Quantitative assessment of single-cell RNA-sequencing methods

Cancer systems immunology

Tumor immunology is undergoing a renaissance due to the recent profound clinical successes of tumor immunotherapy. These advances have coincided with an exponential growth in the development of -omics technologies. Armed with these technologies and their associated computational and modeling toolsets, systems biologists have turned their attention to tumor immunology in an effort to understand the precise nature and consequences of interactions between tumors and the immune system. Such interactions are inherently multivariate, spanning multiple time and size scales, cell types, and organ systems, rendering systems biology approaches particularly amenable to their interrogation. While in its infancy, the field of 'Cancer Systems Immunology' has already influenced our understanding of tumor immunology and immunotherapy. As the field matures, studies will move beyond descriptive characterizations toward functional investigations of the emergent behavior that govern tumor-immune responses. Thus, Cancer Systems Immunology holds incredible promise to advance our ability to fight this disease.

Comparative RNAseq Analysis of the Insect-Pathogenic Fungus Metarhizium anisopliae Reveals Specific Transcriptome Signatures of Filamentous and Yeast-Like Development

The fungus Metarhizium anisopliae is a facultative insect pathogen used as biological control agent of several agricultural pests worldwide. It is a dimorphic fungus that is able to display two growth morphologies, a filamentous phase with formation of hyphae and a yeast-like phase with formation of single-celled blastospores. Blastospores play an important role for M. anisopliae pathogenicity during disease development. They are formed solely in the hemolymph of infected insects as a fungal strategy to quickly multiply and colonize the insect's body. Here, we use comparative genome-wide transcriptome analyses to determine changes in gene expression between the filamentous and blastospore growth phases in vitro to characterize physiological changes and metabolic signatures associated with M. anisopliae dimorphism. Our results show a clear molecular distinction between the blastospore and mycelial phases. In total 6.4% (n = 696) out of 10,981 predicted genes in M. anisopliae were differentially expressed between the two phases with a fold-change > 4. The main physiological processes associated with up-regulated gene content in the single-celled yeast-like blastospores during liquid fermentation were oxidative stress, amino acid metabolism (catabolism and anabolism), respiration processes, transmembrane transport and production of secondary metabolites. In contrast, the up-regulated gene content in hyphae were associated with increased growth, metabolism and cell wall re-organization, which underlines the specific functions and altered growth morphology of M. anisopliae blastospores and hyphae, respectively. Our study revealed significant transcriptomic differences between the metabolism of blastospores and hyphae. These findings illustrate important aspects of fungal morphogenesis in M. anisopliae and highlight the main metabolic activities of each propagule under in vitro growth conditions.

Physiological blood-brain transport is impaired with age by a shift in transcytosis

The vascular interface of the brain, known as the blood-brain barrier (BBB), is understood to maintain brain function in part via its low transcellular permeability^1-3. Yet, recent studies have demonstrated that brain ageing is sensitive to circulatory proteins^4,5. Thus, it is unclear whether permeability to individually injected exogenous tracers-as is standard in BBB studies-fully represents blood-to-brain transport. Here we label hundreds of proteins constituting the mouse blood plasma proteome, and upon their systemic administration, study the BBB with its physiological ligand. We find that plasma proteins readily permeate the healthy brain parenchyma, with transport maintained by BBB-specific transcriptional programmes. Unlike IgG antibody, plasma protein uptake diminishes in the aged brain, driven by an age-related shift in transport from ligand-specific receptor-mediated to non-specific caveolar transcytosis. This age-related shift occurs alongside a specific loss of pericyte coverage. Pharmacological inhibition of the age-upregulated phosphatase ALPL, a predicted negative regulator of transport, enhances brain uptake of therapeutically relevant transferrin, transferrin receptor antibody and plasma. These findings reveal the extent of physiological protein transcytosis to the healthy brain, a mechanism of widespread BBB dysfunction with age and a strategy for enhanced drug delivery.

Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety

Motivation

The effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon.

Results

To explore this hypothesis, we analyzed the expression variability of protein-coding genes, and particularly drug target genes, across individuals. For this, we developed a novel variability measure, termed local coefficient of variation (LCV), which ranks the expression variability of each gene relative to genes with similar expression levels. Unlike commonly used methods, LCV neutralizes expression levels biases without imposing any distribution over the variation and is robust to data incompleteness. Application of LCV to RNA-sequencing profiles of 19 human tissues and to target genes of 1076 approved drugs revealed that drug target genes were significantly more variable than protein-coding genes. Analysis of 113 drugs with available effectiveness scores showed that drugs targeting highly variable genes tended to be less effective in the population. Furthermore, comparison of approved drugs to drugs that were withdrawn from the market showed that withdrawn drugs targeted significantly more variable genes than approved drugs. Last, upon analyzing gender differences we found that the variability of drug target genes was similar between men and women. Altogether, our results suggest that expression variability of drug target genes could contribute to the variable responsiveness and effectiveness of drugs, and is worth considering during drug treatment and development.

Availability and implementation

LCV is available as a python script in GitHub (https://github.com/eyalsim/LCV).

Supplementary information

Supplementary data are available at Bioinformatics online.

Microfluidics in Single-Cell Virology: Technologies and Applications

Microfluidics has proven to be a powerful tool for probing biology at the single-cell level. However, it is only in the past 5 years that single-cell microfluidics has been used in the field of virology. An array of strategies based on microwells, microvalves, and droplets is now available for tracking viral infection dynamics, identifying cell subpopulations with particular phenotypes, as well as high-throughput screening. The insights into the virus-host interactions gained at the single-cell level are unprecedented and usually inaccessible by population-based experiments. Therefore, single-cell microfluidics, which opens new avenues for mechanism elucidation and development of antiviral therapeutics, would be a valuable tool for the study of viral pathogenesis.

