Early transcriptional and epigenetic regulation of CD8+ T cell differentiation revealed by single-cell RNA sequencing.

During microbial infection, responding CD8+ T lymphocytes differentiate into heterogeneous subsets that together provide immediate and durable protection. To elucidate the dynamic transcriptional changes that underlie this process, we applied a single-cell RNA-sequencing approach and analyzed individual CD8+ T lymphocytes sequentially throughout the course of a viral infection in vivo. Our analyses revealed a striking transcriptional divergence among cells that had undergone their first division and identified previously unknown molecular determinants that controlled the fate specification of CD8+ T lymphocytes. Our findings suggest a model for the differentiation of terminal effector cells initiated by an early burst of transcriptional activity and subsequently refined by epigenetic silencing of transcripts associated with memory lymphocytes, which highlights the power and necessity of single-cell approaches.

Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses.

T lymphocytes responding to microbial infection give rise to effector cells that mediate acute host defense and memory cells that provide long-lived immunity, but the fundamental question of when and how these cells arise remains unresolved. Here we combined single-cell gene-expression analyses with 'machine-learning' approaches to trace the transcriptional 'roadmap' of individual CD8(+) T lymphocytes throughout the course of an immune response in vivo. Gene-expression signatures predictive of eventual fates could be discerned as early as the first T lymphocyte division and may have been influenced by asymmetric partitioning of the receptor for interleukin 2 (IL-2Rα) during mitosis. Our findings emphasize the importance of single-cell analyses in understanding fate determination and provide new insights into the specification of divergent lymphocyte fates early during an immune response to microbial infection.

Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dynamics during Neuron Differentiation.

Alternative splicing (AS) generates isoform diversity for cellular identity and homeostasis in multicellular life. Although AS variation has been observed among single cells, little is known about the biological or evolutionary significance of such variation. We developed Expedition, a computational framework consisting of outrigger, a de novo splice graph transversal algorithm to detect AS; anchor, a Bayesian approach to assign modalities; and bonvoyage, a visualization tool using non-negative matrix factorization to display modality changes. Applying Expedition to single pluripotent stem cells undergoing neuronal differentiation, we discover that up to 20% of AS exons exhibit bimodality. Bimodal exons are flanked by more conserved intronic sequences harboring distinct cis-regulatory motifs, constitute much of cell-type-specific splicing, are highly dynamic during cellular transitions, preserve reading frame, and reveal intricacy of cell states invisible to conventional gene expression analysis. Systematic AS characterization in single cells redefines our understanding of AS complexity in cell biology.

Profiling of Short-Tandem-Repeat Disease Alleles in 12,632 Human Whole Genomes.

Short tandem repeats (STRs) are hyper-mutable sequences in the human genome. They are often used in forensics and population genetics and are also the underlying cause of many genetic diseases. There are challenges associated with accurately determining the length polymorphism of STR loci in the genome by next-generation sequencing (NGS). In particular, accurate detection of pathological STR expansion is limited by the sequence read length during whole-genome analysis. We developed TREDPARSE, a software package that incorporates various cues from read alignment and paired-end distance distribution, as well as a sequence stutter model, in a probabilistic framework to infer repeat sizes for genetic loci, and we used this software to infer repeat sizes for 30 known disease loci. Using simulated data, we show that TREDPARSE outperforms other available software. We sampled the full genome sequences of 12,632 individuals to an average read depth of approximately 30× to 40× with Illumina HiSeq X. We identified 138 individuals with risk alleles at 15 STR disease loci. We validated a representative subset of the samples (n = 19) by Sanger and by Oxford Nanopore sequencing. Additionally, we validated the STR calls against known allele sizes in a set of GeT-RM reference cell-line materials (n = 6). Several STR loci that are entirely guanine or cytosines (G or C) have insufficient read evidence for inference and therefore could not be assayed precisely by TREDPARSE. TREDPARSE extends the limit of STR size detection beyond the physical sequence read length. This extension is critical because many of the disease risk cutoffs are close to or beyond the short sequence read length of 100 to 150 bases.

Regulation of asymmetric division and CD8+ T lymphocyte fate specification by protein kinase Cζ and protein kinase Cλ/ι.

During an immune response against a microbial pathogen, activated naive T lymphocytes give rise to effector cells that provide acute host defense and memory cells that provide long-lived immunity. It has been shown that T lymphocytes can undergo asymmetric division, enabling the daughter cells to inherit unequal amounts of fate-determining proteins and thereby acquire distinct fates from their inception. In this study, we show that the absence of the atypical protein kinase C (PKC) isoforms, PKCζ and PKCλ/ι, disrupts asymmetric CD8(+) T lymphocyte division. These alterations were associated with aberrant acquisition of a pre-effector transcriptional program, detected by single-cell gene expression analyses, in lymphocytes that had undergone their first division in vivo and enhanced differentiation toward effector fates at the expense of memory fates. Together, these results demonstrate a role for atypical PKC in regulating asymmetric division and the specification of divergent CD8(+) T lymphocyte fates early during an immune response.

