bioRxivNeurosciencePreprint
Direct RNA Sequencing reveals epitranscriptomic regulation of brain cells and Alzheimer's Disease pathology
Ashley ByrneiD, Jonathan HooveriD, Jessica M. Lund, Ai Zhang, Xiwei ShaniD, Anup Dutt SharmaiD, Xuewen Chen, Ana Xavier‐MagalhãesiD, Lilian Phu, Meena ChoiiD, Ahmet Kurdoglu, Christopher M. RoseiD, Alessandro OriiD, Yuxin Liang, Alicia Nugent, Claire G. JeongiD, Zora D. ModrusaniD, William StephensoniD, Daniel LeiD
Abstract
Alternative mRNA splicing and post-transcriptional RNA modification are key mechanisms that regulate transcript function; however, their role in neuronal activity and neurodegenerative disease remains poorly defined. In this study, we evaluated two nanopore-based long-read sequencing (LR-seq) formats: cDNA-PCR sequencing (CPS) and direct RNA sequencing (DRS). We then applied DRS to profile both full-length isoforms and RNA modifications in major brain cell types derived from induced pluripotent stem cells (iPSCs) and post-mortem Alzheimers disease (AD) brains. Relative to CPS, DRS achieved higher accuracy and sensitivity for transcript quantification, de novo transcript model construction, and open reading frame (ORF) annotation across neuropathological gene sets. Focusing on iPSC-derived neurons, we built a multi-omic atlas to connect transcriptional output with translational engagement and protein abundance, by integrating DRS-based mRNA abundance, N6-methyladenosine (m6A) status and poly(A) tail length with ribosome profiling (Ribo-seq) and mass spectrometry (MS). The combination of DRS and Ribo-seq data demonstrated synergism in predicting protein abundance. This analysis also uncovered a significant inverse relationship between m6A modification and mRNA abundance, which was dependent on the engagement of the ribosomal A-site. Lastly, we applied DRS to the epitranscriptomic analysis of AD brain samples, demonstrating that m6A profiles can be used to distinguish early-versus late-stage disease.
- organism: human
- assay: long-read sequencing
- assay: proteomics
- assay: metagenomics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- research focus: benchmarking
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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