Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics

Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.

Discovering metabolic vulnerability using spatially resolved metabolomics for antitumor small molecule-drug conjugates development as a precise cancer therapy strategy

Against tumor-dependent metabolic vulnerability is an attractive strategy for tumor-targeted therapy.However,metabolic inhibitors are limited by the drug resistance of cancerous cells due to their metabolic plasticity and heterogeneity.Herein,choline metabolism was discovered by spatially resolved metabolomics analysis as metabolic vulnerability which is highly active in different cancer types,and a choline-modified strategy for small molecule-drug conjugates(SMDCs)design was developed to fool tumor cells into indiscriminately taking in choline-modified chemotherapy drugs for targeted cancer therapy,instead of directly inhibiting choline metabolism.As a proof-of-concept,choline-modified SMDCs were designed,screened,and investigated for their druggability in vitro and in vivo.This strategy improved tumor targeting,preserved tumor inhibition and reduced toxicity of paclitaxel,through targeted drug delivery to tumor by highly expressed choline transporters,and site-specific release by carboxylesterase.This study expands the strategy of targeting metabolic vulnerability and provides new ideas of developing SMDCs for precise cancer therapy.

Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients

Background:The response to neoadjuvant chemotherapy(NAC)differs substantially among individual patients with non-small cell lung cancer(NSCLC).Major pathological response(MPR)is a histomorphological read-out used to assess treatment response and prognosis in patientsNSCLC afterNAC.Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes,it has not yet been utilized to assess therapy responses in patients with NSCLC.We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC,using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning.Methods:Resected NSCLC tissue specimens obtained after NAC(n=88)were subjected to high-resolution mass spectrometry,and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC.The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy-naive patients with NSCLC(n=85).Results:The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6%accuracy,whereas the stroma metabolic classifier displayed 78.4%accuracy.By contrast,the accuracies of MPR and TNM staging for stratification were 62.5%and 54.1%,respectively.The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier.In multivariate analysis,metabolic classifiers were the only independent prognostic factors identified(tumor:P=0.001,hazards ratio[HR]=3.823,95%confidence interval[CI]=1.716-8.514;stroma:P=0.049,HR=2.180,95%CI=1.004-4.737),whereasMPR(P=0.804;HR=0.913;95%CI=0.445-1.874)and TNM staging(P=0.078;HR=1.223;95%CI=0.977-1.550)were not independent prognostic factors.Using Kaplan-Meier survival analyses,both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders(P<0.001)and non-responders(P<0.001).Conclusions:Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology-based assessment of tumor response.

Dissecting the brain with spatially resolved multi-omics

Recent studies have highlighted spatially resolved multi-omics technologies,including spatial genomics,transcriptomics,proteomics,and metabolomics,as powerful tools to decipher the spatial heterogeneity of the brain.Here,we focus on two major approaches in spatial transcriptomics(next-generation sequencing-based technologies and image-based technologies),and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics.Furthermore,we discuss their applications in neuroscience,including building the brain atlas,uncovering gene expression patterns of neurons for special behaviors,deciphering the molecular basis of neuronal communication,and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders.However,further efforts are still needed toward the integrative application of multi-omics technologies,including the real-time spatial multi-omics analysis in living cells,the detailed gene profile in a whole-brain view,and the combination of functional verification.

Promise of spatially resolved omics for tumor research

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures.By interacting with the microenvironment,tumor cells undergo dynamic changes in gene expression and metabolism,resulting in spatiotemporal variations in their capacity for proliferation and metastasis.In recent years,the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis.By preserving location information while obtaining a large number of gene and molecular data,spatially resolved metabolomics(SRM)and spatially resolved transcriptomics(SRT)approaches can offer new ideas and reliable tools for the in-depth study of tumors.This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques,as well as a review of their applications in cancer-related fields.

Design and characterizing of robust probes for enhanced mass spectrometry imaging and spatially resolved metabolomics

As for the emerging and cut edge spatially resolved metabolomics,mass spectrometry imaging(MSI)is a powerful tool that can map thousands of metabolites from bio-tissue sections without chemical labels.However,the stability,sensitivity and spatial resolution of MSI are always limited by the performance of its ionization probe.Herein,two types of probes(fine probe(P-100)and large probe(P-200))were designed and characterized to perform air-flow assisted desorption electrospray ionization(AFA-DESI)MSI analysis for spatially resolved metabolomics.It was determined that the spray introduced by P-100 was homogenous and stable under the spray solvent at a flow rate of 5-10μL/min,while P-200 can endure a high flow rate of up to 10-30μL/min.Moreover,the MSI images were acquired by AFA-DESI-MSI with P-100 from rat brain tissue section and with P-200 from whole-body tissue section of mouse,and these results presented unambiguous tissue structure with the distribution information of numerous metabolites.Furthermore,the spatially resolved metabolomic analysis of tumor tissue was successfully realized to discover the tumor associated biomarkers.As the key parts of AFA-DESI-MSI system,it has been demonstrated that the designed probs have excellent performance for spatially resolved metabolomics,and it will further promote its application in life science,and drug research and development.

Single-cell and spatial multi-omics in the plant sciences:Technical advances,applications,and perspectives

Plants contain a large number of cell types and exhibit complex regulatory mechanisms.Studies at the single-cell level have gradually become more common in plant science.Single-cell transcriptomics,spatial transcriptomics,and spatial metabolomics techniques have been combined to analyze plant development.These techniques have been used to study the transcriptomes and metabolomes of plant tissues at the single-cell level,enabling the systematic investigation of gene expression and metabolism in specific tissues and cell types during defined developmental stages.In this review,we present an overview of significant breakthroughs in spatial multi-omics in plants,and we discuss how these approaches may soon play essential roles in plant research.

Recent advances in proteomics and metabolomics in plants

Over the past decade,systems biology and plant-omics have increasingly become the main stream in plant biology research.New developments in mass spectrometry and bioinformatics tools,and methodological schema to inte-grate multi-omics data have leveraged recent advances in proteomics and metabolomics.These progresses are driv-ing a rapid evolution in the field of plant research,greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment.Here,we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response,gene/protein function characterization,metabolic and signaling pathways exploration,and natural product discovery.We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology.This review is intended to provide readers with an overview of how advanced MS technology,and integrated application of proteomics and metabolomics can be used to advance plant system biology research.