Besides cardiomyocytes (CM), the heart contains numerous interstitial cell types which play key roles in heart repair, regeneration and disease, including fibroblast, vascular and immune cells. However, a comprehensive understanding of this interactive cell community is lacking. We performed single-cell RNA-sequencing of the total non-CM fraction and enriched (Pdgfra-GFP+) fibroblast lineage cells from murine hearts at days 3 and 7 post-sham or myocardial infarction (MI) surgery. Clustering of >30,000 single cells identified >30 populations representing nine cell lineages, including a previously undescribed fibroblast lineage trajectory present in both sham and MI hearts leading to a uniquely activated cell state defined in part by a strong anti-WNT transcriptome signature. We also uncovered novel myofibroblast subtypes expressing either pro-fibrotic or anti-fibrotic signatures. Our data highlight non-linear dynamics in myeloid and fibroblast lineages after cardiac injury, and provide an entry point for deeper analysis of cardiac homeostasis, inflammation, fibrosis, repair and regeneration.

Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1188-0) contains supplementary material, which is available to authorized users.

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program.

iSyTE is a publicly available Web resource that can be used to prioritize candidate genes within mapped genomic intervals associated with congenital cataract for further investigation. Extension of this approach to other ocular tissue components will facilitate eye disease gene discovery.

Many vertebrate organs form through the sequential and reciprocal exchange of signaling molecules between juxtaposed epithelial and mesenchymal tissues. We undertook a systems biology approach that combined the generation and analysis of large-scale spatiotemporal gene expression data with mouse genetic experiments to gain insight into the mechanisms that control epithelial-mesenchymal signaling interactions in the developing mouse molar tooth. We showed that the shift in instructive signaling potential from dental epithelium to dental mesenchyme was accompanied by temporally coordinated genome-wide changes in gene expression in both compartments. To identify the mechanism responsible, we developed a probabilistic technique that integrates regulatory evidence from gene expression data and from the literature to reconstruct a gene regulatory network for the epithelial and mesenchymal compartments in early tooth development. By integrating these epithelial and mesenchymal gene regulatory networks through the action of diffusible extracellular signaling molecules, we identified a key epithelial-mesenchymal intertissue Wnt-Bmp (bone morphogenetic protein) feedback circuit. We then validated this circuit in vivo with compound genetic mutations in mice that disrupted this circuit. Moreover, mathematical modeling demonstrated that the structure of the circuit accounted for the observed reciprocal signaling dynamics. Thus, we have identified a critical signaling circuit that controls the coordinated genome-wide expression changes and reciprocal signaling molecule dynamics that occur in interacting epithelial and mesenchymal compartments during organogenesis.

ObjectiveParental obesity can induce metabolic phenotypes in offspring independent of the inherited DNA sequence. Here we asked whether such non-genetic acquired metabolic traits can be passed on to a second generation that has never been exposed to obesity, even as germ cells.MethodsWe examined the F1, F2, and F3 a/a offspring derived from F0 matings of obese prediabetic Avy/a sires and lean a/a dams. After F0, only lean a/a mice were used for breeding.ResultsWe found that F1 sons of obese founder males exhibited defects in glucose and lipid metabolism, but only upon a post-weaning dietary challenge. F1 males transmitted these defects to their own male progeny (F2) in the absence of the dietary challenge, but the phenotype was largely attenuated by F3. The sperm of F1 males exhibited changes in the abundance of several small RNA species, including the recently reported diet-responsive tRNA-derived fragments.ConclusionsThese data indicate that induced metabolic phenotypes may be propagated for a generation beyond any direct exposure to an inducing factor. This non-genetic inheritance likely occurs via the actions of sperm noncoding RNA.

Background-Alternative mRNA splicing is an important mechanism for regulation of gene expression. Altered mRNA splicing occurs in association with several types of cancer, and a small number of disease-associated changes in splicing have been reported in heart disease. However, genome-wide approaches have not been used to study splicing changes in heart disease. We hypothesized that mRNA splicing is different in diseased hearts compared with control hearts. Methods and Results-We used the Affymetrix Exon array to globally evaluate mRNA splicing in left ventricular myocardial RNA from controls (nϭ15) and patients with ischemic cardiomyopathy (nϭ15). We observed a broad and significant decrease in mRNA splicing efficiency in heart failure, which affected some introns to a greater extent than others. The profile of mRNA splicing separately clustered ischemic cardiomyopathy and control samples, suggesting distinct changes in mRNA splicing between groups. Reverse transcription-polymerase chain reaction validated 9 previously unreported alternative splicing events. Furthermore, we demonstrated that splicing of 4 key sarcomere genes, cardiac troponin T (TNNT2), cardiac troponin I (TNNI3), myosin heavy chain 7 (MYH7), and filamin C, gamma (FLNC), was significantly altered in ischemic cardiomyopathy and in dilated cardiomyopathy and aortic stenosis. In aortic stenosis samples, these differences preceded the onset of heart failure. Remarkably, the ratio of minor to major splice variants of TNNT2, MYH7, and FLNC classified independent test samples as control or disease with Ͼ98% accuracy. Conclusions-Our data indicate that mRNA splicing is broadly altered in human heart disease and that patterns of aberrant RNA splicing accurately assign samples to control or disease classes. (Circ Cardiovasc Genet. 2010;3:138-146.)

Motivation: Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significantincrease or decrease in expression variability (variance). Asthese observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting.Results: Here we propose a novel analysis for changes in gene expression variability between groups of amples, which we call differential variability analysis. We introduce the concept of differential variability (DV), and present a simple procedure for identifying DV genes from microarray data. Our procedure is evaluated with simulated and real microarray datasets. The effect of data preprocessing methods on identification of DV gene is investigated. The biological significance of DV analysis is demonstrated with four human disease datasets. The relationships among DV, DE and DC genes are investigated. The results suggest that changes in expression variability are associated with changes in coexpression pattern, which imply that DV is not merely stochastic noise, but informative signal.Availability: The R source code for differential variability analysis is available from the contact authors upon request.Contact: joshua@it.usyd.edu.au; mcharleston@it.usyd.edu.au