Metabolic Heterogeneity Research Articles

Abstract C016: Optical Techniques to Democratize Intravital Quantification of Metabolic Heterogeneity

Abstract Since metabolic and vascular adaptations are essential for tumor cell survival, a technique capable of tracking metabolic heterogeneity at both the bulk tumor and cellular scales over extended timelines is necessary. To address this technological need, we have developed a multi-scale optical microscope relying on exogenous fluorescent reporters for dynamic spatiotemporal imaging of major metabolic pathways in vivo. This microscope utilizes a low-cost CMOS detector and LEDs for excitation to enable fluorescence microscopy in resource-limited settings. Here, we implement our system to assess changes in bulk tumor and cellular metabolic heterogeneity as vasculature is perturbed. 4T1 breast tumor-bearing mice were treated with combretastatin A-1 (a vascular disruptor), implanted with window chambers, and temporally imaged (n=5/group). Fluorescence imaging and Gabor filter/Djikstra segmentation approaches enabled metabolic and vascular analyses, respectively. We demonstrated the system’s ability to acquire co-registered images of vasculature, morphology, and distinct metabolic endpoints reporting on glucose uptake, fatty acid uptake, and mitochondrial metabolism at both wide-field and high resolutions. At 12 hours post-treatment, the tumors’ metabolism shifted from mitochondrial respiration to glycolysis in avascular regions. Further, we report a higher average vascular density in untreated versus treated mice at 108 hours post-treatment (p<0.05, Kolmogorov-Smirnov test). On exploring the relationship between metabolic activity and vasculature, regions with high local tortuosity in the untreated group exhibited a higher reliance on fatty acid uptake than high tortuosity regions in the treatment group, indicating these cells’ reliance on fatty acids within a complex vascular network. Our multiplexed imaging strategy enables the quantification of tumor metabolism and vasculature over multiple length scales, which could be leveraged to study adaptations following clinical therapies. Citation Format: Enakshi Sunassee. Optical Techniques to Democratize Intravital Quantification of Metabolic Heterogeneity [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C016.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Neuroblastoma with high ASPM reveals pronounced heterogeneity and poor prognosis

ObjectiveWe explored the preliminary value of abnormal spindle-like microcephaly- associated (ASPM) protein in aiding precise risk sub-stratification, prediction of metabolic heterogeneity, and prognosis of neuroblastoma (NB).MethodsThis retrospective study enrolled newly diagnosed patients with NB who underwent positron emission tomography/computed tomography (PET/CT) before therapy, and tumor tissue was collected after surgery. Regression analysis was used to evaluate ASPM expression and risk stratification in patients with NB. The expression levels of ASPM, clinical information, and PET/CT text features were analyzed using univariate and multivariate survival analyses. Finally, a correlation analysis was used to explore the relationship between ASPM and tumor metabolic heterogeneity.ResultsThere were 48 patients with NB in this study (35 boys and 13 girls); 22 patients progressed and 16 died. We found that the level of ASPM was highly associated with risk stratification (OR = 5.295, 95%IC: 1.348–41.722, p = 0.021). Patients with NB and high-risk stratification with high ASPM level had a lower 3-year progression-free survival (PFS) rate (14.28%) and 1-year PFS rate (57.14%) than those with low ASPM level (57.14% and 93.75%, respectively). Using univariate and multivariate survival analyses, this study revealed that ASPM and LDH were independent risk factors for both PFS and overall survival (OS), whales GLZLM_ZLNU was only a risk factor for PFS.ConclusionASPM holds promise as a novel biomarker for refining current risk stratification and predicting prognosis in neuroblastoma. Elevated levels of ASPM, LDH, and GLZLM_ZLNU may be associated with poorer survival outcomes in neuroblastoma patients.

