MITI minimum information guidelines for highly multiplexed tissue images.

@article{Schapiro2022MITIMI,
  title={MITI minimum information guidelines for highly multiplexed tissue images.},
  author={Denis Schapiro and Clarence Yapp and Artem Sokolov and Sheila M. Reynolds and Yu-An Chen and Damir Sudar and Yubin Xie and Jeremy L. Muhlich and Raquel Arias-Camison and Sarah Arena and Adam J Taylor and Milen Nikolov and Madison Tyler and Jia-Ren Lin and Erik A. Burlingame and Young Hwan Chang and Samouil L. Farhi and V{\'e}steinn Thorsson and Nithya Venkatamohan and Julia L. Drewes and Dana Pe’er and David A. Gutman and Markus D. Herrmann and Nils Gehlenborg and Peter Bankhead and Joseph T. Roland and John M. Herndon and Michael Paul Snyder and Michael Angelo and Garry P. Nolan and Jason R. Swedlow and Nikolaus D. Schultz and Daniel T Merrick and Sarah A Mazzili and Ethan G. Cerami and Scott J. Rodig and Sandro Santagata and Peter K. Sorger},
  journal={Nature methods},
  year={2022},
  volume={19 3},
  pages={
          262-267
        }
}
The imminent release of tissue atlases combining multichannel microscopy with single-cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards to 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 for other microscopy data to highly multiplexed tissue images and traditional histology. 

MIAAIM: Multi-omics image integration and tissue state mapping using topological data analysis and cobordism learning

MIAAIM (Multi-omics Image Alignment and Analysis by Information Manifolds) is introduced a modular, reproducible computational framework for aligning data across bioimaging technologies, modeling continuities in tissue states, and translating multimodal measures across tissue types.

Multi-modal digital pathology for colorectal cancer diagnosis by high-plex immunofluorescence imaging and traditional histology of the same tissue section

Using data from 40 human colorectal cancer resections, it is shown that IF and H&E images provide human experts and machine learning algorithms with complementary information and the automated generation and ranking of computational models that are highly predictive of progression-free survival are demonstrated.

Stitching and registering highly multiplexed whole-slide images of tissues and tumors using ASHLAR

The development and testing of ASHLAR, a Python tool for coordinated stitching and registration of 103 or more individual multiplexed images to generate accurate whole-slide mosaics, are described and it is shown that it performs better than existing open source and commercial software.

Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data.

The ecosystem of software available for the analysis of tissue images is described and the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists are discussed, via a software interface (Minerva) that integrates multi-omic and tissue-atlas features.

A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels

A structural similarity index measure (SSIM) guided conditional Generative Adversarial Network (cGAN) that generatively performs image-to-image (i2i) synthesis to generate photo-accurate protein channels in multiplexed spatial proteomics images to allow researchers and clinicians to save time and money.

Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology

This narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods, by providing a comparison of different methods.

The emerging landscape of spatial profiling technologies

It is envisioned that spatial methods will map entire organs and enable next-generation pathology, and the developing landscape of in situ spatial transcriptome, genome and proteome technologies are reviewed to highlight their impact on basic and translational research.

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

An overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications are provided.

Developing image analysis methods for digital pathology

  • P. Bankhead
  • Computer Science
    The Journal of pathology
  • 2022
The need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms is described, and it is argued that openness, implementation, and usability deserve more attention among digital pathology researchers.

The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution

It is found that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor.

References

SHOWING 1-10 OF 94 REFERENCES

Online narrative guides for illuminating tissue atlas data and digital pathology images

There is a need for interactive guides or “digital docents” that allow experts to help make complex images intelligible that are being integrated into multi-omic browsers for effective dissemination of atlas data.

MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging

A modular and open-source computational pipeline (MCMICRO) for performing the sequential steps needed to transform large, multi-channel whole slide images into single-cell data is described, providing a solid foundation for the continued development of tissue imaging software.

Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser

The ecosystem of software available for atlas and histopathology images is reviewed and a new Web-based software tool, Minerva Story, is introduced that addresses a critical unmet need.

REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology.

Draft metadata guidelines are proposed to begin addressing the needs of diverse communities within light and electron microscopy and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.

QuPath: Open source software for digital pathology image analysis

QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images, making it suitable for a wide range of additional image analysis applications across biomedical research.

Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging

This protocol provides step-by-step procedures for confirming the selectivity and specificity of antibodies used in fluorescence-based tissue imaging and for the construction and validation of antibody panels for FFPE tissue sections using cyclic immunofluorescence (t-CyCIF) or other multiplexed imaging methods.

Multiplexed ion beam imaging ( MIBI ) of human breast tumors

A method that uses secondary ion mass spectrometry to image antibodies tagged with isotopically pure elemental metal reporters to suggest that MIBI will provide new insights by integrating tissue microarchitecture with highly multiplexed protein expression patterns, and will be valuable for basic research, drug discovery and clinical diagnostics.

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

This work demonstrates how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology, and introduces a new hierarchical approach to keep track of analysis steps and data subsets created by users.

Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes

Tissue-based cyclic immunofluorescence method for highly multiplexed immuno-fluorescence imaging of formalin-fixed, paraffin-embedded specimens mounted on glass slides, the most widely used specimens for histopathological diagnosis of cancer and other diseases is described.
...