Catchii: Empowering literature review screening in healthcare

A systematic review is a type of literature review that aims to collect and analyse all available evidence from the literature on a particular topic. The process of screening and identifying eligible articles from the vast amounts of literature is a time-consuming task. Specialised software has been developed to aid in the screening process and save significant time and labour. However, the most suitable software tools that are available often come with a cost or only offer either a limited or a trial version for free. In this paper, we report the release of a new software application, Catchii, which contains all the important features of a systematic review screening application while being completely free. It supports a user at different stages of screening, from detecting duplicates to creating the final flowchart for a publication. Catchii is designed to provide a good user experience and streamline the screening process through its clean and user-friendly interface on both computers and mobile devices. All in all, Catchii is a valuable addition to the current selection of systematic review screening applications. It enables researchers without financial resources to access features found in the best paid tools, while also diminishing costs for those who have previously relied on paid applications. Catchii is available at https://catchii.org.

Benchmarking single-cell hashtag oligo demultiplexing methods

Sample multiplexing is often used to reduce cost and limit batch effects in single-cell RNA sequencing (scRNA-seq) experiments. A commonly used multiplexing technique involves tagging cells prior to pooling with a hashtag oligo (HTO) that can be sequenced along with the cells' RNA to determine their sample of origin. Several tools have been developed to demultiplex HTO sequencing data and assign cells to samples. In this study, we critically assess the performance of seven HTO demultiplexing tools: hashedDrops, HTODemux, GMM-Demux, demuxmix, deMULTIplex, BFF (bimodal flexible fitting) and HashSolo. The comparison uses data sets where each sample has also been demultiplexed using genetic variants from the RNA, enabling comparison of HTO demultiplexing techniques against complementary data from the genetic 'ground truth'. We find that all methods perform similarly where HTO labelling is of high quality, but methods that assume a bimodal count distribution perform poorly on lower quality data. We also suggest heuristic approaches for assessing the quality of HTO counts in an scRNA-seq experiment.

Variants in SART3 cause a spliceosomopathy characterised by failure of testis development and neuronal defects

Squamous cell carcinoma antigen recognized by T cells 3 (SART3) is an RNA-binding protein with numerous biological functions including recycling small nuclear RNAs to the spliceosome. Here, we identify recessive variants in SART3 in nine individuals presenting with intellectual disability, global developmental delay and a subset of brain anomalies, together with gonadal dysgenesis in 46,XY individuals. Knockdown of the Drosophila orthologue of SART3 reveals a conserved role in testicular and neuronal development. Human induced pluripotent stem cells carrying patient variants in SART3 show disruption to multiple signalling pathways, upregulation of spliceosome components and demonstrate aberrant gonadal and neuronal differentiation in vitro. Collectively, these findings suggest that bi-allelic SART3 variants underlie a spliceosomopathy which we tentatively propose be termed INDYGON syndrome (Intellectual disability, Neurodevelopmental defects and Developmental delay with 46,XY GONadal dysgenesis). Our findings will enable additional diagnoses and improved outcomes for individuals born with this condition.

Toblerone: detecting exon deletion events in cancer using RNA-seq

Cancer is driven by mutations of the genome that can result in the activation of oncogenes or repression of tumour suppressor genes. In acute lymphoblastic leukemia (ALL) focal deletions in IKAROS family zinc finger 1 (IKZF1) result in the loss of zinc-finger DNA-binding domains and a dominant negative isoform that is associated with higher rates of relapse and  poorer patient outcomes. Clinically, the presence of IKZF1 deletions informs prognosis and treatment options. In this work we developed a method for detecting exon deletions in genes using RNA-seq with application to IKZF1. We developed a pipeline that first uses a custom transcriptome reference consisting of transcripts with exon deletions.  Next, RNA-seq reads are mapped using a pseudoalignment algorithm to identify reads that uniquely support deletions. These are then evaluated for evidence of the deletion with respect to gene expression and other samples. We applied the algorithm, named Toblerone, to a cohort of 99 B-ALL paediatric samples including validated IKZF1 deletions. Furthermore, we developed a graphical desktop app for non-bioinformatics users that can quickly and easily identify and report deletions in IKZF1 from RNA-seq data with informative graphical outputs.

Propeller: testing for differences in cell type proportions in single cell data

Motivation

Single cell RNA-Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. This technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments, which has been difficult to directly address with bulk RNA-seq data, is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportion estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions.

Results

We have developed propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. Using simulated cell type proportions data, we show that propeller performs well under a variety of scenarios. We applied propeller to test for significant changes in cell type proportions related to human heart development, ageing and COVID-19 disease severity.

Availability and implementation

The propeller method is publicly available in the open source speckle R package (https://github.com/phipsonlab/speckle). All the analysis code for the article is available at the associated analysis website: https://phipsonlab.github.io/propeller-paper-analysis/. The speckle package, analysis scripts and datasets have been deposited at https://doi.org/10.5281/zenodo.7009042.

Supplementary information

Supplementary data are available at Bioinformatics online.

Large-scale manipulation of promoter DNA methylation reveals context-specific transcriptional responses and stability

Background

Cytosine DNA methylation is widely described as a transcriptional repressive mark with the capacity to silence promoters. Epigenome engineering techniques enable direct testing of the effect of induced DNA methylation on endogenous promoters; however, the downstream effects have not yet been comprehensively assessed.