Abstract C045: Targeting metabolic heterogeneity in pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death in the US. This poor prognosis is related to the complex tumor microenvironment and tumor heterogeneity that drives resistance to chemotherapy. Here the heterogeneity of the stromal, immune, and malignant epithelial cell populations enables signaling and metabolic crosstalk mechanisms that drive immune suppression and resistance to therapy. Recently, our group unveiled that multiple distinct metabolic subclasses of cancer cells coexist within individual PDAC tumors. Among these, two PDAC clonal populations can engage in metabolic symbiosis that supports mitochondrial metabolism. One subtype has a constitutively high integrated stress response (ISR), enabling resistance to mitochondrial inhibitors. Further, these cells release asparagine that supports the mitochondrial metabolism of the other subtype that are more sensitive to mitochondrial inhibition. Given these observations, we are developing approaches to disrupt this metabolic symbiosis. Clonal PDAC cells with constitutively active ISR exhibit increased sensitivity to inhibition of the redox activity of Redox factor 1/Apurinic-apyrimidinic endonuclease 1 (Ref-1). Importantly, this can be leveraged to disrupt the support of the mitochondrial metabolism of clonal symbiotic pairs. Moreover, polyclonal PDAC cell lines representative of the heterogeneity seen in PDAC patients show a highly synergistic response to the combination of Ref-1 redox inhibitors with mitochondrial inhibitors. These data suggest this treatment combination will be synergistic in polyclonal tumors that exist in most PDAC patients. Overall, these data have the potential to lead to new avenues to directly target heterogenous populations of cancer cells to treat pancreatic cancer- an urgent clinical need. Citation Format: Taryn Morningstar, Cecily Anaraki, Lorenzo Scipioni, Giulia Tedeschi, Michelle Digman, Aimee Edinger, Claus Jorgensen, Melissa Fishel, Christopher J Halbrook. Targeting metabolic heterogeneity in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C045.

Trehalose catalytic shift is an intrinsic factor in Mycobacterium tuberculosis that enhances phenotypic heterogeneity and multidrug resistance.

Drug-resistance (DR) in many bacterial pathogens often arises from the repetitive formation of drug-tolerant bacilli, known as persisters. However, it is unclear whether Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), undergoes a similar phenotypic transition. Recent metabolomics studies have identified that a change in trehalose metabolism is necessary for Mtb to develop persisters and plays a crucial role in metabolic networks of DR-TB strains. The present study used Mtb mutants lacking the trehalose catalytic shift and showed that the mutants exhibited a significantly lower frequency of the emergence of DR mutants compared to wildtype, due to reduced persister formation. The trehalose catalytic shift enables Mtb persisters to survive under bactericidal antibiotics by increasing metabolic heterogeneity and drug tolerance, ultimately leading to development of DR. Intriguingly, rifampicin (RIF)-resistant bacilli exhibit cross-resistance to a second antibiotic, due to a high trehalose catalytic shift activity. This phenomenon explains how the development of multidrug resistance (MDR) is facilitated by the acquisition of RIF resistance. In this context, the heightened risk of MDR-TB in the lineage 4 HN878 W-Beijing strain can be attributed to its greater trehalose catalytic shift. Genetic and pharmacological inactivation of the trehalose catalytic shift significantly reduced persister formation, subsequently decreasing the incidence of MDR-TB in HN878 W-Beijing strain. Collectively, the trehalose catalytic shift serves as an intrinsic factor of Mtb responsible for persister formation, cross-resistance to multiple antibiotics, and the emergence of MDR-TB. This study aids in the discovery of new TB therapeutics by targeting the trehalose catalytic shift of Mtb.

Multi-modal, Label-free, Optical Mapping of Cellular Metabolic Function and Oxidative Stress in 3D Engineered Brain Tissue Models.

Brain metabolism is essential for the function of organisms. While established imaging methods provide valuable insights into brain metabolic function, they lack the resolution to capture important metabolic interactions and heterogeneity at the cellular level. Label-free, two-photon excited fluorescence imaging addresses this issue by enabling dynamic metabolic assessments at the single-cell level without manipulations. In this study, we demonstrate the impact of spectral imaging on the development of rigorous intensity and lifetime label-free imaging protocols to assess dynamically over time metabolic function in 3D engineered brain tissue models comprising human induced neural stem cells, astrocytes, and microglia. Specifically, we rely on multi-wavelength spectral imaging to identify the excitation/emission profiles of key cellular fluorophores within human brain cells, including NAD(P)H, LipDH, FAD, and lipofuscin. These enable development of methods to mitigate lipofuscin's overlap with NAD(P)H and flavin autofluorescence to extract reliable optical metabolic function metrics from images acquired at two excitation wavelengths over two emission bands. We present fluorescence intensity and lifetime metrics reporting on redox state, mitochondrial fragmentation, and NAD(P)H binding status in neuronal monoculture and triculture systems, to highlight the functional impact of metabolic interactions between different cell types. Our findings reveal significant metabolic differences between neurons and glial cells, shedding light on metabolic pathway utilization, including the glutathione pathway, OXPHOS, glycolysis, and fatty acid oxidation. Collectively, our studies establish a label-free, non-destructive approach to assess the metabolic function and interactions among different brain cell types relying on endogenous fluorescence and illustrate the complementary nature of information that is gained by combining intensity and lifetime-based images. Such methods can improve understanding of physiological brain function and dysfunction that occurs at the onset of cancers, traumatic injuries and neurodegenerative diseases.

Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer

While heterogeneity is a key feature of cancer, understanding metabolic heterogeneity at the single-cell level remains a challenge. Here we present 13C-SpaceM, a method for spatial single-cell isotope tracing that extends the previously published SpaceM method with detection of 13C6-glucose-derived carbons in esterified fatty acids. We validated 13C-SpaceM on spatially heterogeneous models using liver cancer cells subjected to either normoxia-hypoxia or ATP citrate lyase depletion. This revealed substantial single-cell heterogeneity in labelling of the lipogenic acetyl-CoA pool and in relative fatty acid uptake versus synthesis hidden in bulk analyses. Analysing tumour-bearing brain tissue from mice fed a 13C6-glucose-containing diet, we found higher glucose-dependent synthesis of saturated fatty acids and increased elongation of essential fatty acids in tumours compared with healthy brains. Furthermore, our analysis uncovered spatial heterogeneity in lipogenic acetyl-CoA pool labelling in tumours. Our method enhances spatial probing of metabolic activities in single cells and tissues, providing insights into fatty acid metabolism in homoeostasis and disease.

Potential molecular metabolic mechanisms underlying the effects of cimifugin in gastric cancer through single-cell and bulk RNA sequencing combined with network pharmacology.

Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, posing a significant clinical challenge due to its complex tumor microenvironment (TME) and metabolic heterogeneity. Despite continuous improvements in treatment strategies including surgery, chemotherapy, and targeted therapies, the metabolic reprogramming in GC continues to impede treatment efficacy, highlighting an urgent need for the development of novel therapeutic strategies. This persistent issue underscores the urgent need for novel therapeutic approaches that can effectively address the diverse and dynamic characteristics of GC. Cimifugin, a traditional Chinese medicine (TCM), has garnered attention for its potential role in alleviating inflammation, neurological disorders, pain, and metabolic disorders. Its multi-targeting properties and minimal side effects suggest a broad potential for cancer management, which is currently being explored. This study aims to delineate the molecular mechanisms that cimifugin may impact within the TME and metabolic pathways of GC, with the expectation of contributing to a deeper understanding of GC and the development of innovative treatment strategies. We identified the GC-related TME cell types and metabolic profiles and pathways by using relevant data from the single-cell RNA sequencing (scRNA-seq) database GSE134520 and the stomach adenocarcinoma (STAD) data set from The Cancer Genome Atlas (TCGA). We also assessed the effects of cimifugin on MKN28 cell proliferation, invasion, and migration. By using six public platforms, we comprehensively predicted the potential biological targets of cimifugin. Clinical prognosis and immunohistochemistry (IHC), molecular docking, and dynamics simulations were used to confirm the clinical relevance and stability of the aforementioned targets. Cimifugin inhibited MKN28 cell proliferation, migration, and invasion. Cimifugin may potentially act on various metabolic pathways in GC, including folate biosynthesis, xenobiotic metabolism via cytochrome P450 (CYP), glutathione metabolism, steroid hormone biosynthesis, and tryptophan metabolism. Cimifugin was noted to stably bind to three significant core targets associated with metabolic reprogramming in GC: AKR1C2, MAOB, and PDE2A; all three targets were strongly expressed in endocrince cells, pit mucous cells (PMCs), and common myeloid progenitors (CMPs). We verified the pharmacological effects of cimifugin on GC cell proliferation, invasion, and migration. AKR1C2, MAOB, and PDE2A were identified as the key targets of cimifugin in GC-related metabolic reprogramming and pathogenesis. Our research provides preliminary insights into the potential therapeutic effects of cimifugin, which could be considered for future exploration in the context of GC treatment.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling

The integration of spatial omics technologies can provide important insights into the biology of tissues. Here we combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on a single tissue section to reveal metabolic heterogeneity at single-cell resolution within tissues and its association with specific cell populations such as cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components.

