scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq.

Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity.

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

Cellular diversity and lineage trajectory: insights from mouse single cell transcriptomes.

Single cell RNA-sequencing (scRNA-seq) technology has matured to the point that it is possible to generate large single cell atlases of developing mouse embryos. These atlases allow the dissection of developmental cell lineages and molecular changes during embryogenesis. When coupled with single cell technologies for profiling the chromatin landscape, epigenome, proteome and metabolome, and spatial tissue organisation, these scRNA-seq approaches can now collect a large volume of multi-omic data about mouse embryogenesis. In addition, advances in computational techniques have enabled the inference of developmental lineages of differentiating cells, even without explicitly introduced genetic markers. This Spotlight discusses recent advent of single cell experimental and computational methods, and key insights from applying these methods to the study of mouse embryonic development. We highlight challenges in analysing and interpreting these data to complement and expand our knowledge from traditional developmental biology studies in relation to cell identity, diversity and lineage differentiation.

FlowGrid enables fast clustering of very large single-cell RNA-seq data.

MOTIVATION: Scalable clustering algorithms are needed to analyze millions of cells in single cell RNA-seq (scRNA-seq) data. RESULTS: Here, we present an open source python package called FlowGrid that can integrate into the Scanpy workflow to perform clustering on very large scRNA-seq datasets. FlowGrid implements a fast density-based clustering algorithm originally designed for flow cytometry data analysis. We introduce a new automated parameter tuning procedure, and show that FlowGrid can achieve comparable clustering accuracy as state-of-the-art clustering algorithms but at a substantially reduced run time for very large single cell RNA-seq datasets. For example, FlowGrid can complete a one-hour clustering task for one million cells in about five min. AVAILABILITY AND IMPLEMENTATION: https://github.com/holab-hku/FlowGrid. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dynamic changes in antibiotic resistance genes and gut microbiota after Helicobacter pylori eradication therapies.

BACKGROUND: Short-term antibiotics exposure is associated with alterations in microbiota and antibiotic resistance genes (ARGs) in the human gut. While antibiotics are critical in the successful eradication of Helicobacter pylori, the short-term and long-term impacts on the composition and quantity of antibiotics resistance genes after H. pylori eradication are unclear. This study used whole-genome shotgun metagenomic of stool samples to characterize the gut microbiota and ARGs, before and after H. pylori eradication therapy. RESULTS: Forty-four H. pylori-infected patients were recruited, including 21 treatment naïve patients who received clarithromycin-based triple therapy (CLA group) and 23 patients who failed previous therapies, in which 10 received levofloxacin-based quadruple therapy (LEVO group) and 13 received other combinations (OTHER group). Stool samples were collected at baseline (before current treatment), 6 week and 6 month after eradication therapy. At baseline, there was only a slight difference among the three groups on ARGs and gut microbiota. After eradication therapy, there was a transient but significant increase in gut ARGs 6 week post-therapy, among which the LEVO group had the most significant ARGs alteration compared to other two groups. For treatment naïve patients, those with higher ErmF abundance were prone to fail CLA eradication and gain more ARGs after treatment. For gut microbiota, the bacteria richness decreased at 6 week and there was a significant difference in microbiota community among the three groups at 6 week. CONCLUSIONS: Our findings demonstrated the dynamic alterations in gut microbiota and ARGs induced by different eradication therapies, which could influence the choices of antibiotics in eradication therapy.

Light-focusing human micro-lenses generated from pluripotent stem cells model lens development and drug-induced cataract in vitro.

Cataracts cause vision loss and blindness by impairing the ability of the ocular lens to focus light onto the retina. Various cataract risk factors have been identified, including drug treatments, age, smoking and diabetes. However, the molecular events responsible for these different forms of cataract are ill-defined, and the advent of modern cataract surgery in the 1960s virtually eliminated access to human lenses for research. Here, we demonstrate large-scale production of light-focusing human micro-lenses from spheroidal masses of human lens epithelial cells purified from differentiating pluripotent stem cells. The purified lens cells and micro-lenses display similar morphology, cellular arrangement, mRNA expression and protein expression to human lens cells and lenses. Exposing the micro-lenses to the emergent cystic fibrosis drug Vx-770 reduces micro-lens transparency and focusing ability. These human micro-lenses provide a powerful and large-scale platform for defining molecular disease mechanisms caused by cataract risk factors, for anti-cataract drug screening and for clinically relevant toxicity assays.

dv-trio: a family-based variant calling pipeline using DeepVariant.

MOTIVATION: In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery. RESULTS: To further improve DeepVariant's variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant. AVAILABILITY AND IMPLEMENTATION: dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/). CONTACT: e.giannoulatou@victorchang.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells.