Results

Here, we simultaneously induce methylation at thousands of promoters in human cells using an engineered zinc finger-DNMT3A fusion protein, enabling us to test the effect of forced DNA methylation upon transcription, chromatin accessibility, histone modifications, and DNA methylation persistence after the removal of the fusion protein. We find that transcriptional responses to DNA methylation are highly context-specific, including lack of repression, as well as cases of increased gene expression, which appears to be driven by the eviction of methyl-sensitive transcriptional repressors. Furthermore, we find that some regulatory networks can override DNA methylation and that promoter methylation can cause alternative promoter usage. DNA methylation deposited at promoter and distal regulatory regions is rapidly erased after removal of the zinc finger-DNMT3A fusion protein, in a process combining passive and TET-mediated demethylation. Finally, we demonstrate that induced DNA methylation can exist simultaneously on promoter nucleosomes that possess the active histone modification H3K4me3, or DNA bound by the initiated form of RNA polymerase II.

Conclusions

These findings have important implications for epigenome engineering and demonstrate that the response of promoters to DNA methylation is more complex than previously appreciated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02728-5.

RNA-Seq of amniotic fluid cell-free RNA: a discovery phase study of the pathophysiology of congenital cytomegalovirus infection

BACKGROUND

Congenital cytomegalovirus infection is the most common perinatal infection and a significant cause of sensorineural hearing loss, cerebral palsy, and neurodevelopmental disability. There is a paucity of human gene expression studies examining the pathophysiology of cytomegalovirus infection.

OBJECTIVE

This study aimed to perform a whole transcriptomic assessment of amniotic fluid from pregnancies with live fetuses to identify differentially expressed genes and enriched Gene Ontology categories associated with congenital cytomegalovirus infection.

STUDY DESIGN

Amniotic fluid supernatant was prospectively collected from pregnant women undergoing amniocentesis for suspected congenital cytomegalovirus infection because of first-trimester maternal primary infection or ultrasound features suggestive of fetal infection. Women who had received therapy to prevent fetal infection were excluded. Congenital cytomegalovirus infection was diagnosed via viral polymerase chain reaction of amniotic fluid; cytomegalovirus-infected fetuses were paired with noninfected controls, matched for gestational age and fetal sex. Paired-end RNA sequencing was performed on amniotic fluid cell-free RNA with the Novaseq 6000 at a depth of 30 million reads per sample. Following quality control and filtering, reads were mapped to the human genome and counts summarized across genes. Differentially expressed genes were identified using 2 approaches: voomWithQualityWeights in conjunction with limma and RUVSeq with edgeR. Genes with a false discovery rate <0.05 were considered statistically significant. Differential exon use was analyzed using DEXSeq. Functional analysis was performed using gene set enrichment analysis and Ingenuity Pathway Analysis. Manual curation of differentially regulated genes was also performed.

RESULTS

Amniotic fluid samples were collected from 50 women; 16 (32%) had congenital cytomegalovirus infection confirmed by polymerase chain reaction. After excluding 3 samples without matched controls, 13 cytomegalovirus-infected samples collected at 18 to 23 weeks and 13 cytomegalovirus-negative gestation-matched controls were submitted for RNA sequencing and analysis (N=26). Ten of the 13 pregnancies with cytomegalovirus-infected fetuses had amniocentesis because of serologic evidence of maternal primary infection with normal fetal ultrasound, and 3 had amniocentesis because of ultrasound abnormality suggestive of cytomegalovirus infection. Four cytomegalovirus-infected pregnancies ended in termination (n=3) or fetal death (n=1), and 9 resulted in live births. Pregnancy outcomes were available for 11 of the 13 cytomegalovirus-negative controls; all resulted in live births of clinically-well infants. Differential gene expression analysis revealed 309 up-regulated and 32 down-regulated genes in the cytomegalovirus-infected group compared with the cytomegalovirus-negative group. Gene set enrichment analysis showed significant enrichment of multiple Gene Ontology categories involving the innate immune response to viral infection and interferon signaling. Of the 32 significantly down-regulated genes, 8 were known to be involved in neurodevelopment and preferentially expressed by the brain. Six specific cellular restriction factors involved in host defense to cytomegalovirus infection were up-regulated in the cytomegalovirus-infected group. Ingenuity Pathway Analysis predicted the activation of pathways involved in progressive neurologic disease and inflammatory neurologic disease.

CONCLUSION

In this next-generation sequencing study, we revealed new insights into the pathophysiology of congenital cytomegalovirus infection. These data on the up-regulation of the intraamniotic innate immune response to cytomegalovirus infection and the dysregulation of neurodevelopmental genes may inform future approaches to developing prognostic markers and assessing fetal responses to in utero therapy.

STRipy: a graphical application for enhanced genotyping of pathogenic short tandem repeats in sequencing data

Expansions of short tandem repeats (STRs) have been implicated as the causal variant in over 50 diseases known to date. There are several tools which can genotype STRs from high‐throughput sequencing (HTS) data. However, running these tools out of the box only allows around half of the known disease‐causing loci to be genotyped. Furthermore, the genotypes estimated at these loci are often underestimated with maximum lengths limited to either the read or fragment length, which is less than the pathogenic cutoff for some diseases. Although analysis tools can be customized to genotype extra loci, this requires proficiency in bioinformatics to set up, limiting their widespread usage by other researchers and clinicians. To address these issues, we have developed a new software called STRipy, which is able to target all known disease‐causing STRs from HTS data. We created an intuitive graphical interface for STRipy and significantly simplified the detection of STRs expansions. Moreover, we genotyped all disease loci for over two and half thousand samples to provide population‐wide distributions to assist with interpretation of results. We believe the simplicity and breadth of STRipy will increase the genotyping of STRs in sequencing data resulting in further diagnoses of rare STR diseases.

