Nuclear compartmentalization of TERT mRNA and TUG1 lncRNA is driven by intron retention

Dumbović, Gabrijela; Braunschweig, Ulrich; Langner, Heera K.; Smallegan, Michael; Biayna, Josep; Hass, Evan P.; Jastrzebska, Katarzyna; Blencowe, Benjamin; Cech, Thomas R.; Caruthers, Marvin H.; Rinn, John L.

doi:10.1038/s41467-021-23221-w

Download PDF

Article
Open access
Published: 03 June 2021

Nuclear compartmentalization of TERT mRNA and TUG1 lncRNA is driven by intron retention

Nature Communications volume 12, Article number: 3308 (2021) Cite this article

8324 Accesses
23 Citations
27 Altmetric
Metrics details

Subjects

A Publisher Correction to this article was published on 21 October 2021

This article has been updated

Abstract

The spatial partitioning of the transcriptome in the cell is an important form of gene-expression regulation. Here, we address how intron retention influences the spatio-temporal dynamics of transcripts from two clinically relevant genes: TERT (Telomerase Reverse Transcriptase) pre-mRNA and TUG1 (Taurine-Upregulated Gene 1) lncRNA. Single molecule RNA FISH reveals that nuclear TERT transcripts uniformly and robustly retain specific introns. Our data suggest that the splicing of TERT retained introns occurs during mitosis. In contrast, TUG1 has a bimodal distribution of fully spliced cytoplasmic and intron-retained nuclear transcripts. We further test the functionality of intron-retention events using RNA-targeting thiomorpholino antisense oligonucleotides to block intron excision. We show that intron retention is the driving force for the nuclear compartmentalization of these RNAs. For both RNAs, altering this splicing-driven subcellular distribution has significant effects on cell viability. Together, these findings show that stable retention of specific introns can orchestrate spatial compartmentalization of these RNAs within the cell. This process reveals that modulating RNA localization via targeted intron retention can be utilized for RNA-based therapies.

The pan-cancer lncRNA PLANE regulates an alternative splicing program to promote cancer pathogenesis

Article Open access 18 June 2021

The transcriptional terminator XRN2 and the RNA-binding protein Sam68 link alternative polyadenylation to cell cycle progression in prostate cancer

Article 07 November 2022

A cancer-associated RNA polymerase III identity drives robust transcription and expression of snaR-A noncoding RNA

Article Open access 30 May 2022

Introduction

Dynamic regulation of subcellular RNA localization is critical for biological processes ranging from organismal development to cellular activity^1,2,3,4,5,6. While the underlying mechanisms have been found for some transcripts, new aspects of RNA localization regulation continue to arise^7,8. Studies have found that splicing affects RNA localization^9,10. Over the last years, intron retention has emerged as a regulator of the subcellular distribution and nuclear retention of many messenger RNAs and non-coding RNAs^{4,11,12,13,14,15,16}. Nuclear retention of nonfunctional, partially spliced RNAs can serve as a cellular defense mechanism against the translation of RNAs with erroneous splicing. Recent studies show new functions of intron retention such as buffering protein quantity and rapid response to external stimuli^4,12,13,17. Some intron retention events can be explained by slow post-transcriptional splicing kinetics^{18,19,20,21,22,23}. Alternatively, very specific introns can be stably retained, adding an additional regulatory layer to RNA functionality^24,25. For instance, intron retention in long noncoding RNAs (lncRNAs) can give rise to transcripts with unique functions in terms of sequence variability and subcellular distribution²⁶. Similarly, some coding RNAs retain specific introns, which alters the subcellular localization and availability of their transcripts for translation^27,28.

Recent technological advances enable spatially resolving and quantifying both coding and non-coding RNA distribution on a single-cell, single-transcript, and sub-cellular level^{15,29,30,31,32,33}. Here we explored single-molecule localization dynamics of two developmentally and clinically relevant RNAs, telomerase reverse transcriptase (TERT) mRNA, and taurine-upregulated gene 1 (TUG1) lncRNA, across many human cell types. TERT encodes the catalytic subunit of the ribonucleoprotein complex telomerase, which elongates and maintains telomeres³⁴. Telomerase is reactivated in most tumors from almost all cancer types, and it is needed for the maintenance of telomeres, which is critical for long-term proliferation of cancer cells^35,36. The TERT gene is silenced in differentiated cells, hence TERT has been considered as a promising therapeutic target in cancer^37,38. We and others recently showed that the majority of TERT transcripts are nuclear^39,40, suggesting a potential regulatory mechanism acting at the RNA level.

TUG1 lncRNA has a role in many cellular processes and is associated with malignancies, where it has an oncogenic role (inferred as onco-lncRNA)^{41,42,43,44,45,46,47,48,49}. On a subcellular level, molecular activities of TUG1 lncRNA were pinpointed to the nucleus where it was shown to suppress cell-cycle-related gene expression by interacting with target genomic loci. In the cytoplasm, TUG1 RNA might have a protein-coding role^50,51. Correspondingly, we and others showed that TUG1 RNA is located in both the nucleus and cytoplasm^31,51,52. Nevertheless, the mechanism by which TERT and TUG1 maintain dual localization in the nucleus and cytoplasm remains unknown, but could potentially provide important insights into their biology and targeting in cancer. Thus, TERT and TUG1 transcripts represent an opportunity to elucidate when and where splicing occurs, which introns are retained, and how this in turn affects the cellular state. Furthermore, these RNAs provide an opportunity to test if unspliced introns are indeed sufficient to cause nuclear retention, instead of simply being associated with nuclear retention.

We address these questions using two orthogonal approaches: (i) mapping the subcellular localization of TERT and TUG1 fully processed transcripts and splicing intermediates using single-molecule RNA FISH (smRNA FISH) and (ii) using modified antisense oligonucleotides (ASOs) (thiomorpholinos, TMOs) to direct specific splicing events. We show that retention of specific introns drives the nuclear retention of both TERT pre-mRNA and TUG1 lncRNA transcripts. In the case of TERT transcripts, intron retention is regulated during the cell cycle, with two specific introns retained during interphase and spliced out during mitosis. TUG1 transcripts have two distinct populations of fully spliced, cytoplasmic, and intron-retained, nuclear RNAs. To test the functional significance of these intron-retention events, we developed a TMO approach that further drives intron retention and results in a shift in the subcellular localization of the targeted RNA. Altering the nuclear-cytoplasmic distribution of TERT and TUG1 transcripts had significant functional consequences on a cellular scale and reduced cell viability in vitro. Collectively, our findings provide new evidence for a causative relationship between intron retention and spatio-temporal regulation of non-coding and protein-coding RNAs. We suggest the modulation of RNA localization by modified ASOs as a potential therapeutic approach.

Results

Nuclear TUG1 lncRNA and TERT mRNA retain introns

We and others previously observed that TERT transcripts are unexpectedly more abundant in the nucleus than the cytoplasm^39,40. Somewhat similarly, smRNA FISH revealed that the TUG1 lncRNA is evenly distributed between the nucleus and cytoplasm (Fig. 1)^31,51. We sought to determine molecular features or splicing patterns that could differentiate nuclear versus cytoplasmic localization of these transcripts. By analyzing available poly(A)⁺ RNA-seq data from the ENCODE consortium⁵³, high read coverage across both TUG1 introns were observed (Fig. 1a and Supplementary Fig. 1a), while TERT had high read coverage across three of its introns, intron 1, intron 11 and intron 14 (Fig. 1b and Supplementary Fig. 1b). Next, we calculated the splicing efficiency of each intron using published poly(A)⁺ RNA-seq data from human induced pluripotent stem (iPS) cells. Percent intron retention (PIR) was calculated using vast-tools⁵⁴ as described previously¹¹. Briefly, intron retention was evaluated as the ratio of read counts mapping to exon–intron junctions relative to the total number of exon–intron junction reads plus spliced exon-exon junction reads (see “Methods”). The results show that TERT specifically retains introns 11 and 14 in iPS cells (PIR of 30% and 31%, respectively) while the other introns are efficiently spliced (Fig. 1c and Supplementary data 1; of note, intron 1 did not have enough mappable reads to estimate PIR with vast-tools). Further, we confirmed the retention of the first intron in TUG1; it has a PIR of 46% in iPS cells whereas TUG1 intron 2 is absent from the VastDB human database.

**Fig. 1: Retention of specific introns correlates with nuclear localization of TERT mRNA and TUG1 lncRNA in hES/iPS cells.**

We hypothesized that the transcripts with retained introns would be nuclear localized. To determine RNA localization, we designed smRNA FISH probes tiling across TUG1 and TERT exons and introns (Fig. 1a, b). Dual-color smRNA FISH probes independently targeted TUG1 exons and intron 1 or intron 2, and TERT exons and intron 11 or intron 14 (Fig. 1d). We further applied smRNA FISH against TERT intron 2 and GAPDH intron 2 as controls for non-retained introns. Imaging datasets were processed, and co-localized exon and intron spots were quantified as intron-retaining transcripts, while the exon-only signal was quantified as transcripts with that specific intron spliced out (Fig. 1d).

RNA imaging on a human iPS cell line showed an even distribution of TUG1 in the nucleus and cytoplasm (average ~48% and ~52%, respectively) (Fig. 1e). The majority of the transcripts in the nuclear fraction had retained introns, whereas those in the cytoplasmic fraction did not (Fig. 1e). More specifically, the average nuclear PIR (expressed as a percentage of nuclear intron-retaining transcripts over total nuclear transcripts) for TUG1 intron 1 and intron 2 in iPS cells was 62% and 56%, respectively, with significant correlations between the magnitude of detected intron retention and nuclear TUG1 transcript levels (R² = 0.63 intron 1, R² = 0.47 intron 2; P = 6.77 × 10⁻¹² and P = 3.33 × 10⁻⁸, respectively, Pearson correlation, Fig. 1e).

