# ASTRO: Automated Spatial-Transcriptome whole RNA Output

## TL;DR

ASTRO is a new automated pipeline for analyzing spatial transcriptomics data from FFPE tissues, enabling detection of both coding and non-coding RNAs.

## Contribution

ASTRO introduces a specialized pipeline for whole-transcriptome analysis of FFPE samples, including non-coding RNA detection.

## Key findings

- ASTRO optimizes spatial barcode calling and includes a specialized filtering step to handle low RNA quality in FFPE tissues.
- The pipeline enables robust quantification of both coding and non-coding RNA species in FFPE samples.
- ASTRO is publicly available with code on GitHub and Zenodo.

## Abstract

Despite significant advances in spatial transcriptomics, the analysis of formalin-fixed paraffin-embedded (FFPE) tissues, which constitute most clinically available samples, remains challenging. Additionally, capturing both coding and non-coding RNAs in a spatial context poses significant challenges. We recently introduced Patho-DBiT, a technology designed to address these unmet needs. However, the marked differences between Patho-DBiT and existing spatial transcriptomics protocols necessitate specialized computational tools for comprehensive whole-transcriptome analysis in FFPE samples.

Here, we present ASTRO, an automated pipeline developed to process spatial transcriptomics data. In addition to supporting standard datasets, ASTRO is optimized for whole-transcriptome analyses of FFPE samples, enabling the detection of various RNA species, including non-coding RNAs such as miRNAs. To compensate for the reduced RNA quality in FFPE tissues, ASTRO incorporates a specialized filtering step and optimizes spatial barcode calling, increasing the mapping rate. These optimizations allow ASTRO to spatially quantify coding and non-coding RNA species in the entire transcriptome and achieve robust performance in FFPE samples.

Codes are available at GitHub (https://github.com/gersteinlab/ASTRO) and Zenodo (doi: 10.5281/zenodo.17913760).