JAFFAL: detecting fusion genes with long-read transcriptome sequencing

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .

DNA Methylation Profiles of Purified Cell Types in Bronchoalveolar Lavage: Applications for Mixed Cell Paediatric Pulmonary Studies

In epigenome-wide association studies analysing DNA methylation from samples containing multiple cell types, it is essential to adjust the analysis for cell type composition. One well established strategy for achieving this is reference-based cell type deconvolution, which relies on knowledge of the DNA methylation profiles of purified constituent cell types. These are then used to estimate the cell type proportions of each sample, which can then be incorporated to adjust the association analysis. Bronchoalveolar lavage is commonly used to sample the lung in clinical practice and contains a mixture of different cell types that can vary in proportion across samples, affecting the overall methylation profile. A current barrier to the use of bronchoalveolar lavage in DNA methylation-based research is the lack of reference DNA methylation profiles for each of the constituent cell types, thus making reference-based cell composition estimation difficult. Herein, we use bronchoalveolar lavage samples collected from children with cystic fibrosis to define DNA methylation profiles for the four most common and clinically relevant cell types: alveolar macrophages, granulocytes, lymphocytes and alveolar epithelial cells. We then demonstrate the use of these methylation profiles in conjunction with an established reference-based methylation deconvolution method to estimate the cell type composition of two different tissue types; a publicly available dataset derived from artificial blood-based cell mixtures and further bronchoalveolar lavage samples. The reference DNA methylation profiles developed in this work can be used for future reference-based cell type composition estimation of bronchoalveolar lavage. This will facilitate the use of this tissue in studies examining the role of DNA methylation in lung health and disease.

splatPop: simulating population scale single-cell RNA sequencing data

Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmarking methods but current scRNA-seq simulation frameworks do not simulate population-scale data with genetic effects. Here, we present splatPop, a model for flexible, reproducible, and well-documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci. splatPop can also simulate complex batch, cell group, and conditional effects between individuals from different cohorts as well as genetically-driven co-expression.

Slinker: Visualising novel splicing events in RNA-Seq data

Visualisation of the transcriptome relative to a reference genome is fraught with sparsity. This is due to RNA sequencing (RNA-Seq) reads being predominantly mapped to exons that account for just under 3% of the human genome. Recently, we have used exon-only references, superTranscripts, to improve visualisation of aligned RNA-Seq data through the omission of supposedly unexpressed regions such as introns. However, variation within these regions can lead to novel splicing events that may drive a pathogenic phenotype. In these cases, the loss of information in only retaining annotated exons presents significant drawbacks. Here we present Slinker, a bioinformatics pipeline written in Python and Bpipe that uses a data-driven approach to assemble sample-specific superTranscripts. At its core, Slinker uses Stringtie2 to assemble transcripts with any sequence across any gene. This assembly is merged with reference transcripts, converted to a superTranscript, of which rich visualisations are made through Plotly with associated annotation and coverage information. Slinker was validated on five novel splicing events of rare disease samples from a cohort of primary muscular disorders. In addition, Slinker was shown to be effective in visualising deletion events within transcriptomes of tumour samples in the important leukemia gene, IKZF1. Slinker offers a succinct visualisation of RNA-Seq alignments across typically sparse regions and is freely available on Github.

MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

Calling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combining de novo assembly of transcripts with differential expression analysis to identify up-regulated novel variants in a case sample. We compare MINTIE with eight other approaches, detecting > 85% of variants while no other method is able to achieve this. We posit that MINTIE will be able to identify new disease variants across a range of disease types.

Detecting copy number alterations in RNA-Seq using SuperFreq

MOTIVATION

Calling copy number alterations (CNAs) from RNA sequencing (RNA-Seq) is challenging, because of the marked variability in coverage across genes and paucity of single nucleotide polymorphisms (SNPs). We have adapted SuperFreq to call absolute and allele sensitive CNAs from RNA-Seq. SuperFreq uses an error-propagation framework to combine and maximise information from read counts and B-allele frequencies (BAFs).

RESULTS

We used datasets from The Cancer Genome Atlas (TCGA) to assess the validity of CNA calls from RNA-Seq. When ploidy estimates were consistent, we found agreement with DNA SNP-arrays for over 98% of the genome for acute myeloid leukaemia (TCGA-AML, n = 116) and 87% for colorectal cancer (TCGA-CRC, n = 377). The sensitivity of CNA calling from RNA-Seq was dependent on gene density. Using RNA-Seq, SuperFreq detected 78% of CNA calls covering 100 or more genes with a precision of 94%. Recall dropped for focal events, but this also depended on signal intensity. For example, in the CRC cohort SuperFreq identified all cases (7/7) with high-level amplification of ERBB2, where the copy number was typically >20, but identified only 6% of cases (1/17) with moderate amplification of IGF2, which occurs over a smaller interval. SuperFreq offers an integrated platform for identification of CNAs and point mutations. As evidence of how SuperFreq can be applied, we used it to reproduce the established relationship between somatic mutation load and CNA profile in CRC using RNA-Seq alone.

