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

Background/Objectives: 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). Methods: 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. Results: 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. Conclusions: 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.