TERT transcripts were retained in the nucleus to an even higher degree than TUG1 transcripts, with on average 86% of the total detected TERT RNAs retained in the nucleus of iPS cells (Fig. 1f). Intron 11 of TERT had a high nuclear PIR (90%) which correlates with the quantity of detected nuclear TERT RNA (R² = 0.92, P < 2.2 × 10⁻¹⁶, Pearson correlation). Intron 14 was also retained, albeit at a lower proportion (nuclear PIR = 68%), and it also showed a significant correlation with the quantity of nuclear TERT (R² = 0.63, P = 7.1 × 10⁻¹², Pearson correlation). These results indicate that TERT intron 11 might have a greater impact on the nuclear retention of TERT RNA than intron 14. TERT intron 2, a control for a non-retained intron in iPS cells, had an average nuclear PIR = 18% with no correlation with the quantity of nuclear TERT (R² = 0.0092, P = 0.48, Pearson correlation). GAPDH intron 2 smRNA FISH showed on average between 1 and 2 punctate signals per cell, which overlapped with GAPDH exon signal and marked the active transcription sites, supporting the specificity of the smRNA FISH approach detecting intron retention in TUG1 and TERT (Supplementary Fig. 1c). Of note, with smRNA FISH we can detect the absolute number of intron-retained and spliced transcripts per cell/compartment in interphase and mitosis. Vast-tools analysis was done on bulk RNA from poly(A)-selected samples, therefore likely under-representing the pool of nuclear unspliced or partially spliced transcripts that we detect by smRNA FISH.

TUG1 and TERT intron retention across cancer cell types

The nuclear localization and intron retention observed in healthy iPS cells that endogenously express TUG1 and TERT led us to explore whether this phenomenon also occurs in other cell types and contexts such as cancer, where TERT expression is reactivated and TUG1 is expressed. We performed the same analysis for TUG1 on four cancer cell lines (osteosarcoma U-2 OS, cervical cancer HeLa, colorectal cancer HCT116, and glioblastoma LN-18) and two non-tumor-derived cell types (embryonic kidney HEK293T and BJ fibroblasts); and for TERT on 4 cell lines with TERT re-activation (HeLa, HCT116, HEK293T, and LN-18).

RNA-seq analysis showed high read coverage across TUG1 intron 1 and 2 (Fig. 2a) in all cell lines, indicating that a large fraction of this lncRNA has retained introns. Our smRNA FISH revealed a consistent nuclear/cytoplasmic localization for TUG1 regardless of the cell or cancer type (Fig. 2b and Supplementary Fig. 2a for fibroblasts). As before, there was a significant correlation between the quantity of nuclear TUG1 and intron retention (R² ≥ 0.5, P < 0.001 in all cell lines tested, Pearson correlation) (Fig. 2b and Supplementary Fig. 2a for fibroblasts). There were modest differences in the nuclear PIR between cell lines for both intron 1 and intron 2 (mean value ranging from 52 to 75% for intron 1, and from 52 to 67% for intron 2) (Fig. 2c left). Cell lines with higher total PIR of intron 1 and intron 2 tended to have more nuclear TUG1, indicating a correlation between the extent of TUG1 intron retention and nuclear localization (R² = 0.86 for nuclear enrichment vs. total PIR intron 1; R² = 0.93 for nuclear enrichment vs. total PIR intron 1, P = 0.0015 and P = 0.0004, respectively, Pearson correlation) (Fig. 2c right and Supplementary Fig. 2b). Overall, TUG1 showed dual localization and retention of both introns across all analyzed cell lines, with corresponding differences in the ratios of nuclear vs. cytoplasmic TUG1 transcripts and PIR values between cell lines, thereby opening the possibility that this process is being fine-tuned and regulated in a cell-type dependent manner.

**Fig. 2: TUG1 intron retention is common and fluctuates across cell lines.**

We next explored TERT intron retention across cell lines in a similar manner as for TUG1 (Fig. 3). RNA-seq analysis showed high read coverage in introns 11 and 14 (and in HCT116 cells, intron 2), suggesting their retention (Fig. 3a). By smRNA FISH on the HCT116, HEK293T, and LN-18 cell lines we detected TERT expressed in the majority of cells (Fig. 3b). The LN-18 and HEK293T cell lines showed similar nuclear enrichment of TERT as the iPS cell line (average, 82% and 89% TERT RNA in the nucleus, respectively) (Fig. 3c). This was accompanied by high retention of intron 11 (nuclear PIR = 91% and 89% for LN-18 and HEK293T, respectively). Retention of intron 11 had a significant correlation with the quantity of nuclear TERT (R² = 0.94 and 0.96 for LN-18 and HEK293T, respectively, P < 2.2 × 10⁻¹⁶ for both cell lines, Pearson correlation). As in iPS cells, intron 14 was retained to a lesser extent (nuclear PIR = 61% and 47% for LN-18 and HEK293T, respectively) with a modest, but significant, correlation with the quantity of nuclear TERT (R² = 0.28 and 0.67 for LN-18 and HEK293T, respectively, P = 7.0 × 10⁻⁵ and 2.8 × 10⁻¹³, respectively, Pearson correlation). HeLa cells were excluded from this analysis because they had very few detectable molecules of TERT RNA per cell (Supplementary Fig. 3).

**Fig. 3: Retention of TERT intron 11 is robust across cell lines.**

The HCT116 cell line showed some differences compared to iPS, LN-18 and HEK293T cell lines described above. First, very rarely were spliced TERT transcripts detected in the cytoplasm; on average 96% were found in the nucleus (Fig. 3b, c). While intron 2 was not significantly retained in other cell lines examined, HCT116 retained intron 2 in nuclear TERT (nuclear PIR = 78%, R² = 0.91 with quantity of nuclear TERT, P < 2.2 × 10⁻¹⁶, Pearson correlation), alongside intron 11 (nuclear PIR = 69%, R² = 0.86 with quantity of nuclear TERT, P < 2.2 × 10⁻¹⁶, Pearson correlation), while intron 14 was retained less efficiently (nuclear PIR = 40%, R² = 0.50 with quantity of nuclear TERT, P = 6.5 × 10⁻⁹, Pearson correlation). This atypical retention of intron 2 can also be observed in the corresponding RNA-seq for HCT116 (Fig. 3a).

Based on our analysis, we find intron 11 robustly retained across different cell lines, while intron 14 showed less and more variable retention, similar to what was observed in iPS cells. Furthermore, these data illustrate the need to analyze possible splicing aberrations that might influence subcellular localization of TERT in certain cell types or cancer, as shown here for intron 2 in the HCT116 cell line.

Intron retention is conserved across species, but different introns are retained

We reasoned that if these specific intron retention events for TUG1 and TERT were biologically relevant, they would show evolutionary conservation. To address this, we performed several analyses between human and mouse. The TUG1 locus has high sequence conservation between human and mouse, maintaining the same gene organization (3 exons and 2 introns) and exhibiting 62.5% overall sequence conservation, 70.2% in exons, and 52.2% in introns (Fig. 4a). We observed that the 5′ exon has slight differences in the annotation compared to human TUG1 (smaller than the corresponding exon in human) (Fig. 4b). Similarly, the TERT locus maintains the same gene organization (16 exons and 15 introns) between human and mouse (Fig. 4a). The overall nucleotide sequence conservation between human and mouse TERT loci is only 29.1%, which is mostly due to the low intron sequence conservation (25.5%) while coding sequences exhibit higher conservation (60.7%). Thus, TUG1 and TERT show similar evolutionary conservation in their exonic structures.

**Fig. 4: Evolutionary conservation of TERT and TUG1 intron retention.**

We sought to determine whether intron retention is a conserved phenomenon in TUG1 and TERT transcripts in comparable cell types across species. We analyzed the splicing efficiency of individual Tert and Tug1 introns using published RNA-seq data from mouse iPS (miPS) and mouse embryonic stem (mES) cells (Fig. 4b). In mES and miPS cells, Tug1 intron 1 is not highly retained (PIR of ~6% in miPS and mES), while mouse intron 2 had a higher PIR of ~20%. We applied smRNA FISH to further determine retention of both introns and subcellular localization patterns of Tug1 in mES cells (Fig. 4c). We observed conserved dual localization of Tug1 in the nucleus and cytoplasm (average 61% nuclear Tug1). Intron 2 is highly retained in nuclear Tug1 (PIR = 62%). However, intron 1 is less retained (PIR = 24%), thereby suggesting a more efficient splicing of intron 1 in mouse compared to human TUG1.

Tert introns 11 and 14 are more efficiently spliced in mouse (PIR = 3.4% and 0%, respectively), contrary to their high retention in human cells (PIR = 30.2% and 31.4%, respectively) (Fig. 4b). In contrast, intron 3 and intron 7 are highly retained in mouse Tert (PIR = 24.6% and 23%, respectively, in mES, and 13.3% and 17.2%, respectively, in miPS). Analysis of poly(A) enriched RNA-seq from mES cells showed predominantly nuclear localization of Tert (Fig. 4d).

Intrigued by the retention of different TERT introns between human and mouse, we extended the analysis to seven mammalian species across multiple tissues and cell line samples (Fig. 4e and Supplementary data 2). The analysis revealed an intriguing difference in selective intron retention, even among mammals of the same order. The retention of introns 1 and 11 is conserved across different tissue and cell types in primates. Retention of intron 14 appears to be primarily a human-specific phenomenon. Variability exists in the retention of other introns (for instance, intron 15 is highly retained in macaque). High retention of introns 3 and 7 is conserved between rat and mouse, while rat Tert additionally retains other introns, including intron 11. These data demonstrate that both intron sequences and intron retention have undergone evolutionary divergence between mammalian orders. Intriguingly, TERT contains retained introns in all species investigated, thus opening the possibility that regulation of TERT via subcellular localization may be an evolutionarily conserved mechanism.

We sought to determine more globally whether the observed retention of specific introns in TERT and TUG1 is a common or atypical phenomenon among coding and lncRNA genes. To this end, we analyzed PIR of each intron for every mRNA and lncRNA across hiPS, mES, and miPS cells (Fig. 4f and Supplementary data 3, 4). For each gene, we plotted the maximum PIR among all introns in a given transcript, together with the minimum PIR, if applicable. LncRNAs are known to be spliced less efficiently^11,12,55. Accordingly, intron retention is generally high in lncRNA genes, extending previous observations that introns in UTRs and non-coding genes are particularly highly retained^11,56. TUG1 has a maximum PIR typical for a lncRNA gene (Fig. 4f). Interestingly, this analysis revealed that many coding genes have at least one retained intron alongside fully spliced introns. Importantly, PIR of TERT retained introns is among the top 20% of intron retention events in coding genes in both species.

Collectively, these results show that the phenomenon of intron retention of TERT and TUG1 transcripts is conserved across species, but the identity of which introns are retained has evolved.

