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De novo annotation of lncRNA HOTAIR transcripts by long-read RNA capture-seq reveals a differentiation-driven isoform switch



LncRNAs are tissue-specific and emerge as important regulators of various biological processes and as disease biomarkers. HOTAIR is a well-established pro-oncogenic lncRNA which has been attributed a variety of functions in cancer and native contexts. However, a lack of an exhaustive, cell type-specific annotation questions whether HOTAIR functions are supported by the expression of multiple isoforms.


Using a capture long-read sequencing approach, we characterize HOTAIR isoforms expressed in human primary adipose stem cells. We find HOTAIR isoforms population displays varied splicing patterns, frequently leading to the exclusion or truncation of canonical LSD1 and PRC2 binding domains. We identify a highly cell type-specific HOTAIR isoform pool regulated by distinct promoter usage, and uncover a shift in the HOTAIR TSS usage that modulates the balance of HOTAIR isoforms at differentiation onset.


Our results highlight the complexity and cell type-specificity of HOTAIR isoforms and open perspectives on functional implications of these variants and their balance to key cellular processes.

Peer Review reports


Long non-coding RNAs (lncRNAs) are increasingly recognized as major regulators of physiological processes and have emerged as biomarkers for disease diagnosis and prognosis [1]. HOTAIR (HOX Transcript Antisense RNA) is a long antisense transcript of ~ 500–2200 base pairs (bp) located within, and extending upstream of, the HOXC11 gene, in the HOXC locus on chromosome 12. HOTAIR is mostly studied in cancer models where its overexpression promotes cell migration and metastasis by altering gene expression [2,3,4,5]. HOTAIR is also the most differentially expressed gene between upper- and lower-body adipose tissue, and its expression is induced during adipose differentiation of gluteofemoral adipose stem cells (hereafter referred to as ASCs) [6, 7]. The function of HOTAIR in adipose tissue remains, however, unclear. In contrast to its effect in cancer cell lines [8,9,10,11], HOTAIR overexpression does not affect adipose progenitors’ gene expression or proliferation rates [7, 12]. These observations point to different functions and mechanisms of action of HOTAIR in cancer vs. mesenchymal progenitor cells.

LncRNAs interact with proteins, DNA and other RNAs to regulate gene expression at multiple levels. HOTAIR has been detected in the cytoplasm where it can promote ubiquitin-mediated proteolysis by associating with E3 ubiquitin ligases [13] or function as a microRNA sponge [14]. HOTAIR is also found in the nucleus, where it can bind chromatin [13, 15, 16] and act as scaffold for chromatin-modifying complexes through binding to the Polycomb repressor complex PRC2 subunit EZH2 [17], a histone H3K27 methyltransferase, and to LSD1/KDM1A, the H3K4/K9 demethylase of the REST/CoREST complex [18]. Recent evidence demonstrates that HOTAIR-PRC2 binding can be modulated by changes in HOTAIR structure mediated by RNA binding protein (RBP)-RNA-lncRNA interactions [19].

LncRNA folding into secondary and tertiary structures dictates their interactome, making their function dependent on structural conservation [20]. While the full-length HOTAIR sequence is poorly evolutionarily conserved, folding prediction has identified two well-conserved structures in the 5’ and 3’ ends of HOTAIR [21]. The currently reported primary structure of the HOTAIR gene is complex, with 2 predicted promoters, multiple predicted transcription start sites (TSSs) and potential splice sites leading to several isoforms [22,23,24,25]. HOTAIR transcripts can harbor small exon length variations [26], or alternative splice site usage that eliminates the PRC2 [18] or RBP [19] binding domains. Therefore, distinct HOTAIR splice variants likely have distinct functions, warranting an isoform-specific annotation in relevant tissues.

Current reference annotations for lncRNAs are incomplete due to their overall low expression level, weak evolutionary conservation, and high tissue specificity [27]. Identification of lncRNA isoforms using short-read RNA sequencing (RNA-seq) is challenging because almost every exon can be alternatively spliced [28], and short reads cannot resolve the connectivity between distant exons. Long-read sequencing technologies can address this challenge by covering the entire RNA sequence in a single read, enabling mapping of isoform changes that may impact lncRNA structure and function [29].

Here, we combine long-read Capture-seq and Illumina short-read RNA-seq to resolve changes in the composition of HOTAIR isoforms in a well-characterized adipogenic differentiation system [30]. We uncover a temporal shift in the composition of HOTAIR isoforms upon induction of differentiation, regulated by distinct promoter usage and hormonal and nutrient-sensing pathways.


HOTAIR is highly expressed in ASCs and regulated during adipogenesis

We first assessed HOTAIR expression level in ASCs versus cancer cell lines where HOTAIR function has been previously studied [28]. We find that HOTAIR expression level in ASCs from two unrelated donors is higher or comparable to that of cancer cell lines (Fig. 1a), confirming the relevance of primary ASCs as a model system to assess the relative abundance of HOTAIR isoforms.

Fig. 1
figure 1

HOTAIR expression during adipogenic differentiation. a Quantitative RT-PCR analysis of HOTAIR expression in HeLa, MCF7, MDA-MB-231, and ASCs from two independent donors (ASC-1 and ASC-2). b Differential expression of HOTAIR between differentiation time points analyzed by short-read RNA-seq (mean ± SD; *p < 0.05, **p < 0.01, ***p < 0.001, limma moderated t-statistic; n = 3). c RT-qPCR analysis of HOTAIR relative expression, normalized to D0 ASCs, during adipogenic and osteogenic differentiation (mean fold difference ± SD; ****p < 0.0001, two-way ANOVA; n = 3)

We examined by short-read RNA-seq the transcriptome of ASCs in the proliferating stage (Pro), after cell cycle arrest (day 0; D0), and after 1, 3 and 9 days of adipogenic induction (D1, D3, D9). Hierarchical clustering of differentially expressed genes across time (α < 0.01 between at least two consecutive time points) confirms the upregulation of genes pertaining to the hallmark “Adipogenesis”, including the master adipogenic transcription factor PPARG (Additional file 1, Fig. S1). Moreover, HOTAIR displays a diphasic expression profile in this time course, with increased levels upon cell cycle arrest on D0, followed by a progressive downregulation (Fig. 1b). HOTAIR expression is maintained during osteogenic differentiation (Fig. 1c), indicating a lineage-specific mode of regulation.

