Biology subjects

Doan, M.

Publications and source records attributed to Doan, M..

5 recordsLinked to original sources

bioRxiv2021

Discovery of small molecule pathway regulators by image profile matching

Identifying chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual profile-based screening approach instead identifies compounds that match to pathways based on phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes, and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds. One sentence summaryIf a genetic perturbation impacts cell morphology, a computational query can reveal compounds whose morphology "matches".

bioinformatics↗

bioRxiv2021

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25 - 5.0 {micro}g/mL) and/or carbendazim (0.8 - 1.6 {micro}g/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for mononucleates, binucleates, mononucleates with MN and binucleates with MN, respectively. Successful classifications of trinucleates (90%) and tetranucleates (88%) in addition to other or unscorable phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful ([≤] 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent dose regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

pharmacology and toxicology↗

bioRxiv2020

Quantitative sub-cellular acyl-CoA analysis reveals distinct nuclear regulation

Quantitative sub-cellular metabolomic measurements can yield crucial insights into the roles of metabolites in cellular processes, but are subject to multiple confounding factors. We developed Stable Isotope Labeling of Essential nutrients in cell Culture - Sub-cellular Fractionation (SILEC-SF), which uses isotope labeled internal standard controls that are present throughout fractionation and processing to quantify acyl-Coenzyme A thioesters in sub-cellular compartments by liquid chromatography-mass spectrometry. We tested SILEC-SF in a range of sample types and examined the compartmentalized responses to oxygen tension, cellular differentiation, and nutrient availability. Application of SILEC-SF to the challenging analysis of the nuclear compartment revealed a nuclear acyl-CoA profile distinct from that of the cytosol, with notable nuclear enrichment of propionyl-CoA. Using isotope tracing we identified the branched chain amino acid (BCAA) isoleucine as a major metabolic source of nuclear propionyl-CoA and histone propionylation, thus revealing a new mechanism of crosstalk between metabolism and the epigenome.

biochemistry↗

bioRxiv2018

Label-free assessment of red blood cell storage lesions by deep learning

Blood transfusion is a life-saving clinical procedure. With millions of units needed globally each year, it is a growing concern to improve product quality and recipient outcomes.\n\nStored red blood cells (RBCs) undergo continuous degradation, leading to structural and biochemical changes. To analyze RBC storage lesions, complex biochemical and biophysical assays are often employed.\n\nWe demonstrate that label-free imaging flow cytometry and deep learning can characterize RBC morphologies during 42-day storage, replacing the current practice of manually quantifying a blood smear from stored blood units. Based only on bright field and dark field images, our model achieved 90% accuracy in classifying six different RBC morphologies associated with storage lesions versus human-curated manual examination. A model fitted to the deep learning-extracted features revealed a pattern of morphological changes within the aging blood unit that allowed predicting the expiration date of stored blood using solely morphological assessment.\n\nDeep learning and label-free imaging flow cytometry could therefore be applied to reduce complex laboratory procedures and facilitate robust and objective characterization of blood samples.

bioinformatics↗

bioRxiv2018

Artefactual formation of pyruvate from in-source conversion of lactate

RationaleLactate and pyruvate are high abundance products of glucose metabolism. Analysis of both molecules as part metabolomics studies in cellular metabolism and physiology have been aided by advances in liquid chromatography-mass spectrometry (LC-MS).\n\nMethodsWe used ion pairing-chromatography and negative ion mode ESI on an QExactive HF to perform stable isotope assisted metabolomics profiling of lactate and pyruvate metabolism.\n\nResultsUsing an LC-MS method for polar metabolite analysis we discovered an artefactual formation of pyruvate from in-source fragmentation of lactate. Surprisingly, this in-source fragmentation has not been previously described, thus we report this identification to warn other investigators. This artefact was detected by baseline chromatographic resolution of lactate and pyruvate by LC with confirmation of this artefact by stable isotope labeling of lactate and pyruvate.\n\nConclusionsThese findings have immediate implications for metabolomics studies by LC-MS and direct infusion MS, especially in negative ion mode, whereby users should resolve lactate from pyruvate or robustly quantify the potential formation of pyruvate from higher abundance lactate in their assays.

biochemistry↗