AVAILABILITY

SuperFreq is implemented in R and the code is available through GitHub: https://github.com/ChristofferFlensburg/SuperFreq.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gene set enrichment analysis for genome-wide DNA methylation data

DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

Sex-Specific Control of Human Heart Maturation by the Progesterone Receptor

BACKGROUND

Despite in-depth knowledge of the molecular mechanisms controlling embryonic heart development, little is known about the signals governing postnatal maturation of the human heart.

METHODS

Single-nucleus RNA sequencing of 54 140 nuclei from 9 human donors was used to profile transcriptional changes in diverse cardiac cell types during maturation from fetal stages to adulthood. Bulk RNA sequencing and the Assay for Transposase-Accessible Chromatin using sequencing were used to further validate transcriptional changes and to profile alterations in the chromatin accessibility landscape in purified cardiomyocyte nuclei from 21 human donors. Functional validation studies of sex steroids implicated in cardiac maturation were performed in human pluripotent stem cell-derived cardiac organoids and mice.

RESULTS

Our data identify the progesterone receptor as a key mediator of sex-dependent transcriptional programs during cardiomyocyte maturation. Functional validation studies in human cardiac organoids and mice demonstrate that the progesterone receptor drives sex-specific metabolic programs and maturation of cardiac contractile properties.

CONCLUSIONS

These data provide a blueprint for understanding human heart maturation in both sexes and reveal an important role for the progesterone receptor in human heart development.

Human yolk sac-like haematopoiesis generates RUNX1-, GFI1- and/or GFI 1B-dependent blood and SOX17-positive endothelium

The genetic regulatory network controlling early fate choices during human blood cell development are not well understood. We used human pluripotent stem cell reporter lines to track the development of endothelial and haematopoietic populations in an in vitro model of human yolk-sac development. We identified SOX17⁻CD34⁺CD43⁻ endothelial cells at day 2 of blast colony development, as a haemangioblast-like branch point from which SOX17⁻CD34⁺CD43⁺ blood cells and SOX17⁺CD34⁺CD43⁻ endothelium subsequently arose. Most human blood cell development was dependent on RUNX1. Deletion of RUNX1 only permitted a single wave of yolk sac-like primitive erythropoiesis, but no yolk sac myelopoiesis or aorta-gonad-mesonephros (AGM)-like haematopoiesis. Blocking GFI1 and/or GFI1B activity with a small molecule inhibitor abrogated all blood cell development, even in cell lines with an intact RUNX1 gene. Together, our data define the hierarchical requirements for RUNX1, GFI1 and/or GFI1B during early human haematopoiesis arising from a yolk sac-like SOX17-negative haemogenic endothelial intermediate.

Highlighted Article: The hierarchical requirements for RUNX1, GFI1 and/or GFI1B during early human haematopoiesis arising from a yolk sac-like haemogenic endothelial intermediate.

Sierra: discovery of differential transcript usage from polyA-captured single-cell RNA-seq data

High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3 'UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/VCCRI/Sierra .

Accuracy of short tandem repeats genotyping tools in whole exome sequencing data

Background: Short tandem repeats are an important source of genetic variation. They are highly mutable and repeat expansions are associated dozens of human disorders, such as Huntington's disease and spinocerebellar ataxias. Technical advantages in sequencing technology have made it possible to analyse these repeats at large scale; however, accurate genotyping is still a challenging task. We compared four different short tandem repeats genotyping tools on whole exome sequencing data to determine their genotyping performance and limits, which will aid other researchers in choosing a suitable tool and parameters for analysis. Methods: The analysis was performed on the Simons Simplex Collection dataset, where we used a novel method of evaluation with accuracy determined by the rate of homozygous calls on the X chromosome of male samples. In total we analysed 433 samples and around a million genotypes for evaluating tools on whole exome sequencing data. Results: We determined a relatively good performance of all tools when genotyping repeats of 3-6 bp in length, which could be improved with coverage and quality score filtering. However, genotyping homopolymers was challenging for all tools and a high error rate was present across different thresholds of coverage and quality scores. Interestingly, dinucleotide repeats displayed a high error rate as well, which was found to be mainly caused by the AC/TG repeats. Overall, LobSTR was able to make the most calls and was also the fastest tool, while RepeatSeq and HipSTR exhibited the lowest heterozygous error rate at low coverage. Conclusions: All tools have different strengths and weaknesses and the choice may depend on the application. In this analysis we demonstrated the effect of using different filtering parameters and offered recommendations based on the trade-off between the best accuracy of genotyping and the highest number of calls.

Publisher Correction: The role of cardiac transcription factor NKX2-5 in regulating the human cardiac miRNAome

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

Targeted therapy and disease monitoring in CNTRL-FGFR1-driven leukaemia

We report two patients with leukaemia driven by the rare CNTRL-FGFR1 fusion oncogene. This fusion arises from a t(8;9)(p12;q33) translocation, and is a rare driver of biphenotypic leukaemia in children. We used RNA sequencing to report novel features of expressed CNTRL-FGFR1, including CNTRL-FGFR1 fusion alternative splicing. From this knowledge, we designed and tested a Droplet Digital PCR assay that detects CNTRL-FGFR1 expression to approximately one cell in 100 000 using fusion breakpoint-specific primers and probes. We also utilised cell-line models to show that effective tyrosine kinase inhibitors, which may be included in treatment regimens for this disease, are only those that block FGFR1 phosphorylation.

Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk

Recent advances in the generation of kidney organoids and the culture of primary nephron progenitors from mouse and human have been based on knowledge of the molecular basis of kidney development in mice. Although gene expression during kidney development has been intensely investigated, single cell profiling provides new opportunities to further subsect component cell types and the signalling networks at play. Here, we describe the generation and analysis of 6732 single cell transcriptomes from the fetal mouse kidney [embryonic day (E)18.5] and 7853 sorted nephron progenitor cells (E14.5). These datasets provide improved resolution of cell types and specific markers, including subdivision of the renal stroma and heterogeneity within the nephron progenitor population. Ligand-receptor interaction and pathway analysis reveals novel crosstalk between cellular compartments and associates new pathways with differentiation of nephron and ureteric epithelium cell types. We identify transcriptional congruence between the distal nephron and ureteric epithelium, showing that most markers previously used to identify ureteric epithelium are not specific. Together, this work improves our understanding of metanephric kidney development and provides a template to guide the regeneration of renal tissue.

Bazam: a rapid method for read extraction and realignment of high-throughput sequencing data

The vast quantities of short-read sequencing data being generated are often exchanged and stored as aligned reads. However, aligned data becomes outdated as new reference genomes and alignment methods become available. Here we describe Bazam, a tool that efficiently extracts the original paired FASTQ from alignment files (BAM or CRAM format) in a format that directly allows efficient realignment. Bazam facilitates up to a 90% reduction in the time for realignment compared to standard methods. Bazam can support selective extraction of read pairs from focused genomic regions for applications such as targeted region analyses, quality control, structural variant calling, and alignment comparisons.

Using equivalence class counts for fast and accurate testing of differential transcript usage

Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantification estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis compared to exon-level analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing a variety of RNA-seq analysis directly on equivalence class counts (ECCs). Methods: Here we demonstrate that ECCs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECCs have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform differential transcript usage analysis.

Fast and accurate differential transcript usage by testing equivalence class counts

Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantifications estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing differential expression testing directly on equivalence class read counts (ECs). Methods: Here we demonstrate that ECs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECs counts have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform many types of analysis.

Kidney micro-organoids in suspension culture as a scalable source of human pluripotent stem cell-derived kidney cells

Kidney organoids have potential uses in disease modelling, drug screening and regenerative medicine. However, novel cost-effective techniques are needed to enable scaled-up production of kidney cell types in vitro We describe here a modified suspension culture method for the generation of kidney micro-organoids from human pluripotent stem cells. Optimisation of differentiation conditions allowed the formation of micro-organoids, each containing six to ten nephrons that were surrounded by endothelial and stromal populations. Single cell transcriptional profiling confirmed the presence and transcriptional equivalence of all anticipated renal cell types consistent with a previous organoid culture method. This suspension culture micro-organoid methodology resulted in a three- to fourfold increase in final cell yield compared with static culture, thereby representing an economical approach to the production of kidney cells for various biological applications.

Direct reprogramming to human nephron progenitor-like cells using inducible piggyBac transposon expression of SNAI2-EYA1-SIX1

All nephrons in the mammalian kidney arise from a transient nephron progenitor population that is lost close to the time of birth. The generation of new nephron progenitors and their maintenance in culture are central to the success of kidney regenerative strategies. Using a lentiviral screening approach, we previously generated a human induced nephron progenitor-like state in vitro using a pool of six transcription factors. Here, we sought to develop a more efficient approach for direct reprogramming of human cells that could be applied in vivo. PiggyBac transposons are a non-viral integrating gene delivery system that is suitable for in vivo use and allows for simultaneous delivery of multiple genes. Using an inducible piggyBac transposon system, we optimized a protocol for the direct reprogramming of HK2 cells to induced nephron progenitor-like cells with expression of only 3 transcription factors (SNAI2, EYA1, and SIX1). Culture in conditions supportive of the nephron progenitor state further increased the expression of nephron progenitor genes. The refined protocol was then applied to primary human renal epithelial cells, which integrated into developing nephron structures in vitro and in vivo. Such inducible reprogramming to nephron progenitor-like cells could facilitate direct cellular reprogramming for kidney regeneration.

Nephron progenitor commitment is a stochastic process influenced by cell migration

Progenitor self-renewal and differentiation is often regulated by spatially restricted cues within a tissue microenvironment. Here, we examine how progenitor cell migration impacts regionally induced commitment within the nephrogenic niche in mice. We identify a subset of cells that express Wnt4, an early marker of nephron commitment, but migrate back into the progenitor population where they accumulate over time. Single cell RNA-seq and computational modelling of returning cells reveals that nephron progenitors can traverse the transcriptional hierarchy between self-renewal and commitment in either direction. This plasticity may enable robust regulation of nephrogenesis as niches remodel and grow during organogenesis.

Single-cell analysis reveals congruence between kidney organoids and human fetal kidney

BACKGROUND

Human kidney organoids hold promise for studying development, disease modelling and drug screening. However, the utility of stem cell-derived kidney tissues will depend on how faithfully these replicate normal fetal development at the level of cellular identity and complexity.

