ABSTRACT
Genome stability relies on proper coordination of mitosis and cytokinesis, where dynamic microtubules capture and faithfully segregate chromosomes into daughter cells. The role of long noncoding RNAs (lncRNAs) in controlling these processes however remains largely unexplored. To identify lncRNAs with mitotic functions, we performed a high-content RNAi imaging screen targeting more than 2,000 human lncRNAs. By investigating major hallmarks of cell division such as chromosome segregation, mitotic duration and cytokinesis, we discovered numerous lncRNAs with functions in each of these processes. The chromatin-associated lncRNA, linc00899, was selected for in-depth studies due to the robust mitotic delay observed upon its depletion. Transcriptome analysis of linc00899-depleted cells together with gain-of-function and rescue experiments across multiple cell types identified the neuronal microtubule-binding protein, TPPP/p25, as a target of linc00899. Linc00899 binds the genomic locus of TPPP/p25 and suppresses its transcription through a cis-acting mechanism. In cells depleted of linc00899, the consequent upregulation of TPPP/p25 alters microtubule dynamics and is necessary and sufficient to delay mitosis. Overall, our comprehensive screen identified several lncRNAs with roles in genome stability and revealed a new lncRNA that controls microtubule behaviour with functional implications beyond cell division.
INTRODUCTION
Long noncoding RNAs (lncRNAs) are defined as RNAs longer than 200 nucleotides that lack functional open reading frames, and represent a major transcriptional output of the mammalian genome 1, 2. LncRNAs have been implicated in the control of numerous cellular processes including the cell cycle, differentiation, proliferation and apoptosis 3–5 and their deregulation has been associated with human disease including cancer 6, 7. Several lncRNAs regulate the levels of key cell cycle regulators such as cyclins, cyclin-dependent kinases (CDK), CDK inhibitors and p53 8, 9. LncRNAs have been also linked to cell division by regulating the levels of mitotic proteins 10, 11 or by modulating the activity of enzymes involved in DNA replication and cohesion 12. In addition, lncRNAs can regulate chromosome segregation by controlling kinetochore formation via centromeric transcription 13 or by acting as decoys for RNA binding proteins involved in maintaining genome stability 14, 15. All of these lncRNA-dependent functions can occur through transcriptional and post-transcriptional gene regulation, chromatin organisation and/or post-translational regulation of protein activity 5. These mechanisms usually involve lncRNAs establishing interactions with proteins and/or nucleic acids which allows lncRNA-containing complexes to be recruited to specific RNA or DNA targets 16. Although lncRNAs represent more than 25% of all human genes (GENCODE v24), the biological significance of the majority of lncRNAs remains unknown.
Systematic screens in human cells have identified new protein-coding genes involved in cell survival, cell cycle progression and chromosome segregation 17–20. Similar loss-of function screens have been performed to identify lncRNAs with new functions. For example, CRISPR interference (CRISPRi) has been used in high-throughput screens to identify lncRNA loci important for cell survival 21 and revealed lncRNAs whose functions were highly cell type-specific. Similar results were obtained in a CRISPR/Cas9 screen targeting lncRNA splice sites 22. In a recent RNAi screen targeting human cancer-relevant lncRNAs, Nötzold and colleagues used time-lapse microscopy imaging of Hela Kyoto cells and identified 26 lncRNAs linked to cell cycle regulation and cell morphology 23. However, this screen only studied ∼600 lncRNAs in the genome with respect to a limited number of phenotypes.
With an aim to identify lncRNAs with new functions in cell division, we performed a more comprehensive high-content imaging RNAi screen involving the depletion of 2231 lncRNAs in HeLa cells. We developed new image analysis pipelines to quantify a diverse set of mitotic features in fixed cells, and discovered multiple lncRNAs with roles in mitotic progression, chromosome segregation and cytokinesis. We focused on linc00899, a hitherto uncharacterised lncRNA that regulates mitotic progression by repressing the transcription of the microtubule-stabilising protein TPPP/p25. Our study demonstrates the regulatory function of linc00899 in mitotic microtubule behaviour and provides a comprehensive imaging data resource for further investigation of the roles of lncRNAs in cell division.
RESULTS
High-content phenotypic screen identified new lncRNAs involved in mitotic progression, chromosome segregation and cytokinesis
To identify novel lncRNAs involved in regulating cell division, we performed two consecutive RNAi screens (screen A and B). Briefly, we transfected HeLa cells with the human Lincode small interfering RNA (siRNA) library targeting 2231 lncRNAs (Fig. 1a; Supplementary Table 1) and examined their effects using high-content screening of mitotic phenotypes. Each lncRNA was targeted with a SMARTpool of four different siRNAs. Following 48 hr incubation, cells were fixed and processed for immunostaining and subsequent automated image acquisition and analysis. In screen A, antibodies targeting CEP215 (to label centrosomes), α-tubulin (to label themicrotubule cytoskeleton), phalloidin (to label the actin cytoskeleton) and Hoechst (to label nuclei) were used. In screen B (Fig. 1b-d), phospho-histone H3 (PHH3; to specifically label mitotic cells), α-tubulin, γ-tubulin (to label centrosomes) and Hoechst was used. We used these two screens as independent approaches to robustly identify lncRNAs with new functions in mitotic progression, chromosome segregation and cytokinesis.
In each screen, we employed automated image analysis to segment the cells and developed in-house pipelines to quantify defects in each of abovementioned categories upon lncRNA depletion (Supplementary Fig. 1a). First, for defects in mitotic progression, we determined the percentage of mitotic cells (also called mitotic index), because an increase in the mitotic index implies a delay or block in mitotic progression. We performed nuclear segmentation and computed the mitotic index (Supplementary Fig. 1b) where mitotic cells were identified by the presence of mitotic spindle staining (detected by α-tubulin and CEP215) in screen A or by positive PHH3 staining of chromosomes in screen B. Second, for quantification of chromosome segregation defects, a category that includes chromatin bridges and lagging chromatids, we identified anaphase cells based on α-tubulin staining between the separating nuclei, in addition to Hoechst (DNA) signal (Supplementary Fig. 1c). Third, to evaluate defects in the execution of cytokinesis, we segmented the cytoplasm of interphase cells and scored the number of cells with cytokinetic bridges based on α-tubulin staining (Supplementary Fig. 1d).
a. Schematic representation of the high-throughput RNAi imaging screen for lncRNAs regulating three mitotic processes: mitotic progression, chromosome segregation and cytokinesis. The screen depleted each of 2231 lncRNAs in HeLa cells using the Human Lincode siRNA library (Dharmacon).
b. Z-scores for mitotic progression defects upon depletion of each lncRNA in the siRNA library. Each point corresponds to a single lncRNA where the Z-score was computed based on the mean mitotic index (MI). siRNAs against the protein-coding gene Ch-TOG were used as positive controls, in addition to negative control siRNAs (Ctl, from Ambion). Representative images from the top candidate (linc00899) are also shown, with PHH3 in yellow indicating mitotic cells (white circles).
c. Z-scores for chromosome segregation defects upon lncRNA depletion, similar to (b). The Z-score per lncRNA was computed from the mean number of chromosome segregation errors (CSE). Here, NORAD (grey) was used as a positive control. Top candidates are highlighted in purple. Representative images from one of the top candidates (linc00883) are also shown with staining for α-tubulin (red), PHH3 (green) and γ-tubulin (yellow). Inset depicts normal anaphase cell (blue area) or anaphase cell with CSE (yellow area).
d. Z-scores for cytokinesis defects upon lncRNA depletion, similar to (a). The Z-score for each lncRNA was computed based on the mean number of cells with cytokinetic bridges (CB). Representative images from the top candidate (linc00840) are shown with staining for α-tubulin (red) and DNA (blue). CB are depicted in white circles.
All Z-scores shown here are from screen B. Some of the top candidates are shown in colour and labelled in each plot. The scale bar for all images is 20 μm. Tables below each panel represent the raw data and calculated Z-scores for top lncRNA candidates for each category from two independent screens (A and B).
For each category, we ascertained lncRNAs for which depletion increased the frequency of defects relative to the mean across all lncRNAs in the siRNA library. Negative control siRNA and cells without any siRNA treatment were used as controls, for which we observed no systematic differences in the frequencies of each phenotype (Fig. 1b-d). We identified candidate lncRNAs involved in mitotic progression (Fig. 1a; linc00899 and C1QTNF1-AS1), chromosome segregation (Fig. 1b; PP7080 and linc00883) and cytokinesis (Fig. 1c; linc00840 and loc729970). As a positive control for mitotic progression defects, we used a SMARTpool against the protein-coding gene Ch-TOG/CKAP5, whose depletion leads to mitotic delay and increased mitotic index 24 (Fig. 1b). For chromosome segregation, we successfully identified the lncRNA NORAD (Fig. 1c), depletion of which increases the rate of chromosome segregation errors 14, 15. Supplementary Table 2 contains raw data and computed Z-scores for each lncRNA and phenotype.
To confirm our findings, we conducted a validation screen targeting the top 25 lncRNA candidates identified in the initial screens for mitotic progression and cytokinesis (Supplementary Table 3). Depletion of each lncRNA was performed in two biological (and in total eight technical) replicates. For mitotic progression, this screen corroborated the increase in mitotic index following depletion of linc00899 and C1QTNF1-AS1 (Supplementary Fig. 2a). Although loc100289019-depleted cells also displayed an elevated mitotic index, levels of the lncRNA did not change upon RNAi 25. For the cytokinesis category, we observed an increase in the number of cells with cytokinetic bridges after linc00840 depletion and a decrease after loc729970 depletion, but neither led to multinucleation (Supplementary Fig. 2b, c). Furthermore, elevated mitotic index and cytokinesis defects were not associated with reduced cell viability for these lncRNAs (Supplementary Fig. 2d). As positive controls, we used Ch-TOG and ECT2 (a key regulator of cytokinesis 26, the depletion of which led to expected phenotypes: an increased number of mitotic and multinucleated cells, respectively (Supplementary Fig. 2a-c).
Mitotic perturbations caused by depletion of the lncRNA candidates were further characterised by time-lapse microscopy imaging to investigate the dynamics of each phenotype. As expected, a marked mitotic delay was observed in HeLa cells depleted of linc00899 and C1QTNF1-AS1, lncRNAs associated with increased mitotic index (Supplementary Fig. 3). Next, we depleted linc00883 and PP7080, lncRNAs with potential functions in chromosome segregation (Supplementary Fig. 4). Using time-lapse microscopy imaging of HeLa Kyoto cells stably expressing histone H2B-mCherry (a chromatin marker) and eGFP-α-tubulin (a microtubule marker) 18, we found that depletion of linc00883 and PP7080 increased the rate of chromosome segregation errors to a similar extent as that of NORAD. We then depleted linc00840 and loc729970 (Supplementary Fig. 5), lncRNAs from the cytokinesis category, and found that knockdown of linc00840 doubled the time required for cells to cleave the cytokinetic bridge, whereas knockdown of loc729970 resulted in shorter cytokinesis. Overall, our screen identified novel functions of lncRNAs in the control of cell division, supporting the idea that lncRNAs play an important role in cell cycle progression.
