Cross-level Cross-Scale Inference and Imputation of Single-cell Spatial Proteomics

TL;DR

This paper introduces scProSpatial, a deep learning framework that improves the prediction of spatial protein data from single-cell RNA sequencing, enabling better multi-level biological analysis.

Contribution

The novel contribution is a unified multi-modal framework that infers and imputes spatial proteomics data from scRNA-seq with high fidelity and robust generalization.

Findings

scProSpatial accurately predicts protein spatial abundances without shared transcriptomics features.

The framework expands protein coverage by 50 times compared to existing methods.

It generalizes well to out-of-distribution scenarios and is validated in a metastatic breast cancer case study.

Abstract

High-throughput single-cell and spatial omics technologies have transformed biological research. Despite these advances, reliably identifying the molecular drivers and their interplays across biological levels and scales remains a significant challenge. Current experimental methods are limited by batch effects, the lack of simultaneous multi-modal measurements in individual cells, limited coverage of measured proteins, poor generalization to unseen conditions, and insufficient spatial context at a single-cell resolution. To overcome these challenges, we introduce scProSpatial, a unified, multi-modal, multi-scale deep learning framework designed to infer and impute high fidelity single-cell spatial proteomics from scRNA-seqs. Through comprehensive evaluations, scProSpatial accurately predicts spatial abundances of proteins in the absence of shared transcriptomics features, expands protein coverages by 50 times, and generalizes robustly to out-of-distribution scenarios. A case study in metastatic breast cancer further illustrates its utility, demonstrating scProSpatial’s potential to drive cross-level, cross-scale multi-omics integration and analysis and reveal deeper insights into complex biological systems.

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