Interleukin 27 Protects From Gastric Atrophy and Metaplasia During Chronic Autoimmune Gastritis

BACKGROUND & AIMS

The association between chronic inflammation and gastric carcinogenesis is well established, but it is not clear how immune cells and cytokines regulate this process. We investigated the role of interleukin 27 (IL27) in the development of gastric atrophy, hyperplasia, and metaplasia (preneoplastic lesions associated with inflammation-induced gastric cancer) in mice with autoimmune gastritis.

METHODS

We performed studies with TxA23 mice (control mice), which express a T-cell receptor against the H+/K+ adenosine triphosphatase α chain and develop autoimmune gastritis, and TxA23xEbi3-/- mice, which develop gastritis but do not express IL27. In some experiments, mice were given high-dose tamoxifen to induce parietal cell atrophy and spasmolytic polypeptide-expressing metaplasia (SPEM). Recombinant IL27 was administered to mice with mini osmotic pumps. Stomachs were collected and analyzed by histopathology and immunofluorescence; we used flow cytometry to measure IL27 and identify immune cells that secrete IL27 in the gastric mucosa. Single-cell RNA sequencing was performed on immune cells that infiltrated stomach tissues.

RESULTS

We identified IL27-secreting macrophages and dendritic cell in the corpus of mice with chronic gastritis (TxA23 mice). Mice deficient in IL27 developed more severe gastritis, atrophy, and SPEM than control mice. Administration of recombinant IL27 significantly reduced the severity of inflammation, atrophy, and SPEM in mice with gastritis. Single-cell RNA sequencing showed that IL27 acted almost exclusively on stomach-infiltrating CD4+ T cells to suppress expression of inflammatory genes.

CONCLUSIONS

In studies of mice with autoimmune gastritis, we found that IL27 is an inhibitor of gastritis and SPEM, suppressing CD4+ T-cell-mediated inflammation in the gastric mucosa.

Immuno-oncology gene expression profiling of formalin-fixed and paraffin-embedded clear cell renal cell carcinoma: Performance comparison of the NanoString nCounter technology with targeted RNA sequencing

Inflammatory gene signatures are currently being explored as predictive biomarkers for immune checkpoint blockade, and particularly for the treatment of renal cell cancers. From a diagnostic point of view, the nCounter analysis platform and targeted RNA sequencing are emerging alternatives to microarrays and comprehensive transcriptome sequencing in assessing formalin-fixed and paraffin-embedded (FFPE) cancer samples. So far, no systematic study has analyzed and compared the technical performance metrics of these two approaches. Filling this gap, we performed a head-to-head comparison of two commercially available immune gene expression assays, using clear cell renal cell cancer FFPE specimens. We compared the nCounter system that utilizes a direct hybridization technology without amplification with an NGS assay that is based on targeted RNA-sequencing with preamplification. We found that both platforms displayed high technical reproducibility and accuracy (Pearson coefficient: ≥0.96, concordance correlation coefficient [CCC]: ≥0.93). A density plot for normalized expression of shared genes on both platforms showed a comparable bi-modal distribution and dynamic range. RNA-Seq demonstrated relatively larger signaling intensity whereas the nCounter system displayed higher inter-sample variability. Estimated fold changes for all shared genes showed high correlation (Spearman coefficient: 0.73). This agreement is even better when only significantly differentially expressed genes were compared. Composite gene expression profiles, such as an interferon gamma (IFNg) signature, can be reliably inferred by both assays. In summary, our study demonstrates that focused transcript read-outs can reliably be achieved by both technologies and that both approaches achieve comparable results despite their intrinsic technical differences.

A Public BCR Present in a Unique Dual-Receptor-Expressing Lymphocyte from Type 1 Diabetes Patients Encodes a Potent T Cell Autoantigen

T and B cells are the two known lineages of adaptive immune cells. Here, we describe a previously unknown lymphocyte that is a dual expresser (DE) of TCR and BCR and key lineage markers of both B and T cells. In type 1 diabetes (T1D), DEs are predominated by one clonotype that encodes a potent CD4 T cell autoantigen in its antigen binding site. Molecular dynamics simulations revealed that this peptide has an optimal binding register for diabetogenic HLA-DQ8. In concordance, a synthetic version of the peptide forms stable DQ8 complexes and potently stimulates autoreactive CD4 T cells from T1D patients, but not healthy controls. Moreover, mAbs bearing this clonotype are autoreactive against CD4 T cells and inhibit insulin tetramer binding to CD4 T cells. Thus, compartmentalization of adaptive immune cells into T and B cells is not absolute, and violators of this paradigm are likely key drivers of autoimmune diseases.

Visualization of cardiovascular development, physiology and disease at the single-cell level: Opportunities and future challenges

Single-cell RNA sequencing (scRNA-seq), a method of transcriptome sequencing at the single-cell level, has recently emerged as a revolutionary technology in the field of biomedical research. Compared to conventional gene expression profiling in bulk, scRNA-seq resolves biological differences among individual cells and enables the identification of rare cell populations that are easily overlooked. This review introduces the method of scRNA-seq, summarizes its applications in the field of cardiovascular disease research, and discusses existing limitations and prospects for future applications.

On-Demand Droplet Collection for Capturing Single Cells

Droplet-based microfluidic techniques are extensively used in efficient manipulation and genome-wide analysis of individual cells, probing the heterogeneity among populations of individuals. However, the extraction and isolation of single cells from individual droplets remains difficult due to the inevitable sample loss during processing. Herein, an automated system for accurate collection of defined numbers of droplets containing single cells is presented. Based on alternate sorting and dispensing in three branch channels, the droplet number can be precisely controlled down to single-droplet resolution. While encapsulating single cells and reserving one branch as a waste channel, sorting can be seamlessly integrated to enable on-demand collection of single cells. Combined with a lossless recovery strategy, this technique achieves capture and culture of individual cells with a harvest rate of over 95%. The on-demand droplet collection technique has great potential to realize quantitative processing and analysis of single cells for elucidating the role of cell-to-cell variations.

