SPgen: Proteome-wide Spatial Proteomics generation using multi-modality foundation models
Li, J.; Yang, K.; Che, Q.; Zheng, D.; Wei, W.; Jin, C.; Yuan, Y.
Show abstract
Spatial proteomics (SP) measures the spatial distribution of proteins within tissues, providing important insights into tissue function, disease, and therapeutic response. However, current SP technologies profile only a small fraction of the proteome and are limited by cost and measurement noise. Recent AI approaches enable predicting spatial protein expression from transcriptomic or histopathological data, but are typically restricted to paired datasets covering only tens of proteins, limiting their ability to generalize beyond experimentally measured protein panels. Here we present SPgen, a multi-modal foundation-model framework for proteome-wide spatial protein prediction. SPgen integrates protein sequences, functional annotations, transcriptomic profiles, and spatial information to learn transferable representations that enable inference beyond experimentally profiled proteins. Across diverse spatial proteomics datasets, SPgen accurately reconstructs measured spatial patterns, reduces measurement noise, and enables proteome-wide spatial prediction.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- LEOPARD: missing view completion for multi-timepoints omics data via representation disentanglement and temporal knowledge transfer 96%
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model 95%
- TrimNN: Characterizing cellular community motifs for studying multicellular topological organization in complex tissues 95%
Similar papers in this journal
Similar papers in this journal
- PIFiA: Self-supervised Approach for Protein Functional Annotation from Single-Cell Imaging Data 95%
- hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments 95%
- Turnover and replication analysis by isotope labeling (TRAIL) reveals the influence of tissue context on protein and organelle lifetimes 94%
Similar papers in this journal
- PEPerMINT: Peptide Abundance Imputation in Mass Spectrometry-based Proteomics using Graph Neural Networks 95%
- SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis 94%
- Statistical batch-aware embedded integration, dimension reduction and alignment for spatial transcriptomics 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.