Label-free sensing of cells with fluorescence lifetime imaging: the quest for metabolic heterogeneity

Molecular, morphological and physiological heterogeneity is the inherent property of cells, which governs differences in their response to external influence. The tumor cells metabolic heterogeneity is of a special interest due to its clinical relevance to the tumor progression and therapeutic outcomes. Rapid, sensitive and non-invasive assessment of metabolic heterogeneity of cells is of a great demand for biomedical sciences. Fluorescence lifetime imaging (FLIM), which is an all-optical technique is an emerging tool for sensing and quantifying cellular metabolism by measuring fluorescence decay parameters (FDPs) of endogenous fluorophores, such as NAD(P)H. To achieve the accurate discrimination between metabolically diverse cellular subpopulations, appropriate approaches to FLIM data collection and analysis are needed. In this report, the unique capability of FLIM to attain the overarching goal of discriminating metabolic heterogeneity has been demonstrated. This has been achieved using a novel approach to data analysis based on the non-parametric analysis, which revealed a much better sensitivity to the presence of metabolically distinct subpopulations as compare more traditional approaches of FLIM measurements and analysis. The new approach was further validated for imaging cultured cancer cells treated with chemotherapy. Those results pave the way for an accurate detection and quantification of cellular metabolic heterogeneity using FLIM, which will be valuable for assessing therapeutic vulnerabilities and predicting clinical outcomes.

Metabolic phenotyping of marine heterotrophs on refactored media reveals diverse metabolic adaptations and lifestyle strategies

Microbial communities, through their metabolism, drive carbon cycling in marine environments. These complex communities are composed of many different microorganisms including heterotrophic bacteria, each with its own nutritional needs and metabolic capabilities. Yet, models of ecosystem processes typically treat heterotrophic bacteria as a "black box", which does not resolve metabolic heterogeneity nor address ecologically important processes such as the successive modification of different types of organic matter. Here we directly address the heterogeneity of metabolism by characterizing the carbon source utilization preferences of 63 heterotrophic bacteria representative of several major marine clades. By systematically growing these bacteria on 10 media containing specific subsets of carbon sources found in marine biomass, we obtained a phenotypic fingerprint that we used to explore the relationship between metabolic preferences and phylogenetic or genomic features. At the class level, these bacteria display broadly conserved patterns of preference for different carbon sources. Despite these broad taxonomic trends, growth profiles correlate poorly with phylogenetic distance or genome-wide gene content. However, metabolic preferences are strongly predicted by a handful of key enzymes that preferentially belong to a few enriched metabolic pathways, such as those involved in glyoxylate metabolism and biofilm formation. We find that enriched pathways point to enzymes directly involved in the metabolism of the corresponding carbon source and suggest potential associations between metabolic preferences and other ecologically-relevant traits. The availability of systematic phenotypes across multiple synthetic media constitutes a valuable resource for future quantitative modeling efforts and systematic studies of inter-species interactions.

Multiomics Approach to Precision Sports Nutrition: Limits, Challenges, and Possibilities

Most sports nutrition guidelines are based on group average responses and professional opinion. Precision nutrition for athletes aims to improve the individualization of nutrition practices to optimize long-term performance and health. This is a 2-step process that first involves the acquisition of individual-specific, science-based information using a variety of sources including lifestyle and medical histories, dietary assessment, physiological assessments from the performance lab and wearable sensors, and multiomics data from blood, urine, saliva, and stool samples. The second step consists of the delivery of science-based nutrition advice, behavior change support, and the monitoring of health and performance efficacy and benefits relative to cost. Individuals vary widely in the way they respond to exercise and nutritional interventions, and understanding why this metabolic heterogeneity exists is critical for further advances in precision nutrition. Another major challenge is the development of evidence-based individualized nutrition recommendations that are embraced and efficacious for athletes seeking the most effective enhancement of performance, metabolic recovery, and health. At this time precision sports nutrition is an emerging discipline that will require continued technological and scientific advances before this approach becomes accurate and practical for athletes and fitness enthusiasts at the small group or individual level. The costs and scientific challenges appear formidable, but what is already being achieved today in precision nutrition through multiomics and sensor technology seemed impossible just two decades ago.

