Density-based detection of cell transition states to construct disparate and bifurcating trajectories.

Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects.

Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.

Sequencing dropout-and-batch effect normalization for single-cell mRNA profiles: a survey and comparative analysis.

Single-cell mRNA sequencing has been adopted as a powerful technique for understanding gene expression profiles at the single-cell level. However, challenges remain due to factors such as the inefficiency of mRNA molecular capture, technical noises and separate sequencing of cells in different batches. Normalization methods have been developed to ensure a relatively accurate analysis. This work presents a survey on 10 tools specifically designed for single-cell mRNA sequencing data preprocessing steps, among which 6 tools are used for dropout normalization and 4 tools are for batch effect correction. In this survey, we outline the main methodology for each of these tools, and we also compare these tools to evaluate their normalization performance on datasets which are simulated under the constraints of dropout inefficiency, batch effect or their combined effects. We found that Saver and Baynorm performed better than other methods in dropout normalization, in most cases. Beer and Batchelor performed better in the batch effect normalization, and the Saver-Beer tool combination and the Baynorm-Beer combination performed better in the mixed dropout-and-batch effect normalization. Over-normalization is a common issue occurred to these dropout normalization tools that is worth of future investigation. For the batch normalization tools, the capability of retaining heterogeneity between different groups of cells after normalization can be another direction for future improvement.

Aberration-corrected ultrafine analysis of miRNA reads at single-base resolution: a k-mer lattice approach.

Raw sequencing reads of miRNAs contain machine-made substitution errors, or even insertions and deletions (indels). Although the error rate can be low at 0.1%, precise rectification of these errors is critically important because isoform variation analysis at single-base resolution such as novel isomiR discovery, editing events understanding, differential expression analysis, or tissue-specific isoform identification is very sensitive to base positions and copy counts of the reads. Existing error correction methods do not work for miRNA sequencing data attributed to miRNAs' length and per-read-coverage properties distinct from DNA or mRNA sequencing reads. We present a novel lattice structure combining kmers, (k - 1)mers and (k + 1)mers to address this problem. The method is particularly effective for the correction of indel errors. Extensive tests on datasets having known ground truth of errors demonstrate that the method is able to remove almost all of the errors, without introducing any new error, to improve the data quality from every-50-reads containing one error to every-1300-reads containing one error. Studies on experimental miRNA sequencing datasets show that the errors are often rectified at the 5' ends and the seed regions of the reads, and that there are remarkable changes after the correction in miRNA isoform abundance, volume of singleton reads, overall entropy, isomiR families, tissue-specific miRNAs, and rare-miRNA quantities.

Instance-based error correction for short reads of disease-associated genes.

BACKGROUND: Genomic reads from sequencing platforms contain random errors. Global correction algorithms have been developed, aiming to rectify all possible errors in the reads using generic genome-wide patterns. However, the non-uniform sequencing depths hinder the global approach to conduct effective error removal. As some genes may get under-corrected or over-corrected by the global approach, we conduct instance-based error correction for short reads of disease-associated genes or pathways. The paramount requirement is to ensure the relevant reads, instead of the whole genome, are error-free to provide significant benefits for single-nucleotide polymorphism (SNP) or variant calling studies on the specific genes. RESULTS: To rectify possible errors in the short reads of disease-associated genes, our novel idea is to exploit local sequence features and statistics directly related to these genes. Extensive experiments are conducted in comparison with state-of-the-art methods on both simulated and real datasets of lung cancer associated genes (including single-end and paired-end reads). The results demonstrated the superiority of our method with the best performance on precision, recall and gain rate, as well as on sequence assembly results (e.g., N50, the length of contig and contig quality). CONCLUSION: Instance-based strategy makes it possible to explore fine-grained patterns focusing on specific genes, providing high precision error correction and convincing gene sequence assembly. SNP case studies show that errors occurring at some traditional SNP areas can be accurately corrected, providing high precision and sensitivity for investigations on disease-causing point mutations.

Argonaute proteins: key players in RNA silencing.

During the past decade, small non-coding RNAs have rapidly emerged as important contributors to gene regulation. To carry out their biological functions, these small RNAs require a unique class of proteins called Argonautes. The discovery and our comprehension of this highly conserved protein family is closely linked to the study of RNA-based gene silencing mechanisms. With their functional domains, Argonaute proteins can bind small non-coding RNAs and control protein synthesis, affect messenger RNA stability and even participate in the production of a new class of small RNAs, Piwi-interacting RNAs.

Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information.

BACKGROUND: A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs. RESULTS: We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks. CONCLUSION: Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding.

Another "loophole" in miRNA processing.

In this issue of Molecular Cell, Suzuki et al. (2011) present the intriguing finding that an RNAse known to play an important role in immunity regulates miRNA processing in cancer and inflammation by cleaving the terminal loops of many miRNAs.

miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data.

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org.

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite.

BACKGROUND: MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics. METHODS AND RESULTS: We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs. CONCLUSIONS: With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.

tRNA-Derived RNA Fragments Associate with Human Multisynthetase Complex (MSC) and Modulate Ribosomal Protein Translation.

