MITI minimum information guidelines for highly multiplexed tissue images

The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide 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.

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“…One of the other challenges of high-plex spatial imaging concerns imaging data management and metadata management [101].…”

Section: Data Managementmentioning

confidence: 99%

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Dam

Baars

Vercoulen

2022

Cancers

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“…Much, therefore, remains to be discovered from the images we have collected. Full resolution Level 3 images (104) and associated single-cell data are therefore being released in their entirety, without restriction, for follow-on analysis.…”

Section: Limitations Of This Studymentioning

confidence: 99%

“…A specimen from a single patient MEL1 (samples MEL1-1, MEL1-2, and MEL1-3) was selected for deeper profiling with CyCIF and high-resolution imaging, in addition to microregion transcriptomics (PickSeq, GeoMX). The clinical, biospecimen, and imaging level metadata were all collected following the MITI standards (104).…”

Section: Clinical Samplesmentioning

confidence: 99%

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

Nirmal

Maliga

Vallius

et al. 2021

Preprint

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Cutaneous melanoma is a highly immunogenic disease, surgically curable at early stages, but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially-resolved micro-region transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis from precursor states to melanoma in situ to invasive tumor. Hallmarks of immunosuppression are detectable by the precursor stage, and when tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1 and by PDL1-expressing macrophages and dendritic cells engaging activated T cells. However, a few mm away, T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can co-exist within a few millimeters of each other in a single specimen.

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“…ASHLAR operates in three broad phases to convert a multi-cycle multi-tile (Level 1) dataset into a cohesive (Level 2) mosaic image (Schapiro et al, 2022b) ( Figure 1 ): (i) tiles within the first imaging cycle are stitched; (ii) tiles from the second and subsequent cycles are registered to corresponding tiles from the first cycle; and (iii) all tiles from all cycles are merged into a mosaic image. The output of stitching and registration is a list of new, corrected positions for all tiles in each cycle.…”

Section: Methodsmentioning

confidence: 99%

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

Yuan

Russell

et al. 2021

Preprint

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Widespread use of highly multiplexed microscopy to study normal and diseased tissues at a single-cell level is complicated by underdevelopment of the necessary software. This is particularly true of high resolution whole-slide imaging (WSI), which involves gigapixel datasets of specimens as large as 5 cm2. WSI is necessary for accurate spatial analysis and a diagnostic necessity. High resolution WSI requires collection of successive image tiles; multiplexing commonly involves successive data acquisition cycles, each with a subset of dyes, antibodies or oligonucleotides. We describe a new Python tool, ASHLAR (Alignment by Simultaneous Harmonization of Layer/Adjacency Registration), that coordinates stitching and registration and scales to 103 or more image tiles over many imaging cycles to generate accurate, high-plex image mosaics, the key type of data for downstream visualization and computational analysis. ASHLAR is more robust and accurate than existing methods and compatible with any scanner or microscope conforming to Open Microscopy Environment standards.

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MITI minimum information guidelines for highly multiplexed tissue images

Cited by 41 publications

References 41 publications

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

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

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

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