Source 1primary paper2026MED
Toolkit/multiplex pseudocolored immunohistochemistry
multiplex pseudocolored immunohistochemistry
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Subset-specific ICOS expression was evaluated using multiplex pseudocolored immunohistochemistry in 10 representative cases selected from the ICOS+ TIL-high group and validated using a publicly available single-cell RNA-seq dataset.
Usefulness & Problems
Why this is useful
This assay was used to determine which T-cell subsets express ICOS within LUAD tissue sections. It showed the highest ICOS positivity among Tregs, followed by CD4+ non-Tregs and CD8+ TILs.; subset-specific ICOS expression analysis in tumor-infiltrating lymphocytes; resolving ICOS positivity across Treg, CD4+ non-Treg, and CD8+ TIL compartments
Source:
This assay was used to determine which T-cell subsets express ICOS within LUAD tissue sections. It showed the highest ICOS positivity among Tregs, followed by CD4+ non-Tregs and CD8+ TILs.
Source:
subset-specific ICOS expression analysis in tumor-infiltrating lymphocytes
Source:
resolving ICOS positivity across Treg, CD4+ non-Treg, and CD8+ TIL compartments
Problem solved
It addresses the need to assign ICOS expression to specific T-cell subsets rather than treating ICOS-positive TILs as a single pooled population.; enables cell-subset-resolved localization of ICOS expression within tumor tissue
Source:
It addresses the need to assign ICOS expression to specific T-cell subsets rather than treating ICOS-positive TILs as a single pooled population.
Source:
enables cell-subset-resolved localization of ICOS expression within tumor tissue
Problem links
enables cell-subset-resolved localization of ICOS expression within tumor tissue
LiteratureIt addresses the need to assign ICOS expression to specific T-cell subsets rather than treating ICOS-positive TILs as a single pooled population.
Source:
It addresses the need to assign ICOS expression to specific T-cell subsets rather than treating ICOS-positive TILs as a single pooled population.
Published Workflows
Objective: To define subset-specific ICOS expression and test whether ICOS-positive TIL density together with tumor HLA class I and HLA-DR expression provides immune context and prognostic information in lung adenocarcinoma.
Why it works: The workflow combines cohort-scale immunohistochemistry for broad clinicopathological association testing with higher-resolution multiplex tissue analysis and orthogonal single-cell RNA-seq corroboration to assign ICOS expression to specific T-cell subsets.
T-cell costimulatory receptor expressionHLA-mediated tumor recognitionimmunohistochemistrymultiplex pseudocolored immunohistochemistrysingle-cell RNA-seq validation
Stages
- 1.Cohort immunohistochemistry profiling(broad_screen)
This stage establishes cohort-level relationships between ICOS-positive TIL density, tumor HLA expression, T-cell subset densities, and clinical outcomes.
Selection: Assessment of ICOS-positive TIL density and tumor HLA class I and HLA-DR status with comparison to T-cell subset densities and postsurgical outcomes.
- 2.Subset-resolved multiplex tissue analysis(secondary_characterization)
This stage resolves which T-cell subsets account for ICOS-positive infiltrates observed in the broader cohort analysis.
Selection: Representative cases were selected from the ICOS-positive TIL-high group for subset-specific ICOS expression analysis.
- 3.Orthogonal transcriptomic validation(confirmatory_validation)
This stage provides orthogonal support for the subset-specific ICOS expression pattern observed in multiplex tissue analysis.
Selection: Validation of subset-specific ICOS expression findings using a publicly available single-cell RNA-seq dataset.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete measurement method used to characterize an engineered system.
Mechanisms
multiplex antibody-based immunodetectionpseudocolored image-based cell subset discriminationTechniques
Functional AssayTarget processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: sensor
It requires representative tumor tissue sections and multiplex immunohistochemical analysis with markers sufficient to distinguish Treg, CD4+ non-Treg, and CD8+ compartments.; requires tumor tissue specimens; requires multiplex immunohistochemical staining and interpretation across multiple T-cell markers
It does not by itself establish functional causality or broad cohort-level prevalence beyond the selected representative cases.; applied to only 10 representative cases in the abstracted study; selection was restricted to the ICOS-positive TIL-high group
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Because CD4-positive non-Tregs were numerically predominant, ICOS-positive CD4-positive non-Tregs constituted the largest ICOS-positive fraction in lung adenocarcinoma.