Features of retained introns

We next analyzed intron sequence features that could potentially discriminate retained from efficiently spliced introns. It was shown that retained introns are significantly associated with elevated CG content, reduced length, and relatively weak donor and acceptor splice sites²⁸. Introns 3, 7, 11, and 14 are in general longer in human than mouse (Supplementary data 5). No significant differences in GC content were found, except for intron 7 having lower GC content in mouse. We further analyzed the conservation and strength of splice sites of all TERT introns, focusing on highly retained TERT introns in either human or mouse (intron 3, 7, 11, and 14). In all instances, the canonical GT-AG pair is present (Supplementary Fig. 4a). The acceptor and donor splice sites are classified as strong, with no significant differences in the strength of splice sites correlating with intron retention, with the exception of intron 7 which has a weaker donor splice site in mouse (Supplementary Fig. 4b). In contrast, TUG1 introns are highly conserved between human and mouse in length, GC content, and splice site strength (Supplementary Fig. 4, Supplementary data 5).

To begin to assess the influence of RNA binding proteins (RBPs) on intron retention, we analyzed RBP motif instances and binding events detected across all of ENCODE’s eCLIP datasets in TERT introns. We reasoned that having highly retained and spliced introns of the same transcript would facilitate discerning binding profiles between spliced and retained introns. The overall binding profile of introns 11 and 14 clustered together in hierarchical clustering of eCLIP peak coverage intron-wide (Supplementary Fig. 5a). This seems to be primarily driven by higher peak coverage indicating increased overall RBP binding in TERT’s retained introns (Supplementary Fig. 6), especially around their 5′ and 3′ splice sites, despite their motif density being on par with the other introns (Supplementary Fig. 5b). However, we did not observe proteins that bound uniquely to retained introns 11 and 14, nor proteins that bound all efficiently spliced introns and not those retained. Collectively, these analyses demonstrate that intron 11 and 14 share the property of intron retention and the highest amount of RBP binding. Yet, if the RBP density is due to intron retention or driving intron retention remains unknown.

Intron-retained nuclear TUG1 and TERT are stable RNAs that remain in the nucleus after transcription termination

Some RNA processing intermediates retain certain introns due to slow post-transcriptional splicing kinetics^12,23. To test whether TUG1 and TERT retain introns due to slow splicing kinetics or if those are stable transcripts, we treated cells with Actinomycin D (ActD). ActD inhibits Pol I, Pol II, and Pol III by intercalating in the DNA and preventing transcription elongation^57,58. Cell lines that endogenously co-express TUG1 and TERT (iPS, LN-18, HEK293T) were treated for up to 4.5 hours and harvested for RT-qPCR at several time points (0 h, 40 min, 2.5 h and 4.5 h). We monitored the stability of TUG1 and TERT exons and retained introns. As a stable RNA control, we used GAPDH, while GAPDH intron 2 and pre-ribosomal RNA (45S rRNA) were used as controls for nascent RNAs. We observed an immediate decrease in the nascent 45S rRNA and GAPDH intron 2 after 40 min of ActD treatment. In contrast, in healthy and cancer cell lines, intron-containing TUG1 and TERT RNAs were highly stable even after 4.5 h of transcription inhibition (Fig. 5a). To differentiate nascent transcripts and those that have terminated, we compared the abundance and stability of intron-retained transcripts in cDNA synthesized with random primers or oligo(dT) during the iPS ActD treatment. The majority of detected TUG1 and TERT intron-retained transcripts had a poly(A) tail in the untreated state and after transcription inhibition, indicating that those transcripts have been 3′-end processed (Fig. 5b).

**Fig. 5: Intron-retained nuclear TUG1 and TERT are long-lived transcripts, stably retained in the nucleus.**

We applied smRNA FISH during ActD treatment to determine the stability and spatial localization of intron-retained and spliced TUG1 and TERT. Specifically, nuclear TUG1 remained stable across the ActD time course (Fig. 5c for LN-18 and Supplementary Fig. 7a for iPS; no decrease and ~1.2-fold decrease, respectively). In contrast, cytoplasmic TUG1 gradually decreased in both cell lines during ActD time points (~2.2-fold decrease, P ≤ 0.001, unpaired t-test for LN-18; ~2-fold decrease, P ≤ 0.001, unpaired t-test for iPS). Furthermore, retention of intron 1 and 2 remained high even after 4.5 h of treatment, and unspliced TUG1 remained nuclear. Thus, the nuclear, intron-retained TUG1 fraction is more stable than the fully spliced cytoplasmic fraction.

TERT followed a similar trend, with nuclear, unspliced TERT RNA (assessed by retention of intron 11) being highly stable and retained in the nucleus even after 4.5 h of ActD treatment (Fig. 5d and Supplementary Fig. 7b). Nuclear TERT was highly stable during the course of ActD treatment, while cytoplasmic TERT gradually decreased after transcription inhibition (~4-fold decrease, P ≤ 0.001, unpaired t-test for LN-18; ~3-fold decrease, P ≤ 0.001, unpaired t-test for iPS). Retention of intron 11 remained high (no significant decrease) for LN-18 and iPS, and the unspliced transcript remained in the nucleus.

As a control for transcription inhibition, we monitored GAPDH transcription sites visualized by smRNA FISH GAPDH exon/intron 2 overlap. GAPDH transcription sites were decreased in the majority of cells after 40 min of treatment, while after 2.5 and 4.5 h the signal was not detectable (Supplementary Fig. 8). Collectively, these results show that intron-containing TUG1 and TERT are stable, long-lived transcripts, stably retained in the nucleus relative to their spliced cytoplasmic counterparts. We note that nuclear RNAs with retained introns were shown to be hyperadenylated upon transcription inhibition, which may contribute to their stabilization⁵⁹.

TERT pre-mRNA splicing is cell-cycle specific occurring at mitosis

During our smRNA FISH analyses of intron 11 retention, we observed that TERT pre-mRNA was spliced during cell division (after late prophase). Specifically, we used DNA staining with Hoechst to distinguish cells in interphase and mitosis (either prophase, metaphase, anaphase or telophase). We quantified unspliced TERT (co-localized intron 11 and exon signal), spliced TERT (exon signal only), and solo intron 11 in each stage after late prophase. In contrast to interphase, during mitosis, all TERT RNA molecules could be readily visualized as spliced. We observed that TERT intron 11 was in the form of a solo intron, i.e., not co-localized with exons (Fig. 6a, b). The quantity of spliced TERT was increased in mitosis compared to interphase cells (mean value 9.4 vs. 3.8 of spliced TERT molecules per mitotic or interphase cell, respectively, P ≤ 0.001, unpaired t-test). Moreover, the quantity of unspliced TERT was reduced from a mean value of 10.4 molecules/cell in interphase cells to 1.0 in mitosis (P ≤ 0.001, unpaired t-test). Lastly, while intron 11 was included in the vast majority of nuclear TERT mRNA in interphase cells, in mitosis the quantity of free intron 11 greatly increased (mean value 0.0 vs. 8.2, respectively, P ≤ 0.001, unpaired t-test).

**Fig. 6: Splicing of TERT intron 11 occurs upon mitosis.**

Notably, the quantity of free intron 11 was comparable with the number of newly spliced TERT RNA molecules (mean value 8.2 vs. 9.4, respectively). Since the mitotically spliced intron was observed by smRNA FISH, it further indicates that the intron was stable, presumably in the form of a lariat. However, given that the free intron was not observed in other stages of the cell cycle, neither in the cytoplasm nor in the nucleus, it indicates that the stability of solo intron 11 is limited to mitosis.

We performed the same analysis for TUG1. In contrast to TERT, a great portion of TUG1 transcripts remained unspliced during mitosis compared to interphase cells (Fig. 6c, d, mean value 10.6 vs. 16.4 for Δintron1, respectively; 10.9 vs. 14.9 for Δintron2, respectively), implying that TUG1 splicing is not dependent on mitosis. Together, our smRNA FISH analysis found that TERT splicing of retained intron 11 appears to be regulated in mitosis, opening an intriguing possibility of mitotic inheritance of fully spliced, cytoplasmic TERT mRNA.

Modified ASOs block splicing and affect subcellular RNA localization

All our observations above of nuclear TERT and TUG1 intron retention are correlative and do not show a causality of intron retention driving their subcellular localization. To test the causality, we applied chemically modified ASOs called Thiomorpholinos (TMOs). TMOs are oligonucleotides in which the bases (thymine, cytosine, adenine, and guanine) are attached to morpholine, and these nucleosides are joined through thiophosphoramidate internucleotide linkages (Fig. 7a)⁶⁰. They show increased hybridization stability towards complementary RNA (10 °C increased melting temperature compared to an unmodified control duplex of identical sequence). TMOs are also highly stable towards exonuclease enzymes; minimal degradation is observed when treated with snake venom phosphodiesterase I for over 23 h. Unlike DNA:RNA duplexes, they do not elicit RNase H1 activity, making them ideal candidates for splicing studies.

**Fig. 7: Intron retention drives nuclear compartmentalization of TUG1.**

We hypothesized that increasing intron retention via blocking excision of the retained introns would affect the subcellular localization of TUG1. We further wanted to test the physiological role of TUG1 subcellular localization in cancer cell lines, as it has been implicated in many cancers. Specifically, we chose U-2 OS and HeLa for TUG1 lncRNA based on the previous studies^48,49,61. To test this hypothesis, we designed 20-mer TMOs against the two TUG1 donor splice sites, each hybridizing to 2 nt of the exon and 18 nt of the intron sequence (designated TUG1 TMO1 and TMO2) (Fig. 7b). To control for cell effects that could be caused by TMO intake, we designed a control TMO (randomized sequence of TMO1).

TMOs were transfected at an increasing concentration to U-2 OS and HeLa cells, after which cells were harvested for monitoring intron retention via intron-spanning RT-qPCR and smRNA FISH (Fig. 7c, d). The mixture of TUG1 TMO1 and TMO2 inhibited splicing and achieved retention of both introns in a dose-dependent manner already 24 h after treatment (Fig. 7e). Sanger sequencing of the spliced and unspliced RT-PCR products confirmed that the complete introns were retained (Fig. 7f). Interestingly, alongside the expected spliced and intron-retained amplicons, the intron-spanning PCR revealed additional isoforms amplified by the specified primer sets. Some of those less abundant isoforms displayed a shift in size upon the TMO treatment (Supplementary Fig. 9a, labeled with a star).

