ProFormer: generalizable classification of single-cell and plasma proteomes using deep learning
Abstract
Proteins are the main drivers of cell function and disease, making proteomics a powerful technique for biomarker discovery and defining cell identity. While current technologies can profile thousands of proteins and reach single-cell sensitivity, limited throughput restricts applications such as robust classification of large biological or clinical cohorts. To close this gap, we present a deep-learning (DL) approach for the direct analysis of mass spectrometric (MS) data, assigning proteomic profiles to sample identity. Specifically, we introduce the ProFormer, a transformer pipeline that classifies samples by tabular MS1-level features derived from peptide ions, eliminating the need for time-consuming data interpretation. The ProFormer outperforms traditional machine-learning and image-based convolutional neural networks (CNNs), demonstrating enhanced classification and generalization over 14 evaluated architectures. We further provide detailed insights into how the ProFormer dynamically aggregates MS1 data, while preserving signal contribution and enabling explainable AI at single-peptide resolution. Applied to diverse LC-MS data sets, the ProFormer accurately classified single-cell proteomes by cell type, cycle stage or differentiation trajectory, as well as patient disease status from plasma proteomes. Thereby, the ProFormer provides a versatile and rapid framework for the classification of proteomic data, with important implications for patient stratification, early detection and single-cell analysis.
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BibTeX
@article{krull2026proformer,
title = {{ProFormer: generalizable classification of single-cell and plasma proteomes using deep learning}},
author = {Karl K. Krull and Arlene Kühn and Julia Höhn and Syed Azmal Ali and Martin H. Otterbein and Titus J. Brinker and Jeroen Krijgsveld},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78143-2},
url = {https://www.nature.com/articles/s41467-026-78143-2},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - ProFormer: generalizable classification of single-cell and plasma proteomes using deep learning
AU - Karl K. Krull
AU - Arlene Kühn
AU - Julia Höhn
AU - Syed Azmal Ali
AU - Martin H. Otterbein
AU - Titus J. Brinker
AU - Jeroen Krijgsveld
PY - 2026
DA - 2026/10/05
JO - Nature Communications
DO - 10.1038/s41467-026-78143-2
UR - https://www.nature.com/articles/s41467-026-78143-2
AB - Proteins are the main drivers of cell function and disease, making proteomics a powerful technique for biomarker discovery and defining cell identity. While current technologies can profile thousands of proteins and reach single-cell sensitivity, limited throughput restricts applications such as robust classification of large biological or clinical cohorts. To close this gap, we present a deep-learning (DL) approach for the direct analysis of mass spectrometric (MS) data, assigning proteomic profiles to sample identity. Specifically, we introduce the ProFormer, a transformer pipeline that classifies samples by tabular MS1-level features derived from peptide ions, eliminating the need for time-consuming data interpretation. The ProFormer outperforms traditional machine-learning and image-based convolutional neural networks (CNNs), demonstrating enhanced classification and generalization over 14 evaluated architectures. We further provide detailed insights into how the ProFormer dynamically aggregates MS1 data, while preserving signal contribution and enabling explainable AI at single-peptide resolution. Applied to diverse LC-MS data sets, the ProFormer accurately classified single-cell proteomes by cell type, cycle stage or differentiation trajectory, as well as patient disease status from plasma proteomes. Thereby, the ProFormer provides a versatile and rapid framework for the classification of proteomic data, with important implications for patient stratification, early detection and single-cell analysis.
SN - 2041-1723
ER -