Genes encoding cell-surface proteins control nervous system development and are implicated in neurological disorders. These genes produce alternative mRNA isoforms which remain poorly characterized, impeding understanding of how disease-associated mutations cause pathology. Here we introduce a strategy to define complete portfolios of full-length isoforms encoded by individual genes. Applying this approach to neural cell-surface molecules, we identify thousands of unannotated isoforms expressed in retina and brain. By mass spectrometry we confirm expression of newly-discovered proteins on the cell surface in vivo. Remarkably, we discover that the major isoform of a retinal degeneration gene, CRB1, was previously overlooked. This CRB1 isoform is the only one expressed by photoreceptors, the affected cells in CRB1 disease. Using mouse mutants, we identify a function for this isoform at photoreceptor-glial junctions and demonstrate that loss of this isoform accelerates photoreceptor death. Therefore, our isoform identification strategy enables discovery of new gene functions relevant to disease.
Most genes generate multiple mRNA isoforms. Mechanisms such as alternative splicing, intron retention, and alternative transcription start/stop sites serve to diversify mRNA sequences, yielding isoforms that often differ in their protein-coding capacity1,2,3,4. These mechanisms are especially common in the central nervous system (CNS), where alternative isoform use is particularly prevalent1,5. The diverse portfolio of CNS isoforms contributes in important ways to a wide range of neural functions6,7,8. Moreover, dysregulation of isoform expression is implicated in neurological disorders9,10,11. For these reasons, there is increasing awareness that genetic studies of CNS development, function, and disease will need to take isoform diversity into account.
Despite this clear importance, information about the number and the identity of CNS mRNA isoforms remains surprisingly scarce—even within the major transcriptome annotation databases12. RNA-sequencing (RNA-seq) has led to an explosion of new information about alternative splicing; however, because typical RNA-seq read lengths are <200 bp, the method is not able to resolve the full-length sequence of multi-kilobase transcripts. Therefore, by relying on RNA-seq alone, it is impossible to determine the number of isoforms produced by any given gene, or their full-length sequences.
In the absence of reliable full-length transcript annotations, the design and interpretation of genetic experiments become exceedingly difficult. For example, unless transcript sequences are known, it is difficult to be certain that a “knockout” mouse allele has been properly designed to fully eliminate expression of all isoforms. Unannotated isoforms can also be problematic for understanding how mutations lead to pathology in human genetic disease. Hidden isoforms may possess uncharacterized protein-coding sequences or unexpected expression patterns, which could cause the molecular and cellular consequences of disease-linked mutations to be misinterpreted. Thus, lack of comprehensive isoform sequence information remains a major impediment to our understanding of both normal gene function and the phenotypic consequences of gene dysfunction12.
Here we sought to address this deficiency by uncovering the isoform diversity of genes encoding CNS cell-surface molecules. We focused on this gene set for two reasons. First, some of the most striking examples of functionally significant isoform diversity are found among genes of this class. These include the Drosophila Dscam1 gene13, the mammalian clustered protocadherins14, and the neurexin gene family15,16. Each of these genes produces hundreds of protein isoforms with distinct binding specificity, diversifying the molecular recognition events that mediate assembly of the nervous system17,18,19. From these examples it seems clear that, to understand the molecular basis for neural circuit wiring, it will be necessary to define the precise repertoire of cell-surface protein isoforms expressed in the developing CNS. A second reason for focusing on cell-surface molecules is that genetic alterations affecting them have been implicated in numerous CNS disorders. These include autism20, epilepsy21,22, and neurodegeneration23,24,25,26. However, in the vast majority of these cases, it remains unclear why certain mutations increase disease risk. Comprehensive isoform identification has great potential to reveal how these genetic variants cause disease pathology.
Here we devised a strategy that leverages Pacific Biosciences (PacBio) long-read sequencing technology to generate comprehensive catalogs of CNS cell-surface molecules. Long-read sequencing is ideal for full-length transcript identification; however, sequencing depth is not yet sufficient to reveal the full scope of isoform diversity27,28,29,30. To overcome this limitation we adapted a strategy from short-read sequencing, in which targeted cDNAs are pulled down with biotinylated probes against known exons31,32. This approach yielded major improvements in long-read coverage, revealing an unexpectedly rich diversity of isoforms encoded by the targeted genes. To make sense of these complex datasets, we developed bioinformatics tools for the classification and comparison of isoforms, and for determining their expression patterns using short-read RNA-seq data.
To demonstrate how our approach can illuminate gene function, we analyzed one gene, Crb1, in detail. Crb1 is a member of the evolutionarily conserved Crumbs gene family, which encode cell-surface proteins that mediate apico-basal epithelial polarity33. In the retina, CRB1 localizes to the outer limiting membrane (OLM), a set of structurally important junctions between photoreceptors and neighboring glial cells known as Müller glia26. OLM junctions form at precise subcellular domains within each cell type, suggesting a high degree of molecular specificity in the establishment of these intercellular contacts34. There is great interest in understanding the function of CRB1 at OLM junctions, because loss-of-function mutations in human CRB1 cause a spectrum of retinal degenerative disorders35. It has been proposed that loss of OLM integrity might play a role in disease pathogenesis26,36, but studies in mice have yet to convincingly support this model: Deletion of the known Crb1 isoform neither disrupts the OLM nor causes significant photoreceptor degeneration37. Here we identify a new Crb1 isoform that is far more abundant—in both mouse and human retina—than the canonical isoform. Using mutant mice, we show that this isoform is required for OLM integrity and that its removal is required to adequately phenocopy the human degenerative disease. These results call for a major revision to prevailing models of CRB1 disease genetics and pathobiology. Thus, our findings provide a striking example of how comprehensive isoform characterization can unveil important gene functions that were previously overlooked, enabling new insights into many biological questions including the biology of disease-associated genes.
Cataloging isoforms via long-read capture sequencing
To define the isoform diversity of CNS cell surface molecules, we first manually screened RNA-seq data from mouse retina and brain38,39 to identify genes that showed unannotated mRNA diversity. We focused on cell surface receptors of the epidermal growth factor (EGF), Immunoglobulin (Ig), and adhesion G-protein coupled receptor superfamilies, as these genes have known roles in cell-cell recognition. For each gene screened (n = 402), we assessed whether it was expressed during CNS development, and if so, whether RNA-seq reads supported existence of unannotated exons or splice junctions (Fig. 1a). We found that ~15% of genes (60/402) showed strong evidence of multiple unannotated features. These genes were selected as candidates for long-read sequencing.
To comprehensively identify these genes’ transcripts, we developed a method to improve PacBio sequencing depth for large (>4 kb) and moderately expressed cDNAs, such as those on our candidate gene list. We term this strategy long-read capture sequencing (lrCaptureSeq), because we adapted prior CaptureSeq approaches31,32,40 to enable characterization of protein-coding cDNAs with the long-read PacBio platform. In lrCaptureSeq (Fig. 1b, c), biotinylated probes are designed to tile known exons without crossing splice junctions, so as to avoid targeting particular isoforms. These probes are used to pull down cDNAs from libraries that have been size-selected to filter truncated cDNAs. This size selection was essential to obtaining full-length reads (Supplementary Fig. 1a), because shorter fragments tend to dominate the sequencing output15.
To implement lrCaptureSeq, we first filtered the initial candidate list down to 30 that were predicted to encode cDNAs of similar length (4–8 kb). The final target list included genes involved in axon guidance, synaptogenesis, and neuron-glial interactions; it also included one gene, Crb1, which is implicated in inherited photoreceptor degeneration. Some targeted genes were known to generate many isoforms (Nrxn1, Nrxn3), but in most cases isoform diversity had not previously been characterized. When captured cDNAs were sequenced on the PacBio platform, ~132,000 full-length reads were generated per experiment (Supplementary Fig. 1c). These reads were strongly enriched for the targeted genes (Supplementary Fig. 1b), and the vast majority of reads were within the targeted length range (Fig. 1c). Thus, lrCaptureSeq can achieve deep full-length coverage of larger cDNAs that are underrepresented in other long-read datasets.
A comprehensive isoform catalog generated by lrCaptureSeq
To catalog isoforms for all 30 genes across development and across CNS regions, we performed lrCaptureSeq at a variety of timepoints in mouse retina and brain (Fig. 1c; Supplementary Fig. 1c). The number of isoforms, and reads comprising each, were determined using PacBio Iso-Seq software, together with custom software we developed for the analysis of isoform populations (IsoPops41). After this processing pipeline, the lrCaptureSeq catalog contained 4116 isoforms of the 30 targeted genes (Fig. 2a, b; Supplementary Data 1 and 2)—approximately one order of magnitude greater than the number of isoforms currently annotated for this gene set in public databases (Fig. 2b). It was also far higher than the number of isoforms predicted by popular short-read transcriptome assembly software (Supplementary Fig. 2a). Only 9% of lrCaptureSeq isoforms appeared in any of the databases we examined, suggesting most of them are novel.
To ensure that these unannotated isoforms are real, we used independent datasets to validate their transcription start sites and exon junctions. Cap analysis of gene expression42 (CAGE) reads from adult mouse retina43 corroborated 97.7% of transcription start sites identified by lrCaptureSeq (1051/1076 adult retina isoforms had CAGE-seq coverage at their 5′ end; Supplementary Fig. 1d). Moreover, CAGE-seq reads mapped selectively to 5′ ends of lrCaptureSeq isoforms (Supplementary Fig. 1d, e), further supporting the accuracy of our transcription start site annotations.
