Mannose Promotes Metabolic Discrimination of Osteosarcoma Cells at Single-Cell Level by Mass Spectrometry

Identification of microtubule-associated biomarker using machine learning methods in osteonecrosis of the femoral head and osteosarcoma

Background

This study aims to explore the microtubule-associated gene signatures and molecular processes shared by osteonecrosis of the femoral head (ONFH) and osteosarcoma (OS).

Methods

Datasets from the TARGET and GEO databases were subjected to bioinformatics analysis, including the functional enrichment analysis of genes shared by ONFH and OS. Prognostic genes were identified using univariate and multivariate Cox regression analyses to develop a risk score model for predicting overall survival and immune characteristics. Furthermore, LASSO and SVM-RFE algorithms identified biomarkers for ONFH, which were validated in OS. Function prediction, ceRNA network analysis, and gene-drug interaction network construction were subsequently conducted. Biomarker expression was then validated on clinical samples by using qPCR.

Results

A total of 14 microtubule-associated disease genes were detected in ONFH and OS. Subsequently, risk score model based on four genes was then created, revealing that patients with low-risk exhibited superior survival outcomes compared with those with high-risk. Notably, ONFH with low-risk profiles may manifest an antitumor immune microenvironment. Moreover, by utilizing LASSO and SVM-RFE algorithms, four diagnostic biomarkers were pinpointed, enabling effective discrimination between patients with ONFH and healthy individuals as well as between OS and normal tissues. Additionally, 21 drugs targeting these biomarkers were predicted, and a comprehensive ceRNA network comprising four mRNAs, 71 miRNAs, and 98 lncRNAs was established. The validation of biomarker expression in clinical samples through qPCR affirmed consistency with the results of bioinformatics analysis.

Conclusion

Microtubule-associated genes may play pivotal roles in OS and ONFH. Additionally, a prognostic model was constructed, and four genes were identified as potential biomarkers and therapeutic targets for both diseases.

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Microfluidic Sampling of Undissolved Components from Subcellular Regions of Living Single Cells for Mass Spectrometry Analysis

Precise sampling of undissolved chemical components from subcellular regions of living single cells is a prerequisite for their in-depth analysis, which could promote understanding of subtle early stage physiological or pathological processes. Here we report a microfluidic method to extract undissolved components from subcellular regions for MS analysis. The target single cell was isolated by the microchamber beneath the microfluidic probe and washed by the injected biocompatible isotonic glucose aqueous solution (IGAS). Then, the sampling solvent was injected to extract undissolved components from the expected subcellular region of the living single cell, where the position and size of the sampling region could be controlled. The components immobilized by undissolved cellular structures were proven to be successfully extracted. Since unextracted subcellular regions were protected by IGAS, the single cell could survive after a tiny part was extracted, providing the possibility of repetitive sampling of the same living cell. Phospholipids extracted from the subcellular regions were successfully identified. The results demonstrated the feasibility of our method for subcellular sampling and analysis.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

Biological function, regulatory mechanism, and clinical application of mannose in cancer

Studies examining the regulatory roles and clinical applications of monosaccharides other than glucose in cancer have been neglected. Mannose, a common type of monosaccharide found in human body fluids and tissues, primarily functions in protein glycosylation rather than carbohydrate metabolism. Recent research has demonstrated direct anticancer effects of mannose in vitro and in vivo. Simply supplementing cell culture medium or drinking water with mannose achieved these effects. Moreover, mannose enhances the effectiveness of current cancer treatments including chemotherapy, radiotherapy, targeted therapy, and immune therapy. Besides the advancements in basic research on the anticancer effects of mannose, recent studies have reported its application as a biomarker for cancer or in the delivery of anticancer drugs using mannose-modified drug delivery systems. This review discusses the progress made in understanding the regulatory roles of mannose in cancer progression, the mechanisms underlying its anticancer effects, and its current application in cancer diagnosis and treatment.

