MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure
By Leeat Keren, M. Bosse, S. Thompson, Tyler T. Risom, Kausalia Vijayaragavan, E. Mccaffrey, Diana Marquez, Roshan Angoshtari, N. Greenwald, Harris G. Fienberg and 9 more. Science Advances, 2019
373 citations recorded by the source corpus.
Multiplexed, high resolution analysis with MIBI-TOF reveals surprising features of the immune environment across tumor regions. Understanding tissue structure and function requires tools that quantify the expression of multiple proteins while preserving spatial information. Here, we describe MIBI-TOF (multiplexed ion beam imaging by time of flight), an instrument that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. We demonstrate quantitative, full periodic table coverage across a five-log dynamic range, imaging 36 labeled antibodies simultaneously with histochemical stains and endogenous elements. We image fields of view up to 800 μm × 800 μm at resolutions down to 260 nm with sensitivities approaching single-molecule detection. We leverage these properties to interrogate intrapatient heterogeneity in tumor organization in triple-negative breast cancer, revealing regional variability in tumor cell phenotypes in contrast to a structured immune response. Given its versatility and sample back-compatibility, MIBI-TOF is positioned to leverage existing annotated, archival tissue cohorts to explore emerging questions in cancer, immunology, and neurobiology.
Subfields: Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/77bea22796aecff8c1f62500552c09f8ad06cd18
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In Molecular Biology
Citations in the corpus
- An introduction to spatial transcriptomics for biomedical research (2022, methodology): “A recent method, multiplexed ion beam imaging by time of flight (MIBI-TOF), and imaging mass cytometry (IMC) have resolutions of 1 μm or less when profiling 36–40 proteins [23, 68].”
- Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning (2021, methodology): “Developing this algorithm required two innovations: (1) a scalable approach for generating large volumes of pixel-level training data and (2) an integrated deep learning pipeline that uses these data to achieve humanlevel performance.”
- DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, methodology): “MERFISH (11), 4i (9) and MIBI-TOF (10)), in particular, three-dimensional (3D) expression domains are extracted on MERFISH data.”