CASPER: Cross-modal Alignment of Spatial and single-cell Profiles for Expression Recovery

2025-11-19

AI Summary

Spatial transcriptomics is limited by low experimental throughput and high cost, permitting measurement of only a sparse subset of genes—necessitating reliable imputation of unmeasured gene expression. To address this, we propose a cross-attention–based multimodal deep learning framework that leverages cell-type centroids derived from single-cell RNA-seq to infer spatially resolved gene expression across modalities. Crucially, our method operates without paired samples and explicitly models shared gene co-expression structure between spatial and single-cell modalities to achieve accurate cross-modal alignment and expression recovery. We systematically evaluate the framework on four public spatial transcriptomics datasets. Across 12 quantitative metrics, our approach significantly outperforms state-of-the-art methods in 9, substantially improving prediction accuracy for unmeasured genes. The framework establishes a scalable, interpretable paradigm for multimodal integration in spatial omics, enabling robust inference of spatial gene expression patterns from complementary single-cell data.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Spatial Transcriptomics enables mapping of gene expression within its native tissue context, but current platforms measure only a limited set of genes due to experimental constraints and excessive costs. To overcome this, computational models integrate Single-Cell RNA Sequencing data with Spatial Transcriptomics to predict unmeasured genes. We propose CASPER, a cross-attention based framework that predicts unmeasured gene expression in Spatial Transcriptomics by leveraging centroid-level representations from Single-Cell RNA Sequencing. We performed rigorous testing over four state-of-the-art Spatial Transcriptomics/Single-Cell RNA Sequencing dataset pairs across four existing baseline models. CASPER shows significant improvement in nine out of the twelve metrics for our experiments. This work paves the way for further work in Spatial Transcriptomics to Single-Cell RNA Sequencing modality translation. The code for CASPER is available at https://github.com/AI4Med-Lab/CASPER.

Problem

• Predicting unmeasured gene expression in spatial transcriptomics
• Integrating single-cell RNA sequencing with spatial transcriptomics data
• Overcoming limited gene measurement in spatial transcriptomics platforms

Innovation

• Uses cross-attention framework for gene prediction
• Leverages centroid representations from single-cell data
• Integrates spatial and single-cell transcriptomics modalities