Single cell mass spectrometry studies reveal metabolomic features and potential mechanisms of drug-resistant cancer cell lines

Single-Cell Mass Spectrometry Studies of Secondary Drug Resistance of Tumor Cells

Patients with epidermal growth factor receptor mutant nonsmall cell lung cancer (NSCLC) often fail to treat gefitinib because of secondary drug resistance. The development of tumor drug resistance is closely related to variations in cancer cell metabolism. Single-cell metabolomics analysis can provide unique information about tumor drug resistance. Herein, we constructed a platform to study the secondary resistance of tumor cells based on single-cell metabolomics (sSRTC-scM). A gefitinib-resistant NSCLC cell line (PC9GR) was constructed by increasing the dose step by step. The metabolic profiles of parental PC9 cells and PC9GR cells with different drug resistance levels were detected by intact living-cell electrolaunching ionization mass spectrometry at the single-cell level. The data were analyzed by statistical methods such as t-SNE, variance, volcano plot, heat map, and metabolic pathway analysis. Using this platform, we found that the metabolic fingerprints of PC9GR cells can evaluate drug resistance degrees. The metabolic fingerprints continue to be altered with the increase of drug resistance. We revealed 19 metabolic markers of secondary resistance by variance analysis and clarified that the glycerophospholipid metabolic pathway of PC9GR cells changed significantly. In addition, we found that with the increase in drug resistance levels, the heterogeneity of single-cell metabolism became greater and the number of cells with weak drug resistance gradually decreased. This phenomenon can be utilized to illustrate the drug resistance degrees of PC9GR cells. This study provides diagnostic markers for evaluating the drug resistance of tumors and gives new insight into overcoming the secondary resistance of tumors.

MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells

Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastatic phenotypes. Our results show that compared with standard transfer learning models, MetaPhenotype can rapidly predict and achieve a high accuracy of over 90% with fewer new training samples. Overall, our work opens the possibility of accurate cell phenotype classification based on fewer SCMS samples, thus lowering the demand for sample acquisition.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Artificial intelligence-enabled multipurpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics

An active-matrix electrowetting-on-dielectric (AM-EWOD) system integrates hundreds of thousands of active electrodes for sample droplet manipulation, which can enable simultaneous, automatic, and parallel on-chip biochemical reactions. A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps. In this work, we demonstrated an artificial intelligence (AI)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks. We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods. We used the YOLOv8 model to monitor the droplet-splitting process online. A 97.76% splitting success rate was observed with 18 different AM-EWOD chips. A 99.982% model precision rate and a 99.980% model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets. Compared with manual verification, the model achieved 99.260% and 99.193% precision and recall rates, respectively. In addition, single-cell droplet sorting and routing experiments were demonstrated. With an AI-based smart detection system, AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.

Proteomics Analysis of Interactions between Drug-Resistant and Drug-Sensitive Cancer Cells: Comparative Studies of Monoculture and Coculture Cell Systems

Cell-cell interactions, which allow cells to communicate with each other through molecules in their microenvironment, are critical for the growth, health, and functions of cells. Previous studies show that drug-resistant cells can interact with drug-sensitive cells to elevate their drug resistance level, which is partially responsible for cancer recurrence. Studying protein targets and pathways involved in cell-cell communication provides essential information for fundamental cell biology studies and therapeutics of human diseases. In the current studies, we performed direct coculture and indirect coculture of drug-resistant and drug-sensitive cell lines, aiming to investigate intracellular proteins responsible for cell communication. Comparative studies were carried out using monoculture cells. Shotgun bottom-up proteomics results indicate that the P53 signaling pathway has a strong association with drug resistance mechanisms, and multiple TP53-related proteins were upregulated in both direct and indirect coculture systems. In addition, cell-cell communication pathways, including the phagosome and the HIF-signaling pathway, contribute to both direct and indirect coculture systems. Consequently, AK3 and H3-3A proteins were identified as potential targets for cell-cell interactions that are relevant to drug resistance mechanisms. We propose that the P53 signaling pathway, in which mitochondrial proteins play an important role, is responsible for inducing drug resistance through communication between drug-resistant and drug-sensitive cancer cells.

