Conrrero, A. | bioRxiv Science

bioRxiv2026

Single-Cell proteomics discerns patient-specific subpopulations in pediatric B-cell acute lymphoblastic leukemia

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer, representing ~30% of pediatric cancers and ~80-85% of pediatric ALL cases. Despite high remission rates, relapse remains a major challenge, often driven by therapy-resistant subpopulations, which are masked in bulk analysis, thus limiting our understanding of disease progression and optimal therapeutic intervention. Precision oncology enables proteome-level characterization of patient specific cancer samples and their subpopulations, essential for improving prognostic accuracy and individualized therapies. Single-cell proteomics by mass spectrometry (SCP-MS) enables quantification of hundreds to thousands of proteins at single cell level, uncovering cellular programs that may contribute to minimal residual disease (MRD) and relapse. Here, employing single-cell sorting of leukemic cells coupled with high-sensitivity SCP-MS, we profile individual leukemic blasts and normal immature B-cells from pediatric B-ALL bone marrow aspirates alongside age-matched controls. SCP-MS deconvoluted cellular heterogeneity and revealed subpopulations with variable leukemia-marker expression, highlighting its potential for early detection of relapse-prone phenotypes and personalized pediatric therapy.

cancer biology↗

bioRxiv2025

ProteoForge: An Imputation-Aware Framework for Differential Proteoform Discovery in Bottom-Up Proteomics

The human genome contains approximately 20,000 protein-coding genes. However, millions of diverse protein variants, called proteoforms, exist. Despite originating from the same gene, proteoforms often have distinct biological roles. In bottom-up proteomics, the aggregation of peptide measurements into protein-level quantities often obscures this information. Existing methods for proteoform deconvolution are limited by their handling of missing data, which can introduce significant bias. To address this we developed ProteoForge, which builds on an imputation-aware statistical model to identify and group co-varying peptides into quantitatively differential proteoforms (dPFs). Benchmarking against existing deconvolution methods demonstrated that ProteoForge provides high accuracy and stability in datasets with high rates of missing values, complex experimental designs, or varying signal strengths. Application of ProteoForge to proteomics data from lung cancer cells under hypoxia revealed extensive proteoform-level regulation hidden by standard protein-level analysis.

bioinformatics↗