Comparison of RNA isolation procedures for analysis of adult murine brain and spinal cord astrocytes

BACKGROUND

Molecular analyses of cell populations and single cells have been instrumental in the advancement of our understanding of the physiology and pathologic processes of the nervous system. However, the limitation of these methods is the dependence on a gentle, efficient and specific enrichment procedure for the target cell population. In particular, this has been challenging for tightly interconnected cells, for example central nervous system (CNS) endogenous cells such as astrocytes.

NEW METHOD

Here we adopted one of the most common methods of cell extraction, namely, enzymatic tissue digestion followed by fluorescence-activated cell sorting (FACS) of individual cells. We evaluated different enzymatic/mechanical tissue dissociation procedures and analyzed different astrocyte lineage transgenic models. Furthermore, we compared the cell extraction efficiency from spinal cord vs. brain.

RESULTS

Enzymatic digestion of CNS tissue of Glast-Cre^ERT2x tdTomato^fl/fl or Aldh1l1-Cre^ERT2x tdTomato^fl/fl followed by FACS resulted in highly purified astrocytes. Automated tissue digestion strongly improved the isolated cell numbers. Aldh1l1-Cre^ERT2 identified more astrocytes than Glast-Cre^ERT2; isolation from brain yields higher numbers than from spinal cord.

COMPARISON WITH EXISTING METHODS

We compared the efficiency and purity of the enzymatic dissociation/FACS approach with a more modern procedure consisting of tissue homogenization followed by translating ribosome affinity purification (TRAP).

CONCLUSION

We found that both methods result in highly enriched astrocytic RNA. However, only TRAP isolation resulted in reliably detectable RNA concentrations from spinal cord tissue on a single animal level. Depending on the aim of the study both methods have advantages and disadvantages but both are acceptable for astrocytic RNA analysis.

DTD: An R Package for Digital Tissue Deconvolution

Digital tissue deconvolution (DTD) estimates the cellular composition of a tissue from its bulk gene-expression profile. For this, DTD approximates the bulk as a mixture of cell-specific expression profiles. Different tissues have different cellular compositions, with cells in different activation states, and embedded in different environments. Consequently, DTD can profit from tailoring the deconvolution model to a specific tissue context. Loss-function learning adapts DTD to a specific tissue context, such as the deconvolution of blood, or a specific type of tumor tissue. We provide software for loss-function learning, for its validation and visualization, and for applying the DTD models to new data.

Understanding the complexity of retina and pluripotent stem cell derived retinal organoids with single cell RNA sequencing: current progress, remaining challenges and future prospective

Single-cell sequencing technologies have emerged as a revolutionary tool with transformative new methods to profile genetic, epigenetic, spatial, and lineage information in individual cells. Single-cell RNA sequencing (scRNA-Seq) allows researchers to collect large datasets detailing the transcriptomes of individual cells in space and time and is increasingly being applied to reveal cellular heterogeneity in retinal development, normal physiology, and disease, and provide new insights into cell-type specific markers and signaling pathways. In recent years, scRNA-Seq datasets have been generated from retinal tissue and pluripotent stem cell-derived retinal organoids. Their cross-comparison enables staging of retinal organoids, identification of specific cells in developing and adult human neural retina and provides deeper insights into cell-type sub-specification and geographical differences. In this article, we review the recent rapid progress in scRNA-Seq analyses of retina and retinal organoids, the questions that remain unanswered and the technical challenges that need to be overcome to achieve consistent results that reflect the complexity, functionality, and interactions of all retinal cell types.

Single‑cell RNA sequencing of t(8;21) acute myeloid leukemia for risk prediction

Single-cell RNA sequencing (scRNA-seq) of bone marrow or peripheral blood samples from patients with acute myeloid leukemia (AML) enables the characterization of heterogeneous malignant cells. A total of 87 cells from two patients with t(8;21) AML were analyzed using scRNA-seq. Clustering methods were used to separate leukemia cells into different sub-populations, and the expression patterns of specific marker genes were used to annotate these populations. Among the 31 differentially expressed genes in the cells of a patient who relapsed after hematopoietic stem cell transplantation, 13 genes were identified to be associated with leukemia. Furthermore, three genes, namely AT-rich interaction domain 2, lysine methyltransferase 2A and synaptotagmin binding cytoplasmic RNA interacting protein were validated as possible prognostic biomarkers using two bulk expression datasets. Taking advantage of scRNA-seq, the results of the present study may provide clinicians with several possible biomarkers to predict the prognostic outcomes of t(8;21) AML.

CLEAR: coverage-based limiting-cell experiment analysis for RNA-seq

Background

Direct cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cell populations without the need for sample pooling or RNA extraction. We term the use of single-cell chemistries for sequencing low numbers of cells limiting-cell RNA-seq (lcRNA-seq). Currently, there is no customized algorithm to select robust/low-noise transcripts from lcRNA-seq data for between-group comparisons.

Methods

Herein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed genes (DEG) analysis. Total RNA obtained from primary chronic lymphocytic leukemia (CLL) CD5+ and CD5− cells were used to develop the CLEAR algorithm. Once established, the performance of CLEAR was evaluated with FACS-sorted cells enriched from mouse Dentate Gyrus (DG).

Results

When using CLEAR transcripts vs. using all transcripts in CLL samples, downstream analyses revealed a higher proportion of shared transcripts across three input amounts and improved principal component analysis (PCA) separation of the two cell types. In mouse DG samples, CLEAR identifies noisy transcripts and their removal improves PCA separation of the anticipated cell populations. In addition, CLEAR was applied to two publicly-available datasets to demonstrate its utility in lcRNA-seq data from other institutions. If imputation is applied to limit the effect of missing data points, CLEAR can also be used in large clinical trials and in single cell studies.