We next used smRNA FISH to determine whether forced intron inclusion would affect the subcellular localization and availability of spliced TUG1 in the cytoplasm. Specifically, we performed dual-color smRNA FISH in U-2 OS and HeLa cell lines treated with TUG1-targeting TMOs (TUG1 TMO1 + TMO2) and a control TMO. The TUG1-targeting TMOs gave a drastic shift in the subcellular localization and splicing of TUG1 (Fig. 7g and Supplementary Fig. 9b). On average, in U-2 OS TUG1 decreased ~2.4-fold in the cytoplasm (mean 29 molecules in control vs. 12 in TUG1 TMO1 + 2), while TUG1 increased ~1.8-fold in the nucleus (mean 21 molecules in control vs. 38 in TUG1 TMO1 + 2). Similarly, in HeLa cells, TUG1 decreased ~2.7-fold in the cytoplasm (mean 22 molecules in control vs. 8 in TUG1 TMO1 + 2), and it increased ~1.7-fold in the nucleus (mean count 29 in control vs. 48 in TUG1 TMO1 + 2) (Fig. 7g and Supplementary Fig. 9b). After TUG1 TMO1 + TMO2 application, intron retention in nuclear TUG1 was significantly increased in U-2 OS and HeLa cells (PIR intron 1 increased from 51 to 85%, PIR intron 2 increased from 52 to 84% in U2-OS; PIR intron 1 increased from 67 to 92%, PIR intron 2 increased from 57 to 92% in HeLa).

In parallel, we used TUG1 TMO1 labeled with FITC (TUG1 TMO1-FITC) to determine the subcellular localization of the TMO after transfection. TUG1 TMO1-FITC showed predominantly nuclear localization, and it was stably localized in the nucleus 96 h after transfection (later time points were not assessed) (Supplementary Fig. 9c), consistent with these oligos being able to alter nuclear splicing processes. Together, our results demonstrate that TMOs can be used to achieve increased intron retention and in turn increased nuclear localization of transcripts.

Functional consequences of enforced intron retention for TUG1 and TERT

Having observed that increasing intron retention increases nuclear localization of TUG1, we wanted to determine if this redistribution of transcripts has a functional cellular consequence. Specifically, U-2 OS and HeLa cell lines were transfected with 25 nM of TUG1 TMO1 and TMO2 and cell viability was assessed 48 h and 72 h post-transfection relative to transfection agent only and a control TMO (Fig. 7h). Both cell lines showed a reduction in cell viability after 48 h of TUG1 TMO treatment compared to controls (mean 24% and 59% reduced viability for HeLa and U-2 OS, respectively, P ≤ 0.01, unpaired t-test), and after 72 h (mean 29% and 57% reduced viability for HeLa and U-2 OS, respectively, P ≤ 0.01, unpaired t-test). Thus, in both cases altering the subcellular distribution of TUG1 impaired cell viability.

To determine if our TMO strategy is also applicable to pre-mRNAs we focused on TERT. Briefly, we designed a TMO to retain specified intron 11 of TERT targeting TERT exon 11/ intron 11 junctions and determined the cellular consequences thereof (Fig. 8a). Cell lines with uniform reactivation of TERT expression, LN-18, and HEK293T, were transfected with TERT TMO and control TMO. Because TERT intron 11 is quite long (3.8 kb), intron-spanning PCR was not feasible to assess intron retention. Thus, we applied exon/intron junction RT-qPCR to assess the efficiency of intron retention (Fig. 8b, c). We found that TMOs enforcing intron 11 retention decreased splicing of intron 11 by ~60% compared to the control TMO. In contrast, intron 11-containing TERT (assessed by monitoring intron 11 and exon 11 to intron 11 junction) was increased ~32% compared to control TMO. As additional controls, we applied primers at the upstream exon 10 to exon 11 junction, which was not affected with TERT TMO treatment, and exon 10 to exon 12 junction, which was decreased ~50%, in accordance with the decrease in exon 11 to exon 12 junction (Fig. 8c).

**Fig. 8: TMO-based prevention of TERT splicing reduces cell viability in vitro.**

We further leveraged the specific retention of intron 11 and the restriction of splicing to mitosis. We observed that the total number of TERT RNA molecules (assessed by overall exon signal) was not altered during mitosis between control and TERT TMO-treated samples (mean values 6.3 and 6.7, respectively). Consistent with the above results, we observed a significant effect of TERT TMO on splicing of intron 11 only during mitosis (Fig. 8d). More specifically, the majority of intron 11 was spliced out and observed in the form of a solo intron with control TMO, and these solo introns were significantly decreased in cells treated with TERT TMO (mean value 4.0 solo intron 11 per mitosis in control TMO and 1.3 solo intron 11 per mitosis in TERT TMO, P ≤ 0.001, unpaired t-test).

The observation of solo intron 11 during mitosis is in accordance with the observation made in iPS cells (Fig. 6a, b). While during mitosis most of TERT RNA was in the form of spliced RNA in the control TMO samples, the quantity of spliced TERT significantly decreased in TERT TMO samples (mean values 5.4 and 2.2 molecules per mitosis, respectively, P ≤ 0.001, unpaired t-test). Consequently, the quantity of unspliced TERT increased in TERT TMO compared to control TMO samples (mean values 4.5 and 0.8 molecules per mitosis, respectively, P ≤ 0.001, unpaired t-test). Overall, the RT-qPCR and smRNA FISH confirmed that TMOs can specifically inhibit splicing of intron 11 from TERT pre-mRNA.

We next sought to determine whether inhibiting the availability of spliced TERT by TERT TMO would affect cell growth of LN-18 and HEK293T cell lines. Both cell lines were transfected with 25 nM of TERT TMO, and cell proliferation was assessed (Fig. 8e). LN-18 cell line showed a reduction in cell viability after 48 h of treatment compared to transfection agent only and control TMO (mean reduction of 18%, P ≤ 0.05), which was further enhanced after 72 h (mean reduction of 28% in cell viability, P ≤ 0.01). HEK293T showed a delayed response and cell viability was reduced after 72 h treatment with TERT TMO (mean reduction of 29% in cell viability, P ≤ 0.01) compared to control TMO and transfection agent only.

It is intriguing that cell viability is compromised so quickly after reduction of translatable TERT mRNA, given that telomere shrinkage due to inhibition of telomerase typically takes many population doublings before it gives a growth defect⁶². Indeed, we detected only mild telomere shortening after 5 days of treatment with TERT TMO by Southern blot (Supplementary Fig. 10a). This result coincides with previous cases where telomerase inhibition was found to decrease cell viability through telomere length-independent processes^63,64,65,66. More specifically, we observed an induction of γH2A.X foci and G2/M arrest as has been shown to occur upon short-term telomerase inhibition (Supplementary Fig. 10b, c). To test the specificity of the TERT TMO effect on cell viability, we co-transfected spliced TERT (therefore, not a target of the TERT TMO) in combination with TERT TMO. Effects specific to inhibiting excision of intron 11 with TERT TMO, reduction of cell viability, G2/M arrest, and γH2A.X foci, were absent when co-transfecting spliced TERT, supporting the specificity of these effects (Fig. 8f).

Collectively, these findings demonstrate that TMOs effectively block splicing and change cellular localization and availability of the RNA. Moreover, we find that this perturbed subcellular transcript distribution has a functional consequence on cell viability.

Discussion

It has long been known that the spatio-temporal distribution and compartmentalization of RNA in the cell is tightly coupled with its subcellular function^1,2,3,4,5,6. Studies of underlying mechanisms have pinpointed RNA motifs and structural features that target RNA subcellular localization^67,68. Splicing has been shown to strongly influence RNA localization^9,10. For example, lncRNAs are inefficiently spliced, display increased intron retention relative to coding mRNAs, and are more nuclear than mRNAs^11,55,56. Despite these intriguing findings, the causality of splicing events, such as intron retention, in the molecular events driving nuclear retention of RNAs has remained unclear. A surprising finding is that the majority of TERT transcripts are nuclear, and therefore translationally inert, yet the underlying mechanism remained unknown^39,40.

Here we addressed this question using multiple approaches including single-molecule RNA FISH to spatio-temporally measure specific splicing events that may alter subcellular RNA localization. We focused on two cancer-related transcripts, TERT mRNA and TUG1 lncRNA. We find that both mRNA and lncRNA localization patterns are driven by consistent retention of specific introns. While such RNAs could be nonfunctional, partially spliced byproducts, or serve as nuclear non-coding RNAs, it has been shown that some of the nuclear, intron-retained RNAs are poised or ‘detained’ for a signal for post-transcriptional splicing, hence serving as a reservoir of RNAs readily available depending on cellular activity^12,13. In this regard, the striking splicing of retained TERT intron 11 after cells’ entry to mitosis was an intriguing indication that fully spliced TERT might be generated mitotically. Retention of specific introns would compartmentalize TERT RNA in the nucleus of interphase cells, while upon cells’ entry to mitosis, retained introns would be spliced out and daughter cells would inherit fully spliced TERT. Mitotic inheritance of spliced TERT would ensure that telomere elongation occurs only in mitotically active cells, still allowing telomerase assembly during the later stages of the cell cycle when DNA is replicated and telomeres elongated^69,70,71.

Together, these findings raise the question of how intron 11 is retained in order to specifically be spliced out during mitosis and produce a cytoplasmic transcript for translation. In this regard, it is interesting to consider that TERT intron retention may be regulated as part of a broader program of differential intron retention (and other forms of alternative splicing) that is controlled by the SR protein splicing factor kinase CLK1 during the cell cycle⁷². Alternatively, possibly some other signaling pathway could regulate the splicing of the retained introns and nuclear export of TERT for translation during interphase.

We found that the lncRNA TUG1 is equally distributed between the nucleus and cytoplasm across multiple cell lines. Hence the same locus gives rise to equal amounts of either efficiently spliced cytoplasmic TUG1 or intron-retained nuclear TUG1, where intron retention dictates nuclear/cytoplasmic transcript distribution. This interesting splicing balance could have important implications: (i) the longer TUG1 lncRNA with retained introns could exert a specific nuclear RNA function in this longer form, which is consistent with the strong conservation of TUG1 intronic sequences—an infrequent property of lncRNA or mRNA introns; (ii) intron sequences could give rise to distinct functions; (iii) the efficiently spliced cytoplasmic TUG1 could be destined to encode a protein, as has been proposed by recent studies⁵¹; (iv) the conserved distribution of TUG1 in the nucleus and cytoplasm could represent a translational buffering or two distinct functionalities. One of these mechanisms, or their combination, potentially underlies a 100% penetrant male infertility phenotype in TUG1 knock-out mouse models⁵¹.