To validate lrCaptureSeq splice junctions we tested for their existence within short-read RNA-seq datasets from retina and brain38,39. Most lrCaptureSeq exon junctions (98.9%) occurred at canonical splice sites (n = 80,590 junctions), so we expected that this analysis would corroborate their validity. Indeed, we found that the independent datasets supported the vast majority (98.1%) of lrCaptureSeq junctions (n = 79,020). This included complete junction coverage for 71% of lrCaptureSeq isoforms (n = 2925). The unconfirmed junctions were likely absent from the RNA-seq data due to low expression levels, since the isoforms that did not show complete coverage were significantly less abundant (Supplementary Fig. 2b). Consistent with this interpretation, unconfirmed junctions could be detected by sequencing of RT-PCR products, suggesting that they were simply below RNA-seq detection threshold (n = 9/12 absent RNA-seq junctions in Megf11 gene were detected by RT-PCR). Together, these analyses strongly support the validity of the full-length sequences within the lrCaptureSeq isoform catalog.
Efficient isoform detection by lrCaptureSeq
To probe the accuracy and sensitivity of isoform detection, we compared our lrCaptureSeq data to previous studies of the Nrxn1 and Nrxn3 genes. In these studies, the α and β classes of Nrxn transcripts were amplified by PCR and then characterized using PacBio sequencing15,16. The total number of Nrxn1 and Nrxn3 isoforms we identified was similar in scale to the previous studies (Fig. 2a), despite radically different library preparation and bioinformatic methods. Patterns of exon usage in alternative splice sites (AS)1-AS4 were also similar (Supplementary Table 1). For example, we confirmed a deterministic AS4 splicing event identified in the previous work, wherein Nrxn3 exon 24 always splices to exon 25a (n = 76 exon 24-containing isoforms, all spliced to exon 25). These findings suggest that our Nrxn isoform catalog largely matches those generated by past studies. Nevertheless, we found features of the neurexin genes not noted in the previous catalogs. Because our method was not biased by PCR primer placement, we identified isoforms that did not contain canonical α or β transcript start/termination sites, one of which accounted for 64% of our Nrxn3α reads (Supplementary Table 1). Further, we detected 7 unannotated transcription termination sites, used by 16 different Nrxn3α isoforms, that truncate the mRNA upstream of the transmembrane domain (Supplementary Table 1). All seven of these new sites were corroborated with junction coverage from RNA-seq data. Together, these findings demonstrate the utility of lrCaptureSeq in recovering isoform diversity with high efficiency.
Many isoforms contribute to overall gene expression
Having identified a large number of isoforms within our lrCaptureSeq catalog, we next addressed whether this extensive isoform diversity is positioned to impact gene function. For diversity to be functionally significant, two conditions must be met: (1) multiple isoforms of individual genes should be expressed at meaningful levels; and (2) the sequences of the isoforms must differ enough to encode functional differences. To investigate isoform expression levels, we assessed how each gene’s overall expression was distributed across its isoform portfolio (Fig. 2c, e; Supplementary Fig. 2d). Some genes—for example, Egflam and Crb1—were dominated by a small number of isoforms. However, other genes distributed their expression far more equitably across isoforms (Fig. 2c, e). Indeed, the genes that produced the largest number of isoforms also tended to be the most equitable, with a high fraction of similarly-abundant isoforms (Fig. 2c). Using the Shannon diversity index44, we rank-ordered genes based on the diversity of their expressed mRNA species. Nrnx3 was the top-ranked gene; however, several others of the latrophilin and protein tyrosine phosphatase receptor (PTPR) families scored nearly as high (Fig. 2d). Thus, Nrxn3 is far from unique in expressing a large number of isoforms. We conclude that, for the genes in our dataset, much of the isoform diversity is expressed at appreciable levels.
Predicted functional diversity of lrCaptureSeq isoforms
We next investigated the extent of sequence differences across the isoforms of each gene in our dataset. Most of the 30 genes encoded isoforms that varied widely in length and number of exons (Supplementary Fig. 2e, f), suggesting the potential for great functional diversity. To identify isoforms that are most likely to diverge functionally, unsupervised clustering methods were used to group isoforms based on their sequence similarity (Fig. 2f,g; Supplementary Fig. 2g). For most genes, isoforms clustered into distinct groups of related isoforms that made similar choices among alternative mRNA elements (Fig. 2f, g). Thus, major sequence differences exist within the isoform portfolio of individual genes, which can be traced to the inclusion of specific exon sequences by families of related isoforms.
To learn whether these sequence differences might diversify protein output, we analyzed predicted open reading frames (ORFs; Supplementary Data 1). The 4116 RNA isoforms in our dataset were predicted to express 2247 unique ORFs. A small subset of genes expressed great mRNA diversity but no equivalent ORF diversity (Fig. 3a); this was largely due to variations in 5′ UTRs or systematic intron retention (Supplementary Fig. 3c, d). Overall, however, there was a strong correlation between the number of isoforms and the number of predicted proteins (Fig. 3a). The amount of expressed ORF diversity varied by gene; but similar to mRNAs, a large amount of this predicted protein diversity was expressed at appreciable levels (Fig. 3b–d; Supplementary Fig. 3a, b). Remarkably, the genes with the most ORF diversity tended to encode a specific type of cell-surface protein: The top genes by Shannon diversity index all encode trans-synaptic adhesion molecules (Fig. 3c). Thus, a major function of mRNA diversity may be the generation of protein variants that are positioned to influence formation or stability of synaptic connections.
To understand how mRNA diversity alters protein sequences, we studied the predicted protein output of individual genes. In many cases, predicted proteins varied substantially in their inclusion of well-characterized features or functional domains. This phenomenon is exemplified by the Megf11 gene, which encodes a transmembrane EGF repeat protein implicated in cell-cell recognition during retinal development45. Megf11 undergoes extensive alternative splicing: Out of 26 protein-coding exons, 21 are alternatively spliced (81%). In fact, we documented only ten constitutive splice junctions within the 234 Megf11 isoforms identified in three independent long-read sequencing experiments (Fig. 4a, b; Supplementary Fig. 4). Examination of predicted proteins revealed a potential reason for such extensive splicing: Most of the EGF repeats comprising the extracellular domain are encoded by individual exons, such that alternative splicing causes them to be deployed in a modular fashion (Fig. 4a–d). As a result of this modularity, predicted MEGF11 proteins showed substantial variability in the number and/or identity of included EGF repeats (Fig. 4d). The most variable EGF repeats were encoded by exons 14-16b (Fig. 4b); however, most of the EGF repeats were subject to alternative usage. Intracellular domain exons also showed potential for modularity in the use of ITAM or ITIM signaling motifs (Fig. 4a–d), similar to the situation in the Drosophila Megf11 homolog Draper46. Using BaseScopeTM in situ hybridization47,48, we confirmed that each of the most variable exon junctions are expressed by retinal neurons in vivo (Fig. 4e). Remarkably, individual Megf11-expressing cells used all of the exon junctions we tested, suggesting that extensive Megf11 isoform diversity is present even within individual neurons (Fig. 4e). Therefore, similar to insect Dscam1, Megf11 uses alternative splicing of modular extracellular domain features to create a large family of isoforms encoding distinct cell-surface molecules. Together with our analysis of the full lrCaptureSeq dataset, these findings strongly suggest that isoform diversity serves to diversify the repertoire of neuronal cell-surface proteins.
lrCaptureSeq isoforms encode cell-surface proteins in vivo
To determine whether lrCaptureSeq isoforms are translated into proteins, we surveyed the retinal cell-surface proteome using mass spectrometry. Cell-surface protein samples were obtained from developing retina using cell-impermeant reagents that either cleaved or biotinylated extracellular epitopes (Fig. 3e, f). To learn whether these samples contained protein isoforms identified by lrCaptureSeq, we generated a database of possible trypsin peptide products derived from the isoforms within the lrCaptureSeq catalog. This was essential because protein identification requires comparison of raw mass spectrometry data to a reference peptide database. On generation of this predicted peptide database, we found that it contained ~25% more putative peptides for our 30 genes than the UniProt Mouse Reference Database typically used in most proteomics experiments (Supplementary Fig. 3e). The extra putative peptides represent the additional protein sequence complexity that is predicted by the lrCaptureSeq catalog.
Using this database as a reference, our mass spectrometry experiment identified 686 total peptides corresponding to 28 of the genes. 35 of these peptides were absent from the UniProt standard reference database, and were present only in our lrCaptureSeq reference (Fig. 3g; Supplementary Data 3). This fraction represents unannotated peptides, predicted from our lrCaptureSeq isoform catalog, that would have gone undetected in a typical mass spectrometry experiment. Unannotated peptides were found for 14 of our 30 genes, validating predicted exonic sequences, splice junctions, and splice acceptor sites (Supplementary Data 3). These findings strongly suggest that at least some of the proteins predicted by lrCaptureSeq are expressed on the surface of retinal cells in vivo.
Identification of an abundant retina-specific Crb1 isoform
To investigate whether newly-discovered isoforms can provide insight into gene function, we focused on Crb1, a well-known retinal disease gene. Our Crb1 catalog contained 15 isoforms, several of which were tissue-specific and developmentally-regulated (Fig. 5a, b; Supplementary Fig. 5b, c). In mature retina, Crb1 expression was dominated by a single isoform—but not the one that has been the subject of virtually all previous Crb1 studies. Instead, the dominant isoform was a retina-specific variant bearing unique 5′ and 3′ exons (Figs. 5a, 6a; Supplementary Fig. 6a) and a unique putative promoter site just upstream of the 5′ exon (Fig. 5c). We named this isoform Crb1-B, to distinguish it from the canonical Crb1-A isoform.