Conclusions

lcRNA-seq coupled with CLEAR is widely used in our institution for profiling immune cells (circulating or tissue-infiltrating) for its transcript preservation characteristics. CLEAR fills an important niche in pre-processing lcRNA-seq data to facilitate transcriptome profiling and DEG analysis. We demonstrate the utility of CLEAR in analyzing rare cell populations in clinical samples and in murine neural DG region without sample pooling.

Robust RNA-Seq of aRNA-amplified single cell material collected by patch clamp

Most single cell RNA sequencing protocols start with single cells dispersed from intact tissue. High-throughput processing of the separated cells is enabled using microfluidics platforms. However, dissociation of tissue results in loss of information about cell location and morphology and potentially alters the transcriptome. An alternative approach for collecting RNA from single cells is to re-purpose the electrophysiological technique of patch clamp recording. A hollow patch pipette is attached to individual cells, enabling the recording of electrical activity, after which the cytoplasm may be extracted for single cell RNA-Seq ("Patch-Seq"). Since the tissue is not disaggregated, the location of cells is readily determined, and the morphology of the cells is maintained, making possible the correlation of single cell transcriptomes with cell location, morphology and electrophysiology. Recent Patch-Seq studies utilizes PCR amplification to increase amount of nucleic acid material to the level required for current sequencing technologies. PCR is prone to create biased libraries - especially with the extremely high degrees of exponential amplification required for single cell amounts of RNA. We compared a PCR-based approach with linear amplifications and demonstrate that aRNA amplification (in vitro transcription, IVT) is more sensitive and robust for single cell RNA collected by a patch clamp pipette.

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

Technological advances and computational approaches for alternative splicing analysis in single cells

Alternative splicing of RNAs generates isoform diversity, resulting in different proteins that are necessary for maintaining cellular function and identity. The discovery of alternative splicing has been revolutionized by next-generation transcriptomic sequencing mainly using bulk RNA-sequencing, which has unravelled RNA splicing and mis-splicing of normal cells under steady-state and stress conditions. Single-cell RNA-sequencing studies have focused on gene-level expression analysis and revealed gene expression signatures distinguishable between different cellular types. Single-cell alternative splicing is an emerging area of research with the promise to reveal transcriptomic dynamics invisible to bulk- and gene-level analysis. In this review, we will discuss the technological advances for single-cell alternative splicing analysis, computational strategies for isoform detection and quantitation in single cells, and current applications of single-cell alternative splicing analysis and its potential future contributions to personalized medicine.

Progress and applications of single-cell sequencing techniques

Single-cell sequencing (SCS) is a next-generation sequencing method that is mainly used to analyze differences in genetic and protein information between cells, to obtain genetic information on microorganisms that are difficult to cultivate at a single-cell level and to better understand their specific roles in the microenvironment. By sequencing the whole genome, transcriptome and epigenome of a single cell, the complex heterogeneous mechanisms involved in disease occurrence and progression can be revealed, further improving disease diagnosis, prognosis prediction and monitoring of the therapeutic effects of drugs. In this study, we mainly summarized the methods and application fields of SCS, which may provide potential references for its future clinical applications, including the analysis of embryonic and organ development, the immune system, cancer progression, and parasitic and infectious diseases as well as stem cell research, antibody screening, and therapeutic research and development.

Single-Cell Analysis Uncovers a Vast Diversity in Intracellular Viral Defective Interfering RNA Content Affecting the Large Cell-to-Cell Heterogeneity in Influenza A Virus Replication

Virus replication displays a large cell-to-cell heterogeneity; yet, not all sources of this variability are known. Here, we study the effect of defective interfering (DI) particle (DIP) co-infection on cell-to-cell variability in influenza A virus (IAV) replication. DIPs contain a large internal deletion in one of their eight viral RNAs (vRNA) and are, thus, defective in virus replication. Moreover, they interfere with virus replication. Using single-cell isolation and reverse transcription polymerase chain reaction, we uncovered a large between-cell heterogeneity in the DI vRNA content of infected cells, which was confirmed for DI mRNAs by single-cell RNA sequencing. A high load of intracellular DI vRNAs and DI mRNAs was found in low-productive cells, indicating their contribution to the large cell-to-cell variability in virus release. Furthermore, we show that the magnitude of host cell mRNA expression (some factors may inhibit virus replication), but not the ribosome content, may further affect the strength of single-cell virus replication. Finally, we show that the load of viral mRNAs (facilitating viral protein production) and the DI mRNA content are, independently from one another, connected with single-cell virus production. Together, these insights advance single-cell virology research toward the elucidation of the complex multi-parametric origin of the large cell-to-cell heterogeneity in virus infections.

Methods for Single-Cell Isolation and Preparation

Within the last decade, single-cell analysis has revolutionized our understanding of cellular processes and heterogeneity across all disciplines of life science. As the transcriptome, genome, or epigenome of individual cells can nowadays be analyzed at low cost and in high-throughput within a few days by modern techniques, tremendous improvements in disease diagnosis on the one hand and the investigation of disease-relevant mechanisms on the other were achieved so far. This relies on the parallel development of reliable cell capturing and single-cell sequencing approaches that have paved the way for comprehensive single-cell studies. Apart from single-cell isolation methods in high-throughput, a variety of methods with distinct specializations were developed, allowing for correlation of transcriptomics with cellular parameters like electrophysiology or morphology.For all single-cell-based approaches, accurate and reliable isolation with proper quality controls is prerequisite, whereby different options exist dependent on sample type and tissue properties. Careful consideration of an appropriate method is required to avoid incorrect or biased data that may lead to misinterpretations.In this chapter, we will provide a broad overview of the current state of the art in matters of single-cell isolation methods mostly applied for sequencing-based downstream analysis, and their respective advantages and drawbacks. Distinct technologies will be discussed in detail addressing key parameters like sample compatibility, viability, purity, throughput, and isolation efficiency.

