SUMMARY 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 controlling CD8+ T lymphocyte fate specification. These findings suggest a model of terminal effector cell differentiation initiated by an early burst of transcriptional activity and subsequently refined by epigenetic silencing of transcripts associated with memory lymphocytes, highlighting the power and necessity of single-cell approaches.

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.

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 combine 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 be influenced by asymmetric partitioning of the interleukin-2 receptor during mitosis. These findings underscore 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.

Surface arrays of single-stranded DNA are at the center of some of the most active areas in biological research. These include conventional applications in genome sequencing and disease diagnostics, as well as more novel emerging examples, such as combinatorial drug and reaction discovery. 1 Most methods of oligonucleotide immobilization rely on traditional nucleophilicelectrophilic reactions to achieve coupling of the oligonucleotide to the surface. 2 Unfortunately, this strategy is susceptible to side reactions, for instance, with amino groups on the nucleotides or the small molecule contaminants inherent to oligonucleotide synthesis. 3 Additionally, popular reactive electrophiles, such as N-hydroxysuccinimide esters, are prone to hydrolysis before and during the coupling reaction, which both reduce coupling yields and can make the yields irreproducible. 4 Accurate and reproducible detection of target oligonucleotides depends on the accuracy and reproducibility with which the surface can be functionalized. In this paper, we report a chemoselective approach to the formation of oligonucleotide probe surfaces using copper(I) tris(benzyltriazolylmethyl)amine (TBTA)-catalyzed triazole formation between a controlled density of azide groups on densely packed selfassembled monolayers (SAMs) and acetylene groups on the oligonucleotide probe to be immobilized. We have found this strategy to be highly predictable, very fast, and resistant to side reactions, unaffected even by the presence of excess nucleophilic or electrophilic impurities.Recently, we have demonstrated that Sharpless "click" chemistry can be used to covalently attach acetylene-bearing molecules to azide-terminated SAMs. 5,6 The surface reaction is quantitative and regioselective, exclusively yielding a single product at a single orientation. The chemistry is orthogonal to most typical organic transformations and thus is chemoselective. 7 Recent studies have demonstrated that the chemistry is well suited for the coupling of biomolecules to surfaces. 8 Although application of this chemistry to the attachment of oligonucleotides may appear straightforward, the majority of past work used free Cu(I), typically generated and maintained in aqueous solution by an excess of reducing agent. Unfortunately, in the presence of dioxygen, Cu(I) rapidly damages DNA via the generation of reactive oxygen species. 9 In order for a surface array of oligonucleotides to be useful as a sensor, the structure of the oligonucleotides must be preserved. Recently, the Sharpless group has introduced a triazolylamine copper ligand, tris-(benzyltriazolylmethyl)amine, that can accelerate the cycloaddition reaction. 10 At the same time, this ligand was found to significantly deter the redox chemistry of Cu(I) with oxygen, which is essential for preventing damage to the oligonucleotides. Encouraged by the highly desirable features of the Cu(I)TBTA-catalyzed azide-alkyne cycloaddition, we studied its applicability to the attachment of oligonucleotide probes onto well-defined SAMs.Oligode...

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.

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.

Human neural progenitors derived from pluripotent stem cells develop into electrophysiologically active neurons at heterogeneous rates, which can confound disease-relevant discoveries in neurology and psychiatry. By combining patch clamping, morphological and transcriptome analysis on single human neurons in vitro, we defined a continuum of poor to highly functional electrophysiological states of differentiated neurons. The strong correlations between action potentials, synaptic activity, dendritic complexity and gene expression highlight the importance of methods for isolating functionally comparable neurons for in vitro investigations of brain disorders. While whole-cell electrophysiology is the gold standard for functional evaluation, it often lacks the scalability required for disease modeling studies. Here, we demonstrate a multimodal machine-learning strategy to identify new molecular features that predict the physiological states of single neurons, independently of the time spent in vitro. As further proof of concept, we selected one of the potential neurophysiological biomarkers identified in this study – GDAP1L1 – to isolate highly functional live human neurons in vitro.

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. Biological processes exhibit a hierarchical structure in which the basic working units, proteins, physically associate to form stoichiometrically stable complexes. Complexes interact with individual proteins or other complexes to form functional modules and pathways that carry out most cellular processes. Such higher level interactions are more transient than those within complexes and are highly dependent on temporal and spatial context. The function of each protein or complex depends on its interaction partners. Therefore, a faithful reconstruction of the entire set of complexes in the cell is essential to identifying the function of individual proteins and complexes as well as serving as a building block for understanding the higher level organization of the cell, such as the interactions of complexes and proteins within cellular pathways. Here we describe a novel method for reconstruction of complexes from a variety of biological assays and a method for predicting the network of interactions relating these core cellular units (complexes and proteins).Our reconstruction effort focuses on the yeast Saccharomyces cerevisiae. Yeast serves as the prototypical case study for the reconstruction of protein-protein interaction networks. Moreover the yeast complexes often have conserved orthologs in other organisms, including human, and are of interest in their own right. Several studies (1-4) using a variety of assays have generated high throughput data that directly measure protein-protein inte...