MOTIVATION: New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity. RESULTS: We introduce a highly scalable graph-based clustering algorithm PARC-Phenotyping by Accelerated Refined Community-partitioning-for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis. AVAILABILITY AND IMPLEMENTATION: https://github.com/ShobiStassen/PARC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The short isoform of the CEACAM1 receptor in intestinal T cells regulates mucosal immunity and homeostasis via Tfh cell induction.

Carcinoembryonic antigen cell adhesion molecule like I (CEACAM1) is expressed on activated T cells and signals through either a long (L) cytoplasmic tail containing immune receptor tyrosine based inhibitory motifs, which provide inhibitory function, or a short (S) cytoplasmic tail with an unknown role. Previous studies on peripheral T cells show that CEACAM1-L isoforms predominate with little to no detectable CEACAM1-S isoforms in mouse and human. We show here that this was not the case in tissue resident T cells of intestines and gut associated lymphoid tissues, which demonstrated predominant expression of CEACAM1-S isoforms relative to CEACAM1-L isoforms in human and mouse. This tissue resident predominance of CEACAM1-S expression was determined by the intestinal environment where it served a stimulatory function leading to the regulation of T cell subsets associated with the generation of secretory IgA immunity, the regulation of mucosal commensalism, and defense of the barrier against enteropathogens.

Ularcirc: visualization and enhanced analysis of circular RNAs via back and canonical forward splicing.

Circular RNAs (circRNA) are a unique class of transcripts that can only be identified from sequence alignments spanning discordant junctions, commonly referred to as backsplice junctions (BSJ). Canonical splicing is also linked with circRNA biogenesis either from the parental transcript or internal to the circRNA, and is not fully utilized in circRNA software. Here we present Ularcirc, a software tool that integrates the visualization of both BSJ and forward splicing junctions and provides downstream analysis of selected circRNA candidates. Ularcirc utilizes the output of CIRI, circExplorer, or raw chimeric output of the STAR aligner and assembles BSJ count table to allow multi-sample analysis. We used Ularcirc to identify and characterize circRNA from public and in-house generated data sets and demonstrate how it can be used to (i) discover novel splicing patterns of parental transcripts, (ii) detect internal splicing patterns of circRNA, and (iii) reveal the complexity of BSJ formation. Furthermore, we identify circRNA that have potential open reading frames longer than their linear sequence. Finally, we detected and validated the presence of a novel class of circRNA generated from ApoA4 transcripts whose BSJ derive from multiple non-canonical splicing sites within coding exons. Ularcirc is accessed via https://github.com/VCCRI/Ularcirc.

NAD Deficiency, Congenital Malformations, and Niacin Supplementation.

BACKGROUND: Congenital malformations can be manifested as combinations of phenotypes that co-occur more often than expected by chance. In many such cases, it has proved difficult to identify a genetic cause. We sought the genetic cause of cardiac, vertebral, and renal defects, among others, in unrelated patients. METHODS: We used genomic sequencing to identify potentially pathogenic gene variants in families in which a person had multiple congenital malformations. We tested the function of the variant by using assays of in vitro enzyme activity and by quantifying metabolites in patient plasma. We engineered mouse models with similar variants using the CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 system. RESULTS: Variants were identified in two genes that encode enzymes of the kynurenine pathway, 3-hydroxyanthranilic acid 3,4-dioxygenase (HAAO) and kynureninase (KYNU). Three patients carried homozygous variants predicting loss-of-function changes in the HAAO or KYNU proteins (HAAO p.D162*, HAAO p.W186*, or KYNU p.V57Efs*21). Another patient carried heterozygous KYNU variants (p.Y156* and p.F349Kfs*4). The mutant enzymes had greatly reduced activity in vitro. Nicotinamide adenine dinucleotide (NAD) is synthesized de novo from tryptophan through the kynurenine pathway. The patients had reduced levels of circulating NAD. Defects similar to those in the patients developed in the embryos of Haao-null or Kynu-null mice owing to NAD deficiency. In null mice, the prevention of NAD deficiency during gestation averted defects. CONCLUSIONS: Disruption of NAD synthesis caused a deficiency of NAD and congenital malformations in humans and mice. Niacin supplementation during gestation prevented the malformations in mice. (Funded by the National Health and Medical Research Council of Australia and others.).

Multi-omic profiling reveals associations between the gut mucosal microbiome, the metabolome, and host DNA methylation associated gene expression in patients with colorectal cancer.

