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OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration

Bethany Hunter, Ioana Nicorescu, Emma Foster, David McDonald, Gillian Hulme, Andrew Fuller, Amanda Thomson, Thibaut Goldsborough, Catharien M. U. Hilkens, Joaquim Majo, Luke Milross, Andrew Fisher, Peter Bankhead, John Wills Orcid Logo, Paul Rees Orcid Logo, Andrew Filby, George Merces

Cytometry Part A, Volume: 105, Issue: 1, Pages: 36 - 53

Swansea University Author: Paul Rees Orcid Logo

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DOI (Published version): 10.1002/cyto.a.24803

Abstract

Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states,...

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Published in: Cytometry Part A
ISSN: 1552-4922 1552-4930
Published: Wiley 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68202
Abstract: Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states, or spatial relationships compared to reference data from single-cell suspension technologies. To this end we have developed the “OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)” framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood analysis. We found that single-cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using “classical” bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing phenograph in terms of cell type identification. We also found that neighborhood analysis was influenced by the method used for finding neighboring cells with a “disc” pixel expansion outperforming a “bounding box” approach combined with the need for filtering objects based on size and image-edge location. Importantly, OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.
Keywords: image analysis; image cytometry; imaging mass cytometry; tissue segmentation
College: Faculty of Science and Engineering
Funders: Medical Research Council UK Research and Innovations / NIHR UK Coronavirus Immunology Consortium. Grant Number: MR/V028448 European Union's Horizon 2020 research and innovation program. Grant Number: 860003 JGW Patterson Foundation United Kingdom Research and Innovation. Grant Number: EP/S02431X/1
Issue: 1
Start Page: 36
End Page: 53

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