Challenges in estimating percent inclusion of alternatively spliced junctions from RNA-seq data.

Transcript quantification is a long-standing problem in genomics and estimating the relative abundance of alternatively-spliced isoforms from the same transcript is an important special case. Both problems have recently been illuminated by high-throughput RNA sequencing experiments which are quickly generating large amounts of data. However, much of the signal present in this data is corrupted or obscured by biases resulting in non-uniform and non-proportional representation of sequences from different transcripts. Many existing analyses attempt to deal with these and other biases with various task-specific approaches, which makes direct comparison between them difficult. However, two popular tools for isoform quantification, MISO and Cufflinks, have adopted a general probabilistic framework to model and mitigate these biases in a more general fashion. These advances motivate the need to investigate the effects of RNA-seq biases on the accuracy of different approaches for isoform quantification. We conduct the investigation by building models of increasing sophistication to account for noise introduced by the biases and compare their accuracy to the established approaches. We focus on methods that estimate the expression of alternatively-spliced isoforms with the percent-spliced-in (PSI) metric for each exon skipping event. To improve their estimates, many methods use evidence from RNA-seq reads that align to exon bodies. However, the methods we propose focus on reads that span only exon-exon junctions. As a result, our approaches are simpler and less sensitive to exon definitions than existing methods, which enables us to distinguish their strengths and weaknesses more easily. We present several probabilistic models of of position-specific read counts with increasing complexity and compare them to each other and to the current state-of-the-art methods in isoform quantification, MISO and Cufflinks. On a validation set with RT-PCR measurements for 26 cassette events, some of our methods are more accurate and some are significantly more consistent than these two popular tools. This comparison demonstrates the challenges in estimating the percent inclusion of alternatively spliced junctions and illuminates the tradeoffs between different approaches.

Error correction of high-throughput sequencing datasets with non-uniform coverage.

MOTIVATION: The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open. RESULTS: In this article, we develop the method Hammer for error correction without any uniformity assumptions. Hammer is based on a combination of a Hamming graph and a simple probabilistic model for sequencing errors. It is a simple and adaptable algorithm that improves on other tools on non-uniform single-cell data, while achieving comparable results on normal multi-cell data. AVAILABILITY: http://www.cs.toronto.edu/~pashadag. CONTACT: pmedvedev@cs.ucsd.edu.

A complex-based reconstruction of the Saccharomyces cerevisiae interactome.

Most cellular processes are performed by proteomic units that interact with each other. These units are often stoichiometrically stable complexes comprised of several proteins. To obtain a faithful view of the protein interactome we must view it in terms of these basic units (complexes and proteins) and the interactions between them. This study makes two contributions toward this goal. First, it provides a new algorithm for reconstruction of stable complexes from a variety of heterogeneous biological assays; our approach combines state-of-the-art machine learning methods with a novel hierarchical clustering algorithm that allows clusters to overlap. We demonstrate that our approach constructs over 40% more known complexes than other recent methods and that the complexes it produces are more biologically coherent even compared with the reference set. We provide experimental support for some of our novel predictions, identifying both a new complex involved in nutrient starvation and a new component of the eisosome complex. Second, we provide a high accuracy algorithm for the novel problem of predicting transient interactions involving complexes. We show that our complex level network, which we call ComplexNet, provides novel insights regarding the protein-protein interaction network. In particular, we reinterpret the finding that "hubs" in the network are enriched for being essential, showing instead that essential proteins tend to be clustered together in essential complexes and that these essential complexes tend to be large.

Transcriptome-Wide Off-Target Effects of Steric-Blocking Oligonucleotides.

Steric-blocking oligonucleotides (SBOs) are short, single-stranded nucleic acids designed to modulate gene expression by binding to RNA transcripts and blocking access from cellular machinery such as splicing factors. SBOs have the potential to bind to near-complementary sites in the transcriptome, causing off-target effects. In this study, we used RNA-seq to evaluate the off-target differential splicing events of 81 SBOs and differential expression events of 46 SBOs. Our results suggest that differential splicing events are predominantly hybridization driven, whereas differential expression events are more common and driven by other mechanisms (including spurious experimental variation). We further evaluated the performance of in silico screens for off-target splicing events, and found an edit distance cutoff of three to result in a sensitivity of 14% and false discovery rate (FDR) of 99%. A machine learning model incorporating splicing predictions substantially improved the ability to prioritize low edit distance hits, increasing sensitivity from 4% to 26% at a fixed FDR of 90%. Despite these large improvements in performance, this approach does not detect the majority of events at an FDR <99%. Our results suggest that in silico methods are currently of limited use for predicting the off-target effects of SBOs, and experimental screening by RNA-seq should be the preferred approach.