Because CD4+ non-Tregs were numerically predominant, ICOS+CD4+ non-Tregs constituted the largest ICOS+ fraction.
Multiplex analysis showed that ICOS positivity was highest among Tregs, followed by CD4-positive non-Tregs and then CD8-positive tumor-infiltrating lymphocytes.
Multiplex analysis showed the highest ICOS positivity among Tregs, followed by CD4+ non-Tregs and CD8+ TILs.
Single-cell RNA-seq analysis corroborated the subset-specific ICOS expression findings from multiplex immunohistochemistry.
Single-cell RNA-seq analysis corroborated these findings.
Approval Evidence
1 source3 linked approval claimsfirst-pass slug multiplex-pseudocolored-immunohistochemistry
Subset-specific ICOS expression was evaluated using multiplex pseudocolored immunohistochemistry in 10 representative cases selected from the ICOS+ TIL-high group and validated using a publicly available single-cell RNA-seq dataset.
Source:
compositionsupports
Because CD4-positive non-Tregs were numerically predominant, ICOS-positive CD4-positive non-Tregs constituted the largest ICOS-positive fraction in lung adenocarcinoma.
Because CD4+ non-Tregs were numerically predominant, ICOS+CD4+ non-Tregs constituted the largest ICOS+ fraction.
Source:
expression patternsupports
Multiplex analysis showed that ICOS positivity was highest among Tregs, followed by CD4-positive non-Tregs and then CD8-positive tumor-infiltrating lymphocytes.
Multiplex analysis showed the highest ICOS positivity among Tregs, followed by CD4+ non-Tregs and CD8+ TILs.
Source:
validationsupports
Single-cell RNA-seq analysis corroborated the subset-specific ICOS expression findings from multiplex immunohistochemistry.
Single-cell RNA-seq analysis corroborated these findings.
Source:
Comparisons
Source-stated alternatives
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Source:
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Source-backed strengths
supports subset-specific analysis within tissue context; findings were corroborated by single-cell RNA-seq analysis
Source:
supports subset-specific analysis within tissue context
Source:
findings were corroborated by single-cell RNA-seq analysis
Compared with RNA sequencing
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Shared frame: source-stated alternative in extracted literature
Strengths here: supports subset-specific analysis within tissue context; findings were corroborated by single-cell RNA-seq analysis.
Relative tradeoffs: applied to only 10 representative cases in the abstracted study; selection was restricted to the ICOS-positive TIL-high group.
Source:
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Compared with single-cell RNA-seq analysis
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Shared frame: source-stated alternative in extracted literature
Strengths here: supports subset-specific analysis within tissue context; findings were corroborated by single-cell RNA-seq analysis.
Relative tradeoffs: applied to only 10 representative cases in the abstracted study; selection was restricted to the ICOS-positive TIL-high group.
Source:
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Compared with single-cell RNA sequencing
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Shared frame: source-stated alternative in extracted literature
Strengths here: supports subset-specific analysis within tissue context; findings were corroborated by single-cell RNA-seq analysis.
Relative tradeoffs: applied to only 10 representative cases in the abstracted study; selection was restricted to the ICOS-positive TIL-high group.
Source:
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Compared with single-cell transcriptomics
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Shared frame: source-stated alternative in extracted literature
Strengths here: supports subset-specific analysis within tissue context; findings were corroborated by single-cell RNA-seq analysis.
Relative tradeoffs: applied to only 10 representative cases in the abstracted study; selection was restricted to the ICOS-positive TIL-high group.
Source:
The study used publicly available single-cell RNA-seq as an orthogonal validation approach.
Ranked Citations
- 1.