Screen technical noise in single cell RNA sequencing data

We proposed a data cleaning pipeline for single cell (SC) RNA-seq data, where we first screen genes (gene-wise screening) followed by screening cell libraries (library-wise screening). Gene-wise screening is based on the expectation that for a gene with a low technical noise, a gene's count in a library will tend to increase with the increase of library size, which was tested using negative binomial regression of gene count (as dependent variable) against library size (as independent variable). Library-wise screening is based on the expectation that across-library correlations for housekeeping (HK) genes is expected to be higher than the correlations for non-housekeeping (NHK) genes in those libraries with low technical noise. We removed those libraries, whose mean pairwise correlation for HK genes is NOT significantly higher than that for NHK genes. We successfully applied the pipeline to two large SC RNA-seq datasets. The pipeline was also developed into an R package.

The Mammalian Spermatogenesis Single-Cell Transcriptome, from Spermatogonial Stem Cells to Spermatids

Spermatogenesis is a complex and dynamic cellular differentiation process critical to male reproduction and sustained by spermatogonial stem cells (SSCs). Although patterns of gene expression have been described for aggregates of certain spermatogenic cell types, the full continuum of gene expression patterns underlying ongoing spermatogenesis in steady state was previously unclear. Here, we catalog single-cell transcriptomes for >62,000 individual spermatogenic cells from immature (postnatal day 6) and adult male mice and adult men. This allowed us to resolve SSC and progenitor spermatogonia, elucidate the full range of gene expression changes during male meiosis and spermiogenesis, and derive unique gene expression signatures for multiple mouse and human spermatogenic cell types and/or subtypes. These transcriptome datasets provide an information-rich resource for studies of SSCs, male meiosis, testicular cancer, male infertility, or contraceptive development, as well as a gene expression roadmap to be emulated in efforts to achieve spermatogenesis in vitro.

Improving single-cell transcriptome sequencing efficiency with a microfluidic phase-switch device

In this paper, we present a novel method to improve the efficiency of single-cell transcriptome sequencing for analyzing valuable cell samples. The microfluidic device we designed integrates multiple single-cell isolation chambers with hydrodynamic traps and achieves a nearly 100% single-cell capture rate and minimal cell loss, making it particularly suitable for samples with limited numbers of cells. Single cells were encapsulated using a novel phase-switch method into picoliter-sized hydrogel droplets and easily recovered for subsequent reactions. Minimizing the reaction volume resulted in a high reverse transcription (RT) efficiency for RNA sequencing (RNA-Seq). With this novel microfluidic platform, we captured dozens of hESCs (H9) simultaneously and obtained live cells in individual picoliter volumes, thus allowing for the convenient construction of a high-quality library for deep single-cell RNA-Seq. Our single-cell RNA-Seq results confirmed that a spectrum of pluripotency existed within an H9 colony. This integrated microfluidic platform can be applied to various cell types for the investigation of rare cellular events, and the phase-switch single-cell processing strategy will improve the efficiency and accessibility of single-cell transcriptome sequencing analysis.

Quantifying pluripotency landscape of cell differentiation from scRNA-seq data by continuous birth-death process

Modeling cell differentiation from omics data is an essential problem in systems biology research. Although many algorithms have been established to analyze scRNA-seq data, approaches to infer the pseudo-time of cells or quantify their potency have not yet been satisfactorily solved. Here, we propose the Landscape of Differentiation Dynamics (LDD) method, which calculates cell potentials and constructs their differentiation landscape by a continuous birth-death process from scRNA-seq data. From the viewpoint of stochastic dynamics, we exploited the features of the differentiation process and quantified the differentiation landscape based on the source-sink diffusion process. In comparison with other scRNA-seq methods in seven benchmark datasets, we found that LDD could accurately and efficiently build the evolution tree of cells with pseudo-time, in particular quantifying their differentiation landscape in terms of potency. This study provides not only a computational tool to quantify cell potency or the Waddington potential landscape based on scRNA-seq data, but also novel insights to understand the cell differentiation process from a dynamic perspective.

Transcriptomic analysis of Macrobrachium rosenbergii (giant fresh water prawn) post-larvae in response to M. rosenbergii nodavirus (MrNV) infection: de novo assembly and functional annotation

Background

Macrobrachium rosenbergii, is one of a major freshwater prawn species cultured in Southeast Asia. White tail disease (WTD), caused by Macrobrachium rosenbergii nodavirus (MrNV), is a serious problem in farm cultivation and is responsible for up to 100% mortality in the post larvae stage. Molecular data on how M. rosenbergii post-larvae launches an immune response to an infection with MrNV is not currently available. We therefore compared the whole transcriptomic sequence of M. rosenbergii post-larvae before and after MrNV infection.

Results

Transcriptome for M. rosenbergii post-larvae demonstrated high completeness (BUSCO Complete: 83.4%, fragmentation: 13%, missing:3.3%, duplication:16.2%; highest ExN50 value: 94%). The assembled transcriptome consists of 96,362 unigenes with N₅₀ of 1308 bp. The assembled transcriptome was successfully annotated against the NCBI non-redundant arthropod database (33.75%), UniProt database (26.73%), Gene Ontology (GO) (18.98%), Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups (EggNOG) (20.88%), and Kyoto Encyclopedia of Genes and Genome pathway (KEGG) (20.46%). GO annotations included immune system process, signaling, response to stimulus, and antioxidant activity. Differential abundance analysis using EdgeR showed 2413 significantly up-regulated genes and 3125 significantly down-regulated genes during the infection of MrNV.

Conclusions

This study reported a highly complete transcriptome from the post-larvae stage of giant river prawn, M. rosenbergii. Differential abundant transcripts during MrNV infection were identified and validated by qPCR, many of these differentially abundant transcripts as key players in antiviral immunity. These include known members of the innate immune response with the largest expression change occurring in the M. rosenbergii post-larvae after MrNV infection such as antiviral protein, C-type lectin, prophenol oxidase, caspase, ADP ribosylation factors, and dicer.

