Long-read sequencing transcriptome quantification with lr-kallisto.

RNA abundance quantification has become routine and affordable thanks to high-throughput "short-read" technologies that provide accurate molecule counts at the gene level. Similarly accurate and affordable quantification of definitive full-length, transcript isoforms has remained a stubborn challenge, despite its obvious biological significance across a wide range of problems. "Long-read" sequencing platforms now produce data-types that can, in principle, drive routine definitive isoform quantification. However some particulars of contemporary long-read datatypes, together with isoform complexity and genetic variation, present bioinformatic challenges. We show here, using ONT data, that fast and accurate quantification of long-read data is possible and that it is improved by exome capture. To perform quantifications we developed lr-kallisto, which adapts the kallisto bulk and single-cell RNA-seq quantification methods for long-read technologies.

Flexible parsing, interpretation, and editing of technical sequences with splitcode.

MOTIVATION: Next-generation sequencing libraries are constructed with numerous synthetic constructs such as sequencing adapters, barcodes, and unique molecular identifiers. Such sequences can be essential for interpreting results of sequencing assays, and when they contain information pertinent to an experiment, they must be processed and analyzed. RESULTS: We present a tool called splitcode, that enables flexible and efficient parsing, interpreting, and editing of sequencing reads. This versatile tool facilitates simple, reproducible preprocessing of reads from libraries constructed for a large array of single-cell and bulk sequencing assays. AVAILABILITY AND IMPLEMENTATION: The splitcode program is available at http://github.com/pachterlab/splitcode.

Genome organization around nuclear speckles drives mRNA splicing efficiency.

The nucleus is highly organized, such that factors involved in the transcription and processing of distinct classes of RNA are confined within specific nuclear bodies1,2. One example is the nuclear speckle, which is defined by high concentrations of protein and noncoding RNA regulators of pre-mRNA splicing3. What functional role, if any, speckles might play in the process of mRNA splicing is unclear4,5. Here we show that genes localized near nuclear speckles display higher spliceosome concentrations, increased spliceosome binding to their pre-mRNAs and higher co-transcriptional splicing levels than genes that are located farther from nuclear speckles. Gene organization around nuclear speckles is dynamic between cell types, and changes in speckle proximity lead to differences in splicing efficiency. Finally, directed recruitment of a pre-mRNA to nuclear speckles is sufficient to increase mRNA splicing levels. Together, our results integrate the long-standing observations of nuclear speckles with the biochemistry of mRNA splicing and demonstrate a crucial role for dynamic three-dimensional spatial organization of genomic DNA in driving spliceosome concentrations and controlling the efficiency of mRNA splicing.

The impact of package selection and versioning on single-cell RNA-seq analysis.

Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.

The T-cell niche tunes immune function through modulation of the cytoskeleton and TCR-antigen forces.

Obesity is a major public health crisis given its rampant growth and association with an increased risk for cancer. Interestingly, patients with obesity tend to have an increased tumor burden and decreased T-cell function. It remains unclear how obesity compromises T-cell mediated immunity. To address this question, we modeled the adipocyte niche using the secretome released from adipocytes as well as the niche of stromal cells and investigated how these factors modulated T-cell function. We found that the secretomes altered antigen-specific T-cell receptor (TCR) triggering and activation. RNA-sequencing analysis identified thousands of gene targets modulated by the secretome including those associated with cytoskeletal regulation and actin polymerization. We next used molecular force probes to show that T-cells exposed to the adipocyte niche display dampened force transmission to the TCR-antigen complex and conversely, stromal cell secreted factors lead to significantly enhanced TCR forces. These results were then validated in diet-induced obese mice. Importantly, secretome-mediated TCR force modulation mirrored the changes in T-cell functional responses in human T-cells using the FDA-approved immunotherapy, blinatumomab. Thus, this work shows that the adipocyte niche contributes to T-cell dysfunction through cytoskeletal modulation and reduces TCR triggering by dampening TCR forces consistent with the mechanosensor model of T-cell activation.

Nuclear to cytoplasmic transport is a druggable dependency in MYC-driven hepatocellular carcinoma.

