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Assessing long-distance RNA sequence connectivity via RNA-templated DNA–DNA ligation

  1. Christian K Roy
  2. Sara Olson
  3. Brenton R Graveley
  4. Phillip D Zamore Is a corresponding author
  5. Melissa J Moore Is a corresponding author
  1. Howard Hughes Medical Institute, University of Massachusetts Medical School, United States
  2. University of Massachusetts Medical School, United States
  3. University of Connecticut Health Center, United States
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Cite as: eLife 2015;4:e03700 doi: 10.7554/eLife.03700

Abstract

Many RNAs, including pre-mRNAs and long non-coding RNAs, can be thousands of nucleotides long and undergo complex post-transcriptional processing. Multiple sites of alternative splicing within a single gene exponentially increase the number of possible spliced isoforms, with most human genes currently estimated to express at least ten. To understand the mechanisms underlying these complex isoform expression patterns, methods are needed that faithfully maintain long-range exon connectivity information in individual RNA molecules. In this study, we describe SeqZip, a methodology that uses RNA-templated DNA–DNA ligation to retain and compress connectivity between distant sequences within single RNA molecules. Using this assay, we test proposed coordination between distant sites of alternative exon utilization in mouse Fn1, and we characterize the extraordinary exon diversity of Drosophila melanogaster Dscam1.

https://doi.org/10.7554/eLife.03700.001

eLife digest

A flow chart can show how an outcome can be achieved from a particular start point by breaking down an activity into a list of possible steps. Often, a flow chart contains several alternative steps, not all of which are taken every time the flow chart is used. The same can be said of genes, which are biological instructions that often contain many options within their DNA sequences.

Proteins—which perform many roles in cells—are built following the instructions contained in genes. First, the DNA sequence of the gene is copied. This produces a molecule of ribonucleic acid (RNA), which is able to move around the cell to find the machinery that can use the genetic information to make a protein. Genes and their RNA copies contain instructions with more steps—called exons—than are necessary to make a working protein, so extra exons are removed (‘spliced’) from the RNA copies. Different combinations of exons can be removed, so splicing can make different versions of the RNA called isoforms. These allow a single gene to build many different proteins. In fruit flies, for example, the different exons of the gene Dscam1 can be spliced into one of 38,016 unique RNA isoforms.

Current technology only allows researchers to deduce the sequence of RNA molecules by combining sequences recorded from short fragments of the molecule. However, before splicing, RNA molecules tend to be much longer than this, so this restricts our understanding of the RNA isoforms found in cells. Here, Roy et al. devised and tested a new method called SeqZip to solve this problem.

SeqZip uses short fragments of DNA called ligamers that can only stick to the sections of RNA that will remain after the molecule has been spliced. After splicing, the ligamers can be stuck together to make a DNA replica of the spliced RNA. The end product is at least 49 times shorter than the original RNA, so it is easier to sequence. In addition, the combinations of the ligamers in the DNA replica show which exons of a specific gene are kept and which ones are spliced out.

To test the method, Roy et al. studied a mouse gene that has six RNA isoforms. SeqZip reduced the length of the RNA by five times and made it possible to measure how frequently the different isoforms naturally arise. Roy et al. also used SeqZip to work out which isoforms of the Dscam1 gene are used at different stages in the life of fruit fly larvae. SeqZip can provide insights into how complex organisms like flies, mice, and humans have evolved with relatively few—a little over 20,000—genes in their genomes.

https://doi.org/10.7554/eLife.03700.002

Introduction

One of the most important drivers of metazoan gene expression is the ability to produce multiple mRNA isoforms from a single gene. Around 58% of Drosophila melanogaster genes and >95% of human genes produce more than one transcript (Pan et al., 2008; Wang et al., 2008; Brown et al., 2014), with most human genes expressing 10 or more distinct isoforms (Djebali et al., 2012). Alternative promoter use, alternative splicing, and alternative polyadenylation all contribute to isoform diversity. In genes with multiple alternative transcription start and/or pre-mRNA processing sites, their combinatorial potential exponentially increases the number of possible products, with some human genes predicted to express >100 mRNA isoforms. In D. melanogaster, the number of isoforms observed per gene correlates with open reading frame length, suggesting that isoform complexity is a function of transcript length (Brown et al., 2014). The current record holder in this regard is Dscam1, in which four regions of mutually exclusive cassette exons combine to generate a remarkable 38,016 distinct >7000 nt mRNAs, each encoding a unique protein isoform (Schmucker et al., 2000).

In Dscam1, the four regions of mutually exclusive cassette exon splicing are separated by one to eight constitutive exons. This feature of multiple alternative splicing regions separated by constitutive exons is shared by more than a quarter of human genes (Fededa et al., 2005). In many cases, these regions are separated by >500 nts, the current limit for contiguous sequence output on most deep sequencing platforms. Further, high-throughput sequencing of RNA (RNA-Seq) generally requires its reverse transcription, with the processivity of available reverse transcriptases (RTs) limiting even single molecule cDNA sequencing (e.g., Pacific Biosciences) to <2500 nt (Sharon et al., 2013). Thus, existing high-throughput technologies cannot readily retain connectivity information between very distant sequences within individual mRNA molecules. Instead, full-length transcripts must be inferred by piecing together multiple short overlapping reads (Garber et al., 2011; Grabherr et al., 2011; Haas et al., 2013; Boley et al., 2014). For widely separated regions of alternative exon use, this loss of connectivity significantly limits our abilities to catalog isoform abundance and understand the mechanisms underlying alternative isoform generation.

Here, we describe SeqZip, a method for profiling multiple distant (>1000 nt) sites of alternative splicing within individual RNA molecules. SeqZip uses sets of DNA oligonucleotides termed ‘ligamers’. Each ∼40–60 nt ligamer hybridizes to the 5′ and 3′ ends of a single alternatively spliced exon or the beginning and end of a large block of constitutively included exons, looping out the sequences in between. These loops can be hundreds to thousands of nucleotides long. Juxtaposed ligamers hybridized to single RNA molecules are then joined by enzymatic ligation with T4 RNA ligase 2 (Rnl2) (Ho and Shuman, 2002). The resultant DNA ligation products both capture the intramolecular connectivity among exons of interest and compress the sequence space necessary to identify those exons. Exon connectivity is subsequently decoded by assessing the sizes or sequences of the ligation products. Because SeqZip does not include an RT step and is therefore not subject to RT processivity and template-switching limitations, it can be used to assess intramolecular connectivity between regions separated by thousands of nucleotides. Further, relative ligation product abundance accurately reports spliced isoform abundance in the original sample. As a proof-of-principle, we here used SeqZip to test proposed connectivity relationships among alternatively spliced exons in mouse Fibronectin (Fn1) and to define the molecular diversity of fly Dscam1.

Results

A reverse transcription-free method to assess sequence connectivity

The general idea of SeqZip is schematized in Figure 1. This method requires efficient ligation of multiple DNA oligonucleotides (oligos) hybridized to an RNA template with little or no non-templated ligation. Although many ligases can join DNA or RNA oligos hybridized to a DNA template (Bullard and Bowater, 2006), when we initiated this study, only T4 DNA ligase was reported to join DNA fragments templated by RNA (Nilsson et al., 2001). While T4 DNA ligase is the basis of multiple RNA-templated DNA ligation methods (Nilsson et al., 2001; Yeakley et al., 2002; Conze et al., 2010; Li et al., 2012), it also catalyzes non-templated DNA ligation (Kuhn and Frank-Kamenetskii, 2005), which would reduce SeqZip fidelity.

Figure 1 with 2 supplements see all
Principles of SeqZip.

The target RNA is hybridized with a set of DNA oligonucleotides (‘ligamers’). Ligamers targeting outermost sequences contain one region of complementarity and primer sequences for subsequent amplification. Internal ligamers contain two regions of complementarity separated by a spacer sequence. Hybridization of the internal ligamers causes the RNA between the hybridization sites to loop out. Hybridized ligamers are ligated, amplified, and analyzed.

https://doi.org/10.7554/eLife.03700.003

To find a suitable ligase for SeqZip, we tested the ability of several other commercially available enzymes to ligate four or five 5′ 32P-radiolabeled 20-nt DNA oligos hybridized to adjacent positions on either DNA or RNA (Figure 2A). Although all DNA ligases tested could efficiently join multiple oligos hybridized to the DNA template (Figure 2A; Bullard and Bowater, 2006), only T4 DNA ligase and RNA ligase 2 (Rnl2) joined the DNA oligos when hybridized to the RNA template. Of the two, Rnl2 was more active for RNA-templated DNA ligation (data not shown) and produced <1/7 as much non-templated product as T4 DNA ligase (Figure 2A). Moreover, Rnl2 could not ligate DNA oligos hybridized to the DNA template, eliminating the possibility of contaminating genomic DNA confounding SeqZip (Figure 2A). We note that Chlorella virus DNA ligase was recently commercialized for the purpose of RNA-templated DNA–DNA ligation (SplintR ligase; NEB) (Lohman et al., 2014). We found, however, that SplintR ligase produces more non-templated DNA–DNA ligation events than Rnl2 (Figure 2—figure supplement 1). Also, while our paper was under review, another group reported RNA-templated DNA–DNA ligation by Rnl2 (Larman et al., 2014), further validating its use in SeqZip.

Figure 2 with 1 supplement see all
T4 RNA Ligase 2 catalyzes RNA-templated DNA-to-DNA ligation.

(A) Left panel: ligase screen for RNA-templated DNA–DNA ligation activity. Ligases were incubated with an unlabeled single-stranded DNA (left) or RNA (right) template hybridized to a common pool of 5′ end 32P-labeled (circled P) DNA oligonucleotides for 1 hr. Both T4 DNA ligase and T4 RNA ligase 2 (Rnl2) catalyze RNA-templated DNA–DNA ligation. Also note the inability of Rnl2 to ligate >2 oligos on the DNA template. For both templates, ligases are left to right: Tth DNA ligase (Thermo), Tsc DNA ligase (Prokaria), Thermostable DNA ligase (Bioline), T4 DNA ligase (NEB), T4 Rnl2 (NEB), E. coli DNA ligase (NEB). The three rightmost lanes are 32P-oligos only, 32P-labeled RNA template, and a 32P-labeled low-molecular weight DNA ladder (NEB, N3233S). Right panel: Rnl2 and T4 DNA ligase time course for oligos hybridized to the RNA template. Templated ligation products (–x2 through –x5); non-templated ligation product (*–x6). (B) Rnl2 can join multiple 32P-labeled ligamers each looping out sections of the template but only when they are adjacently hybridized. Gray or white square: ligamer present or absent, respectively. No template (-T); no enzyme (-E). (C) Cis- and trans-transcript hybridization and ligation using a ligamer (W) spanning 1046 nt common to two RNAs (XWY and VWZ). Template concentrations (nM) were as indicated above each lane (ranging from 0.01 to 100 nM), ligamers were held constant at 10 nM. Left panel, phosphoimage; right panel, SybrGold stained. (D) The ability of SeqZip to accurately report on relative input RNA concentrations was investigated using various ratios of two RNAs (XWZ and VWY) and a six ligamer pool. Observed product ratios were calculated from radioactive PCR band intensities.

https://doi.org/10.7554/eLife.03700.006

The SeqZip design requires efficient ligation of multiple DNA oligos (ligamers), some spanning loops in the RNA template (Figure 1). To test the ability of Rnl2 to ligate these species, we designed four different 26 nt ligamers to loop out various lengths of a 307 nt transcript (Figure 2B). Each 26 nt ligamer contained 10 nt of complementarity on either side of the loop, with a 6 nt spacer opposite the loop. The four ligamers—individually, pairwise, in threes, or as a complete set—were annealed to the template RNA and incubated with Rnl2. Ligation products were only observed when ligamers bound to adjacent RNA sequences; four-way ligation products were obtained only when all ligamers were present. Thus, ligamers designed to loop out various lengths of a template RNA can be used to condense by more than twofold the information required to assess RNA connectivity—244 nt of the target RNA was condensed to a 104 nt DNA. Subsequent ligamer designs condensed connectivity information by >49-fold.