Both TERT and TUG1 are upregulated in many cancers and thus represent important therapeutic targets. To this end, we tested a RNA-based strategy to alter TERT and TUG1 splicing and subcellular distribution. We found that this TMO antisense approach was highly effective and specific at blocking TERT and TUG1 splicing events. Importantly, altering these specific splicing patterns using our TMO approach not only affected subcellular distribution but, in both cases, decreased cell viability. Thus, TMO-based strategies could be universally applicable not only to other transcripts that retain specific introns, but to a variety of oncogene transcripts that could be rendered inert in the nucleus.

Methods

Cell lines and cell culture

HCT116 (CCL-247), HeLa (CCL-2), HEK293T (CRL-3216), LN-18 (CRL-2610), U-2 OS (HTB-96) cell lines were obtained from ATCC and cultured according to recommended protocols. Human iPSC WTC-11 (Coriell Institute) cells were cultured on Vitronectin (Thermo Fisher Scientific) coated 6-well plates or glass coverslips (for smRNA FISH purposes) in Essential 8 Flex medium (Thermo Fisher Scientific) with E8 supplement (Thermo Fisher Scientific), Rock inhibitor and 2.5% penicillin-streptomycin. iPS cells we passaged with EDTA in dPBS. Mouse embryonic stem cells (Harvard Stem cell institute) were cultured on top of gelatin (0.1%, EMD Millipore) coated plates or glass coverslips (for smRNA FISH purposes). Embryonic stem cell media was prepared as follows: KnockOut DMEM medium (Thermo Fisher) supplemented with ESC FCS (Millipore Sigma), non-essential amino acids (Thermo Fisher), GlutaMAX supplement (Thermo Fisher), penicillin-streptomycin (Thermo Fisher), 50 mM 2-mercaptoethanol, LIF, CHIR99021 and PD0325901 (Sigma-Aldrich).

Actinomycin D (Sigma-Aldrich) was used at final concentration of 5 μg/mL in full growth media. Cell pellets and coverslips were harvested at 0, 40 min, 2.5 h and 4.5 h after adding Actinomycin D, and processed for RNA extraction and smRNA FISH as described below.

RNA extraction

After the corresponding treatments, cell pellets were harvested and RNA extraction was performed with Maxwell LEV Simply RNA tissue kit (Promega) following manufacturer’s instructions with DNase I treatment. Each sample was tested for DNA contamination by qPCR after each extraction. RNA quality was assessed on 2% agarose gel and Bioanalyzer (RNA Nano Assay: 25–500 ng/μL).

Analysis of intron retention from RNA-seq

Vast-tools v2.2.2⁵⁴ (https://github.com/vastgroup/vast-tools) was used to calculate PIR values. We used two previously published poly(A)⁺ RNA-seq data from human iPS cells⁷³

(GSM1023087 and GSM1023070), and one ribosomal RNA depleted RNA-seq from human iPS cells (GSM808734). For mouse, we used two previously published poly(A)⁺ RNA-seq datasets from mouse iPS cells (GSM1032506, GSM1032518) and three poly(A)⁺ RNA-seq datasets from mES cells generated in this study and available from GSE169743.

Reads mapping to mid-intron sequences and balanced counts of reads aligning to upstream and downstream exon–intron sequences were used to evaluate intron retention levels. PIR was measured as a percentage of mean retention reads over the sum of retained and spliced intron reads. Raw values were filtered based on reported quality scores, requiring at least 15 total reads per event and absence of a positive result (P < 0.05) for the binomial test for upstream/downstream junction read balance. PIR values for human TERT intron 11 were reported by vast-tools as imbalanced due to an alternative exon within the intron and were therefore re-calculated based solely on the downstream intron-exon junction reads. Similarly, TUG1 intron 1 was re-calculated based on upstream exon–intron junction reads due to an alternative acceptor site, and TUG1 intron 2 was absent from the VastDB database. For the analysis of global levels of maximum and minimum PIR in coding and non-coding genes, gene biotype annotations were taken from GENCODE v29 (human) and vM23 (mouse) and simplified to ‘coding’, ‘lncRNA’, and ‘other’ (not shown).

Methods for analysis of splicing conservation in TERT

Vast-tools results from poly(A)⁺ RNA-seq datasets for introns in TERT in seven mammalian species across multiple tissue and cell line samples (H. sapiens, 128; P. troglodytes, 38; M. mulatta, 66; R. norvegicus, 127; M. musculus, 151; B. taurus, 58; M. domestica, 44) were kindly shared by M. Irimia (CRG, Barcelona). Vast-tools results were filtered as described for RNA-seq analysis in iPS cells.

Analysis of RBPs interactions in introns

eCLIP peak bed files for 223 experiments in HEPG2 and K562 cell lines were retrieved from the ENCODE Data Portal and merged for each RBP using GenomicRanges (R, version 3.6.0; Bioconductor, version 3.10). Intron windows for the primary transcript of TERT (ENST00000310581.9) were generated by taking 40 bp upstream and downstream of the 3′ and 5′ splice sites and the remaining interior of the intron was partitioned into five equal tiles. The fraction of base pairs in each window was visualized using ComplexHeatmap⁷⁴. For the RBP motif analysis, motifs were retrieved from the ATtRACT motif database and converted to MEME format using chen2meme from the MEME suite (version 5.1.1)^75,76. FIMO from the MEME suite was used to search for motif matches in the intronic sequence and a p-value cut-off of P < 1e−4 was used to filter low-quality motif instances. Since multiple motifs can refer to the same RBP, for ease of visualization motif matches were collated by RBP and visualized in a heatmap using the same windows as for eCLIP. The color in the motif heatmap corresponds to the maximum FIMO score within that window for a given RBP.

cDNA synthesis and qPCR analysis

Reverse transcription was performed with SuperScript® IV First-Strand Synthesis System (Thermo Fisher Scientific) with Superase RNase inhibitor (Ambion) and random hexamers hexamers or oligo dT (indicated in the figure legend) on 0.2–1 μg of RNA. Relative expression was determined by qPCR using SYBR Green I master mix (Thermo Fisher Scientific) according to manufacturer’s instructions using the following amplification conditions: 95 °C 10′; 45 cycles of 95 °C 15″, 57.5 °C 20″ and 72 °C 25″. Expression levels were normalized using GAPDH. A list of primers used in qPCR analyses are summarized in Supplementary Table 1. Their efficiencies were compared to ensure analysis by the comparative Ct method. Relative expression data were analyzed comparing the Ct values of the gene of interest with Ct values of the reference gene for every sample. We used the formula 2ΔΔCt, ΔΔCt being the difference between the Ct of the RNA of interest and the Ct of the housekeeping gene. Duplicates or triplicates were made for each sample and primer set.

RT-PCR and Sanger sequencing

cDNA was amplified with Q5® High-Fidelity DNA Polymerase PCR System (NEB). PCR conditions: initial denaturing at 95 °C 2’; 40 cycles of denaturing at 95 °C 30”, annealing at 58 °C 30” and extension at 72 °C 2’ 30”; followed by final extension at 72 °C 7’. PCR product was examined on a 1% agarose gel for correct size and specificity. Bands corresponding to spliced or unspliced TUG1 were cut from the gel and DNA was extracted with Gel extraction kit (Qiagen) according to instructions. Extracted DNA was cloned with TOPO PCR Cloning Kit (Thermo Fisher Scientific), and positive colonies selected on ampicillin agar plates. Minipreps from ∼5 colonies for each amplicon were sent for Sanger sequencing to Genewiz using T3 or T7 primers.

Single-molecule RNA FISH

For in situ RNA detection, single-molecule RNA FISH was employed³⁰. Tiled oligonucleotides targeting human and mouse TUG1 exons, TERT intron 2, TERT exons, GAPDH intron 2, and GAPDH exons labeled with either Quasar 570 or Quasar 670 were used in our previous studies^40,51. For this study, we custom designed tiled oligonucleotides targeting human and mouse TUG1 intron 1 (Quasar 570) and intron 2 (Quasar 570), TERT intron 11 (Quasar 670), and TERT intron 14 (Quasar 670) using LGC Biosearch Technologies’ Stellaris online RNA FISH probe designer (Stellaris Probe Designer, version 4.2) which were produced by LGC Biosearch Technologies.

Cells were seeded on glass coverslips coated with poly-L-lysine (10 μg/mL in PBS), vitronectin (human iPS cells) or gelatine (mouse ES cells). The iPS cells were seeded at lower density the day before harvesting the coverslips to facilitate the quantification process. Coverslips were washed 2 times with PBS, fixed in 3.7% formaldehyde in PBS for 10 min at room temperature (RT), followed by washing 2 times with PBS and immersed in 70% EtOH at 4 °C for a minimum of 1 h. Prior hybridization, coverslips were washed with 2 mL of wash buffer A (LGC Biosearch Technologies) supplemented with 10% deionized formamide (Agilent) at RT for 5 min. Cells were hybridized with 80 μL of hybridization buffer (LGC Biosearch Technologies) supplemented with 10% deionized formamide (Agilent) containing 1:100 dilution of smRNA FISH probes overnight at 37 °C in a humid chamber. The next day, cells were washed with 1 mL of wash buffer A with 10% formamide for 30 min at 37 °C, followed by a wash with wash buffer A with 10% formamide containing Hoechst DNA stain (1:1000; Thermo Fisher Scientific) for 30 min at 37 °C. Coverslips were washed with 1 mL of wash buffer B (LGC Biosearch Technologies) for 5 min at RT, equilibrated 5 min in base glucose buffer (2× SSC, 0.4% glucose solution, 20 mM Tris pH 8.0 in RNase-free H₂O), and then incubated 5 min in Base Glucose buffer supplemented with 1:100 dilution of glucose oxidase (stock 3.7 mg/mL) and catalase (stock 4 mg/mL). Afterwards, the coverslips were mounted with ProlongGold or ProlongGlass (Life Technologies) on a glass slide and left to curate overnight before proceeding to image acquisition (see below).

