Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures

Zang, Qingce; Sun, Chenglong; Chu, Xiaoping; Li, Limei; Gan, Wenxia; Zhao, Zitong; Song, Yongmei; Zhang, Ruiping; Abliz, Zeper

doi:10.1016/j.aca.2021.338342

Cited by 34 publications

(29 citation statements)

References 45 publications

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“…A similar approach could therefore give a valuable insight into 3D tumour aggregoid models of OS, providing comprehensive information of biochemical pathways that influence the cancer pathogenesis. In addition to this, a recent paper demonstrated the combination of oesophageal cancer multicellular tumour spheroids (MCTS) and clinical tissue with MALDI-MSI as an approach to determine the metabolic relevance of the models to in vivo [ 15 ]. With that in mind, detailed molecular characterisation of in vitro OS models and clinical tissue would improve the understanding of cancer metabolism within the 3D cultures and highlight biomarkers of patient outcome.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Osteosarcoma Aggregated Tumour Models with Human Tissue by Multimodal Mass Spectrometry Imaging

et al. 2021

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Osteosarcoma (OS) is the most common primary bone malignancy and largely effects adolescents and young adults, with 60% of patients under the age of 25. There are multiple cell models of OS described in vitro that express the specific genetic alterations of the sarcoma. In the work reported here, multiple mass spectrometry imaging (MSI) modalities were employed to characterise two aggregated cellular models of OS models formed using the MG63 and SAOS-2 cell lines. Phenotyping of the metabolite activity within the two OS aggregoid models was achieved and a comparison of the metabolite data with OS human tissue samples revealed relevant fatty acid and phospholipid markers. Although, annotations of these species require MS/MS analysis for confident identification of the metabolites. From the putative assignments however, it was suggested that the MG63 aggregoids are an aggressive tumour model that exhibited metastatic-like potential. Alternatively, the SAOS-2 aggregoids are more mature osteoblast-like phenotype that expressed characteristics of cellular differentiation and bone development. It was determined the two OS aggregoid models shared similarities of metabolic behaviour with different regions of OS human tissues, specifically of the higher metastatic grade.

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Section: Introductionmentioning

confidence: 99%

Comparison of Osteosarcoma Aggregated Tumour Models with Human Tissue by Multimodal Mass Spectrometry Imaging

et al. 2021

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“…In addition, similarities in metabolic signatures including the glutamine, fatty acid and phospholipid metabolism pathways were recently identified between KYSE-30 oesophageal cancer spheroids and clinical patient tissue samples determined by MALDI-MSI at a greater spatial resolution (12 µm) (Zang et al, 2021). The corresponding detection of metabolites to in vivo demonstrated the spheroid model as an accurate representative of cancer metabolism.…”

Section: Characterisation Of Tumour Spheroids By Msimentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

“…MALDI-MSI is the most used technique applied to metabolomics studies of disease characterisation and drug effects (Irie et al, 2014;Swales et al, 2018;Rzagalinski et al, 2019). The study of metabolomics by MALDI-MSI has been extended to understand the pathophysiology of 3D cell cultures (Tucker et al, 2019;Zang et al, 2021). Additionally, the detection of drug metabolism and druginduced metabolic changes within phenotypic regions of 3D models has also been demonstrated (Liu et al, 2013;Palubeckaitė et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Applications of MALDI-MSI have characterised colon carcinoma and breast cancer spheroid models based on their endogenous metabolite and protein distributions(Li & Hummon, 2011;Hiraide et al, 2016; Tucker et al, 2019). More recently, MALDI-MSI detected similar tendencies in metabolic activity between KYSE-30 oesophageal cancer MCTS and clinical tissue samples(Zang et al, 2021). In addition, progress in the development of the ambient technique, single-probe MSI, was able to spatially resolve endogenous metabolites within colon carcinoma MCTS(Tian et al,…”

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confidence: 97%

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Imaging Three-Dimensional Cell-Culture Models for Pre-Clinical Biopharmaceutical Testing

Flint¹

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Mass spectrometry imaging (MSI) is a multiplex methodology that has the capability to map molecular distributions within biological tissues via an unlabelled approach. The ability of MSI to detect a drug, its active metabolites and the molecular changes to treatment has demonstrated value for pre-clinical efficacy and toxicity studies in the pharmaceutical industry. Parallel to the developments in MSI, three-dimensional (3D) cell cultures have emerged as biologically relevant models for pre-clinical therapeutic research in part as an attempt to address the economic and societal issues of the use of animals in science. The combination of MSI with 3D cell culture models has previously been shown to provide an elegant method for small molecule drug analysis; however, with recent progress in the development of biopharmaceuticals there are increasing demands for advancements in this field.

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Metabolomics and cellular altered pathways in cancer biology: A review

Bhardwaj,

Siwach,

Sachdeva

2024

J Biochem & Molecular Tox

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Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other “‐omics” technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

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Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures

Cited by 34 publications

References 45 publications

Comparison of Osteosarcoma Aggregated Tumour Models with Human Tissue by Multimodal Mass Spectrometry Imaging

Comparison of Osteosarcoma Aggregated Tumour Models with Human Tissue by Multimodal Mass Spectrometry Imaging

Imaging Three-Dimensional Cell-Culture Models for Pre-Clinical Biopharmaceutical Testing

Metabolomics and cellular altered pathways in cancer biology: A review

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