Even though Crb1-B was the most abundant of the 4116 isoforms in our dataset (Fig. 2d), it was not annotated in the major genome databases (RefSeq, GENCODE, or UCSC). Nor, to our knowledge, was it documented in the literature. CRB1-B is also the most abundant isoform in human retina, as shown by a lrCaptureSeq dataset generated from human retinal cDNA (Figs. 5d and 6b). A third variant, CRB1-C, was also expressed in human retina at moderate levels—much higher than in mouse—but it was still not as abundant as CRB1-B (Figs. 5d and 6b). As in mouse, ATAC-seq revealed a putative B isoform promoter in human retina (Fig. 5c–e). Using short-read datasets38,49, we corroborated the mouse and human findings (Fig. 6c, d) and extended them to several other vertebrate species (Supplementary Fig. 5a). Together, these results demonstrate that the major retinal isoform of an important disease gene had previously been overlooked: Across a range of vertebrate species, CRB1-B is the predominant CRB1 isoform in the retina.
Crb1 isoforms are expressed in different retinal cell types
Crb1-B is predicted to encode a transmembrane protein sharing significant extracellular domain overlap with CRB1-A, but an entirely different intracellular domain (Fig. 7a, b). We therefore asked whether this protein is expressed and, if so, where the protein is localized. Western blotting with an antibody raised against the intracellular domain demonstrated that CRB1-B protein exists in vivo (Fig. 7c). Moreover, it exists in the configuration predicted by lrCaptureSeq (Fig. 7a), because intracellular domain expression was absent in mice engineered to lack the Crb1-B promoter and 5′ exon (Fig. 7c; see below for mouse design). Consistent with the notion that CRB1-B is a transmembrane protein, it was detected in the membrane fraction but not the soluble fraction of retinal lysates (Fig. 7d). Further, when expressed in heterologous cells, CRB1-B trafficked to the plasma membrane in a manner strongly resembling CRB1-A (Supplementary Fig. 6c). These data suggest that both major CRB1 isoforms localize at the cell surface.
To determine the expression patterns of Crb1-A and Crb1-B, we developed a strategy to evaluate expression of lrCaptureSeq isoforms within single-cell (sc) RNA-seq datasets. Applying this strategy to scRNA-seq data from developing mouse retina50, we found distinct expression patterns for each isoform. Crb1-A was expressed largely by Müller glia (Fig. 8a, b; Supplementary Fig. 6d), consistent with previous immunohistochemical studies37,51. Crb1-B, by contrast, was expressed by rod and cone photoreceptors (Fig. 8a, b; Supplementary Fig. 6b, d). These cell-type-specific expression patterns were validated using two independent methods: First, ATAC-seq data from rods and cones showed that photoreceptors selectively use the Crb1-B promoter (Fig. 5c). Second, BaseScope staining confirmed mutually exclusive expression of the two isoforms, with Crb1-A localizing to Müller cells and Crb1-B to photoreceptors (Fig. 8c).
To examine CRB1-B protein localization, we initially attempted immunohistochemistry but found that our antibody was not suitable. Therefore, we turned to a technique that combines serial tangential cryosectioning of the retina with Western blotting52,53. Each tangential section contains a specific subset of cellular and subcellular structures that are recognized by representative protein markers (Fig. 8d). This approach confirmed expression of CRB1-B in the photoreceptor layer, predominantly within the inner and outer segments. This localization is in marked contrast to CRB1-A which has been localized to the apical tips of Müller cells, within the OLM (Fig. 8a), using antibodies specific to this isoform37,51.
CRB1-B is required for outer limiting membrane integrity
We next investigated the function of the CRB1-B isoform. Photoreceptors and Müller glia, the two cell types that express the major CRB1 isoforms (Fig. 8), engage in specialized cell-cell junctions that form the OLM (Fig. 9b, c). It has been suggested that degenerative pathology in CRB1 disease may result from disruption of these junctions, but mouse studies have failed to clarify whether CRB1 is in fact required for OLM integrity. The two existing Crb1 mutant strains (Fig. 9a) have conflicting OLM phenotypes: Mice bearing a Crb1 point mutation known as rd8 show sporadic OLM disruptions36, whereas a Crb1 “knockout” allele, here denoted Crb1ex1, fails to disturb OLM junctions37. Our lrCaptureSeq data revealed a key difference between these two alleles: rd8 affects both Crb1-A and Crb1-B isoforms, whereas the “knockout” ex1 allele leaves Crb1-B intact (Fig. 9a). Therefore, we hypothesized that Crb1-B influences the integrity of photoreceptor-Müller junctions at the OLM. To test this hypothesis, we generated two new mutant alleles (Fig. 9a; Supplementary Fig. 7a, b). The first, Crb1delB, abolishes Crb1-B while preserving other isoforms including Crb1-A. The second, Crb1null, is a large deletion designed to disrupt all Crb1 isoforms.
Using electron microscopy to evaluate OLM integrity, we found that Crb1null mutants exhibit disruptions at the OLM whereby photoreceptor nuclei invaded the inner segment layer, disturbing the structure of the outer retina (Fig. 9b–e; Supplementary Fig. 7d). Within the disrupted regions, photoreceptor inner segments lacked their characteristic electron-dense junctions with apical Müller processes, indicating that OLM gaps arose due to disruption of photoreceptor-Müller contacts (Fig. 9f). A similar phenotype was also observed in Crb1rd8 mutants, as previously reported36 (Fig. 9f, g, j; Supplementary Fig. 7d–f). To explore the contribution of each isoform to the OLM phenotype, we examined mice bearing various combinations of the Crb1null and Crb1delB alleles. In Crb1delB/delB mice, which lack Crb1-B but retain two copies of Crb1-A, the OLM phenotype was still evident but was weaker than in rd8 or null homozygotes (Fig. 9h, j). By contrast, the OLM phenotype was equivalent to rd8 and null mutants in Crb1delB/null mice, which lack Crb1-B but retain one copy of Crb1-A (Fig. 9e, j; Supplementary Fig. 7f). These findings indicate that both Crb1 isoforms are needed for OLM junctional integrity, but the role of Crb1-B is particularly important, given that OLM disruptions can arise even when Crb1-A remains present.
Retinal degeneration in mice lacking all Crb1 isoforms
Finally, we asked whether insight into CRB1 isoforms could be used to improve animal models of CRB1 degenerative disease. Photoreceptor degeneration is absent or extremely slow in existing Crb1 mutant mice, making them poor models of human degenerative phenotypes36,37,54. We hypothesized that previously unannotated Crb1 isoforms, such as Crb1-B, might help explain these mild phenotypes. Consistent with this possibility, we noted that neither Crb1ex1 nor Crb1rd8 completely eliminates all Crb1 isoforms (Fig. 9a). To test the contribution of new Crb1 isoforms to photoreceptor degeneration, we took advantage of our newly-generated Crb1delB and Crb1null strains (Fig. 9a). Quantification of photoreceptor numbers in young adult mice (P100) revealed that both Crb1-A and Crb1-B isoforms are required for photoreceptor survival. Crb1delB mutants had normal photoreceptor numbers (Fig. 10a, d; Supplementary Fig. 7c), similar to the previously-reported Crb1ex1 mutant37. Therefore, removing either major isoform by itself has minimal degenerative effects. By contrast, deletion of all isoforms in Crb1null mice caused marked photoreceptor degeneration (Fig. 10a–d). Thus, significant cell loss requires compromise of both Crb1-A and Crb1-B. No degeneration was evident yet at P100 in Crb1rd8 mutants (Fig. 10b–d), consistent with previous reports that significant degeneration takes ~2 years36,54,55. Together, these genetic experiments support the conclusion that multiple Crb1 isoforms contribute to photoreceptor survival—including Crb1-B. Thus, modeling of human disease can be achieved by rational design of mutant alleles guided by lrCaptureSeq isoform catalogs.
Despite recent advances in sequencing technology, the true diversity of the transcriptome remains murky12. For most genes, only a small subset of the full isoform portfolio has been documented. Here we show that lrCaptureSeq can unveil the rich diversity of the CNS transcriptome. LrCaptureSeq is accurate and efficient, with sufficient depth to reveal the full-length sequence of even low-abundance isoforms. To facilitate interpretation of lrCaptureSeq data we provide a companion R software package for analyzing and visualizing isoform catalogs. Applying these tools to the developing nervous system, we uncovered a vast diversity of isoforms encoding cell surface proteins, most of which were unannotated elsewhere. Many were predicted to alter functional protein domains. Further, we found that Crb1-B, the most abundant isoform in our entire dataset, has a distinct expression pattern and function from the canonical Crb1 isoform, endowing it with disease-relevant functions. CRB1 therefore serves as a striking example of the value of comprehensive full-length isoform identification. Application of lrCaptureSeq to other cell types and tissues has great potential to unlock many new insights into gene function and dysfunction—both in the CNS and beyond.
LrCaptureSeq is successful because it enables deep long-read sequencing for transcripts that would be poorly represented in existing PacBio transcriptomes, due to their cDNA size and expression levels. Even with short-read sequencing, it is challenging to achieve sufficient sequencing depth for isoform identification31,56. Targeted CaptureSeq approaches improve short-read detection of low-abundance transcripts31,57; here we show the same is true for full-length sequencing of protein-coding mRNAs. It is clear from the distribution of isoform abundances (Fig. 2c) that only the least abundant isoforms escaped detection. Therefore, we consider the lrCaptureSeq isoform catalogs to be largely complete. However, we cannot exclude the possibility that certain transcripts are missing from the catalogs. For example, some isoforms smaller than 4.5 kb may have evaded detection due to the size selection step of our library preparation protocol (Fig. 1b). We suspect this is a small minority of transcripts because, even with size selection, we still cataloged numerous smaller isoforms (Supplementary Fig. 2e)—including Crb1-B (3.0 kb). Thus, while the catalogs may lack certain short and/or rare transcripts, we conclude that we have detected most of the isoforms expressed in our targeted tissues. We achieved this depth by targeting 30 genes for parallel sequencing, but higher-throughput PacBio instruments are now available; these should allow substantially more targeted genes to be sequenced in parallel without sacrificing isoform coverage.