Identification of main HOTAIR isoforms

To identify HOTAIR isoforms, we performed PacBio single-molecule, long-read isoform sequencing of captured polyadenylated HOTAIR transcripts (PacBio Capture-seq) in proliferating ASCs and during adipose differentiation. Full-length reads were clustered into non-redundant transcripts and aligned to the hg38 genome assembly, providing excellent coverage over the HOTAIR locus with sharp exon boundaries (Fig. 2a,b). The Isoseq3 pipeline yielded ~ 6000 HOTAIR isoforms; these were further filtered and merged both across time points and based on internal junctions using Cupcake ToFU [31, 32] or TAMA [33] (Fig. 2a), resulting in 34 isoforms (Fig. 2a,c; Additional file 2, Table S1, Additional file 3).

Fig. 2
figure 2

Identification of HOTAIR isoforms in differentiating ASCs. a Bioinformatic pipeline for identification of HOTAIR isoforms. b Representative integrative genomics viewer (IGV) tracks for short-read Illumina RNA-seq (upper) and PacBio capture-seq (lower) coverage tracks on HOTAIR (D0). c Venn diagram showing the overlap between HOTAIR isoforms identified using TAMA and Cupcake ToFU. d-h SQANTI characterization of 34 HOTAIR transcripts, with: (d) Number of isoforms per SQANTI categories and presence of non-canonical splice sites. e Distance in base pairs from the isoform start to the nearest CAGE peak summit from Ref. [34]. f HOTAIR exon E7 diagram showing 3’ end polyA tails. g Cumulative read counts of the isoforms shown by SQANTI categories. h Cumulative read number per isoform vs. number of samples in which the isoform was detected (cutoff: 0.1 × 103 reads per isoform). i Exon structure of 23 high confidence HOTAIR isoforms colored by SQANTI categories. FSM: full splice match; ISM: incomplete splice match; NIC: novel in catalog; NNC: novel not in catalog

To characterize these HOTAIR isoforms, we first classified transcripts into four main categories using SQANTI [34]. Out of the 34 aforementioned isoforms, we find (i) 8 full splice matches (FSM) transcripts, (ii) 6 incomplete splice matches (ISM) of an annotated (known) transcript, (iii) 12 novel in catalog (NIC) transcripts containing new combinations of annotated splice sites, and (iv) 8 novel not in catalog (NNC) transcripts using at least one unannotated splice site (Fig. 2d, Additional file 1, Fig. S2). Second, we assessed isoform variations at the 5’ and 3’ ends. Transcripts start sites supported by Cap analysis of gene expression (CAGE) data are distributed evenly across SQANTI categories, with 15 transcripts starting within 15 bp of a CAGE peak summit (Fig. 2e). Transcripts with a TSS located more than 250 bp away from a CAGE peak likely represent lowly expressed or highly cell type-specific isoforms of HOTAIR, explaining the absence of a dedicated CAGE peak [35, 36]. HOTAIR full length transcripts also show variable 3’ ends (exon 7; E7) corresponding to the alternative usage of 4 different canonical polyA signals for transcription termination (Fig. 2f, Additional file 2, Table S2). Third, we find the highest number of reads for transcripts in FSM, ISM and NIC SQANTI categories (Fig. 2g), indicating that most HOTAIR transcripts identified here have a known exon and splice junction composition.

To identify the top isoforms expressed across differentiation, we further filtered candidates based on read counts (Fig. 2h). Only 23 transcripts accumulate more than 100 reads, and these are also detected in at least 2 samples. These 23 high-confidence isoforms arise from multiple TSS usage, alternative splicing and intron retention events, as well as polyA site usage (Fig. 2i). Altogether, our PacBio sequencing analysis identifies with high confidence known and novel uncharacterized HOTAIR transcript isoforms in our adipose differentiation system, with notable variation in their TSS and polyA site usage.

HOTAIR splicing affects LSD1 and PRC2 interacting domains

HOTAIR has been described as a scaffold for LSD1 [18] and PRC2 [18], epigenetic modifiers involved in the regulation of adipogenesis [37,38,39]. The LSD1 binding domain lies in the last 500 bp of HOTAIR exon 7 (E7), whereas the PRC2 binding domain spans exons 4 and 5 [40] (Fig. 3a).

Fig. 3
figure 3

HOTAIR splicing across functional domains during adipogenesis. a Schematic representation of HOTAIR 3’ exons (exons E4 to E7) containing LSD1 and PRC2 binding domains. Alternative splicing events and polyA (pA) sites usage detected in 5 representative HOTAIR high confidence isoforms are represented. b Proportion of long-read Capture-Seq reads for each E7 length across differentiation time points. c Schematic representation of RT-qPCR amplicons (upper panel) and qRT-PCR analysis of HOTAIR E7 length variation during adipose differentiation (lower panel). d PacBio Capture-Seq analysis of HOTAIR exon E5 alternative splice site usage and e of the proportion of major exon E5 splice variants during adipogenic differentiation. f Schematic representation of PCR amplicons (upper panel) and semi-quantitative RT-PCR analysis of HOTAIR expression using primers located in exons E3 and E5 (E3-E5; lower panel). SF3A1 is shown as a loading control. RT-: no reverse transcriptase control; NTC: no template control. Full-length gels are presented in Additional file 4, Fig. S4

Inclusion of the LSD1 binding domain in HOTAIR isoforms depends on polyA site usage (see Figs. 3a and 2f,i); thus we examined potential changes in exon E7 length during adipogenesis. The proportion of PacBio reads for the long forms of E7, harboring the LSD1 binding domain, does not significantly vary during differentiation (Fig. 3b). In agreement, the expression profile of LSD1-containing HOTAIR isoforms (E7 long and medium) is similar to that of the total isoform pool (E7 short) (Fig. 3c, see Fig. 1bc), confirming that alternative polyadenylation pattern of HOTAIR is maintained during differentiation (Fig. 3c). Hence, variations in HOTAIR isoforms during adipogenesis do not in principle impact its LSD1 scaffolding function.