Evolutionary and molecular characterisation of linc00899 and C1QTNF1-AS1
Two lncRNAs associated with delayed mitotic progression, linc00899 and C1QTNF1-AS, were selected for in-depth functional analysis. Linc00899 and C1QTNF1-AS1 are spliced and polyadenylated lncRNAs. Linc00899 (also known as loc100271722 or ENSG00000231711) is a multi-exonic intergenic lncRNA located on chromosome 22 and is ∼1.6kb long. C1QTNF1-AS1 (also known as ENSG00000265096) is a lncRNA on chromosome 17 that is ∼1kb long and is antisense to a protein-coding gene “C1q And TNF Related 1” (C1QTNF1/CTRP1). Both lncRNAs are annotated in Gencode, show signs of active transcription (Fig. 2a) and have low protein-coding potential (Supplementary Fig. 6a). Although, we did not identify a syntenic ortholog for linc00899 in the mouse genome, short stretches of conserved regions 27 are present within exon 1 (Supplementary Fig. 6b). This places linc00899 in a group of lncRNAs with conserved exonic sequences embedded in a rapidly evolving transcript architecture 28. Based on the syntenic position of protein-coding gene C1QTNF1, we found a mouse ortholog for C1QTNF1-AS1 (GM11747, ENSMUG000000086514) that is also antisense to the mouse C1qtnf1. Thus, C1QTNF1-AS1 is a lncRNA that is conserved across mouse and human, while linc00899 contains short conserved stretches at its 5’ end representing possible functional domains 29, 30.
a. Schematic representation of the genomic landscape surrounding linc00899 (annotated in RefSeq as NR_027036; Gencode gene ENSG00000231711; chr22:46039907-46044868, hg38) and C1QTNF1-AS1 (annotated in RefSeq as NR_040018/NR_040019; Gencode gene ENSG00000265096; chr17:79019209-79027601, hg38). Marks of active transcription in HeLa cells (H3K4me3 and H3K27ac, obtained from ENCODE via the UCSC browser) and conservation scores by PhastCons are also shown. Putative conserved mouse lncRNAs based on syntenic conservation are shown below.
b. Expression of linc00899 and C1QTNF1-AS1 in the nucleus and cytosol of ENCODE cell lines, shown as reads per kilobase of exon per million reads mapped.
c. Single-molecule RNA FISH using exonic probes (white) against linc00899 and C1QTNF1-AS1. Nuclei were stained with DAPI (blue). Scale bar, 5µm.
d. qPCR quantification of lncRNA levels in RNA fractions extracted from different cellular compartments (C, cytoplasm; N, nucleoplasm; Chr, chromatin). U1 small nuclear RNA was used as a positive control for the chromatin fraction. Errors bars represent the standard error of the mean ±S.E.M from at least five independent experiments.
We then validated the expression of both lncRNAs in HeLa cells using a variety of techniques. cDNA generated from polyadenylated RNA was used for rapid amplification of cDNA ends (RACE) to define the locations of the 5’ cap and 3’ end identifying several isoforms for linc00899 and C1QTNF1-AS1 (Supplementary Fig. 6c). The diversity of isoforms for both lncRNAs is consistent with a previous study on lncRNA annotation 27. Expression data from ENCODE cell lines indicated that linc00899 and C1QTNF1-AS1 were present in both the nucleus and cytoplasm of HeLa-S3 cells (Fig. 2b), which we further confirmed by RNA fluorescence in situ hybridisation (RNA FISH) (Fig. 2c). qPCR analyses on RNA extracted from different cellular fractions revealed that linc00899 but not C1QTNF1-AS1 is associated with chromatin (Fig. 2d). Although some linc00899 foci remain detectable in mitotic cells (Supplementary Fig. 6d), they do not associate with mitotic chromatin, arguing against a mitotic bookmarking role for these lncRNAs. Linc00899 and C1QTNF1-AS1 are estimated to occur, on average, in five and two copies per cell, respectively, and do not show cell cycle-dependency in their expression (Supplementary Fig. 6e, f). In the Genotype-Tissue Expression (GTEx) RNA-seq dataset 31 from human tissue, C1QTNF1-AS1 was highly expressed in adrenal gland and spleen, while linc00899 was broadly expressed in most of the tissues, with uterus being the highest (Supplementary Fig. 6g).
Linc0889 and C1QTNF1-AS1 facilitate timely mitotic progression
To characterise the mitotic phenotype further, we depleted linc00899 and C1QTNF1-AS1 in HeLa and HeLa Kyoto cells and examined the effect on mitotic progression with immunofluorescence and time-lapse microcopy imaging, respectively. Quantification of lncRNA-depleted cells using RNAi revealed an increase in the mitotic index compared to cells treated with control siRNA (Fig. 3a, b). This was confirmed by time-lapse microscopy imaging of HeLa Kyoto cells where depletion of linc00899 and C1QTNF1-AS1 resulted in increased mitotic duration (Supplementary Fig. 7a-c). We found that cells treated with control siRNA initiated anaphase onset at 40 min ± 10 min (median ± SD), whereas the cells depleted of linc00899 and C1QTNF1-AS1 initiate anaphase onset at 100 ± 173 and 240 ± 146 min, respectively (Fig. 3c). Linc00899-depleted cells assembled bipolar spindles and congressed chromosomes with normal kinetics, but exhibited a delay in metaphase to anaphase transition (Fig. 3d upper panels and Supplementary Movies S1, 2). By contrast, C1QTNF1-AS1-depleted cells showed a delay in chromosome congression to the metaphase plate (Fig. 3d lower panels and Supplementary Movie S3).
a. Expression of linc00899 and C1QTNF1-AS1 after RNAi depletion, as measured by qPCR. Results are also shown for negative control siRNAs (Ctl, from Ambion) and for cells treated with transfection reagent only (Cells). Data are shown as mean ± S.E.M. n = 3 (C1QTNF1-AS1) and 6 (linc00899) biological replicates, ****P<0.0001 by two-tailed Student’s t-test. Expression values are presented relative to control siRNA.
b. Changes in the mitotic index (MI, based on PHH3 and Hoechst staining) after RNAi-mediated depletion of linc00899 and C1QTNF1-AS1 determined 48 hours after siRNA transfection. Data are shown as mean ± S.E.M. n = 4 biological replicates, *P<0.05 by two-tailed Student’s t-test.
c. Quantification of mitotic duration from time-lapse microscopy imaging after depletion of linc00899 and C1QTNF1-AS1 in HeLa Kyoto cells. We analysed n=183 for cells treated with negative control siRNAs (Ctl), n=241 for cells with transfection reagent only (Cells), n=163 for linc00899 siRNAs and n=147 for C1QTNF1-AS1 siRNAs. Bars show the median and the interquartile range from 3 biological replicates. ****P<0.0001 by Mann-Whitney test.
d. Representative still images from time-lapse microscopy from (c). Scale bar, 5µm.
e. Expression of linc00899 and C1QTNF1-AS1 after depletion using two different LNA gapmers targeting the mature lncRNA transcripts, as measured by qPCR. Results are also shown for negative control LNAs (Ctl LNA A and B) and for cells treated with transfection reagent only (Cells). Data are shown as mean ± S.E.M. n = 3 biological replicates, ***P<0.001 and ****P<0.0001 by two-tailed Student’s t-test. Expression levels are presented relative to Ctl LNA A.
f. Changes in the MI after depletion of linc00899 and C1QTNF1-AS1 using LNA1 gapmers, as measured in (B). Data are shown as mean ± S.E.M. n = 4 biological replicates, *P<0.05 by two-tailed Student’s t-test.
g. Quantification of mitotic duration as in (c) after depleting linc00899 and C1QTNF1-AS1 with LNA1 gapmers. We analysed n=274 for negative control LNA (Ctl LNA A), n=312 for cells with transfection reagent alone (Cells), n=138 for linc00899 LNA1 and n=349 for C1QTNF1-AS1 LNA1. Bars show the median and interquartile range from 3 biological replicates. ****P<0.0001 by Mann-Whitney test.
h. Representative still images from time-lapse microscopy from (g). Scale bar, 5µm.
Mitotic duration in (c) and (g) was defined from NEBD (t=0 min) to anaphase onset.
a. Venn diagram of DEGs detected by RNA-seq after depletion of linc00899 and C1QTNF1-AS1 using RNAi and LNA1 gapmers. The total number of genes in this analysis was 17022 and DEGs were detected at a FDR of 5% with a log2-fold change threshold of 0.5.
b. Heat map of DEGs detected with RNA-seq after depletion of linc00899 using RNAi and LNA gapmers. 3 - 4 biological replicates were generated for each condition and compared to appropriate negative controls (Ctl) – A and B for LNA gapmers, Ambion (Amb) and Dharmacon (Dh) for RNAi. Only DEGs changing in the same direction with both LOF methods are shown. Linc00899 itself is shown as a reference.
c. Genome tracks of RNA-seq coverage for linc00899 before and after depletion of linc00899 using RNAi or LNA1 gapmers. The tracks were constructed from averages of three to four biological replicates for each condition, see abbreviations in (b).
d. Genome tracks of RNA-seq coverage for TPPP, as in (c).
e. Representative Western blot of TPPP levels after depletion of linc00899. ß-tubulin and p150 were used as two loading controls. Unprocessed original scans of blots are shown in Supplementary Fig. 14.
f. Densitometric analysis of TPPP levels in (e). n = 5 biological replicates for each LOF method. Bar charts depict mean ± S.E.M.
g. Quantification of mitotic duration from time-lapse microscopy imaging of HeLa cells after single and double knockdown of linc00899 and TPPP using RNAi or LNAs. We analyzed n=154 for cells treated with transfection reagent alone (Cells), n=188 for negative control siRNA (Ctl, from Ambion), n=208 for TPPP siRNAs, n=98 for linc00899 siRNAs, n=205 for linc00899 and TPPP siRNAs, n=183 for negative control LNA (Ctl LNA A), n=116 for linc00899 LNA1 and n=247 for linc00899 LNA1 and TPPP siRNA. For each condition, we show the median with interquartile range from 2 biological replicates. ****P<0.0001 by Mann-Whitney test. Mitotic duration was defined from NEBD (t=0 min) to anaphase onset.
h. Expression of linc00899 and TPPP after single or double knockdown with RNAi, as quantified with qPCR. Results are also shown for cells treated with transfection reagent alone (Cells) or negative control siRNA (Ctl). Data are shown as mean ± S.E.M. n = 3 biological replicates, *P<0.05, ***P<0.001 and ****P<0.0001 by two-tailed Student’s t-test. Expression values are presented relative to expression with the control siRNA.
i. Expression of linc00899 and TPPP after single or double knockdown as in (h), but using LNA1 gapmers to deplete linc000899. All expression values are presented relative to expression with the negative control LNA A (Ctl LNA A).
We next asked whether these phenotypes could be recapitulated by a loss-of-function (LOF) method other than RNAi. We used locked nucleic acid (LNA) successfully depleted both linc00899 and C1QTNF1-AS1 with two different LNAs and (Fig. 3e). Similar to RNAi, we observed an increase in the mitotic index following LNA1-mediated depletion of linc00899 and C1QTNF1-AS1 (Fig. 3f) and confirmed the mitotic delay with time-lapse microcopy imaging of HeLa Kyoto cells (Fig. 3g, h; Supplementary Fig. 7d-f and Supplementary Movies S4-6). Together, these data indicate that linc00899 and C1QTNF1-AS1 have biological functions in regulation of mitotic progression, albeit through different mechanisms.
To further confirm the phenotype of C1QTNF1-AS1 depletion, we inserted a polyadenylation poly(A) signal (pAS) 34 downstream of the C1QTNF1-AS1 transcriptional starts site (TSS) using CRISPR-Cas9 gene editing. This method allows lncRNAs to be transcribed but terminates them prematurely due to the inserted pAS, preventing the expression of full-length lncRNA transcripts. We obtained four homozygous clones for C1QTNF1-AS1 with similar knockdown efficiency, all of which displayed mitotic delay similar to RNAi- and LNA-mediated depletion of C1QTNF1-AS1 (Supplementary Fig. 8). These results suggest that the 5 C1QTNF1-AS1 transcript, and not transcription at the C1QTNF1-AS1 locus, is required for the regulation of mitotic progression. Similar experiments for linc00899 were hindered by the presence of over 4 copies of linc00899 in the HeLa genome (https://cansar.icr.ac.uk/cansar/cell-lines/HELA/copy_number_variation/) preventing the generation of homozygous clones despite the use of two different guide RNAs targeting different regions of linc00899 (data not shown).
Linc0889-dependent regulation of TPPP/p25, a microtubule associated protein, is responsible for mitotic progression
To reveal transcriptional regulatory functions of linc00899 and C1QTNF1-AS1, we performed RNA sequencing (RNA-seq) of cells following RNAi- and LNA-mediated depletion of each lncRNA. We selected the subset of differentially expressed genes (DEG) that changed in a consistent direction with both LOF methods to minimise any potential method specific off-target effects as shown previously 25. For C1QTNF1-AS1, a single DEG was detected, which was C1QTNF1-AS1 itself (Fig. 4a) arguing against a transcriptional role for C1QTNF1-AS1 in mitotic progression. For linc00899, we identified eight DEGs in common across the two LOF methods (Fig. 4a), of which four were changing in the same direction with both methods (Fig. 4b). One of these candidates was TPPP/p25, a tubulin polymerisation promoting protein with established roles in microtubule dynamics and mitosis 35, 36. TPPP was upregulated upon RNAi and LNA-mediated depletion of linc00899 (Fig. 4c, d), which we validated at the protein level in asynchronous (Fig. 4e, f) as well as in mitotic cells (Supplementary Fig. 9a).