Dynamic metabolic change of cancer cells induced by natural killer cells at single-cell level studied by label-free mass cytometry

Natural killer cells (NK cells) are important immune cells which have attracted increasing attention in cancer immunotherapy. Due to the heterogeneity of cells, individual cancer cells show different resistance to NK cytotoxicity, which has been revealed by flow cytometry. Here we used label-free mass cytometry (CyESI-MS) as a new tool to analyze the metabolites in Human Hepatocellular Carcinoma (HepG2) cells at the single-cell level after the interaction with different numbers of NK92 MI cells. A large amount of chemical information from individual HepG2 cells was obtained showing the process of cell apoptosis induced by NK cells. Nineteen metabolites which consecutively change during cell apoptosis were revealed by calculating their average relative intensity. Four metabolic pathways were impacted during cell apoptosis which hit 4 metabolites including glutathione (GSH), creatine, glutamic acid and taurine. We found that the HepG2 cells could be divided into two phenotypes after co-culturing with NK cells according to the bimodal distribution of concentration of these 4 metabolites. The correlation between metabolites and different apoptotic pathways in the early apoptosis cell group was established by the 4 metabolites at the single-cell level. This is a new idea of using single-cell specific metabolites to reveal the metabolic heterogeneity in cell apoptosis which would be a powerful means for evaluating the cytotoxicity of NK cells.

Label-free mass cytometry is utilized to study the dynamic metabolic change during apoptosis in HepG2 cells induced by NK92 MI cells at the single-cell level. The metabolic heterogeneity of individual HepG2 cells during apoptosis was revealed.

Mannose and phosphomannose isomerase regulate energy metabolism under glucose starvation in leukemia

Diverse metabolic changes are induced by various driver oncogenes during the onset and progression of leukemia. By upregulating glycolysis, cancer cells acquire a proliferative advantage over normal hematopoietic cells; in addition, these changes in energy metabolism contribute to anticancer drug resistance. Because leukemia cells proliferate by consuming glucose as an energy source, an alternative nutrient source is essential when glucose levels in bone marrow are insufficient. We profiled sugar metabolism in leukemia cells and found that mannose is an energy source for glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway. Leukemia cells express high levels of phosphomannose isomerase (PMI), which mobilizes mannose to glycolysis; consequently, even mannose in the blood can be used as an energy source for glycolysis. Conversely, suppression of PMI expression or a mannose load exceeding the processing capacity of PMI inhibited transcription of genes related to mitochondrial metabolism and the TCA cycle, therefore suppressing the growth of leukemia cells. High PMI expression was also a poor prognostic factor for acute myeloid leukemia. Our findings reveal a new mechanism for glucose starvation resistance in leukemia. Furthermore, the combination of PMI suppression and mannose loading has potential as a novel treatment for driver oncogene-independent leukemia.

Phosphomannose Isomerase High Expression Associated with Better Prognosis in Pancreatic Ductal Adenocarcinoma

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the United States. The need for increased patient survival has not been met for PDAC. The addition of mannose to conventional chemotherapy leads to accumulation of mannose metabolite in cancer cells and increases subsequent cell death. This susceptibility to mannose depends on the levels of phosphomannose isomerase (PMI). The cancer cells with lower levels of PMI are more sensitive to mannose than cells with higher levels. In this study, we investigated the association of PMI expression with clinical and pathological features of PDAC cases.

Methods

PMI antibody immunohistochemistry (AbCam) was performed on tissue microarrays from 235 PDAC by a standard protocol on Ventana automated immunostainer. The PMI intensity was graded (0–3) and the proportion of positivity was scored. Correlation of PMI expression with staging and survival was analyzed.

Results

Of the 235 cases, 51.5% (n=121) cases demonstrated grade 2 intensity with 90.1% of these (n=109) showing positivity in ≥70% of tumor cells. Ninety-eight (41.7%) cases exhibited grade 3 intensity with 94.9% (n=93) of these cases showing ≥70% reactivity. Sixteen cases (6.8%) were nonreactive (intensity grade 0–1). Intensity of PMI expression was associated with significantly better prognosis as assessed by median survival in months (M): grade 0–1 intensity group: 11.2 M; grade 2 intensity group: 25.2 M; and grade 3 intensity group: 33.2 M (p=0.03). A minority (6.8%) of PDACs show non-high PMI expression with poorer prognosis.

