iss-nf: A Nextflow-based end-to-end in situ sequencing decoding workflow

Abstract

In situ sequencing (ISS) offers a powerful approach for spatially resolved gene expression profiling within tissue samples, but the complexity of analyzing the resulting data has limited its broader use. Here, we present iss-nf, a Nextflow-based, end-to-end workflow designed to streamline the decoding of large ISS datasets. The workflow automates critical steps, including image registration, fluorescent spot detection, transcript decoding, and quality control (QC), offering a scalable, reproducible, and user-friendly solution for ISS data analysis. We successfully applied iss-nf on multiple large datasets, including publicly available mouse brain and breast cancer tissue datasets, as well as an in-house non-small cell lung cancer (NSCLC) ISS dataset. The workflow is designed to be accessible for both experienced researchers, as well as newcomers to spatial transcriptomics, providing a robust tool for analyzing large-scale ISS data. Our results suggest that iss-nf is a valuable contribution to the growing field of spatial transcriptomics, enabling precise, modular, reproducible, and, by means of automated tiling and parallelization, scalable analysis of tissue-specific gene expression.