To address whether the mitotic delay in linc00899-depleted 5 cells is due to increased TPPP levels, we performed single and double knockdowns of linc00899 and TPPP and analysed the mitotic progression (Fig. 4g). Whereas TPPP knockdown alone did not affect mitotic timing, depletion of linc00899 alone in HeLa cells led to a mitotic delay, consistent with results from HeLa Kyoto cells (Fig. 3c, g). However, cells with simultaneous depletion of linc00899 and TPPP progressed through mitosis with near-normal timing, a phenotype observed with both LOF methods. We confirmed that co-depletion of linc00899 and TPPP rescued the previously observed upregulation of the latter with qPCR (Fig. 4h, i). Similar results were obtained with an additional LNA oligonucleotide targeting the first intron of linc00899 (Supplementary Fig. 9b, c). These data suggest that linc00899 needs to be depleted at least by 50% to attain TPPP upregulation and mitotic delay in HeLa cells (Supplementary Fig. 9d, e).
Linc00889 controls microtubule behaviour in mitotic cells
TPPP is known to localise to the mitotic spindle, and its overexpression influences microtubule dynamics and stability in mammalian cells 35–38. Overexpression of TPPP increases tubulin acetylation, and also microtubule stability via microtubule-bundling 36. Thus, we tested whether depletion of linc00899, which leads to upregulation of TPPP, had a similar effect. Immunofluorescence of linc00899-depleted cells using antibodies against α-tubulin (a microtubule marker) and acetylated α-tubulin (a marker of long-lived microtubules) 39 showed a marked increase in acetylated α-tubulin levels (Fig. 5a), consistent with linc00899-depleted cells containing more long-lived microtubules. This was confirmed by quantification of acetylated α-tubulin within the mitotic spindle in linc00899-depleted cells (Fig. 5b).
a. Acetylated α-tubulin levels in HeLa cells after RNAi-mediated linc00899 depletion or treatment with a negative control siRNA (Ctl, from Ambion), based on immunofluorescence after staining with antibodies against α-tubulin (microtubules, in red) and acetylated α-tubulin (long-lived microtubules, in green). Representative images are shown and correspond to maximum intensity projections of confocal micrographs. DNA was stained with Hoechst and is shown in blue in the merged images.
b. Quantification of acetylated α-tubulin intensity over the total level of α-tubulin from maximum intensity projections obtained in (a). Numbers of cells analysed is n=85 for negative control siRNAs (Ctl) and n=92 for linc00899 RNAi. Swarm plots represent values from single mitotic cells, with the median denoted by the horizontal line. ****P<0.0001 by two-tailed Student’s t-test.
c. EB1 signal in HeLa cells after RNAi-mediated linc00899 depletion or treatment with a negative control siRNA (Ctl), based on immunofluorescence after staining with antibodies against α-tubulin (in red) and EB1 (in green). Images correspond to maximum intensity projections of confocal micrographs. DNA is shown in blue.
d. Quantification of EB1 intensity from maximum intensity projections obtained in the experiment (c). Numbers of cells analysed is n=39 for negative control siRNAs (Ctl) and n=38 for linc00899 RNAi. Swarm plots represent values from single mitotic cells, with the median represented by the horizontal line. ****P<0.0001 by two-tailed Student’s t-test.
e. Expression of linc00899 and TPPP after RNAi-mediated depletion of linc00899 in normal retina cell line (hTert-RPE1) based on qPCR. Results are also shown for negative control siRNAs (Ctl, from Ambion) and cells treated with transfection reagent alone (Cells). Data are shown mean ± S.E.M. n = 3 biological replicates, *P<0.05 and ****P<0.0001 by two-tailed Student’s t-test.
f. Quantification of mitotic duration from time-lapse microscopy imaging after depletion of linc00899 in RPE1 cells. Number of cells analysed is n=135 for negative control siRNAs (Ctl), n=98 for cells with transfection reagent alone (Cells) and n=103 for linc00899 RNAi. Data are shown as median with interquartile range from 2 biological replicates. ****P<0.0001 by Mann-Whitney test. Depletion of linc00899 in RPE1 leads to an ∼12min delay (p<0.0001) compared to cells treated with control siRNA.
g. Expression of linc00899 and TPPP after RNAi-mediated depletion of linc00899 in non-tumorigenic epithelial breast cells (MCF10A) based on qPCR. Controls are as described in (e). Data are shown mean ± S.E.M. n = 3 biological replicates, *P<0.05 and ****P<0.0001 by two-tailed Student’s t-test.
h. Quantification of mitosis duration from time-lapse microscopy imaging after depletion of linc00899 in MCF10A cells, as described in (f). Number of cells analysed is n=143 for negative control siRNAs (Ctl), n=142 for cells with transfection reagent alone (Cells) and n=63 for linc00899 siRNAs. Data are shown as median with interquartile range from 2 biological replicates. ****P<0.0001 by Mann-Whitney test. Depletion of linc00899 in MCF10A leads to ∼16min delay (p< 0.0001) compared to cells treated with control siRNA.
i. Expression of linc00899 and TPPP after RNAi-mediated depletion of linc00899 in normal primary umbilical vein endothelial cells (HUVEC) based on qPCR. Controls are as described in (e). Data are shown mean ± S.E.M. n = 4 biological replicates, *P<0.05 and **P<0.01 by two-tailed Student’s t-test.
j. Quantification of mitotic duration from time-lapse microscopy imaging after depletion of linc00899 in HUVEC cells, as described in (f). Number of cells analysed is n=59 for negative control siRNAs (Ctl), n=129 for cells with transfection reagent alone (Cells) and n=181 for linc00899 RNAi. Data are shown as median with interquartile range from 3 biological replicates. ****P<0.0001 by Mann-Whitney test. Depletion of linc00899 in HUVEC leads to an ∼8min delay (p=0.0002) compared to cells treated with control siRNA.
Mitotic duration in (f), (h) and (j) was defined from NEBD (t=0 min) to anaphase using bright-field microscopy. Scale bar, 5µm.
As TPPP influences the growth velocity of microtubules by affecting their stability 36, we examined the localisation of EB1 protein, which specifically associates with growing microtubule plus-ends where it regulates microtubule dynamics 40. As expected, EB1 staining was apparent at the spindle pole and throughout the spindle in control cells but it was much reduced in intensity upon linc00899 depletion (Fig. 5c). Importantly, reduced EB1 signal was not due to diminished microtubule levels, because α-tubulin staining of the mitotic spindle was comparable between linc00899-depleted and control cells. Quantification of EB1 signal in mitotic cells confirmed the decrease in EB1 levels, indicating that linc00899 depletion lessens the number of growing microtubule ends, a phenotype consistent with a reduction in microtubule dynamics (Fig. 5d). Taken together, these data demonstrate that by controlling TPPP expression levels, linc00899 limits the number of long-lived microtubules and maintains normal microtubule dynamics in mitotic HeLa cells.
We next asked whether linc00899-mediated regulation of TPPP occurs in cell lines other than HeLa. RNAi-mediated depletion of linc00899 resulted in upregulation of TPPP in three normal diploid cell lines (hTERT-RPE1, retinal pigment epithelial cells; MCF10A, untransformed breast epithelial cells; HUVEC, primary umbilical vein endothelial cells) where mitosis is ∼ 25 min (Fig. 5e, g, i). Elevated TPPP levels were accompanied with a mitotic delay from ∼8 to 16 min upon linc00899 knockdown (Fig. 5f, h, j). This delay was smaller compared to a mitotic delay in HeLa cells and most likely is due to the presence of at least 82 chromosomes in HeLa cells that need to be aligned at the metaphase plate, compared to 46 chromosomes present in normal diploid cells. Indeed, the mitotic timing in HeLa cells is at least ∼15 min longer than in RPE1, MCF10A and HUVEC cells. Thus, linc00899 regulates mitotic progression by controlling TPPP levels in multiple cell lines.
In addition to its mitotic functions, TPPP is crucial for microtubule organisation in the brain. Although TPPP is present in multiple tissues, expression of TPPP is highest in the brain in both mouse 41 and human (Supplementary Fig. 10a). In GTEx brain samples, high TPPP expression is accompanied by low levels of linc00899, while in multiple sclerosis patients TPPP and linc00899 expression were negatively correlated (Supplementary Fig. 10b, c). This suggests that the regulatory relationship observed in our study may be physiologically relevant in human brain tissue and neuropathological diseases.
Gain-of-function and rescue studies define linc00899 as a repressor of TPPP expression through cis-acting mechanism
Linc00089 can regulate TPPP expression in cis (locally) or in trans (distally), as shown for multiple lncRNAs 3, 5, 42. Despite linc00089 and TPPP being located on different chromosomes, a cis-acting mechanism whereby lncRNA interacts with its target site by being tethered to its site of synthesis cannot be excluded. To elucidate the mode of action by which linc00899 regulates TPPP, we compared effects of ectopic and endogenous overexpression of linc00899 on TPPP levels. Despite an increase in linc00899 expression after its ectopic overexpression using the expression plasmid encoding linc00899 cDNA, no changes in TPPP levels were observed in HeLa and RPE1 cells (Fig. 6a). These results argue against a trans function of linc00899 in TPPP regulation. Indeed, rescue experiments using expression plasmid after RNAi- or LNA-mediated depletion of linc00899 failed to reduce TPPP levels (Fig. 6b). However, exogenous lncRNA expression is not expected to rescue phenotypes associated with depletion of cis-acting chromatin-associated lncRNAs 42–44. To assess a possible cis effect, we employed the CRISPR activation (CRISPRa) system 45, which uses catalytically inactive dCas9 fused to transcriptional activator VP64, and guide RNAs (gRNAs) targeting different regions of the linc00899 promoter. We observed two-fold overexpression of linc00899 from its endogenous locus, which led to downregulation of TPPP in the normal diploid RPE1 but not in HeLa cells (Fig. 6c). The lack of TPPP downregulation in HeLa cells may stem from the presence of multiple linc00899 and TPPP alleles; for instance, CRISPRa may not induce overexpression of all (4 or more) linc00899 loci. In summary, our data support the presence of a cis-acting mechanism for linc00899 to repress TPPP expression.
a. Schematic diagram of ectopic overexpression of linc00899. Linc00899 and TPPP expression were analysed by qPCR after lentiviral overexpression using lincXpress vector encoding linc00899 cDNA in HeLa (left) and RPE1 cells (right). The expression was normalised to the scrambled linc00899 vector (negative control). Data are shown mean ± S.E.M. n = 3 - 4 biological replicates, *P<0.05 by two-tailed Student’s t-test.
b. Rescue of linc00899 function after RNAi-or LNA-mediated depletion of linc00899 in HeLa cells through ectopic overexpression of linc00899. Data are shown mean ± S.E.M. n = 3 biological replicates, *P<0.05 and **P<0.01 by two-tailed Student’s t-test.
c. Schematic diagram of endogenous overexpression of linc00899 using CRISPRa. Linc00899 and TPPP expression were analysed by qPCR after transduction of dCas9-VP64 and gRNAs targeting different regions of the linc00899 promoter in HeLa (left) and RPE1 cells (right). The expression was normalised to the negative guide (negative control). Data are shown mean ± S.E.M. n = 4 biological replicates, *P<0.05, ***P<0.001 and ****P<0.0001 by two-tailed Student’s t-test.