Insights into metabolic heterogeneity of colorectal cancer gained from fluorescence lifetime imaging.

Heterogeneity of tumor metabolism is an important, but still poorly understood aspect of tumor biology. Present work is focused on the visualization and quantification of cellular metabolic heterogeneity of colorectal cancer using fluorescence lifetime imaging (FLIM) of redox cofactor NAD(P)H. FLIM-microscopy of NAD(P)H was performed in vitro in four cancer cell lines (HT29, HCT116, CaCo2 and CT26), in vivo in the four types of colorectal tumors in mice and ex vivo in patients' tumor samples. The dispersion and bimodality of the decay parameters were evaluated to quantify the intercellular metabolic heterogeneity. Our results demonstrate that patients' colorectal tumors have significantly higher heterogeneity of energy metabolism compared with cultured cells and tumor xenografts, which was displayed as a wider and frequently bimodal distribution of a contribution of a free (glycolytic) fraction of NAD(P)H within a sample. Among patients' tumors, the dispersion was larger in the high-grade and early stage ones, without, however, any association with bimodality. These results indicate that cell-level metabolic heterogeneity assessed from NAD(P)H FLIM has a potential to become a clinical prognostic factor.

Insights into metabolic heterogeneity of colorectal cancer gained from fluorescence lifetime imaging

Heterogeneity of tumor metabolism is an important, but still poorly understood aspect of tumor biology. Present work is focused on the visualization and quantification of cellular metabolic heterogeneity of colorectal cancer using fluorescence lifetime imaging (FLIM) of redox cofactor NAD(P)H. FLIM-microscopy of NAD(P)H was performed in vitro in four cancer cell lines (HT29, HCT116, CaCo2 and CT26), in vivo in the four types of colorectal tumors in mice and ex vivo in patients’ tumor samples. The dispersion and bimodality of the decay parameters were evaluated to quantify the intercellular metabolic heterogeneity. Our results demonstrate that patients’ colorectal tumors have significantly higher heterogeneity of energy metabolism compared with cultured cells and tumor xenografts, which was displayed as a wider and frequently bimodal distribution of a contribution of a free (glycolytic) fraction of NAD(P)H within a sample. Among patients’ tumors, the dispersion was larger in the high-grade and early stage ones, without, however, any association with bimodality. These results indicate that cell-level metabolic heterogeneity assessed from NAD(P)H FLIM has a potential to become a clinical prognostic factor.

Attenuated effector T cells are linked to control of chronic HBV infection

Hepatitis B virus (HBV)-specific CD8+ T cells play a dominant role during acute-resolving HBV infection but are functionally impaired during chronic HBV infection in humans. These functional deficits have been linked with metabolic and phenotypic heterogeneity, but it has remained unclear to what extent different subsets of HBV-specific CD8+ T cells still suppress viral replication. We addressed this issue by deep profiling, functional testing and perturbation of HBV-specific CD8+ T cells during different phases of chronic HBV infection. Our data revealed a mechanism of effector CD8+ T cell attenuation that emerges alongside classical CD8+ T cell exhaustion. Attenuated HBV-specific CD8+ T cells were characterized by cytotoxic properties and a dampened effector differentiation program, determined by antigen recognition and TGFβ signaling, and were associated with viral control during chronic HBV infection. These observations identify a distinct subset of CD8+ T cells linked with immune efficacy in the context of a chronic human viral infection with immunotherapeutic potential.

The heterogeneity of cellular metabolism in the tumour microenvironment of hepatocellular carcinoma with portal vein tumour thrombus.