Minimal trans-transcript hybridization and ligation

A ligamer designed to loop out the sequences in between widely spaced regions of complementarity has the potential to bridge two RNA molecules. Such intermolecular (trans) hybridization would interfere with measurement of intramolecular (cis) RNA connectivity, producing artifacts akin to template switching in RT-based methods (Figure 2C; Houseley and Tollervey, 2010). To test the frequency of such trans events, we mixed two RNAs, each comprising a common 1106 nt internal sequence flanked by unique 5′ and 3′ sequences, with a ligamer set in which a single internal ligamer (W) looped out 1046 nt of the shared internal sequence (Figure 2C). Because the terminal ligamers (X, Y, V, and Z) varied in length, polymerase chain reaction (PCR) of SeqZip reactions yielded 177 and 143 nt cis-templated products and 165 and 155 nt trans-templated products. Trans hybridization of ligamer W, a tri-molecular interaction, should be much more sensitive to RNA concentration than bimolecular cis hybridization. Consistent with this, whereas cis products were detected by end point PCR at every target RNA concentration tested down to 0.01 nM, trans products were only detected when target RNAs were ≥10 nM, (Figure 2C, lower half). But, even when both targets were present at 50 nM, semi-quantitative radioactive PCR revealed that the cis hybridization products predominated (Figure 2C, lower left). Nonetheless, to disfavor trans hybridization, the general conditions for SeqZip described below use cellular RNA concentrations (10–40 ng/ml polyA+ RNA) at which most individual mRNAs are present at <1 nM.

To be useful as a quantitative method, SeqZip should accurately report on input RNA abundances. To test this, we mixed two target RNAs at ratios varying from 100:1 to 1:100 (a 100-fold dynamic range). Radioactive PCR revealed that their respective SeqZip product ratios paralleled these input ratios over the entire series (Figure 2D).

SeqZip vs RT-based analysis of CD45 spliced isoforms

As a first test of SeqZip with a biological sample, we used it to assess alternative exon inclusion in endogenous human CD45 (PTPRC) mRNA (Zikherman and Weiss, 2008). CD45 isoforms contain various combinations of exons 4, 5, and 6 (Figure 3A). Jurkat cells (resembling naïve, primary T cells) predominantly express isoforms containing exons 5 and 6 (R56), only exon 5 (R5), or no cassette exon (R0). U-937 cells (resembling activated T cells) predominantly express the R56 isoform and one containing exons 4, 5, and 6 (R456; Yeakley et al., 2002). The three adjacent cassette exons occupy only 585 nt, making this region amenable to analysis by both reverse transcription and SeqZip. Reverse transcription-PCR (RT-PCR) products ranged from 365 to 848 nt, while SeqZip products ranged from 132 to 260 nt (Figure 3B), representing a ∼threefold compression of connectivity information.

SeqZip assay to measure endogenous mRNA isoform expression.

(A) The SeqZip strategy to detect human CD45 mRNA isoforms. (B) Denaturing PAGE gels showing products of reverse transcriptase (RT) (top left) or SeqZip (bottom left) CD45 mRNA obtained from two different human Jurkat and U-937 T-cell lines, or a 1:1 mixture of the two. Top right: quantified band intensities from gels at left. Bottom right: mirrored lane profiles from the mix lanes (RT—left; SeqZip—right). (C) The six possible combinations of EDA (blue; + or −) and V (light blue; 120, 95 and 0) alternative splicing within mouse Fn1 transcripts. Filled boxes depict exons, diagonal lines indicate isoform sequences not shown, and straight lines show absence of exon(s) in the final mRNA. (D) Detailed schematic of ligamer pools used to analyze indicated regions of Fn1 RNA. (E) SeqZip ligation products from immortalized MEFs with indicated Fn1 genotypes. Radioactive PCR separated on a native acrylamide gel. (F) Fn1 isoform abundance measured by SeqZip and PacBio. Black bars indicate observed individual exon (‘Individual Pool’; EDA, V) or combination frequencies (‘Combination A + V pool’, [EDA, V]). Shown in light gray are expected combination isoform intensities, and where available, the frequency of PacBio reads (mid-gray, lower bars).

https://doi.org/10.7554/eLife.03700.008

Using RT-PCR and SeqZip, we measured CD45 isoforms from Jurkat or U-937 poly(A)-selected RNA or a 1:1 mixture of the two. Both methods reported the expected isoform abundances (Figure 3B). Importantly, even though SeqZip detection of R456, R56, R5, and R0 required different numbers of ligation events, all relative abundances were accurately reported, even in the mixture containing all four isoforms (Figure 3B, lower right).

SeqZip and PacBio analysis of mouse Fn1 isoform connectivity

For a more complex splicing pattern, we next turned to fibronectin (Fn). Mouse Fn1 contains three well-characterized regions of alternative splicing: (1) the EDB exon included in embryos and adult brain but not other adult tissues, (2) the EDA exon variably included or excluded across multiple developmental and adult tissue types, and (3) the variable (V) region in which use of three alternative 3′ splice sites leads to inclusion of 120, 95, or 0 additional amino acids in the FN1 protein (Figure 3C). The original suggestion that an upstream splicing decision can affect a downstream splicing decision came from analysis of the EDA and V regions where it was reported that EDA exclusion promotes use of the promoter-proximal 3′ splice site (‘120’) in the V region (Fededa et al., 2005). The EDA and V regions are separated by six constitutively included exons, comprising 813 nt; thus, RT-PCR products including the EDA and V regions range from 1 to 1.6 kbp (Figure 3D). Both the overall length of the RT-PCR products and the extensive region of similar sequence identity in the middle that can promote template switching (see below) confound RT-PCR analysis of the six possible EDA and V exon combinations. In comparison, our SeqZip ligation products were >fivefold smaller (139–318 nt; Figure 3D,E), and they contained no intervening region of extensive nucleotide identity. Thus, SeqZip provided a new means to test the possibility of connectivity between Fn1 EDA and V splicing decisions.

The effects of EDA inclusion or exclusion on V region splicing were previously tested by creating mice via homologous recombination with intronic splicing enhancers modified to favor either constitutive inclusion (+/+) or exclusion (−/−) of the EDA exon (Chauhan et al., 2004). That study also analyzed mice heterozygous for the modified locus (+/−) and the wild-type parental strain (wt/wt). We obtained immortalized mouse embryonic fibroblasts generated from all four mouse lines and performed SeqZip analysis (Figure 3E,F). Three different ligamer pools allowed us to analyze each region in isolation (individual pools A and V) or both regions together (combination pool A + V) (Figure 3D). EDA and V isoform ratios determined from low cycle, radioactive PCR band intensities of the A and V pool ligation products (SeqZip: Observed) were used to calculate expected EDA:V isoform abundances, assuming no interdependence between the two regions (SeqZip: Expected). We also generated cDNAs by low-cycle RT-PCR and sequenced them on a Pacific Biosciences RSII instrument (PacBio:Observed), a single molecule platform with sufficient read length to maintain connectivity between the EDA and V regions (Sharon et al., 2013).

In both the SeqZip and PacBio data sets, constitutive EDA inclusion or exclusion was as expected in the +/+ and −/− cells, respectively. Unexpectedly, however, we could not detect any EDA inclusion in the +/− cells despite confirming the presence of both alleles in gDNA (data not shown). Regardless, neither SeqZip nor PacBio yielded any evidence for an effect of EDA inclusion or exclusion on V region splice site choice. That is, in no case was the observed frequency of any A + V combination statistically different from the frequency expected for independent events. This was also our observation in primary mouse embryonic fibroblasts (MEFs) from wild-type mice (Figure 3F). Our results thus support the view that the EDA and V regions of mouse Fn1 are spliced autonomously (Chauhan et al., 2004).

SeqZip eliminates template-switching artifacts in the analysis of Dscam1 isoforms

For the Drosophila Dscam1 gene, alternative splicing of four blocks of mutually exclusive cassette exons (exons 4, 6, 9, and 17) can potentially produce 38,016 possible mRNA isoforms (Figure 4A). Previous studies suggest that all isoforms can be generated (Neves et al., 2004; Zhan et al., 2004; Sun et al., 2013), with all 12 exon 4 variants being stochastically incorporated in individual neurons (Miura et al., 2013).

Figure 4 with 1 supplement see all
Analysis of Dscam1 isoforms via high-throughput sequencing.

(A) Architecture of Dscam1. Black: constitutively included exons; colors: variant exons. Only one cassette exon per variant region is included in the mRNA. (B) Sequence similarity between 1000 random isoforms of Dscam1 in cDNA, circularized cDNA, and SeqZip ligation product form. All lengths are shown to scale. (C) Strategy to measure Dscam1 isoform diversity using SeqZip on the MiSeq platform. (D) Strategy to measure Dscam1 isoform diversity by triple-read sequencing on the Illumina MiSeq platform.

https://doi.org/10.7554/eLife.03700.009

Previous high-throughput methods for examining Dscam1 exon connectivity relied on RT-PCR, a technique potentially confounded by long stretches of sequence identity in the constitutive exons separating each cluster and by sequence similarity among exon 4, 6, and 9 variants (Figure 4B). Long regions of sequence homology promote template switching during both RT and PCR (Judo et al., 1998; Houseley and Tollervey, 2010); this can generate novel isoforms not originally present in the biological sample. SeqZip can both dramatically reduce these regions of sequence of identity (Figure 4B) and introduce new exon-specific codes (Figure 4C). Thus, in addition to maintaining connectivity information, SeqZip both compresses sequence length and increases sequence heterogeneity, thereby greatly decreasing the potential for template switching compared to cDNAs created by standard RT or circularized cDNA approaches.

Prior to our development of SeqZip, we had attempted to use a RT-PCR-based triple-read sequencing method to determine exon connectivity between Dscam1 alternative splicing regions 4, 6, and 9 (Figure 4D, Figure 4—figure supplement 1B, ‘Materials and methods’). To measure the extent of template switching, we generated four RNA transcripts corresponding to distinct isoforms. As expected, this RT-based method detected many novel transcript isoforms containing exon combinations not present in the four input transcripts (Figure 4—figure supplement 1B). These template-switched isoforms represented 34–55% of the isoforms detected, with many being significantly more abundant than one or more of the input isoforms.

