Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.
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Embargo End Date
ICR Authors
Authors
Magness, A
Colliver, E
Enfield, KSS
Lee, C
Shimato, M
Daly, E
Moore, DA
Sivakumar, M
Valand, K
Levi, D
Hiley, CT
Hobson, PS
van Maldegem, F
Reading, JL
Quezada, SA
Downward, J
Sahai, E
Swanton, C
Angelova, M
Colliver, E
Enfield, KSS
Lee, C
Shimato, M
Daly, E
Moore, DA
Sivakumar, M
Valand, K
Levi, D
Hiley, CT
Hobson, PS
van Maldegem, F
Reading, JL
Quezada, SA
Downward, J
Sahai, E
Swanton, C
Angelova, M
Document Type
Journal Article
Date
2024-06-15
Date Accepted
2024-05-16
Abstract
The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its use on different tissue types, tissue fixation conditions, image sizes and antibody panels. As PHLEX is an automated and containerised Nextflow pipeline, manual assessment, programming skills or pathology expertise are not essential. PHLEX offers an end-to-end solution in a growing field of highly multiplexed data and provides clinically relevant insights.
Citation
Nature Communications, 2024, 15 (1), pp. 5135 -
Source Title
Nature Communications
Publisher
NATURE PORTFOLIO
ISSN
2041-1723
eISSN
2041-1723
2041-1723
2041-1723
Collections
Research Team
Lung Cancer Group