Publisher Correction: Subsets of ILC3-ILC1-like cells generate a diversity spectrum of innate lymphoid cells in human mucosal tissues.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Subsets of ILC3-ILC1-like cells generate a diversity spectrum of innate lymphoid cells in human mucosal tissues.

Innate lymphoid cells (ILCs) are tissue-resident lymphocytes categorized on the basis of their core regulatory programs and the expression of signature cytokines. Human ILC3s that produce the cytokine interleukin-22 convert into ILC1-like cells that produce interferon-γ in vitro, but whether this conversion occurs in vivo remains unclear. In the present study we found that ILC3s and ILC1s in human tonsils represented the ends of a spectrum that included additional discrete subsets. RNA velocity analysis identified an intermediate ILC3-ILC1 cluster, which had strong directionality toward ILC1s. In humanized mice, the acquisition of ILC1 features by ILC3s showed tissue dependency. Chromatin studies indicated that the transcription factors Aiolos and T-bet cooperated to repress regulatory elements active in ILC3s. A transitional ILC3-ILC1 population was also detected in the human intestine. We conclude that ILC3s undergo conversion into ILC1-like cells in human tissues in vivo, and that tissue factors and Aiolos were required for this process.

Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols.

In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.

BACKGROUND: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. RESULTS: Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. CONCLUSIONS: Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.

canvasDesigner: a versatile interactive high-resolution scientific multi-panel visualization toolkit.

Summary: We present a bioinformatics and systems biology visualization toolkit harmonizing real time interactive exploring and analyzing of big data, full-fledged customizing of look-n-feel and producing multi-panel publication-ready figures in PDF format simultaneously. Availability and implementation: Source code and detailed user guides are available at http://canvasxpress.org, https://baohongz.github.io/canvasDesigner and https://baohongz.github.io/canvasDesigner/demo_video.html. Supplementary information: Supplementary data are available at Bioinformatics online.

QuickMIRSeq: a pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing.

BACKGROUND: Genome-wide miRNA expression data can be used to study miRNA dysregulation comprehensively. Although many open-source tools for microRNA (miRNA)-seq data analyses are available, challenges remain in accurate miRNA quantification from large-scale miRNA-seq dataset. We implemented a pipeline called QuickMIRSeq for accurate quantification of known miRNAs and miRNA isoforms (isomiRs) from multiple samples simultaneously. RESULTS: QuickMIRSeq considers the unique nature of miRNAs and combines many important features into its implementation. First, it takes advantage of high redundancy of miRNA reads and introduces joint mapping of multiple samples to reduce computational time. Second, it incorporates the strand information in the alignment step for more accurate quantification. Third, reads potentially arising from background noise are filtered out to improve the reliability of miRNA detection. Fourth, sequences aligned to miRNAs with mismatches are remapped to a reference genome to further reduce false positives. Finally, QuickMIRSeq generates a rich set of QC metrics and publication-ready plots. CONCLUSIONS: The rich visualization features implemented allow end users to interactively explore the results and gain more insights into miRNA-seq data analyses. The high degree of automation and interactivity in QuickMIRSeq leads to a substantial reduction in the time and effort required for miRNA-seq data analysis.

Evaluation and comparison of computational tools for RNA-seq isoform quantification.

BACKGROUND: Alternatively spliced transcript isoforms are commonly observed in higher eukaryotes. The expression levels of these isoforms are key for understanding normal functions in healthy tissues and the progression of disease states. However, accurate quantification of expression at the transcript level is limited with current RNA-seq technologies because of, for example, limited read length and the cost of deep sequencing. RESULTS: A large number of tools have been developed to tackle this problem, and we performed a comprehensive evaluation of these tools using both experimental and simulated RNA-seq datasets. We found that recently developed alignment-free tools are both fast and accurate. The accuracy of all methods was mainly influenced by the complexity of gene structures and caution must be taken when interpreting quantification results for short transcripts. Using TP53 gene simulation, we discovered that both sequencing depth and the relative abundance of different isoforms affect quantification accuracy CONCLUSIONS: Our comprehensive evaluation helps data analysts to make informed choice when selecting computational tools for isoform quantification.

QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization.

BACKGROUND: RNA sequencing (RNA-seq), a next-generation sequencing technique for transcriptome profiling, is being increasingly used, in part driven by the decreasing cost of sequencing. Nevertheless, the analysis of the massive amounts of data generated by large-scale RNA-seq remains a challenge. Multiple algorithms pertinent to basic analyses have been developed, and there is an increasing need to automate the use of these tools so as to obtain results in an efficient and user friendly manner. Increased automation and improved visualization of the results will help make the results and findings of the analyses readily available to experimental scientists. RESULTS: By combing the best open source tools developed for RNA-seq data analyses and the most advanced web 2.0 technologies, we have implemented QuickRNASeq, a pipeline for large-scale RNA-seq data analyses and visualization. The QuickRNASeq workflow consists of three main steps. In Step #1, each individual sample is processed, including mapping RNA-seq reads to a reference genome, counting the numbers of mapped reads, quality control of the aligned reads, and SNP (single nucleotide polymorphism) calling. Step #1 is computationally intensive, and can be processed in parallel. In Step #2, the results from individual samples are merged, and an integrated and interactive project report is generated. All analyses results in the report are accessible via a single HTML entry webpage. Step #3 is the data interpretation and presentation step. The rich visualization features implemented here allow end users to interactively explore the results of RNA-seq data analyses, and to gain more insights into RNA-seq datasets. In addition, we used a real world dataset to demonstrate the simplicity and efficiency of QuickRNASeq in RNA-seq data analyses and interactive visualizations. The seamless integration of automated capabilites with interactive visualizations in QuickRNASeq is not available in other published RNA-seq pipelines. CONCLUSION: The high degree of automation and interactivity in QuickRNASeq leads to a substantial reduction in the time and effort required prior to further downstream analyses and interpretation of the analyses findings. QuickRNASeq advances primary RNA-seq data analyses to the next level of automation, and is mature for public release and adoption.

A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification.

BACKGROUND: RNA-Seq has become increasingly popular in transcriptome profiling. One aspect of transcriptome research is to quantify the expression levels of genomic elements, such as genes, their transcripts and exons. Acquiring a transcriptome expression profile requires genomic elements to be defined in the context of the genome. Multiple human genome annotation databases exist, including RefGene (RefSeq Gene), Ensembl, and the UCSC annotation database. The impact of the choice of an annotation on estimating gene expression remains insufficiently investigated. RESULTS: In this paper, we systematically characterized the impact of genome annotation choice on read mapping and transcriptome quantification by analyzing a RNA-Seq dataset generated by the Human Body Map 2.0 Project. The impact of a gene model on mapping of non-junction reads is different from junction reads. For the RNA-Seq dataset with a read length of 75 bp, on average, 95% of non-junction reads were mapped to exactly the same genomic location regardless of which gene models was used. By contrast, this percentage dropped to 53% for junction reads. In addition, about 30% of junction reads failed to align without the assistance of a gene model, while 10-15% mapped alternatively. There are 21,958 common genes among RefGene, Ensembl, and UCSC annotations. When we compared the gene quantification results in RefGene and Ensembl annotations, 20% of genes are not expressed, and thus have a zero count in both annotations. Surprisingly, identical gene quantification results were obtained for only 16.3% (about one sixth) of genes. Approximately 28.1% of genes' expression levels differed by 5% or higher, and of those, the relative expression levels for 9.3% of genes (equivalent to 2038) differed by 50% or greater. The case studies revealed that the gene definition differences in gene models frequently result in inconsistency in gene quantification. CONCLUSIONS: We demonstrated that the choice of a gene model has a dramatic effect on both gene quantification and differential analysis. Our research will help RNA-Seq data analysts to make an informed choice of gene model in practical RNA-Seq data analysis.

Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap.

BACKGROUND: While RNA-sequencing (RNA-seq) is becoming a powerful technology in transcriptome profiling, one significant shortcoming of the first-generation RNA-seq protocol is that it does not retain the strand specificity of origin for each transcript. Without strand information it is difficult and sometimes impossible to accurately quantify gene expression levels for genes with overlapping genomic loci that are transcribed from opposite strands. It has recently become possible to retain the strand information by modifying the RNA-seq protocol, known as strand-specific or stranded RNA-seq. Here, we evaluated the advantages of stranded RNA-seq in transcriptome profiling of whole blood RNA samples compared with non-stranded RNA-seq, and investigated the influence of gene overlaps on gene expression profiling results based on practical RNA-seq datasets and also from a theoretical perspective. RESULTS: Our results demonstrated a substantial impact of stranded RNA-seq on transcriptome profiling and gene expression measurements. As many as 1751 genes in Gencode Release 19 were identified to be differentially expressed when comparing stranded and non-stranded RNA-seq whole blood samples. Antisense and pseudogenes were significantly enriched in differential expression analyses. Because stranded RNA-seq retains strand information of a read, we can resolve read ambiguity in overlapping genes transcribed from opposite strands, which provides a more accurate quantification of gene expression levels compared with traditional non-stranded RNA-seq. In the human genome, it is not uncommon to find genomic loci where both strands encode distinct genes. Among the over 57,800 annotated genes in Gencode release 19, there are an estimated 19 % (about 11,000) of overlapping genes transcribed from the opposite strands. Based on our whole blood mRNA-seq datasets, the fraction of overlapping nucleotide bases on the same and opposite strands were estimated at 2.94 % and 3.1 %, respectively. The corresponding theoretical estimations are 3 % and 3.6 %, well in agreement with our own findings. CONCLUSIONS: Stranded RNA-seq provides a more accurate estimate of transcript expression compared with non-stranded RNA-seq, and is therefore the recommended RNA-seq approach for future mRNA-seq studies.

[Study on Vibrational Spectra Characteristics of Gem-Quality Natrolite].

Recently, there is a batch of colorless faceted gem-quality natrolite appear in the international jewelry market. In order to provide some information that can help us to distinguish them from the imitations. The infrared spectrometer and Raman spectrometer were employed to study the characteristics of the vibrational spectrum of three natrolite samples in this article. The typical infrared spectra shows that: the absorption region 4000~1200 cm(-1) is induced by stretching vibration of the hydroxyl group, the strong absorption peaks range from 1200~600 cm(-1) are relative with the anti-symmetry and symmetry stretching vibration of tetrahedral T-O bonds (T=Si or Al). The Raman spectra scattering peaks are located in the range of 300~600 and 700~1200 cm(-1). The low intensity Raman scattering spectrum in the range of 300~360 cm(-1) corresponds to the vibration of the water molecules in the crystal. The medium intensity Raman scattering spectrum is assigned to the deformation of SiO4 tetrahedra. The Raman spectra scattering peak at 726 cm(-1) is assigned to the stretching vibration of Al-O; The Si-O stretching vibration displays the Raman spectra scattering peaks at 974, 1038 and 1084 cm(-1).

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing.

BACKGROUND: Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. RESULTS: Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. CONCLUSIONS: Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html.

The Type I Interferon Signature Reflects Multiple Phenotypic and Activity Measures in Dermatomyositis.