We next investigated alternative splicing events affecting the PRC2 binding domain. Analysis of exon E5 alternative splicing reveals two major alternative splice sites (site 1 and site 3) (Fig. 3d). Induction of differentiation promotes splicing at site 3, leading to a slight increase in proportion of the shorter E5 variant (Fig. 3e). This splicing event can be readily detected by semi-quantitative RT-PCR analysis of exon E3-E5 expression in ASCs from two independent individuals (Fig. 3f, Additional file 1, Fig. S3a,b,c), emphasizing the robustness of the PacBio approach in capturing relative variations in HOTAIR transcript levels during differentiation. Overall, PacBio Capture-seq analysis reveals that HOTAIR is expressed as a pool of isoforms with differential ability to bind LSD1 and PRC2. However, adipogenic induction does not significantly affect the proportion of isoforms with LSD1 or PRC2 binding capacity.

Adipogenic induction triggers a switch in HOTAIR isoform start sites

One noticeable feature of HOTAIR isoform pool is the presence of 9 distinct start sites (Fig. 4a, see Fig. 2i). To assess TSS usage, we first quantified the total number of reads for each exon start category in PacBio Capture-Seq dataset (Fig. 4b). Only transcripts starting from exons E2, E3, E3.1 and E5 cumulated more than 500 reads during differentiation. We next quantified the proportion of each of HOTAIR 23 high-confidence isoform at each time point (Fig. 4c). Strikingly, while E3.1-starting isoforms contribute to the majority of reads prior to differentiation onset (Pro, D0), adipogenic induction triggers a decrease in the E3.1-starting isoform pool which is compensated by an increase in E3-starting isoform expression (Fig. 4c,d,e). Short-read RNA-seq analysis confirms this switch in TSS usage, with exon E3 becoming significantly more expressed than E3.1 upon differentiation onset (D1; Fig. 4f). The sharp drop in E3.1 starting isoforms is readily observed by semi-quantitative RT-PCR using primers spanning exons E3.1-E4, while expression of E3-E5 containing isoforms is maintained during early adipogenesis (Fig. 4g, left; Additional file 1, Fig. S3b). Importantly, osteogenic differentiation of ASCs maintains E3.1 isoforms expression (Fig. 4g, right; Additional file 1, Fig. S3a), indicating an adipose-specific regulation of HOTAIR isoforms. We conclude that adipogenic commitment triggers a switch in HOTAIR TSS usage, potentially impacting functional binding domains located in 5’ exons [19].

Fig. 4
figure 4

Remodeling of the HOTAIR isoform pool during adipogenesis. a Schematic representation of HOTAIR 5’ exons (exons E1 to E5) showing novel HOTAIR exons and 9 alternative TSSs detected by PacBio Capture-Seq. b Cumulative number of long-reads for each HOTAIR starting exon. c Heatmap of the proportion of isoforms expressed at each time in the adipogenesis time course. Matching full length reads from PacBio long-read Capture-Seq are compared with each time point. d, e Proportion of read coverage for each start exon quantified from long-read data for (d) HOTAIR isoforms cumulating > 500 reads over the time course and (e) E3- and E3.1-starting isoforms. f Normalized short-read RNA-seq read count per kilobase of exon sequence for E3 and E3.1 exons during differentiation, quantified with featureCounts with strict parameters (-f -g exon_id -p -s 2 -O –fraction -B -C) (n = 3, ***p < 0.001 paired t-test with Benjamini–Hochberg adjusted p value) g Semi-quantitative RT-PCR analysis of HOTAIR expression using primers located in HOTAIR exons E3.1-E4 and E3-E5. SF3A1 is shown as a loading control. A representative image from one of three independent experiments is shown for adipogenesis (left) and osteogenesis (right); see also Additional file 1 Fig. S3b,c. RT-: no reverse transcriptase control; NTC: no template control. Full-length gels are presented in Additional file 4 Fig. S4,5,6

ASCs express a cell type-specific HOTAIR isoform

To assess cell type- and tissue-specificity of E3.1 starting HOTAIR isoforms, we used Snaptron [41], a search engine for querying splicing patterns in publicly available RNA-seq datasets (Additional file 2, Table S3). We found only 24 cell samples with more than 10 reads containing the E3.1-E4 junction, while the E3-E4 junction was detected in 672 samples (Fig. 5a; Additional file 2, Table S4). We confirmed by semi-quantitative RT-PCR the expression of exon E3.1 in cultured primary myoblasts, BJ fibroblasts and HEK 293 T cells – albeit to lower levels than in ASCs – and its absence in HeLa cells (Fig. 5b).

Fig. 5
figure 5

HOTAIR isoform expression is highly cell-type specific. a Number of SRA samples with 10 or more junction spanning reads for exons E3.1-E4 and E3-E4. b Semi-quantitative RT-PCR analysis of HOTAIR expression in ASCs, primary myoblasts, BJ fibroblasts, HEK293T and HeLa cells using primers located in HOTAIR exons E3.1-E4 and E3-E5. SF3A1 is shown as a loading control. Full-length gels are presented in Additional file 4 Fig.S7. c IGV tracks over HOTAIR locus showing predicted HOTAIR regulatory elements (REMs) from the EpiRegio database [42, 43] (red: activating, blue: repressing), average ATAC-Seq tracks from ASCs [44, 45], Myoblasts [46] and HeLa cells [47], and ChIP primers location. d ChIP-qPCR analysis of histone modifications during adipogenesis of ASCs (mean ± SEM of n ≥ 3 independent differentiation experiments; **p < 0.01 Mixed effect analysis)

To gain insight into the regulation of HOTAIR isoform expression, we examined the chromatin accessibility landscape of the HOTAIR locus in published Assay for Transposable Accessible Chromatin (ATAC)-seq data [44,45,46,47] (Fig. 5c). We find two regions of accessible (‘open’) chromatin in ASCs (R3, R4), which our chromatin immunoprecipitation (ChIP)-seq data show are also enriched in H3K4me3 and H3K27ac, histone modifications characterizing active regulatory sites (Fig. 5c, d). Region R3 coincides with both activating and repressing regulatory element (REM) annotations by EpiRegio [42], suggesting it constitutes a bivalent regulator for HOTAIR or nearby genes expression. Region R4, located immediately upstream of exon E3.1 is also in an ‘open’ chromatin in myoblasts but not in HeLa cells, which respectively do and do not express E3.1-starting transcripts (Fig. 5b,c), and likely represents the active promoter for E3.1-starting isoforms. In contrast, these regions are in a ‘closed’ configuration in mature adipocytes, consistent with HOTAIR downregulation during adipogenesis (see Fig. 1b). Thus, HOTAIR displays active regulatory sites in ASCs, in line with the expression of specific isoforms. However, HOTAIR upregulation upon cell cycle arrest is not accompanied by changes of H3K4me3 and H3K27ac levels, and loss of H3K4me3 at region 4 occurs only at later differentiation time points (D3 and D9). Thus, modulation of HOTAIR levels during adipogenesis is not mediated by epigenetic regulation (Fig. 5d).

