Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution

Advances in Sampling and Analytical Techniques for Single-Cell Metabolomics: Exploring Cellular Heterogeneity

Single-cell metabolomics is an emerging and powerful technology that uncovers intercellular heterogeneity and reveals microenvironmental dynamics in both physiological and pathological conditions. This technology enables detailed observations of cellular interactions, providing valuable insights into processes such as aging, immune responses, and disease development. Despite significant advances, the need for detailed discussions on sampling and analytical methods in single-cell metabolomics continues to grow, with increasing focus on selecting the most suitable techniques for diverse research objectives. This review addresses these challenges by exploring key sampling and analytical strategies used in single-cell metabolomics. We focus on three main approaches: the capture and isolation of specific cell types, the precise aspiration of individual cells, and in situ mass spectrometry imaging. These methods are critically assessed to highlight strategies for achieving accurate metabolite detection at the single-cell level across diverse research applications.

In situ mass spectrometry imaging reveals metabolite alterations in gray and white matter after spinal cord injury

It is known that spinal cord injury (SCI) causes metabolic disorders, such as disrupted lipid and amino acid metabolism. However, the in situ changes of metabolites in the spinal cord remain elusive. In the present study, adult Sprague-Dawley rats underwent spinal cord transection surgery. Ambient air flow-assisted desorption electrospray ionization mass spectrometry imaging was performed to identify the localization of metabolites in the white and gray matter and the ventral and dorsal horns within the spinal cord sections. The results revealed that 42 metabolites were specifically increased in the gray matter, and 90 metabolites were increased in the white matter compared with that in the sham group. In the ventral and dorsal horns, 86 and 103 metabolites, respectively, exhibited specific increases after injury. By contrast, numerous metabolites, especially lipids, were significantly decreased after SCI. Phosphatidylserine and phosphatidylethanolamine were mainly decreased in the gray matter, while cholesterol and ceramide were mainly decreased in the white matter. Specifically, phosphatidylethanolamine was detected at low levels in the dorsal horn following injury. However, the phosphatidylserine level decreased in the ventral horn. The functional enrichment of these metabolites via Kyoto Encyclopedia of Genes and Genomes analysis further confirmed the profile differences in the white and gray matter, as well as in the ventral and dorsal horns after SCI. These results provide valuable information on the metabolite profiles across various anatomical regions of the spinal cord following SCI, which may support the development of precise treatment strategies for patients.

Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study

BACKGROUND

Ovarian cancer (OC) ranks as the most lethal gynaecological malignancy worldwide, with early diagnosis being crucial yet challenging. Current diagnostic methods like transvaginal ultrasound and blood biomarkers show limited sensitivity/specificity. This study aimed to identify and validate serum metabolic biomarkers for OC diagnosis using the largest cohort reported to date.

METHODS

We constructed a large-scale OC-associated cohort of 1432 subjects, including 662 OC, 563 benign ovarian disease, and 207 healthy control subjects, across retrospective (n = 1073) and set-aside validation (n = 359) cohorts. Serum metabolic fingerprints (SMFs) were recorded using nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI-MS). A diagnostic panel was developed through machine learning of SMFs in the discovery cohort and validated in independent verification and set-aside validation cohorts. The identified metabolic biomarkers were further validated using liquid chromatography MS and their biological functions were assessed in OC cell lines.

FINDINGS

We identified a metabolic biomarker panel including glucose, histidine, pyrrole-2-carboxylic acid, and dihydrothymine. This panel achieved consistent areas under the curve (AUCs) of 0.87-0.89 for distinguishing between malignant and benign ovarian masses across all cohorts, and improved to AUCs of 0.95-0.99 when combined with risk of ovarian malignancy algorithm (ROMA). In vitro validation provided initial biological context for the metabolic alterations observed in our diagnostic panel.

INTERPRETATION

Our study established a reliable serum metabolic biomarker panel for OC diagnosis with potential clinical translations. The NELDI-MS based approach offers advantages of fast analytical speed (∼30 s/sample) and low cost (∼2-3 dollars/sample), making it suitable for large-scale clinical applications.

