A cytoplasmic index for quantifying immune-related A-to-I RNA editing

Distinguishing self from non-self is a major challenge for the immune system. Endogenous cytoplasmic double-stranded RNA (dsRNA) can mimic viral RNA and activate immune sensors like MDA5. ADAR1-mediated adenosine-to-inosine editing disrupts base-pairing to suppress immunogenicity of these endogenous structures. Global editing indices are widely used to probe this crucial ADAR1 function. However, they are dominated by nuclear pre‑mRNA edits with limited immune relevance. Here we present the cytoplasmic editing index (CEI) that quantifies editing specifically within inverted Alu repeats in 3' untranslated regions of mature cytoplasmic transcripts, which potentially form cytosolic dsRNA structures carrying higher immunological risk.

Analyzing over 25,000 RNA-sequencing samples, we demonstrate CEI captures ADAR1p150 activity and outperforms the global editing index in terms of sensitivity and signal-to-noise, enabling sharper tissue-specific profiling, enhanced detection power of infection‑induced editing changes, and stronger association with cancer prognoses. An open-source, cloud-native pipeline delivers end‑to‑end, reproducible analysis at very low cost, supporting immediate, scalable adoption.

CEI provides a refined metric for quantifying immune-relevant RNA editing, revealing previously obscured tissue- and disease-specific editing landscapes. The accompanying open-source, cloud-native pipeline enables broad adoption of high-quality editing analysis across research settings. This approach offers new opportunities for investigating ADAR1's role in immunity, infection, and cancer, with potential applications in biomarker development and therapeutic intervention strategies.