Our results suggest many potential uses for lrCaptureSeq in transcriptome annotation. One particularly exciting use case is identification of cell-type-specific isoform expression patterns. We show that lrCaptureSeq data can be integrated with existing short-read RNA-seq datasets, including single-cell data, to reveal the time and place of isoform expression. As of now, this approach works best for isoforms that differ at their 3′ ends, due to 3′ bias inherent in most single-cell library preparation methods. As scRNA-seq methods are refined to improve depth and coverage, we expect that other types of isoforms will become amenable to mapping in this way. With this methodology, it will not be necessary to generate lrCaptureSeq catalogs for each cell type in the nervous system; rather, cell-type-specific isoform expression can be determined bioinformatically by combining different types of sequencing data.
How many mRNA isoforms are produced by any given gene? While our 30 genes probably have more isoforms than the average gene, given that they were selected because they showed evidence of transcript diversity (Fig. 1a), our results suggest that the number is substantially higher than suggested by present database annotations. For the 30 genes in our dataset the median number of RefSeq isoforms was 11.5, and no gene had more than 51. By contrast, the median number of isoforms in our lrCaptureSeq catalog was 50, while the most diverse gene, Nrxn3, had nearly 900. A previous CaptureSeq study of long noncoding RNAs found only two-fold more isoforms40, but this study was not focused on the nervous system. Thus, it remains to be determined whether the diversity we observed is a specific feature of CNS cell surface molecules, or if instead it is typical of other gene classes and tissues. Broader application of lrCaptureSeq should help resolve this question.
It has long been suspected that extensive cell-surface protein diversity might mediate formation of precise neuronal connections5,8,58. However, the need for numerous cell-surface cues has recently been called into question59. Here we show that extensive isoform diversity is widespread across many cell-surface receptor genes, and that individual neurons most likely express numerous of isoforms of certain genes (e.g., Megf11; Fig. 4e). Furthermore, our Megf11 results show that insect Dscam1 is not unique in using large-scale modular alternative splicing to swap extracellular domain motifs. The phenomenon of modular EGF-repeat swapping through alternative splicing has been observed before, albeit at smaller scale, for Netrin-G proteins60. Therefore, other EGF-repeat genes may also generate large families of cell surface proteins using a similar strategy. Overall, our results establish the molecular prerequisite for models of CNS wiring that require numerous cell surface cues. Whether such models are correct remains to be determined; however, in this regard it is striking that the genes with the most predicted protein diversity share a common function as trans-synaptic, synaptogenic cell adhesion molecules19,61,62 (Fig. 3c). Therefore, these diverse molecular cues are likely positioned in exactly the right place to influence the precision of synaptic connections.
Our studies of CRB1 illustrate the value and importance of documenting the complete isoform output of individual genes. CRB1 is a major causal gene for inherited retinal degenerative diseases, including Leber’s congenital amaurosis, retinitis pigmentosa, and macular dystrophy63,64,65. As such, both mouse Crb1 and human CRB1 have been studied intensively. CRB1-B may have been overlooked because its 5′ and 3′ exons are the only parts of the transcript that distinguish it from CRB1-A. With short-read sequencing it is difficult to tell that these two distant exons are typically used together in the same transcript. By contrast, lrCaptureSeq clearly showed that the most abundant retinal CRB1 isoform was a variant containing these unconventional 5′ and 3′ exons.
Due to their distinct 5′ and 3′ ends (Fig. 7a), Crb1-A and -B differ in crucial ways that likely endow them with distinct functions. Their 5′ exons have different promoters that drive expression in different cell types—Crb1-A in Müller glia and Crb1-B in photoreceptors—while their 3′ exons encode different intracellular domains. The CRB1-A intracellular domain, like other vertebrate homologs of Drosophila Crumbs, contains two highly-conserved motifs mediating interactions with polarity proteins66. These motifs localize Crumbs homologs to apical junctions, where they are required for maintaining epithelial structural integrity and apico-basal polarity33. CRB1-B lacks these conserved motifs, suggesting a model whereby CRB1-A and -B operate in different cell types through different intracellular interaction partners.
Our findings have implications for the prevailing model of CRB1 disease, which posits that CRB1 is required for integrity of OLM junctions between Müller glia and photoreceptors26. A major challenge for this model has been the absence of OLM phenotypes or photoreceptor degeneration in Crb1ex1 mutants, which lack only Crb1-A (Fig. 9a) but was thought to be a null allele37. As such, the weak mutant phenotype suggested that CRB1 might be dispensable for photoreceptor survival in mice26. Here we show that CRB1 is indeed required for OLM integrity and photoreceptor survival, but the mechanism involves the photoreceptor-specific CRB1-B isoform. Moreover, we show a genetic interaction between the two isoforms, revealing OLM integrity and pro-survival functions for CRB1-A that were obscured in Crb1ex1. We propose that the concerted action of CRB1-A in glia and CRB1-B in photoreceptors controls OLM integrity and photoreceptor health, perhaps through the assembly or maintenance of the junctional protein complex in each respective cell type.
The notion of concerted Crb1-A and Crb1-B function is further supported by the fact that Crb1rd8 (Fig. 9a) has a more severe phenotype than Crb1ex136,37,55. However, it remains unclear why Crb1rd8 is less severe than Crb1null (Fig. 10), even though A and B isoforms are affected in both mutants. One possible explanation is that Crb1rd8 may not be a mRNA or protein null55. Another possibility is that the Crb1-C isoform may play a compensatory role, as it is unaffected by Crb1rd8 (Fig. 9a). Either way, our results show that the design of mouse disease models is significantly enhanced when a complete isoform catalog is available.
Overall, our work highlights the value of building complete and accurate full-length isoform catalogs. Lack of such information can cause key gene functions to be overlooked and can lead to misinterpretation of genetic experiments and disease phenotypes. We expect the transcriptomic ground truth provided by deep long-read capture sequencing will be an important addition to the transcriptome annotation toolbox, enabling discovery of specific mRNA isoforms that contribute to a wide range of normal and disease processes.
Resources and reagents
All key reagents used in this study, including antibodies, primers, datasets, and animal strains, are listed in a Key Resources table (Supplementary Table 2).
Animals and human tissue samples
Mouse experiments in this study were approved by the Duke University Institutional Animal Care and Use Committee (protocols A005-16-01 and A274-18-12). The mice were housed under a 12 h light-dark cycle with ad lib access to food and water. Heat and humidity were maintained within the parameters specified in the National Institute of Health Guide for the Care and Use of Laboratory Animals. Experimental procedures were also consistent with this Guide.
Human donor eyes were obtained from Miracles in Sight (Winston Salem, NC), which were distributed by BioSight (Duke University Shared Resource). Ethical procedures, including procedures for obtaining informed consent from donors, were reviewed and approved by the Duke University Institutional Review Board (protocol #PRO-00050810). Postmortem human donor eyes were enucleated and stored on ice in PBS until dissection. Retinas were dissected from posterior poles and proceeded to RNA isolation. Donors with a history of retinal disease were excluded from the study.
Knockout mouse generation
For the generation of Crb1delB, CRISPR guides were designed to target genomic coordinates chr1:139,256,486 and 139,254,837 and validated in vitro on genomic DNA prior to injection. A C57Bl6J/SJL F1 hybrid mouse line was used for injection; both strains are wild-type at the Crb1 locus (i.e., they do not carry rd8). Founders were genotyped using PCR primers to distinguish the alleles (see Supplementary Table 2 for primer sequences). Two founder lines with genomic deletions were maintained. One carrying the deletion 139,254,836–139,256,488 (Δ1652 bp) plus two additional cytosines, and the other 139,254,836–139,256,488 (Δ1652 bp). Both alleles effectively delete the entire first exon of Crb1-B and the promoter region and are currently phenotypically indistinguishable. For the generation of Crb1null, CRISPR guides were designed to target genomic coordinates chr1:139,256,486–139,243,407 and validated in vitro on genomic DNA prior to injection. A C57Bl6J/SJL F1 hybrid mouse line was used for injection and founders were genotyped using PCR primers (Supplementary Table 2) to distinguish the alleles. Two founder lines with genomic deletions were maintained. One carrying the deletion chr1: 139,256,844–139,243,411 (Δ13,433 bp) and the other 139,257,194–139,243,411 (Δ13,783 bp). Both alleles effectively delete the entire first exon of Crb1-B and the promoter region in addition to exon 6 and part of exon 7 of Crb1-A. This deletion would eliminate the exon 7 splice acceptor and is predicted to exclude exon 7 altogether. Splicing from exons 5 to 8 (as in Crb1-A) and 4 to 8 (as in Crb1-A2) would result in frameshifts. The Crb1-C-specific retained intron after exon 6 is also entirely deleted. Founder animals were backcrossed with C57Bl6J mice for at least two generations before analysis and genotyped to ensure they were not carrying rd1 mutation from the SJL background. Animals generated in this study will be made available to the research community for non-commercial use.
