Single-cell RNA-sequencing (scRNA-seq) technologies, such as the Chromium Single Cell platform from 10x Genomics, are widely used to uncover cellular diversity, with a primary focus on distinguishing the expression patterns of coding genes. However, understanding complex disease mechanisms and biological processes requires the exploration of a broader spectrum of RNA species, including small or long non-coding or circular RNAs. Traditional scRNA-seq methods, which rely on poly(dT) primers or probes to capture polyadenylated or target-specific RNAs, respectively, exhibit inherent biases, such as a preference for the 3′ ends of transcripts and the inability to capture non-polyadenylated or non-target RNAs. Moreover, these methods face challenges when applied to archived samples, especially formalin-fixed paraffin-embedded (FFPE) samples, which represent a valuable resource for both clinical practice and research.

The optimized single-nucleus isolation and random primer-based RNA capture strategies enable snRandom-seq to generate comprehensive total transcriptome profiles at the single-nucleus level across various archived sample types. We have demonstrated that snRandom-seq captures a diverse array of RNA biotypes, including abundant non-coding RNAs, and exhibits minimal 3′- or 5′-end biases across gene bodies. The detection of extensive unspliced transcripts in snRandom-seq results, as demonstrated in mouse testis samples, enhances its effectiveness for velocity analysis. Additionally, snRandom-seq is well suited for archived samples, including FFPE specimens stored for extended periods. This capability is particularly advantageous for rare clinical samples and retrospective studies involving large-scale datasets collected over decades. For instance, snRandom-seq provided comprehensive molecular insights into the single-nucleus landscape of various glioma subtypes, including rare clinical cases and matched primary–recurrent glioblastomas.