HOTAIR stability increases upon cell cycle arrest

HOTAIR half-life varies according to cell type, from 4 h in HeLa cells [13] to more than 7 h in primary trophoblast cells [48]. We therefore asked whether increased HOTAIR levels upon cell cycle arrest (D0; see Fig. 1b) could relate to a change in lncRNA stability. Treatment with Flavopiridol to inhibit RNA polymerase II (Pol II) transcription reveals an increase in HOTAIR half-life from 3 h in proliferating ASCs to > 4 h in D0 cells (Fig. 6a), while cell cycle arrest does not impact the stability of control short-lived CEBPD or long-lived GAPDH mRNA (Fig. 6b,c). Thus, while ATAC-seq data indicate that the HOTAIR isoform balance is likely regulated by cell type-specific transcription factors, the global increase in HOTAIR levels observed at D0 results at least in part from an increase in its stability.

Fig. 6
figure 6

HOTAIR stability increases upon growth arrest. RT-qPCR analysis of a HOTAIR, b CEBPD and c GAPDH levels after flavopiridol treatment (mean ± SD; *p < 0.05, **p < 0.01, two-way ANOVA with Šídák's multiple comparisons test; n = 3 independent experiments)


LncRNAs can modulate important biological and pathophysiological processes through their interaction with multiple partners. Our long-read Capture-seq of HOTAIR unveils a complex and dynamic pool of isoforms in differentiating ASCs. Adipogenic induction triggers a switch in the expression of main HOTAIR isoforms which likely impacts on its structure and interactome. Our results emphasize the importance of robust lncRNA annotation in the tissue of interest prior to functional characterization.

A single lncRNA locus can generate many transcript variants through alternative TSS usage, polyadenylation sites and splicing events [49]. We find that previously undescribed HOTAIR variants constitutes the major isoforms in human adipose stem cells, and show that not only lncRNA expression level, but also the isoform pool expressed can be highly cell type-specific, adding another layer of complexity to the regulation of biological processes by lncRNAs.

The chromatin landscape of the HOTAIR locus in ASCs is consistent with a promoter upstream of HOTAIR exon E3.1, which is in an ‘open’ and active epigenetic configuration in ASCs and myoblasts, where HOTAIR E3.1 starting isoforms are expressed, but not in HeLa cells. However, adipogenic induction results in only mild and delayed changes in active histone modification at this site, suggesting that expression of various HOTAIR isoforms is rather regulated by differentiation-stage specific transcription factors. In line, alternative promoter usage is elicited downstream of adipogenic, but not osteogenic, signaling pathways. Additionally, increased HOTAIR levels upon cell cycle arrest (D0 cells) also correlates with an increase in its stability. Interestingly, interaction with the RNA-binding protein HuR, a negative regulator of adipogenesis [50], reduces HOTAIR stability [13]. Alternatively, increased HOTAIR stability could result from its binding to a cell stage-specific protein partner. Collectively, our results indicate a tight, multifactorial regulation of isoform pool during adipogenesis, arguing for a functional importance of the isoform switch.

In cancer cell lines, HOTAIR has been reported to scaffold for chromatin regulators with broad effects on gene expression [51,52,53]. Our long-read sequencing data reveal multiple polyA site usage leading to the exclusion of the LSD1 binding domain at the 3’ end, or alternative splicing events affecting the PRC2 minimal binding domain. However, the proportion of HOTAIR isoforms containing LSD1 or PRC2 domains does not vary during adipose differentiation, suggesting that HOTAIR’s role in adipogenesis is independent from its epigenetic scaffold function. Supporting this idea, HOTAIR depletion does not significantly affect PRC2-mediated gene regulation in adipose progenitors [12].

Recent studies have confirmed that a non-protein-coding locus can give rise to functionally distinct transcript isoforms [49, 54,55,56]. Of note, the switch in HOTAIR start site upon differentiation induction leads to the inclusion of HOTAIR exon 3 containing a protein binding domain [19], which likely alters HOTAIR function. Another intriguing possibility is that short sequence variations at the 5’ end impact HOTAIR’s secondary structure and thus the folding of functional domains. Observation of HOTAIR structure using atomic force microscopy reveals multiple dynamic conformations [57]. It is therefore conceivable that HOTAIR functions via conformational changes, induced by or resulting from interactions with protein partners. Hence, variations in isoform composition likely results in cell type specific structures and interactomes, which could account for the divergent roles of HOTAIR in primary cells and cancer cell lines.


We generate the first cell type-specific, comprehensive catalog of HOTAIR isoforms in a physiological context and describe novel HOTAIR isoforms, alternative splicing events, and multiple start site usage. We find that HOTAIR splicing in ASCs often leads to the exclusion or truncation of canonical LSD1 and PRC2 binding domains. We uncover a shift in HOTAIR TSS usage that controls the balance of HOTAIR isoforms during early adipogenesis The variability of HOTAIR isoforms opens new perspectives for studies in (patho)physiological contexts.


All methods were performed in accordance with the guidelines and regulations of the University of Oslo.

Cell culture and differentiation

ASCs from two non-obese donors (ASC-1 and ASC-2) were cultured in DMEM/F12 with 10% fetal calf serum and 20 ng/ml basic fibroblast growth factor (Pro). Upon confluency, growth factor was removed, and cells were cultured for 72 h before induction of differentiation (D0). For adipose differentiation, ASCs were induced with 0.5 µM 1-methyl-3 isobutyl xanthine, 1 µM dexamethasone, 10 µg/ ml insulin and 200 µM indomethacin. For osteogenic differentiation, ASCs were induced with 0.1 μM dexamethasone, 10 mM β-glycerophosphate and 0.05 mM L-ascorbic acid-2 phosphate. Differentiation media was renewed every 3 days, and samples were harvested on D1, D3 and D9 after induction. Differentiation experiments were done in at least biological triplicates. HeLa cells (American Type Culture Collection; CCL-2) were cultured in MEM medium containing Glutamax (Gibco), 1% non-essential amino acids and 10% fetal calf serum. MDA-MB-231 and MCF-7 cells (both from American Type Culture Collection) were cultured in DMEM containing 10% fetal calf serum. Human myoblasts (Lonza) were cultured as described [58]. BJ fibroblasts (American Type Culture Collection) were cultured in DMEM/F12 with 10% fetal calf serum and 20 ng/ml basic fibroblast growth factor. HEK293T (Thermo Scientific) were cultured in DMEM/F12 with 10% fetal calf serum.