Characterization of metabolic compartmentalization in the liver using spatially resolved metabolomics

Cells adapt their metabolism to physiological stimuli, and metabolic heterogeneity exists between cell types, within tissues, and subcellular compartments. The liver plays an essential role in maintaining whole-body metabolic homeostasis and is structurally defined by metabolic zones. These zones are well-understood on the transcriptomic level, but have not been comprehensively characterized on the metabolomic level. Mass spectrometry imaging (MSI) can be used to map hundreds of metabolites directly from a tissue section, offering an important advance to investigate metabolic heterogeneity in tissues compared to extraction-based metabolomics methods that analyze tissue metabolite profiles in bulk. We established a workflow for the preparation of tissue specimens for matrix-assisted laser desorption/ionization (MALDI) MSI and achieved broad coverage of central carbon, nucleotide, and lipid metabolism pathways. We used this approach to visualize the effect of nutrient stress and excess on liver metabolism. Our data revealed a highly organized metabolic compartmentalization in livers, which becomes disrupted under nutrient stress conditions. Fasting caused changes in glucose metabolism and increased the levels of fatty acids in the circulation. In contrast, a prolonged high-fat diet (HFD) caused lipid accumulation within liver tissues with clear zonal patterns. Fatty livers had higher levels of purine and pentose phosphate related metabolites, which generates reducing equivalents to counteract oxidative stress. This MALDI MSI approach allowed the visualization of liver metabolic compartmentalization at high resolution and can be applied more broadly to yield new insights into metabolic heterogeneity in vivo .

Integrated proteogenomic analysis revealed the metabolic heterogeneity in noncancerous liver tissues of patients with hepatocellular carcinoma

Understanding the adjacent liver microenvironment of hepatocellular carcinoma (HCC) with possible metastasis tendency might provide a strategy for risk classification of patients and potential therapies by converting the unique metastasis-inclined microenvironment to a metastasis-averse one. In this study, we performed an integrated proteogenomic analysis to have a comprehensive view on the heterogeneity of hepatic microenvironment contributing to HCC metastasis. Pairing mRNA-protein analysis revealed consistent and discordant mRNA-protein expressions in metabolism regulations and cancer-related pathways, respectively. Proteomic profiling identified three subgroups associated with the recurrence-free survival of patients. These proteomic subgroups demonstrated distinct features in metabolic reprogramming, which was potentially modified by epigenetic alterations. This study raises the point of metabolic heterogeneity in HCC noncancerous tissues and may offer a new perspective on HCC treatment.

DLST-dependence dictates metabolic heterogeneity in TCA-cycle usage among triple-negative breast cancer

Triple-negative breast cancer (TNBC) is traditionally considered a glycolytic tumor with a poor prognosis while lacking targeted therapies. Here we show that high expression of dihydrolipoamide S-succinyltransferase (DLST), a tricarboxylic acid (TCA) cycle enzyme, predicts poor overall and recurrence-free survival among TNBC patients. DLST depletion suppresses growth and induces death in subsets of human TNBC cell lines, which are capable of utilizing glutamine anaplerosis. Metabolomics profiling reveals significant changes in the TCA cycle and reactive oxygen species (ROS) related pathways for sensitive but not resistant TNBC cells. Consequently, DLST depletion in sensitive TNBC cells increases ROS levels while N-acetyl-L-cysteine partially rescues cell growth. Importantly, suppression of the TCA cycle through DLST depletion or CPI-613, a drug currently in clinical trials for treating other cancers, decreases the burden and invasion of these TNBC. Together, our data demonstrate differential TCA-cycle usage in TNBC and provide therapeutic implications for the DLST-dependent subsets.

Metabolomic credentialing of murine carcinogen-induced urothelial cancer

Bladder cancer (BCa) is the most common malignancy of the urinary system with increasing incidence, mortality, and limited treatment options. Therefore, it is imperative to validate preclinical models that faithfully represent BCa cellular, molecular, and metabolic heterogeneity to develop new therapeutics. We performed metabolomic profiling of premalignant and non-muscle invasive bladder cancer (NMIBC) that ensued in the chemical carcinogenesis N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN) mouse model. We identified the enriched metabolic signatures that associate with premalignant and NMIBC. We found that enrichment of lipid metabolism is the forerunner of carcinogen-induced premalignant and NMIBC lesions. Cross-species analysis revealed the prognostic value of the enzymes associated with carcinogen-induced enriched metabolic in human disease. To date, this is the first study describing the global metabolomic profiles associated with early premalignant and NMIBC and provide evidence that these metabolomic signatures can be used for prognostication of human disease.

