Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data

Lei Guo, Jiyang Dong*, Xiangnan Xu, Zhichao Wu, Yinbin Zhang, Yongwei Wang, Pengfei Li, Zhi Tang, Chao Zhao*, Zongwei Cai*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.

Original languageEnglish
Pages (from-to)1924–1932
Number of pages9
JournalAnalytical Chemistry
Volume95
Issue number3
Early online date12 Jan 2023
DOIs
Publication statusPublished - 24 Jan 2023

Scopus Subject Areas

  • Analytical Chemistry

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