haCCA: Multi-module Integrating of spatial transcriptomes and metabolomes.

Abstract

Spatial techniques such as spatial transcriptomes and MALDI-MSI, offering insights into both transcripts and metabolite of tissue sections. However, integrating them with high accuracy is challenge due to no shared spots or features. We present haCCA, a workflow designed to integrate spatial transcriptomes and metabolomes data using high-correlated feature pairs and modified spatial morphological alignment. This approach ensures high-resolution and accurate spot-to-spot data integration across neighbor tissue section. We applied haCCA to both publicly available 10X Visium and MALDI-MSI datasets from mouse brain tissue and a custom spatial transcriptome and MALDI-MSI dataset from an intrahepatic cholangiocarcinoma (ICC) model, exploring the metabolic alteration of NETs(neutrophil extracellular traps) on ICC, and finding a potential mechanism that NETs upregulated Scd1 to activate fatty acid metabolism. Providing new insights into the dynamic crosstalk between genes and metabolites that regulates the tumor biological behavior and drives the response to treatment. We developed and published an easy-to-use Python package to facilitate its use.