The MYC oncogene is often dysregulated in human cancer, including hepatocellular carcinoma (HCC). MYC is considered undruggable to date. Here, we comprehensively identify genes essential for survival of MYChigh but not MYClow cells by a CRISPR/Cas9 genome-wide screen in a MYC-conditional HCC model. Our screen uncovers novel MYC synthetic lethal (MYC-SL) interactions and identifies most MYC-SL genes described previously. In particular, the screen reveals nucleocytoplasmic transport to be a MYC-SL interaction. We show that the majority of MYC-SL nucleocytoplasmic transport genes are upregulated in MYChigh murine HCC and are associated with poor survival in HCC patients. Inhibiting Exportin-1 (XPO1) in vivo induces marked tumor regression in an autochthonous MYC-transgenic HCC model and inhibits tumor growth in HCC patient-derived xenografts. XPO1 expression is associated with poor prognosis only in HCC patients with high MYC activity. We infer that MYC may generally regulate and require altered expression of nucleocytoplasmic transport genes for tumorigenesis.

Avenanthramide and ß-Glucan Therapeutics Accelerate Wound Healing Via Distinct and Nonoverlapping Mechanisms.

Objective: Given the significant economic, health care, and personal burden of acute and chronic wounds, we investigated the dose dependent wound healing mechanisms of two Avena sativa derived compounds: avenanthramide (AVN) and ß-Glucan. Approach: We utilized a splinted excisional wound model that mimics human-like wound healing and performed subcutaneous AVN and ß-Glucan injections in 15-week-old C57BL/6 mice. Histologic and immunohistochemical analysis was performed on the explanted scar tissue to assess changes in collagen architecture and cellular responses. Results: AVN and ß-Glucan treatment provided therapeutic benefits at a 1% dose by weight in a phosphate-buffered saline vehicle, including accelerated healing time, beneficial cellular recruitment, and improved tissue architecture of healed scars. One percent AVN treatment promoted an extracellular matrix (ECM) architecture similar to unwounded skin, with shorter, more randomly aligned collagen fibers and reduced inflammatory cell presence in the healed tissue. One percent ß-Glucan treatment promoted a tissue architecture characterized by long, thick bundles of collagen with increased blood vessel density. Innovation: AVN and ß-Glucan have previously shown promise in promoting wound healing, although the therapeutic efficacies and mechanisms of these bioactive compounds remain incompletely understood. Furthermore, the healed ECM architecture of these wounds has not been characterized. Conclusions: AVN and ß-Glucan accelerated wound closure compared to controls through distinct mechanisms. AVN-treated scars displayed a more regenerative tissue architecture with reduced inflammatory cell recruitment, while ß-Glucan demonstrated increased angiogenesis with more highly aligned tissue architecture more indicative of fibrosis. A deeper understanding of the mechanisms driving healing in these two naturally derived therapeutics will be important for translation to human use.

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression.

There are an estimated 300,000 mammalian viruses from which infectious diseases in humans may arise. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012 virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify putative novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.

kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq.

The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.

Growth hormone insensitivity and adipose tissue: tissue morphology and transcriptome analyses in pigs and humans.

PURPOSE: Growth hormone receptor knockout (GHR-KO) pigs have recently been developed, which serve as a large animal model of Laron syndrome (LS). GHR-KO pigs, like individuals with LS, are obese but lack some comorbidities of obesity. The purpose of this study was to examine the histological and transcriptomic phenotype of adipose tissue (AT) in GHR-KO pigs and humans with LS. METHODS: Intraabdominal (IA) and subcutaneous (SubQ) AT was collected from GHR-KO pigs and examined histologically for adipocyte size and collagen content. RNA was isolated and cDNA sequenced, and the results were analyzed to determine differentially expressed genes that were used for enrichment and pathway analysis in pig samples. For comparison, we also performed limited analyses on human AT collected from a single individual with and without LS. RESULTS: GHR-KO pigs have increased adipocyte size, while the LS AT had a trend towards an increase. Transcriptome analysis revealed 55 differentially expressed genes present in both depots of pig GHR-KO AT. Many significant terms in the enrichment analysis of the SubQ depot were associated with metabolism, while in the IA depot, IGF and longevity pathways were negatively enriched. In pathway analysis, multiple expected and novel pathways were significantly affected by genotype, i.e. KO vs. controls. When GH related gene expression was analyzed, SOCS3 and CISH showed species-specific changes. CONCLUSION: AT of GHR-KO pigs has several similarities to that of humans with LS in terms of adipocyte size and gene expression profile that help describe the depot-specific adipose phenotype of both groups.