We used Pierce Custom antibody service (Thermo Fisher Scientific) to generate a CRB1-B specific antibody. The antigen was the last 16 amino acids (RMNDEPVVEWGAQENY) of CRB1-B, which are predicted to be exclusive to this isoform at the protein level. Antibodies were made in rabbit according to their 90-day protocol with initial inoculation followed by 3 boosts. The antibody was affinity-purified and validated by western blot with a Crb1delB knockout control. CRB1-B produces a band of ~150 kDa, larger than the predicted size of 110 kDa. This discrepancy in experimental vs predicted size is likely due to post translational modifications such as glycosylation, since addition of PNGase F lowered the band size. Antibodies generated in this study will be made available to the research community for non-commercial use.
For PacBio sequencing experiments and qRT-PCR, C57Bl6/J mice were anesthetized at P1, P6, P10, or P35 (adult) with isoflurane or cryoanesthesia (neonates only) followed by decapitation. Eyes were enucleated and retinas were dissected out, or brain was dissected from the skull and the cerebral cortex was removed. Total RNA was isolated using Tri Reagent (ThermoFisher Scientific AM9738) according to the manufacturer’s protocol. Tissue was mechanically homogenized in Tri Reagent followed by phase separation with chloroform and isopropanol precipitation. RNA samples were stored at −80 °C. RIN number was calculated using a Bioanalyzer. Only RIN values above 9 were used for sequencing.
PacBio library preparation for mouse samples
Reverse transcription was carried out using the Clontech SMARTer cDNA kit according to the manufacturer’s protocol. cDNA was amplified with KAPA HiFi DNA Polymerase for 12 cycles followed by size selection (4.5–10 Kb). For capture, 1 ug of cDNA was denatured and blocked with DTT primer and Clontech primer then mixed with Nimblegen’s SeqCap EZ Developer (≤200 Mb) custom baits at 47 °C for 20 h. Biotynaylated cDNAs were pulled down with streptavidin beads and washed with Nimblegen hybridization buffers to minimize non-specific binding. Targeted cDNA library was amplified 11 cycles with Takara LA Taq. SMRT bell library was constructed then additional size selection was performed (4.5–10 Kb) followed by binding of Polymerase with P6-C4 chemistry (RSII). Library was loaded onto SMRT cell using MagBead loading at 80pM (RSII). For PacBio Sequel library, sequencing primer version 2.1 was annealed and bound using polymerase version 2.0. The bound complex was cleaned with PB Ampure beads and loaded by diffusion at 6 pM with 120 min pre-extension.
PacBio library prep for human retina
Reverse transcription was carried out using Clontech SMARTer cDNA kit according to the manufacturer’s protocol. cDNA was amplified with Prime Star GXL Polymerase for 14 cycles followed by Blue Pippin size selection (4.5–10 Kb). For capture, 1ug denatured cDNA was used then incubated with Twist Custom Probes at 70 °C for 20 h. Biotynaylated cDNAs were pulled down with streptavidin beads and washed with Twist hybridization buffers to reduce non-specific binding. Targeted cDNA library was amplified 11 cycles with Takara LA Taq yielding 650 ng of enriched cDNA for library prep. SMRTbell Template Prep Kit 1.0 post exonuclease was used for library prep followed by a Blue Pippin size selection (4Kb to 50KB). Post size selection yielded 120 ng of DNA. Sequencing primer version 3.0 was annealed and bound using polymerase version 2.0. The bound complex was cleaned with PB Ampure beads and loaded onto PacBio Sequel instrument by diffusion at 6pM.
Processing of PacBio raw data
Iso-Seq software was used for initial post-processing of raw PacBio data. For lrCaptureSeq experiments, reads of insert were generated from PacBio raw reads using ConsensusTools.sh with the parameters–minFullPasses 1 –minPredictedAccuracy 80 –parameters /smrtanalysis/current/analysis/etc/algorithm_parameters/2014-09/. From the reads of insert full-length, non-chimeric reads (FLNC reads) were generated using pbtranscript.py classify with the parameters –min_seq_len 500 and presence of 5′ and 3′ Clontech primers in addition to a polyA tail preceding the 3′ primer. For Megf11 PCR product sequencing, parameters were the same except that full-length reads were distinguished by the presence of Megf11-specific primer sequences (5′ GGCTCCGGGGTATAGGA; 3′ sequence CTGGCTGCATTGCATTGG for Megf11 long or GGTGTCCAATAAAGTC for Megf11 short).
Isoform level clustering
Clustering of FLNC reads into isoforms was performed using ToFU, which consists of two parts: (1) Isoform-level clustering algorithm ICE (Iterative Clustering for Error Correction), used to generate consensus isoforms; and (2) Quiver, used to polish consensus isoforms. Transcript isoforms were generated using the ToFU_wrap script with the parameters –bin_manual “(0,4,6,9,30)” –quiver –hq_quiver_min_accuracy 0.99 (0.98 for Megf11 PCR data). This generated high-quality full-length transcripts with ≥ 99% post correction accuracy (≥ 98% for Megf11 PCR data). Isoforms were aligned to the mouse genome mm10 using GMAP (version 1.3.3b) with default values of alignment accuracy (0.85) and coverage (0.99). To prevent over clustering based on 5′ end lengths, redundant clusters were removed by collapsing all transcripts that share exactly the same exon structure. To minimize the impact truncated mRNAs may have on inflating isoform numbers, we set a threshold of ≥ 2 independent full-length reads that must cluster together in order to define an isoform.
To generate the entire isoform catalog, the complete dataset (all timepoints, retina and cortex) was analyzed using the cluster function of Iso-Seq (version 3), with default parameters. Only the highest-quality full-length reads (≥99% accuracy or QV ≥ 20) from each experiment were passed to this analysis. At the conclusion of Iso-Seq 8287 isoforms of our 30 genes were identified. HQ reads were mapped to the genome (mm10 for mouse, hg19 for human) Cupcake ToFU67 was used to further reduce overclustering of isoform subdivisions.
Finally, additional filtering of putative spurious isoforms was performed with our IsoPops software. The goal of this filtering was to remove artifacts arising from cDNA truncations or poly-A mispriming within genomic DNA. Details of the filtering methodology are provided below in the section describing the software package. Applying these filters yielded the final catalog of 4116 isoforms. We did not exclude isoforms that contained non-canonical junctions, because many such isoforms were highly abundant; however, even if they were excluded, overall isoform counts would be only slightly reduced (Supplementary Fig. 2c).
The final isoform catalog specified not only the number of isoforms, but also the number of full-length reads obtained for each isoform. We have reported these read counts for some of our analyses (e.g., Fig. 2c, e; Fig. 3b, d). These data aid in understanding how the overall expression of a particular gene is distributed across its isoform portfolio. We have avoided making conclusions about the expression level of particular isoforms, unless the PacBio data are supported by independent short-read RNA-seq data (e.g., Fig. 6a–d).
IsoPops R package
We developed a package of R software for convenient analysis and viewing of PacBio transcriptome sequence output. The IsoPops R package allows users to perform many of the analyses described in this study on their own long-read data.
The package offers the following features. First, it permits filtering of truncated and spurious isoforms to facilitate downstream analysis. Second, it displays maps of exon usage enabling the user to visually compare how isoforms differ. Third, it generates plots summarizing expression levels of isoforms within an individual gene and across a dataset. These include tree plots (Fig. 2e) and a variant on the Lorenz plot that we have termed a jellyfish plot (Fig. 2c). Fourth, it clusters similar isoforms and displays the data in various dimension-reducing plots such as dendrograms and 3-dimensional PCA plots. Fifth, it provides summary statistics such as the length distribution of a gene’s isoforms or the number of exons used in each isoform. Finally, it performs cross-correlations, enabling the user to ask if certain exons tend to appear together in the same transcripts. Methods relevant to these features are described below.
Filtering: The IsoPops isoform filtering process consists of three steps: First, transcripts containing fewer than n exons are removed. For our study, n was set to 4, because we did not expect any such short isoforms for the genes in our dataset. To quantify exon number, we did not reference exon annotations, but instead defined the number of non-contiguous genomic segments (or the number of junctions plus one) as the exon count for each isoform. This filtering step removed most spurious transcripts arising from genomic poly-A mispriming, as these sequences typically contained only a single exon as defined by this mapping procedure. Second, the least abundant 5% of isoforms for each gene were filtered out, on the assumption that these extremely low-abundance isoforms might constitute experimental or biological noise.
Finally, we filtered out truncation artifacts. To identify truncated isoforms, we developed an algorithm designed to filter as thoroughly as possible without discarding potentially valuable unique transcripts. In particular, we wanted to preserve all unique splicing events and tolerate unique transcription start sites (TSS) and transcription termination sites (TTS) modestly. The algorithm compares the set of exon boundaries (coordinates of acceptor and donor splice sites) for an isoform pair A and B and applies the following two rules. Rule 1: If all the exon boundaries in B form a contiguous subset of the exon boundaries in A, then B is a truncation of A. We required the subset to be contiguous to avoid filtering transcripts with retained introns. Rule 2: If all three of the following conditions are met, B is a truncation of A. (1) The TSS of B falls within an exon in A; (2) the TTS of B is either found in A or within/beyond the 3′-most exon of the gene; (3) internal exon boundaries of B (i.e., excluding the 5′- and 3′-most exon boundaries of B) are a contiguous subset of A.
Pearson correlation: This function enables analysis of exon co-occurrence across isoforms. Each isoform in a given gene was labeled with a series of binary values representing the exons called within its cDNA sequence. Exon calls were determined by searching for exact matches of either the first 30 bp or last 30 bp of each exon within the transcript. Exon definitions were derived from PacBio isofom GFF file. Isoforms were weighted by their full-length read counts before pairwise Pearson correlations between exon calls were calculated.