FUNDING

MOST (2021YFA0910100), NSFC (82421001, 823B2050, 824B2059, and 82173077), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021GD02, YG2024ZD07, and YG2023ZD08), Shanghai Science and Technology Committee Project (23JC1403000), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Shanghai Jiao Tong University Inner Mongolia Research Institute (2022XYJG0001-01-16), Sichuan Provincial Department of Science and Technology (2024YFHZ0176), Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute, Guangdong Basic and Applied Basic Research Foundation (2024A1515013255).

MMEASE: enhanced analytical workflow for single-cell metabolomics

Metabolomics is essential for providing an overview of what chemical processes are taking place. A clear shift from bulk metabolomics to single-cell metabolomics (SCM) is observed in current research, and an integral workflow enabling the analysis of SCM data is therefore in great demand. However, no such workflow has been available to date. Herein, MMEASE, previously designed for analyzing bulk metabolomic data, was therefore updated to its 2.0 version by developing the first comprehensive and in-depth workflow analyzing SCM data. First, it provided all sequential steps of modern SCM research (from SCM data processing, to cellular heterogeneity analysis, then to high-resolution metabolite annotation, and finally to cell-based biological interpretation). Second, compared with the existing tools, MMEASE 2.0 was superior by incorporating the widest variety of methods at every step of the SCM analyses. The originality and functionality of our MMEASE were extensively validated and explicitly described by case studies on benchmark data. All in all, MMEASE 2.0 was unique in accomplishing comprehensive and in-depth analyses of SCM data, which could be considered as an indispensable complement to the existing tools. Now, the latest version of MMEASE is freely accessible by all users at: https://idrblab.org/mmease/.

Clonal Analysis of Peripheral Blood T Cells in patients with Hashimoto's Thyroiditis at Different Stages using TCR Sequencing

BACKGROUND

Hashimoto's Thyroiditis (HT) is a prevalent autoimmune disorder characterized by progressive thyroid damage mediated by T cells. The clonal diversity and expansion of T cells play a critical role in the pathogenesis of HT, but detailed insights into these characteristics at various disease stages are lacking.

OBJECTIVE

To analyze and compare the clonal diversity and T cell receptor (TCR) repertoire in peripheral blood T cells across different stages of Hashimoto's Thyroiditis using TCR sequencing.

METHODS

Peripheral blood samples from 19 HT patients at different disease stages and 4 healthy controls were collected. TCR sequencing was performed to assess T cell diversity and clonal expansion. Statistical analyses, including Shannon's entropy and Simpson's index, were employed to compare TCR diversity. Additionally, differential VJ gene usage and amino acid sequence diversity were analyzed.

RESULTS

The results revealed significant differences in TCR diversity between HT patients and healthy controls, with HT patients showing lower diversity. Notably, the frequency of hyperexpanded clones was higher in HT patients. Specific VJ genes exhibited differential usage patterns across disease stages, and a set of unique amino acid sequences was identified in each stage.

CONCLUSION

TCR sequencing highlights distinct clonal T cell expansions and shifts in VJ gene usage in HT patients, providing insights into the immune mechanisms driving disease progression.

Hollow Mesoporous Carbon Nanospheres/Ni Hybrids Aid in Metabolic Encoding for COVID-19 Recovery Assessment in Mothers and Fetuses

Metabolite analysis of body fluids is an advanced method for disease diagnosis and status assessment. Laser desorption/ionization-mass spectrometry (LDI-MS) has been widely employed for metabolic analysis due to the fast detection speed and simple sample pretreatment. Here, we designed and synthesized hollow mesoporous carbon nanospheres anchored with Ni (HMCSs/Ni) to simultaneously enhance the ionization and thermal desorption processes of the LDI process owing to their hollow and mesoporous structure, large surface area, and abundant Ni-N bonds. Based on HMCSs/Ni, we built an LDI-MS platform that can be used for metabolic information extraction and achieved the rapid detection (about seconds per sample) of metabolic fingerprints in trace serum samples (∼0.1 μL) without complicated preprocessing procedures. Then, we conducted serum metabolic screening in a cohort of COVID-19-recovered pregnant women. The optimized machine learning model could distinguish recovered pregnant women from uninfected pregnant women based on metabolic features with an AUC value of 0.901. In addition, the model indicates that maternal COVID-19 infection does not significantly affect the metabolic fingerprints of the fetuses. Overall, our work shows the prospect of HMCSs/Ni-assisted LDI-MS in disease recovery assessment and metabolite analysis.

