V.Gerayeli, F. | bioRxiv Science

bioRxiv2026

A standardized atlas of human bronchoalveolar lavage cells built using scalable ensemble annotation and cross-study robust markers

BackgroundBronchoalveolar lavage (BAL) single-cell RNA sequencing (scRNA-seq) offers rich insights into pulmonary immune dynamics, yet consistent cell-type annotation remains elusive. Existing methods often rely on a single reference, risking inconsistency and domain shift across datasets. A BAL-specific, high-resolution annotation framework is critically needed. MethodsWe developed BAL-EA (BAL Ensemble Annotation), a BAL-centric automated annotation framework that integrates robust, cross-study marker discovery with ensemble machine learning. BAL-EA harmonizes BAL cell identities into a three-tier taxonomy (11 major lineages, 13 refined classes, 21 fine-grained subtypes) compatible with the Human Lung Cell Atlas (HLCA) while capturing lavage-enriched biology. Marker catalogues were derived via reproducibility-guided differential expression across at least 10 independent sub-studies, ensuring resilience to dataset-specific bias. Comparative benchmarking was performed against six leading annotation tools using independent BAL datasets. ResultsWe assembled the largest BAL scRNA-seq atlas to date, integrating more than 347,333 lung cells from HLCA, multiple public BAL datasets, and the largest inhouse BAL cohort ever reported (241,924 cells from 30 individuals). BAL-EA outperformed existing annotation tools, achieving balanced macro-F1 scores over 0.95 for key lineages such as alveolar macrophages (AM), non-alveolar macrophages, and epithelial cells. Application to Chronic Obstructive Pulmonary Disease (COPD) BAL samples revealed reproducible disease-associated shifts, including increased neutrophils and CCL2-positive macrophages alongside reduced AM in COPD patients, findings validated in independent COVID-19 BAL datasets. The released atlas includes harmonized multi-resolution annotations, robust marker panels, pretrained models. ConclusionsThis work contributes the most comprehensive BAL scRNA-seq atlas, introduces a novel BAL-specific annotation framework (BAL-EA), standardizes BAL taxonomy at three resolutions, and provides rigorously validated marker gene resources. Together, these advances deliver a powerful reference for reproducible BAL scRNA-seq analysis and lay the foundation for clinical and translational applications in respiratory disease research.

genomics↗

bioRxiv2023

Single-cell RNA-sequencing of bronchoscopy specimens: development of a rapid, minimal-handling protocol

Single-cell RNA-sequencing (scRNA-seq) is an important tool for understanding disease pathophysiology, including airways diseases. Currently, the majority of scRNA-seq studies in airways diseases have used invasive methods (airway biopsy, surgical resection) which carry inherent risks and thus present a major limitation to scRNA-seq investigation of airway pathology. Bronchial brushing, where the airway mucosa is sampled using a cytological brush, is a viable, less invasive method of obtaining airway cells for scRNA-seq. Here we are describing the development of a rapid and minimal-handling protocol for preparing single cell suspensions from bronchial brush specimens for scRNA-seq. Our optimized protocol maximises cell recovery and cell quality, and may facilitate large-scale profiling of the airway transcriptome at single cell resolution. Lay abstractSingle-cell RNA-sequencing (scRNA-seq) measures the gene expression of individual cells, and may be useful for understanding disease processes. scRNA-seq may be used to investigate lung diseases, but using invasive methods such as biopsy or surgery limits our ability to conduct large research studies. Bronchial brushing, where a soft brush is used to collect cells from inside the lungs, is a safer method but we need a better way to isolate individual cells from the brush specimens. We developed a method that is faster and involves less handling of the specimens compared to other published methods. Our method may therefore be useful for conducting large scRNA-seq studies in lung diseases.

genomics↗