ASTRO: Automated Spatial Whole-Transcriptome RNA-Expression Workflow
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| Published in: | bioRxiv (Feb 5, 2025) |
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| Main Author: | |
| Other Authors: | , , , , , |
| Published: |
Cold Spring Harbor Laboratory Press
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| Subjects: | |
| Online Access: | Citation/Abstract Full text outside of ProQuest |
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| Abstract: | Motivation: 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 noncoding 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. Results: 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. Availability: Codes are available at GitHub (https://github.com/gersteinlab/ASTRO).Competing Interest StatementThe authors have declared no competing interest.Footnotes* Manuscript resubmitted; Figure 1 quality improved; Figure 2 quality improved; Figure 3 quality improved. |
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| ISSN: | 2692-8205 |
| DOI: | 10.1101/2025.01.24.634814 |
| Source: | Biological Science Database |