A similar circularized cDNA method, CAMSeq, has also been used to assess Dscam1 exon connectivity (Sun et al., 2013). In light of the high rate of template switching in our triple-read sequencing approach, we reexamined the published CAMSeq control data to assess the extent of template-switching events (Figure 4—figure supplement 1B). Indeed, template-switched isoforms were present in the CAMSeq data, with many template-switched isoforms being more abundant than the low abundance input isoforms. Moreover, we detected 5386–5914 additional isoforms whose presence could not be explained by either the composition of the 8 input RNA isoforms or by template switching. Thus, while CAMSeq was a clear improvement over both previous linear RT-PCR-based approaches and our triple-read sequencing approach, template-switching artifacts remained a substantial problem.

By eliminating RT and using exon-specific barcodes to ensure unambiguous isoform assignment (Figure 4C), SeqZip should greatly reduce template switching. To measure this directly, we mixed together three different in vitro-transcribed Dscam1 isoforms in the presence of total RNA from a mouse hepatoma cell line (Hepa 1–6c; Figure 5A). This mixture was then divided into two separate ligation reactions, each containing a complete 97 ligamer pool that differed only in the 7 nt ligamer barcode assigned to two exons in each cluster (* in Figure 5A). Following ligation, the differentially coded samples were mixed together, subjected to PCR, and sequenced on the MiSeq platform (on which paired-end reads can cover a total of 500 nts). Of the 50,475 reads obtained in these control reactions, none were indicative of template switching (i.e., no ligation product contained both pool 1 and pool 2 barcodes; Figure 5B). Moreover, when the same differential coding approach was applied to Drosophila S2 cell poly(A)-selected RNA, just 17 of 111,242 reads (0.015%) corresponded to template-switched isoforms (Figure 5B, Figure 4—figure supplement 1B). Thus, the SeqZip design greatly diminishes template switching.

Figure 5 with 1 supplement see all
SeqZip Dscam1 control experiments.

(A) Three in vitro-transcribed cDNAs used as controls containing the exon variants indicated and mixed in a 100:10:1 relative ratio. Also shown are a schematic of the ligamer pool, with each ligamer targeting a different variant exon, the six ligamers (*) containing having different codes in pool 1 and pool 2, and a workflow for identifying near-cognate ligation and template-switching events. (B) Schematic showing how template-switched isoforms were identified as an incorrect combination of barcodes unique to the differentially coded pools shown in (A). Also shown are the observed numbers of un-switched and template-switched reads and isoforms for controls in (A) and S2 cellular RNA. (C) Quantification of in vitro-transcribed control cDNAs analyzed by SeqZip according to the workflow shown in (A).

https://doi.org/10.7554/eLife.03700.011

Sequences at the ends of target exons specify where ligamers bind (Figure 1). The high similarity among the cassette exon sequences within each cluster raised the possibility that ligamers would bind near-cognate as well as cognate sequences. To assess the potential for mis-pairing, we calculated the free energy of hybridization (Reuter and Mathews, 2010) between each ligamer and all exon variants within its target cluster (Figure 5—figure supplement 1A). Cognate ligamer–exon pairs had predicted hybridization energies lower than ΔG° = −67 kcal/mol; the closest near-cognate pair was ≥12 kcal/mole higher. In the control experiments containing just three Dscam1 isoforms, only 642 of 50,475 high-confidence alignments (1.3%) contained ligamers for exons not present in any input transcript, with the majority of these species (221/236) represented by three or fewer reads (Figure 4—figure supplement 1B). Nonetheless, two near-cognate hybridization products with >100 reads were detected. Although both were less abundant (2.4- and 372-fold lower) than reads corresponding to cognate targets (Figure 5C and Figure 4—figure supplement 1B), this does raise a cautionary note with regard to interpretation of extremely low abundance ligation products in experiments wherein near-cognate ligation is a possibility. On the other hand, SeqZip accurately reported input cognate isoform abundances over 3 orders of magnitude (Figure 5C). Thus, as with CD45 and Fn1 isoforms, SeqZip proved highly quantitative for assessing the majority of Dscam1 isoforms.

SeqZip analysis of Dscam1 isoforms

We next used SeqZip to measure Dscam1 isoform identity and abundance in S2 cells, as well as 4–6 hr and 14–16 hr D. melanogaster embryos. Ligamers targeting every exon variant in clusters 4, 6, and 9 together with ligamers for constitutive exons 3, 5, 7, 8, and 10 (97 ligamers in all) reduced the median size of mRNA sequences analyzed from 1734 nt (1722–1751 nt for exons 3–10) to 356 nt for a seven-ligamer product formed by six ligation events. This approximately fivefold length reduction allowed the products to be fully sequenced using 250 bp, paired-end reads in a single Illumina MiSeq run (Figure 4C). Between 449,113 and 946,110, high-confidence alignments were obtained for each sample (Supplementary file 2). Across all three samples, SeqZip detected 8397 of the 18,612 possible isoforms (Figure 6A). Individual isoform abundances were highly correlated between both technical and biological replicates (r = 0.8–0.95, p < 2.2 × 10−16, Fisher z-transformation; Figure 6—figure supplement 1). Of the 97 possible exons represented in our ligamer set, all were detected except exon 6.11, which is generally thought to be an unused pseudo-exon (Neves et al., 2004; Zhan et al., 2004; Watson et al., 2005; Miura et al., 2013; Sun et al., 2013). The absence of exon 6.11 reads from our libraries provides additional evidence for the specificity of SeqZip. Further, with two exceptions, the patterns of individual exon use in S2 cells were directly comparable between the SeqZip and CAMSeq data sets (r = 0.87, p < 2.2 × 10−16, Fisher z-transformation; Figure 7—figure supplement 1): exon 6.47 was well represented in the CAMSeq data but undetectable by SeqZip, and exon 9.31 was more abundantly represented in our data.

Figure 6 with 1 supplement see all
SeqZip captures diverse Dscam1 isoform expression and exon use.

(A) Rank-order of isoform expression by sample type (S2, 4–6 hr, 14–16 hr). (B) Individual exon usage per library for each replicate (differently shaded bars).

https://doi.org/10.7554/eLife.03700.013

Comparison of exon usage patterns across three different biological samples revealed increasing isoform diversity with tissue complexity: S2 cells were the least diverse, 4–6 hr embryos had intermediate isoform diversity, and 14–16 hr embryos showed the greatest isoform diversity (Figure 6B). As previously shown, cluster 4 and 9 exon usage patterns change during development, whereas the cluster 6 pattern remains more static (Celotto and Graveley, 2001; Neves et al., 2004; Zhan et al., 2004; Miura et al., 2013; Sun et al., 2013). In S2 cells, Dscam1 mRNAs incorporate very little of exon 4 cassettes 2 and 9 and use almost exclusively exon 9 cassettes 6, 9, 13, 30, and 31. This pattern is the characteristic of hemocytes (Watson et al., 2005) and consistent with the macrophage-like nature of S2 cells (Schneider, 1972). Whereas 4–6 hr embryos are similar to S2 cells in exon clusters 4 and 9, 14–16 hr embryos show increased exon diversity, particularly in cluster 9. Figure 7 shows that Dscam1 isoforms associated with hemocytes (i.e., those lacking exon 4.2 and 4.9 or containing exon 9 cassettes 6, 9, 13, 30, or 31) are the most abundant in all three samples, but other isoforms emerge as development proceeds.

Figure 7 with 1 supplement see all
Observed vs expected Dscam1 isoform abundance.

Two-way (4:6, 6:9, and 4:9) and three-way (4:6:9) expected isoform abundances, calculated from the individual inclusion frequency for each variant exon (Figure 6B) in indicated sample type (S2 cells, 4–6, or 14–16 hr embryos), plotted against observed isoform abundances in that sample type. Isoforms are colored according to hemocyte-indicative (red) or non-hemocyte-indicative (blue) exon variants.

https://doi.org/10.7554/eLife.03700.015

To examine the possibility of coordinated splicing, we calculated expected pairwise and three-way exon combination frequencies for every transcript isoform observed in each sample (S2 cells, 4–6 hr and 14–16 hr embryos), assuming a null hypothesis of no coordination (see ‘Materials and methods’). Comparison of expected and observed frequencies (Figure 7) revealed no statistically significant differences (q ≤ 0.05) between expectation and observation for S2 cells and 4–6 hr embryos. For 14–16 hr embryos, however, 17 of 371 observed exon 4:9 combination frequencies, and 14 of 2004 observed 4:6:9 combination frequencies, did fall outside of the expected range (q < 0.05, Supplementary file 4). The only pattern we could deduce was that the variant 4:9 combinations were all non-hemocyte combinations (Figure 7). Because the majority of 4:6:9 combination frequencies (99.3%) were consistent with the null hypothesis of no coordination, our data agree with previous studies (Neves et al., 2004; Sun et al., 2013) that individual cassettes in Dscam clusters 4, 6, and 9 are chosen independently, with exon choice in one cluster having no detectable effect on subsequent exon choice in another cluster.

Discussion

SeqZip provides a new strategy to retain and compress key sequence information in long RNAs while maintaining connectivity between distant regions within individual molecules. Free from the template-switching artifacts that limit RT-based methods, SeqZip can be used to quickly and efficiently examine complex alternative splicing patterns and quantify isoform expression for genes harboring multiple distal regions of alternative splicing. Here, we used SeqZip to examine the possibility of coordination between distant alternative splicing regions in mouse Fn1 and D. melanogaster Dscam1 mRNAs. Consistent with other studies, SeqZip revealed no evidence of such coordination.

Coordination between alternative processing events

The idea that promoter-proximal (upstream) gene regions can affect distal (downstream) alternative splicing was first reported for the mammalian Fn1 gene almost two decades ago (Cramer et al., 1997). Expressed sequence tag and oligonucleotide microarray data were subsequently interpreted to suggest coordination among different regions of alternative splicing in numerous other genes (Fededa et al., 2005; Fagnani et al., 2007). One potential explanation for this effect is promoter-dependent polymerase speed: slow RNA synthesis favors inclusion of cassette exons with weak splicing signals, while fast RNA synthesis favors their exclusion (Fededa et al., 2005; Dujardin et al., 2014). This polymerase speed effect does not necessitate any dependence of a downstream splicing decision on an upstream decision—the multiple sites of alternative splicing can be independently but similarly influenced by polymerase speed. In this case, although alternative splicing decisions in different regions within a transcript might correlate with one another, they would not depend on one another.

Neither oligonucleotide microarrays nor short high-throughput sequencing reads preserve long-range exon connectivity within individual mRNA molecules. Thus, neither approach is able to unambiguously distinguish between dependent coordination, wherein alternative processing at an upstream site causes alternative processing changes at a downstream site, and independent coordination, wherein multiple regulated exons are simply subject to similar external influences (Calarco et al., 2007). SeqZip, however, does preserve single molecule connectivity, so is perfectly suited to investigate coordination mechanisms. Our proof-of-principle SeqZip experiments with the mouse Fn1 gene revealed no evidence of dependent coordination between the two regions of alternative splicing we examined (Figure 3). Therefore, consistent with other findings (Chauhan et al., 2004), we conclude that any apparent coordination between the Fn1 EDA and V regions is due to their independent response to external influences.

Deconvoluting Dscam1

With 38,016 possible isoforms, D. melanogaster Dscam1 produces the greatest known isoform diversity of any single gene. Diverse Dscam1 isoforms enable the developing nervous and immune systems to discriminate between heterotypic and homotypic connections (Wojtowicz et al., 2004; Watson et al., 2005; Zipursky and Grueber, 2013). While flies engineered to produce only 4752 unique isoforms display neurite formation functionally equivalent to wild-type controls, flies expressing just 1152 isoforms display neuronal branching defects. This supports the view that what is essential for biological function is molecular diversity not any particular sequence (Hattori et al., 2009). Both D. melanogaster and Anopheles gambiae (AgDscam) also express Dscam in hemocytes, where isoform diversity has been implicated in opsonizing invading pathogens (Watson et al., 2005; Dong et al., 2006).

Complete characterization of Dscam1 isoform diversity presents an extreme technical challenge (Hattori et al., 2008). The four regions of mutually exclusive cassette exons span >4300 nt in full-length mRNAs, so maintaining connectivity among all cassettes or even just cassettes 4, 6, and 9, which span >1700 nt, is all but impossible when sequencing with current high-throughput technologies (Black, 2000; LeGault and Dewey, 2013; Zipursky and Grueber, 2013). Single molecule methods capable of longer reads (e.g., Pacific Biosciences) have limited read depths, making it difficult to fully analyze transcripts expressed over many orders of magnitude. Finally, many Dscam1 exon variants arose from exon-duplication events, so their sequences are highly similar (Lee et al., 2010). This high-sequence similarity, combined with the long stretches of identical constitutive exons separating the distant alternative splicing regions, strongly favors template switching by RT (Judo et al., 1998; Houseley and Tollervey, 2010).

SeqZip has no RT step, it eliminates long intervening regions of common sequence, and the unique exon-specific barcodes introduced during the ligation step further discourage template switching during subsequent amplification. Using pools of 97 individual ligamers targeting every exon in clusters 4, 6, and 9, we analyzed Dscam1 diversity in S2 cells, 4–6 hr, and 14–16 hr embryos. In all three samples, we observed individual exon use frequencies similar to those observed with CAMSeq (Figure 7—figure supplement 1; Sun et al., 2013). SeqZip and CAMSeq both detected significant exon usage changes in clusters 4 and 9 between S2 cells and embryos. Analysis of 4–6 hr and 14–16 hr embryos allowed the timing of exon 4 and 9 usage changes to be narrowed to >6 and <16 hr (Figure 5A), a developmental window when neurogenesis is occurring (Goodman et al., 1993). In S2 cells and 4–6 hr embryos, we found no evidence of inter-cluster connectivity with regard to exon choice (Figure 7). In 14–16 hr embryos, we found weak evidence for such connectivity (Supplementary file 4). Multiple cell types expressing characteristic, but different, cluster 4 and 9 exon variants, however, likely confound determination of coordination in 14–16 hr embryos. Therefore, consistent with previous reports (Neves et al., 2004; Miura et al., 2013; Sun et al., 2013), we conclude that individual Dscam1 isoforms are produced via stochastic alternative splicing.

In mammalian neuronal development, cells use tandem arrays of protocadherin and neurexin genes to distinguish their own neurites from those originating from different cells. Some tandem arrays are capable of generating >1000 different spliced isoforms (Ushkaryov et al., 1992; Wu and Maniatis, 1999; Rowen et al., 2002). A recent analysis of mouse neurexin genes using long reads (Pacific Biosciences) of individual cDNA molecules showed that while these genes do produce many different isoforms, there is also no coordination among their alternative processing choices (Treutlein et al., 2014).

SeqZip uses and limitations

A potentially routine and robust use of SeqZip is highlighted by our Fn1 analyses, where we simultaneously measured 12 different alternative splicing isoforms and determined their relative expression by simple gel electrophoresis without sequencing (Figure 3E). This application is similar to the multiple-exon-skipping detection assay (MESDA) used to study SMN1 and SMN2 isoform expression in different Batten disease cell lines (Singh et al., 2012). MESDA measured the relative expression of >6 SMN isoforms and even identified a novel isoform, providing a useful tool for researchers working on spinal muscular atrophy. Measurement of SMN isoforms could also be performed using SeqZip, with several advantages over MESDA including reduction in amplicon size, lower propensity for template switching during amplification, and no RT step.

One limitation of SeqZip is the number of ligamers required to create a ligation product. To achieve necessary sequence specificity, ligamers need to be 40–60 nt. Current illumina-based sequencing platforms can read ∼500 nt of contiguous sequence. Thus, ∼8–12 ligamers is currently the upper limit for high-throughput sequencing analyses. Although our quantitative analysis of CD45 showed no difference in ligation efficiency for isoforms requiring two ligations compared to those requiring five (Figure 3B), other transcripts could theoretically differ. Because exon-excluded isoforms require fewer ligations, as the number of sites being examined grows, it is possible that detection of shorter (i.e., exon-excluded) transcripts will be favored. Thus, if a SeqZip experiment requires different numbers of ligation events for different RNA isoforms, it is crucial to perform the necessary controls to ensure quantitative detection of all desired isoforms. In our experiments, we were able to demonstrate accurate isoform abundance reporting over >4 orders of magnitude for Dscam (Figure 5B). At the low end of this range, however, near-cognate ligation events began to be problematic. As for sensitivity, we have been able to obtain detectable ligation products from as few as ∼900 (5 × 10−17 M; 0.05 fM) target RNA molecules (data not shown). Because the limit of detection for SeqZip is likely more than a single molecule, lack of detection of a particular isoform should only be interpreted as that isoform being below the SeqZip detection limit. Nonetheless, when properly controlled, SeqZip is a sensitive quantitative method for assessing complex isoform abundance patterns over a wide dynamic range.

The easiest ‘complex’ form of alternative splicing for SeqZip analysis was Dscam1, where alternative processing is limited to mutually exclusive exons (i.e., all spliced isoforms contain the same number of exons, and therefore, ligamer ligation events). However, many mammalian transcripts have more varied alternative splicing, including alternative 5′ and 3′ splice site usage and intron retention. Ligamer design against these types of alternative splicing quickly becomes unwieldy. For example, characterization of alternative transcriptional start and polyadenylation sites requires different terminal ligamers for each different start or polyadenylation site. Thus, while we were able to simultaneously assess two different types of alternative splicing in Fn1 (exon inclusion/exclusion and alternative 3′ splice sites), other mammalian genes displaying even more numerous forms of alternative processing (e.g., Kcnma1) would require significantly more complicated ligamer pools. Indeed, there may be genes with splicing patterns that cannot be readily interrogated with a single ligamer pool capable of generating a unique ligation product for every possible isoform. In such cases, analysis using multiple ligamer pools each targeting a select region may still be advantageous for estimating splicing frequencies compared to more traditional methods like quantitative RT-PCR. To assist readers in designing their own ligamer pools, we have included a schematic of our ligamer design process for mouse Fn1 (Figure 1—figure supplement 1 and Supplementary file 3). For highly complex pools, this process can be automated by writing a simple Python, Perl, or R script specific to the problem being addressed.

One potential future application of SeqZip is the detection of multiple single-nucleotide polymorphisms (SNPs) on a single molecule of a long RNA. By placing the ligation sites over each SNP, one could take advantage of the requirement by Rnl2 for complete complementarity at a ligation junction; mismatches would inhibit efficient ligamer joining (Landegren et al., 1988; Chauleau and Shuman, 2013). Further, any sequence can be placed in between the two regions of target complementarity within each ligamer. Therefore, sequences for custom priming, restriction digestion, recombination, etc, can be introduced, allowing for quantification or subsequent manipulation of ligation products. Analysis of ligation products can even be multiplexed, allowing for simultaneous generation and analysis using internal controls. These applications and others are shown in Figure 1—figure supplement 2.

As demonstrated by our investigation of Dscam1, SeqZip ligation products can be analyzed by high-throughput sequencing via incorporation of platform-appropriate priming sequences in the terminal ligamers or PCR primers or in the spacer sequences of internal ligamers. SeqZip could also be used to assess the integrity of very long RNAs, such as piRNA-precursor transcripts (Li et al., 2013) or mRNAs with extended 3′ UTRs (Wang and Yi, 2013). Thus, SeqZip, which retains sequence connectivity and overcomes template-switching artifacts of RT-based methods, represents a useful and adaptable new tool for detecting and quantifying numerous features of individual molecules of long RNA.

Materials and methods

All oligo and ligamer sequences are provided in Supplementary file 1. U-937 (CRL-1593.2), Jurkat (TIB-152), and S2 (CRL-1963) cell lines were from ATCC. Primary C57BL/6J MEF cells were from Jackson Labs. MEF lines were immortalized using SV40 retroviral infection. D. melanogaster embryos were reared at 25°C.

Proof-of-concept experiments

The template sequence for the initial ligase screen (Figure 2A) was a 307 nt section of mouse DDX1 mRNA (NM_134040.1; see Supplementary file 1); ssDNA and RNA templates were a synthetic oligonucleotide and in vitro transcript, respectively. Enzymes tested were Tth DNA ligase (AB-0325; Thermo, Waltham, MA), Tsc DNA ligase (Dlig 119; Prokaria, Reykjavik, Iceland), thermostable DNA ligase (BIO-27045; Bioline, Taunton, MA), T4 DNA ligase (M0202S; NEB, Ipswich, MA), Escherichia coli DNA ligase (M0205S; NEB), Rnl2 (M0239; NEB), SplintR ligase (M0375; NEB). Templates and oligos were hybridized by heating to 65°C for 1 min, followed by slow cooling to room temperature. After buffer and enzyme addition, reactions were incubated at the manufacturer-specified optimal ligation temperature (16–65°C depending on enzyme) for time indicated; denaturing polyacrylamide gels were quantified by phosphorimaging. Final ligation conditions in Figure 2A were (left panel) 1.5 μM ssDNA or RNA template, 5′-32P-labeled oligos (10 μM each), and 1 μl of neat indicated enzyme (specific units varied according to the manufacturer and enzyme) in manufacturer's recommended buffer; (right panel) 250 nM RNA template, 5′-32P-labeled oligos (500 nM each), and 10 U/μl Rnl2 or 20 U/μl T4 DNA ligase. Reactions in Figure 2B contained 1.25 μM DDX1 RNA template, 5 μM each 5′-32P-labeled oligo, and 10 U/μl Rnl2 were incubated for 4 hr at 37°C and separated on a 11.25% denaturing polyacrylamide gel. Reaction conditions in Figure 2C,D were as described in the SeqZip section (see below), using indicated RNA templates in a background of 10 ng/μl total mouse embryo fibroblast (MEF) RNA. RNA templates in Figure 2C,D were runoff transcripts from PCR products generated with different oligo combinations (Supplementary file 1) having partial complementarity to human eIF4A3 cDNA (RefSeq: NM_014740).

Radioactive and end point PCR

For radioactive PCR using Taq Polymerase (PN-M712; Promega, GoTaq Green Master Mix, Madison, WI), one PCR oligo was 32P-5′-end-labeled, and cycle numbers were confined to a range predetermined to yield a per cycle log2 linear increase in signal intensity (typically 15–23 cycles). After resolution on a denaturing polyacrylamide gel, bands were quantified using a Typhoon imager (GE Healthcare, Chicago, IL) and the ImageQuant software package (GE Healthcare). End point PCR was typically 35 cycles at a hybridization temperature 5°C below the lowest primer TM. End point PCR products were resolved on native 29:1 (acrylamide: bis-acrylamide) polyacrylamide gels, visualized by staining with SybrGold (Invitrogen, Grand Island, NY), and also imaged/quantified as above.