De novo compartment deconvolution and weight estimation of tumor samples using DECODER

Tumors are mixtures of different compartments. While global gene expression analysis profiles the average expression of all compartments in a sample, identifying the specific contribution of each compartment remains a challenge. With the increasing recognition of the importance of non-neoplastic components, the ability to breakdown the gene expression contribution of each is critical. Here, we develop DECODER, an integrated framework which performs de novo deconvolution and single-sample compartment weight estimation. We use DECODER to deconvolve 33 TCGA tumor RNA-seq data sets and show that it may be applied to other data types including ATAC-seq. We demonstrate that it can be utilized to reproducibly estimate cellular compartment weights in pancreatic cancer that are clinically meaningful. Application of DECODER across cancer types advances the capability of identifying cellular compartments in an unknown sample and may have implications for identifying the tumor of origin for cancers of unknown primary.

Separating different cell compartments from bulk gene expression data can be challenging. Here the authors present DECODER, which can perform de novo deconvolutions on non-negative matrices including microarray, RNA-seq and ATAC-seq data sets.

Microfluidic SlipChip device for multistep multiplexed biochemistry on a nanoliter scale

We have developed a multistep microfluidic device that expands the current SlipChip capabilities by enabling multiple steps of droplet merging and multiplexing. Harnessing the interfacial energy between carrier and sample phases, this manually operated device accurately meters nanoliter volumes of reagents and transfers them into on-device reaction wells. Judiciously shaped microfeatures and surface-energy traps merge droplets in a parallel fashion. Wells can be tuned for different volumetric capacities and reagent types, including for pre-spotted reagents that allow for unique identification of original well contents even after their contents are pooled. We demonstrate the functionality of the multistep SlipChip by performing RNA transcript barcoding on-device for synthetic spiked-in standards and for biologically derived samples. This technology is a good candidate for a wide range of biological applications that require multiplexing of multistep reactions in nanoliter volumes, including single-cell analyses.

Using Large Datasets to Understand Nanotechnology

Advances in sequencing technologies have made studying biological processes with genomics, transcriptomics, and proteomics commonplace. As a result, this suite of increasingly integrated techniques is well positioned to study drug delivery, a process that is influenced by many biomolecules working in concert. Omics-based approaches can be used to study the vast nanomaterial chemical space as well as the biological factors that affect the safety, toxicity, and efficacy of nanotechnologies. The generation and analysis of large datasets, methods to interpret them, and dataset applications to nanomaterials to date, are demonstrated here. Finally, new approaches for how sequencing-generated datasets can answer fundamental questions in nanotechnology based drug delivery are proposed.

The first enhancer in an enhancer chain safeguards subsequent enhancer-promoter contacts from a distance

Background

Robustness and evolutionary stability of gene expression in the human genome are established by an array of redundant enhancers.

Results

Using Hi-C data in multiple cell lines, we report a comprehensive map of promoters and active enhancers connected by chromatin contacts, spanning 9000 enhancer chains in 4 human cell lines associated with 2600 human genes. We find that the first enhancer in a chain that directly contacts the target promoter is commonly located at a greater genomic distance from the promoter than the second enhancer in a chain, 96 kb vs. 45 kb, respectively. The first enhancer also features higher similarity to the promoter in terms of tissue specificity and higher enrichment of loop factors, suggestive of a stable primary contact with the promoter. In contrast, a chain of enhancers which connects to the target promoter through a neutral DNA segment instead of an enhancer is associated with a significant decrease in target gene expression, suggesting an important role of the first enhancer in initiating transcription using the target promoter and bridging the promoter with other regulatory elements in the locus.

Conclusions

The widespread chained structure of gene enhancers in humans reveals that the primary, critical enhancer is distal, commonly located further away than other enhancers. This first, distal enhancer establishes contacts with multiple regulatory elements and safeguards a complex regulatory program of its target gene.

Electronic supplementary material

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Transcriptome Analysis of the Effects of Fasting Caecotrophy on Hepatic Lipid Metabolism in New Zealand Rabbits

Simple Summary

Caecotrophy in small herbivores, including rabbits, is the instinctive behavior of eating soft feces. Little is known about the impact of caecotrophy on growth and metabolism. In the present study, we used an Elizabeth circle to prevent rabbits from eating soft feces and measured changes in feed intake, body weight, internal organ weight, serum biochemical indices and liver lipid droplet accumulation. Liver tissue was also used for transcriptome sequencing. Results indicated that fasting caecotrophy decreased rabbit growth and lipid synthesis in the liver.

Abstract

In order to investigate the effects of fasting caecotrophy on hepatic lipid metabolism in rabbits, 12 weaned female New Zealand white rabbits were randomly divided into (n = 6/group) a control and fasting caecotrophy group. Rabbits in the experimental group were treated with an Elizabeth circle to prevent them from eating their own soft feces for a 60-day period. Growth and blood biochemical indices, transcriptome sequencing and histology analysis of the liver were performed. Compared with the control group, final weight, weight gain, liver weight, growth rate and feed conversion ratio, all decreased in the experimental group (p < 0.05). RNA sequencing (RNA-seq) analysis revealed a total of 301.2 million raw reads (approximately 45.06 Gb of high-quality clean data) that were mapped to the rabbit genome. After a five-step filtering process, 14,964 genes were identified, including 444 differentially expressed genes (p < 0.05, foldchange ≥ 1). A number of differently expressed genes linked to lipid metabolism were further analyzed including CYP7A1, SREBP, ABCA1, GPAM, CYP3A1, RBP4 and RDH5. The KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of the differentially expressed genes indicated that main pathways affected were pentose and glucuronide interactions, starch and sucrose metabolism, retinol metabolism and PPAR signaling. Overall, the present study revealed that preventing caecotrophy reduced growth and altered lipid metabolism, both of which will help guide the development of new approaches for rabbits’ feeding and production. These data also provide a reference for studying the effects of soft feces in other small herbivores.