Glycogen drives tumour initiation and progression in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is an aggressive cancer defined by oncogenic drivers and metabolic reprogramming. Here we leverage next-generation spatial screens to identify glycogen as a critical and previously underexplored oncogenic metabolite. High-throughput spatial analysis of human LUAD samples revealed that glycogen accumulation correlates with increased tumour grade and poor survival. Furthermore, we assessed the effect of increasing glycogen levels on LUAD via dietary intervention or via a genetic model. Approaches that increased glycogen levels provided compelling evidence that elevated glycogen substantially accelerates tumour progression, driving the formation of higher-grade tumours, while the genetic ablation of glycogen synthase effectively suppressed tumour growth. To further establish the connection between glycogen and cellular metabolism, we developed a multiplexed spatial technique to simultaneously assess glycogen and cellular metabolites, uncovering a direct relationship between glycogen levels and elevated central carbon metabolites essential for tumour growth. Our findings support the conclusion that glycogen accumulation drives LUAD cancer progression and provide a framework for integrating spatial metabolomics with translational models to uncover metabolic drivers of cancer.

Energy-Confinement 3D Flower-Shaped Cages for AI-Driven Decoding of Metabolic Fingerprints in Cardiovascular Disease Diagnosis

Rapid and accurate detection plays a critical role in improving the survival and prognosis of patients with cardiovascular disease, but traditional detection methods are far from ideal for those with suspected conditions. Metabolite analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is considered to be a promising technique for disease diagnosis. However, the performance of core nanomatrixes has limited its clinical application. In this study, we constructed 3D flower-shaped cages based on controllable structured metal-organic frameworks and iron oxide nanoparticles with low thermal conductivity and significant photothermal effects. The elongation of the incident light path through multilayer reflection significantly enhances the effective light absorption area of the nanomatrixes. Concurrently, the alternating layered structure confines the thermal energy, reducing thermal losses. Moreover, the 3D structure increases affinity sites, expanding the detection coverage. This approach effectively enhances the laser ionization and thermal desorption efficiency during the LDI process. We applied this technology to analyze the serum metabolomes of patients with myocardial infarction, heart failure, and heart failure combined with myocardial infarction, achieving cost-effective, high-throughput, highly accurate, and user-friendly detection of cardiovascular diseases. Subsequently, deep analysis of detected serum fingerprints via artificial intelligence models screens potential metabolic biomarkers, providing a new paradigm for the accurate diagnosis of cardiovascular diseases.

Deep Learning-Enabled Rapid Metabolic Decoding of Small Extracellular Vesicles via Dual-Use Mass Spectroscopy Chip Array

The increasing focus of small extracellular vesicles (sEVs) in liquid biopsy has created a significant demand for streamlined improvements in sEV isolation methods, efficient collection of high-quality sEV data, and powerful rapid analysis of large data sets. Herein, we develop a high-throughput dual-use mass spectroscopic chip array (DUMSCA) for the rapid isolation and detection of plasma sEVs. The DUMSCA realizes more than a 50% increase in speed compared to traditional method and confirms proficiency in robust storage, reuse, high-efficiency desorption/ionization, and metabolite quantification. With the collected metabolic data matrix of sEVs, a deep learning model achieves high-performance diagnosis of Crohn's disease. Furthermore, discovered biomarkers by feature sparsification and tandem mass spectrometry experiments also exhibited remarkable performance in diagnosis. This work demonstrates the rapidity and validity of DUMSCA for disease diagnosis, enabling the diagnosis of diseases without the necessity for prior knowledge and providing a high-throughput technology for sEV-based liquid biopsy that will empower its vigorous development.