Ligamer design

For SeqZip of endogenous CD45, Fn1, and Dscam1 mRNAs, individual ligamers were designed as follows (Figure 1—figure supplement 1). The 5′- and 3′-termini of each target sequence (e.g., one or multiple exons) were obtained from online databases (Ensembl and UCSD). For terminal ligamers, the length of complementarity necessary to obtain a predicted hybridization Tm nearest but not exceeding 65°C was calculated using the BioPerl Bio::SeqFeature::Primer Tm module with default [Na+] and [oligo] settings (Allawi and Santalucia, 1997). This complementary sequence was then appended to the desired PCR-primer hybridization sequence. For internal ligamers, the length of complementarity at each end (generally 12–25 nt) was adjusted to achieve a Tm of 60 ± 5°C for each end separately in order to maintain an overall length of ≤60 nt. End sequences were joined via a short spacer that could include a barcode. Ligamers were ordered from Integrated DNA Technologies, with or without a 5′ phosphate as required, and used directly in SeqZip reactions.

SeqZip

Total RNA (200–800 ng per ligation reaction) isolated from cells using Tri Reagent (MRC) was bound to Poly(A)Purist MAG magnetic beads (Ambion AM1922; 2.25 μl slurry per ligation reaction) according to the manufacturer's instructions. After removal of unbound RNA, ligamers (10 nM (f.c.)) were hybridized to bead-bound poly(A) RNA in hybridization buffer (60 mM Tris-HCl, pH 7.5 at 25°C, 1.2 mM DTT, 2.4 mM MgCl2, 480 μM ATP) by heating samples to 62°C for 5 min in a thermocycler and then slow cooling to 45°C via a 3°C drop every 10 min. After 1 hr at 45°C, the temperature was again decreased 3°C every 10 min to 37°C, where samples were held until T4 RNA ligase 2 (2 U/μl (f.c.), NEB, M0239) addition. At this point, the samples were in 1× ligation buffer (51 mM Tris-HCl, pH 7.5 at 25°C, 2 mM DTT, 5 mM KCl, 2 mM MgCl2, 400 μM ATP, 3.5 mM (NH4)2SO4, 5% (vol/vol) glycerol). After 8–16 hr at 37°C, beads were used directly for PCR using a polymerase appropriate for the downstream application (e.g., Taq for gel analysis, Herculase for sequencing).

Reverse transcription and PacBio FN1 analysis

RT reactions in Figure 3 used SuperScript III (10 U/μl, Invitrogen) at 55°C, 200 ng poly(A) selected RNA, and either anchored oligo(dT) or a gene-specific antisense primer. Fn1 amplicons were prepared using 12 cycles of Herculase II Fusion DNA Polymerase and primers targeting the sequence between the EDB and V regions (Supplementary file 1). Amplicons were submitted for library construction using The DNA Template Prep Kit 2.0 (Pacific Biosciences) and sequenced on a PacBio RS II. Circular consensus reads were aligned to an index of FN1 isoforms using BLAT.

Triple-read sequencing

RT was performed using 5 μg total RNA, Superscript II (Invitrogen), and random hexamers at 42°C for 1 hr. Strand-switching control experiments were performed by mixing plasmids encoding Dscam isoforms 1.33.9, 12.32.9, 1.24.6, and 7.9.6 at 3:3:1:1, 1:1:1:5, and 1:1:1:1. PCR with Phusion polymerase (NEB) (annealing temperature, 55°C; 1 min extension) was used to amplify cDNA or plasmids containing the region encompassing exons 4, 6, and 9 with exon 3 (Not1Ex3For: TAT CGG CGG CCG CGG ACG TCC ATG TGC GAG CCG) and exon 10 (Ex10RevNot1: ATA TCG CGG CCG CGA GGA TCC ATC TGG GAG GTA) primers. Both primers contained a 5′ end NotI restriction site. PCR products were gel purified and digested with NotI for 2 hr at 37°C, followed by a heat inactivation at 65°C for 20 min. The digested PCR products (0.5 μg) were circularized in 500 μl 1× T4 DNA ligase buffer (NEB) with 1 μl T4 DNA ligase (0.8 U/μl, (f.c.), NEB, M0202) at 18°C overnight. Inverse PCR was then performed using Phusion polymerase (annealing temperature, 55°C; 30 s extension) with primers specific to exons 7 (PEex7Rev: CAA GCA GAA GAC GGC ATA CGA GAT CGG TCT CGG CAT TCC TGC TGA ACC GCT CTT CCG ATC TAT GAA CTT GTA CCA T) and 8 (PEex8For: AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT GTT TCC CTA CAC GAC GCT CTT CCG ATC TAA GTG CAA GTC ATG G) that contained Illumina paired-end clustering sequences. Libraries were gel purified, quantified via Nanodrop (Thermo), and clustered on a Genome Analyzer IIx (GAIIx) paired-end flow cell on an Illumina cluster station using the standard clustering protocol.

Sequencing was performed on an Illumina GAIIx by modifying the protocol for paired-end sequencing with an index read. Briefly, read 1 was performed for 24 cycles with a primer complementary to the 5′ end of exon 8 (Ex8For: ACG ACG CTC TTC CGA TCT AAG TGC AAG TCA TGG). The flow cell was denatured to remove the exon 9 sequencing products, a primer complimentary to exon 3 (Ex3For: CCC GGG ACG TCC ATG TGC GAG CCG) was annealed, and read 2 sequenced for 12 cycles. Next, the flow cell was re-clustered using the paired-end protocol, and read 3 performed for 20 cycles using a primer complementary to exon 7 (Ex7Rev: GAA CCG CTC TTC CGA TCT ATG AAC TTG TAC CAT).

Base calling was performed from the raw images using the Firecrest, Bustard, and Gerald software modules of GAPipeline-1.4.0 and a matrix.txt file for a PhiX lane from a previous flow cell for calibration. This generated a single FastQ file per lane containing the three catenated reads from each cluster. The reads within the FastQ files were parsed to separate the three reads, the identity of each exon determined, and then the full isoform determined by matching to a database of known exon sequences.

MiSeq library preparation

SeqZip ligation reactions were amplified via PCR (Agilent, Herculase II Fusion DNA Polymerase, Catalog Number—600675) for 12 cycles using common primers. Reactions were resolved on a 5% polyacrylamide native gel, and DNA in the size range appropriate for full-length ligation products quantified by fluorescence imaging, cut and eluted from the gel, and precipitated. Equal DNA quantities based on the gel imaging were amplified for another 22 cycles using primers containing Illumina priming sequences with integrated barcodes. PCR products were purified (28104; Qiagen, QIAquick PCR Purification Kit) and quantified using a Bioanalyzer 2100 (Agilent) and High-Sensitivity DNA chip. Samples were mixed and submitted for sequencing on the MiSeq instrument using the paired-end 250 nt read option. Sequencing data are available at Short Read Archive accession SRP043516.

MiSeq read analysis

All Dscam1 SeqZip products were shorter than 400 nt; therefore, paired-end MiSeq 250 nt reads contained overlapping 3′ sequences. Using these overlapping sequences, paired reads were combined into one sequence using the Paired-End Assembler (pear, v. 0.8.1) and default options (Zhang et al., 2014). An index of all possible Dscam1 ligamer combinations was created using a single PERL script that permuted all possible ligamer combinations with correct 5′ to 3′ exons 4, 6, and 9 ligamer arrangements. Paired MiSeq reads were aligned against this index using Bowtie2 v. 2.1.0 (Langmead and Salzberg, 2012) in the very-sensitive-local mode and constrained using no-discordant to only look for reads where both pairs aligned to the same isoform. Using the SAMtools (v. 0.1.19) software package (Li et al., 2009), alignments were further filtered for alignments containing quality 31 and above (-q 31) and read counts per isoform extracted. Count analysis was performed and graphs generated using R (R Development Core Team, 2008).

Differential expression of Dscam1 isoforms

For differential expression analysis, we treated each Dscam1 isoform as though it was its own gene. The percent use of individual exons for each cluster (4, 6, and 9) was determined. Expected use of all possible combinations of 4:6, 4:9, 6:9, and 4:6:9 was calculated by multiplying the percentages of individual exon use. Expected use was compared to observed use of the equivalent combination. The DESeq differential gene expression R package (Anders and Huber, 2010) was used to identify isoforms whose observed and expected abundances were ‘differential’.

Determining sequencing similarity of Dscam1 sequences

Endogenous Dscam1 sequences were obtained from genomic build DM3 using BEDTools (Quinlan and Hall, 2010). All possible Dscam1 isoform sequences between exons 4 and 10 were assembled using a PERL script. Five hundred random isoforms were obtained and aligned using TCOFFEE (Di Tommaso et al., 2011) in the Jalview package (Waterhouse et al., 2009). Consensus scores of alignments were exported and graphed in R. The same analysis was performed on Dscam1 ligation products, except ligamer sequences were used in place of endogenous exonic sequences.

Statistical analysis

Error bars represent the standard error of the mean of experimental replicates. Errors were propagated from individual standard deviations according to standard methods (Goodman, 1960; Natrella, 2012).

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Decision letter

  1. Aviv Regev
    Reviewing Editor; Broad Institute of MIT and Harvard, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for sending your work entitled “Assessing long-distance RNA sequence connectivity via RNA-templated DNA-DNA ligation” for consideration at eLife. Your article has been favorably evaluated by Aviv Regev (Senior editor), a Reviewing editor, and three reviewers.

Roy and colleagues present a new method for the assessment of complex alternative splicing systems. Most current methods for profiling the expressed alternatively spliced isoforms of a gene use short read RNAseq to assess the linkage of individual exons and thus require different positions of alternative splicing within a transcript to be examined independently. This is a problem for assembling the correct set of complete mRNAs from a gene as information on the combinatorial relationships and possible coregulation of distant exons is lost, although these relationships can frequently be deduced by statistical inference. The new method, “SeqZip”, uses oligo ligation directed by the mRNA template to report on exon-exon joints present in the mRNA and to condense distant regions of splice variation into a much shorter sequences assayable by sequencing or simply by size. The method is clever and the biochemistry is very well done, but no new biological insights are presented. The interest here is in the technology and the widespread need for such an assay. However, as a methods paper the study has several failings that need to be addressed.

Major points:

1) From the data presented, it is not clear how well a ligation event for an oligo pair will linearly report on a particular exon-exon joint when the pair is part of a complex set of ligamers. This has several complications.

A) From Figure 2, it appears that increasing the total number of ligation events decreases the efficiency of product generation. This will lead to a favoring of exon skipping events in the assay. In Figure 3, the authors compare the exon inclusion ratios of CD45 measured by RT/PCR and SeqZip and find that they are similar. However, RT/PCR is known to be biased towards exon-skipped products, especially when comparing size differences as large as those here. Do the similar results from the two assays indicate a similar exon-skipping bias from SeqZip?

