Abstract 4881: Detecting cancer microbiota using unmapped RNA reads on spatial transcriptomics
Authors: Park Jeongbin , Park Seo Hye , Lee Dongjoo , Lee Jae Eun , Lee Daeseung , Na Kwon Joong , Choi Hongyoon , Im Hyung-Jun 
Published: March 2024

Background: Recently, the study of microbiota has emerged as a novel area in cancer research. While next-generation sequencing (NGS) technologies are becoming more prevalent in the area, the acquisition of spatial information in microbiota is still challenging. Despite the possibility of detecting microbial RNAs on Visium (10X genomic, USA), methods depending on predefined host and microbial references cannot reliably capture microbial RNA reads, since Visium inherently lacks the ability to capture RNAs that are missing poly-A tails. Therefore, a reference-independent microbial RNA detection method needs to be developed for more trustworthy research on cancer microbiota on Visium.

Methods: Initially, we reconstructed Visium data from a public raw dataset (Project: PRJNA811533, European Nucleotide Archive) derived from colorectal cancer (CRC) and oral squamous cell carcinoma (OSCC). BAM files were obtained by running Space Ranger on a human reference, GRCh38. These BAM files were firstly separated according to spatial barcodes with samtools and xargs. Then, microbial scores for each barcode were obtained using PathSeq. On the other hand, barcode-wise unmapped reads were extracted from the original BAM files simply by remaining unmapped reads, sorting them by spatial barcodes, and counting the number of reads. Then, the unmapped reads originating from host RNA reads were eliminated by regression analysis, remaining the microbial RNA reads. Subsequently, we conducted a comparison of microbial RNA reads from two different techniques.

Results: By filtering out the unmapped reads associated with the host, microbial reads could be isolated. While these remaining microbial RNA reads show a significant correlation with the PathSeq scores, the unmapped reads were significantly higher than the PathSeq scores, increasing the statistical power for the downstream analysis with unmapped reads. Additionally, tissue regions with low PathSeq scores and high volumes of non-human unmapped reads were identified. These regions may indicate the presence of microbial sequences or RNA species that were not detected in the PathSeq analysis.

Conclusion: The use of unmapped reads enables us to capture a greater number of RNA transcripts compared to PathSeq to indirectly analyze cancer microbiota. This means that we can observe hard-to-identify RNA variants, such as unstable RNAs or new species originating from the interaction between microbial RNAs and host RNAs. Our methods can provide plausible explanations for the existence of microbial RNAs on Visium, and we suggest a reference-independent detection of cancer microbota reads by utilizing unmapped reads.

Citation Format: Jeongbin Park, Seo Hye Park, Dongjoo Lee, Jae Eun Lee, Daeseung Lee, Kwon Joong Na, Hongyoon Choi, Hyung-Jun Im. Detecting cancer microbiota using unmapped RNA reads on spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4881.