BACKGROUND: The human gut microbiome plays a critical role in the carcinogenesis of colorectal cancer (CRC). However, a comprehensive analysis of the interaction between the host and microbiome is still lacking. RESULTS: We found correlations between the change in abundance of microbial taxa, butyrate-related colonic metabolites, and methylation-associated host gene expression in colonic tumour mucosa tissues compared with the adjacent normal mucosa tissues. The increase of genus Fusobacterium abundance was correlated with a decrease in the level of 4-hydroxybutyric acid (4-HB) and expression of immune-related peptidase inhibitor 16 (PI16), Fc Receptor Like A (FCRLA) and Lymphocyte Specific Protein 1 (LSP1). The decrease in the abundance of another potentially 4-HB-associated genus, Prevotella 2, was also found to be correlated with the down-regulated expression of metallothionein 1 M (MT1M). Additionally, the increase of glutamic acid-related family Halomonadaceae was correlated with the decreased expression of reelin (RELN). The decreased abundance of genus Paeniclostridium and genus Enterococcus were correlated with increased lactic acid level, and were also linked to the expression change of Phospholipase C Beta 1 (PLCB1) and Immunoglobulin Superfamily Member 9 (IGSF9) respectively. Interestingly, 4-HB, glutamic acid and lactic acid are all butyrate precursors, which may modify gene expression by epigenetic regulation such as DNA methylation. CONCLUSIONS: Our study identified associations between previously reported CRC-related microbial taxa, butyrate-related metabolites and DNA methylation-associated gene expression in tumour and normal colonic mucosa tissues from CRC patients, which uncovered a possible mechanism of the role of microbiome in the carcinogenesis of CRC. In addition, these findings offer insight into potential new biomarkers, therapeutic and/or prevention strategies for CRC.

Comparative analysis of metazoan chromatin organization.

Genome function is dynamically regulated in part by chromatin, which consists of the histones, non-histone proteins and RNA molecules that package DNA. Studies in Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular mechanisms of genome function in humans, and have revealed conservation of chromatin components and mechanisms. Nevertheless, the three organisms have markedly different genome sizes, chromosome architecture and gene organization. On human and fly chromosomes, for example, pericentric heterochromatin flanks single centromeres, whereas worm chromosomes have dispersed heterochromatin-like regions enriched in the distal chromosomal 'arms', and centromeres distributed along their lengths. To systematically investigate chromatin organization and associated gene regulation across species, we generated and analysed a large collection of genome-wide chromatin data sets from cell lines and developmental stages in worm, fly and human. Here we present over 800 new data sets from our ENCODE and modENCODE consortia, bringing the total to over 1,400. Comparison of combinatorial patterns of histone modifications, nuclear lamina-associated domains, organization of large-scale topological domains, chromatin environment at promoters and enhancers, nucleosome positioning, and DNA replication patterns reveals many conserved features of chromatin organization among the three organisms. We also find notable differences in the composition and locations of repressive chromatin. These data sets and analyses provide a rich resource for comparative and species-specific investigations of chromatin composition, organization and function.

iSyTE 2.0: a database for expression-based gene discovery in the eye.

Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches.

Identification of satellite cells from anole lizard skeletal muscle and demonstration of expanded musculoskeletal potential.

The lizards are evolutionarily the closest vertebrates to humans that demonstrate the ability to regenerate entire appendages containing cartilage, muscle, skin, and nervous tissue. We previously isolated PAX7-positive cells from muscle of the green anole lizard, Anolis carolinensis, that can differentiate into multinucleated myotubes and express the muscle structural protein, myosin heavy chain. Studying gene expression in these satellite/progenitor cell populations from A. carolinensis can provide insight into the mechanisms regulating tissue regeneration. We generated a transcriptome from proliferating lizard myoprogenitor cells and compared them to transcriptomes from the mouse and human tissues from the ENCODE project using XGSA, a statistical method for cross-species gene set analysis. These analyses determined that the lizard progenitor cell transcriptome was most similar to mammalian satellite cells. Further examination of specific GO categories of genes demonstrated that among genes with the highest level of expression in lizard satellite cells were an increased number of genetic regulators of chondrogenesis, as compared to mouse satellite cells. In micromass culture, lizard PAX7-positive cells formed Alcian blue and collagen 2a1 positive nodules, without the addition of exogenous morphogens, unlike their mouse counterparts. Subsequent quantitative RT-PCR confirmed up-regulation of expression of chondrogenic regulatory genes in lizard cells, including bmp2, sox9, runx2, and cartilage specific structural genes, aggrecan and collagen 2a1. Taken together, these data suggest that tail regeneration in lizards involves significant alterations in gene regulation with expanded musculoskeletal potency.

Cloud accelerated alignment and assembly of full-length single-cell RNA-seq data using Falco.