Linc0889 binds and regulates transcription of TPPP
As shown in Figure 2, linc00899 is a nuclear and chromatin-enriched lncRNA, raising the possibility that it could directly regulate transcription of TPPP. To test this, we performed Cleavage Under Targets and Release Using Nuclease (CUT&RUN) 46 that allows genome-wide profiling of active and repressive histone modifications as well as transcription factors from low cell numbers. We observed an increase in trimethylation of lysine 4 on histone H3 (H3K4me3), a mark of active transcription, at the promoter of TPPP after RNAi- and LNA-mediated depletion of linc00899 (Fig. 7a). Thus, elevated levels of TPPP mRNA in linc00899-depleted cells is likely to arise from increased transcription at the TPPP locus. No significant changes were seen in other active and repressive histone modifications at the TPPP locus (Supplementary Fig. 11).
a. CUT&RUN profiling of H3K4me3 enrichment after depletion of linc00899 with RNAi and LNAs at the TPPP locus in HeLa cells. An increase in H3K4me3 was observed at the TPPP promoter upon depletion of linc00899 with both LOF methods (arrow). Tracks were constructed from averages of two biological replicates for each condition. TSS, transcriptional start site.
b. Enrichment of linc00899 using a cocktail of 4 oligonucleotides complementary to accessible regions of linc00899 (as determined by RNase H mapping, Supplementary Fig. 12a), compared to a mixture of two control oligonucleotides containing parts of the linc00899 sense sequence, as measured by qPCR. The 5.8S was used as a negative control region. Data are shown mean ± S.E.M. n = 4 biological replicates.
c. Profiling of linc00899 binding by CHART-seq after RNase H elution at the TPPP locus in HeLa cells. The region with the most significant increase in linc00899 binding compared to the sense control was present in the second intron of TPPP (arrow). All binding sites were detected using an empirical FDR threshold of 30%.
d. Validation of linc00899 colocalization with the TPPP locus by co-RNA FISH in HeLa cells. Linc00899 (exonic probes against the mature transcript) colocalized to the genomic region of TPPP (intronic probes against the premature transcript) in ∼3% of the cells (n=376). The nucleus was stained with DAPI (blue). Scale bar, 5 μm.
Based on these findings, we further hypothesized that linc00899 binds to regulatory regions of the TPPP locus. Therefore, we performed capture hybridization analysis of RNA targets with sequencing (CHART-seq), a method used to identify lncRNA binding sites on chromatin 47–50. We first mapped antisense oligonucleotides whose binding is accessible to linc00899 transcript (Supplementary Fig. 12a) and using the antisense cocktail found ∼10-fold enrichment of linc00899 transcript compared to the control oligonucleotides (Fig. 7b). No enrichment upon pulldown was detected with the negative control transcript 5.8S. After sequencing the enriched genomic DNA using the CHART-seq protocol 51 we identified prominent linc00899 binding site in the intron of TPPP (Fig. 7c; Supplementary Fig. 12 b-d); however, we were not able to identify regions with sequence complementarity to the linc00899 transcript (Supplementary Fig. 13). Thus, transcriptional upregulation of TPPP is likely to be mediated by linc00899 binding to the TPPP locus through protein interactions. This could occur through 3D proximity-guided localisation mechanism which allows low-abundant lncRNAs, such as linc00899, to identify its target genes even on different chromosomes 52. Such proximity-guided search has been observed for Firre and CISTR-ACT lncRNAs 53, 54 and could explain how linc00899, which is encoded on chromosome 22, may bind and regulate TPPP encoded on chromosome 5.
To corroborate that linc00899 act as a transcriptional repressor of TPPP, we performed co-RNA FISH using intronic probes against the premature TPPP and the mature linc00899 transcripts (Fig. 7d). TPPP has 8 alleles in the HeLa cell genome (https://cansar.icr.ac.uk/cansar/cell-lines/HELA/copy_number_variation), yet, premature TPPP transcripts were detected only in one third of the cells and presented mostly as a single focus. ∼3% of cells showed colocalization between mature linc00899 and premature TPPP transcript. Given that mature linc00899 transcripts can be detected in most cells, this low level of colocalization is consistent with effective linc00899-mediated suppression of TPPP transcription.
DISCUSSION
Previous studies have attributed the regulation of the cell cycle primarily to multi-protein networks. Here, we performed a high-content imaging screen to identify new lncRNAs with functions in cell division. Development of in-house image analysis pipelines coupled with targeted validation of lncRNA-induced phenotypes allowed us to quantify the impact of lncRNA depletion on cell division. Among other lncRNAs, this study identified linc00899 and C1QTNF1-AS1 as novel lncRNAs involved in the control of mitotic progression.
Our results revealed that linc00899 controls mitotic progression by regulating TPPP, a protein that binds and stabilises the microtubule network at all stages of the cell cycle 35–38, 41, 55. TPPP also binds to and inhibits histone deacetylase 6, an enzyme responsible for tubulin deacetylation. This binding results in increased tubulin acetylation 36, a phenotype also observed upon linc00899 depletion. Fine-tuning TPPP protein levels seems particularly important for mitosis. Indeed, TPPP overexpression in human cells suppresses microtubule growth velocity and normal microtubule dynamics, thereby impeding timely spindle assembly and cell division 36. Previous studies have shown that TPPP levels are subject to regulation by microRNAs 56 and protein kinases 41, 55, 57; our study now reveals lncRNA-mediated transcriptional control as a new regulatory layer.
Our mechanistic studies indicate that linc00899 regulates TPPP transcription in cis. In particular, this notion is supported by our findings that i) linc00899 is a nuclear and chromatin-enriched lncRNA, ii) there is no change in TPPP levels after ectopic overexpression of linc00899, iii) H3K4me3 levels increase at the TPPP promoter in linc00899-depleted cells, and iv) linc00899 binds at the TPPP genomic locus. It is possible that linc00899 contributes to the repressive chromatin landscape at the TPPP locus by altering local chromatin accessibility as observed with other lncRNAs 58. The mechanism whereby linc00899 binds to the TPPP genomic locus and represses its transcription remains to be fully defined. Given the limited colocalization between linc00899 and the premature TPPP transcript, it is more likely that linc00899 uses “proximity-guided search” where transcription site of linc00899 resides in close spatial proximity of TPPP in the nucleus. This would allow linc00899 to act immediately upon its transcription and suppress TPPP expression possibly via interactions with protein complexes such as chromatin regulators 52. It is also possible that the linc00899-mediated regulation of TPPP transcription depends on linc00899 release from the chromatin, with linc00899 target specificity being guided by the pre-established chromosomal proximity, as shown for lncRNA A-ROD 59. As we did not observe a strong sequence complementarity between the linc00899 transcript and the TPPP DNA sequence, we excluded the possibility of direct binding of linc00899 to TPPP locus. Instead, our data suggest that the linc00899 function could be mediated through linc00899-protein interactions. Further studies will be required to determine the in vivo linc00899-protein interactome and the relevance of these interactions in TPPP transcriptional regulation and cell division.
TPPP is crucial for microtubule organization in the brain. In Drosophila, TPPP mutants (also known as Ringmaker) exhibit defects in axonal extension 60. In mammals it is primarily expressed in oligodendrocytes where it stabilizes microtubule networks, and its depletion in progenitors inhibits oligodendrocyte differentiation 56. Indeed, TPPP-deficient mice display convulsive seizures, consistent with a defect in myelinating oligodendrocytes 61. In humans, an inverse correlation between TPPP and linc00899 across all tissues is consistent with a regulatory relationship, with the highest expression of TPPP (and lowest expression of linc00899) being observed in the brain. This suggests that linc00899-dependent suppression of TPPP could be used to fine-tune TPPP expression, and hence microtubule behaviour in a developmental stage- and tissue-specific manner. Intriguingly, altered TPPP protein levels have been observed in a number of neurodegenerative disorders including multiple sclerosis and Parkinson’s disease 62, 63.
In this study, we have comprehensively explored the activity of thousands of lncRNAs in cell cycle regulation, and identified an assortment of lncRNAs that are involved in controlling mitotic progression, chromosome segregation and cytokinesis. While our analysis encompassed several cellular features, the imaging data from our screen allows us to extract phenotypes at any stage of cell division upon lncRNA depletion. As interest in the regulatory functions of lncRNAs increases, we anticipate that our data will serve as a powerful resource for prioritizing lncRNA candidates for further studies in the RNA and cell cycle fields.
METHODS
Cell lines
HeLa and 293FT cells were maintained in Dulbecco’s modified Eagle’s medium (Gibco, 41966-029) supplemented with 10% Fetal bovine serum (FBS, Thermo Fisher Scientific, 10500064). MCF10A (human breast epithelial cell line) were cultured in Mammary Epithelial Cell Growth Medium (Lonza, CC-3151) with supplements and growth factors (Lonza, CC-4136). HUVEC (primary Human Umbilical Vein Endothelial Cells, CC2517, LOT 0000482213) were maintained in Endothelial Cell Growth Medium Lonza (Lonza, CC-3121) with supplements and growth factors (Lonza, CC-4133). RPE1 cells were maintained in Dulbecco’s modified Eagle’s medium F12 Nutrient Mixture (Gibco, 31331-028) supplemented with 10% Fetal bovine serum (FBS, Thermo Fisher Scientific, 10500064, LOT 2025814K). All cell lines were obtained from the American Type Culture Collection (ATCC) and were cultured at 37°C with 5% CO2. HeLa Kyoto (EGFP-α-tubulin/ H2B-mCherry) cells were obtained from ATCC/Jan Ellenberg (EMBL Heidelberg) 18 and cultured in DMEM with 10% FBS. All cell lines were verified by short tandem repeat (STR) profiling and tested negative for mycoplasma contamination.
High-content imaging screen: Lincode siRNA library
The Lincode siRNA Library (GE Dharmacon, G-301000) is a collection of siRNA reagents targeting 2231 human lncRNAs (1860 unique human lncRNA genes and 371 lncRNA transcripts associated with protein-coding genes). The design of this library is based on RefSeq version 54 and the siRNAs are arrayed as SMARTpools. The library was purchased at 0.1 nmol in a 96-well format. The library was diluted to a 5 μM stock with 1x siRNA buffer (GE Dharmacon, B-002000-UB-100) and arrayed onto black 384-well PerkinElmer Cell Carrier plates (Perkin Elmer, 6007550) using the Echo Liquid Handler (Labcyte). The black 384-well PerkinElmer Cell Carrier plates were prepared in advance and stored at −80°C. The final siRNA concentration per well was 20 nM. An siRNA targeting exon 1 of lncRNA GNG12-AS1 (Silencer select, Life Technologies, S59962)64 and SMARTpool siRNAs targeting protein-coding gene CKAP5/Ch-TOG (GE Dharmacon, L-006847-00) were also included on each plate. CKAP5/Ch-TOG was used as a positive control as its depletion leads to mitotic delay and increased mitotic index 24. ECT2 SMARTpool siRNAs (GE Dharmacon, L-006450-00-0005) were used as a positive control in the third validation screen as its depletion results in multinucleated cells 26.
High-content imaging screen: Reverse transfection
To redissolve the siRNA in the black 384-well PerkinElmer Cell Carrier plates (10 plates in total), 5 μl of OptiMEM medium (Thermo Fisher Scientific, 31985047) was added. The plates were centrifuged (1 min, 900g) and incubated at room temperature (RT) for 5 min. Lipofectamine RNAimax (Thermo Fisher Scientific, 13778150) was added in OptiMEM medium to a final concentration of 8 μl Lipofectamine to 1 ml OptiMEM medium and incubated at RT for 5 min. 5 μl of OptiMEM/Lipofectamine mix was then added to the plates. Plates were centrifuged and incubated at RT for 20 min. In the meantime, HeLa cells were trypinised and counted using an automated cell counter (Countess, Thermo Fisher Scientific). Cells were centrifuged at 1000g for 4 min, the medium was removed and the cells were resuspended in OptiMEM medium to a final concentration of 2000 cells/well. 10 μl of cell suspension was added to the plates, plates were centrifuged and incubated at 37°C for 4 hours, before adding 10 μl of DMEM + 30% FBS + 3% P/S (penicillin/streptomycin, P/S) was added (final concentration 10% FBS, 1% P/S). Plates were centrifuged and incubated at 37°C for 48 hours before fixation. A Multidrop Combi Reagent dispenser (Thermo Fisher Scientific) was used throughout the transfection protocol to ensure even liquid addition.
