TY - JOUR
T1 - Divide and Conquer
T2 - A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data
AU - Guo, Lei
AU - Dong, Jiyang
AU - Xu, Xiangnan
AU - Wu, Zhichao
AU - Zhang, Yinbin
AU - Wang, Yongwei
AU - Li, Pengfei
AU - Tang, Zhi
AU - Zhao, Chao
AU - Cai, Zongwei
N1 - Funding Information:
The work was supported by the National Natural Science Foundation of China (91843301, 81871445, 22176195 and 21876116); the National Key Research Program of China (2017YFC1600505 and 2017YFE0191000); the Natural Science Foundation of Fujian Province, China (2022Y0003); the Natural Science Foundation of Guangdong Province, China (2021A1515010171); Key Program of Fundamental Research in Shenzhen (JCYJ20210324115811031); and Shenzhen Key Laboratory for Accurate Diagnosis and Treatment of Depression (2023).
Publisher copyright:
© 2023 The Authors. Published by American Chemical Society
PY - 2023/1/24
Y1 - 2023/1/24
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85146323221&partnerID=8YFLogxK
U2 - 10.1021/acs.analchem.2c04045
DO - 10.1021/acs.analchem.2c04045
M3 - Article
C2 - 36633187
VL - 95
SP - 1924
EP - 1932
JO - Analytical Chemistry
JF - Analytical Chemistry
SN - 0003-2700
IS - 3
ER -