RT-qPCR and semi-quantitative PCR

Total RNA was isolated using the RNeasy kit (QIAGEN) and 1 µg was used for cDNA synthesis using the High-Capacity cDNA Reverse Transcription Kit (ThermoFisher). RT-PCR was done using IQ SYBR green (Biorad) with SF3A1 as a reference gene. PCR conditions were 95 °C for 3 min and 40 cycles of 95 °C for 30 s, 60 °C for 30 s, and 72 °C for 20 s. Semi-quantitative PCR was done using a PCR Master Mix (ThermoFisher) with the following conditions: 95 °C for 3 min and 30 cycles of 95 °C for 30 s, 60 °C for 30 s, and 72 °C for 30 s. Products were separated in a 2.5% agarose gel with Tris–Borate-EDTA buffer. PCR primers are listed in Additional file 2 Table S5. Uncropped gels are presented in Additional file 4, Fig. S4 to S7.

Short-read Illumina RNA-sequencing and data analysis.

Differentiation time courses from ASC-1 with 5 time points were sequenced in biological triplicates with short (40 bp), paired end reads on Illumina NextSeq. Reads were aligned to the hg38 genome (ensembl v95 annotation) using hisat2, and counted with featureCounts (–fraction –M). To further analyze differentially expressed genes, edgeR and limma packages were used. Low abundance genes were filtered using edgeR's function "filterByExpr". Genes with an adjusted p-value < 0.01 (eBayes method, limma package) between two or more consecutive time points were clustered with DPGP software. FDR adjusted p-value for Adipogenesis cluster was generated by overrepresentation analysis using Hallmark gene sets from MSigDb (v7.4) with all human genes as background. FeatureCounts with strict parameters (-f -g exon_id -p -s 2 -O –fraction -B -C) was used to quantify exon coverage as normalized counts per kilobase of exon sequence.

PacBio capture-seq

PacBio Capture-seq was performed on duplicate differentiation time courses from ASC-1. For each differentiation, RNA samples from 5 differentiation time points were used to synthesize full-length barcoded cDNA libraries using the Template Switching RT Enzyme Mix (NEB). Libraries were prepared using Pacific Biosciences protocol for cDNA Sequence Capture Using IDT xGen® Lockdown® probes ( A pool of 100 probes against all known HOTAIR isoform sequences was designed using the IDT web tool. Full-length cDNA was cleaned up using Pronex beads, and 1200 ng was used for each hybridization reactions. Library was sequenced in one 8 M SMRT cell on a Sequel II instrument using Sequel II Binding kit 2.1 and Sequencing chemistry v2.0.

Transcript identification from targeted long-read sequencing

CCS sequences were generated for the entire dataset using the Circular Consensus Sequence pipeline (SMRT Tools v with minimum number of passes 3 and minimum accuracy 0.99. CCS reads were demultiplexed using the Barcoding pipeline (SMRT Tools v7.0.0.63823). Iso-Seq analysis was performed using the Iso-Seq pipeline (SMRT Link v7.0.0.63985) with default settings. Only clustered isoforms with at least 2 subreads, 0.99 quality score and containing a polyA tail of at least 20 base pairs (bp) were used. Isoforms were aligned to the hg38 genome with minimap2 v.2.17 [59]. Primary alignments to the HOTAIR locus (chr12: 53,962,308–53,974,956) were selected as target HOTAIR transcripts if they had a mapping quality above 20 and less than 50 clipped nucleotides (samclip; Single exon and sense transcripts were also filtered.

HOTAIR transcripts were collapsed with both Cupcake Tofu [31] ( and TAMA [33] ( Parameters –dun-merge-5-shorter and –x capped were used for cupcake and TAMA respectively, to prevent shorter transcript models from being merged into longer ones. For TAMA, –z 100 was also set to increase the allowed 3’ variability. To combine transcript lists between timepoints, collapsed transcripts with at least 50 full length reads within one sample were merged using cupcake or Initially this produced ~ 80 HOTAIR transcripts, with much of the variation in the ends of 3' and 5' exons. To achieve a final transcript list, collapsed transcripts were merged on internal junctions only by increasing the allowed 5' and 3' variability (with TAMA options -a 300 -z 2000) and ranking libraries by the number of polyadenylated HOTAIR reads. This resulted in 34 TAMA isoforms.

Final cross-validation was conducted by running SQANTI with unclustered ccs reads as the “novel long read-defined transcriptome” and the top 34 TAMA isoforms as the “reference annotation”. This assigned full length reads to their corresponding TAMA isoform and reads annotated as full-splice match were counted for each isoform. SQANTI was used again to classify transcripts against existing reference annotations and to search for polyA motifs near transcript ends from a list of potential human motifs (Additional file 2, Tables S1, S2) [34] ( Reference isoforms were collected from four sources: Ensembl (v95), RefSeq, UCSC browser’s "lincRNA and TUCP transcripts" and Fantom CAT (Hon 2015), the last of which included isoforms from encode, hubmap and miTranscriptome (Additional File 1 Fig. S2) SQANTI categories are defined relative to the reference isoforms as follows (i) FSM: the number of exons and all internal junctions are concordant with the reference isoform. (ii) ISM: the isoform has less 5' exons than the reference, and yet the internal junctions are perfectly consistent. (iii) NIC: all donor and acceptor sites exist in the reference isoform list but their combination in a single isoform is novel. (iv) NNC: at least one of the donor or acceptor sites in the isoform is not found in the reference list. For all categories, the exact length of the 5' and 3' ends of first and last exons, respectively, can differ by any amount.


