A graphical data processing pipeline for mass spectrometry imaging-based spatially resolved metabolomics on tumor heterogeneity

Huang, Luojiao; Mao, Xinxin; Sun, Chenglong; Luo, Zhigang; Song, Xiaowei; X, Li; Zhang, Ruiping; Lv, Yiwei; Chen, Jie; Abliz, Zeper

doi:10.1016/j.aca.2019.05.068

Cited by 28 publications

(18 citation statements)

References 41 publications

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“…Further, another advantage of DESI is its capability for highthroughput analysis as minimal sample preparation is needed, where, for example, coating samples with matrices is not required. DESI-MSI-based spatially resolved metabolomics in different cancer studies have provided new insights into the understanding of tumorassociated metabolic reprogramming and provided a novel approach for functional metabolites based molecular histology (He et al 2018;Huang et al 2019;Sun et al 2019). In addition, DESI-MSI has previously been employed to analyze kidney tissues and other tissue samples (Eberlin et al 2013(Eberlin et al , 2014Wiseman et al 2008;Zhang et al 2017) although diabetic changes in mouse kidneys have not previously reported.…”

Section: Introductionmentioning

confidence: 99%

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

Zhang

DeHoog

et al. 2020

Metabolomics

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Section: Introductionmentioning

confidence: 99%

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

Zhang

DeHoog

et al. 2020

Metabolomics

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“…In the same way, spatial analysis of esophageal cancer showed that metabolite profile can be extremely variable within the cancerous tissue: in particular, metabolites such as amino acids, fatty acids, and nucleosides were found to be unevenly distributed in the the tumoral milieu [114]. Heterogenous metabolite distribution within single tumors was also confirmed in colorectal [115], gastric [116], prostate [117], and papillary thyroid cancers [118].…”

Section: Metabolic Intra-tumor Heterogeneitymentioning

confidence: 82%

Metabolic Constrains Rule Metastasis Progression

Roda

Gambino

2020

Cells

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Metastasis formation accounts for the majority of tumor-associated deaths and consists of different steps, each of them being characterized by a distinctive adaptive phenotype of the cancer cells. Metabolic reprogramming represents one of the main adaptive phenotypes exploited by cancer cells during all the main steps of tumor and metastatic progression. In particular, the metabolism of cancer cells evolves profoundly through all the main phases of metastasis formation, namely the metastatic dissemination, the metastatic colonization of distant organs, the metastatic dormancy, and ultimately the outgrowth into macroscopic lesions. However, the metabolic reprogramming of metastasizing cancer cells has only recently become the subject of intense study. From a clinical point of view, the latter steps of the metastatic process are very important, because patients often undergo surgical removal of the primary tumor when cancer cells have already left the primary tumor site, even though distant metastases are not clinically detectable yet. In this scenario, to precisely elucidate if and how metabolic reprogramming drives acquisition of cancer-specific adaptive phenotypes might pave the way to new therapeutic strategies by combining chemotherapy with metabolic drugs for better cancer eradication. In this review we discuss the latest evidence that claim the importance of metabolic adaptation for cancer progression.

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“…The commercial format of raw data files obtained by DESI-MSI was first converted into the commonly accessible date format cdf files using Xcalibur (Thermo Fisher Scientific, San Jose, CA, US). Massimager (Chemmind Technologies, Beijing, China) was employed for the construction of ion images and co-localisation with the optical image of H&E staining [ 25 , 26 ]. The ion images were plotted and manually segmented into regions of interest (five regions), as determined by histopathological evaluation.…”

Section: Methodsmentioning

confidence: 99%

In situ DESI-MSI lipidomic profiles of mucosal margin of oral squamous cell carcinoma

et al. 2021

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Background Although there is consensus that the optimal safe margin is ≥ 5mm, obtaining clear margins (≥5 mm) intraoperatively seems to be the major challenge. We applied a molecular diagnostic method at the lipidomic level to determine the safe surgical resection margin of OSCC by desorption electrospray ionisation mass spectrometry imaging (DESI-MSI). Methods By overlaying mass spectrometry images with hematoxylin-eosin staining (H&E) from 18 recruited OSCC participants, the mass spectra of all pixels across the diagnosed tumour and continuous mucosal margin regions were extracted to serve as the training and validation datasets. A Lasso regression model was used to evaluate the test performance. Findings By leave-one-out validation, the Lasso model achieved 88.6% accuracy in distinguishing between tumour and normal regions. To determine the safe surgical resection distance and margin status of OSCC, a set of 14 lipid ions that gradually decreased from tumour to normal tissue was assigned higher weight coefficients in the Lasso model. The safe surgical resection distance of OSCC was measured using the developed 14 lipid ion molecular diagnostic model for clinical reference. The overall accuracy of predicting tumours, positive margins, and negative margins was 92.6%. Interpretation The spatial segmentation results based on our diagnostic model not only clearly delineated the tumour and normal tissue, but also distinguished the different status of surgical margins. Meanwhile, the safe surgical resection margin of OSCC on frozen sections can also be accurately measured using the developed diagnostic model. Funding This study was supported by Nanjing Municipal Key Medical Laboratory Constructional Project Funding (since 2016) and the Centre of Nanjing Clinical Medicine Tumour (since 2014).

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A graphical data processing pipeline for mass spectrometry imaging-based spatially resolved metabolomics on tumor heterogeneity

Cited by 28 publications

References 41 publications

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules

Metabolic Constrains Rule Metastasis Progression

In situ DESI-MSI lipidomic profiles of mucosal margin of oral squamous cell carcinoma

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