ATP7B variant c.1934T > G p.Met645Arg causes Wilson disease by promoting exon 6 skipping.

Wilson disease is a recessive genetic disorder caused by pathogenic loss-of-function variants in the ATP7B gene. It is characterized by disrupted copper homeostasis resulting in liver disease and/or neurological abnormalities. The variant NM_000053.3:c.1934T > G (Met645Arg) has been reported as compound heterozygous, and is highly prevalent among Wilson disease patients of Spanish descent. Accordingly, it is classified as pathogenic by leading molecular diagnostic centers. However, functional studies suggest that the amino acid change does not alter protein function, leading one ClinVar submitter to question its pathogenicity. Here, we used a minigene system and gene-edited HepG2 cells to demonstrate that c.1934T > G causes ~70% skipping of exon 6. Exon 6 skipping results in frameshift and stop-gain, leading to loss of ATP7B function. The elucidation of the mechanistic effect for this variant resolves any doubt about its pathogenicity and enables the development of genetic medicines for restoring correct splicing.

Paternally inherited cis-regulatory structural variants are associated with autism.

The genetic basis of autism spectrum disorder (ASD) is known to consist of contributions from de novo mutations in variant-intolerant genes. We hypothesize that rare inherited structural variants in cis-regulatory elements (CRE-SVs) of these genes also contribute to ASD. We investigated this by assessing the evidence for natural selection and transmission distortion of CRE-SVs in whole genomes of 9274 subjects from 2600 families affected by ASD. In a discovery cohort of 829 families, structural variants were depleted within promoters and untranslated regions, and paternally inherited CRE-SVs were preferentially transmitted to affected offspring and not to their unaffected siblings. The association of paternal CRE-SVs was replicated in an independent sample of 1771 families. Our results suggest that rare inherited noncoding variants predispose children to ASD, with differing contributions from each parent.

SeeSite: Characterizing Relationships between Splice Junctions and Splicing Enhancers.

RNA splicing is a cellular process driven by the interaction between numerous regulatory sequences and binding sites, however, such interactions have been primarily explored by laboratory methods since computational tools largely ignore the relationship between different splicing elements. Current computational methods identify either splice sites or other regulatory sequences, such as enhancers and silencers. We present a novel approach for characterizing co-occurring relationships between splice site motifs and splicing enhancers. Our approach relies on an efficient algorithm for approximately solving Consensus Sequence with Outliers , an NP-complete string clustering problem. In particular, we give an algorithm for this problem that outputs near-optimal solutions in polynomial time. To our knowledge, this is the first formulation and computational attempt for detecting co-occurring sequence elements in RNA sequence data. Further, we demonstrate that SeeSite is capable of showing that certain ESEs are preferentially associated with weaker splice sites, and that there exists a co-occurrence relationship with splice site motifs.

The dsRBP and inactive editor ADR-1 utilizes dsRNA binding to regulate A-to-I RNA editing across the C. elegans transcriptome.

Inadequate adenosine-to-inosine editing of noncoding regions occurs in disease but is often uncorrelated with ADAR levels, underscoring the need to study deaminase-independent control of editing. C. elegans have two ADAR proteins, ADR-2 and the theoretically catalytically inactive ADR-1. Using high-throughput RNA sequencing of wild-type and adr mutant worms, we expand the repertoire of C. elegans edited transcripts over 5-fold and confirm that ADR-2 is the only active deaminase in vivo. Despite lacking deaminase function, ADR-1 affects editing of over 60 adenosines within the 3' UTRs of 16 different mRNAs. Furthermore, ADR-1 interacts directly with ADR-2 substrates, even in the absence of ADR-2, and mutations within its double-stranded RNA (dsRNA) binding domains abolish both binding and editing regulation. We conclude that ADR-1 acts as a major regulator of editing by binding ADR-2 substrates in vivo. These results raise the possibility that other dsRNA binding proteins, including the inactive human ADARs, regulate RNA editing through deaminase-independent mechanisms.

Chemoselective covalent coupling of oligonucleotide probes to self-assembled monolayers.

A chemoselective route to routinely and rapidly attach oligonucleotide probes to well-defined surfaces is presented. Cu(I) tris(benzyltriazolylmethyl)amine-catalyzed coupling of terminal acetylenes to azides on a self-assembled monolayer is used instead of traditional nucleophilic-electrophilic coupling reactions. The reaction proceeds well even in the presence of purposely introduced nucleophilic and electrophilic impurities. The density of oligonucleotide probes can be controlled by controlling the amount of azide functionality. Although most of our work was done on gold surfaces, this technique should be readily applicable to any surface on which an azide-containing monolayer can be assembled as we have preliminarily demonstrated by derivatizing azidotrimethoxysilane-modified glass slides with fluorescein-containing oligonucleotides.