The functionality of small RNAs from abundant species of "housekeeping" noncoding RNAs (e.g., rRNA, tRNA, snRNA, snoRNA, etc.) remains a highly studied topic. The current state of research on short RNAs derived from transfer RNA (tRNA), called tRNA-derived fragments (tRFs), has been restricted largely to expression studies and limited functional studies. 5' tRFs are known translational inhibitors in mammalian cells, yet little is known about their functionality. Here we report on the first experimental evidence of the tRF protein interactome, identifying the mammalian multisynthetase complex as the primary interactor of the 5' tRF Gln19. We also present proteome-wide SILAC evidence that 5' tRFs increase ribosomal and poly(A)-binding protein translation.

The miRNA biogenesis factors, p72/DDX17 and KHSRP regulate the protein level of Ago2 in human cells.

MicroRNAs (miRNAs) are short (21-23nt long) RNAs that post-transcriptionally regulate gene expression in plants and animals. They are key regulators in all biological processes. In mammalian cells miRNAs are loaded into one of the four members of the Argonaute (Ago) protein family to form the RNA-induced silencing complex (RISC). RISCs inhibit the translation of mRNAs that share sequence complementarity with their loaded miRNAs. miRNA processing and miRNA-mediated gene regulation are highly regulated processes and involve many RNA-binding proteins as auxiliary factors. Here we show that the two RNA-binding proteins, p72 and KHSRP, both with known roles in promoting miRNA biogenesis, regulate the protein level of human Ago2 in transformed human cells. We determined that p72 and KHSRP influence Ago2 stability by regulating miRNA levels in the cell and that loss of p72/KHSRP results in a decrease of unloaded Ago2.

The human Piwi protein Hiwi2 associates with tRNA-derived piRNAs in somatic cells.

The Piwi-piRNA pathway is active in animal germ cells where its functions are required for germ cell maintenance and gamete differentiation. Piwi proteins and piRNAs have been detected outside germline tissue in multiple phyla, but activity of the pathway in mammalian somatic cells has been little explored. In particular, Piwi expression has been observed in cancer cells, but nothing is known about the piRNA partners or the function of the system in these cells. We have surveyed the expression of the three human Piwi genes, Hiwi, Hili and Hiwi2, in multiple normal tissues and cancer cell lines. We find that Hiwi2 is ubiquitously expressed; in cancer cells the protein is largely restricted to the cytoplasm and is associated with translating ribosomes. Immunoprecipitation of Hiwi2 from MDAMB231 cancer cells enriches for piRNAs that are predominantly derived from processed tRNAs and expressed genes, species which can also be found in adult human testis. Our studies indicate that a Piwi-piRNA pathway is present in human somatic cells, with an uncharacterised function linked to translation. Taking this evidence together with evidence from primitive organisms, we propose that this somatic function of the pathway predates the germline functions of the pathway in modern animals.

RNA Binding Proteins in the miRNA Pathway.

microRNAs (miRNAs) are short ~22 nucleotides (nt) ribonucleic acids which post-transcriptionally regulate gene expression. miRNAs are key regulators of all cellular processes, and the correct expression of miRNAs in an organism is crucial for proper development and cellular function. As a result, the miRNA biogenesis pathway is highly regulated. In this review, we outline the basic steps of miRNA biogenesis and miRNA mediated gene regulation focusing on the role of RNA binding proteins (RBPs). We also describe multiple mechanisms that regulate the canonical miRNA pathway, which depends on a wide range of RBPs. Moreover, we hypothesise that the interaction between miRNA regulation and RBPs is potentially more widespread based on the analysis of available high-throughput datasets.

Rule discovery and distance separation to detect reliable miRNA biomarkers for the diagnosis of lung squamous cell carcinoma.

BACKGROUND: Altered expression profiles of microRNAs (miRNAs) are linked to many diseases including lung cancer. miRNA expression profiling is reproducible and miRNAs are very stable. These characteristics of miRNAs make them ideal biomarker candidates. METHOD: This work is aimed to detect 2-and 3-miRNA groups, together with specific expression ranges of these miRNAs, to form simple linear discriminant rules for biomarker identification and biological interpretation. Our method is based on a novel committee of decision trees to derive 2-and 3-miRNA 100%-frequency rules. This method is applied to a data set of lung miRNA expression profiles of 61 squamous cell carcinoma (SCC) samples and 10 normal tissue samples. A distance separation technique is used to select the most reliable rules which are then evaluated on a large independent data set. RESULTS: We obtained four 2-miRNA and three 3-miRNA top-ranked rules. One important rule is that: If the expression level of miR-98 is above 7.356 and the expression level of miR-205 is below 9.601 (log2 quantile normalized MirVan miRNA Bioarray signals), then the sample is normal rather than cancerous with specificity and sensitivity both 100%. The classification performance of our best miRNA rules remarkably outperformed that by randomly selected miRNA rules. Our data analysis also showed that miR-98 and miR-205 have two common predicted target genes FZD3 and RPS6KA3, which are actually genes associated with carcinoma according to the Online Mendelian Inheritance in Man (OMIM) database. We also found that most of the chromosomal loci of these miRNAs have a high frequency of genomic alteration in lung cancer. On the independent data set (with balanced controls), the three miRNAs miR-126, miR-205 and miR-182 from our best rule can separate the two classes of samples at the accuracy of 84.49%, sensitivity of 91.40% and specificity of 77.14%. CONCLUSION: Our results indicate that rule discovery followed by distance separation is a powerful computational method to identify reliable miRNA biomarkers. The visualization of the rules and the clear separation between the normal and cancer samples by our rules will help biology experts for their analysis and biological interpretation.