B) Another source of error in the assay will likely be differences in annealing and ligation efficiency between different ligamer pairs. One oligo can have quite different ligation activities when joined to different partners. Different oligos have such drastically different annealing and ligation efficiency especially since they can also form various inter- and intra-molecular interactions when the complexity is increased. As a result, the poorest one will be the rate-limiting factor for the whole isoform presentation. For individual isoform pairs under different conditions, we use ratio/ratio to infer changes, but in the current study, this is not feasible. They may argue that a poor oligo will affect all isoforms equally, and thus, it is not a problem. However, other isoforms that lack the targeting exon for such poor oligo will be over represented. This problem may not surface with low complexity system. But, it is likely that it will soon be encountered by others, if the system is adopted, and if not properly addressed, the method will have little impact. This is a critical point for the authors to assess.

C) Related to B, they make passing reference to matching the free energy of binding for each hybridizing segment, but it is not clear how well this works or whether it makes all oligo pairs join equally well. Similarly, the authors discuss the question of distinguishing highly similar exon sequences in DSCAM, but it is not clear if their approach is a general solution. Through assays of constructed pools they show they can distinguish exons 4.1 and 4.7, exons 6.9, 6.33, and 6.24, and exons 9.6 and 9.9, using their complex set of ligamers. However, it is not clear if these are the most similar of all DSCAM exon pairs or that others of the many possibilities might be more difficult to distinguish.

2) Although the authors present a complex oligo set for DSCAM whose ligated product can be assayed in a length accessible by MiSeq, it is not clear how general these conditions are for other complex systems, where there are more sites of variation and more types of splicing change. It seems likely that some regions will still need to be assayed separately and combinations predicted statistically. What are the limitations here? How complex a set of ligamers is feasible? Is there an upper limit on length that can be sequenced as a single fragment? Given such a length limit, what determines the number and spacing of alternative events that can be assayed by a ligamer set targeting all the possible products? Exactly how did the DSCAM mapping and variant quantification work? It appears that DSCAM isoforms were identified and counted by aligning to a library of isoform sequences and somehow scoring for sequence match. This needs to be described in much more detail. For this to be a useful method, it will need to be applicable to other complex transcript sets. It is not clear how feasible this is. Without understanding the mapping strategy, it is not clear how one might develop this for another gene.

Minor points:

1) The paper is very haphazardly written and it is often difficult to follow exactly what is being done. At the same time, the authors often overstate the novelty and relative performance of the assay. Oligo Ligation as an assay for specific sequences in a complex mixture goes back at least to Landegren and Hood. The method here is basically an extension of RASL-seq. It doesn't take away from the advance to reference these precedents. The authors comment on the better performance on SeqZip over CamSeq and PacBio long read sequencing. However, examining Figures 2E and Supplement 6A, they appear comparable. When discussing CamSeq, in the section “SeqZip eliminates Dscam1 isoform template switching analysis artifacts”, it is not clear what the authors mean by the sentence: “Although template-switched and unexplained isoforms together represented just 0.6-0.94% of all reads, they account for 99.9% of multi-exon isoforms sequenced…” The authors state that detection of template switched isoforms is much lower for SeqZip, but later report that 1.3% of the alignments in the sequencing of the DSCAM ligation products contained exons not present in the input. From this it would seem that the error rate for SeqZip is potentially higher than CamSeq.

2) It is often unclear what the figures are presenting. More labels and lane numbers are needed. In Figure 2E, it was not clear that each bar represented a different variant for each region and should thus sum to 100%. In this Figure, the EDA bars for the EDA+/- cells show only one isoform (they are not labeled as to which is which). Shouldn't both isoforms be present in these cells? The point of Figure 4B is also not clear.

3) As mentioned above, how were the isoform abundances calculated? This should be described in detail with a figure showing the conversion from read counts to isoforms. Also needed are the data tables and sequencing statistics for the MiSeq runs. Exactly how were these sequences mapped to transcripts to identify the exon combinations? The authors report the use of DESeq to determine differential expression of DSCAM isoforms. They should be aware that this is considered to be a bad idea generally and specifically that SeqZip libraries likely violate several crucial assumptions made by DESeq. This should be clarified in detail.

4) The authors stress that PCR amplification must be minimized, but report 12 cycles of PCR after ligation, followed by 22 cycles to add the Illumina primers. How were these numbers arrived at? Was it the same for the biological samples and the artificial pools? How will this be affected by transcript abundance for targets that are less abundant than DSCAM? The DSCAM splice variants are all the same length, but this is not usually the case for systems of complex splicing. How will this PCR step affect the quantification when some variants are much shorter than others?

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled “Assessing long-distance RNA sequence connectivity via RNA-templated DNA-DNA ligation” for further consideration at eLife. Your revised article has been favorably evaluated by Aviv Regev (Senior editor), a member of the Board of Reviewing Editors, and the original reviewers. The manuscript has been improved, but all the reviewers feel that there are some comments that would require a minor revision. Once these are introduced to the description of the results, methods and/or discussion (no further experiments or analyses are requested) we will be able to accept the paper. We emphasize that we do not anticipate re-sending the manuscript to the reviewers.

Specifically:

1) The authors should add a discussion of areas for future analytics to better improve the reliability and quantifiability of the inferred isoforms. These should accompany the currently well-described experimental limitations of SeqZip. As Reviewer 1 notes in further detail (and agreed by all reviewers): (1) there may be genes and isoforms that cannot readily be assayed with ligamer pools that uniquely quantify combinations of splicing events, but proper inference would still be able to help estimate splicing frequency; (2) guidelines for a method/algorithm to design a valid ligamer pool for a given gene are important to highlight in terms of what is already available and what is a future analytic direction.

2) Several critical details are missing or quite unclear, especially in the Methods sections, which were missing before or even removed in this version, per the items detailed in Reviewer 2's review. In particular, we believe that the information on number of PCR cycles is absolutely critical to reinstate to the Methods section.

3) There are some lingering concerns with the extent to which the method is (1) quantitative and (2) has false negatives (the two are inter-related). Again, we would like to see the discussion section clarify to the reader, as much as possible, what they can and cannot expect from the method. This is important for its long-term impact in the field.

Reviewer 1:

The authors have clarified a number of concerns I had with this revised version of the manuscript. The authors' section on the limitations of SeqZip clearly specifies most of the experimental limitations that remain. I would have liked to see a more thorough discussion of the analytic limitations or areas for improvement in the Discussion. For example:

1) As the authors note, there may be genes and isoforms that cannot readily be assayed with ligamer pools that uniquely quantify combinations of splicing events. However, SeqZip could still provide major advantages over conventional RNA-Seq for these genes, if it were coupled with statistical inference to estimate the frequency of splicing event combinations. The requirement that reads uniquely quantify isoforms is helpful but probably unnecessary for recovering accurate values. Using DESeq will only work well with ligamer pools that uniquely specify splicing combinations. For more complex pools, the authors’ current pipeline is likely to return unreliable numbers.

2) The authors do not describe an algorithm that generates a valid ligamer pool for a given gene. How are users of SeqZip to know that the pool they design meets the requirements of the assay and the analysis pipeline? What if they make a mistake, and leave a particular combination of splicing outcomes “uncovered” by the ligamer pool? A rigorous design tool for making ligamer pools would ensure that the current analysis pipeline returns valid quantification. Alternatively, the analysis pipeline itself could be improved (as noted in #1) to support more relaxed ligamer designs.

Both of these issues seem like good potential future directions. I would just suggest that the authors note them as limitations or opportunities in the Discussion.

Reviewer 2:

While the authors addressed most of the concerns raised earlier, this “final” version still has various problems:

1) The optimal PCR cycle number. The authors argued that under their conditions, the PCR cycle up to 22 did not cause any problem, yet still recommended to keep the cycle minimal in the previous version. Now, the authors removed such recommendation because they no longer think it is a critical issue? They did not even specify in their method section how many PCR cycles used. This information is needed for others to use the method.

2) Template switching was not a major problem with the amount of starting materials the authors used. However, most experiments use total RNA from a large number of cells. The authors need to provide instruction in their Methods section for the optimal range of RNA concentrations to be used in their assays.

3) I am still confused by the phrase in the section “SeqZip eliminates template-switching artifacts in the analysis of Dscam1 isoforms”: “Although the percentage of total reads aligning to template-switched and unexplained isoforms was low (0.6-0.94%), these few reads support the majority of Dscam1 isoforms sequenced (99.9%)”. Did the authors mean the majority of artifacts sequenced? If this is what they meant, SeqZip detected 1.3% artifacts, which is higher than CAMSeq.

4) In the Discussion, the authors cited “slow RNA synthesis favors inclusion of cassette exons with weak splicing signals, while fast RNA synthesis favors exclusion (Kornblihtt et al. 2013)”. They need to cite a recent paper in Mol Cell from the lab that has corrected this simple version of the model.

5) The authors added additional references in the text, but many did not show up in the reference list. Seem to need to update their EndNote. They should cite the Hood Science paper, which is the first to describe the ligation approach.

Reviewer 3:

The SeqZip method presented by the authors does address an important issue that is of general interest. The concept for the method has been around previously. Thus, I consider it essential that a high-profile publication reporting this method contains extensive validation of the approach.

The authors have made an attempt to address the issues raised in the review. However, the major issue remains: under which circumstances can the SeqZip method be considered as quantitative? To ensure the quantification aspect of SeqZip, several conditions have to be examined:

i) the specificity of the ligamers;

ii) the equal hybridization and ligation efficiencies according to ligamer sequences;

iii) the equal ligation efficiencies across various numbers of ligation events.

These key parameters differ for each target and rigorous control criteria are essential to set up suitable conditions for each target. Without prior validation of these criteria, SeqZip cannot be considered as quantifiable. The only conclusion that could thus be addressed is that combination of alternative events is present within one transcript if there is a signal (but absence of signal doesn't necessary mean that the isoform doesn't exist).

The authors have selected different targets to validate different aspects of the method (CD45, FN1 and Dscam1) but their major figure describing SeqZip method (Figure 2) highlights the strong bias to underrepresent mRNA isoforms that require multiple ligation events.

The Author response image 2 increases the concern that also ligamers can be non-specific. Nevertheless, the authors strongly argue against relying on the CAMseq experiments, which at the same time they consider as not reliable. Again, a more rigorous, systematic inclusion of controls is needed. For example, the authors should have selected control constructs according to the sequence similarity rather than to the availability of clones.

https://doi.org/10.7554/eLife.03700.021

Author response

Major points:

1) From the data presented, it is not clear how well a ligation event for an oligo pair will linearly report on a particular exon-exon joint when the pair is part of a complex set of ligamers. This has several complications.

A) From Figure 2, it appears that increasing the total number of ligation events decreases the efficiency of product generation. This will lead to a favoring of exon skipping events in the assay. In Figure 3, the authors compare the exon inclusion ratios of CD45 measured by RT/PCR and SeqZip and find that they are similar. However, RT/PCR is known to be biased towards exon-skipped products, especially when comparing size differences as large as those here. Do the similar results from the two assays indicate a similar exon-skipping bias from SeqZip?

Yes, we agree that under suboptimal conditions (e.g., insufficient extension times) PCR can favor shorter over longer products. In the RT-PCR reactions shown in Figure 3, products representing the four CD45 isoforms ranged from 367 to 850 nt. Our PCR conditions included a 1 min extension step, which should be more than sufficient to allow complete replication of all four isoforms (Taq DNA polymerase extension guidelines are typically 1 min per 1,000 nt). Supporting this, we observed no differences in isoform ratios after 18, 21, and 24 cycles of PCR (Author response image 1). For the data shown in Figure 3B, we used 21 cycles. Therefore, under the conditions we used here, there is little or no bias PCR toward shorter isoforms. Similarly, we observed no bias toward shorter isoforms with different PCR cycles in the SeqZip samples. Because SeqZip yielded the same isoform ratios as RT-PCR, we conclude that there was no bias toward shorter isoforms in the ligation reactions shown in Figure 3B.

