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Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic. Image 1 • Single cell metabolomics was incorporated with machine learning models to predict different degrees of drug resistance. • Rapid and reliable predictions were achieved in the testing set and validation set. • Our proof-of-concept research demonstrated potentials towards future point-of-care diagnosis. [ABSTRACT FROM AUTHOR]

Irinotecan (Iri) is a key drug to treat metastatic colorectal cancer, but its clinical activity is often limited by de novo and acquired drug resistance. Studying the underlying mechanisms of drug resistance is necessary for developing novel therapeutic strategies. In this study, we used both regular and irinotecan-resistant (Iri-resistant) colorectal cell lines as models, and performed single cell mass spectrometry (SCMS) metabolomics studies combined with analyses from cytotoxicity assay, western blot, flow cytometry, quantitative real-time polymerase chain reaction (qPCR), and reactive oxygen species (ROS). Our SCMS results indicate that Iri-resistant cancer cells possess higher levels of unsaturated lipids compared with the regular cancer cells. In addition, multiple protein biomarkers and their corresponding mRNAs of colon cancer stem cells are overexpressed in Iri-resistance cells. Particularly, stearoyl-CoA desaturase 1 (SCD1) is upregulated with the development of drug resistance in Iri-resistant cells, whereas inhibiting the activity of SCD1 efficiently increase their sensitivity to Iri treatment. In addition, we demonstrated that SCD1 directly regulates the expression of ALDH1A1, which contributes to the cancer stemness and ROS level in Iri-resistant cell lines. [Display omitted] • Live single cell mass spectrometry was combined with other bioanalytical techniques. • Cells' molecular profiles were correlated to their resistance to anticancer drug. • Metabolomic biomarkers and relevant proteins reflecting cancer cells' drug resistance were proposed. • Drug resistant cancer cells possess molecular features similar to cancer stem cells. [ABSTRACT FROM AUTHOR]

Irinotecan (IRI), a topoisomerase I inhibitor blocking DNA synthesis, is a widely used chemotherapy drug for metastatic colorectal cancer. Despite being an effective chemotherapy drug, its clinical effectiveness is limited by both intrinsic and acquired drug resistance. Previous studies indicate IRI induces cancer stemness in irinotecan-resistant (IRI-resistant) cells. Metformin, an oral antidiabetic drug, was recently reported for anticancer effects, likely due to its selective killing of cancer stem cells (CSCs). Given IRI-resistant cells exhibiting high cancer stemness, we hypothesize metformin can sensitize IRI-resistant cells and rescue the therapeutic effect. In this work, we utilized the Single-probe mass spectrometry technique to analyze live IRI-resistant cells under different treatment conditions. We discovered that metformin treatment was associated with the downregulation of lipids and fatty acids, potentially through the inhibition of fatty acid synthase (FASN). Importantly, certain species can be only detected from cells in their living status. The level of synergistic effect of metformin and IRI in their co-treatment of IRI-resistant cells was evaluated using Chou-Talalay combinational index. Using enzymatic activity assay, we determined that the co-treatment exhibit the highest FASN inhibition compared with the mono-treatment of IRI or metformin. To our knowledge, this is the first single-cell MS metabolomics study demonstrating metformin-IRI synergistic effect overcoming drug resistance in IRI-resistant cells. [Display omitted] • Ambient single cell mass spectrometry was used to study live cells. • Other analytical techniques (e.g., cytotoxicity assay, enzymatic activity assay, and HPLC/MS) were used to provide complementary information. • Drug-resistant cancer cells' viabilities and molecular profiles were affected by antidiabetic drug metformin. • Unique lipids and metabolites were observed only in live single cells. • Inhibition of fatty acid synthase (FASN) is a potential mechanism related to metformin treatment of drug-resistant cancer cells. [ABSTRACT FROM AUTHOR]

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research. Image 1 • Live single cell metabolomics was performed using MS and comprehensive data analysis. • Cells' metabolomic response to anticancer drugs was investigated. • Phenotypic biomarkers reflecting drug treatment were discovered and identified. • Biological pathways related to drug treatment were revealed at the single cell level. [ABSTRACT FROM AUTHOR]

Ion exchange (IE)-vapor generation (VG)-inductively coupled plasma (ICP)-mass spectrometry (MS) method has been employed to simultaneously determine trace amounts of As, Se, and Hg in acidic conditions. Before hydride generation, anion-exchange column was used to separate the analytes from the matrix. Effects of sample solution acidity, eluant conditions and concentrating time were investigated and optimized. The method sensitivity was improved, as well as the ability to refrain interference caused by chloride, mental ions and other hydride-forming elements compared with (CF)VG-ICP-MS method. Limits of detection (3σ, n = 10) for As, Se, and Hg were As, 0.0021 ng/ml; Se, 0.0022 ng/ml; Hg, 0.0007 ng/ml, respectively; and recovery values for interference experiments were between 95.6 and 100.3%. The developed method was applied to four standard biological and geological materials, and the determined results of As, Se, and Hg were consistent with the certified values. [Copyright &y& Elsevier]

A newly developed hydride generation–inductively coupled plasma–mass spectrometry (HG–ICP–MS) system was employed to determine trace amounts of lead in geological and biological samples. Laboratory-made single-stage and double-stage gas–liquid separators (GLSs) were investigated in order to replace the consumable membrane-GLS. Possible reasons were given why double-stage GLSs were superior over the single-stage ones according to the factors such as GLS volume and configuration, carrier gas inlet mode and flow rate. Interferences in liquid and vapor phase from concomitant ions and their products were investigated employing different flow mani-folds. Memory effects contributed to the blank values, but could be reduced employing a special wash protocol. Internal and external standardization were combined to improve the accuracy of the method, with bismuth as the internal standard according to its similarity with lead in the HG–ICP–MS system. Compared with ICP–MS and hydride generation–atomic fluorescence spectrometry (HG–AFS) methods, the system performance of HG–ICP–MS was characterized with improved detection limit to 0.002 ng/ml and acceptable short- and long-term stabilities. The linear dynamic range of this method was up to 50 ng/ml lead. Three Chinese national certified reference materials: poplar leaves, human hair and copper ore, were analyzed for method validation, and the results agreed well with the certified values. At last, the method was also employed to determine wide range of lead concentrations in lightweight limestone and nervous tissue samples from infants of albino rats with recoveries between 95 and 105% (n=10). [Copyright &y& Elsevier]