Introns occupy nearly one-third of the human genome, yet whether they encode widespread regulatory functions remains unclear. Here, we identify a pervasive class of intron-derived Kink-loop RNAs (klRNAs) that locally control pre-mRNA processing. Using complementary enrichment strategies, we uncovered approximately 15,200 and 3,500 previously unannotated klRNAs in humans and mice, respectively. Bound by the conserved RNA-binding protein 15.5K, klRNAs constitute a distinct family of structured RNAs that arise predominantly from introns, lack the canonical stems of Kink-turn motifs, and do not exhibit antisense complementarity to known non-coding RNAs. Depletion of 15.5K disrupts klRNA biogenesis and leads to their global loss. Transcriptome-wide analyses reveal that klRNAs function predominantly in cis to suppress excision of their host introns in a kink-loop structure-dependent manner, thereby enabling introns to regulate their own processing. Together, our findings establish RNA structure-encoded autoregulation as a pervasive mechanism controlling intron fate and reveal that introns are active sources of regulatory RNAs. One Sentence Summary A huge class of orphan backward K-loop RNAs
Bin Li, Q. Lin, Anrui Liu et al.· bioRxiv· 0 citations
Detecting rare cell populations that drive development, differentiation, and disease-associated transformation remains a central challenge in biology and medicine. Although these populations often represent promising targets for intervention, they are difficult to resolve from single-cell transcriptomic data because most methods rely on homophily-based cell–cell similarity, which can merge rare cells into dominant populations and mask their subtle transcriptional signatures. The challenge is further amplified in multi-sample analyses, where batch correction can dilute rare-cell-specific signals. Here, we present scFormer, a heterogeneous graph transformer (HGT) framework for sensitive and robust rare-cell discovery. scFormer constructs a Z-score-guided cell-gene heterogeneous graph in which highly specific marker genes serve as informational bridges, embedding rare-cell features directly into the graph topology rather than inferring them from global neighbors. This design provides a clear biological rationale for rare-cell recovery, as low-abundance cells can remain connected through shared high-specificity genes even when local cell–cell neighborhoods are sparse. An integrated optimization strategy jointly performs representation learning, clustering, and optional batch correction, enabling rare-cell discovery while preserving biological structure. Across 125 simulated and 18 real datasets, scFormer consistently achieved competitive or superior performance relative to existing approaches. Applied to diverse multi-sample single-cell and spatial transcriptomics datasets, scFormer recovered known but weakly represented populations and revealed previously obscured cell states, including proliferative club cells in the airway epithelium, revival stem cells during intestinal regeneration, and rare embryonic cell states from spatial transcriptomics. Overall, scFormer provides a unified framework for identifying biologically meaningful rare populations while mitigating batch effects in multi-sample datasets.
Jinjing Huang, Xuanzhe Xia, Feng Luo et al.· Advanced Biotechnology· 0 citations
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