Discussion

Mannose may be a particularly useful adjunct with chemotherapy to treat this aggressive subgroup. PMI expression is also a potential biomarker to predict the prognosis of PDAC.

The limitless applications of single-cell metabolomics

Single-cell metabolomics (SCM) is currently one of the most powerful tools for performing high-throughput metabolic analysis at the cellular level. The power of single-cell metabolomics to determine the metabolic profiles of individual cells makes it very suitable for decoding cell heterogeneity. SCM bears great potential in cell type identification and differentiation within cell colonies. With the development of various equipment and techniques, SCM analysis has become possible for a wide range of biological samples. Many fields have incorporated this cutting-edge analytic tool to generate fruitful findings. This review article pays close attention to the prevalent techniques utilized in SCM and the exciting new findings and applications developed by studies in phytology, neurology, and oncology using SCM.

Emerging open microfluidics for cell manipulation

Cell manipulation is the foundation of biochemical studies, which demands user-friendly, multifunctional and precise tools. Based on flow confinement principles, open microfluidics can control the movement of microscale liquid in open space. Every position of the circuit is accessible to external instruments, making it possible to perform precise treatment and analysis of cells at arbitrary target positions especially at the single-cell/sub-cell level. Benefiting from its unique superiority, various manipulations including patterned cell culture, 3D tissue modelling, localized chemical stimulation, online cellular factor analysis, single cell sampling, partial cell treatment, and subcellular free radical attack can be easily realized. In this tutorial review, we summarize two basic ideas to design open microfluidics: open microfluidic networks and probes. The principles of mainstream open microfluidic methods are explained, and their recent important applications are introduced. Challenges and developing trends of open microfluidics are also discussed.

Single Cell Mass Spectrometry with a Robotic Micromanipulation System for Cell Metabolite Analysis

ObjectiveThe increasing demand for unraveling cellular heterogeneity has boosted single cell metabolomics studies. However, current analytical methods are usually labor-intensive and hampered by lack of accuracy and efficiency.

METHODS

we developed a first-ever automated single cell mass spectrometry system (named SCMS) that facilitated the metabolic profiling of single cells. In particular, extremely small droplets of sub nano-liter were generated to extract the single cells, and the underlying mechanism was verified theoretically and experimentally. This was crucial to minimize the dilution of the trace cellular contents and enhance the analytical sensitivity. Based on the precise 3D positioning of the pipette tip, we established a visual servoing robotic micromanipulation platform on which single cells were sequentially extracted, aspirated, and ionized, followed by the mass spectrometry analyses.

RESULTS

With the SCMS system, inter-operator variability was eliminated and working efficiency was improved. The performance of the SCMS system was validated by the experiments on bladder cancer cells. MS and MS2 analyses of single cells enable us to identify several cellular metabolites and the underlying inter-cell heterogeneity.

CONCLUSION

In contrast to traditional methods, the SCMS system functions without human intervention and realizes a robust single cell metabolic analysis.

SIGNIFICANCE

the SCMS system upgrades the way how single cell metabolites were analyzed, and has the potential to be a powerful tool for single cell metabolomics studies.

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

High-throughput screening for high-efficiency small-molecule biosynthesis

Systems metabolic engineering faces the formidable task of rewiring microbial metabolism to cost-effectively generate high-value molecules from a variety of inexpensive feedstocks for many different applications. Because these cellular systems are still too complex to model accurately, vast collections of engineered organism variants must be systematically created and evaluated through an enormous trial-and-error process in order to identify a manufacturing-ready strain. The high-throughput screening of strains to optimize their scalable manufacturing potential requires execution of many carefully controlled, parallel, miniature fermentations, followed by high-precision analysis of the resulting complex mixtures. This review discusses strategies for the design of high-throughput, small-scale fermentation models to predict improved strain performance at large commercial scale. Established and promising approaches from industrial and academic groups are presented for both cell culture and analysis, with primary focus on microplate- and microfluidics-based screening systems.