Single Cell mass spectrometry: Towards quantification of small molecules in individual cells

Studying cell heterogeneity can provide a deeper understanding of biological activities, but appropriate studies cannot be performed using traditional bulk analysis methods. The development of diverse single cell bioanalysis methods is in urgent need and of great significance. Mass spectrometry (MS) has been recognized as a powerful technique for bioanalysis for its high sensitivity, wide applicability, label-free detection, and capability for quantitative analysis. In this review, the general development of single cell mass spectrometry (SCMS) field is covered. First, multiple existing SCMS techniques are described and compared. Next, the development of SCMS field is discussed in a chronological order. Last, the latest quantification studies on small molecules using SCMS have been described in detail.

Differential lipid analysis of oxaliplatin-sensitive and resistant HCT116 cells reveals different levels of drug-induced lipid droplet formation

Lipid droplets (LDs) are intracellular storage vesicles composed of a neutral lipid core surrounded by a glycerophospholipid membrane. LD accumulation is associated with different stages of cancer progression and stress responses resulting from chemotherapy. In previous work, a novel dual nano-electrospray ionization source and data-dependent acquisition method for measuring the relative abundances of lipid species between two extracts were described and validated. Here, this same source and method were used to determine if oxaliplatin-sensitive and resistant cells undergo similar lipid profile changes, with the goal of identifying potential signatures that could predict the effectiveness of an oxaliplatin-containing treatment. Oxaliplatin is commonly used in the treatment of colorectal cancer. When compared to a no-drug control, oxaliplatin dosing caused significant increases in triglyceride (TG) and cholesterol ester (CE) species. These increases were more pronounced in the oxaliplatin-sensitive cells than in oxaliplatin-resistant cells. The increased neutral lipid abundance correlated with LD formation, as confirmed by confocal micrographs of Nile Red-stained cells. Untargeted proteomic analyses also support LD formation after oxaliplatin treatment, with an increased abundance of LD-associated proteins in both the sensitive and resistant cells.

Advances in regulation and function of stearoyl-CoA desaturase 1 in cancer, from bench to bed

Stearoyl-CoA desaturase 1 (SCD1) converts saturated fatty acids to monounsaturated fatty acids. The expression of SCD1 is increased in many cancers, and the altered expression contributes to the proliferation, invasion, sternness and chemoresistance of cancer cells. Recently, more evidence has been reported to further support the important role of SCD1 in cancer, and the regulation mechanism of SCD1 has also been focused. Multiple factors are involved in the regulation of SCD1, including metabolism, diet, tumor microenvironment, transcription factors, non-coding RNAs, and epigenetics modification. Moreover, SCD1 is found to be involved in regulating ferroptosis resistance. Based on these findings, SCD1 has been considered as a potential target for cancer treatment. However, the resistance of SCD1 inhibition may occur in certain tumors due to tumor heterogeneity and metabolic plasticity. This review summarizes recent advances in the regulation and function of SCD1 in tumors and discusses the potential clinical application of targeting SCD1 for cancer treatment.

Detection, mechanisms, and therapeutic implications of oncometabolites

Metabolic abnormalities are a hallmark of cancer cells and are essential to tumor progression. Oncometabolites have pleiotropic effects on cancer biology and affect a plethora of processes, from oncogenesis and metabolism to therapeutic resistance. Targeting oncometabolites, therefore, could offer promising therapeutic avenues against tumor growth and resistance to treatments. Recent advances in characterizing the metabolic profiles of cancer cells are shedding light on the underlying mechanisms and associated metabolic networks. This review summarizes the diverse detection methods, molecular mechanisms, and therapeutic targets of oncometabolites, which may lead to targeting oncometabolism for cancer therapy.

Toward a Unifying Hypothesis for Redesigned Lipid Catabolism as a Clinical Target in Advanced, Treatment-Resistant Carcinomas

We review extensive progress from the cancer metabolism community in understanding the specific properties of lipid metabolism as it is redesigned in advanced carcinomas. This redesigned lipid metabolism allows affected carcinomas to make enhanced catabolic use of lipids in ways that are regulated by oxygen availability and is implicated as a primary source of resistance to diverse treatment approaches. This oxygen control permits lipid catabolism to be an effective energy/reducing potential source under the relatively hypoxic conditions of the carcinoma microenvironment and to do so without intolerable redox side effects. The resulting robust access to energy and reduced potential apparently allow carcinoma cells to better survive and recover from therapeutic trauma. We surveyed the essential features of this advanced carcinoma-specific lipid catabolism in the context of treatment resistance and explored a provisional unifying hypothesis. This hypothesis is robustly supported by substantial preclinical and clinical evidence. This approach identifies plausible routes to the clinical targeting of many or most sources of carcinoma treatment resistance, including the application of existing FDA-approved agents.