Given the growing interest in the metabolic heterogeneity of hepatocellular carcinoma (HCC) and portal vein tumour thrombus (PVTT). This study comprehensively analysed the metabolic heterogeneity of HCC, PVTT, and normal liver samples using multi-omics combinations. A single-cell RNA sequencing dataset encompassing six major cell types was obtained for integrated analysis. The optimal subtypes were identified using cluster stratification and validated using spatial transcriptomics and fluorescent multiplex immunohistochemistry. Then, a combined index based meta-cluster was calculated to verify its prognostic significance using multi-omics data from public cohorts. Our study first depicted the metabolic heterogeneity landscape of non-malignant cells in HCC and PVTT at multiomics levels. The optimal subtypes interpret the metabolic characteristics of PVTT formation and development. The combined index provided effective predictions of prognosis and immunotherapy responses. Patients with a higher combined index had a relatively poor prognosis (p <0.001). We also found metabolism of polyamines was a key metabolic pathway involved in conversion of metabolic heterogeneity in HCC and PVTT, and identified ODC1 was significantly higher expressed in PVTT compared to normal tissue (p =0.03). Our findings revealed both consistency and heterogeneity in the metabolism of non-malignant cells in HCC and PVTT. The risk stratification based on cancer-associated fibroblasts and myeloid cells conduce to predict prognosis and guide treatment. This offers new directions for understanding disease development and immunotherapy responses.

Biofluorescence imaging-guided spatial metabolic tracing: In vivo tracking of metabolic activity in circulating tumor cell-mediated multi-organ metastases

Circulating tumor cells (CTC) are considered metastatic precursors that are shed from the primary or metastatic deposits and navigate the bloodstream before undergoing extravasation to establish distant metastases. Metabolic reprogramming appears to be a hallmark of metastatic progression, yet current methods for evaluating metabolic heterogeneity within organ-specific metastases in vivo are limited. To overcome this challenge, we present Biofluorescence Imaging-Guided Spatial Metabolic Tracing (BIGSMT), a novel approach integrating in vivo biofluorescence imaging, stable isotope tracing, stain-free laser capture microdissection, and liquid chromatography-mass spectrometry. This innovative technology obviates the need for staining or intricate sample preparation, mitigating metabolite loss, and substantially enhances detection sensitivity and accuracy through chemical derivatization of polar metabolites in central carbon pathways. Application of BIGSMT to a preclinical CTC-mediated metastasis mouse model revealed significant heterogeneity in the in vivo carbon flux from glucose into glycolysis and the tricarboxylic acid (TCA) cycle across distinct metastatic sites. Our analysis indicates that carbon predominantly enters the TCA cycle through the enzymatic reaction catalyzed by pyruvate dehydrogenase. Thus, our spatially resolved BIGSMT technology provides fresh insights into the metabolic heterogeneity and evolution during melanoma CTC-mediated metastatic progression and points to novel therapeutic opportunities.

Combined High-Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer.

Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironment (TME) variability. Intra-tumor heterogeneity and its connections with metabolic reprogramming and epithelial-mesenchymal transition (EMT) were investigated with explorative shotgun proteomics complemented by a Random Forest (RF) machine-learning approach. Deep and superficial tumor regions and distant-site non-tumor samples from the same patients (n = 16) were analyzed. Among the 2009 proteins analyzed, 91 proteins, including 23 novel potential CRC hallmarks, showed significant quantitative changes. In addition, a 98.4% accurate classification of the three analyzed tissues was obtained by RF using a set of 21 proteins. Subunit E1 of 2-oxoglutarate dehydrogenase (OGDH-E1) was the best classifying factor for the superficial tumor region, while sorting nexin-18 and coatomer-beta protein (beta-COP), implicated in protein trafficking, classified the deep region. Down- and up-regulations of metabolic checkpoints involved different proteins in superficial and deep tumors. Analogously to immune checkpoints affecting the TME, cytoskeleton and extracellular matrix (ECM) dynamics were crucial for EMT. Galectin-3, basigin, S100A9, and fibronectin involved in TME-CRC-ECM crosstalk were found to be differently variated in both tumor regions. Different metabolic strategies appeared to be adopted by the two CRC regions to uncouple the Krebs cycle and cytosolic glucose metabolism, promote lipogenesis, promote amino acid synthesis, down-regulate bioenergetics in mitochondria, and up-regulate oxidative stress. Finally, correlations with the Dukes stage and budding supported the finding of novel potential CRC hallmarks and therapeutic targets.

