# 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.

## Key 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.