METHODS

Here, we present an integrated analysis of single cell datasets from human kidney organoids and human fetal kidney to assess similarities and differences between the component cell types.

RESULTS

Clusters in the combined dataset contained cells from both organoid and fetal kidney with transcriptional congruence for key stromal, endothelial and nephron cell type-specific markers. Organoid enriched neural, glial and muscle progenitor populations were also evident. Major transcriptional differences between organoid and human tissue were likely related to technical artefacts. Cell type-specific comparisons revealed differences in stromal, endothelial and nephron progenitor cell types including expression of WNT2B in the human fetal kidney stroma.

CONCLUSIONS

This study supports the fidelity of kidney organoids as models of the developing kidney and affirms their potential in disease modelling and drug screening.

Single-cell analysis reveals congruence between kidney organoids and human fetal kidney

Background

Human kidney organoids hold promise for studying development, disease modelling and drug screening. However, the utility of stem cell-derived kidney tissues will depend on how faithfully these replicate normal fetal development at the level of cellular identity and complexity.

Methods

Here, we present an integrated analysis of single cell datasets from human kidney organoids and human fetal kidney to assess similarities and differences between the component cell types.

Results

Clusters in the combined dataset contained cells from both organoid and fetal kidney with transcriptional congruence for key stromal, endothelial and nephron cell type-specific markers. Organoid enriched neural, glial and muscle progenitor populations were also evident. Major transcriptional differences between organoid and human tissue were likely related to technical artefacts. Cell type-specific comparisons revealed differences in stromal, endothelial and nephron progenitor cell types including expression of WNT2B in the human fetal kidney stroma.

Conclusions

This study supports the fidelity of kidney organoids as models of the developing kidney and affirms their potential in disease modelling and drug screening.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0615-0) contains supplementary material, which is available to authorized users.

Clinker: visualizing fusion genes detected in RNA-seq data

Background

Genomic profiling efforts have revealed a rich diversity of oncogenic fusion genes. While there are many methods for identifying fusion genes from RNA-sequencing (RNA-seq) data, visualizing these transcripts and their supporting reads remains challenging.

Findings

Clinker is a bioinformatics tool written in Python, R, and Bpipe that leverages the superTranscript method to visualize fusion genes. We demonstrate the use of Clinker to obtain interpretable visualizations of the RNA-seq data that lead to fusion calls. In addition, we use Clinker to explore multiple fusion transcripts with novel breakpoints within the P2RY8-CRLF2 fusion gene in B-cell acute lymphoblastic leukemia.

Conclusions

Clinker is freely available software that allows visualization of fusion genes and the RNA-seq data used in their discovery.

Necklace: combining reference and assembled transcriptomes for more comprehensive RNA-Seq analysis

Background

RNA sequencing (RNA-seq) analyses can benefit from performing a genome-guided and de novo assembly, in particular for species where the reference genome or the annotation is incomplete. However, tools for integrating an assembled transcriptome with reference annotation are lacking.

Findings

Necklace is a software pipeline that runs genome-guided and de novo assembly and combines the resulting transcriptomes with reference genome annotations. Necklace constructs a compact but comprehensive superTranscriptome out of the assembled and reference data. Reads are subsequently aligned and counted in preparation for differential expression testing.

Conclusions

Necklace allows a comprehensive transcriptome to be built from a combination of assembled and annotated transcripts, which results in a more comprehensive transcriptome for the majority of organisms. In addition RNA-seq data are mapped back to this newly created superTranscript reference to enable differential expression testing with standard methods.

Ximmer: a system for improving accuracy and consistency of CNV calling from exome data

Background

While exome and targeted next-generation DNA sequencing are primarily used for detecting single nucleotide changes and small indels, detection of copy number variants (CNVs) can provide highly valuable additional information from the data. Although there are dozens of exome CNV detection methods available, these are often difficult to use, and accuracy varies unpredictably between and within datasets.

Findings

We present Ximmer, a tool that supports an end-to-end process for evaluating, tuning, and running analysis methods for detection of CNVs in germline samples. Ximmer includes a simulation framework, implementations of several commonly used CNV detection methods, and a visualization and curation tool that together enable interactive exploration and quality control of CNV results. Using Ximmer, we comprehensively evaluate CNV detection on four datasets using five different detection methods. We show that application of Ximmer can improve accuracy and aid in quality control of CNV detection results. In addition, Ximmer can be used to run analyses and explore CNV results in exome data.

Conclusions

Ximmer offers a comprehensive tool and method for applying and improving accuracy of CNV detection methods for exome data.

STRetch: detecting and discovering pathogenic short tandem repeat expansions

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Most existing tools for detecting STR variation with short reads do so within the read length and so are unable to detect the majority of pathogenic expansions. Here we present STRetch, a new genome-wide method to scan for STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting STR expansions using short-read whole-genome sequencing data at known pathogenic loci as well as novel STR loci. STRetch is open source software, available from github.com/Oshlack/STRetch.