Deciphering the Complexities of Adult Human Steady State and Stress-Induced Hematopoiesis: Progress and Challenges

Human hematopoietic stem cells (HSCs) have traditionally been viewed as self-renewing, multipotent cells with enormous potential in sustaining essential steady state blood and immune cell production throughout life. Indeed, around 86% (1011-1012) of new cells generated daily in a healthy young human adult are of hematopoietic origin. Therapeutically, human HSCs have contributed to over 1.5 million hematopoietic cell transplants (HCTs) globally, making this the most successful regenerative therapy to date. We will commence this review by briefly highlighting selected key achievements (from 1868 to the end of the 20th century) that have contributed to this accomplishment. Much of our knowledge of hematopoiesis is based on small animal models that, despite their enormous importance, do not always recapitulate human hematopoiesis. Given this, we will critically review the progress and challenges faced in identifying adult human HSCs and tracing their lineage differentiation trajectories, referring to murine studies as needed. Moving forward and given that human hematopoiesis is dynamic and can readily adjust to a variety of stressors, we will then discuss recent research advances contributing to understanding (i) which HSPCs maintain daily steady state human hematopoiesis, (ii) where these are located, and (iii) which mechanisms come into play when homeostatic hematopoiesis switches to stress-induced or emergency hematopoiesis.

Ascorbate deficiency increases quiescence and self-renewal in hematopoietic stem cells and multipotent progenitors

ABSTRACT

Ascorbate (vitamin C) limits hematopoietic stem cell (HSC) function and suppresses leukemia development, partly by promoting the function of the Tet2 tumor suppressor. In humans, ascorbate is obtained from the diet, whereas in mice, it is synthesized in the liver. In this study, we show that deletion of the Slc23a2 ascorbate transporter from hematopoietic cells depleted ascorbate to undetectable levels in HSCs and multipotent hematopoietic progenitors (MPPs) without altering the plasma ascorbate levels. Slc23a2 deficiency increased HSC reconstituting potential and self-renewal potential upon transplantation into irradiated mice. Slc23a2 deficiency also increased the reconstituting and self-renewal potentials of MPPs, conferring the ability to reconstitute irradiated mice long term. Slc23a2-deficient HSCs and MPPs divided much less frequently than control HSCs and MPPs. Increased self-renewal and reconstituting potential were observed particularly in quiescent Slc23a2-deficient HSCs and MPPs. The effect of Slc23a2 deficiency on MPP self-renewal was not mediated by reduced Tet2 function. Ascorbate thus regulates quiescence and restricts self-renewal potential in HSCs and MPPs such that ascorbate deficiency confers MPPs with long-term self-renewal potential.

Hematopoietic stem cell a reservoir of innate immune memory

Hematopoietic stem cells (HSCs) are a rare, long-lived and multipotent population that give rise to majority of blood cells and some tissue-resident immune cells. There is growing evidence that inflammatory stimuli can trigger persistent reprogramming in HSCs that enhances or inhibits the cellular functions of these HSCs and their progeny in response to subsequent infections. This newly discovered property makes HSCs a reservoir for innate immune memory. The molecular mechanisms underlying innate immune memory in HSCs are similar to those observed in innate immune cells, although their full elucidation is still pending. In this review, we examine the current state of knowledge on how an inflammatory response leads to reprogramming of HSCs. Understanding the full spectrum of consequences of reshaping early hematopoiesis is critical for assessing the potential benefits and risks under physiological and pathological conditions.

A system-level model reveals that transcriptional stochasticity is required for hematopoietic stem cell differentiation

HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.

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.

Metabolic regulation in normal and leukemic stem cells

Hematopoietic stem cells (HSCs) and leukemic stem cells (LSCs) are crucial for ensuring hematopoietic homeostasis and driving leukemia progression, respectively. Recent research has revealed that metabolic adaptations significantly regulate the function and survival of these stem cells. In this review, we provide an overview of how metabolic pathways regulate oxidative and proteostatic stresses in HSCs during homeostasis and aging. Furthermore, we highlight targetable metabolic pathways and explore their interactions with epigenetics and the microenvironment in addressing the chemoresistance and immune evasion capacities of LSCs. The metabolic differences between HSCs and LSCs have profound implications for therapeutic strategies.

Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

Tuberculosis (TB) stands as the second most fatal infectious disease after COVID-19, the effective treatment of which depends on accurate diagnosis and phenotyping. Metabolomics provides valuable insights into the identification of differential metabolites for disease diagnosis and phenotyping. However, TB diagnosis and phenotyping remain great challenges due to the lack of a satisfactory metabolic approach. Here, a metabolomics-based diagnostic method for rapid TB detection is reported. Serum metabolic fingerprints are examined via an automated nanoparticle-enhanced laser desorption/ionization mass spectrometry platform outstanding by its rapid detection speed (measured in seconds), minimal sample consumption (in nanoliters), and cost-effectiveness (approximately $3). A panel of 14 m z-1 features is identified as biomarkers for TB diagnosis and a panel of 4 m z-1 features for TB phenotyping. Based on the acquired biomarkers, TB metabolic models are constructed through advanced machine learning algorithms. The robust metabolic model yields a 97.8% (95% confidence interval (CI), 0.964-0.986) area under the curve (AUC) in TB diagnosis and an 85.7% (95% CI, 0.806-0.891) AUC in phenotyping. In this study, serum metabolic biomarker panels are revealed and develop an accurate metabolic tool with desirable diagnostic performance for TB diagnosis and phenotyping, which may expedite the effective implementation of the end-TB strategy.

Proteomics reveals dynamic metabolic changes in human hematopoietic stem progenitor cells from fetal to adulthood

BACKGROUND

Hematopoietic stem progenitor cells (HSPCs) undergo phenotypical and functional changes during their emergence and development. Although the molecular programs governing the development of human hematopoietic stem cells (HSCs) have been investigated broadly, the relationships between dynamic metabolic alterations and their functions remain poorly characterized.

METHODS

In this study, we comprehensively described the proteomics of HSPCs in the human fetal liver (FL), umbilical cord blood (UCB), and adult bone marrow (aBM). The metabolic state of human HSPCs was assessed via a Seahorse assay, RT‒PCR, and flow cytometry-based metabolic-related analysis. To investigate whether perturbing glutathione metabolism affects reactive oxygen species (ROS) production, the metabolic state, and the expansion of human HSPCs, HSPCs were treated with buthionine sulfoximine (BSO), an inhibitor of glutathione synthetase, and N-acetyl-L-cysteine (NAC).

RESULTS

We investigated the metabolomic landscape of human HSPCs from the fetal, perinatal, and adult developmental stages by in-depth quantitative proteomics and predicted a metabolic switch from the oxidative state to the glycolytic state during human HSPC development. Seahorse assays, mitochondrial activity, ROS level, glucose uptake, and protein synthesis rate analysis supported our findings. In addition, immune-related pathways and antigen presentation were upregulated in UCB or aBM HSPCs, indicating their functional maturation upon development. Glutathione-related metabolic perturbations resulted in distinct responses in human HSPCs and progenitors. Furthermore, the molecular and immunophenotypic differences between human HSPCs at different developmental stages were revealed at the protein level for the first time.

CONCLUSION

The metabolic landscape of human HSPCs at three developmental stages (FL, UCB, and aBM), combined with proteomics and functional validations, substantially extends our understanding of HSC metabolic regulation. These findings provide valuable resources for understanding human HSC function and development during fetal and adult life.

Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

Metabolism and HSC fate: what NADPH is made for

Mitochondrial metabolism plays a central role in the regulation of hematopoietic stem cell (HSC) biology. Mitochondrial fatty acid oxidation (FAO) is pivotal in controlling HSC self-renewal and differentiation. Herein, we discuss recent evidence suggesting that NADPH generated in the mitochondria can influence the fate of HSCs. Although NADPH has multiple functions, HSCs show high levels of NADPH that are preferentially used for cholesterol biosynthesis. Endogenous cholesterol supports the biogenesis of extracellular vesicles (EVs), which are essential for maintaining HSC properties. We also highlight the significance of EVs in hematopoiesis through autocrine signaling. Elucidating the mitochondrial NADPH-cholesterol axis as part of the metabolic requirements of healthy HSCs will facilitate the development of new therapies for hematological disorders.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

Targeted Quantification of Glutathione/Arginine Redox Metabolism Based on a Novel Paired Mass Spectrometry Probe Approach for the Functional Assessment of Redox Status

Glutathione (GSH) redox control and arginine metabolism are critical in regulating the physiological response to injury and oxidative stress. Quantification assessment of the GSH/arginine redox metabolism supports monitoring metabolic pathway shifts during pathological processes and their linkages to redox regulation. However, assessing the redox status of organisms with complex matrices is challenging, and single redox molecule analysis may not be accurate for interrogating the redox status in cells and in vivo. Herein, guided by a paired derivatization strategy, we present a new ultraperformance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)-based approach for the functional assessment of biological redox status. Two structurally analogous probes, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) and newly synthesized 2-methyl-6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (MeAQC), were set for paired derivatization. The developed approach was successfully applied to LPS-stimulated RAW 264.7 cells and HDM-induced asthma mice to obtain quantitative information on GSH/arginine redox metabolism. The results suggest that the redox status was remarkably altered upon LPS and HDM stimulation. We expect that this approach will be of good use in a clinical biomarker assay and potential drug screening associated with redox metabolism, oxidative damage, and redox signaling.

Pt/NiFe-LDH hybrids for quantification and qualification of polyphenols

Polyphenols with antioxidant properties are of significant interest in medical and pharmaceutical applications. Given the diverse range of activities of polyphenols in vivo, accurate detection of these compounds plays a crucial role in nutritional surveillance and pharmaceutical development. Yet, the efficient quantitation of polyphenol contents and qualification of monomer compositions present a notable challenge when studying polyphenol bioavailability. In this study, platinum-modified nickel-iron layered double hydroxide (Pt/NiFe-LDH hybrids) were designed to mimic peroxidases for colorimetric analysis and act as enhanced matrices for laser desorption/ionization mass spectrometry (LDI MS) to quantify and qualify polyphenols. The hybrids exhibited an enzymatic activity of 33.472 U/mg for colorimetric assays, facilitating the rapid and direct quantitation of total tea polyphenols within approximately 1 min. Additionally, the heterogeneous structure and exposed hydroxyl groups on the hybrid surface contributed to photoelectric enhancement and in-situ enrichment of polyphenols in LDI MS. This study introduces an innovative approach to detect polyphenols using advanced materials, potentially inspiring the future development and applications of other photoactive nanomaterials.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Multidimensional Interactive Cascading Nanochips for Detection of Multiple Liver Diseases via Precise Metabolite Profiling

It is challenging to detect and differentiate multiple diseases with high complexity/similarity from the same organ. Metabolic analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is a promising platform for disease diagnosis, while the enhanced property of its core nanomatrix materials has plenty of room for improvement. Herein, a multidimensional interactive cascade nanochip composed of iron oxide nanoparticles (FeNPs)/MXene/gold nanoparticles (AuNPs), IMG, is reported for serum metabolic profiling to achieve high-throughput detection of multiple liver diseases. MXene serves as a multi-binding site and an electron-hole source for ionization during NMALDI-MS analysis. Introduction of AuNPs with surface plasmon resonance (SPR) properties facilitates surface charge accumulation and rapid energy conversion. FeNPs are integrated into the MXene/Au nanocomposite to sharply reduce the thermal conductivity of the nanochip with negligible heat loss for strong thermally-driven desorption, and construct a multi-interaction proton transport pathway with MXene and AuNPs for strong ionization. Analysis of these enhanced serum fingerprint signals detected from the IMG nanochip through a neural network model results in differentiation of multiple liver diseases via a single pass and revelation of potential metabolic biomarkers. The promising method can rapidly and accurately screen various liver diseases, thus allowing timely treatment of liver diseases.