Immunofluorescence

Cells were seeded on top of poly-L-lysine coated glass coverslips. After treatment, coverslips were rinsed 2× with PBS, crosslinked in 3.7% formaldehyde in PBS, washed 3× for 5 min in PBS/0.1% Triton-X. Cells were blocked with PBS/0.1% Triton-X/3% BSA for 30 min at RT. Cells were incubated with primary antibody against phospho-Histone H2A.X (1:100, Millipore Sigma) in blocking solution and washed 3× for 5 min in PBS. Secondary antibody (anti-mouse labeled with Alexa Fluor 488, 1:800 in blocking solution, ab150113, Abcam) was incubated for 1 h at RT. Cells were washed 3× for 5 min in PBS, 15 min in PBS with Hoechst DNA stain (1:1000; Thermo Fisher Scientific), rinsed with PBS and mounted with ProlongGlass (Life Technologies) on top of a glass slide.

Microscopy and image analysis

Z stacks with 200–250 nm z-step capturing the entire cell volume were acquired with a GE wide-field DeltaVision Elite microscope with an Olympus UPlanSApo 100×/1.40-numerical aperture oil objective lens and a PCO Edge sCMOS camera using appropriate filters. The built-in DeltaVision SoftWoRx Imaging software was used to deconvolve the three-dimensional stacks. Maximum intensity projections were generated in Fiji and subjected for quantification using Fiji. To outline cell borders, we used manual segmentation. For an accurate manual cell segmentation, the saturation of the FISH channel was increased to visualize cytoplasm contours. Overlapping cells were not quantified. The brightness and contrast of each channel was adjusted. Overlapping exon/intron spots were considered as intron-retained transcripts, while exon only transcripts as spliced transcripts. Each imaging experiment was performed at least two times quantifying at least 50 cells across independently acquired datasets. For ActD treatment and mitosis, less cells/mitosis were quantified per treatment, as indicated in the figure legend. Analysis of z-stacked was additionally performed in 3D in Imaris to confirm that nuclear intron-retained transcripts were within the nucleus. γH2A.X foci were quantified with ImageJ plugin Spot Counter v.0.14 maintaining the same spot size between conditions.

TMO synthesis

Prior to thiomorpholino oligonucleotide (TMO) synthesis, appropriately protected morpholino nucleosides of adenine, guanine, thymine and cytosine and their corresponding phosphorodiamidites were synthesized as reported elsewhere⁶⁰. All TMOs were synthesized using an Applied Biosystems Model 394 Automated DNA Synthesizer using conventional DNA synthesis reagents that were purchased from Glen Research, VA. Briefly, 1.0 μM succinyl CPG support was detritylated using 3% trichloroacetic acid in dichloromethane. The 5′-unprotected nucleoside was allowed to react with a 0.1 M solution of the appropriate morpholinonucleoside phosphorodiamidite in acetonitrile in the presence of 0.12 M 5-ethylthio-1H-tetrazole (600 s coupling time). After sulfurization using 0.05 M sulfurizing reagent II in pyridine/acetonitrile, the capping step was carried using conventional Cap Mix A (acetic anhydride/tetrahydrofuran) and Cap Mix B (1-methylimidazole in acetonitrile), completing one synthesis cycle. Multiple synthesis cycles were repeated until a TMO oligonucleotide of the desired sequence was obtained. The 5′-DMT group on the solid-support bound final oligonucleotide was not detritylated so that purification could be carried out using the DMT-On/Off procedure⁷⁷. Cleavage and deprotection was carried out using 28% aqueous ammonia at 55 °C for 16 h. After cooling to 25 °C followed by evaporation of the ammonia mixture, the oligonucleotides were purified by ion-pair reversed phase HPLC. During this process, the total reaction mixture (after evaporation to dryness) was dissolved in 3% aqueous acetonitrile and injected into an Agilent 1100 HPLC equipped with a manual injector. Due to the lipophilicity of the DMT handle, the DMT-On TMO oligonucleotide could be easily separated from failure products using a gradient of 50 mM Triethylammonium bicarbonate in acetonitrile (Agilent Zorbax C18 column, 2.0 mL flow rate). The DMT-On fractions were pooled, evaporated to dryness and treated with 50% aqueous acetic acid for 5 min. After quenching with triethylamine, the mixture was evaporated to dryness. The resulting solids were dissolved in 3% aqueous acetonitrile and the deprotected TMO oligonucleotides were re-purified by ion-pair RP-HPLC. All oligonucleotides were desalted prior to use. Graphical illustration of thiomorpholino oligonucleotide synthesis shown in Supplementary Fig. 11.

TMO and plasmid transfection

Cells were plated at 200,000 cells/well in a 6-well plate, or 100,000 cells/well in a 12-well plate, the day prior to transfection. Each cell line was transfected with increasing quantity of TMOs with two different transfection agents (Lipofectamine RNAiMAX (Thermo Fisher Scientific) and Xtreme Gene siRNA transfection agent (Sigma)) to determine the optimal transfection conditions for each cell line. Fluorescently labeled TMO was used to assess transfection efficiency, while intron-spanning RT-PCR (only for TUG1), RT-qPCR and smRNA FISH were used to assess the efficiency of intron inclusion. Lipid-oligo complexes were prepared at room temperature in OptiMem medium (Thermo Fisher Scientific) according to the manufacturer’s instructions. After incubation time, lipid-oligo complexes were added dropwise to wells containing freshly added full growth media. U-2 OS was most efficiently transfected with Xtreme Gene, while HeLa, LN-18 and HEK293T cell lines were more efficiently transfected with Lipofectamine RNAiMAX. 25 nM TMO was chosen as the lowest quantity achieving maximum intron inclusion efficiency. 1 μg of a plasmid expressing hTERT (kind gift from Joachim Lingner, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland⁷⁸) was transiently co-transfected with corresponding TMO as described above.

Cell viability assays

Cells were plated at density of 1000 cells/well in a 96 well plate. After 24 h, cells were transfected with 25 nM of the corresponding TMO. 48 h and 72 h post-transfection, cell culture media was replaced by 10% of AlamarBlue reagent (DAL1100, ThermoFisher Scientific) in full growth media 2–4 h prior to reading fluorescence. Fluorescent data was collected using the CLARIOstar microplate reader from BMG Labtech fluorescence plate reader following the manufacturer’s recommendations.

Cell cycle analysis with flow cytometry

Cells were washed with PBS, collected with trypsinization and spun down. After a rinse with PBS, cells were fixed in 70% ethanol for at least 2 h at −20 °C. Cells were spun down; supernatant was removed and cells were resuspended in a PBS containing 35 μg/mL propidium iodide (Sigma) and 100 μg/mL RNAse A (Roche). Data was acquired on a BD FACSCelesta Flow Cytometer and analyzed with ModFit LT 5.0 software. Gating strategy used to identify single-cell populations is shown in Supplementary Fig. 12.

DNA extraction

Genomic DNA was extracted from pelleted cells using Monarch^® Genomic DNA Purification Kit (NEB).

Southern blot

Telomere length analysis via Southern blotting was performed as described previously⁴⁰. Briefly, ~1.5 μg of genomic DNA from each cell sample was digested with RsaI and HinfI. Fragments were resolved on a 0.8% agarose gel, and then transferred to Hybond N + Nylon membrane (GE) via capillary transfer. The fragments were crosslinked to the membrane, and the membrane was probed for telomeric sequence using radiolabeled (TTAGGG)₄ DNA oligo. After washing off nonspecifically bound probe, the membrane was exposed on a phosphor screen and imaged using a Typhoon FLA 9500 Variable Mode Imager (GE). Median telomere length was calculated by extracting signal profiles from each lane using ImageJ, finding the median point of each distribution using Microsoft Excel, and converting those pixel coordinates into base pair lengths using a λ-HindIII molecular weight marker (NEB).

TUG1 and TERT conservation analysis

Human and mouse TUG1 and TERT genomic sequences were downloaded from hg38 and mm10, respectively. Alignments were prepared in Geneious using MAFFT v7.388^79,80. Alignments were imported in CLC main workbench (Qiagen) where sequence conservation was further analyzed by pairwise sequence comparison and visualized.

Splice site strength analysis

MaxEntScan⁸¹ was used to calculate maximum entropy scores for 9 nt donor splice sites and 20 nt acceptor splice sites.

RNA sequencing and read alignment

Total RNA from U-2 OS, HeLa and mES cell lines was extracted with Maxwell LEV Simply RNA isolation kit. RNA quality was assessed with BioAnalyzer. One microgram of total RNA from U-2 OS was subjected to poly(A) RNA enrichment and library preparation with NEBNext® Poly(A) mRNA Magnetic Isolation Module (NEB E7490) and NEBNext® Ultra RNA Library Prep Kit from Illumina® (E7530), and sequenced on the HiSeq4000. One microgram of total RNA from HeLa and mES was subjected to poly(A) RNA enrichment and library preparation with TruSeq kit v2 according to manufacturer’s instructions and sequenced on HiSeq4000 (HeLa) or HiSeq2500 (mES). We retrieved RNA-seq data for HEK293 (poly(A)⁺), LN-18 (poly(A)⁺), HCT116 (poly(A)⁺) and fibroblasts (poly(A)⁺) (accession numbers: HEK293: SRR3997506, LN-18: SRR8769945, HCT116: SRR8615282, fibroblasts: SRR5420980) and gene annotations were retrieved from Gencode (vM23). Raw reads were mapped to GRCm38 using the nf-core RNA-seq pipeline (v1.4.2)⁸². Raw reads from poly(A)⁺ RNA sequencing of chromatin-associated, nuclear soluble, and cytoplasmic fractions of mES cells were retrieved (GSE80262)⁸³. Relative abundance in each fraction was calculated from transcript per million TPM values (for instance, chromatin TPM divided by the sum of TPMs in all compartments).

Primers and TMOs

List of primer and TMO sequences used in this study are listed in Supplementary Table 1.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data supporting the findings of this study are available from the corresponding authors upon reasonable request. The sequencing data are accessible through GEO Series accession number GSE169743. Browser tracks can be found at: https://genome.ucsc.edu/s/GabrijelaD/TERT_multiple_cell_lines_Share (HEK293, LN-18, HCT116, HeLa and BJ fibroblasts for TERT); https://genome.ucsc.edu/s/GabrijelaD/TUG1_multiple_cell_lines_Share (U2-OS, HeLa, BJ fibroblasts, LN-18, HEK292, HCT116 for TUG1). Source data are provided with this paper.