B) Another source of error in the assay will likely be differences in annealing and ligation efficiency between different ligamer pairs. One oligo can have quite different ligation activities when joined to different partners. Different oligos have such drastically different annealing and ligation efficiency especially since they can also form various inter- and intra-molecular interactions when the complexity is increased. As a result, the poorest one will be the rate-limiting factor for the whole isoform presentation. For individual isoform pairs under different conditions, we use ratio/ratio to infer changes, but in the current study, this is not feasible. They may argue that a poor oligo will affect all isoforms equally, and thus, it is not a problem. However, other isoforms that lack the targeting exon for such poor oligo will be over represented. This problem may not surface with low complexity system. But, it is likely that it will soon be encountered by others, if the system is adopted, and if not properly addressed, the method will have little impact. This is a critical point for the authors to assess.

To maintain modular cassette-exon ligamer design, ligation sites must be at splice sites. Also, analysis of exonic splice-site sequences support more uniform terminal exonic nucleotides, especially for the 5´ splice site (Lim & Burge, 2001). However, results should be cross-compared to traditional isoform analysis methods (RT-PCR). Finally, problematic “poor” oligo hybridization could be addressed using modified nucleotides such as LNA.

C) Related to B, they make passing reference to matching the free energy of binding for each hybridizing segment, but it is not clear how well this works or whether it makes all oligo pairs join equally well. Similarly, the authors discuss the question of distinguishing highly similar exon sequences in DSCAM, but it is not clear if their approach is a general solution. Through assays of constructed pools they show they can distinguish exons 4.1 and 4.7, exons 6.9, 6.33, and 6.24, and exons 9.6 and 9.9, using their complex set of ligamers. However, it is not clear if these are the most similar of all DSCAM exon pairs or that others of the many possibilities might be more difficult to distinguish.

We have supplied a multiple sequence alignment and sequence logo for the variant exons in clusters 4, 6, and 9 as Author response image 2. These data show that there is low sequence variability between the alternative exons within a cluster. However, the high-degree of similarity between our data and those presented by Sun2013 support our Dscam1 ligamer design's ability to discriminate between these highly-similar exons (Sun et al., 2013). The exons in our constructed pools were chosen based on availability of clones and overall high expression in S2 cells, not by sequence similarity.

2) Although the authors present a complex oligo set for DSCAM whose ligated product can be assayed in a length accessible by MiSeq, it is not clear how general these conditions are for other complex systems, where there are more sites of variation and more types of splicing change. It seems likely that some regions will still need to be assayed separately and combinations predicted statistically. What are the limitations here? How complex a set of ligamers is feasible? Is there an upper limit on length that can be sequenced as a single fragment? Given such a length limit, what determines the number and spacing of alternative events that can be assayed by a ligamer set targeting all the possible products? Exactly how did the DSCAM mapping and variant quantification work? It appears that DSCAM isoforms were identified and counted by aligning to a library of isoform sequences and somehow scoring for sequence match. This needs to be described in much more detail. For this to be a useful method, it will need to be applicable to other complex transcript sets. It is not clear how feasible this is. Without understanding the mapping strategy, it is not clear how one might develop this for another gene.

We have now added a paragraph to the Discussion specifically addressing these points.

Minor points:

1) The paper is very haphazardly written and it is often difficult to follow exactly what is being done. At the same time, the authors often overstate the novelty and relative performance of the assay. Oligo Ligation as an assay for specific sequences in a complex mixture goes back at least to Landegren and Hood. The method here is basically an extension of RASL-seq. It doesn't take away from the advance to reference these precedents.

We have added references to previous ligation-based assays in both the Results and Discussion sections.

The authors comment on the better performance on SeqZip over CamSeq and PacBio long read sequencing. However, examining Figures 2E and Supplement 6A, they appear comparable.

We assume the reviewers intended to write ‘3E’ as Figure 2 does not have a panel E. Yes we fully agree that SeqZip, CamSeq, and PacBio all yield comparable usage for individual exons. But the clear advantage of SeqZip is that it is much less prone to template switching during library preparation and amplification.

When discussing CamSeq, in the section “SeqZip eliminates Dscam1 isoform template switching analysis artifacts”, it is not clear what the authors mean by the sentence: “Although template-switched and unexplained isoforms together represented just 0.6-0.94% of all reads, they account for 99.9% of multi-exon isoforms sequenced…” The authors state that detection of template switched isoforms is much lower for SeqZip, but later report that 1.3% of the alignments in the sequencing of the DSCAM ligation products contained exons not present in the input. From this it would seem that the error rate for SeqZip is potentially higher than CamSeq.

There is a major difference between these two ‘error rates’. Using our differential barcode approach, which uses sets of ligamers with different barcodes but whose ligation products are amplified together, we measure template-switching based on improper barcode combinations. We conclude that the vast majority of the reads aligning to ‘unintended’ isoforms originate from incorporation of near-cognate ligamers. Therefore, while a slightly higher overall percentage of SeqZip reads align to non-input isoforms, the source of most of these alignments (i.e. near-cognate ligamer usage) is known. In contrast, we assigned CamSeq reads to ‘template switched’ isoforms based logical rearrangement of high abundance control isoform exons. As these two ‘error rates’ are from different sources (template-switching vs. near-cognate ligamer usage), they cannot be compared. Finally, we believe that modest changes in ligamer design (i.e. longer hybridization regions, LNA incorporation) would further reduce near-cognate ligamer incorporation. In contrast, approaches to reduce of template-switching beyond the low rate that CamSeq has already achieved are not obvious to us.

2) It is often unclear what the figures are presenting. More labels and lane numbers are needed. In Figure 2E, it was not clear that each bar represented a different variant for each region and should thus sum to 100%. In this Figure, the EDA bars for the EDA+/- cells show only one isoform (they are not labeled as to which is which). Shouldn't both isoforms be present in these cells?

In Figure 3E, we have now added the requested labels for isoform variants. We also now indicate which sets of bars should sum to 100%. Regarding the EDA+/– bars in the EDA+/– cells, like the Reviewer we also expected to observe both isoforms. Therefore we were surprised when we only detected EDA- isoform expression. We verified the presence of the EDA+ and EDA- alleles in gDNA by PCR. We tentatively conclude that the EDA+ allele is transcriptionally silenced in these cells. We now point out this apparent discrepancy in the text.

The point of Figure 4B is also not clear.

The point of this figure is to show the extent of sequence identity when using different methods of RNA->DNA conversion for Dscam1 isoforms. SeqZip greatly reduces the length of completely identical sequences thus minimizing the likelihood of template switching. We have now explained this figure in more detail in the text.

3) As mentioned above, how were the isoform abundances calculated? This should be described in detail with a figure showing the conversion from read counts to isoforms.

We now provide a new supplementary figure (Figure 4–figure supplement 1) showing our workflow for converting from read counts to isoforms.

Also needed are the data tables and sequencing statistics for the MiSeq runs. Exactly how were these sequences mapped to transcripts to identify the exon combinations?

We apologize for not including these tables in the full submission. They are now included in the revised submission. Mapping of sequence to transcripts is explained in Figure 4–figure supplement 1.

The authors report the use of DESeq to determine differential expression of DSCAM isoforms. They should be aware that this is considered to be a bad idea generally and specifically that SeqZip libraries likely violate several crucial assumptions made by DESeq. This should be clarified in detail.

Our ability to assign each ligation product uniquely to a particular combination ensures the independence of the read counts between the isoforms, hence fulfilling a key assumption of DESeq. The isoform abundances vary over 5-logs, and exhibit similar dispersion to RNA-Seq samples. We therefore believe DESeq is a suitable tool for identifying population outliers, which in our case would be indicative of isoforms displaying linked exon usage.

4) The authors stress that PCR amplification must be minimized, but report 12 cycles of PCR after ligation, followed by 22 cycles to add the Illumina primers. How were these numbers arrived at? Was it the same for the biological samples and the artificial pools?

SeqZip of Dscam1 by HTS is equivalent to single-gene RNA-Seq. That is, rather than making a library of the entire transcriptome, we made libraries from transcripts from a single gene expressed at a low level. One cannot get the amount of material needed without many cycles of PCR. We have now removed the phrase “and subsequent PCR amplification is kept to a minimum” from the text. We have also inserted text in the Methods explaining how we normalized between samples the amount of Dscam1 DNA from the first PCR reaction into the second PCR reaction. We note that all of our ligation reactions were performed in duplicate with two different barcodes. The duplicate samples were mixed immediately upon completion of the ligation reaction. Having two barcodes in the same PCR sample enabled us to detect template-switched isoforms in the final library. If any library contained >0.1% template-switched isoforms it was considered over amplified and not used.

How will this be affected by transcript abundance for targets that are less abundant than DSCAM? The DSCAM splice variants are all the same length, but this is not usually the case for systems of complex splicing. How will this PCR step affect the quantification when some variants are much shorter than others?

Dscam1 is a low to moderate abundance transcript in embryos (See Author response image 3). Our ability to characterize specific Dscam1 isoforms extracted from whole animals demonstrates the sensitivity of our method. Some amount of empirical investigation will likely be required to adapt SeqZip for extremely rare transcripts, but this would be true of any technique.

Author response image 3

Dscam1 expression as measured by ENCODE high-throughput sequencing. Accessed 2014-10-12 via http://flybase.org/reports/FBgn0033159.html

https://doi.org/10.7554/eLife.03700.025

SeqZip compresses the connectivity information in a long RNAs into ligation products <500 nt long but >100 nt. In this size range, preferential PCR of smaller fragments is much less of issue than for methods where full length cDNAs are measured and lengths might vary by thousands of nucleotides.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The manuscript has been improved, but all the reviewers feel that there are some comments that would require a minor revision. Once these are introduced to the description of the results, methods and/or discussion (no further experiments or analyses are requested) we will be able to accept the paper. We emphasize that we do not anticipate re-sending the manuscript to the reviewers.

Specifically:

1) The authors should add a discussion of areas for future analytics to better improve the reliability and quantifiability of the inferred isoforms. These should accompany the currently well-described experimental limitations of SeqZip.

As detailed below, we have now added the requested verbiage to the Discussion.

As Reviewer 1 notes in further detail (and agreed by all reviewers): (1) there may be genes and isoforms that cannot readily be assayed with ligamer pools that uniquely quantify combinations of splicing events, but proper inference would still be able to help estimate splicing frequency;

We have now made this clear in the Discussion.

Guidelines for a method/algorithm to design a valid ligamer pool for a given gene are important to highlight in terms of what is already available and what is a future analytic direction.

To help the reader understand ligamer pool design, we have now added a schematic (Figure 1figure supplement 7) showing our design process for terminal and internal mouse Fn ligamers. Because each individual application will necessitate a ligamer design specific to that application, we have not attempted to generate a universal computerized ligamer design implementation. Instead we think it more useful for the reader to fully understand how such an algorithm might work and then generate their own.