K-mer vectorization: IsoPops enables quantification of sequence differences between isoforms. To quantify relative differences between isoforms, we calculated the Euclidean distances between vectorizations of each isoform’s cDNA sequence (or their predicted ORF amino acid sequence). We used the text2vec R package to generate a vector for each isoform, where each element in the vector equals the number of times a certain k-mer (sequence fragment) appears within the isoform. We counted all possible 6-mers within isoforms, choosing k = 6 to maximize k-mer count uniqueness between isoforms without requiring excessive computational resources. Each isoform’s vector of k-mer counts was then normalized to sum to 1, so that isoform distances calculated from these vectors would not be dominated by differences in length between transcripts.
Isoform clustering: To cluster isoforms, we calculated pairwise euclidean distances between isoforms’ k-mer count vectorizations. We then performed hierarchical agglomerative clustering using the R base algorithm hclust using default settings and the “complete” agglomeration method. Dendrogram plots of clusterings were generated by the dendextend R package.
Dimension reduction: PCA and t-SNE were performed directly on the k-mer count vectorizations. We used the R base algorithm prcomp for PCA with default settings. For t-SNE, we ran the Rtsne package’s algorithm for exact t-SNE (theta = 0, maximum iterations = 1000, perplexity = 35), which includes a round of PCA for data pre-processing. t-SNE results are plotted in the same number of dimensions as output by the algorithm (i.e., 3D t-SNE plots were generated with ndim = 3).
Lorenz (Jellyfish) plot: Cumulative percent abundance was calculated independently for the isoforms of each gene. First, full-length read counts were normalized across the gene and labeled percent abundance. Next, isoforms for a given gene were rank-ordered by percent abundance in descending order. Finally, a cumulative percent abundance was calculated for each isoform, via partial summation of percent abundances in descending order. Isoforms were then plotted in this order along the y-axis and positioned according to cumulative percent abundance along the x-axis.
ORF prediction and proteomics reference library
Sqanti68 (version 1.2) was used for ORF prediction and genomic correction of PacBio isoforms. To generate the lrCaptureSeq reference peptide library for proteomics, amino acid sequences were trypsinized in silico using the python program trypsin with default settings. The proline rule was followed which did not cut lysine or arginine if it immediately preceded a proline.
RNA-seq fastq files were downloaded from NCBI GEO (www.ncbi.nlm.nih.gov/geo/) and the data were mapped with Hisat2 (version 2.1.0) to reference build mm10 (for mouse), hg19 (for human), bosTau8 (bovine), danRer11 (zebrafish), and rn6 (rat). Datasets GSE101986 and GSE74660 were quantified with Cufflinks (version 2.2.1). Datasets GSE94437, GSE101544, GSE59911 and GSE84932 were quantified with StringTie (version 1.3.3b). All reference annotations for isoform quantification analysis were generated from corresponding reference GTF files merged with the Iso-Seq GFF output using the top 3 most abundant isoforms for each of the 30 genes.
Isoform predictions from RNA-seq data
Computational prediction of isoforms was performed on the RNA-seq data set GSE101986 and GSE79416 using Cufflinks (version 2.2.1) or Stringtie (version 1.3.3b) without a reference assembly. Resulting assemblies were merged using Cuffmerge to create the final reference assembly. Isoform matching between datasets was performed using Sqanti. Isoforms were considered a match if they were identified as full-splice match by Sqanti. All other isoforms were considered non-matching.
Matching of lrCaptureSeq isoforms to other databases
Sqanti was used for validation of isoforms in public databases, as well as Cufflinks/Stringtie predicted isoform databases. Validation was performed using the reference GTF (either from computational assembly, NCBI RefSeq, or UCSC Genes) as input. Isoforms were validated if they returned a full-splice match to the reference. All other isoforms were considered distinct.
Validation of isoform splice junctions and 5′ ends
Junction coverage of PacBio isoforms by RNA-seq data was assessed using Sqanti software. The junction input file for Sqanti was generated using STAR (STAR_2.6.0a) by mapping mouse retina and cortex RNA-seq data (GSE101986 and GSE79416) to the mm10 genome with a custom index made using the PacBio GFF output. Junctions were classified as either canonical (GT-AG, GC-AG, and AT-AC) or noncanonical (all other combinations).
CAGE RNA-seq data (available from the DDJB sequence read archive https://ddbj.nig.ac.jp/) from adult mouse retina (DRA002410) was used for validation of 5′ ends. Samples Sham1 (DRX019832), Sham2 (DRX019833), and Sham3 (DRX019834) were aligned to the genome (mm10) using Hisat2. Read coverage at exon 1 of the lrCaptureSeq isoforms was determined using BedTools (version 2.29.2). CAGE data coverage across normalized isoform lengths was performed using Qualimap (version 2.2.1).
Publicly available ATAC-seq data was used to assess chromatin accessibility (i.e., putative promoter sites) in mouse and human retina69,70,71. DNAse I hypersensitivity data from the ENCODE project was used for assessment of mouse cortex72. All raw fastq files were downloaded from SRA or aligned bam files from ENCODE data portal. Reads were trimmed using fastqc (version 0.11.3) and trim galore (version 0.4.1) and mapped to either the mm9 or hg19 genomes using bowtie2 (version 2.2.5). Aligned bam files were filtered for quality (>Q30) and mitochondrial and blacklisted regions were removed. Files were converted to bigwigs using deeptools (version 3.1.0) and visualized in IGV (version 2.4.16). All tracks from the same experiment are group scaled.
Shannon diversity index
The Shannon index was calculated with the R package Vegan73 according to the following equation:
In this equation pi is the proportion of isoforms found in a gene:
where ni is the number of reads for isoform i and N is the total number of reads for a gene.
Sashimi plots were generated using Gviz (version 1.24.0) with the PacBio generated GFF file. The reads for the plot were generated by mapping the PacBio FLNC.fastq (≥ 85% accuracy) file to the genome (mm10, hg19) with GMAP (version 2014-09-30). Because the FLNC reads had relatively high error rates that had not been filtered out like in our final datasets, and because expression varied by gene, minimum junction coverage was variable for each plot. Minimum junction coverage was set to 60 for Crb1 mouse retina, 4 for Crb1 Cortex, 11 for human CRB1, and 4 for Megf11.
Raw scRNAseq data profiling mouse retinal development (GSE118614)50 were aligned to a custom mm10 mouse genome/transcriptome using CellRanger (v3.0, 10X Genomics). mm10 reference genome and transcriptomes were downloaded from 10X Genomics and the GTF file was modified to identify the dominant Crb1 isoforms (Crb1-A and Crb1-B) as independent genes. As the CellRanger count function only considers alignments that uniquely map to a single gene, output files only report reads that map within the independent 3′ exons or splice into these from the most distal last shared exon.
The resulting CellRanger count output matrices (expression and barcode matrices) were imported into R and manually aggregated. The aggregate matrices were used to generate a Monocle (v3.0) cell data set, using gene annotations as the feature matrix. Transcript expression across cells was normalized to transcript copies per 10,000 (CPT)74. Established cell type annotations were imported from GSE11861450.
BaseScope in situ hybridization
Eyes were enucleated and retinas were dissected from the eyecup, washed in PBS, and fixed at RT for 24 h in PBS supplemented with 4% formaldehyde. Retinas were cryoprotected by osmotic equilibrium overnight at 4 ° in PBS supplemented with 30% sucrose. Retinas were imbedded in Tissue Freezing Medium and flash frozen in 2-methyl butane chilled by dry ice. Retina tangential sections were cut to 18 μm on a Thermo Scientific Microm HM 550 Cryostat and adhered to Superfrost Plus slides.
Probes were designed against splice junctions to detect various splicing events (see Supplementary Table 2 for sequences). Probe detection was performed using the Red detection kit. BaseScope in situ hybridization was performed according to the manufacturers protocol with slight modifications. Fixed frozen retinas were baked in an oven at 60 °C for 1 h then proceeded with standard fixed frozen pretreatment conditions with the following exceptions: Incubation in Pretreatment 2 was reduced to 2 min and Pretreatment 3 was reduced to 13 min at RT. BaseScope probes were added to the tissue and hybridized for 2 h at 40 °C. Slides were washed with wash buffer and probes were detected using the Red Singleplex detection kit. Immunostaining was performed after probe detection by incubation with primary antibodies overnight. For Megf11 BaseScope, α-Calbindin antibodies were used to label starburst amacrine cells and horizontal cells. Tissue was washed 3 times with PBS and Alexa 488-conjugated secondary antibodies were applied and incubated for 1 h at RT. Slides were washed once again and coverslips mounted.
Expression of CRB1 isoforms in K562 cells
Tagged CRB1 constructs were built by cloning YFP in-frame at the C-terminus of CRB1-A and CRB1-B. The tagged constructs were cloned into the pCAG-YFP plasmid (Addgene #11180).