OBJECTIVE: The type 1 interferon (IFN) pathway is up-regulated in dermatomyositis (DM). We sought to define how organ-specific disease activity as well as autoantibodies and other clinical factors are independently associated with systemic type I IFN activity in adult patients with DM. METHODS: RNA sequencing was performed on 355 whole blood samples collected from 202 well-phenotyped DM patients followed up during the course of their clinical care. A previously defined 13-gene type I IFN score was modeled as a function of demographic, serologic, and clinical variables using both cross-sectional and longitudinal data. RESULTS: The pattern of type I IFN-driven transcriptional response was stereotyped across samples with a sequential modular activation pattern strikingly similar to systemic lupus erythematosus. The median type I IFN score was higher or lower in patients with anti-melanoma differentiation-associated protein 5 (anti-MDA-5) or anti-Mi-2 antibodies, respectively, compared to patients without these antibodies. Absolute type I IFN score was independently associated with muscle and skin disease activity, interstitial lung disease, and anti-MDA-5 antibodies. Changes in the type I IFN score over time were significantly associated with changes in skin or muscle disease activity. Stratified analysis accounting for heterogeneity in organ involvement and antibody class revealed high correlation between changes in the type I IFN score and skin disease activity (Spearman's ρ = 0.84-0.95). CONCLUSION: The type I IFN score is independently associated with skin and muscle disease activity as well as certain clinical and serologic features in DM. Accounting for the effect of muscle disease and anti-MDA-5 status revealed that the type I IFN score is strongly correlated with skin disease activity, providing support for type I IFN blockade as a therapeutic strategy for DM.

A protein kinase C α and ß inhibitor blunts hyperphagia to halt renal function decline and reduces adiposity in a rat model of obesity-driven type 2 diabetes.

Type 2 diabetes (T2D) and its complications can have debilitating, sometimes fatal consequences for afflicted individuals. The disease can be difficult to control, and therapeutic strategies to prevent T2D-induced tissue and organ damage are needed. Here we describe the results of administering a potent and selective inhibitor of Protein Kinase C (PKC) family members PKCα and PKCß, Cmpd 1, in the ZSF1 obese rat model of hyperphagia-induced, obesity-driven T2D. Although our initial intent was to evaluate the effect of PKCα/ß inhibition on renal damage in this model setting, Cmpd 1 unexpectedly caused a marked reduction in the hyperphagic response of ZSF1 obese animals. This halted renal function decline but did so indirectly and indistinguishably from a pair feeding comparator group. However, above and beyond this food intake effect, Cmpd 1 lowered overall animal body weights, reduced liver vacuolation, and reduced inguinal adipose tissue (iWAT) mass, inflammation, and adipocyte size. Taken together, Cmpd 1 had strong effects on multiple disease parameters in this obesity-driven rodent model of T2D. Further evaluation for potential translation of PKCα/ß inhibition to T2D and obesity in humans is warranted.

QuickIsoSeq for Isoform Quantification in Large-Scale RNA Sequencing.

RNA-sequencing (RNA-seq) is a powerful technology for transcriptome profiling. While most RNA-seq projects focus on gene-level quantification and analysis, there is growing evidence that most mammalian genes are alternatively spliced to generate different isoforms that can be subsequently translated to protein molecules with diverse or even opposing biological functions. Quantifying the expression levels of these isoforms is key to understanding the genes biological functions in healthy tissues and the progression of diseases. Among open source tools developed for isoform quantification, Salmon, Kallisto, and RSEM are recommended based upon previous systematic evaluation of these tools using both experimental and simulated RNA-seq datasets. However, isoform quantification in practical RNA-seq data analysis needs to deal with many QC issues, such as the abundance of rRNAs in mRNA-seq, the efficiency of globin RNA depletion in whole blood samples, and potential sample swapping. To overcome these practical challenges, QuickIsoSeq was developed for large-scale RNA-seq isoform quantification along with QC. In this chapter, we describe the pipeline and detailed the steps required to deploy and use it to analyze RNA-seq datasets in practice. The QuickIsoSeq package can be downloaded from https://github.com/shanrongzhao/QuickIsoSeq.

Single-cell analyses of Crohn's disease tissues reveal intestinal intraepithelial T cells heterogeneity and altered subset distributions.

