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Nuclear speckles are nuclear membraneless organelles in higher eukaryotic cells playing a vital role in gene expression. Using an in situ reverse transcription–based sequencing method, we study nuclear speckle–associated human transcripts. Our data indicate the existence of three gene groups whose transcripts demonstrate different speckle localization properties: stably enriched in nuclear speckles, transiently enriched in speckles at the pre–messenger RNA stage, and not enriched. We find that stably enriched transcripts contain inefficiently excised introns and that disruption of nuclear speckles specifically affects splicing of speckle-enriched transcripts. We further reveal RNA sequence features contributing to transcript speckle localization, indicating a tight interplay between transcript speckle enrichment, genome organization, and splicing efficiency. Collectively, our data highlight a role of nuclear speckles in both co- and posttranscriptional splicing regulation. Last, we show that genes with stably enriched transcripts are over-represented among genes with heat shock–up-regulated intron retention, hinting at a connection between speckle localization and cellular stress response. 

RNA molecules often play critical roles in assisting the formation of membraneless organelles in eukaryotic cells. Yet, little is known about the organization of RNAs within membraneless organelles. Here, using super-resolution imaging and nuclear speckles as a model system, we demonstrate that different sequence domains of RNA transcripts exhibit differential spatial distributions within speckles. Specifically, we image transcripts containing a region enriched in binding motifs of serine/arginine-rich (SR) proteins and another region enriched in binding motifs of heterogeneous nuclear ribonucleoproteins (hnRNPs). We show that these transcripts localize to the outer shell of speckles, with the SR motif-rich region localizing closer to the speckle center relative to the hnRNP motif-rich region. Further, we identify that this intra-speckle RNA organization is driven by the strength of RNA-protein interactions inside and outside speckles. Our results hint at novel functional roles of nuclear speckles and likely other membraneless organelles in organizing RNA substrates for biochemical reactions. 

Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: Despite their excellent accuracy, they cannot describe how they arrived at their predictions. Here, using an “interpretable-by-design” approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model’s interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed uncharacterized components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.