ArticleNature communications2024
Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Spatial and Temporal Heterogeneity of Macrophage Efferocytosis in Tumors: Emerging Implications for Immunotherapy Biomarkers and Therapeutic Timing.Cancer medicine · 2026Review
- PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.bioRxiv : the preprint server for biology · 2026Article
- Review
- Spatial Multiomics Reveal Insights Into ADC Efficacy.European journal of immunology · 2026Review
- CROCHET: a versatile pipeline for automated analysis and visual atlas creation from single-cell spatialomic data.bioRxiv : the preprint server for biology · 2026Article
- Spatial proteomics in precision medicine: technologies, bioinformatics, and translational applications.Precision clinical medicine · 2026Review
- Deconvolution of cell types and states in spatial multiomics utilizing TACIT.Nature communications · 2025Article
- PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images.PloS one · 2025Article
- Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues.The Plant cell · 2024Article
- Scalable analysis of whole slide spatial proteomics with Harpy.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
19 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.