Source 1primary paper2026MED
Toolkit/single-cell RNA-seq analysis
single-cell RNA-seq analysis
Also known as: single-cell RNA-seq
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
Single-cell RNA-seq analysis was used as a validation modality for the subset-specific ICOS expression pattern observed by multiplex immunohistochemistry.; orthogonal validation of subset-specific ICOS expression patterns; transcriptomic corroboration of T-cell subset composition findings
Source:
Single-cell RNA-seq analysis was used as a validation modality for the subset-specific ICOS expression pattern observed by multiplex immunohistochemistry.
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orthogonal validation of subset-specific ICOS expression patterns
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transcriptomic corroboration of T-cell subset composition findings
Problem solved
It provides orthogonal transcriptomic support for the tissue-based subset assignments.; provides an independent validation modality for subset-specific ICOS expression patterns
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It provides orthogonal transcriptomic support for the tissue-based subset assignments.
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provides an independent validation modality for subset-specific ICOS expression patterns
Problem links
provides an independent validation modality for subset-specific ICOS expression patterns
LiteratureIt provides orthogonal transcriptomic support for the tissue-based subset assignments.
Source:
It provides orthogonal transcriptomic support for the tissue-based subset assignments.
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
orthogonal validation of cell subset-specific gene expressionsingle-cell transcript quantificationTechniques
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 a publicly available single-cell RNA-seq dataset and computational analysis to compare ICOS expression across T-cell subsets.; requires access to a suitable public single-cell RNA-seq dataset; requires computational analysis of transcriptomic cell subsets
The abstract does not identify the dataset or provide enough detail to recover the exact analysis workflow.; the exact dataset accession is not provided in the abstract; the abstract does not specify analysis details
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 source1 linked approval claimfirst-pass slug single-cell-rna-seq-analysis
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.
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validationsupports
Single-cell RNA-seq analysis corroborated the subset-specific ICOS expression findings from multiplex immunohistochemistry.
Single-cell RNA-seq analysis corroborated these findings.
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Comparisons
Source-stated alternatives
The primary tissue-based method in this study was multiplex pseudocolored immunohistochemistry.
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The primary tissue-based method in this study was multiplex pseudocolored immunohistochemistry.
Source-backed strengths
corroborated multiplex immunohistochemistry findings
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corroborated multiplex immunohistochemistry findings
Compared with immunohistochemistry
The primary tissue-based method in this study was multiplex pseudocolored immunohistochemistry.
Shared frame: source-stated alternative in extracted literature
Strengths here: corroborated multiplex immunohistochemistry findings.
Relative tradeoffs: the exact dataset accession is not provided in the abstract; the abstract does not specify analysis details.
Source:
The primary tissue-based method in this study was multiplex pseudocolored immunohistochemistry.
Compared with multiplex pseudocolored immunohistochemistry
The primary tissue-based method in this study was multiplex pseudocolored immunohistochemistry.
Shared frame: source-stated alternative in extracted literature
Strengths here: corroborated multiplex immunohistochemistry findings.
Relative tradeoffs: the exact dataset accession is not provided in the abstract; the abstract does not specify analysis details.
Source:
The primary tissue-based method in this study was multiplex pseudocolored immunohistochemistry.
Ranked Citations
- 1.