Bailey, M. L. | bioRxiv Science

bioRxiv2026

Recovering biological structure in sparse single-cell proteomics with GIRAFI

Single-cell proteomics (SCP) based on liquid-chromatography mass-spectrometry resolves protein-level cellular heterogeneity, but interpretation remains limited by detection-linked sparsity. SCP profiles continuous, peptide-derived intensities and has lower throughput than single-cell RNA sequencing, making denoising methods for large-scale, count-based transcriptomics difficult to apply. Here we present GIRAFI, a graph-informed statistical learning framework that imputes missing values and reveals reproducible cell states by constraining inference to dataset-aware, prior-knowledge-informed protein neighborhoods. We evaluated GIRAFI across SCP datasets spanning diverse biological/technical contexts. In masking-based recovery experiments and cell-type-specific protein-protein interaction inference, GIRAFI outperformed existing methods, and matched bulk proteomics comparisons corroborated recovery accuracy and ablations supported the graph-informed design. Beyond reduced replicate- and source-associated technical structure, GIRAFI recovered ground-truth cell-type annotations, improved cell state-resolved pathway analysis, and enabled trajectory inference consistent with known time courses. These results establish graph-constrained imputation as an effective strategy for improving SCP robustness, biological structure, interpretation, and cross-dataset comparability.

bioinformatics↗

bioRxiv2026

Bridging Simplicity and Depth in Single-Cell Proteomics: A Cost-Effective Workflow and Expanded Framework for Data Evaluation

Single-cell proteomics (SCP) offers direct insight into functional protein states that drive cellular heterogeneity, complementing genomic and transcriptomic analyses. Although recent reports have demonstrated improved proteome coverage, their reliance on specialized instrumentation limits broader adoption. Additionally, current evaluation practices remain largely centered on protein and peptide identification counts, which alone do not fully reflect data quality or biological interpretability. Here, we describe an accessible, label-free SCP workflow which implements easily accessible laboratory equipment: a single-cell dispenser, conventional multiwell plates, and an incubator with water-bath-based humidity control. Using trapped ion mobility spectrometry-time-of-flight mass spectrometry (timsTOF), we systematically optimize key sample preparation variables, including trypsin concentration, incubation time, reduction/alkylation, digestion conditions, and plate types, which together maximize data quality and reproducibility. We further introduce a data-quality framework that moves beyond identification counts, emphasizing quantitative consistency and biological interpretability via individual protein coverage completeness across cells, coefficients of variation across technical replicates, peptide-to-protein ratios, and single-cell-to-bulk correlations. Collectively, our approach lowers technical barriers to accessing SCP while enabling more rigorous, interpretable, and scalable SCP analysis across diverse research contexts.

biochemistry↗