BACKGROUND: Read alignment and transcript assembly are the core of RNA-seq analysis for transcript isoform discovery. Nonetheless, current tools are not designed to be scalable for analysis of full-length bulk or single cell RNA-seq (scRNA-seq) data. The previous version of our cloud-based tool Falco only focuses on RNA-seq read counting, but does not allow for more flexible steps such as alignment and read assembly. RESULTS: The Falco framework can harness the parallel and distributed computing environment in modern cloud platforms to accelerate read alignment and transcript assembly of full-length bulk RNA-seq and scRNA-seq data. There are two new modes in Falco: alignment-only and transcript assembly. In the alignment-only mode, Falco can speed up the alignment process by 2.5-16.4x based on two public scRNA-seq datasets when compared to alignment on a highly optimised standalone computer. Furthermore, it also provides a 10x average speed-up compared to alignment using published cloud-enabled tool for read alignment, Rail-RNA. In the transcript assembly mode, Falco can speed up the transcript assembly process by 1.7-16.5x compared to performing transcript assembly on a highly optimised computer. CONCLUSION: Falco is a significantly updated open source big data processing framework that enables scalable and accelerated alignment and assembly of full-length scRNA-seq data on the cloud. The source code can be found at https://github.com/VCCRI/Falco.

Crim1 regulates integrin signaling in murine lens development.

The developing lens is a powerful system for investigating the molecular basis of inductive tissue interactions and for studying cataract, the leading cause of blindness. The formation of tightly controlled cell-cell adhesions and cell-matrix junctions between lens epithelial (LE) cells, between lens fiber (LF) cells, and between these two cell populations enables the vertebrate lens to adopt a highly ordered structure and acquire optical transparency. Adhesion molecules are thought to maintain this ordered structure, but little is known about their identity or interactions. Cysteine-rich motor neuron 1 (Crim1), a type I transmembrane protein, is strongly expressed in the developing lens and its mutation causes ocular disease in both mice and humans. How Crim1 regulates lens morphogenesis is not understood. We identified a novel ENU-induced hypomorphic allele of Crim1, Crim1(glcr11), which in the homozygous state causes cataract and microphthalmia. Using this and two other mutant alleles, Crim1(null) and Crim1(cko), we show that the lens defects in Crim1 mouse mutants originate from defective LE cell polarity, proliferation and cell adhesion. Crim1 adhesive function is likely to be required for interactions both between LE cells and between LE and LF cells. We show that Crim1 acts in LE cells, where it colocalizes with and regulates the levels of active ß1 integrin and of phosphorylated FAK and ERK. The RGD and transmembrane motifs of Crim1 are required for regulating FAK phosphorylation. These results identify an important function for Crim1 in the regulation of integrin- and FAK-mediated LE cell adhesion during lens development.

Identification of clinically actionable variants from genome sequencing of families with congenital heart disease.

PURPOSE: Congenital heart disease (CHD) affects up to 1% of live births. However, a genetic diagnosis is not made in most cases. The purpose of this study was to assess the outcomes of genome sequencing (GS) of a heterogeneous cohort of CHD patients. METHODS: Ninety-seven families with probands born with CHD requiring surgical correction were recruited for genome sequencing. At minimum, a proband-parents trio was sequenced per family. GS data were analyzed via a two-tiered method: application of a high-confidence gene screen (hcCHD), and comprehensive analysis. Identified variants were assessed for pathogenicity using the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines. RESULTS: Clinically relevant genetic variants in known and emerging CHD genes were identified. The hcCHD screen identified a clinically actionable variant in 22% of families. Subsequent comprehensive analysis identified a clinically actionable variant in an additional 9% of families in genes with recent disease associations. Overall, this two-tiered approach provided a clinically relevant variant for 31% of families. CONCLUSIONS: Interrogating GS data using our two-tiered method allowed identification of variants with high clinical utility in a third of our heterogeneous cohort. However, association of emerging genes with CHD etiology, and development of novel technologies for variant assessment and interpretation, will increase diagnostic yield during future reassessment of our GS data.

PBrowse: a web-based platform for real-time collaborative exploration of genomic data.

Genome browsers are widely used for individually exploring various types of genomic data. A handful of genome browsers offer limited tools for collaboration among multiple users. Here, we describe PBrowse, an integrated real-time collaborative genome browser that enables multiple users to simultaneously view and access genomic data, thereby harnessing the wisdom of the crowd. PBrowse is based on the Dalliance genome browser and has a re-designed user and data management system with novel collaborative functionalities, including real-time collaborative view, track comment and an integrated group chat feature. Through the Distributed Annotation Server protocol, PBrowse can easily access a wide range of publicly available genomic data, such as the ENCODE data sets. We argue that PBrowse represents a paradigm shift from using a genome browser as a static data visualization tool to a platform that enables real-time human-human interaction and knowledge exchange in a collaborative setting. PBrowse is available at http://pbrowse.victorchang.edu.au, and its source code is available via an open source BSD 3 license at http://github.com/VCCRI/PBrowse.

Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest.

BACKGROUND: It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs - a motif grammar - located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model. RESULTS: We present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar. CONCLUSIONS: Our bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific.