PXD038876

PXD038876 is an original dataset announced via ProteomeXchange.

Title	Delineating Regions-of-interest forMass Spectrometry Imaging byMultimodally Corroborated Spatial Segmentation
Description	Mass spectrometry imaging, which quantifies molecules in a tag-free, spatially-resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate Regions-of-Interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (\#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate \#Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A Deep Learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire H\&E image. Clustering is then similarly performed to produce Histology-segmentation. Since ROIs originating from instrumental noise or artifacts wouldn't be reproduced cross-modally, the consistency between histology- and MSI-segmentation becomes an effective measure of the biological validity of the results. So, \#Clusters that maximizes the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.
HostingRepository	iProX
AnnounceDate	2023-03-08
AnnouncementXML	Submission_2023-04-18_19:22:33.602.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Ang Guo
SpeciesList	scientific name: Mus musculus; NCBI TaxID: 10090;
ModificationList	No PTMs are included in the dataset
Instrument	SYNAPT G2-Si

Revision	Datetime	Status	ChangeLog Entry
0	2022-12-17 20:24:41	ID requested
1	2022-12-17 20:24:53	announced
⏵ 2	2023-04-18 19:22:35	announced	2023-04-19: Update publication information.

Guo A, Chen Z, Li F, Luo Q, Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation. Gigascience, 12():(2022) [pubmed]

submitter keyword: Mass spectrometry imaging, Spatial segmentation, Multimodal data fusion

Qian Luo
contact affiliation	Shenzhen Institute of Science and Technology, Chinese Academy of Sciences
contact email	qian.luo@siat.ac.cn
lab head
Ang Guo
contact affiliation	Shenzhen Institute of Science and Technology, Chinese Academy of Sciences
contact email	ang.guo@siat.ac.cn
dataset submitter

iProX dataset URI