Crohn's disease (CD) is a chronic transmural inflammation of intestinal segments caused by dysregulated interaction between microbiome and gut immune system. Here, we profile, via multiple single-cell technologies, T cells purified from the intestinal epithelium and lamina propria (LP) from terminal ileum resections of adult severe CD cases. We find that intraepithelial lymphocytes (IEL) contain several unique T cell subsets, including NKp30+γδT cells expressing RORγt and producing IL-26 upon NKp30 engagement. Further analyses comparing tissues from non-inflamed and inflamed regions of patients with CD versus healthy controls show increased activated TH17 but decreased CD8+T, γδT, TFH and Treg cells in inflamed tissues. Similar analyses of LP find increased CD8+, as well as reduced CD4+T cells with an elevated TH17 over Treg/TFH ratio. Our analyses of CD tissues thus suggest a potential link, pending additional validations, between transmural inflammation, reduced IEL γδT cells and altered spatial distribution of IEL and LP T cell subsets.

[Crystallography orientation by molecular spectrum of structural hydroxide in pyroxene megacryst].

Crystal can be crystallographically oriented by molecular spectrum with the change in band shift, band intensity, and band shape. The three groups of band shift of augite are different between vertical to c-axis and parallel to c-axis: the first peak value 3 629-3 633 red shifts to 3 601-3 616; the same as the second peak value 3 514-3 543; on the contrary, the third peak value 3 460-3 465 is blue shift. The intensity of the first band is equivalent in two directions, while the second and the third parallel to c-axis are much stronger than the vertical. The wave shape is not quite different. The intensity of the Raman spectra vertical to c-axis is stronger than that parallel to c-axis in general, while the band shift and the shape show little difference. It was indicated that the geological tectonic environment can be reflected by the spectra of structural hydroxide with different crystal directions.

Alternative splicing, RNA-seq and drug discovery.

Alternative splicing, hereafter referred to as AS, is an essential component of gene expression regulation that contributes to the diversity of proteomes. Recent developments in RNA sequencing (RNA-seq) technologies, combined with the advent of computational tools, have enabled transcriptome-wide studies of AS at an unprecedented scale and resolution. RNA mis-splicing can cause human disease, and to target alternative splicing has led to the development of novel therapeutics. Splice variants diversify the repertoire of biomarkers and functionally contribute to drug resistance. Our expanding knowledge of AS variation in human populations holds great promise for improving disease diagnoses and ultimately patient care in the era of sequencing and precision medicine.

Applications of machine learning in drug discovery and development.

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.

Molecular Profiling of Ulcerative Colitis Subjects from the TURANDOT Trial Reveals Novel Pharmacodynamic/Efficacy Biomarkers.

BACKGROUND AND AIMS: To define pharmacodynamic and efficacy biomarkers in ulcerative colitis [UC] patients treated with PF-00547659, an anti-human mucosal addressin cell adhesion molecule-1 [MAdCAM-1] monoclonal antibody, in the TURANDOT study. METHODS: Transcriptome, proteome and immunohistochemistry data were generated in peripheral blood and intestinal biopsies from 357 subjects in the TURANDOT study. RESULTS: In peripheral blood, C-C motif chemokine receptor 9 [CCR9] gene expression demonstrated a dose-dependent increase relative to placebo, but in inflamed intestinal biopsies CCR9 gene expression decreased with increasing PF-00547659 dose. Statistical models incorporating the full RNA transcriptome in inflamed intestinal biopsies showed significant ability to assess response and remission status. Oncostatin M [OSM] gene expression in inflamed intestinal biopsies demonstrated significant associations with, and good accuracy for, efficacy, and this observation was confirmed in independent published studies in which UC patients were treated with infliximab or vedolizumab. Compared with the placebo group, intestinal T-regulatory cells demonstrated a significant increase in the intermediate 22.5-mg dose cohort, but not in the 225-mg cohort. CONCLUSIONS: CCR9 and OSM are implicated as novel pharmacodynamic and efficacy biomarkers. These findings occur amid coordinated transcriptional changes that enable the definition of surrogate efficacy biomarkers based on inflamed biopsy or blood transcriptomics data.ClinicalTrials.gov identifierNCT01620255.