Clustering trees: a visualization for evaluating clusterings at multiple resolutions

Clustering techniques are widely used in the analysis of large datasets to group together samples with similar properties. For example, clustering is often used in the field of single-cell RNA-sequencing in order to identify different cell types present in a tissue sample. There are many algorithms for performing clustering, and the results can vary substantially. In particular, the number of groups present in a dataset is often unknown, and the number of clusters identified by an algorithm can change based on the parameters used. To explore and examine the impact of varying clustering resolution, we present clustering trees. This visualization shows the relationships between clusters at multiple resolutions, allowing researchers to see how samples move as the number of clusters increases. In addition, meta-information can be overlaid on the tree to inform the choice of resolution and guide in identification of clusters. We illustrate the features of clustering trees using a series of simulations as well as two real examples, the classical iris dataset and a complex single-cell RNA-sequencing dataset. Clustering trees can be produced using the clustree R package, available from CRAN and developed on GitHub.

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source and open-science approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records the growth of the field over time.

Patient-iPSC-Derived Kidney Organoids Show Functional Validation of a Ciliopathic Renal Phenotype and Reveal Underlying Pathogenetic Mechanisms

Despite the increasing diagnostic rate of genomic sequencing, the genetic basis of more than 50% of heritable kidney disease remains unresolved. Kidney organoids differentiated from induced pluripotent stem cells (iPSCs) of individuals affected by inherited renal disease represent a potential, but unvalidated, platform for the functional validation of novel gene variants and investigation of underlying pathogenetic mechanisms. In this study, trio whole-exome sequencing of a prospectively identified nephronophthisis (NPHP) proband and her parents identified compound-heterozygous variants in IFT140, a gene previously associated with NPHP-related ciliopathies. IFT140 plays a key role in retrograde intraflagellar transport, but the precise downstream cellular mechanisms responsible for disease presentation remain unknown. A one-step reprogramming and gene-editing protocol was used to derive both uncorrected proband iPSCs and isogenic gene-corrected iPSCs, which were differentiated to kidney organoids. Proband organoid tubules demonstrated shortened, club-shaped primary cilia, whereas gene correction rescued this phenotype. Differential expression analysis of epithelial cells isolated from organoids suggested downregulation of genes associated with apicobasal polarity, cell-cell junctions, and dynein motor assembly in proband epithelial cells. Matrigel cyst cultures confirmed a polarization defect in proband versus gene-corrected renal epithelium. As such, this study represents a "proof of concept" for using proband-derived iPSCs to model renal disease and illustrates dysfunctional cellular pathways beyond the primary cilium in the setting of IFT140 mutations, which are established for other NPHP genotypes.

Cord Blood CD8+ T Cells Have a Natural Propensity to Express IL-4 in a Fatty Acid Metabolism and Caspase Activation-Dependent Manner

How T cells differentiate in the neonate may critically determine the ability of the infant to cope with infections, respond to vaccines and avert allergies. Previously, we found that naïve cord blood CD4⁺ T cells differentiated toward an IL-4-expressing phenotype when activated in the presence of TGF-β and monocyte-derived inflammatory cytokines, the latter are more highly secreted by infants who developed food allergy. Here, we show that in the absence of IL-2 or IL-12, naïve cord blood CD8⁺ T cells have a natural propensity to differentiate into IL-4-producing non-classic T_C2 cells when they are activated alone, or in the presence of TGF-β and/or inflammatory cytokines. Mechanistically, non-classic T_C2 development is associated with decreased expression of IL-2 receptor alpha (CD25) and glycolysis, and increased fatty acid metabolism and caspase-dependent cell death. Consequently, the short chain fatty acid, sodium propionate (NaPo), enhanced IL-4 expression, but exogenous IL-2 or pan-caspase inhibition prevented IL-4 expression. In children with endoscopically and histologically confirmed non-inflammatory bowel disease and non-infectious pediatric idiopathic colitis, the presence of TGF-β, NaPo, and IL-1β or TNF-α promoted T_C2 differentiation in vitro. In vivo, colonic mucosa of children with colitis had significantly increased expression of IL-4 in CD8⁺ T cells compared with controls. In addition, activated caspase-3 and IL-4 were co-expressed in CD8⁺ T cells in the colonic mucosa of children with colitis. Thus, in the context of colonic inflammation and limited IL-2 signaling, CD8⁺ T cells differentiate into non-classic T_C2 that may contribute to the pathology of inflammatory/allergic diseases in children.

NKX2-5 regulates human cardiomyogenesis via a HEY2 dependent transcriptional network

Congenital heart defects can be caused by mutations in genes that guide cardiac lineage formation. Here, we show deletion of NKX2-5, a critical component of the cardiac gene regulatory network, in human embryonic stem cells (hESCs), results in impaired cardiomyogenesis, failure to activate VCAM1 and to downregulate the progenitor marker PDGFRα. Furthermore, NKX2-5 null cardiomyocytes have abnormal physiology, with asynchronous contractions and altered action potentials. Molecular profiling and genetic rescue experiments demonstrate that the bHLH protein HEY2 is a key mediator of NKX2-5 function during human cardiomyogenesis. These findings identify HEY2 as a novel component of the NKX2-5 cardiac transcriptional network, providing tangible evidence that hESC models can decipher the complex pathways that regulate early stage human heart development. These data provide a human context for the evaluation of pathogenic mutations in congenital heart disease.

A gene regulatory network, including the transcription factor Nkx2-5, regulates cardiac development. Here, the authors show that on deletion of NKX2-5 from human embryonic stem cells, there is impaired cardiomyogenesis and changes in action potentials, and that this is regulated via HEY2.

