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Genes2026; 17(7); 773; doi: 10.3390/genes17070773

Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.

Abstract: Bulk RNA sequencing (RNA-seq) averages signals across heterogeneous cell populations. Computational deconvolution methods aim to infer cell type composition and cell type-specific gene expression from bulk data, but their performance in equine samples has not been evaluated. In this study, we assessed the ability of computational deconvolution to recover cellular composition and differential expression signals in bronchoalveolar lavage fluid (BALF) from horses with severe equine asthma (SEA) and controls (CTL). Cryopreserved BALF samples from six SEA and five CTL horses previously analyzed by scRNA-seq were used to generate bulk RNA-seq data. The matched scRNA-seq dataset served as the reference for deconvolution. Performance was evaluated by comparing deconvolution raw and mRNA-corrected estimates with scRNA-seq cell proportions. Differential expression between SEA and CTL was analyzed on bulk RNA-seq, deconvoluted expression profiles, and scRNA-seq pseudobulk data. Deconvolution primarily captured mRNA-derived cell type proportions rather than true cell counts: agreement with scRNA-seq cell counts was moderate (r = 0.62; 95% CI 0.45-0.75) but improved after mRNA content correction (r = 0.83; 95% CI 0.74-0.89). Comparison with mRNA-weighted scRNA-seq proportions showed near-perfect concordance (r = 0.98; 95% CI 0.97-0.99). Cell type-specific performance varied, with stronger correlations for B cells and dendritic cells and weaker performance for neutrophils, T cells and monocytes/macrophages. Recovery of cell type-specific differential expression was inconsistent, frequently showing cross-lineage signal spillover. Although both approaches detected a Th17 signature in SEA, most deconvolution-derived differentially expressed genes overlapped with conventional bulk RNA-seq results. Deconvolution of bulk RNA-seq did not reliably estimate cell counts or provide substantial biological insight beyond conventional bulk analysis, highlighting the value of scRNA-seq for resolving cell type-specific disease mechanisms in equine asthma.
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Publication Date: 2026-06-30 PubMed ID: 42510813PubMed Central: PMC13408615DOI: 10.3390/genes17070773Google Scholar: Lookup
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  • Journal Article
  • Comparative Study

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study evaluates computational methods to estimate cell type composition and gene expression from bulk RNA sequencing data of horse lung fluid, comparing these estimates to matched single-cell RNA sequencing data.
  • The research focuses on horses with severe equine asthma and healthy controls, assessing how well bulk data deconvolution reveals cell types and disease-associated gene expression patterns.

Background

  • Bulk RNA-seq vs. Single-cell RNA-seq: Bulk RNA-seq measures gene expression averaged across all cells in a sample, losing information about individual cell types.
  • Computational Deconvolution: Algorithms attempt to separate bulk RNA-seq data into contributions from different cell types to infer cellular composition and cell type-specific gene expression.
  • Equine BALF: Bronchoalveolar lavage fluid obtained from horse lungs contains a mixture of immune and lung cells relevant for studying equine asthma.
  • Equine Asthma Context: Severe equine asthma (SEA) is an inflammatory lung disease in horses that shares some features with human asthma, making cell type-specific expression analysis important.

Methods

  • Sample Collection: BALF samples were collected from 6 horses with SEA and 5 control horses.
  • Data Generation: The BALF samples were cryopreserved and subjected to both bulk RNA-seq and matched single-cell RNA-seq (scRNA-seq) for detailed cellular profiling.
  • Deconvolution Algorithms: Computational methods were applied to the bulk RNA-seq data using the scRNA-seq dataset as a reference to estimate:
    • Cell type proportions
    • Cell type-specific gene expression profiles
  • Evaluation of Performance: The estimated cell proportions from deconvolution were compared to actual cell proportions observed in scRNA-seq both as raw cell counts and after correcting for mRNA content per cell type.
  • Differential Expression Analysis: Compared bulk RNA-seq, deconvoluted expression data, and scRNA-seq pseudobulk data to identify genes differing between SEA and controls.

Key Findings

  • Cell Proportion Estimation:
    • Deconvolution better estimated the mRNA content contribution of cell types rather than actual cell numbers.
    • Moderate correlation (r = 0.62) was found between raw deconvolution estimates and true scRNA-seq cell counts.
    • Correlation improved substantially to r = 0.83 after correcting for differences in mRNA content per cell type.
    • Near-perfect correlation (r = 0.98) was observed when comparing deconvolution outputs to mRNA-weighted cell proportions from scRNA-seq.
  • Variation by Cell Type:
    • Stronger performance in estimating proportions for B cells and dendritic cells.
    • Poorer estimates for neutrophils, T cells, and monocytes/macrophages, likely because of variable mRNA content or overlapping gene expression profiles.
  • Differential Expression and Biological Insights:
    • Cell-type specific differential expression signals were inconsistently recovered from deconvoluted data.
    • Cross-lineage signal spillover occurred, where gene expression changes attributed to one cell type appeared in others incorrectly.
    • Both scRNA-seq and deconvolution detected a Th17 immune response signature characteristic of SEA.
    • Most differentially expressed genes identified by deconvolution overlapped with those from traditional bulk RNA-seq, indicating limited added value.

Conclusions and Implications

  • Deconvolution of bulk RNA-seq, while useful for estimating mRNA-weighted cell type proportions, does not reliably estimate actual cell counts in equine BALF.
  • It also fails to consistently recover accurate cell type-specific gene expression patterns and disease-associated changes.
  • Single-cell RNA-seq remains a more powerful and reliable approach for understanding cell type-specific disease mechanisms in equine asthma.
  • This highlights limitations in applying current computational deconvolution methods to complex immune cell mixtures in veterinary samples and supports prioritizing single-cell technologies for future equine respiratory disease research.

Cite This Article

APA
Jagannathan V, Leeb T, Gerber V, Sage SE. (2026). Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data. Genes (Basel), 17(7), 773. https://doi.org/10.3390/genes17070773

Publication

Genes
ISSN: 2073-4425
NlmUniqueID: 101551097
Country: Switzerland
Language: English
Volume: 17
Issue: 7
PII: 773

Researcher Affiliations

Jagannathan, Vidhya
  • Institute of Genetics, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.
Leeb, Tosso
  • Institute of Genetics, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.
  • Next Generation Sequencing Platform, University of Bern, 3012 Bern, Switzerland.
Gerber, Vinzenz
  • Swiss Institute of Equine Medicine, Department of Clinical Veterinary Medicine, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.
Sage, Sophie E
  • Swiss Institute of Equine Medicine, Department of Clinical Veterinary Medicine, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland.

MeSH Terms

  • Animals
  • Horses / genetics
  • Bronchoalveolar Lavage Fluid / cytology
  • Bronchoalveolar Lavage Fluid / chemistry
  • RNA-Seq / methods
  • Asthma / genetics
  • Asthma / veterinary
  • Single-Cell Analysis / methods
  • Single-Cell Gene Expression Analysis
  • Horse Diseases / genetics
  • Sequence Analysis, RNA / methods
  • Gene Expression Profiling
  • RNA, Messenger / genetics

Grant Funding

  • 31003A- 162548/1 / Swiss National Science Foundation
  • 33-890 / Internal Research Fund of the Swiss Institute of Equine Medicine

Conflict of Interest Statement

The authors declare no conflicts of interest.

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