Crosstalk between DNA Damage Repair and Metabolic Regulation in Hematopoietic Stem Cells

Self-renewal and differentiation are two characteristics of hematopoietic stem cells (HSCs). Under steady physiological conditions, most primitive HSCs remain quiescent in the bone marrow (BM). They respond to different stimuli to refresh the blood system. The transition from quiescence to activation is accompanied by major changes in metabolism, a fundamental cellular process in living organisms that produces or consumes energy. Cellular metabolism is now considered to be a key regulator of HSC maintenance. Interestingly, HSCs possess a distinct metabolic profile with a preference for glycolysis rather than oxidative phosphorylation (OXPHOS) for energy production. Byproducts from the cellular metabolism can also damage DNA. To counteract such insults, mammalian cells have evolved a complex and efficient DNA damage repair (DDR) system to eliminate various DNA lesions and guard genomic stability. Given the enormous regenerative potential coupled with the lifetime persistence of HSCs, tight control of HSC genome stability is essential. The intersection of DDR and the HSC metabolism has recently emerged as an area of intense research interest, unraveling the profound connections between genomic stability and cellular energetics. In this brief review, we delve into the interplay between DDR deficiency and the metabolic reprogramming of HSCs, shedding light on the dynamic relationship that governs the fate and functionality of these remarkable stem cells. Understanding the crosstalk between DDR and the cellular metabolism will open a new avenue of research designed to target these interacting pathways for improving HSC function and treating hematologic disorders.

Ascorbate depletion increases quiescence and self-renewal potential in hematopoietic stem cells and multipotent progenitors

Ascorbate (vitamin C) limits hematopoietic stem cell (HSC) function and suppresses leukemia development by promoting the function of the Tet2 tumor suppressor. In humans, ascorbate is obtained from the diet while in mice it is synthesized in the liver. In this study, we show that deletion of the Slc23a2 ascorbate transporter severely depleted ascorbate from hematopoietic cells. Slc23a2 deficiency increased HSC reconstituting potential and self-renewal potential upon transplantation into irradiated mice. Slc23a2 deficiency also increased the reconstituting and self-renewal potential of multipotent hematopoietic progenitors (MPPs), conferring the ability to long-term reconstitute irradiated mice. Slc23a2-deficient HSCs and MPPs divided much less frequently than control HSCs and MPPs. Increased self-renewal and reconstituting potential were observed particularly in quiescent Slc23a2-deficient HSCs and MPPs. The effect of Slc23a2 deficiency on MPP self-renewal was not mediated by reduced Tet2 function. Ascorbate thus regulates quiescence and restricts self-renewal potential in HSCs and MPPs such that ascorbate depletion confers MPPs with long-term self-renewal potential.

Assessment of Reference Genes Stability in Cortical Bone of Obese and Diabetic Mice

Introduction

Bone, a pivotal structural organ, is susceptible to disorders with profound health implications. The investigation of gene expression in bone tissue is imperative, particularly within the context of metabolic diseases such as obesity and diabetes that augment the susceptibility to bone fractures. The objective of this study is to identify a set of internal control genes for the analysis of gene expression.

Methods

This study employs reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) to assess gene expression in bone tissue. We selected fourteen housekeeping genes and assessed their stability in the cortical bone of mouse models for obesity and diabetes using four well-established algorithms (GeNorm, BestKeeper, NormFinder, and the comparative Delta Ct method).

Results and Conclusion

We identified Rpl13a as the mostly stably expressed reference gene in cortical bone tissue from mouse models of obesity and diabetes (db/db), while Gapdh was found to be the most stable reference gene in another diabetes model, KKAy mice. Additionally, Ef1a, Ppia, Rplp0, and Rpl22 were identified as alternative genes suitable for normalizing gene expression in cortical bone from obesity and diabetes mouse models. These findings enhance RT-qPCR accuracy and reliability, offering a strategic guide to select reference gene for studying bone tissue gene expression in metabolic disorders.