We searched Snaptron's SRA V2 database [41], which contains ~ 49,000 public samples, for experiments with reads overlapping HOTAIR exon-exon junctions with the web query (Additional File 2, Table S3). Sample IDs for experiments with at least 10 exon spanning reads were extracted and cell type information accessed via a matching metadata query. A simplified version of the exon E3.1 metadata table is presented in Additional file 2 Table S4.

Chromatin immunoprecipitation

Cells (2 × 106/ChIP) were cross-linked with 1% formaldehyde for 10 min and cross-linking was stopped with 125 mM glycine. Cells were lysed for 10 min in ChIP lysis buffer (1% SDS, 10 mM EDTA, 50 mM Tris–HCl, pH 8.0, proteinase inhibitors, 1 mM PMSF, 20 mM Na Butyrate) and sonicated for 30 s ON/OFF for 10 min in a Bioruptor®Pico (Diagenode) to generate 200–500 bp DNA fragments. After sedimentation at 10,000 g for 10 min, the supernatant was collected and diluted 5 times in RIPA buffer (140 mM NaCl, 10 mM Tris–HCl pH 8.0, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% SDS, 0.1% sodium deoxycholate, protease inhibitors, 1 mM PMSF, 20 mM Na Butyrate). After a 100 µL sample was removed (input), diluted chromatin was incubated for 2 h with antibodies (2.5 µg/100 µL) coupled to magnetic Dynabeads Protein A (Invitrogen). ChIP samples were washed 4 times in ice-cold RIPA buffer, crosslinks were reversed and DNA was eluted for 2 h at 68 °C in 50 mM NaCl, 20 mM Tris–HCl pH 7.5, 5 mM EDTA, 1% SDS and 50 ng/µl Proteinase K. DNA was purified and dissolved in H2O. ChIP DNA was used as template for quantitative (q)PCR using SYBR® Green (BioRad), with 95 °C denaturation for 3 min and 40 cycles of 95 oC for 30 s, 60 oC for 30 s, and 72 oC for 30 s. Primers used for ChIP are listed in Additional file 2, Table S5.

Statistical analyses

Statistics were performed with GraphPad Prism 9.2.0 (

Availability of data and materials

The PacBio dataset supporting the conclusions of this article is available in the SRA repository under accession PRJNA730802 ( and the filtered transcript list was submitted to GenBank (see accession numbers in Additional file 1, Table S1).

Bulk short-read RNA-Seq data has been deposited in the Gene Expression Omnibus (GEO) under accession GSE176020.

ATAC-seq data were obtained from GEO accession GSE118500 (, GSE139571 ( and GSE157399 (

CAGE clusters and transcripts from [35] were obtained from FANTOM ( Other HOTAIR transcripts were downloaded from ensembl v95 [60] ( or from the UCSC browser at [61].

Regulatory elements (REMs) [42] associated with the HOTAIR locus were queried from the EpiRegio database (; accessed: 23/04/2021).



Adipose stem cells


Chromatin immunoprecipitation


Gluteofemoral subcutaneous adipose tissue


HOX Transcript Antisense RNA


Long non-coding RNA


Polycomb repressor complex 2


  1. Cantile M, Di Bonito M, De TraceyBellis M, Botti G. Functional Interaction among lncRNA HOTAIR and MicroRNAs in Cancer and Other Human Diseases. Cancers. 2021;13(3):570.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Kogo R, Shimamura T, Mimori K, Kawahara K, Imoto S, Sudo T, et al. Long noncoding RNA HOTAIR regulates polycomb-dependent chromatin modification and is associated with poor prognosis in colorectal cancers. Cancer Res. 2011;71:6320–6.

    Article  PubMed  CAS  Google Scholar 

  3. Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 2010;464:1071–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Xue X, Yang YA, Zhang A, Fong K-W, Kim J, Song B, et al. LncRNA HOTAIR enhances ER signaling and confers tamoxifen resistance in breast cancer. Oncogene. 2016;35:2746–55.

    Article  PubMed  CAS  Google Scholar 

  5. Milevskiy MJG, Al-Ejeh F, Saunus JM, Northwood KS, Bailey PJ, Betts JA, et al. Long-range regulators of the lncRNA HOTAIR enhance its prognostic potential in breast cancer. Hum Mol Genet. 2016;25:3269–83.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Pinnick KE, Nicholson G, Manolopoulos KN, McQuaid SE, Valet P, Frayn KN, et al. Distinct developmental profile of lower-body adipose tissue defines resistance against obesity-associated metabolic complications. Diabetes. 2014;63:3785–97.

    Article  PubMed  CAS  Google Scholar 

  7. Divoux A, Karastergiou K, Xie H, Guo W, Perera RJ, Fried SK, et al. Identification of a novel lncRNA in gluteal adipose tissue and evidence for its positive effect on preadipocyte differentiation. Obesity. 2014;22:1781–5.

    Article  PubMed  CAS  Google Scholar 

  8. Wu Y, Zhang L, Zhang L, Wang Y, Li H, Ren X, et al. Long non-coding RNA HOTAIR promotes tumor cell invasion and metastasis by recruiting EZH2 and repressing E-cadherin in oral squamous cell carcinoma. Int J Oncol. 2015;46:2586–94.

    Article  PubMed  CAS  Google Scholar 

  9. Wu Y, Liu J, Zheng Y, You L, Kuang D, Liu T. Suppressed expression of long non-coding RNA HOTAIR inhibits proliferation and tumourigenicity of renal carcinoma cells. Tumour Biol. 2014;35:11887–94.

    Article  PubMed  CAS  Google Scholar 

  10. Ding W, Ren J, Ren H, Wang D. Long Noncoding RNA HOTAIR Modulates MiR-206-mediated Bcl-w Signaling to Facilitate Cell Proliferation in Breast Cancer. Sci Rep. 2017;7:17261.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Wu Z-H, Wang X-L, Tang H-M, Jiang T, Chen J, Lu S, et al. Long non-coding RNA HOTAIR is a powerful predictor of metastasis and poor prognosis and is associated with epithelial-mesenchymal transition in colon cancer. Oncol Rep. 2014;32:395–402.

    Article  PubMed  CAS  Google Scholar 

  12. Kalwa M, Hänzelmann S, Otto S, Kuo C-C, Franzen J, Joussen S, et al. The lncRNA HOTAIR impacts on mesenchymal stem cells via triple helix formation. Nucleic Acids Res. 2016;44:10631–43.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Yoon J-H, Abdelmohsen K, Kim J, Yang X, Martindale JL, Tominaga-Yamanaka K, et al. Scaffold function of long non-coding RNA HOTAIR in protein ubiquitination. Nat Commun. 2013;4:2939.