K562 cells (ATCC® CCL-243™) were obtained from, validated by, and mycoplasma tested by ATCC. The cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) with 10% bovine growth serum, 4.5 g/L D-glucose, 2.0 mM L-glutamine, 1% Penicillin/Streptomycin in 10 cm cell culture dishes. Cells were passaged every 2–3 days before reaching 2 million cells/ml. Cells were transfected using the Amaxa® Cell Line Nucleofector® Kit V following instructions in the K562 nucleofection manual. Specifically, aliquots of 1 million cells were pelleted through centrifuging at 200 × g for 5 min at room temperature in Eppendorf tubes. Supernatant was completely and cell pellets were suspend in 100 ul Nucleofector® solution per sample. A total of 2 ug of plasmid DNA (pCAG:Crb1A-YFP, pCAG:Crb1B-YFP, or pCAG:YFP) were added and gently mixed with the suspended cells. Cell and DNA mixture were transfected into cuvettes, inserted into the Nucleofector® Cuvette Holder, and transfected with program T-016. Cuvettes were taken out of the holder after program is completed and immediately added with 500ul of pre-equilibrated cultured medium. These transfected cells were then divided and transferred into two wells of the 24-well glass bottom dish (MatTek Corporation). Cells were imaged 24-h post transfection with an inverted confocal microscope (Nikon).
Retina thin sectioning and electron microscopy
Mice were anesthetized with isoflurane followed by decapitation. Superior retina was marked with a low-temperature cautery to track orientation. Eyes were enucleated and fixed overnight at RT in Glut Buffer (40 mM MOPS, 0.005% CaCl2, 2% formaldehyde, 2% glutaraldehyde in H2O). The dorsal-ventral axis was marked at the time of dissection so that superior and inferior retina could subsequently be identified in thin sections. Eyes were transferred to a fresh tube containing PBS for storage 4 °C until prepped for embedding.
For thin sections, the cornea was removed from the eyecup and the eyecup was immersed in 2% osmium tetroxide in 0.1% cacodylate buffer. The eyecup was then dehydrated and embedded in Epon 812 resin. Semi-thin sections of 0.5 µm were cut through the optic nerve head from superior to inferior retina. The sections were counterstained with 1% methylene blue and imaged on an Olympus IX81 bright-filed microscope.
For electron microscopy, tissue was fixed and embedded as for thin sections. Far peripheral retina was trimmed and 65–75 µm sections were prepared on a Leica EM CU7 ultramicrotome. Sections were prepared separately from superior and inferior hemisections of each retina, and counterstained with a solution of 2% uranyl acetate + 3.5% lead citrate. Imaging was performed on a JEM-1400 electron microscope equipped with an Orius 1000 camera.
Retina nuclei counting
Retina semi-thin sections were tile scanned on an Olympus IX81 bright-filed microscope with a 60X oil objective and stitched together with cellSens software. Using Fiji software, a segmented line was drawn from the optic nerve head to the periphery for both superior and inferior retina. At intervals of 500 μm, four boxes of 100 μm were drawn encapsulating the outer nuclear layer so that the center of the box was a factor of 500 μm from the optic nerve head. For each hemisphere of the retina, four boxes were made. Using the count function in ImageJ, the total number of nuclei encapsulated by each box were counted at each position. Counts were averaged across each position and plotted as well as total counts for all 8 measurements for each retina.
Assessment of OLM junctions by electron microscopy
Each section, comprising ~90% of one retinal hemisection (far peripheral retina was trimmed during sectioning), was evaluated on the electron microscope for OLM gaps. Each potential gap was imaged and gaps were subsequently confirmed offline by evaluating the presence of electron-dense OLM junctions on the inner segments of imaged photoreceptors. The number of gaps per section was quantified, along with the size of each gap, using Fiji software. For quantification and statistics, wild-type and null/+ heterozygous controls were grouped together, since neither genotype showed any OLM gaps.
Retina serial sectioning with Western blotting
For the serial sectioning-Western blotting method52,53, mice were anesthetized with isoflurane followed by decapitation. Eyes were enucleated and dissected in ice-cold Ringer’s solution. A retina punch (2 mm diameter) was cut from the eyecup with a surgical trephine positioned adjacent next to the optic disc, transferred onto PVDF membrane with the photoreceptor layer facing up, flat mounted between two glass slides separated by plastic spacers (ca. 240 μm) and frozen on dry ice. The retina surface was aligned with the cutting plane of a cryostat and uneven edges were trimmed away. Progressive 10-μm or 20-μm tangential sections were collected—depending upon endpoint of sectioning (photoreceptors or inner retina, respectively). Blotting was performed with antibodies to CRB1-B, rhodopsin, and GAPDH (see Supplementary Table 2 for antibody details).
Sample preparation for proteomics
Juvenile (P14) mice were anesthetized with isoflurane followed by decapitation. Eyes were enucleated and dissected out of the eyecup in Ringers solution (154 mM NaCl, 5.6 mM KCl, 1 mM MgCl2, 2.2 mM CaCl2, 10 mM glucose, 20 mM HEPES). For trypsin release of ectodomains, retinas were placed in 100 μl Ringers solution containing 5 μg trypsin/lys-c. This preparation was incubated at RT for 10 min with periodic gentle mixing. Contents were then centrifuged at 300 × g for 1.5 min and the supernatant was transferred to a new tube. Urea was added to protein mixture to 8 M then incubated at 50 °C. After 1 h incubation, DTT was added to a final concentration of 10 mM and incubated for 15 min at 50 °C. Peptides were alkylated by adding 3.25 μl of 20 mM Iodoacetamide and incubated for 30 min at room temperature in the dark. Reaction was quenched by adding DTT to 50 mM final concentration. Mixture was diluted 1:3 with ~270 μl of ammonium bicarbonate. Mixture was further digested overnight by adding 1 μg of trypsin/lys-c at 37 °C.
For cell-surface biotin labeling of membrane proteins75, retinas were dissected out of the eyecup into ice-cold HBSS. Retinas were washed with HBSS followed by incubation in HBSS supplemented with EZ-Link Sulfo_NHS-SS-Biotin (0.5 mg/ml in HBSS) for 45 min on ice. Retinas were then washed 3X with HBSS + 100μM lysine to quench remaining reactive esters. Retinas were then collected in 400 μl (200 μl/retina) lysis buffer (1% Triton X-100, 20 mM Tris, 50 mM NaCl, 0.1% SDS, 1 mM EDTA). Retinas were homogenized using short pulses on a sonicator. The lysate was centrifuged at 21,000 × g for 20 min at 4 °C and the soluble fraction was collected. For immunoprecipitation, 75 μg of protein lysate was mixed with 100 μl of Streptavidin Magnetic Beads (Pierce™) and incubated at room temperature while rotating. Streptavidin/biotin complex was sequestered using a magnet and washed with lysis buffer. Proteins were eluted from the beads by incubation with elution buffer (PBS with 0.1% SDS 100 mM DTT) at 50 °C for 30 min. Experimental samples (input, biotin enriched, and non-biotin labeled negative control) were mixed with 4× SDS-PAGE sample buffer and incubated on a heat block at 90 °C for 10 min. Samples were then loaded on a 4–15% mini PROTEAN TGX Stain-Free protein gel. Electrophoresis was carried out at 65 V through the stacking gel then adjusted to 100 V until the dye front reached the end of the gel.
After electrophoresis, the gel was washed twice with H2O, fixed with 50% methanol, 7% acetic acid for 20 min and stained with colloidal Coomassie based GelCode Blue Stain reagent (Thermo Fischer Scientific, cat # 24590) for 30 min. The gel was destained with distilled water at 4 °C for 2 h while rocking. Protein bands were imaged on a Bio-Rad ChemiDoc Touch imager. Using a clean razor blade, bands between 75 and 250 kDa were excised, cut into ~1 × 1 mm pieces and collected in 0.5 ml siliconized (low retention) centrifuge tube. Gel pieces were destained with 200 μl of Destaining Solution (50 mM ammonium bicarbonate, NH4HCO3 in 50:50 acetonitrile:water) at 37 °C for 30 min with shaking. Solution was removed and replaced with 200 μl of Destaining Solution and incubated again at 37 °C for 30 min with shaking. Solution was removed from the gel pieces and peptides were reduced with 20 μl of 20 mM DTT in 50 mM ammonium bicarbonate buffer (pH 7.8)at 60 °C for 15 min. Cysteines were alkylated by adding 50 μl of the alkylation buffer (ammonium bicarbonate buffer with 50 mM Iodoacetamide) and incubated in the dark at room temparature for 1 h. Alkylation buffer was removed from tubes and replaced with 200 μl destaining buffer. Samples were incubated for 30 min at 37 °C with shaking, buffer removed, and washed again with destaining buffer. Gel pieces were dehydrated with 75 μl of acetonitrile and incubated at room temperature for 15 min. Acetonitrile was removed from tubes and shrunken gel pieces were left to dry for 15 min. Trypsin/lys-c (5 ng/μl in 25 μl of ammonium bicarbonate buffer) was added to gel pieces and incubated for 1 h at room temperature. An additional 25 μl of ammonium bicarbonate buffer was added to the tubes and incubated overnight at 37 °C. Sample volume was brought to 125 μl with distilled water, and liquid containing trypsinized peptides was placed in a clean siliconized 0.5 ml tube.
Mass spectrometry analysis
A total of 2 μl aliquots of tryptic digests were analyzed by LC-MS/MS using a nanoAcquity UPLC system coupled to a Synapt G2 HDMS mass spectrometer (Waters Corp, Milford, MA). Peptides were initially trapped on a 180 μm × 20 mm Symmetry C18 column (at the 5 μl/min flow rate for 3 min in 99.9% water, 0.1% formic acid). Peptide separation was then performed on a 75 μm × 150 mm column filled with the 1.7 μm C18 BEH resin (Waters) using the 6–30% acetonitrile gradient with 0.1% formic acid for 90 min at the flow rate of 0.3 μl/min at 35 °C. Eluted peptides were sprayed into the ion source of Synapt G2 using the 10 μm PicoTip emitter (Waters) at the voltage of 3.0 kV.
