Endothelial, epithelial, and fibroblast cells exhibit specific splicing programs independently of their tissue of origin

Abstract

Alternative splicing is the main mechanism of increasing the proteome diversity coded by a limited number of genes. It is well established that different tissues or organs express different splicing variants. However, organs are composed of common major cell types, including fibroblasts, epithelial, and endothelial cells. By analyzing large-scale data sets generated by The ENCODE Project Consortium and after extensive RT-PCR validation, we demonstrate that each of the three major cell types expresses a specific splicing program independently of its organ origin. Furthermore, by analyzing splicing factor expression across samples, publicly available splicing factor binding site data sets (CLIP-seq), and exon array data sets after splicing factor depletion, we identified several splicing factors, including ESRP1 and 2, MBNL1, NOVA1, PTBP1, and RBFOX2, that contribute to establishing these cell type-specific splicing programs. All of the analyzed data sets are freely available in a user-friendly web interface named FasterDB, which describes all known splicing variants of human and mouse genes and their splicing patterns across several dozens of normal and cancer cells as well as across tissues. Information regarding splicing factors that potentially contribute to individual exon regulation is also provided via a dedicated CLIP-seq and exon array data visualization interface. To the best of our knowledge, FasterDB is the first database integrating such a variety of large-scale data sets to enable functional genomics analyses at exon-level resolution.

Figure 1.

Epithelial- and fibroblast-specific splicing variants. (A) Transcriptome analysis of fibroblasts (fibro) as compared to epithelial (epi) cells at both gene and exon levels. The number of genes differentially expressed at the gene and/or exon level when comparing both cell types is shown in the left panel. The middle panel indicates the classification of events corresponding to exon level variations. Differentially expressed exons were classified according to their annotation using publicly available transcripts: alternative first exon (AFE), alternative last exon (ALE), and alternative skipped exon (ASE). Not annotated (NA) corresponds to exons that do not correspond to any of the above-mentioned categories. The cellular functions of genes differentially spliced when comparing fibroblasts to epithelial cells are indicated in the right panel. (B) Heatmap presentation of the splicing index (SI) values for exons differentially spliced when comparing fibroblast to epithelial cells. Each line corresponds to a regulated exon, while each column corresponds to a specific cell. Green boxes (–1.5 < SI < 0) correspond to a low inclusion level in the cell as compared to all the others; red boxes (0 < SI < 1.5), high inclusion level; black boxes (SI = 0), no difference for exon inclusion between cells; and gray boxes, missing values. Exons were computationally split into several groups depending on their inclusion rate that correlates with the two major cell types. (C) RT-PCR validations using RNAs from fibroblasts and epithelial cells, as indicated.

Figure 2.

Epithelial- and endothelial-specific splicing variants. (A) Same as in Figure 1A but comparing endothelial (Endo) to epithelial (Epi) cells. (B) Same as in Figure 1B but comparing endothelial (Endo) to epithelial (Epi) cells. (C) RT-PCR validations using RNAs from endothelial and epithelial cells, as indicated.

Figure 3.

Cell type–specific splicing programs. (A) Heatmap presentation of the SI values for exons differentially spliced across fibroblast, endothelial, and epithelial cells. Exons were computationally split into several groups depending on their inclusion rate in the three major cell types. Every brace corresponds to a group (names are indicated on the left); their exons are surrounded by a yellow rectangle on the heatmap. The different categories of exons are indicated as follows: epithelial-included (EPI+), epithelial-skipped (EPI–), endothelial-included (ENDO+), endothelial-skipped (ENDO–), fibroblast-included (FIBRO+), and fibroblast-skipped (FIBRO–) exons. (B) Inclusion rate of selected exons measured after RT-PCR using cells from different origins. The gene symbol and exon position are indicated. Inclusion rates are given for epithelial cells compared to both fibroblast and endothelial cells (upper panel), fibroblasts compared to both epithelial and endothelial cells (middle panels), and endothelial cells compared to both fibroblast and epithelial cells (lower panel).

Figure 4.

Cell type–specific expression of splicing factors. (A) Heatmap of splicing factor expression level. Each line represents a splicing factor, while each column represents a specific cell. The color of the square corresponds to the variation of the expression level of the splicing factor in each specific cell as compared to the others (green, less expressed in the cell; red, more expressed in the cell; and black, no difference). (B) RT-qPCR analysis of the expression level of ESRP1, PTBP1, MBNL1, RBFOX2, and NOVA1 in a collection of fibroblasts, epithelial and endothelial cells. (C) Spearman correlations between splicing factor expression level and the inclusion rate of epithelial-included (EPI+), epithelial-skipped (EPI–), endothelial-included (ENDO+), endothelial-skipped (ENDO–), fibroblast-included (FIBRO+), and fibroblast-skipped (FIBRO–) exons. Warm colors indicate positive correlation (e.g., a high exon inclusion level that correlates with a high splicing factor expression level), whereas cold colors indicate negative correlation (e.g., a low exon inclusion level that correlates with a high splicing factor expression level). Gray boxes indicate correlations that were discarded because of values that were not statistically significant or insufficient data available to compute correlations. (D) Summary table of epithelial-, fibroblast-, and endothelial-specific exons predicted to be regulated by the ESRP1, PTBP1, and RBFOX2 splicing factors using RNA-seq, exon array, and CLIP-seq data sets (see Supplemental Fig. S4 for more information). The number and percentage of exons predicted to be regulated by each splicing factor in each category are indicated. (E) Schematic representation of splicing factor binding site enrichment in several sets of exons differentially regulated across epithelial, endothelial, or fibroblast cells. Columns define regions in which the binding site searches were done. (F) RT-PCR analyses of the effect of depleting ESRP1, RBFOX2, or PTBP1 on alternative splicing of selected genes in the MCF-7 epithelial cell line, the MDA-MB-231 fibroblast-like cell line, or the HUVEC endothelial cell line, respectively. (G) Venn diagrams representing the number of epithelial-, fibroblast-, and endothelial-specific exons predicted to be regulated by ESRP1, PTBP1, and/or RBFOX2.

Figure 5.

Dedicated CLIP-seq data visualization web interface. (A) Localization of predicted hnRNPH/F binding motifs in the vicinity of the ENAH exon 12 and their link to splicing factor data sets. (B) Reads from a CLIP-seq experiment corresponding to hnRNPF binding sites in the vicinity of the ENAH exon 12. (C) Visualization of exon array probe intensities in hnRNPH/F- or ESRP1-depleted cells as compared to control cells. RT-PCR analysis of the ENAH exon 12 inclusion rate in hnRNPH/F-depleted or control cells.