Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.
Date
2024-06-15ICR Author
Author
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
Type
Journal Article
Metadata
Show full item recordAbstract
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.
Collections
Subject
Humans
Deep Learning
Image Processing, Computer-Assisted
Animals
Software
Spatial Analysis
Single-Cell Analysis
Phenotype
Mice
Image Cytometry
Research team
Lung Cancer Group
Language
eng
Date accepted
2024-05-16
License start date
2024-06-15
Citation
Nature Communications, 2024, 15 (1), pp. 5135 -
Publisher
NATURE PORTFOLIO