High-content imaging screen: Fixation and immunostaining
For screen A, the plates were fixed by adding an equal volume of pre-warmed (37°C) 8% formaldehyde (Thermo Fischer Scientific, 28908)/PBS solution to the wells and incubated at 37°C for 10 min. The cells were permeabilised with pre-warmed PBS/0.2% Triton X-100 (Acros Organics, 327371000) for 15 min at RT. The cells were then blocked in 1% BSA/PBS for 1 hour at RT. To perform the immunostaining, the cells were incubated with primary antibodies against α-tubulin (Dm1α, Sigma, TUB9026), CEP215/CDK5RAP2 65 and Alexa-Fluor® 568 Phalloidin (Thermo Fisher Scientific, A12380) for 2 hours at RT. The cells were washed three times in 1X PBS and incubated for 1 hour at RT with secondary antibodies Alexa Fluor® 488 (Thermo Fisher Scientific, A21206) and Alexa Fluor® 647 (Thermo Fisher Scientific, A31571). After three washes in 1X PBS, the cells were incubated with 1 µg/ml Hoechst (Sigma, H 33258, diluted in PBS) for 15 min at RT before a final wash in 1X PBS and imaging.
For screen B, the same fixation protocol as described for screen A was used. For permeabilization, PBS/0.05% SDS was used for 20 min at RT. Blocking was performed as described above. For the immunostaining, cells were incubated with primary antibodies against γ-tubulin (Sigma, GTU88) and phospho-histone H3 serine 10 (PHH3, Millipore, 06-570) for 2 hours at RT, washed three times with 1X PBS before incubation with secondary antibodies (Thermo Fisher Scientific, A-31571 and A-21206). After three 1X PBS washes, the cells were stained with α-tubulin (Serotec, MCA78G) and incubated with secondary antibody (Thermo Fisher Scientific, A-21434). All primary and secondary antibodies were diluted in 1% BSA/PBS.
The fixation and staining for both replicates was carried out at the Institute for Cancer Research (ICR, London) using the PerkinElmer Cell:Explorer system coupled to automated liquid handling equipment. Solutions were dispensed using a Multidrop Combi Reagent dispenser (Thermo Fisher Scientific) and aspirated/washed using a Biotek washer with 96 pins. All plates were imaged using the PerkinElmer Opera high-content confocal screening platform with spinning disc. Thirty fields of view per well were captured using a 20x air objective, numerical aperture (NA) 0.45.
High-content imaging screen: Image analysis
All image analysis was performed using custom workflows created with the Columbus software (PerkinElmer). Several output parameters were evaluated from high-content images: mitotic index (number of cells in mitosis), multinucleation index (number of multinucleated cells), number of viable cells, number of chromosome segregation errors (chromatin bridges and lagging chromatids) and number of cells with cytokinetic bridges (Supplementary Fig. 1). These are defined in more detail below:
i) Mitotic and multinucleation index. Nuclei were first segmented using Hoechst staining (which defines the total cell number). The false positives (e.g., dead cells) were discarded based on the nucleus area, α-tubulin and γ-tubulin/CEP215 staining intensity. Multinucleated non-dividing cells were retained as a separate subpopulation using a two-step detection process: binucleated cells were isolated using size, aspect ratio and roundness parameters of close nucleus pairs. Other multinucleated cells were then identified among remaining cells for which α- or γ-tubulin intensity was low in the perinuclear region. Further identification of mitotic cells/stages was accomplished using filters based on Hoechst for the nucleus shape and size, in combination with high PHH3 (screen B) or high α-tubulin and low CEP215 staining intensity (screen A). Notably, nuclei of cells in anaphase/telophase stage of mitosis were small, had elongated shape and exhibited low Hoechst integrated intensity (low amount of DNA among mitotic cells). The distance between both nuclei of cells in anaphase/telophase stage was the main criteria to discard two daughter non-mitotic cells (maximum of 0.65 µm and 2.6 µm, respectively). From all these sub-populations, we calculated mitotic and multinucleation index relative to the total number of live cells.
ii) Chromosome segregation errors. We started from the previous identified subpopulation of cells in anaphase/telophase stage. We filtered cells according to α-tubulin staining intensity between nuclei, as cells in anaphase have lower α-tubulin intensity compared to the cells in telophase. This allowed us to identify only the cells in anaphase. To calculate the number of anaphase cells with chromosome segregation errors, the inter-nuclei space was used as the measuring area to calculate the remaining Hoechst signal. This captures both chromatin bridges and lagging chromatids.
iii) Number of viable cells. The total number of viable cells was determined after removal of dead cells and cell debris with anaphase and telophase cells counted as one (despite exhibiting two nuclear segments). The same rule was applied for multinucleated cells.
iv) Number of cytokinetic bridges. Cytokinetic bridges were defined as elongated high-intensity objects split into two parts that are positive for α-tubulin staining. We used prior segmentation of the cytoplasm and the Spot Finder feature to identify bridge half parts and sorted them as doublets by calculating distance between them. We discarded the false positive candidates based on the shape criteria and γ-tubulin staining. The final number of cytokinetic bridges was divided by total number of viable cells.
To minimize the variation in the cell density between different wells among all ten 384-well plates, we divided the output numbers by the total number of cells per well (dead cells were not included). Multinucleated cells were considered to be single cells during counting. The ratios were then normalized between screen plates by calculating the average value per plate and finally the grand average of all ten plates, giving a reference mean ratio. Per-well ratios were scaled so that the per-plate average was equal to the reference. Z-scores (z) were calculated as follows for each parameter:
where x represents the ratio for the feature of interest (e.g., mitotic/multinucleation index), μ represents the reference ratio and σ represents the standard deviation of ratios across wells. All the scripts used for the image analysis are available at https://github.com/MarioniLab/LncScreen2018.
High-content imaging screen: Third pass validation screen
The third-pass validation screen was performed in two replicates with four technical replicates using the top 25 candidates from each of the categories (mitotic progression, cytokinesis) using the same antibodies as in screen B. Correlation coefficients between replicate plates in third screen were calculated by Spearman’s rank correlation (mitotic index = 0.967888, viability = 0.93249, multinucleation index = 0.995897, cytokinetic bridges = 0.898324).
Single-molecule RNA fluorescent in situ hybridization (FISH)
Cells were grown on coverslips in 12-well plates, briefly washed with 1X PBS and fixed with PBS/3.7% formaldehyde at RT for 10 min. Following fixation, cells were washed twice with PBS. The cells were then permeabilized in 70% ethanol for at least 1 hour at 4°C. Stored cells were briefly rehydrated with Wash Buffer (2X SSC, 10% formamide, Biosearch) (Formamide, Thermo Fischer Scientific, BP227-100) before FISH. The Stellaris FISH Probes (linc00899 and C1QTNF1-AS1 exonic probes Q570) were added to the hybridization buffer (2X SSC, 10% formamide, 10% dextran sulfate, Biosearch) at a final concentration of 250 nM. Hybridization was carried out in a humidified chamber at 37°C overnight. The following day, the cells were washed twice with Wash Buffer (Biosearch) at 37°C for 30 min each. The second wash contained DAPI for nuclear staining (5 ng/ml, Sigma, D9542). The cells were then briefly washed with 2X SSC and then mounted in Vectashield (Vector Laboratories, H-1000). Images were captured using a Nikon TE-2000 inverted microscope with NIS-elements software, a Plan Apochromat 100x objective and an Andor Neo 5.5 sCMOS camera. We acquired 25 optical slices at 0.3 µm intervals. Images were projected in two dimensions using ImageJ and deconvolved with Huygens Professional.
For validation of CHART-sequencing, Stellaris FISH Probes for intronic region of TPPP (Q670) were combined with linc00899 exonic probe (Q570) at a final concentration of 250 nM per probe set. To score whether TPPP (intronic signal) and linc00899 (exonic signal) colocalize, we only considered cells in which both signals were present. The sequences of RNA FISH probes are presented in the Supplementary Table 4.
Plasmids
To insert poly(A) signal into lncRNAs, pSpCas9(BB)-2A-GFP vector was used (PX458, Addgene, #48138). For CRISPR activation (CRISPRa), pHAGE EF1alpha dCAS-VP64-HA (Addgene, #50918), pU6-sgRNA EF1Alpha-puro-T2A-BFP (Addgene, #60955), second-generation packaging plasmid psPAX2 (Addgene, #12260) and the envelope plasmid pMD2.G (Addgene, #12259) were used. Linc00899 and C1QTNF1-AS1 constructs were synthesized by Labomics. The full sequence of linc00899 was synthesised as CS-LNC233J-T7 (4283bp, insert was 1610bp based on NR_027036), scrambled linc00899 was CS-LNC236J-T7, full C1QTNF1-AS1 was CS-LNC237JT7 (3638bp, insert was 970bp based on NR_040018) and scrambled C1QTNF1-AS1 was CS-LNC238JT7). Sequences of Labomics lncRNA vectors (pUCLOMT) are presented in the Supplementary Information.
Lentiviral overexpression of lncRNAs
For overexpression of linc00899 in HeLa and RPE1 cells, linc00899 sequence (Labomics) and negative control vector (scrambled linc00899 sequence) were cloned into pLenti6.3/TO/V5-DEST vector (also known as lincXpress; kindly provided by John Rinn, University of Colorado) using Gateway cloning strategy as described previously 64. To overexpress linc00899 from its endogenous locus using CRISPRa, we used pHAGE EF1alpha dCAS9-VP64-HA (Addgene, #50918). Two gRNA sequences targeting different regions of linc00899 (guide 1 and 2) were cloned into pU6-sgRNA EF1Alpha-puro-T2A-BFP (Addgene, #60955). All clones were verified by Sanger sequencing using mU6 forward primers (Supplementary Methods). Viral overexpression was performed as described previously 25. Briefly, the cells were transduced with lentivirus containing dCAS9-VP64 and two gRNA targeting linc00899, or with lentivirus contacting negative guide RNA (NC2) in the presence of polybrene (5µg/ml, Sigma). 24hr after viral transduction the medium was exchanged and RNA was collected 48 hr after for qPCR analysis.
siRNA and LNA depletion experiments
HeLa cells were transfected with Lipofectamine RNAiMax reagent (Thermo Fischer Scientific) following the manufacturer’s instructions. All experiments were done 48 hours after transfection. The pool of 4 siRNA sequences (SMARTpool, Thermo Fischer Scientifc) and LNA Gapmers (Exiqon) were used at a final concentration of 50 nM and 25 nm, respectively. For double knockdown experiments, HeLa cells (10,000 cells/well) were plated on 8-well chamber slides (Ibidi, 80826) for time-lapse microscopy imaging or in 6-well for RNA extraction (Corning, 120,000 cells/well). The cells were transfected the next day with either negative control siRNA (Ctl, from Ambion), a SMARTpool of siRNAs targeting linc00899 or TPPP or siRNAs targeting linc00899 in combination with TPPP. The same final concentration of 50 nM was achieved for both single and double knockdown by adding equal amount of control siRNA sequence (Ctl) to the single SMARTpool targeting linc00899 or TPPP separately. siRNA and LNA sequences are listed in the Supplementary Information.
Western Blot analysis
Cells were grown in a 6-well plate, trypsinized, pelleted and washed twice with 1X PBS. The pellet was lysed in lysis buffer (50 mM Tris-HCl, pH 8, 125 mM NaCl, 1% NP-40, 2 mM EDTA, 1 mM PMSF [Sigma, 93482-50ML-F], and protease inhibitor cocktail [Roche, 000000011836170001) and incubated on ice for 25 min. The samples were centrifuged for 3 min at 12,000g and 4°C. Supernatant was collected and protein concentration was determined using the Direct Detect® Spectrometer (Merck Millipore). The proteins (30 µg) were denatured, reduced, and separated with Bolt® 4-12% Bis-Tris Plus Gel (Thermo Fisher Scientific, NW04120BOX) in MOPS buffer (Thermo Fisher Scientific, B0001-02). Precision Plus Protein Standards (161-0373, Bio-Rad) was used as a protein standard. The proteins were then transferred to nitrocellulose membrane and blocked with 5% nonfat milk in TBS-T (50mM Tris, 150mM NaCl, 0.1% Tween-20) for 1 hour at RT. The membranes were incubated with primary antibodies in 5% milk in TBS-T. After overnight incubation at 4°C, the membranes were washed with TBS-T and incubated with HRP secondary antibodies (GE Healthcare Life Sciences, 1:2000), and immunobands were detected with a Pierce ECL Western Blotting Substrate (Thermo Fischer Scientific, 32106). Quantification of immunoblots normalized against appropriate loading controls was done using ImageJ. Uncropped scans of the immunoblots are shown in Figure S14. The list of primary and secondary antibodies is provided in the Supplementary Information.