    Article  PubMed  CAS  Google Scholar 

  14. Xu F, Zhang J. Long non-coding RNA HOTAIR functions as miRNA sponge to promote the epithelial to mesenchymal transition in esophageal cancer. Biomed Pharmacother. 2017;90:888–96.

    Article  PubMed  CAS  Google Scholar 

  15. Yu G-J, Sun Y, Zhang D-W, Zhang P. Long non-coding RNA HOTAIR functions as a competitive endogenous RNA to regulate PRAF2 expression by sponging miR-326 in cutaneous squamous cell carcinoma. Cancer Cell Int. 2019;19:270.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Zhao Y-H, Liu Y-L, Fei K-L, Li P. Long non-coding RNA HOTAIR modulates the progression of preeclampsia through inhibiting miR-106 in an EZH2-dependent manner. Life Sci. 2020;253:117668.

    Article  PubMed  CAS  Google Scholar 

  17. Rinn JL, Kertesz M, Wang JK, Squazzo SL, Xu X, Brugmann SA, et al. Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell. 2007;129:1311–23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Tsai M-C, Manor O, Wan Y, Mosammaparast N, Wang JK, Lan F, et al. Long noncoding RNA as modular scaffold of histone modification complexes. Science. 2010;329:689–93.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Balas MM, Hartwick EW, Barrington C, Roberts JT, Wu SK, Bettcher R, et al. Establishing RNA-RNA interactions remodels lncRNA structure and promotes PRC2 activity. Sci Adv. 2021;7(16):eabc9191.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Graf J, Kretz M. From structure to function: route to understanding lncRNA mechanism. BioEssays. 2020;42:e2000027.

    Article  PubMed  CAS  Google Scholar 

  21. He S, Liu S, Zhu H. The sequence, structure and evolutionary features of HOTAIR in mammals. BMC Evol Biol. 2011;11:102.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Hajjari M, Rahnama S. HOTAIR Long Non-coding RNA: Characterizing the Locus Features by the In Silico Approaches. Genomics Inform. 2017;15:170–7.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, et al. GENCODE: the reference human genome annotation for the ENCODE Project. Genome Res. 2012;22:1760–74.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733–45.

    Article  PubMed  CAS  Google Scholar 

  25. Schorderet P, Duboule D. Structural and functional differences in the long non-coding RNA hotair in mouse and human. PLoS Genet. 2011;7:e1002071.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Mercer TR, Gerhardt DJ, Dinger ME, Crawford J, Trapnell C, Jeddeloh JA, et al. Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Nat Biotechnol. 2011;30:99–104.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Uszczynska-Ratajczak B, Lagarde J, Frankish A, Guigó R, Johnson R. Towards a complete map of the human long non-coding RNA transcriptome. Nat Rev Genet. 2018;19:535–48.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Zampetaki A, Albrecht A, Steinhofel K. Long non-coding RNA structure and function: is there a link? Front Physiol. 2018;9:1201.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Boquest AC, Collas P. Obtaining freshly isolated and cultured mesenchymal stem cells from human adipose tissue. Methods Mol Biol. 2012;879:269–78.

    Article  PubMed  CAS  Google Scholar 

  30. Meredith EK, Balas MM, Sindy K, Haislop K, Johnson AM. An RNA matchmaker protein regulates the activity of the long noncoding RNA HOTAIR. RNA. 2016;22:995–1010.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Gordon SP, Tseng E, Salamov A, Zhang J, Meng X, Zhao Z, et al. Widespread polycistronic transcripts in fungi revealed by single-molecule mRNA sequencing. PLoS One. 2015;10:e0132628.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Tseng E. cDNA_Cupcake Wiki. Github.

  33. Kuo RI, Cheng Y, Zhang R, Brown JWS, Smith J, Archibald AL, et al. Illuminating the dark side of the human transcriptome with long read transcript sequencing. BMC Genomics. 2020;21:751.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Tardaguila M, de la Fuente L, Marti C, Pereira C, Pardo-Palacios FJ, Del Risco H, et al. SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. Genome Res. 2018.

  35. Hon C-C, Ramilowski JA, Harshbarger J, Bertin N, Rackham OJL, Gough J, et al. An atlas of human long non-coding RNAs with accurate 5’ ends. Nature. 2017;543:199–204.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Imada EL, Sanchez DF, Collado-Torres L, Wilks C, Matam T, Dinalankara W, et al. Recounting the FANTOM CAGE-Associated Transcriptome. Genome Res. 2020;30:1073–81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Musri MM, Carmona MC, Hanzu FA, Kaliman P, Gomis R, Párrizas M. Histone demethylase LSD1 regulates adipogenesis. J Biol Chem. 2010;285:30034–41.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chen Y, Kim J, Zhang R, Yang X, Zhang Y, Fang J, et al. Histone Demethylase LSD1 Promotes Adipocyte Differentiation through Repressing Wnt Signaling. Cell Chem Biol. 2016;23:1228–40.

    Article  PubMed  CAS  Google Scholar 

  39. Wang L, Jin Q, Lee J-E, Su I-H, Ge K. Histone H3K27 methyltransferase Ezh2 represses Wnt genes to facilitate adipogenesis. Proc Natl Acad Sci U S A. 2010;107:7317–22.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wu L, Murat P, Matak-Vinkovic D, Murrell A, Balasubramanian S. Binding interactions between long noncoding RNA HOTAIR and PRC2 proteins. Biochemistry. 2013;52:9519–27.

    Article  PubMed  CAS  Google Scholar 

  41. Wilks C, Gaddipati P, Nellore A, Langmead B. Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples. Bioinformatics. 2018;34:114–6.

    Article  PubMed  CAS  Google Scholar 

  42. Baumgarten N, Hecker D, Karunanithi S, Schmidt F, List M, Schulz MH. EpiRegio: analysis and retrieval of regulatory elements linked to genes. Nucleic Acids Res. 2020;48:W193–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Schmidt F, Marx A, Baumgarten N, et al. Integrative analysis of epigenetics data identifies gene-specific regulatory elements. Nucleic Acids Res. 2021;49(18):10397-10418.