2) Several critical details are missing or quite unclear, especially in the Methods sections, which were missing before or even removed in this version, per the items detailed in Reviewer 2's review. In particular, we believe that the information on number of PCR cycles is absolutely critical to reinstate to the Methods section.

As noted below, the number of PCR cycles was previously included in the Methods section. But perhaps the reviewer could not locate them because the order of the Methods section did not parallel the order of the Results section. This has now been corrected. We have also expanded the Methods section to include detailed descriptions of the experiments presented in all figures.

3) There are some lingering concerns with the extent to which the method is (1) quantitative and (2) has false negatives (the two are inter-related). Again, we would like to see the discussion section clarify to the reader, as much as possible, what they can and cannot expect from the method. This is important for its long-term impact in the field.

We have now added the following verbiage to the Discussion to address this point:

“In our experiments, we were able to demonstrate accurate isoform abundance reporting over >4 orders of magnitude for Dscam1 (Figure 5B). At the low end of this range, however, near-cognate ligation events began to be problematic. As for sensitivity, we have been able to obtain detectable ligation products from as few as ∼900 (5 × 10−17 M; 0.05 fM) target RNA molecules (data not shown). Because the limit of detection for SeqZip is likely more than a single molecule, lack of detection of a particular isoform should only be interpreted as that isoform being below the SeqZip detection limit. Nonetheless, when properly controlled, SeqZip is a sensitive quantitative method for assessing complex isoform abundance patterns over a wide dynamic range.”

Reviewer 1:

The authors have clarified a number of concerns I had with this revised version of the manuscript. The authors' section on the limitations of SeqZip clearly specifies most of the experimental limitations that remain. I would have liked to see a more thorough discussion of the analytic limitations or areas for improvement in the Discussion. For example:

1) As the authors note, there may be genes and isoforms that cannot readily be assayed with ligamer pools that uniquely quantify combinations of splicing events. However, SeqZip could still provide major advantages over conventional RNA-Seq for these genes, if it were coupled with statistical inference to estimate the frequency of splicing event combinations. The requirement that reads uniquely quantify isoforms is helpful but probably unnecessary for recovering accurate values. Using DESeq will only work well with ligamer pools that uniquely specify splicing combinations. For more complex pools, the authors’ current pipeline is likely to return unreliable numbers.

As the reviewer notes, application of DESeq for analyzing DSCAM ligamer isoforms is unique to that experimental design. Further, how best to analyze a particular SeqZip experiment (e.g., radioactive PCR on polyacrylamide gels vs deep sequencing) will depend on the exact experimental design and the number of isoforms being queried.

2) The authors do not describe an algorithm that generates a valid ligamer pool for a given gene. How are users of SeqZip to know that the pool they design meets the requirements of the assay and the analysis pipeline? What if they make a mistake, and leave a particular combination of splicing outcomes “uncovered” by the ligamer pool? A rigorous design tool for making ligamer pools would ensure that the current analysis pipeline returns valid quantification. Alternatively, the analysis pipeline itself could be improved (as noted in #1) to support more relaxed ligamer designs.

Nowhere in this manuscript did we or do we now present any computational “pipeline” for designing ligamer pools or for analyzing the results. Because of the flexibility of the method for addressing many different kinds of questions, it is hard for us to envision (and therefore even more difficult to implement) any single “pipeline” that would be suitable for all applications. Nonetheless, we do see the utility of giving the reader a better idea of how we went about designing large ligamer pools. Therefore we now include as Figure 1–figure supplement 7 a detailed flow diagram of our ligamer design process for the mouse Fn gene.

Both of these issues seem like good potential future directions. I would just suggest that the authors note them as limitations or opportunities in the Discussion.

The following paragraph has now been added to the Discussion:

“The easiest ‘complex’ form of alternative splicing for SeqZip analysis was Dscam1, where alternative processing is limited to mutually exclusive exons […] For highly complex pools, this process can be automated by writing a simple Python, Perl, or R script specific to the problem being addressed. ”

Reviewer 2:

While the authors addressed most of the concerns raised earlier, this “final” version still has various problems:

1) The optimal PCR cycle number. The authors argued that under their conditions, the PCR cycle up to 22 did not cause any problem, yet still recommended to keep the cycle minimal in the previous version. Now, the authors removed such recommendation because they no longer think it is a critical issue? They did not even specify in their method section how many PCR cycles used. This information is needed for others to use the method.

In the previous manuscript version, the number of PCR cycles used was clearly stated in the relevant paragraph of the Methods section for both “MiSeq Library Preparation” and “Radioactive PCR”. In rereading the Methods section, we realized however, that the order in which specific methods were presented was not the order in which they appeared in the Results. This may have led to the reviewer's confusion as to where to find the PCR cycle numbers. We have now reordered the Methods section to make it parallel the Results section, and PCR cycle numbers are clearly stated for every experiment.

2) Template switching was not a major problem with the amount of starting materials the authors used. However, most experiments use total RNA from a large number of cells. The authors need to provide instruction in their Methods section for the optimal range of RNA concentrations to be used in their assays.

The range of total RNA starting concentrations is now explicitly stated in the “SeqZip” methods paragraph.

3) I am still confused by the phrase in the section “SeqZip eliminates template-switching artifacts in the analysis of Dscam1 isoforms”: “Although the percentage of total reads aligning to template-switched and unexplained isoforms was low (0.6-0.94%), these few reads support the majority of Dscam1 isoforms sequenced (99.9%)”. Did the authors mean the majority of artifacts sequenced? If this is what they meant, SeqZip detected 1.3% artifacts, which is higher than CAMSeq.

No we meant that 99.9% of species detected in the CAMSeq control experiments were artifacts. Because this passage was the cause of much confusion, we have now eliminated it and reworked the paragraph to make it clearer.

4) In the Discussion, the authors cited “slow RNA synthesis favors inclusion of cassette exons with weak splicing signals, while fast RNA synthesis favors exclusion (Kornblihtt et al. 2013)”. They need to cite a recent paper in Mol Cell from the lab that has corrected this simple version of the model.

Done.

5) The authors added additional references in the text, but many did not show up in the reference list. Seem to need to update their EndNote. They should cite the Hood Science paper, which is the first to describe the ligation approach.

Fixed.

Reviewer 3:

The SeqZip method presented by the authors does address an important issue that is of general interest. The concept for the method has been around previously. Thus, I consider it essential that a high-profile publication reporting this method contains extensive validation of the approach.

The authors have made an attempt to address the issues raised in the review. However, the major issue remains: under which circumstances can the SeqZip method be considered as quantitative? To ensure the quantification aspect of SeqZip, several conditions have to be examined:

i) the specificity of the ligamers;

ii) the equal hybridization and ligation efficiencies according to ligamer sequences;

iii) the equal ligation efficiencies across various numbers of ligation events.

These key parameters differ for each target and rigorous control criteria are essential to set up suitable conditions for each target. Without prior validation of these criteria, SeqZip cannot be considered as quantifiable. The only conclusion that could thus be addressed is that combination of alternative events is present within one transcript if there is a signal (but absence of signal doesn't necessary mean that the isoform doesn't exist).

The authors have selected different targets to validate different aspects of the method (CD45, FN1 and Dscam1) but their major figure describing SeqZip method (Figure 2) highlights the strong bias to underrepresent mRNA isoforms that require multiple ligation events.

We assume that by “Figure 2”, the author means Figure 2B. In this experiment, each ligamer contained only 10 nts of complementarity to either side of the looped out region, there was no amplification of ligation products, and all ligamers were the same length. Therefore all three 2-way and both 3-way ligation products co-migrate on the gel, whereas there is only one possible 4-way ligation product. This co-migration likely explains the reviewer's conclusion that there is a “strong bias to underrepresent mRNA isoforms that require multiple ligation events”. The results in Figure 3, however, clearly demonstrate that when full-length ligation products were amplified, there was no detectable bias against longer isoforms in favor of shorter isoforms. Indeed the number of ligation events required for the longest CD45 isoform (R456) is 3 more than the number required for the shortest (R0). Of course, the safest comparisons will always be between ligation reactions requiring similar numbers of ligation events (e.g., Dscam), and care should be taken when comparing abundances of ligation products resulting from significantly different (Δ>3) ligation events. We have now added the following cautionary verbiage in the Discussion:

“One limitation of SeqZip is the number of ligamers required to create a ligation product […], it is crucial to perform the necessary controls to ensure quantitative detection of all desired isoforms.”

The Author response image 2 increases the concern that also ligamers can be non-specific. Nevertheless, the authors strongly argue against relying on the CAMseq experiments, which at the same time they consider as not reliable. Again, a more rigorous, systematic inclusion of controls is needed. For example, the authors should have selected control constructs according to the sequence similarity rather than to the availability of clones.

We agree that other control constructs might have been more optimal, but at the time of these experiments such constructs were not available to us. We disagree with the reviewer, however, that we “strongly argue against relying on the CAMseq” approach. It was our intent to provide a disinterested analysis of the advantages and disadvantages of RT-PCR-based approaches (i.e., triple read sequencing and CAMseq) versus a RNA-templated ligation-based approach (i.e., SeqZip). While SeqZip solves the template-switching problem inherent to the RT-PCR-based methods, it obviously has its own limitations. These are now covered in great detail in the “SeqZip uses and limitations” section of the Discussion. We leave it to the reader to decide which approach is most appropriate for his or her needs.

https://doi.org/10.7554/eLife.03700.022

Article and author information

Author details

  1. Christian K Roy

    1. RNA Therapeutics Institute, Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, United States
    2. Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
    Contribution
    CKR, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  2. Sara Olson

    Institute for Systems Genomics, Department of Genetics and Developmental Biology, University of Connecticut Health Center, Farmington, United States
    Contribution
    SO, Acquisition of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  3. Brenton R Graveley

    Institute for Systems Genomics, Department of Genetics and Developmental Biology, University of Connecticut Health Center, Farmington, United States
    Contribution
    BRG, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  4. Phillip D Zamore

    1. RNA Therapeutics Institute, Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, United States
    2. Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
    Contribution
    PDZ, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    phillip.zamore@umassmed.edu
    Competing interests
    PDZ: Reviewing Editor, eLife.
  5. Melissa J Moore

    1. RNA Therapeutics Institute, Howard Hughes Medical Institute, University of Massachusetts Medical School, Worcester, United States
    2. Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
    Contribution
    MJM, Conception and design, Analysis and interpretation of data
    For correspondence
    melissa.moore@umassmed.edu
    Competing interests
    No competing interests declared.

Funding

National Institutes of Health (NIH) (GM62862)

  • Phillip D Zamore

National Institutes of Health (NIH) (GM067842)

  • Sara Olson
  • Brenton R Graveley

The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported in part by National Institutes of Health grants R01 GM067842 to BRG and GM62862 to PDZ. We thank Alper Kucukural for bioinformatics support, Kristen Lynch for plasmids, Andrés Muro for Fn1 cell lines, and members of the Moore and Zamore labs for helpful comments on the manuscript.

Reviewing Editor

  1. Aviv Regev, Reviewing Editor, Broad Institute of MIT and Harvard, United States

Publication history

  1. Received: June 15, 2014
  2. Accepted: April 12, 2015
  3. Accepted Manuscript published: April 13, 2015 (version 1)
  4. Version of Record published: May 26, 2015 (version 2)

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© 2015, Roy et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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