Time-lapse microscopy imaging
HeLa (10,000 cells/well), HeLa Kyoto (10,000 cells/well), RPE1 (10,000 cells/well), MCF10A (15,000 cells/well) and HUVEC cells (15,000 cells/well) were cultured in 8-well chamber slides (Ibidi) with 200 μl/well of corresponding medium. Imaging was performed in their corresponding medium. Time-lapse microscopy imaging was performed for all cell lines 48 hours after transfection with RNAi or LNA gapmers. Mitotic duration was measured as the time from nuclear envelope breakdown (NEBD) until anaphase onset, based on visual inspection of the images. Cytokinesis was measured from anaphase onset to abscission completion. Live-cell imaging was performed using a Zeiss Axio Observer Z1 microscope equipped with a PL APO 0.95NA 40X dry objective (Carl Zeiss Microscopy) fitted with a LED light source (Lumencor) and an Orca Flash 4.0 camera (Hamamatsu). Four positions were placed per well and a z-stack was acquired at each position every 10 minutes (HeLa, HeLa Kyoto) or 5 minutes (MCF10A, HUVEC, RPE1) for a total duration of 12 hours. To detect chromosome segregation errors (chromatin bridges and/or lagging chromatids) Hela Kyoto cells were imaged every 4 min with only 2 positions/well. Voxel size was 0.325 µm x 0.325 µm x 2.5 µm. Zen software (Zeiss) was used for data collection and analysis. Throughout the experiment, cells were maintained in a microscope stage incubator at 37°C in a humidified atmosphere of 5% CO2.
Cell cycle synchronization
HeLa cells were grown to 50% confluency and then synchronized with thymidine for 16 hours (2 mM, Sigma, T1895). The cells were washed three times with 1X PBS and then released into thymidine-free medium for 5 hours. The cells were released for indicated timepoints for RNA collection. Treatment of HeLa cells with monastrol (100 µM for 16hr; Tocris, 1305) coupled with the mitotic shake-off was used to isolate mitotic cells.
Copy number evaluation
To calculate the linc00899 and C1QTNF1-AS1 copy number, a standard curve of Ct values was generated by performing qPCR on a dilution series of known concentration of linc00899 or C1QTNF1-AS1 DNA templates (Labomics). cDNA was prepared from RNA (1 µg) extracted from known number of HeLa cells (500,000 cells). The observed Ct values were fitted on the standard curve and the number of lncRNA molecules per cell was calculated. The final value was multiplied by 2 to account for the fact that cDNA is single stranded and DNA templates used to make the standard curve were double stranded.
RNA isolation, cDNA synthesis and qPCR
RNA (1 µg) was extracted with the RNeasy Kit (QIAGEN, 74106) and treated with DNase I following the manufacturer’s instructions (QIAGEN, 79254). The QuantiTect Reverse Transcription Kit (QIAGEN, 205313) was used for cDNA synthesis including an additional step to eliminate genomic DNA contamination. Quantitative real-time PCR (qPCR) was performed on a QuantStudio 6 Flex (Thermo Fischer Scientific) with Fast SYBR Green Master Mix (Life Technologies). Thermocycling parameters were defined as 95°C for 20 sec followed by 40 cycles of 95°C for 1 sec and 60°C for 20 sec. Two reference genes (GAPDH and RPS18) were used to normalise expression levels using the 2−ΔΔCT method. Sequences of qPCR primers are provided in the Supplementary Information. For linc00899 and C1QTNF1-AS1 primers against exons 2-4 and 1-2, respectively, were used throughout the paper if not indicated otherwise.
RNA library preparation, sequencing and analysis
RNA-seq libraries were prepared from HeLa cells using TruSeq Stranded Total RNA Kit with Ribo-Zero Gold (IIllumina, RS-122-2303). We generated four biological replicates for RNAi and LNA-mediated depletion of linc00899 and C1QTNF1-AS1. Indexed libraries were PCR-amplified and sequenced using 125 bp paired-end reads on an Illumina Hiseq 2500 instrument (CRUK CI Genomics Core Facility). Each library was sequenced to a depth of 20-30 million read pairs. Paired-end reads were aligned to the human genome hg38 with subread and the number of read pairs mapped to the exonic regions of each gene was counted for each library with using the featureCounts 66 function in Rsubread v1.30.0 with Ensembl GRCh38 version 91. Only alignments with mapping quality scores above 10 and with the first read pair on the reverse strand were considered during counting. Approximately 80% of read pairs contained one read that was successfully mapped to the reference, and 74% of all read pairs in each library were assigned into exonic regions. Any outlier samples with very low depth (resulting from failed library preparation or sequencing) were removed prior to further analysis.
The DE analysis was performed using the limma package v3.36.0 67. First, lowly expressed genes with average counts below 3 were filtered out. Normalization was performed using the TMM method to remove composition biases. Log-transformed expression values with combined precision/array weights were computed with the voomWithQualityWeights function 68. The experimental design was parametrized using an additive model with a group factor, where each group was comprised of all samples from one batch/treatment combination; and an experiment factor, representing samples generated on the same day. Robust empirical Bayes shrinkage 69 was performed using the eBayes function to stabilize the variances. Testing for DE genes was performed between pairs of groups using the treat function 70 with a log-fold change threshold of 0.5. Here, the null hypothesis was that the absolute log2-fold change between groups was less than or equal to 0.5. All pairwise contrasts involved groups from the same batch to avoid spurious differences due to batch effects. For each contrast, genes with significant differences in expression between groups were detected at an FDR of 5%. To identify DE genes that were consistent across LOF methods, an intersection-union test was performed 71 on one-sided p-values in each direction, followed by an FDR correction. Coverage tracks for each library were generated using Gviz as previously described.
Capture Hybridization Analysis of RNA targets (CHART) sequencing and analysis
CHART enrichment and RNase H (NEB, M0297S) mapping was performed as previously described 47, 51. Briefly, five 150-mm dishes of HeLa cells were used to prepare the CHART extract for each pulldown. In the first sonication step, the samples were sonicated using Covaris S220 (Covaris, 500217) in microTUBEs (Covaris, 520045) in a final volume of 130 µl (Program conditions: 20% duty cycle, 200 bursts/cycle, Intensity 175, 8 min). The extracts were hybridised with a linc00899 oligonucleotide cocktail (mix of 4 oligos used at final concentration of 25 µM) overnight at RT. MyOne streptavidin beads C1 (Thermo Fischer Scientifc, 65001) were used to capture complexes overnight at RT by rotation. Bound material was washed five times and RNase H (NEB) was added for 30 min at RT to elute RNA-chromatin complexes. To increase recovery yield, the remaining beads were saved and the bound material was eluted with Proteinase K (Thermo Fischer Scientifc, 25530-049) in 100 µl of XLR buffer for 60 min at 55°C. The supernatant was then collected and heated for additional 30 min at 65°C. The RNase H eluate samples were also treated with Proteinase K (Thermo Fischer Scientifc, 25530-049) and cross-links were reversed (55°C for 1hr, followed by 65°C for 30 min). One fifth of the total sample was used to purify RNA using miRNeasy kit (QIAGEN, 217004) and to calculate RNA enrichment, while the rest of the sample was used to purify DNA using Phenol-ChCl3:isoamyl (Thermo Fischer Scientific, 15593-031) extraction and ethanol precipitation. The final CHART DNA was eluted in 12.5 µl of 1X TE buffer (low EDTA, pH 7.4). 10 µl from this reaction was used for sonication using Covaris LE220 (programme 250bp/10sec) to obtain average fragment size of 200-300 bp. For this, microTUBE-15 beads strips (520159, Covaris) were used in a total volume of 15 µl. After sonication, the DNA was measured by Nanodrop (input, diluted 1:20) and Qubit High Sensitivity DNA Assay (eluates were undiluted). CHART material (5 ng) was used for library preparation using the ThruPLEX DNA-Seq library preparation protocol (Rubicon Genomics, UK). For inputs, 6 PCR cycles were performed, while for eluates 8 PCR cycles were performed due to the lower quantity of input DNA. Library fragment size was determined using a 2100 Bioanalyzer (Agilent). Libraries were quantified by qPCR (Kapa Biosystems). Pooled libraries were sequenced on a HiSeq4000 (Illumina) according to manufacturer’s instructions to generate paired-end 150bp reads (CRUK CI Genomics Core Facility). CHART-seq was performed from five biological replicates using a mix of two sense control oligos for linc00899 as a negative control for the linc00899 pulldown. Reads were aligned to the hg38 build of the human genome using subread in paired-end genomic mode. A differential binding (DB) analysis was performed using csaw to identify lncRNA binding sites in the pulldown compared to the sense control. Filtered windows were obtained as described for the CUT&RUN data analysis. Normalization factors were computed by binning read pairs into 5 kbp intervals and applying the TMM method without weighting, to account for composition biases from greater enrichment in the antisense pulldown samples. The filtered windows and normalization factors were then used in a DB analysis with the quasi-likelihood framework in edgeR. This was performed using an additive design for the generalized linear model fit, containing terms for the batch and the pulldown (5 batches in total, sense and antisense pulldowns in each batch). A p-value was computed for each window by testing whether the pulldown term was equal to zero, i.e., no difference in coverage between the antisense pulldown and sense control. The above analysis was repeated using window sizes from 150 to 1000 bp to obtain DB results at varying resolutions. These results were consolidated into a single list of DB regions using consolidateWindows as previously described, yielding a combined p-value for each region. The combined p-values across all regions were then used to compute the empirical false discovery rate (eFDR), defined as the ratio of the number of clusters that exhibited increased coverage in the sense control (i.e., false positives) to the number of clusters with increased coverage in the antisense pulldown (i.e., potentially true discoveries). Putative lncRNA binding sites were defined at an eFDR of 30%. Coverage tracks for each library were generated using Gviz as previously described.
CUT&RUN (Cleavage under targets and release using nuclease)
Chromatin profiling was performed according to Skene et al. (2018) 46 with minor modifications. HeLa cells were plated in 30-mm dishes (220,000 cells/dish) and transfected the next day with either negative control siRNA, linc00899 siRNA pool, negative control LNA A or an LNA gapmer targeting linc00899 (LNA 1). Two biological replicates were performed per condition. Cells were washed once with PBS, spun down at 600g for 3 minutes in swinging-bucket rotor and washed twice with 1.5 mL Wash buffer (20 mM HEPES-KOH (pH 7.5), 150 mM NaCl, 0.5 mM Spermidine and 1X cOmplete™ EDTA-free protease inhibitor cocktail (Roche)). During the cell washes, concanavalin A-coated magnetic beads (Bangs Laboratories, BP531) (10 µl per condition) were washed twice in 1.5 mL Binding Buffer (20 mM HEPES-KOH (pH 7.5), 10 mM KCl, 1 mM CaCl, 1 mM MnCl2) and resuspended in 10 µl Binding Buffer per condition. Cells were then mixed with beads and rotated for 10 minutes at RT and samples were split into aliquots according to number of antibodies profiled per cell type. We used 80-100,000 cells per chromatin mark. Cells were then collected on magnetic beads and resuspended in 50 µl Antibody Buffer (Wash buffer with 0.05% Digitonin and 2mM EDTA) containing one of the following antibodies in 1:100 dilution: H3K4me3 (Millipore 05-1339 CMA304, Lot 2780484), H3K27me3 (Cell Signaling #9733S C36B11, Lot 8), H3K36me3 (Active Motif Cat#61101, Lot 32412003), H3K27ac (Abcam ab4729, Lot GR3211741-1) and Goat Anti-Rabbit IgG H&L (Abcam ab97047, Lot GR254157-8). Cells were incubated with antibodies overnight at 4°C rotating, and then washed once with 1 mL Digitonin buffer (Wash buffer with 0.05% Digitonin). For the mouse anti-H3K4me3 antibody, samples were incubated with 50 µl of a 1:100 dilution in Digitonin buffer of secondary rabbit anti-mouse antibody (Invitrogen, A27033, Lot RG240909) for 10 minutes at RT and then washed once with 1 mL Digitonin buffer. Samples were then incubated in 50 µl Digitonin buffer containing 700 ng/ml Protein A-MNase fusion protein (kindly provided by Steven Henikoff) for 10 minutes at RT followed by two washes with 1 mL Digitonin buffer. Cells were then resuspended in 100 µl Digitonin buffer and cooled down to 4°C before addition of CaCl2 to a final concentration of 2 mM. Targeted digestion was performed for 30 minutes on ice until 100 µl of 2X STOP buffer (340 mM NaCl, 20 mM EDTA, 4 mM EGTA, 0.02% Digitonin, 250 mg RNase A, 250 µg Glycogen, 15 pg/ml yeast spike-in DNA (kindly provided by Steven Henikoff)) was added. Cells were then incubated at 37°C for 10 minutes to release cleaved chromatin fragments, spun down for 5 minutes at 16,000g at 4°C and collected on magnet. Supernatant containing the cleaved chromatin fragments were then transferred and cleaned up using the Zymo Clean & Concentrator Kit. Library preparation was performed as described 72 using the ThruPLEX® DNA-Seq Library Preparation Kit (Rubicon Genomics) with a modified Library Amplification programme: Extension and cleavage for 3 minutes at 72°C followed by 2 minutes at 85°C, denaturation for 2 minutes at 98°C followed by four cycles of 20 seconds at 98°C, 20 seconds at 67°C and 40 seconds at 72°C for the addition of indexes. Amplification was then performed for 12 cycles of 20 seconds at 98°C and 15 seconds at 72°C (14 cycles were used for the Goat Anti-Rabbit antibody due to the lower yield of input DNA). Double-size selection of libraries was performed using Agencourt AMPure XP beads (Beckman Coulter, A63880) according to manufacturer’s instructions. Average library size was determined using an Agilent Tapestation DNA1000 High Sensitivity Screentape and quantification was performed using the KAPA Library Quantification Kit. CUT&RUN libraries were sequenced on a HiSeq2500 using a paired-end 125bp run at the CRUK CI Genomics Core Facility. Reads were aligned to a reference genome containing the hg38 build of the human genome and the R64 build of the yeast genome, using subread v1.6.1 73 in paired-end genomic mode. A DB analysis was performed using the csaw package v1.14.0 74 to identify changes in histone mark enrichment in linc00899 depleted cells compared to the negative controls for each LOF method. Coverage was quantified by sliding a window across the genome and counting the number of sequenced fragments overlapping the window in each sample. Windows were filtered to retain only those with average abundances that were 5-fold greater than the expected coverage due to background non-specific enrichment. Normalization factors were computed by applying the trimmed mean of M-values (TMM) method 75 without weighting to the filtered windows. The filtered windows and normalization factors were then used in a DB analysis with the quasi-likelihood framework in the edgeR package v3.22.0 76. This was performed using an additive design for the generalized linear model fit, containing terms for the batch and the depletion status (2 batches in total, depletion and negative control from LNA and RNAi in each batch). A p-value was computed for each window by first testing whether for a depletion effect in each LOF method, and then taking the larger of the two p-values 71 across both LOF methods. The above analysis was repeated using window sizes from 150 to 1000 bp to obtain DB results at varying resolutions. To consolidate these results into a single list of DB regions, overlapping windows of all sizes were clustered together based on their genomic locations, using a single-linkage approach in the consolidateWindows function. For each cluster of windows, a combined p-value was computed using Simes’ method 77. This represents the evidence against the global null hypothesis for each cluster, i.e., that none of the constituent windows are DB. The Benjamini-Hochberg method was used to define putative DB regions at an FDR of 5%. Coverage tracks were generated by computing the number of sequencing fragments per million overlapping each based, using library sizes adjusted according to the TMM normalization factors for each library. Tracks were visualized using the Gviz package 78.
Generation of C1QTNF1-AS1 CRISPR poly(A) site clones
Insertion of the transcriptional termination signal (poly(A) signal (pAS) 34 into the first exon of C1QTNF1-AS1 was performed using a published CRISPR/CAS9 protocol 79. Briefly, the CRISPR pAS guide oligonucleotides were phosphorylated (T4 poly nucleotide kinase; NEB), annealed and ligated (Quick Ligase kit, NEB) into BbsI (NEB) digested pX458 vector (Addgene, #48138). The oligonucleotides (Sigma-Aldrich, diluted in low TE EDTA buffer at 100 µM) used for the gRNA target sequence are listed in the Supplementary Methods. All inserts were verified with Sanger sequencing. HeLa cells were plated in 6-well plates (200,000 cells/well) and on the following day, transfected with either 2.5 µg of empty PX458 vector as a control or 2.5 µg of PX458 vector where guide oligonucleotides targeting C1QTNF1-AS1 were cloned together with a symmetric single stranded oligonucleotide donor 80 (ssODN; IDT Inc, 4 µl from 10 µM stock) containing the pAS flanked by homology arms (75bp) to the target site. Lipofectamine 3000 reagent (ThermoFisher Scientific, L3000015) was used as transfection reagent according to the manufacturer’s instructions. The sequence of the symmetric ssODNs is as follows (5’ to 3’): 75bp homology left arm-aataaaagatctttattttcattagatctgtgtgttggttttttgtgtg-75 bp homology right arm CTCACTGCGGGGCTCGGGAAGGAGGAAAGGAGTGAGCATGTCCTGCTCCTGC ATGTCCCTGCTTAAGCTCAGGACaataaaagatctttattttcattagatctgtgtgttggttttttgtgtgCCCTTCCAGGCCAAGGACCCCAGCATAGACCCCAGGACAGGGCCCCAAGGA TCCCTGGCTCATGAGAGCGGCTTGC
The exon 1 of C1QTNF1-AS1 is shown in upper case. The pAS site is in bold and contains BglII restriction sites (underlined) that were used for identification of the positive clones. The PAM site (TGG) was abrogated to avoid further cleavage by Cas9.
After 48 hours, the GFP-positive cells were sorted using BD FACSAria Ilu (CRUK Flow Cytometry Core Facility) and plated on 96-well plates with DMEM supplemented with 20% FBS. Single cell clones were expanded and genomic DNA was extracted using Direct PCR Lysis reagent (Viagen, 201-Y; 25 µl/well) with Proteinase K (Thermo Fisher Scientific, 25530-049; 0.4 mg/ml). The plates were incubated for 1 hour at 55°C, followed by 45 min incubation at 85°C. 2 µl of genomic DNA was used for screening by PCR amplification (Phusion® High-Fidelity PCR Master Mix with HF Buffer, NEB, M0531S) of the targeted genomic region. PCR conditions for C1QTNF1-AS1_guide 70 were: 98°C 30 sec, 98°C 30 sec, 70°C 30 sec, 72°C 30 sec (repeat 30X), 72°C 90 sec, 4°C forever. After PCR, 5 µl was used for BglII digestion (NEB, 2 hours at 37°C) and the products were loaded on a 2% agarose gel to confirm the pAS insertion. The rest of PCR reaction (15 µl) was also loaded on the gel as a control. The same genomic PCR product was also ligated into pJET1.2/Blunt and transformed into bacteria (CloneJET; ThermoFisher Scientific, K1231). To ensure representation by both alleles, plasmids were isolated and sequenced from a minimum of 5-10 bacterial colonies (Figure S8). This method revealed that C1QTNF1-AS1 pAS clones were homozygously targeted clones. Four clones were analysed (clone 95, 136, 153 and 169) by qPCR and live-cell imaging for the mitotic phenotype, along with the wild type controls (clone 2 and clone 5) that were transfected with an empty vector PX458. We also attempted to perform pAS insertion into exon 1 of linc00899 using two different sgRNAs. However, we failed to obtain homozygous targeted clones, most likely due to the presence of 4 copies of linc00899 in HeLa cells (https://cansar.icr.ac.uk/cansar/cell-lines/HELA/copy_number_variation/).
Mapping of 5′ and 3′ ends of linc00899 and C1QTNF1-AS1 by RACE
HeLa total RNA (1 µg) was extracted and 5’ and 3’ rapid amplification of cDNA ends (RACE) was performed using the Smarter RACE kit (Clontech, 634858). cDNA was synthesized as described in the kit using 5′ and 3′ RACE CDS primers and SMARTer IIA oligo for template switching for 5′ RACE. cDNA ends were then amplified by touchdown PCR. The first PCR (touchdown) used the following conditions: 5 cycles of 94°C/30 sec, 72°C/3 min; 5 cycles of 94°C/30 sec, 70°C/30 sec, 72°C 3 min; 25 cycles of 94°C/30 sec, 68°C/30 sec, 72°C/3 min. For nested PCR, 2 µl of PCR reaction was diluted in 98 µl Tricine-EDTA buffer and used as a template for the second PCR reaction with following conditions: 25 cycles of 94°C/30 sec, 68°C/30 sec, 72°C/3 min. For linc00899, the first PCR used Universal Primer A mixed with linc00899_5R outer 1=GATTACGCCAAGCTTacatcccggttcccacgaaaagcaacc for the 5′ ends or linc00899_3Routerinner3=GATTACGCCAAGCTTccagggaggggaaaggagtcggcaat for the 3′ ends. The second (nested) PCR used Nested Universal Primer A and linc00899_5Routerinner1=GATTACGCCAAGCTTggagcaggcgaagagggagtgagggg for the 5′ ends or linc00899_3R outerinner1=GATTACGCCAAGCTTggtcacagcctagccaagcccagcca for the 3′ ends. For C1QTNF1-AS1, the first PCR used Universal Primer A mixed with C1QTNF1-AS1_5_RACE_outer_1=GATTACGCCAAGCTTccaggcccctaatgatgtcctttga for the 5′ ends or C1QTNF1-AS1_3RACE_outer_1=GATTACGCCAAGCTTggaggaaaggagtgagcatgtcctg for the 3′ ends. The second nested PCR used Nested Universal Primer A and C1QTNF1-AS1_5RACE_inner_2 = GATTACGCCAAGCTTGTCCTGATCTCCACCTGTCCCAAGC for the 5′ ends or C1QTNF1-AS1_3RACE_inner_2= GATTACGCCAAGCTTGGAAACTTGGCAGACAGATCCAGCC for the 3′ ends.
For linc00899, nine different 5’ sites and four different 3’ sites were identified. For C1QTNF1-AS1, six different 5’ sites and four different 3’ sites were identified. The fragments were purified after agarose gel electrophoresis with the QiaQuick Gel Extraction Kit (QIAGEN), cloned in pRACE vector as per kit instructions and transformed into Stellar component cells (Clontech, 636763). The inserts were verified by Sanger sequencing. Uncropped images of RACE results are presented in the Supplementary Fig. 14.
RACE results for linc00899
We have identified nine different 5‘ start sites (pink) and four 3‘ sites (yellow). The most common starting site for linc00899 was CACGTCC, 87 bp upstream from the linc00899 TSS. The most common termination site for linc00899 was acagcaag. Capital italic letters indicate the region upstream of linc00899 TSS (cggccgcccc) based on UCSC.
