydroxylated metabolites were identified: M'4 and its demethylated derivative M'1 (OH in ortho position on the phenyl substituent in position 1), and M'6 and its demethylated derivative M'3 (OH on the imidazole ring at the C2 position). Three phase II metabolites (Met A, EAPB0602 glucuronide; Met B, M'4 glucuronide; Met C, EAPB0603 glucuronide) were also evidenced. Elucidation of the metabolite structures was performed by comparing the chromatographic behaviors (changes in retention times), by measuring the molecular masses (mass increment), by studying the MS(2) spectral patterns of metabolites with those of parent drugs and for M'1 and M'4 by co-analysis with synthetic standards. The results of the present study provided important structural information relating to the metabolism of EAPB0503

New imidazoquinoxaline derivatives: Synthesis, biological evaluation on melanoma, effect on tubulin polymerization and structure–activity relationships

International audienceMicrotubules are considered as important targets of anticancer therapy. EAPB0503 and its structural imidazo[1,2-a]quinoxaline derivatives are major microtubule-interfering agents with potent anticancer activity. In this study, the synthesis of several new derivatives of EAPB0503 is described, and the anticancer efficacy of 13 novel derivatives on A375 human melanoma cell line is reported. All new compounds show significant antiproliferative activity with IC50 in the range of 0.077-122μM against human melanoma cell line (A375). Direct inhibition of tubulin polymerization assay in vitro is also assessed. Results show that compounds 6b, 6e, 6g, and EAPB0503 highly inhibit tubulin polymerization with percentages of inhibition of 99%, 98%, 90%, and 84% respectively. Structure-activity relationship studies within the series are also discussed in line with molecular docking studies into the colchicine-binding site of tubulin

A Modular Probe Strategy for Drug Localization, Target Identification and Target Occupancy Measurement on Single Cell Level

Late stage failures of candidate drug molecules are frequently caused by off-target effects or inefficient target engagement <i>in vivo</i>. In order to address these fundamental challenges in drug discovery, we developed a modular probe strategy based on bioorthogonal chemistry that enables the attachment of multiple reporters to the same probe in cell extracts and live cells. In a systematic evaluation, we identified the inverse electron demand Diels–Alder reaction between <i>trans</i>-cyclooctene labeled probe molecules and tetrazine-tagged reporters to be the most efficient bioorthogonal reaction for this strategy. Bioorthogonal biotinylation of the probe allows the identification of drug targets in a chemoproteomics competition binding assay using quantitative mass spectrometry. Attachment of a fluorescent reporter enables monitoring of spatial localization of probes as well as drug-target colocalization studies. Finally, direct target occupancy of unlabeled drugs can be determined at single cell resolution by competitive binding with fluorescently labeled probe molecules. The feasibility of the modular probe strategy is demonstrated with noncovalent PARP inhibitors

A Modular Probe Strategy for Drug Localization, Target Identification and Target Occupancy Measurement on Single Cell Level

Late stage failures of candidate drug molecules are frequently caused by off-target effects or inefficient target engagement <i>in vivo</i>. In order to address these fundamental challenges in drug discovery, we developed a modular probe strategy based on bioorthogonal chemistry that enables the attachment of multiple reporters to the same probe in cell extracts and live cells. In a systematic evaluation, we identified the inverse electron demand Diels–Alder reaction between <i>trans</i>-cyclooctene labeled probe molecules and tetrazine-tagged reporters to be the most efficient bioorthogonal reaction for this strategy. Bioorthogonal biotinylation of the probe allows the identification of drug targets in a chemoproteomics competition binding assay using quantitative mass spectrometry. Attachment of a fluorescent reporter enables monitoring of spatial localization of probes as well as drug-target colocalization studies. Finally, direct target occupancy of unlabeled drugs can be determined at single cell resolution by competitive binding with fluorescently labeled probe molecules. The feasibility of the modular probe strategy is demonstrated with noncovalent PARP inhibitors

A Modular Probe Strategy for Drug Localization, Target Identification and Target Occupancy Measurement on Single Cell Level

Late stage failures of candidate drug molecules are frequently caused by off-target effects or inefficient target engagement <i>in vivo</i>. In order to address these fundamental challenges in drug discovery, we developed a modular probe strategy based on bioorthogonal chemistry that enables the attachment of multiple reporters to the same probe in cell extracts and live cells. In a systematic evaluation, we identified the inverse electron demand Diels–Alder reaction between <i>trans</i>-cyclooctene labeled probe molecules and tetrazine-tagged reporters to be the most efficient bioorthogonal reaction for this strategy. Bioorthogonal biotinylation of the probe allows the identification of drug targets in a chemoproteomics competition binding assay using quantitative mass spectrometry. Attachment of a fluorescent reporter enables monitoring of spatial localization of probes as well as drug-target colocalization studies. Finally, direct target occupancy of unlabeled drugs can be determined at single cell resolution by competitive binding with fluorescently labeled probe molecules. The feasibility of the modular probe strategy is demonstrated with noncovalent PARP inhibitors

Bioaccumulation of therapeutic drugs by human gut bacteria.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner

Bioaccumulation of therapeutic drugs by human gut bacteria

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping ofthe interactions between drugs and bacteria has only started recently' and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth ofthe bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine bindsto several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegansto duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner