Back to first-pass browser

First-pass extracted concept

single-cell RNA sequencing

Candidate: concept label3 source documents6 linked claims
Jump to explainersJump to evidenceJump to sourcesJump to claims
Live refresh every 5sNext refresh in 5s

Aliases

scRNA-seq

Extracted Explainers

What the tool is doing

Single-cell RNA sequencing was used to profile cells from 22 fresh PitNET samples and reveal cellular heterogeneity and lineage-specific immune states.

Source 1DOIPubMed

The paper uses single-cell RNA sequencing to identify muscarinic receptor expression patterns in primary DMG samples.

Source 2DOIPubMed

scRNA-seq measures thousands of features in individual cells and is presented as enabling unbiased classification of sensory neuron cell types.

Source 3DOIPubMed

Resources required

The abstract states that fresh surgical PitNET samples were analyzed by scRNA-seq.

Source 1DOIPubMed

The abstract indicates that quality sequencing is required for accurate cell-type assignment.

Source 3DOIPubMed

What problem it solves

It helps resolve heterogeneous immune and stromal populations that are not clear from bulk-level descriptions.

Source 1DOIPubMed

It addresses the limited resolution of older classification approaches that measured only a dozen or so physiological features or a handful of marker genes.

Source 3DOIPubMed

What it does not solve

The abstract states that cell-type assignment can still be compromised by sequencing quality and incomplete RNA capture.

Source 3DOIPubMed

Alternatives

The paper contrasts scRNA-seq with historical classification based on physiology, morphology, target innervation, spinal termination, developmental hierarchy, and neurochemical properties.

Source 3DOIPubMed

Evidence Snippets

This study conducted single-cell RNA sequencing (scRNA-seq) on 22 fresh PitNET samples.
Evidence 1Source 1DOIPubMedprovenance
Single-cell RNA sequencing revealed high CHRM1 and CHRM3 expression in primary DMG samples.
Evidence 2Source 2DOIPubMedprovenance
By contrast, single-cell RNA sequencing (scRNA-seq) can measure thousands of features, is quantitative, and allows for an unbiased classification of cell types.
Evidence 3Source 3DOIPubMedprovenance

Supporting Sources

Linked Claims

Claim 1comparative enrichmentsupports2026Source 1DOIPubMed

CD4+ regulatory T cells and T follicular helper cells are significantly enriched in the PIT1 lineage relative to TPIT and SF1 lineages.

Claim 2comparative enrichmentsupports2026Source 1DOIPubMed

Collagen-expressing CAF3 is significantly enriched in the PIT1 lineage compared with TPIT and SF1 lineages.

Claim 3comparative enrichmentsupports2026Source 1DOIPubMed

Inflammatory CAF5 is predominantly found in the TPIT lineage relative to PIT1 and SF1 lineages.

Claim 4descriptive findingsupports2026Source 1DOIPubMed

PitNETs exhibit significant cellular heterogeneity and a lineage-specific immune landscape.

Claim 5expressionsupports2025Source 2DOIPubMed

Single-cell RNA sequencing revealed high CHRM1 and CHRM3 expression in primary DMG samples.

Quoted textsource-backed
Single-cell RNA sequencing revealed high CHRM1 and CHRM3 expression in primary DMG samples.
Claim 6method capabilitysupports2023Source 3DOIPubMed

Single-cell RNA sequencing can measure thousands of features quantitatively and allows unbiased classification of sensory neuron cell types.

Quoted textsource-backed
By contrast, single-cell RNA sequencing (scRNA-seq) can measure thousands of features, is quantitative, and allows for an unbiased classification of cell types.