Change history

21 October 2021
A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-26492-5

References

Lécuyer, E. et al. Global analysis of mRNA localization reveals a prominent role in organizing cellular architecture and function. Cell 131, 174–187 (2007).
Article PubMed Google Scholar
Martin, K. C. & Ephrussi, A. mRNA localization: gene expression in the spatial dimension. Cell 136, 719–730 (2009).
Article CAS PubMed PubMed Central Google Scholar
Buxbaum, A. R., Haimovich, G. & Singer, R. H. In the right place at the right time: visualizing and understanding mRNA localization. Nat. Rev. Mol. Cell Biol. 131, 174–187 (2014).
Google Scholar
Mauger, O., Lemoine, F. & Scheiffele, P. Targeted intron retention and excision for rapid gene regulation in response to neuronal activity. Neuron 92, 1266–1278 (2016).
Article CAS PubMed Google Scholar
Chin, A. & Eric, L. RNA localization: making its way to the center stage. Biochim. Biophys. Acta Gen. Subj. 1861, 2956–2970 (2017).
Article CAS PubMed Google Scholar
Philip, L. et al. CeFra-seq reveals broad asymmetric mRNA and noncoding RNA distribution profiles in Drosophila and human cells. RNA 24, 98–113 (2018).
Article Google Scholar
Zuckerman, B., Ron, M., Mikl, M., Segal, E. & Ulitsky, I. Gene architecture and sequence composition underpin selective dependency of nuclear export of long RNAs on NXF1 and the TREX complex. Mol. Cell 79, 1–17 (2020).
Article Google Scholar
Goering, R. et al. FMRP promotes RNA localization to neuronal projections through interactions between its RGG domain and G-quadruplex RNA sequences. Elife 9, 1–31 (2020).
Article Google Scholar
Valencia, P., Dias, A. P. & Reed, R. Splicing promotes rapid and efficient mRNA export in mammalian cells. Proc. Natl Acad. Sci. USA 105, 3386–3391 (2008).
Article ADS CAS PubMed PubMed Central Google Scholar
Wu, K. E., Parker, K. R., Fazal, F. M., Chang, H. Y. & Zou, J. RNA- GPS predicts high-resolution RNA subcellular localization and highlights the role of splicing. RNA 26, 851–865 (2020).
Article CAS PubMed PubMed Central Google Scholar
Braunschweig, U. et al. Widespread intron retention in mammals functionally tunes transcriptomes. Genome Res. 24, 1774–1786 (2014).
Article CAS PubMed PubMed Central Google Scholar
Boutz, P. L., Bhutkar, A. & Sharp, P. A. Detained introns are a novel, widespread class of post-transcriptionally spliced introns. Genes Dev. 29, 63–80 (2015).
Article PubMed PubMed Central Google Scholar
Jacob, A. G. & Smith, C. W. J. Intron retention as a component of regulated gene expression programs. Hum. Genet. 136, 1043–1057 (2017).
Article CAS PubMed PubMed Central Google Scholar
Fazal, F. M. et al. Atlas of subcellular RNA localization revealed by APEX-Seq. Cell 178, 473–490 (2019).
Article CAS PubMed PubMed Central Google Scholar
Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019).
Article CAS PubMed PubMed Central Google Scholar
Ullrich, S. & Guigo, R. Dynamic changes in intron retention are tightly associated with regulation of splicing factors and proliferative activity during B-cell development. Nucleic Acids Res. 48, 1327–1340 (2020).
Article CAS PubMed Google Scholar
Boothby, T. C., Zipper, R. S., Van Der Weele, C. M. & Wolniak, S. M. Removal of retained introns regulates translation in the rapidly developing gametophyte of Marsilea vestita. Dev. Cell 24, 517–529 (2013).
Article CAS PubMed Google Scholar
Baurén, G. & Wieslander, L. Splicing of Balbiani Ring 1 gene pre-mRNA occurs simultaneously with transcription. Cell 76, 183–192 (1994).
Article PubMed Google Scholar
Smith, K. P., Moen, P. T., Wydner, K. L., Coleman, J. R. & Lawrence, J. B. Processing of endogenous pre-mRNAs in association with SC-35 domains is gene specific. J. Cell Biol. 144, 617–629 (1999).
Article CAS PubMed PubMed Central Google Scholar
Melčák, I., Melčáková, Š., Kopsky, V., Večerová, J. & Raška, I. Prespliceosomal assembly on microinjected precursor mRNA takes place in nuclear speckles. Mol. Biol. Cell 12, 393–406 (2001).
Article PubMed PubMed Central Google Scholar
Vargas, D. Y. et al. Single-molecule imaging of transcriptionally coupled and uncoupled splicing. Cell 147, 1054–1065 (2011).
Article CAS PubMed PubMed Central Google Scholar
Hao, S. & Baltimore, D. RNA splicing regulates the temporal order of TNF-induced gene expression. Proc. Natl Acad. Sci. USA 110, 11934–11939 (2013).
Article ADS CAS PubMed PubMed Central Google Scholar
Coté, A. et al. The spatial distributions of pre-mRNAs suggest post-transcriptional splicing of specific introns within endogenous genes. Preprint at https://www.biorxiv.org/content/10.1101/2020.04.06.028092v1 (2020).
Yap, K., Lim, Z. Q., Khandelia, P., Friedman, B. & Makeyev, E. V. Coordinated regulation of neuronal mRNA steady-state levels through developmentally controlled intron retention. Genes Dev. 26, 1209–1223 (2012).
Article CAS PubMed PubMed Central Google Scholar
Wong, J. J.-L. et al. Orchestrated intron retention regulates normal granulocyte differentiation. Cell 154, 583–595 (2013).
Article CAS PubMed Google Scholar
Guo, C.-J. et al. Distinct processing of lncRNAs contributes to non-conserved functions in stem cells. Cell 181, 621–636 (2020).
Article CAS PubMed Google Scholar
Wong, J. J.-L., Au, A. Y. M., Ritchie, W. & Rasko, J. E. J. Intron retention in mRNA: no longer nonsense. Bioesseys 38, 41–49 (2015).
Article Google Scholar
Monteuuis, G., Wong, J. J. L., Bailey, C. G., Schmitz, U. & Rasko, E. J. The changing paradigm of intron retention: regulation, ramifications and recipes. Nucleic Acids Res. 47, 11497–11513 (2019).
CAS PubMed PubMed Central Google Scholar
Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).
Article ADS CAS PubMed Google Scholar
Raj, A., Van Den Bogaard, P., Rifkin, S. A., Van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).
Article CAS PubMed PubMed Central Google Scholar
Cabili, M. N. et al. Localization and abundance analysis of human lncRNAs at single-cell and single-molecule resolution. Genome Biol. 16, 20 (2015).
Article PubMed PubMed Central Google Scholar
Raj, A. & Rinn, J. L. Illuminating genomic dark matter with RNA imaging. Cold Spring Harb. Perspect. Biol. 11, a032094 (2019).
Article CAS PubMed PubMed Central Google Scholar
Dumbović, G., Sanjuan, X., Perucho, M. & Forcales, S.-V. Stimulated emission depletion (STED) super resolution imaging of RNA- and protein-containing domains in fixed cells. Methods 187, 68–76 (2021).
Article PubMed Google Scholar
Weinrich, S. L. et al. RNA component hTR and the catalytic protein subunit hTRT. Nat. Genet. 17, 498–502 (1997).
Article CAS PubMed Google Scholar
Shay, J. W. & Bacchetti, S. A survey of telomerase activity in human cancer. Eur. J. Cancer 33, 787–791 (1997).
Kim, N. W. et al. Specific association of human telomerase activity with immortal cells and cancer. Science 266, 2011–2016 (2011).
Article ADS Google Scholar
Shay, J. W. & Wright, W. E. Telomerase: a target for cancer therapeutics. Cancer Cell 2, 257–265 (2002).
Article CAS PubMed Google Scholar
Harley, C. B. Telomerase and cancer therapeutics. Nature 8, 55–57 (2008).
Google Scholar
Malhotra, S., Freeberg, M. A., Winans, S. J., Taylor, J. & Beemon, K. L. A novel long non-coding RNA in the hTERT promoter region regulates hTERT expression. Non-coding RNA 4, 1–14 (2018).
Article Google Scholar
Rowland, T. J., Dumbovic, G., Hass, E. P., Rinn, J. L. & Cech, T. R. Single-cell imaging reveals unexpected heterogeneity of telomerase reverse transcriptase expression across human cancer cell lines. Proc. Natl Acad. Sci. USA 116, 18488–18497 (2019).
Article CAS PubMed PubMed Central Google Scholar
Young, T. L., Matsuda, T. & Cepko, C. L. The noncoding RNA taurine upregulated gene 1 is required for differentiation of the murine retina. Curr. Biol. 15, 501–512 (2005).
Article CAS PubMed Google Scholar
Zhang, E. et al. P53-regulated long non-coding RNA TUG1 affects cell proliferation in human non-small cell lung cancer, partly through epigenetically regulating HOXB7 expression. Cell Death Dis. 5, 1–12 (2014).
Article Google Scholar
Katsushima, K. et al. Targeting the Notch-regulated non-coding RNA TUG1 for glioma treatment. Nat. Commun. 7, 1–14 (2016).
Article Google Scholar
Jianyin, L. et al. Long noncoding RNA Tug1 regulates mitochondrial bioenergetics in diabetic nephropathy. J. Clin. Invest. 126, 4205–4218 (2016).
Article Google Scholar
Li, Z., Shen, J., Chan, M. T. V. & Wu, W. K. K. TUG1: a pivotal oncogenic long non-coding RNA of human cancers. Cell Prolif. 49, 471–475 (2016).
Article CAS PubMed PubMed Central Google Scholar
Chiu, H.-S. et al. Pan-cancer analysis of lncRNA regulation supports their targeting of cancer genes in each tumor context. Cell Rep. 23, 297–312 (2018).
Article CAS PubMed PubMed Central Google Scholar
Carlevaro-Fita, J. et al. Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs. Commun. Biol. 3, 1–16 (2020).
Article Google Scholar
Zhang, Q., Geng, P., Yin, P., Wang, X. & Jia, J. Down-regulation of long non-coding RNA TUG1 inhibits osteosarcoma cell proliferation and promotes apoptosis. Asian Pac. J. Cancer Prev. 14, 2311–2315 (2013).
Article PubMed Google Scholar
Zhu, J., Shi, H., Liu, H., Wang, X. & Li, F. Long non-coding RNA TUG1 promotes cervical progression by regulating the miR-138-5p-SIRT1 axis. Oncotarget 8, 65253–65264 (2017).
Article PubMed PubMed Central Google Scholar
van Heesch, S. et al. The translational landscape of the human heart. Cell 178, 242–260 (2019).
Article PubMed Google Scholar
Lewandowski, J. P. et al. The Tug1 lncRNA locus is essential for male fertility. Genome Biol. 21, 1–35 (2020).
Article Google Scholar
Khalil, A. M. et al. Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc. Natl Acad. Sci. USA 106, 11667–11672 (2009).
Article ADS CAS PubMed PubMed Central Google Scholar
The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Article ADS PubMed Central Google Scholar
Tapial, J. et al. An atlas of alternative splicing profiles and functional associations reveals new regulatory programs and genes that simultaneously express multiple major isoforms. Genome Res. 27, 1759–1768 (2017).
Article CAS PubMed PubMed Central Google Scholar
Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).
Article ADS CAS PubMed PubMed Central Google Scholar
Melé, M. et al. Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs. Genome Res. 27, 27–37 (2017).
Article PubMed PubMed Central Google Scholar
Perry, R. P. & Kelley, D. E. Inhibition of RNA synthesis by actinomycin D: characteristic dose-response of different RNA species. J. Cell Physiol. 76, 127–139 (1969).
Article Google Scholar
Bensaude, O. Inhibiting eukaryotic transcription. Transcription 2, 103–108 (2011).
Article PubMed PubMed Central Google Scholar
Bresson, S. M., Hunter, O. V., Hunter, A. C. & Conrad, N. K. Canonical poly(A) polymerase activity promotes the decay of a wide variety of mammalian nuclear RNAs. PLoS Genet. 11, 1–25 (2015).
Article CAS Google Scholar
Langner, H. K., Jastrzebska, K. & Caruthers, M. H. Synthesis and characterization of thiophosphoramidate morpholino oligonucleotides and chimeras. J. Am. Chem. Soc. 142, 16240–16253 (2020).
Article CAS PubMed Google Scholar
Li, Y., Zhang, T., Zhang, Y. & Wang, W. Targeting the FOXM1- regulated long noncoding RNA TUG1 in osteosarcoma. Cancer Sci. 109, 3093–3104 (2018).
Article CAS PubMed PubMed Central Google Scholar
Nakashima, M., Nandakumar, J., Sullivan, K. D., Espinosa, J. M. & Cech, T. R. Inhibition of telomerase recruitment and cancer cell death. JCB 288, 33171–33180 (2013).
CAS Google Scholar
Li, S., Crothers, J., Haqq, C. M. & Blackburn, E. H. Cellular and gene expression responses involved in the rapid growth inhibition of human cancer cells by RNA interference-mediated depletion of telomerase RNA. J. Biol. Chem. 280, 23709–23717 (2005).
Article CAS PubMed Google Scholar
Fatemi, A., Safa, M. & Kazemi, A. MST-312 induces G2/M cell cycle arrest and apoptosis in APL cells through inhibition of telomerase activity and suppression of NF-κB pathway. Tumor Biol. 36, 8425–8437 (2015).
Article CAS Google Scholar
Celeghin, A. et al. Short-term inhibition of TERT induces telomere length-independent cell cycle arrest and apoptotic response in EBV-immortalized and transformed B cells. Cell Death Dis. 7, 1–11 (2016).
Article Google Scholar
Thompson, C. A. H. et al. Transient telomerase inhibition with imetelstat impacts DNA damage signals and cell-cycle kinetics. Mol. Cancer Res. 16, 1215–1225 (2018).
Article CAS PubMed Google Scholar
Lubelsky, Y. & Ulitsky, I. Sequences enriched in Alu repeats drive nuclear localization of long RNAs in human cells. Nature 555, 107–111 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Shukla, C. J. et al. High-throughput identification of RNA nuclear enrichment sequences. EMBO J. 37, e98452 (2018).
Article PubMed PubMed Central Google Scholar
Wright, W. E., Tesmer, V. M., Liao, M. L. & Shay, J. W. Normal human telomeres are not late replicating. Exp. Cell Res. 499, 492–499 (1999).
Article Google Scholar
Zou, Y., Gryaznov, S. M., Shay, J. W., Wright, W. E. & Cornforth, M. N. Asynchronous replication timing of telomeres at opposite arms of mammalian chromosomes. Proc. Natl Acad. Sci. USA 101, 12928–12933 (2004).
Article ADS CAS PubMed PubMed Central Google Scholar
Arnoult, N. et al. Replication timing of human telomeres Is chromosome arm–specific, influenced by subtelomeric structures and connected to nuclear localization. PLoS Genet. 6, e1000920 (2010).
Article PubMed PubMed Central Google Scholar
Dominguez, D. et al. An extensive program of periodic alternative splicing linked to cell cycle progression. Elife 5, e10288 (2016).
Article CAS PubMed PubMed Central Google Scholar
Golipour, A. et al. A late transition in somatic cell reprogramming requires regulators distinct from the pluripotency network. Cell Stem Cell 11, 769–782 (2012).
Article CAS PubMed Google Scholar
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
Article CAS PubMed Google Scholar
Bailey, T. L. et al. MEME Suite: tools for motif discovery and searching. Nucleic Acids Res. 37, 202–208 (2009).
Article ADS Google Scholar
Giudice, G., Sánchez-Cabo, F., Torroja, C. & Lara-Pezzi, E. ATtRACT-a database of RNA-binding proteins and associated motifs. Database 1–9 https://doi.org/10.1093/database/baw035 (2016).
Krotz, A. H., Mcelroy, B., Scozzari, A. N., Cole, D. L. & Ravikumar, V. T. Controlled deacetylation of antisense oligonucleotides. Org. Process Res. Dev. 7, 47–52 (2003).
Article CAS Google Scholar
Cristofari, G. & Lingner, J. Telomere length homeostasis requires that telomerase levels are limiting. EMBO J. 25, 565–574 (2006).
Article CAS PubMed PubMed Central Google Scholar
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).
Article CAS PubMed PubMed Central Google Scholar
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Article CAS PubMed PubMed Central Google Scholar
Yeo, G. & Burge, C. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).
Article CAS PubMed Google Scholar
Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 271–278 (2020).
Article Google Scholar
Engreitz, J. M. et al. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539, 452–455 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