Haploinsufficiency for the Six2 gene increases nephron progenitor proliferation promoting branching and nephron number

The regulation of final nephron number in the kidney is poorly understood. Cessation of nephron formation occurs when the self-renewing nephron progenitor population commits to differentiation. Transcription factors within this progenitor population, such as SIX2, are assumed to control expression of genes promoting self-renewal such that homozygous Six2 deletion results in premature commitment and an early halt to kidney development. In contrast, Six2 heterozygotes were assumed to be unaffected. Using quantitative morphometry, we found a paradoxical 18% increase in ureteric branching and final nephron number in Six2 heterozygotes, despite evidence for reduced levels of SIX2 protein and transcript. This was accompanied by a clear shift in nephron progenitor identity with a distinct subset of downregulated progenitor genes such as Cited1 and Meox1 while other genes were unaffected. The net result was an increase in nephron progenitor proliferation, as assessed by elevated EdU (5-ethynyl-2'-deoxyuridine) labeling, an increase in MYC protein, and transcriptional upregulation of MYC target genes. Heterozygosity for Six2 on an Fgf20-/- background resulted in premature differentiation of the progenitor population, confirming that progenitor regulation is compromised in Six2 heterozygotes. Overall, our studies reveal a unique dose response of nephron progenitors to the level of SIX2 protein in which the role of SIX2 in progenitor proliferation versus self-renewal is separable.

MCM3AP in recessive Charcot-Marie-Tooth neuropathy and mild intellectual disability

Defects in mRNA export from the nucleus have been linked to various neurodegenerative disorders. We report mutations in the gene MCM3AP, encoding the germinal center associated nuclear protein (GANP), in nine affected individuals from five unrelated families. The variants were associated with severe childhood onset primarily axonal (four families) or demyelinating (one family) Charcot-Marie-Tooth neuropathy. Mild to moderate intellectual disability was present in seven of nine affected individuals. The affected individuals were either compound heterozygous or homozygous for different MCM3AP variants, which were predicted to cause depletion of GANP or affect conserved amino acids with likely importance for its function. Accordingly, fibroblasts of affected individuals from one family demonstrated severe depletion of GANP. GANP has been described to function as an mRNA export factor, and to suppress TDP-43-mediated motor neuron degeneration in flies. Thus our results suggest defective mRNA export from nucleus as a potential pathogenic mechanism of axonal degeneration in these patients. The identification of MCM3AP variants in affected individuals from multiple centres establishes it as a disease gene for childhood-onset recessively inherited Charcot-Marie-Tooth neuropathy with intellectual disability.

Splatter: simulation of single-cell RNA sequencing data

As single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed, and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available. Here, we present the Splatter Bioconductor package for simple, reproducible, and well-documented simulation of scRNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types, or differentiation paths.

Diagnostic Impact and Cost-effectiveness of Whole-Exome Sequencing for Ambulant Children With Suspected Monogenic Conditions

IMPORTANCE

Optimal use of whole-exome sequencing (WES) in the pediatric setting requires an understanding of who should be considered for testing and when it should be performed to maximize clinical utility and cost-effectiveness.

OBJECTIVES

To investigate the impact of WES in sequencing-naive children suspected of having a monogenic disorder and evaluate its cost-effectiveness if WES had been available at different time points in their diagnostic trajectory.

DESIGN, SETTING, AND PARTICIPANTS

This prospective study was part of the Melbourne Genomics Health Alliance demonstration project. At the ambulatory outpatient clinics of the Victorian Clinical Genetics Services at the Royal Children's Hospital, Melbourne, Australia, children older than 2 years suspected of having a monogenic disorder were prospectively recruited from May 1 through November 30, 2015, by clinical geneticists after referral from general and subspecialist pediatricians. All children had nondiagnostic microarrays and no prior single-gene or panel sequencing.

EXPOSURES

All children underwent singleton WES with targeted phenotype-driven analysis.

MAIN OUTCOMES AND MEASURES

The study examined the clinical utility of a molecular diagnosis and the cost-effectiveness of alternative diagnostic trajectories, depending on timing of WES.

RESULTS

Of 61 children originally assessed, 44 (21 [48%] male and 23 [52%] female) aged 2 to 18 years (mean age at initial presentation, 28 months; range, 0-121 months) were recruited, and a diagnosis was achieved in 23 (52%) by singleton WES. The diagnoses were unexpected in 8 of 23 (35%), and clinical management was altered in 6 of 23 (26%). The mean duration of the diagnostic odyssey was 6 years, with each child having a mean of 19 tests and 4 clinical genetics and 4 nongenetics specialist consultations, and 26 (59%) underwent a procedure while under general anesthetic for diagnostic purposes. Economic analyses of the diagnostic trajectory identified that WES performed at initial tertiary presentation resulted in an incremental cost savings of A$9020 (US$6838) per additional diagnosis (95% CI, A$4304-A$15 404 [US$3263-US$11 678]) compared with the standard diagnostic pathway. Even if WES were performed at the first genetics appointment, there would be an incremental cost savings of A$5461 (US$4140) (95% CI, A$1433-A$10 557 [US$1086- US$8004]) per additional diagnosis compared with the standard diagnostic pathway.

CONCLUSIONS AND RELEVANCE

Singleton WES in children with suspected monogenic conditions has high diagnostic yield, and cost-effectiveness is maximized by early application in the diagnostic pathway. Pediatricians should consider early referral of children with undiagnosed syndromes to clinical geneticists.