Stearoyl-CoA Desaturase 1 as a Therapeutic Biomarker: Focusing on Cancer Stem Cells

The dysregulation of lipid metabolism and alterations in the ratio of monounsaturated fatty acids (MUFAs) to saturated fatty acids (SFAs) have been implicated in cancer progression and stemness. Stearoyl-CoA desaturase 1 (SCD1), an enzyme involved in lipid desaturation, is crucial in regulating this ratio and has been identified as an important regulator of cancer cell survival and progression. SCD1 converts SFAs into MUFAs and is important for maintaining membrane fluidity, cellular signaling, and gene expression. Many malignancies, including cancer stem cells, have been reported to exhibit high expression of SCD1. Therefore, targeting SCD1 may provide a novel therapeutic strategy for cancer treatment. In addition, the involvement of SCD1 in cancer stem cells has been observed in various types of cancer. Some natural products have the potential to inhibit SCD1 expression/activity, thereby suppressing cancer cell survival and self-renewal activity.

FACS-assisted single-cell lipidome analysis of phosphatidylcholines and sphingomyelins in cells of different lineages

Recent advances in single-cell genomics and transcriptomics technologies have transformed our understanding of cellular heterogeneity in growth, development, ageing, and disease; however, methods for single-cell lipidomics have comparatively lagged behind in development. We have developed a method for the detection and quantification of a wide range of phosphatidylcholine and sphingomyelin species from single cells that combines fluorescence-assisted cell sorting with automated chip-based nanoESI and shotgun lipidomics. We show herein that our method is capable of quantifying more than 50 different phosphatidylcholine and sphingomyelin species from single cells and can easily distinguish between cells of different lineages or cells treated with exogenous fatty acids. Moreover, our method can detect more subtle differences in the lipidome between cell lines of the same cancer type. Our approach can be run in parallel with other single-cell technologies to deliver near-complete, high-throughput multi-omics data on cells with a similar phenotype and has the capacity to significantly advance our current knowledge on cellular heterogeneity.

Sánchez B, León C, Nozal L, Mora-Rodríguez JM, Castro F, Crego AL, Díaz-Laviada I. Metabolic fingerprinting of chemotherapy-resistant prostate cancer stem cells. An untargeted metabolomic approach by liquid chromatography-mass spectrometry

Chemoresistance is one of the most important challenges in cancer therapy. The presence of cancer stem cells within the tumor may contribute to chemotherapy resistance since these cells express high levels of extrusion pumps and xenobiotic metabolizing enzymes that inactivate the therapeutic drug. Despite the recent advances in cancer cell metabolism adaptations, little is known about the metabolic adaptations of the cancer stem cells resistant to chemotherapy. In this study, we have undertaken an untargeted metabolomic analysis by liquid chromatography–high-resolution spectrometry combined with cytotoxicity assay, western blot, quantitative real-time polymerase chain reaction (qPCR), and fatty acid oxidation in a prostate cancer cell line resistant to the antiandrogen 2-hydroxiflutamide with features of cancer stem cells, compared to its parental androgen-sensitive cell line. Metabolic fingerprinting revealed 106 out of the 850 metabolites in ESI+ and 67 out of 446 in ESI- with significant differences between the sensitive and the resistant cell lines. Pathway analysis performed with the unequivocally identified metabolites, revealed changes in pathways involved in energy metabolism as well as posttranscriptional regulation. Validation by enzyme expression analysis indicated that the chemotherapy-resistant prostate cancer stem cells were metabolically dormant with decreased fatty acid oxidation, methionine metabolism and ADP-ribosylation. Our results shed light on the pathways underlying the entry of cancer cells into dormancy that might contribute to the mechanisms of drug resistance.