Each sample was subjected to a data-independent analysis (HDMSE) using ion mobility workflow for simultaneous peptide quantitation and identification. For robust peak detection and alignment of individual peptides across all HDMSE runs we performed automatic alignment of ion chromatography peaks representing the same mass/retention time features using Progenesis QI software. To perform peptide assignment to the ion features, ProteinLynx Global Server version 2.5.1 (Waters) was used to generate searchable files that were submitted to the IdentityE search engine incorporated into Progenesis QI for Proteomics (version 4.1). Precursor mass tolerance for the database search was 5 parts per million; fragment mass tolerance was 0.50 Da. For peptide identification we searched against the Iso-Seq/lrCaptureSeq custom database described above (see ORF Prediction subsection), as well as the UniProtKb mouse database. Post-processing using Protein and Peptide Prophet software (Scaffold 4.4) was used to evaluate confidence in peptide matches and to control false discovery rates (FDRs). Spectrum-peptide matches with <50% confidence score were excluded from further analysis. FDR cutoffs for accepting peptide and protein identifications were 1%. In addition, a target-decoy analysis was performed by searching a decoy database—reversed mouse UniProt 2016 database. The FDR measured in this way was 0.16%.
Using these procedures we identified unannotated peptides (Supplementary Data 3) as those that were detected in the custom Iso-Seq database but not in the UniProt database. To distinguish newly discovered peptides from known annotated peptides containing posttranslational modifications, we conducted additional UniProt database searches using the most common protein modifications, including phosphorylation at S,T and Y; glutamylation at E; acetylation at K; methylation at D and E. Each of these modifications was tested individually via a separate database search. No potential false identifications were found. Upon identification of spectra matching unannotated peptides, we took additional quality control steps to ensure the reliability of these matches. First, spectra were inspected manually to confirm alignment of measured and predicted peaks. Second, we compared search engine scores for spectra matching unannotated peptides to those matching known peptides. Both groups had similar search engine scores (Score range: Unannotated = 15–150; Known = 15–130. Mean score: Unannotated = 42.2; Known = 49.9. Median score: Unannotated 38.3; Known = 41.3. Match confidence for median score: Unannotated = 91.8; Known = 91.2).
Retinas from littermate WT and Crb1 mutant mice were briefly sonicated and vortexed in 400 µl of the lysis buffer containing 2% SDS in PBS plus protease inhibitor cocktail (cOmplete; Roche). The lysates were spun at 20,000 × g for 10 min at 22 °C, supernatants collected and total protein concentration determined by the DC protein assay kit (Bio-Rad). Using lysis buffer, the volumes were adjusted to normalize the lysates by total protein concentration before adding 4× SDS-PAGE buffer containing 400 mM DTT and heating the lysates for 10 min at 90 °C. Equal volumes of the lysates, each containing 15 µg total protein, were subjected to SDS-PAGE and proteins were transferred to polyvinylidene fluoride membranes (Bio-Rad). The membranes were blocked in the Odyssey blocking buffer (LiCor Bioscience) and incubated with the appropriate primary antibodies (anti-CRB1-B, anti-Phosducin, and anti-ABCA4) and Alexa Fluor 680 or 800 conjugated secondary antibodies (Invitrogen). Protein bands were imaged by the Odyssey CLx infrared imaging system (LiCor Bioscience). See Supplementary Table 2 for additional information on primary and secondary antibodies.
To separate soluble and insoluble proteins, mouse retinas were briefly sonicated and hypotonically shocked in 300 µl of water on ice. The lysed retinal suspensions were spun at 20,000 × g at 4 °C for 20 min, the resulting supernatant was collected and the pellet was rinsed once with water. The pellet and supernatant were reconstituted in a final volume of 400 µL lysis buffer, containing 2% SDS, 1x PBS, and protease inhibitor cocktail (cOmplete; Roche) Equal volume aliquots of these lysates were used as described above for Western blotting.
Statistics and reproducibility
The lrCaptureSeq experiments were performed once for each condition/age (four mouse retina timepoints; one mouse brain timepoint; one human retina condition). Mouse experiments used one C57Bl6/J animal for each condition. Human experiments used tissue from a single donor (male, age 59). Each proteomics strategy (i.e., cell surface biotinylation and trypsin ectodomain release) was performed once, although the biotinylation and pull-down conditions were worked out in pilot experiments. The gel shown in Fig. 3f is the same one used for the proteomics experiment and was representative of the pilot experiments using similar conditions. For each proteomics strategy, P14 mouse retinal tissue was pooled from multiple littermates. Even though the large-scale sequencing and mass spectrometry experiments were performed only once, we replicated key results using different experimental approaches, such as corroboration with independent short-read datasets (Fig. 6c, d; Supplementary Fig. 2b); CAGE-seq datasets (Supplementary Fig. 1d); and qPCR (Supplementary Fig. 5b, c).
PacBio sequencing was performed on two different retinal Megf11 RT-PCR reactions as shown in Supplementary Fig. 4. Together with lrCaptureSeq, therefore, we had 3 independent PacBio datasets for the Megf11 gene. RT-PCR gel images shown in Supplementary Fig. 4a are representative of many such reactions that were performed. These images are also representative of the reactions that were used for PacBio sequencing.
Mouse mutant phenotypes reported in Figs. 9, 10, and Supplementary Fig. 7b–f have been observed in multiple animals and multiple litters from different founder strains of the mutant alleles. Since the phenotypes were consistent, these groups were pooled for the analysis and reported as a single experiment. Photomicrographs in these figures are representative of the phenotypes, or the range of phenotypes, observed across animals of a given genotype.
Western blots of CRB1-B expression in delB mutants (Fig. 7c, d) are representative examples of results obtained from 3 independent experiments (i.e., 3 biological replicates). The CRB1-B blot from null mutant (Supplementary Fig. 7b) is a representative example of an experiment that was repeated twice (2 biological replicates). The serial section Western blotting experiment was repeated three times (3 biological replicates); images shown in Fig. 8d are from a single experiment. They are representative examples of the results obtained each time.
Images shown in Figs. 4e and 8c are representative of BaseScope staining that was repeated at least three times on retinal tissue from separate animals. Images of transfected K562 cells (Supplementary Fig. 6c) are representative of two independently transfected tissue culture coverslips, which were imaged in parallel. The Crb1 qPCR experiment (Supplementary Fig. 5b) was performed once, although results were consistent with smaller pilot experiments in which primers and conditions were being tested. The Crb1 RT-PCR gel (Supplementary Fig. 5c) was run twice on different RNA samples with identical results.
Statistical analyses comparing two or more groups were performed with Prism software (version 8.3.1). Tests included ordinary one-way ANOVA with Tukey’s post-hoc test, and two-way ANOVA with Sidak’s post-hoc test. All post-hoc tests were performed with corrections for multiple comparisons.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
The data that support this study are available from the corresponding author upon reasonable request. Long-read sequencing data generated in this study have been deposited in the NCBI BioProject repository (accession number PRJNA547800). The Supplementary Data 1 and Supplementary Data 2 files specify the sequence, genomic location, and read number for all isoforms within the lrCaptureSeq dataset. Mass spectrometry proteomics data generated in this study have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD017290. Crb1 isoform cDNA sequences described in this study have been deposited at Genbank with the following accession numbers: MT470365 (human CRB1-A); MT470366 (human CRB1-B); MT470367 (human CRB1-C); MT470368 (mouse Crb1-A); MT470369 (mouse Crb1-B); MT470370 (mouse Crb1-C); and MT470371 (mouse Crb1-A2).
The source data underlying graphs in Figs. 6c, d, 9j, 10c, d, Supplementary Fig. 5a, b, Supplementary Fig. 6b, and Supplementary Fig. 7c, f are provided in a Source Data file. Also see the Source Data file for full gel images related to Figs. 7c, d, 8d, Supplementary Fig. 5c, and Supplementary Fig. 7b. Source data are provided with this paper.
IsoPops code is available at https://kellycochran.github.io/IsoPops/index.html, licensed under the GNU General Public License v3.0. Source data are provided with this paper.
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This work was supported by the National Eye Institute (EY024694 and EY030611 to J.N.K., EY026344 to T.A.R., EY5722 to Duke University, EY027844 to B.S.C.); Pew Charitable Trusts (J.N.K.); McKnight Endowment Fund for Neuroscience (J.N.K.); a Duke University School of Medicine Kahn Neurotechnology Award (J.N.K.); Foundation Fighting Blindness (Individual Investigator Award BR‐CMM‐0619‐0767‐DUKE to J.N.K.); and Research to Prevent Blindness (Unrestricted Grant to Duke University; Unrestricted Grant to Washington University). Duke Research Computing provided computing resources for data analysis. This project benefitted from data storage capacity provided by the Duke Data Commons, a data storage installation underwritten by the NIH (1S10OD018164-01). We thank the Duke Transgenic and Knockout Mouse Shared Resource for their assistance in making mutant mice, Liz Tseng (PacBio) for assistance with Iso-Seq 3, and Ariane Pereira for mouse colony management.
T.A.R. and J.N.K.: Patent pending, “Compositions and Methods for the Diagnosis and Treatment of Retinopathies.” A.L., M.-X.H., X.W., and E.P. were employed by Advanced Cell Diagnostics at the time of this project. All other authors declare no competing interests.
Peer review information Nature Communications thanks Wei Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Ray, T.A., Cochran, K., Kozlowski, C. et al. Comprehensive identification of mRNA isoforms reveals the diversity of neural cell-surface molecules with roles in retinal development and disease. Nat Commun 11, 3328 (2020). https://doi.org/10.1038/s41467-020-17009-7
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