Metabolic and Oxidative Stress Management Heterogeneity in a Panel of Breast Cancer Cell Lines.

Metabolic alterations are recognized as one of the hallmarks of cancer. Among these, alterations in mitochondrial function have been associated with an enhanced production of Reactive Oxygen Species (ROS), which activate ROS-regulated cancer cell signaling pathways. Breast cancer is the main cancer-related cause of death for women globally. It is a heterogeneous disease with subtypes characterized by specific molecular features and patient outcomes. With the purpose of identifying differences in energy metabolism and the oxidative stress management system in non-tumorigenic, estrogen receptor positive (ER+) and triple negative (TN) breast cancer cells, we evaluated ROS production, protein enzyme levels and activities and profiled energy metabolism. We found differences in energetic metabolism and ROS management systems between non-tumorigenic and cancer cells and between ER+ and TN breast cancer cells. Our results indicate a dependence on glycolysis despite different glycolytic ATP levels in all cancer cell lines tested. In addition, our data show that high levels of ROS in TN cells are a result of limited antioxidant capacity in the NADPH producing and GSH systems, mitochondrial dysfunction and non-mitochondrial ROS production, making them more sensitive to GSH synthesis inhibitors. Our data suggest that metabolic and antioxidant profiling of breast cancer will provide important targets for metabolic inhibitors or antioxidant treatments for breast cancer therapy.

ELIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity.

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

Metabolic reprogramming and heterogeneity during the decidualization process of endometrial stromal cells

The human endometrial decidualization is a transformative event in the pregnant uterus that involves the differentiation of stromal cells into decidual cells. While crucial to the establishment of a successful pregnancy, the metabolic characteristics of decidual cells in vivo remain largely unexplored. Here, we integrated the single-cell RNA sequencing (scRNA-seq) datasets on the endometrium of the menstrual cycle and the maternal-fetal interface in the first trimester to comprehensively decrypt the metabolic characteristics of stromal fibroblast cells. Our results revealed that the differentiation of stromal cells into decidual cells is accompanied by increased amino acid and sphingolipid metabolism. Furthermore, metabolic heterogeneity exists in decidual cells with differentiation maturity disparities. Decidual cells with high metabolism exhibit higher cellular activity and show a strong propensity for signaling. In addition, significant metabolic reprogramming in amino acids and lipids also occurs during the transition from non-pregnancy to pregnancy in the uteri of pigs, cattle, and mice. Our analysis provides comprehensive insights into the dynamic landscape of stromal fibroblast cell metabolism, contributing to our understanding of the metabolism at the molecular dynamics underlying the decidualization process in the human endometrium.

Rapid Screening of Biomarkers in KYSE-150 Cells Exposed to Polycyclic Aromatic Hydrocarbons via Inkjet Printing Single-Cell Mass Spectrometry.

Single-cell analysis by mass spectrometry (MS) is emerging as a powerful tool that not only contributes to cellular heterogeneity but also offers an unprecedented opportunity to predict pathology onset and facilitates novel biomarker discovery. However, the development of single-cell MS analysis techniques with a focus on sample extraction, separation, and ionization methods for volume-limited samples and complexity of cellular samples are still a big challenge. In this study, we present a high-throughput approach to inkjet drop on demand printing single-cell MS for rapid screening of biomarkers of polycyclic aromatic hydrocarbon (PAH) exposure at the KYSE-150 cell, aiming to elucidate the pathogenesis of PAH-induced esophageal cancer. With an analytical bulk KYSE-150 cell throughput of up to 51 cells per minute, the method provides a new opportunity for simultaneous single-cell analysis of multiple biomarkers. We screened 930 characteristic ions from 3,683 detected peak signals and identified 91 distinctive molecules that exhibited significant differences under various concentrations of PAH exposure. These molecules have potential as clinical diagnostic biomarkers. Additionally, the current study identifies specific biomarkers that behave completely opposite in single-cell and multicell lipidomics as the concentration of PAH changes. These biomarkers potentially subdivide KYSE-150 cells into PAH-sensitive and PAH-insensitive types, providing a basis for revealing PAH toxicity and disease pathogenesis from the heterogeneity of cellular metabolism.