MITI Minimum Information guidelines for highly multiplexed tissue images
Denis Schapiro, Clarence Yapp, Artem Sokolov, Sheila M. Reynolds,, Yu-An Chen, Damir Sudar, Yubin Xie, Jeremy L. Muhlich, Raquel Arias-Camison,, Sarah Arena, Adam J. Taylor, Milen Nikolov, Madison Tyler, Jia-Ren Lin, Erik, A. Burlingame, Human Tumor Atlas Network, Young H. Chang
TL;DR
The paper introduces MITI, a standard for metadata and data sharing in highly multiplexed tissue imaging, facilitating integration with genomics and histology data for tissue atlases.
Contribution
It proposes the MITI guidelines, adapting best practices from genomics and microscopy to standardize highly multiplexed tissue imaging data.
Findings
MITI standard enables consistent data deposition and curation.
Facilitates integration of tissue imaging with other omics data.
Supports development of comprehensive tissue atlases.
Abstract
The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.
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Taxonomy
TopicsSingle-cell and spatial transcriptomics · Cancer Genomics and Diagnostics · Radiomics and Machine Learning in Medical Imaging