We thank the Rinn lab members for insightful discussions. We thank Roy Parker and Carolyn Decker (University of Colorado Boulder) for access to the DeltaVision Elite microscope. We thank Theresa Nahreini and Nicole Kethley for use of the Cell Culture Facility (University of Colorado Boulder). iPS and LN-18 cells were a gift from Teisha Rowland from the Stem Cell Research and Technology Resource Center (University of Colorado Boulder). We thank Arthur Zaug (T.R.C. laboratory) for assistance with the TERT overexpression and Southern blot experiments. We thank Manuel Irimia (CRG, Barcelona) for sharing vast-tools results from various tissue/cell types from seven mammalian species. U.B. is supported by funds from a Canadian Institutes for Health Research Foundation Grant to B.B. T.R.C. is an investigator of the Howard Hughes Medical Institute (HHMI). J.L.R. is an HHMI Faculty Scholar and holds a Marvin H. Caruthers Endowed Chair for Early Career Faculty. This research was supported by NIH grant P01GM099117 to J.L.R.

Author information

Gabrijela Dumbović
Present address: Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
Katarzyna Jastrzebska
Present address: Department of Bioorganic Chemistry, Centre of Molecular and Macromolecular Studies, Polish Academy of Sciences, Lodz, Poland

Authors and Affiliations

Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA
Gabrijela Dumbović, Heera K. Langner, Evan P. Hass, Katarzyna Jastrzebska, Thomas R. Cech, Marvin H. Caruthers & John L. Rinn
BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
Gabrijela Dumbović, Michael Smallegan, Thomas R. Cech & John L. Rinn
Donnelly Centre, University of Toronto, Ontario, Canada
Ulrich Braunschweig & Benjamin Blencowe
Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
Michael Smallegan
Institute for Research in Biomedicine, Parc Científic de Barcelona, Barcelona, Spain
Josep Biayna
Howard Hughes Medical Institute, University of Colorado Boulder, Boulder, CO, USA
Thomas R. Cech & John L. Rinn

Authors

Gabrijela Dumbović
View author publications
You can also search for this author in PubMed Google Scholar
Ulrich Braunschweig
View author publications
You can also search for this author in PubMed Google Scholar
Heera K. Langner
View author publications
You can also search for this author in PubMed Google Scholar
Michael Smallegan
View author publications
You can also search for this author in PubMed Google Scholar
Josep Biayna
View author publications
You can also search for this author in PubMed Google Scholar
Evan P. Hass
View author publications
You can also search for this author in PubMed Google Scholar
Katarzyna Jastrzebska
View author publications
You can also search for this author in PubMed Google Scholar
Benjamin Blencowe
View author publications
You can also search for this author in PubMed Google Scholar
Thomas R. Cech
View author publications
You can also search for this author in PubMed Google Scholar
Marvin H. Caruthers
View author publications
You can also search for this author in PubMed Google Scholar
John L. Rinn
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

Study conceptualization and design: G.D., T.R.C., M.C., and J.L.R.; experiment design, performance, microscopy and data analysis: G.D.; Vast-tools analysis: U.B.; TMO synthesis: H.K. and K.J.; computational analyses: G.D., U.B., and M.S.; cell cycle analysis: J.B.; Southern blot: E.H., intellectual input: G.D., U.B., H.K., J.B., B.B., T.R.C., M.C. and J.L.R.; funding: M.C. and J.L.R.; writing the paper: G.D. and J.L.R. with input from all of the authors.

Corresponding authors

Correspondence to Gabrijela Dumbović or John L. Rinn.

Ethics declarations

Competing interests

The authors declare no competing interests. T.R.C. is on the Merck board and is a consultant for Storm Therapeutics and Eikon Therapeutics.

Additional information

Peer review information Nature Communications thanks Karen Beemon, Igor Ulitsky and other, anonymous, reviewers for their contributions to the peer review of this work. Peer review reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Peer Review File

Supplementary data 1

Supplementary data 2

Supplementary data 3

Supplementary data 4

Supplementary data 5

Supplementary table 1

Description of additional supplementary files

Reporting Summary

Source data

source data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Dumbović, G., Braunschweig, U., Langner, H.K. et al. Nuclear compartmentalization of TERT mRNA and TUG1 lncRNA is driven by intron retention. Nat Commun 12, 3308 (2021). https://doi.org/10.1038/s41467-021-23221-w

Download citation

Received: 10 July 2020
Accepted: 07 April 2021
Published: 03 June 2021
DOI: https://doi.org/10.1038/s41467-021-23221-w

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.