  44. Divoux A, Sandor K, Bojcsuk D, Talukder A, Li X, Balint BL, et al. Differential open chromatin profile and transcriptomic signature define depot-specific human subcutaneous preadipocytes: primary outcomes. Clin Epigenetics. 2018;10:148.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Divoux A, Sandor K, Bojcsuk D, Yi F, Hopf ME, Smith JS, et al. Fat distribution in women is associated with depot-specific transcriptomic signatures and chromatin structure. J Endocr Soc. 2020;4:bvaa042.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Martone J, Lisi M, Castagnetti F, Rosa A, Di Carlo V, Blanco E, et al. Trans-generational epigenetic regulation associated with the amelioration of Duchenne Muscular Dystrophy. EMBO Mol Med. 2020;12:e12063.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Fraser LCR, Dikdan RJ, Dey S, Singh A, Tyagi S. Reduction in gene expression noise by targeted increase in accessibility at gene loci. Proc Natl Acad Sci U S A. 2021;118:e2018640118.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Tian F-J, He X-Y, Wang J, Li X, Ma X-L, Wu F, et al. Elevated tristetraprolin impairs trophoblast invasion in women with recurrent miscarriage by destabilization of HOTAIR. Mol Ther Nucleic Acids. 2018;12:600–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Ziegler C, Kretz M. The more the merrier-complexity in long non-coding RNA loci. Front Endocrinol. 2017;8:90.

    Article  Google Scholar 

  50. Siang DTC, Lim YC, Kyaw AMM, Win KN, Chia SY, Degirmenci U, et al. The RNA-binding protein HuR is a negative regulator in adipogenesis. Nat Commun. 2020;11:213.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Mozdarani H, Ezzatizadeh V, Rahbar PR. The emerging role of the long non-coding RNA HOTAIR in breast cancer development and treatment. J Transl Med. 2020;18:152.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Hajjari M, Salavaty A. HOTAIR: an oncogenic long non-coding RNA in different cancers. Cancer Biol Med. 2015;12:1–9.

    PubMed  PubMed Central  CAS  Google Scholar 

  53. Bhan A, Mandal SS. LncRNA HOTAIR: a master regulator of chromatin dynamics and cancer. Biochim Biophys Acta. 2015;1856:151–64.

    PubMed  PubMed Central  CAS  Google Scholar 

  54. Guo C-J, Ma X-K, Xing Y-H, Zheng C-C, Xu Y-F, Shan L, et al. Distinct processing of lncRNAs contributes to non-conserved functions in stem cells. Cell. 2020;181:621-36.e22.

    Article  PubMed  CAS  Google Scholar 

  55. Yuan J-H, Liu X-N, Wang T-T, Pan W, Tao Q-F, Zhou W-P, et al. The MBNL3 splicing factor promotes hepatocellular carcinoma by increasing PXN expression through the alternative splicing of lncRNA-PXN-AS1. Nat Cell Biol. 2017;19:820–32.

    Article  PubMed  CAS  Google Scholar 

  56. Li R, Harvey AR, Hodgetts SI, Fox AH. Functional dissection of NEAT1 using genome editing reveals substantial localization of the NEAT1_1 isoform outside paraspeckles. RNA. 2017;23:872–81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Spokoini-Stern R, Stamov D, Jessel H, Aharoni L, Haschke H, Giron J, et al. Visualizing the structure and motion of the long noncoding RNA HOTAIR. RNA. 2020;26:629–36.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Oldenburg AR, Delbarre E, Thiede B, Vigouroux C, Collas P. Deregulation of Fragile X-related protein 1 by the lipodystrophic lamin A p.R482W mutation elicits a myogenic gene expression program in preadipocytes. Hum Mol Genet. 2014;23:1151–62.

    Article  PubMed  CAS  Google Scholar 

  59. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094–100.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Cunningham F, Achuthan P, Akanni W, Allen J, Amode MR, Armean IM, et al. Ensembl 2019. Nucleic Acids Res. 2018;47:D745–51.

    Article  PubMed Central  CAS  Google Scholar 

  61. Schema for lincRNA TUCP - lincRNA and TUCP transcripts. Accessed 28 Sep 2021.

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We thank Anita Løvstad Sørensen for technical assistance and the Norwegian Sequencing Center (Oslo University Hospital) for professional services.


This work was funded by South-East Health Norway (grant 40040) and the Research Council of Norway (grant No. 249734 and 313508).

Author information

Authors and Affiliations



EP and NB generated data. SHP performed bioinformatic analysis. ATK performed HOTAIR capture and long-read sequencing. EP, SHP and NB interpreted data and made figures. NB supervised the work. EP, SHP, PC and NB wrote or revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Philippe Collas or Nolwenn Briand.

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Ethics approval and consent to participate

The study was approved by the Regional Committee for Research Ethics for Southern Norway (REK 2013/2102 and REK 2018–660), and all subjects gave written informed consent.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1:

Figure S1. Validation of adipogenic differentiation efficiency. Figure S2. Genome browser view of reference HOTAIR transcripts for SQANTI characterization. Figure S3. Semi-quantitative RT-PCR replicates.

Additional file 2:

Table S1. SQANTI characterization of HOTAIR isoforms identified with TAMA. Table S2. List of human polyA motifs for SQANTI analysis. Table S3. Snaptron quantification of exon junctions for the HOTAIR locus. Table S4. Snaptron summary of RNA sequencing data containing HOTAIR E3.1-E4 splice junction. Table S5. List of primers used in this study.

Additional file 3.

BED format file of top 34 HOTAIR isoforms identified in the study. High quality isoforms (black) were further studied while lower quality isoforms (red) were filtered out.

Additional file 4:

Figure S4. Uncropped gels for Fig.3f and Additional file 1, FigS3b left panel. Figure S5. Uncropped gels for Additional file 1, Fig. S3c. Figure S6. Uncropped gels for Fig.4g and Additional file 1, Fig. S3b right panel. Figure S7. Uncropped gels for Fig.5b.

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Potolitsyna, E., Hazell Pickering, S., Tooming-Klunderud, A. et al. De novo annotation of lncRNA HOTAIR transcripts by long-read RNA capture-seq reveals a differentiation-driven isoform switch. BMC Genomics 23, 658 (2022).

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