MOSAIK: Multi-Origin Spatial Transcriptomics Analysis and Integration Kit

TL;DR

MOSAIK is an integrated Python toolkit that streamlines spatial transcriptomics data analysis from multiple platforms, improving data integration, cell assignment, and downstream analysis capabilities.

Contribution

It introduces the first end-to-end workflow supporting raw data from NanoString CosMx and 10x Genomics Xenium, unifying data into a compatible format for comprehensive analysis.

Findings

Supports raw data from CosMx and Xenium platforms

Enables advanced analyses like re-segmentation and cell typing

Provides a reproducible and compatible Python environment

Abstract

Spatial transcriptomics (ST) has revolutionised transcriptomics analysis by preserving tissue architecture, allowing researchers to study gene expression in its native spatial context. However, despite its potential, ST still faces significant technical challenges. Two major issues include: (1) the integration of raw data into coherent and reproducible analysis workflows, and (2) the accurate assignment of transcripts to individual cells. To address these challenges, we present MOSAIK, the first fully integrated, end-to-end workflow that supports raw data from both NanoString CosMx Spatial Molecular Imager (CosMx) and 10x Genomics Xenium In Situ (Xenium). MOSAIK (Multi-Origin Spatial Transcriptomics Analysis and Integration Kit) unifies transcriptomics and imaging data into a single Python object based on the spatialdata format. This unified structure ensures compatibility with a broad range of Python tools, enabling robust quality control and downstream analyses. With MOSAIK, users can perform advanced analyses such as re-segmentation (to more accurately assign transcripts to individual cells), cell typing, tissue domain identification, and cell-cell communication within a seamless and reproducible Python environment.