TGGCCTCGGAGGGTCAACACCTGAGGGCCCGAGGGGCATCCATGCCCCCTCC TCCTTCCCTCCAGCCAAAGGTGGGGGGCAAAGCGCAGGGAAGAAAACACCAG GAGAAAACCAGAGAGCTCTCGGTTCTCTTTTAAGAGCCCTGATGGCCGCAGCG CAGAGCCCGAGGGGAGGGAAAATGTCGGGAAAGATTCTCTTCCGAACTTTGCG AGTCTTTGTTTGGGAGGCTGGGGGCTGACTTCGCCGGGGGCCGGGCCGCGG GCTCGGCCGTGCGCTCCGGTGCAGCGGCCGAGGAGCCCCGGCGCCCGCCAC CCCGGGACACGCCCTCGCAGTCGAGCCCGGACCCCGACCCGGACCCCAGCG CCGCCGGGCGAGGGCGGGAGGGGGAGCGCTTACCAGATCGTCCCGAGCGCG CCGCGGTCCAGGCGGGCACAGCGCAGGGTCAAGTTCACGTCCGGCCCGCGG GCTGCCCGAGGTCCCCGGGCGCGGCTGGGGCAGCGGGAGGCGCGGGAGGC CGAGGTCCGGGTGGCCGCCGcggccgcccccgaagcgctgctgtcaccccggccgcgccccccaa ctttctgcacagtcgcggagctggaagtttccgggcttcgcggacacgctgggctgggtttcagtcgcggctccgaggt tggcaacaaagagggaaagaaggaggaaaagcaggccggggaggggaggaagagaaccgcgcggaggcc gcggcgccgagagccccagaacttccaattctacccagaagcttttttcgtcgtgtttttctcttagacatgatcctctctga ggttggtcctgggcttccatacgtgattcatggaagaggtctcagccccaagagcccctgagggtactgtccactcccc ctggaaacttccagaacctgacgtggggctgaagacatagaggctctgagagttacataattgattctgactttggctgt tggtcaacagtgtcataaggtaaaataaggctgttgtagaatctgctcagccagggaggggaaaggagtcggcaat caggtctcctcctgggcacctttgtgaggccagctggcgagagtggggggtgacactgaggtcccagcagctccaa atgcaggcagagccctgtcctcagagaaggtcacagcctagccaagcccagccaggtggatgggcccacggaac gcacaggaacctggaacggaggttgaaagcaggaagcacagtctgtgactccccagcccactctgcattcgacca cttggggcccagaagcttcaggaaaggtgcacaaggtcactgggtcccagtactcccaacaggaaggtctggtcca gggacagggctcttcccgactccccttagccacacgcaccagaagttctgcagtgcccagtgggcatagcagtcccc aagaatgacccagcactgaagctgagccaaagaacttggggagcgagccacaccccctcactccctcttcgcctg ctccagacttgccaggcggttgcttttcgtgggaaccgggatgtcctcaccaccctgtccagggcccagccccatgtcc ctggcctgctacagctggaaaaaaaaaagagagatgtttgtttttatttgtttataaaaagaaaagtgttatatatataac atattatacctcatgaatacatacaattatttgtcaattaacaataaagaaaaatacagcaagcaaaaaagactctcttc cacaaaaatagtgttcattacagaaaagtacaaaaaaaaaaaaaaaaaactaagaggatatttagaattaagaaa aaactaagagggtatttagaattaaaaaataaaaagaaaacaattacccatgaggtaattactgaatgcatttggttg aaagtccttctgctatattttccaaactgtatgtgtatatatgtgcgcatgcatatatgtgtttgtgtgtgtgtacacatatctat atgaatggaccatatcgtaagttataaatgcatacatatattcatgtatatataatcattagatcatactataggttattttac agtctttttgttgaatacgttgtgagcattttatgtcattattttctacaagatttgaaaaataaagtataaataccagttaa.
Based on our sequencing results, linc00899 transcript varied from 1144bp to 1532bp in HeLa cells. We seqeunced at least 10 clones per 5‘ and 3‘ RACE.
RACE results for C1QTNF1-AS1
We have identified six different 5‘ start sites (pink) and four 3‘ sites (yellow). The most common 5‘ site was AGAGAACTA, 54 bp from TSS. The most common 3‘site was tctggaa. Capital italic letters indicate the region upstream of C1QTNF1-AS1 TSS (gaaggagg) based on UCSC.
ATCACCCCAGCACAAGTGTCACACAGCCGTGACCTTGACAAGGACCCAGAGAT AAAGATCCTTCCCACATGGCTCCGAAGCCCCTCCCTTCCTGCTCACACCGCAT GCCTCTCCCAGAGAACTAGAGGCTGCAGCGGCAGCAGTTGGAGCATCTCACTG CGGGGCTCGGgaaggaggaaaggagtgagcatgtcctgctcctgcatgtccctgcttaagctcaggactggc ccttccaggccaaggaccccagcatagaccccaggacagggccccaaggatccctggctcatgagagcggcttg ctgggctgccccaagagagcctgaaggaaacacattgttgagctgagctgacgtcgctgtttcttccagactgctctct aaagtgggcagggtagcgaccggccggctccgatggtgacgtcccactgccaaggggtgggagtggggagagtc tccacagagcttcggagaagctgctaagATGGAAAAGTGGAAACTTGGCAGACAGATCCAG CCTCCCTGGCCACTGGCCCATGCTCGTGGCTCCTGGATGGCGCTGCCACGTTC TGAGCAGCTTGGGACAGGTGGAGATCAGGACTGGCAGCTGCAAGGACACACC AGAGCCACAGAAACTAAAGAGAATTTCCAAAAGGAGTCTATGGTGAAGTCTCTG AGGATGCAAAGAAGACAAGGAGAATGAaaatccaatgaaagcctgattgtatttgttgaccttaagg aaagtgattttatggtacagcctctctggaagggagggtgtgttcgctcacagaatgcaaataccctttgaccccctaat ctttcttctaggagtttctcctacagataaacttagaagggtgctcaaataagtaagttcaaggatatcctctgaagcattg ccgtagtataaaaaaagcacagataccctcaaaggacatcattaggggcctggtaaaataaattccacacagtgga acaccgtgtagctctttagagaataaacagctctctatatgtgatctggaaCAATCTCC.
Based on our sequencing results, C1QTNF1-AS1 transcript varied from 864bp to 952bp in HeLa cells. We seqeunced at least 10 clones per 5‘ and 3‘ RACE.
Immunofluorescence
HeLa cells were seeded on coverslips in 6-well plates (120,000/well), transfected the next day with siRNA or LNA gapmers and fixed 48 hours post-transfection in ice-cold methanol (Acros Organics, 167830025) for 10 min at −20°C. The cells were then washed once in 1X PBS and permeabilized with PBS/0.5%Triton-X100 (Acros Organics, 327371000)/0.5%Tween-20 (Promega, H5151) for 5 min at RT followed by blocking in 5% BSA/PBS/0.001% Sodium Azide for 30 min at RT. Cells were incubated with primary antibodies overnight at 4°C. Antibodies against acetylated α-tubulin (Sigma) and α-tubulin (Serotec) or against EB1 (CRUK,81) and α-tubulin (Serotec) were used. Cells were washed 3 × 10 min with PBS/0.1%Tween-20 and then incubated with secondary antibodies (Supplementary Methods) diluted in blocking-buffer for 1 hour at RT. After washing again 3 × 10 min with PBS/0.1%Tween-20, cells were stained with 1 µg/ml Hoechst (B2261, Sigma, diluted in PBS) for 10 min at RT. Coverslips were mounted onto glass slides using Prolong Diamond Antifade Mountant (ThermoFischer Scientifc, P36961). To determine the mitotic index, cells were stained with antibodies against PHH3 (Millipore) and α-tubulin (Sigma). The mitotic index was calculated by counting the cells in mitosis (positive for PHH3 and α-tubulin) and total number of cells (Hoechst positive). For each sample, at least 100 cells were randomly counted by fluorescence microscopy and mitotic cells were scored from prophase to anaphase/telophase.
Image processing and quantification
Imaging of fixed cells was performed on a Leica Sp8 confocal microscope using a 100 X 1.4 numerical aperture Leica oil objective where z-stacks with 0.5 μm step size were recorded. Images were taken at identical exposure times within each experiment and were imported into ImageJ and Photoshop (CS6, Adobe). Images shown here represent 3D maximum intensity projections. To analyse the ratio of α-tubulin to acetylated α-tubulin, raw integrated intensities were measured (ImageJ) over the total z-stack plane using a circle selection around the mitotic spindle (based on α-tubulin). The measured intensity values were divided by the area of the selection and the background signal was subtracted. Afterwards, the mean of the signal intensity over all z-stacks was calculated and the ratio of the signal of acetylated-tubulin to α-tubulin was determined. The total EB1 levels were quantified as descried above.
Subcellular fractionation
RNA was fractionated as described previously 64. Briefly, cells from a 150-mm dish were used to isolate RNA from cytoplasmic, nucleoplasmic and chromatin fractions by TRIzol extraction. Turbo DNA-free kit (Thermo Fischer Scientifc, AM1907) was used to remove any traces of DNA. Expression of target genes in each fraction was analysed by qPCR. Data were normalised to the geometric mean of GAPDH and ACTB levels in each cellular compartment. U1 small nuclear RNA was used as a positive control for chromatin fraction.
Coding potential assessment for lncRNAs
The Coding-Potential Calculator82 (CPC, http://cpc.cbi.pku.edu.cn) and Coding Potential Assessment Tool83 (CPAT, http://lilab.research.bcm.edu/cpat/index.php) were used to determine noncoding potential. LncRNAs with CPC scores above 1 and CPAT scores above 0.364 were predicted to have protein-coding capacity. The PhyloCSF 84 score was taken from UCSC (https://github.com/mlin/PhyloCSF/wiki).
Statistical analysis
The statistical significance of data was determined by two-tailed Student’s t-test in all experiments using GraphPad Prism unless indicated otherwise. P-values greater than 0.05 were considered statistically not significant.
DATA AVAILABILITY
Sequencing data are available in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress) with the accession codes E-MTAB-7432 (RNA-seq), E-MTAB-7418 (CHART-seq) and E-MTAB-7419 (CUT&RUN). The imaging data have been submitted to the Image Data Resource (https://idr.openmicroscopy.org) under IDR accession number idr0056.
CODE AVAILABILITY
All code used in this analysis is available at https://github.com/MarioniLab/LncScreen2018.
FUNDING
This work was made possible by funding from Cancer Research UK (C14303/A17197 to F. G, A17197 to J.C.M and A20412 to D.T.O). We also acknowledge the support of the University of Cambridge, the Wellcome Trust (WT202878; D.T.O), European Research Council (615584; D.T.O) and Hutchison Whampoa Limited.
AUTHOR CONTRIBUTIONS
Conception and design of study: LS, FG and DTO. Data acquisition: LS, ATLL, PM, CE, AMR, JM, ARB, VB. Data analyses and interpretation: LS, ATLL, PM, ARB, JCM, CB, DTO, FG. Writing the paper: LS, ATLL, DTO and FG with input from all of the authors wrote the manuscript.
DECLARATION OF INTERESTS
None.
ACKNOWLEDGMENTS
We thank all the members of Gergely and Odom groups for helpful discussions and Julia Tischer for critical reading of the manuscript. We also thank the Genomics, Microscopy, FACS and Research Instrumentation and Cell Service Core Facilities at the CRUK Cambridge Institute. We thank Keith Vance (University of Bath, UK) and Matthew Simon (Yale, USA) for the help with the CHART-seq protocol, John Rinn (University of Colorado) for the lincXpress vector, and Steve Henikoff (Fred Hutchinson Cancer Research Center, USA) for providing the reagents for CUT&RUN.
REFERENCES
- 1.↵
- 2.↵
- 3.↵
- 4.
- 5.↵
- 6.↵
- 7.↵
- 8.↵
- 9.↵
- 10.↵
- 11.↵
- 12.↵
- 13.↵
- 14.↵
- 15.↵
- 16.↵
- 17.↵
- 18.↵
- 19.
- 20.↵
- 21.↵
- 22.↵
- 23.↵
- 24.↵
- 25.↵
- 26.↵
- 27.↵
- 28.↵
- 29.↵
- 30.↵
- 31.↵
- 32.
- 33.
- 34.↵
- 35.↵
- 36.↵
- 37.
- 38.↵
- 39.↵
- 40.↵
- 41.↵
- 42.↵
- 43.
- 44.↵
- 45.↵
- 46.↵
- 47.↵
- 48.
- 49.
- 50.↵
- 51.↵
- 52.↵
- 53.↵
- 54.↵
- 55.↵
- 56.↵
- 57.↵
- 58.↵
- 59.↵
- 60.↵
- 61.↵
- 62.↵
- 63.↵
- 64.↵
- 65.↵
- 66.↵
- 67.↵
- 68.↵
- 69.↵
- 70.↵
- 71.↵
- 72.↵
- 73.↵
- 74.↵
- 75.↵
- 76.↵
- 77.↵
- 78.↵
- 79.↵
- 80.↵
- 81.↵
- 82.↵
- 83.↵
- 84.↵