MiRNAs from the Dlk1-Dio3 locus and miR-224/452 cluster contribute to glioblastoma tumor heterogeneity.

Glioblastoma is one of the most common and aggressive brain tumors and has seen few improvements in patient outcomes. Inter-tumor heterogeneity between tumors of different patients as well as intra-tumor heterogeneity of cells within the same tumor challenge the development of effective drugs. MiRNAs play an essential role throughout the developing brain and regulate many key genes involved in oncogenesis, yet their role in driving many of the processes underlying tumor heterogeneity remains unclear. In this study, we highlight miRNAs from the Dlk1-Dio3 and miR-224/452 clusters which may be expressed cell autonomously and have expression that is associated with cell state genes in glioblastoma, most prominently in neural progenitor-like and mesenchymal-like states respectively. These findings implicate these miRNA clusters as potential regulators of glioblastoma intra-tumoral heterogeneity and may serve as valuable biomarkers for cell state identification.

An evolutionarily conserved, alternatively spliced, intron in the p68/DDX5 DEAD-box RNA helicase gene encodes a novel miRNA.

The DEAD-box RNA helicase p68 (DDX5) plays important roles in several cellular processes, including transcription, pre-mRNA processing, and microRNA (miRNA) processing. p68 expression is growth and developmentally regulated, and alterations in p68 expression and/or function have been implicated in tumor development. The p68 gene encodes an evolutionarily conserved, alternatively spliced, intron the function of which has to date remained unclear. Although the intron-containing p68 RNA does not appear to yield an alternative p68 protein, it is differentially expressed in cell lines and tissues, indicating regulation of expression. Here we show that the p68 conserved intron encodes a novel putative miRNA, suggesting a previously unknown possible regulatory function for the p68 intron. We show that this miRNA (referred to as p68 miRNA) is processed from the intron via the canonical miRNA-processing pathway and that it associates with the Argonaute protein Ago2. Finally we show that the p68 miRNA suppresses an mRNA bearing complementary target sequences, suggesting that it is functional. These findings suggest a novel mechanism by which alterations in p68 expression may impact on the cell.

The ribosomal protein RACK1 is required for microRNA function in both C. elegans and humans.

Despite the importance of microRNAs (miRNAs) in gene regulation, it is unclear how the miRNA-Argonaute complex--or miRNA-induced silencing complex (miRISC)--can regulate the translation of their targets in such diverse ways. We demonstrate here a direct interaction between the miRISC and the ribosome by showing that a constituent of the eukaryotic 40S subunit, receptor for activated C-kinase (RACK1), is important for miRNA-mediated gene regulation in animals. In vivo studies demonstrate that RACK1 interacts with components of the miRISC in nematodes and mammals. In both systems, the alteration of RACK1 expression alters miRNA function and impairs the association of the miRNA complex with the translating ribosomes. Our data indicate that RACK1 can contribute to the recruitment of miRISC to the site of translation, and support a post-initiation mode of miRNA-mediated gene repression.

Small RNAs derived from the 5' end of tRNA can inhibit protein translation in human cells.

Recently, it has been shown that tRNA molecules can be processed into small RNAs that are derived from both the 5' and 3' termini. To date, the function of these tRNA fragments (tRFs) derived from the 5' end of tRNA has not been investigated in depth. We present evidence that conserved residues in tRNA, present in all 5' tRFs, can inhibit the process of protein translation without the need for complementary target sites in the mRNA. These results implicate 5' tRFs in a new mechanism of gene regulation by small RNAs in human cells.

A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation.

MicroRNAs (miRNAs) are non-coding small RNAs which play a critical role in the regulation of gene expression in cells. It is known that miRNAs are often expressed as multiple isoforms, called isomiRs, which may have alternative regulatory functions. Despite the recent development of several single cell small RNA sequencing protocols, these methods have not been leveraged to investigate isomiR expression and regulation to better understand their role on a single cell level. Here we integrate sequencing data from three independent studies and find substantial differences in isomiR composition that suggest that cell autonomous mechanisms may drive isomiR processing. We also find evidence of altered regulatory functions of different classes of isomiRs, when compared to their respective wild-type miRNA, which supports a biological role for many of the isomiRs that are expressed.