Single-cell metabolomics reveals the metabolic heterogeneity among microbial cells

In microbial research, the heterogeneity phenomenon is closely associated with microbial physiology in multiple dimensions. For now, A few studies were proposed in transcriptome and proteome analysis to discover the heterogeneity among single cells. However, microbial single cell metabolomics has not been possible yet. Herein, we developed a method, RespectM, based on discontinuous mass spectrometry imaging, which can detect more than 700 metabolites at a rate of 500 cells per hour. While ensuring the high throughput of RespectM, it integrates matrix sublimation, QC-based peak filtering, and batch correction strategies to improve accuracy. The results show that RespectM can distinguish single microbial cells from the blank matrix with an accuracy of 98.4%, depending on classification algorithms. Furthermore, to verify the accuracy of RespectM for distinguishing different single cells, we performed a classification test on Chlamydomonas reinhardtii single cells among allelic strains. The results showed an accuracy of 93.1%, which provides RespectM with enough confidence to perform microbial single cell metabolomics analysis. As we expected, untreated microbial cells will spontaneously undergo metabolic grouping coherence with genetic and biochemical similarities. Interestingly, the pseudo-time analysis also provided intuitive evidence on the metabolic dimension, indicating the cell grouping is based on microbial population heterogeneity. We believe that the RespectM can offer a powerful tool in the microbial study. Researchers can now directly analyze the changes in microbial metabolism at a single-cell level with high efficiency.

TAMI-26. INVESTIGATING TUMOR MICROENVIRONMENT METABOLIC DEPENDENCIES IN GLIOBLASTOMA

Metabolic reprogramming is a hallmark of cancer. Malignant cells must acquire metabolic adaptations in response to a multitude of intrinsic and extrinsic factors to fuel neoplastic progression. Mutations or changes in metabolic gene expression can impose nutrient dependencies in tumors, and even in the absence of metabolic defects, cancer cells can become auxotrophic for particular nutrients or metabolic byproducts generated by other cells in the tumor microenvironment (TME). Altered metabolism in GBM is becoming an increasingly promising area of research to identify novel therapeutic targets and biomarkers, as metabolic rewiring can occur across numerous genotypes. The unique features of the brain TME pose a difficult challenge when studying GBM and other primary brain cancers – currently, the availability of nutrients in the brain, as well as how they influence or are influenced by tumor metabolism, are not well understood. Our group has identified a subgroup of gliomas, hereafter termed TME-dependent, which can only form tumors in the brain TME. While genetically heterogeneous, these tumors share transcriptional identities linked to oligodendrocyte precursor cell (OPC) and neuronal lineages. Systematic molecular profiling of over 75 patient tumors and their matched cell culture and brain orthotopic xenograft derived models revealed that TME-dependent tumors display lipid metabolic signatures linked to signaling and interactions with surrounding neurons and glial cells. Collectively, these data emphasize the metabolic heterogeneity within GBM, and reveal a subset of gliomas that lack metabolic plasticity in fatty acid biosynthetic programs, indicating a potential brain-microenvironment specific metabolic dependency linked to transcriptional identity that can be targeted for therapy.

Alternative Energy: Breaking Down the Diverse Metabolic Features of Lung Cancers

Metabolic reprogramming is a hallmark of cancer initiation, progression, and relapse. From the initial observation that cancer cells preferentially ferment glucose to lactate, termed the Warburg effect, to emerging evidence indicating that metabolic heterogeneity and mitochondrial metabolism are also important for tumor growth, the complex mechanisms driving cancer metabolism remain vastly unknown. These unique shifts in metabolism must be further investigated in order to identify unique therapeutic targets for individuals afflicted by this aggressive disease. Although novel therapies have been developed to target metabolic vulnerabilities in a variety of cancer models, only limited efficacy has been achieved. In particular, lung cancer metabolism has remained relatively understudied and underutilized for the advancement of therapeutic strategies, however recent evidence suggests that lung cancers have unique metabolic preferences of their own. This review aims to provide an overview of essential metabolic mechanisms and potential therapeutic agents in order to increase evidence of targeted metabolic inhibition for the treatment of lung cancer, where novel therapeutics are desperately needed.