Isoform-level gene expression patterns in single-cell RNA-sequencing data

Motivation

RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data.

Results

We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data.

Availability and implementation

The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

Gene expression analysis of rare or heterogeneous cell populations such as disseminated cancer cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-amplification of WTA products, quantification of amplified cDNA yields and final qPCR quantification, to identify the most reliable and accurate workflow for quantitation of gene expression of the ERBB2 gene in DCCs. We found that absolute quantification outperforms relative quantification. We then validated the performance of our method on single cells of established breast cancer cell lines displaying distinct levels of HER2 protein. The different protein levels were faithfully reflected by transcript expression across the tested cell lines thereby proving the accuracy of our approach. Finally, we applied our method to breast cancer DCCs of a patient undergoing anti-HER2-directed therapy. Here, we were able to measure ERBB2 expression levels in all HER2-protein-positive DCCs. In summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of ERBB2 in DCCs.

Alternating EM algorithm for a bilinear model in isoform quantification from RNA-seq data

MOTIVATION

Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform read distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide bias correction step(s), which is based on biological considerations-such as GC content-and applied in single samples separately. The main problem is that not all biases are known.

RESULTS

We have developed a novel method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and is computed based on the simplifying assumptions. In contrast XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by a simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. We use an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes. In a differential-expression analysis of a real single-cell RNA-seq dataset, XAEM achieves substantially better rediscovery rates in independent validation sets.

AVAILABILITY AND IMPLEMENTATION

The method and pipeline are implemented as a tool and freely available for use at http://fafner.meb.ki.se/biostatwiki/xaem/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DBTSS/DBKERO for integrated analysis of transcriptional regulation

DBTSS (Database of Transcriptional Start Sites)/DBKERO (Database of Kashiwa Encyclopedia for human genome mutations in Regulatory regions and their Omics contexts) is the database originally initiated with the information of transcriptional start sites and their upstream transcriptional regulatory regions. In recent years, we updated the database to assist users to elucidate biological relevance of the human genome variations or somatic mutations in cancers which may affect the transcriptional regulation. In this update, we facilitate interpretations of disease associated genomic variation, using the Japanese population as a model case. We enriched the genomic variation dataset consisting of the 13,368 individuals collected for various genome-wide association studies and the reference epigenome information in the surrounding regions using a total of 455 epigenome datasets (four tissue types from 67 healthy individuals) collected for the International Human Epigenome Consortium (IHEC). The data directly obtained from the clinical samples was associated with that obtained from various model systems, such as the drug perturbation datasets using cultured cancer cells. Furthermore, we incorporated the results obtained using the newly developed analytical methods, Nanopore/10x Genomics long-read sequencing of the human genome and single cell analyses. The database is made publicly accessible at the URL (http://dbtss.hgc.jp/).

BCseq: accurate single cell RNA-seq quantification with bias correction

With rapid technical advances, single cell RNA-seq (scRNA-seq) has been used to detect cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in development and disease. However, the potential of scRNA-seq for new discoveries is constrained by the robustness of subsequent data analysis. Here we propose a robust model, BCseq (bias-corrected sequencing analysis), to accurately quantify gene expression from scRNA-seq. BCseq corrects inherent bias of scRNA-seq in a data-adaptive manner and effectively removes technical noise. BCseq rescues dropouts through weighted consideration of similar cells. Cells with higher sequencing depths contribute more to the quantification nonlinearly. Furthermore, BCseq assigns a quality score for the expression of each gene in each cell, providing users an objective measure to select genes for downstream analysis. In comparison to existing scRNA-seq methods, BCseq demonstrates increased robustness in detection of differentially expressed (DE) genes and cell subtype classification.

Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.

Systematic evaluation of RNA-Seq preparation protocol performance

Background

RNA-Seq is currently the most widely used tool to analyze whole-transcriptome profiles. There are numerous commercial kits available to facilitate preparing RNA-Seq libraries; however, it is still not clear how some of these kits perform in terms of: 1) ribosomal RNA removal; 2) read coverage or recovery of exonic vs. intronic sequences; 3) identification of differentially expressed genes (DEGs); and 4) detection of long non-coding RNA (lncRNA). In RNA-Seq analysis, understanding the strengths and limitations of commonly used RNA-Seq library preparation protocols is important, as this technology remains costly and time-consuming.

Results

In this study, we present a comprehensive evaluation of four RNA-Seq kits. We used three standard input protocols: Illumina TruSeq Stranded Total RNA and mRNA kits, a modified NuGEN Ovation v2 kit, and the TaKaRa SMARTer Ultra Low RNA Kit v3. Our evaluation of these kits included quality control measures such as overall reproducibility, 5′ and 3′ end-bias, and the identification of DEGs, lncRNAs, and alternatively spliced transcripts. Overall, we found that the two Illumina kits were most similar in terms of recovering DEGs, and the Illumina, modified NuGEN, and TaKaRa kits allowed identification of a similar set of DEGs. However, we also discovered that the Illumina, NuGEN and TaKaRa kits each enriched for different sets of genes.

Conclusions

At the manufacturers’ recommended input RNA levels, all the RNA-Seq library preparation protocols evaluated were suitable for distinguishing between experimental groups, and the TruSeq Stranded mRNA kit was universally applicable to studies focusing on protein-coding gene profiles. The TruSeq protocols tended to capture genes with higher expression and GC content, whereas the modified NuGEN protocol tended to capture longer genes. The SMARTer Ultra Low RNA Kit may be a good choice at the low RNA input level, although it was inferior to the TruSeq mRNA kit at standard input level in terms of rRNA removal, exonic mapping rates and recovered DEGs. Therefore, the choice of RNA-Seq library preparation kit can profoundly affect data outcomes. Consequently, it is a pivotal parameter to consider when designing an RNA-Seq experiment.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5953-1) contains supplementary material, which is available to authorized users.

SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data

Different tissues and diseases have distinct transcriptional profilings with specifically expressed genes (SEGs). So, the identification of SEGs is an important issue in the studies of gene function, biological development, disease mechanism and biomarker discovery. However, few accurate and easy-to-use tools are available for RNA sequencing (RNA-seq) data to detect SEGs. Here, we presented SEGtool, a tool based on fuzzy c-means, Jaccard index and greedy annealing method for SEG detection automatically and self-adaptively ignoring data distribution. Testing result showed that our SEGtool outperforms the existing tools, which was mainly developed for microarray data. By applying SEGtool to Genotype-Tissue Expression (GTEx) human tissue data set, we detected 3181 SEGs with tissue-related functions. Regulatory networks reveal tissue-specific transcription factors regulating many SEGs, such as ETV2 in testis, HNF4A in liver and NEUROD1 in brain. Applied to a case study of single-cell sequencing (SCS) data from embryo cells, we identified many SEGs in specific stages of human embryogenesis. Notably, SEGtool is suitable for RNA-seq data and even SCS data with high specificity and accuracy. An implementation of SEGtool R package is freely available at http://bioinfo.life.hust.edu.cn/SEGtool/.

A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

Cell types in cell populations change as the condition changes: some cell types die out, new cell types may emerge and surviving cell types evolve to adapt to the new condition. Using single-cell RNA-sequencing data that measure the gene expression of cells before and after the condition change, we propose an algorithm, SparseDC, which identifies cell types, traces their changes across conditions and identifies genes which are marker genes for these changes. By solving a unified optimization problem, SparseDC completes all three tasks simultaneously. SparseDC is highly computationally efficient and demonstrates its accuracy on both simulated and real data.

Single-Cell Transcriptomic Analyses of Cell Fate Transitions during Human Cardiac Reprogramming

Direct cellular reprogramming provides a powerful platform to study cell plasticity and dissect mechanisms underlying cell fate determination. Here, we report a single-cell transcriptomic study of human cardiac (hiCM) reprogramming that utilizes an analysis pipeline incorporating current data normalization methods, multiple trajectory prediction algorithms, and a cell fate index calculation we developed to measure reprogramming progression. These analyses revealed hiCM reprogramming-specific features and a decision point at which cells either embark on reprogramming or regress toward their original fibroblast state. In combination with functional screening, we found that immune-response-associated DNA methylation is required for hiCM induction and validated several downstream targets of reprogramming factors as necessary for productive hiCM reprograming. Collectively, this single-cell transcriptomics study provides detailed datasets that reveal molecular features underlying hiCM determination and rigorous analytical pipelines for predicting cell fate conversion.

Circulating Tumor Cells as a Tool for Assessing Tumor Heterogeneity

Tumor heterogeneity is the major cause of failure in cancer prognosis and prediction. Accurately detecting heterogeneity for the development of biomarkers and the detection of the clones resistant to therapy is one of the main goals of contemporary medicine. Metastases belong to the natural history of cancer. The present review gives an overview on the origin of tumor heterogeneity. Recent progress has made it possible to isolate and characterize circulating tumor cells (CTCs), which are the drivers of the disease between the primary sites and metastatic foci. The most recent methods for characterizing CTCs are summarized and we discuss the power of CTC profiling for analyzing tumor heterogeneity in early and advanced diseases.

Single cell expression analysis reveals anatomical and cell cycle-dependent transcriptional shifts during heart development

The heart is a complex organ composed of multiple cell and tissue types. Cardiac cells from different regions of the growing embryonic heart exhibit distinct patterns of gene expression, which are thought to contribute to heart development and morphogenesis. Single cell RNA sequencing allows genome-wide analysis of gene expression at the single cell level. Here, we have analyzed cardiac cells derived from early stage developing hearts by single cell RNA-seq and identified cell cycle gene expression as a major determinant of transcriptional variation. Within cell cycle stage-matched CMs from a given heart chamber, we found that CMs in the G2/M phase downregulated sarcomeric and cytoskeletal markers. We also identified cell location-specific signaling molecules that may influence the proliferation of other nearby cell types. Our data highlight how variations in cell cycle activity selectively promote cardiac chamber growth during development, reveal profound chamber-specific cell cycle-linked transcriptional shifts, and open the way to deeper understanding of pathogenesis of congenital heart disease.

Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty

Background

Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty.

Results

The goal of this paper is to develop optimal classification of single-cell trajectories accounting for potential model uncertainty. Partially-observed Boolean dynamical systems (POBDS) are used for modeling gene regulatory networks observed through noisy gene-expression data. We derive the exact optimal Bayesian classifier (OBC) for binary classification of single-cell trajectories. The application of the OBC becomes impractical for large GRNs, due to computational and memory requirements. To address this, we introduce a particle-based single-cell classification method that is highly scalable for large GRNs with much lower complexity than the optimal solution.

Conclusion

The performance of the proposed particle-based method is demonstrated through numerical experiments using a POBDS model of the well-known T-cell large granular lymphocyte (T-LGL) leukemia network with noisy time-series gene-expression data.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5720-3) contains supplementary material, which is available to authorized users.

Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data

Background

Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods.

Results

In this article, we try to re-construct a neural network based on Gene Ontology (GO) for dimension reduction of scRNA-seq data. By integrating GO with both unsupervised and supervised models, two novel methods are proposed, named GOAE (Gene Ontology AutoEncoder) and GONN (Gene Ontology Neural Network) respectively.

Conclusions

The evaluation results show that the proposed models outperform some state-of-the-art dimensionality reduction approaches. Furthermore, incorporating with GO, we provide an opportunity to interpret the underlying biological mechanism behind the neural network-based model.