Small genetic variation affecting mRNA isoforms associated with marbling and meat color in beef cattle.

The aim of this study was to identify mRNA isoforms and small genetic variants that may be affecting marbling and beef color in Nellore cattle. Longissimus thoracis muscle samples from 20 bulls with different phenotypes (out of 80 bulls set) for marbling (moderate (n = 10) and low (n = 10) groups) and beef color (desirable (n = 10) and undesirable (n = 9) group) traits were used to perform transcriptomic analysis using RNA sequencing. Fourteen and 15 mRNA isoforms were detected as differentially expressed (DE) (P-value ≤ 0.001) between divergent groups for marbling and meat color traits, respectively. Some of those DE mRNA isoforms have shown sites of splicing modified by small structural variants as single nucleotide variant (SNV), insertion, and/or deletion. Enrichment analysis identified metabolic pathways, such as O2/CO2 exchange in erythrocytes, tyrosine biosynthesis, and phenylalanine degradation. The results obtained suggest potential key regulatory genes associated with these economically important traits for the beef industry and for the consumer.

Integrated omics unveil the secondary metabolic landscape of a basal dinoflagellate.

BACKGROUND: Some dinoflagellates cause harmful algal blooms, releasing toxic secondary metabolites, to the detriment of marine ecosystems and human health. Our understanding of dinoflagellate toxin biosynthesis has been hampered by their unusually large genomes. To overcome this challenge, for the first time, we sequenced the genome, microRNAs, and mRNA isoforms of a basal dinoflagellate, Amphidinium gibbosum, and employed an integrated omics approach to understand its secondary metabolite biosynthesis. RESULTS: We assembled the ~ 6.4-Gb A. gibbosum genome, and by probing decoded dinoflagellate genomes and transcriptomes, we identified the non-ribosomal peptide synthetase adenylation domain as essential for generation of specialized metabolites. Upon starving the cells of phosphate and nitrogen, we observed pronounced shifts in metabolite biosynthesis, suggestive of post-transcriptional regulation by microRNAs. Using Iso-Seq and RNA-seq data, we found that alternative splicing and polycistronic expression generate different transcripts for secondary metabolism. CONCLUSIONS: Our genomic findings suggest intricate integration of various metabolic enzymes that function iteratively to synthesize metabolites, providing mechanistic insights into how dinoflagellates synthesize secondary metabolites, depending upon nutrient availability. This study provides insights into toxin production associated with dinoflagellate blooms. The genome of this basal dinoflagellate provides important clues about dinoflagellate evolution and overcomes the large genome size, which has been a challenge previously.

linc-HOXA1 is a noncoding RNA that represses Hoxa1 transcription in cis.

Recently, researchers have uncovered the presence of many long noncoding RNAs (lncRNAs) in embryonic stem cells and believe they are important regulators of the differentiation process. However, there are only a few examples explicitly linking lncRNA activity to transcriptional regulation. Here, we used transcript counting and spatial localization to characterize a lncRNA (dubbed linc-HOXA1) located ∼50 kb from the Hoxa gene cluster in mouse embryonic stem cells. Single-cell transcript counting revealed that linc-HOXA1 and Hoxa1 RNA are highly variable at the single-cell level and that whenever linc-HOXA1 RNA abundance was high, Hoxa1 mRNA abundance was low and vice versa. Knockdown analysis revealed that depletion of linc-HOXA1 RNA at its site of transcription increased transcription of the Hoxa1 gene cis to the chromosome and that exposure of cells to retinoic acid can disrupt this interaction. We further showed that linc-HOXA1 RNA represses Hoxa1 by recruiting the protein PURB as a transcriptional cofactor. Our results highlight the power of transcript visualization to characterize lncRNA function and also suggest that PURB can facilitate lncRNA-mediated transcriptional regulation.

Identification and visualization of differential isoform expression in RNA-seq time series.

Motivation: As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Results: Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. Availability and implementation: The package is freely available under the LGPL license from the Bioconductor web site. Contact: mj.nueda@ua.es or aconesa@ufl.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Peripheral SMN restoration is essential for long-term rescue of a severe spinal muscular atrophy mouse model.

Spinal muscular atrophy (SMA) is a motor neuron disease and the leading genetic cause of infant mortality; it results from loss-of-function mutations in the survival motor neuron 1 (SMN1) gene. Humans have a paralogue, SMN2, whose exon 7 is predominantly skipped, but the limited amount of functional, full-length SMN protein expressed from SMN2 cannot fully compensate for a lack of SMN1. SMN is important for the biogenesis of spliceosomal small nuclear ribonucleoprotein particles, but downstream splicing targets involved in pathogenesis remain elusive. There is no effective SMA treatment, but SMN restoration in spinal cord motor neurons is thought to be necessary and sufficient. Non-central nervous system (CNS) pathologies, including cardiovascular defects, were recently reported in severe SMA mouse models and patients, reflecting autonomic dysfunction or direct effects in cardiac tissues. Here we compared systemic versus CNS restoration of SMN in a severe mouse model. We used an antisense oligonucleotide (ASO), ASO-10-27, that effectively corrects SMN2 splicing and restores SMN expression in motor neurons after intracerebroventricular injection. Systemic administration of ASO-10-27 to neonates robustly rescued severe SMA mice, much more effectively than intracerebroventricular administration; subcutaneous injections extended the median lifespan by 25 fold. Furthermore, neonatal SMA mice had decreased hepatic Igfals expression, leading to a pronounced reduction in circulating insulin-like growth factor 1 (IGF1), and ASO-10-27 treatment restored IGF1 to normal levels. These results suggest that the liver is important in SMA pathogenesis, underscoring the importance of SMN in peripheral tissues, and demonstrate the efficacy of a promising drug candidate.

Multiple Isoforms of ANRIL in Melanoma Cells: Structural Complexity Suggests Variations in Processing.

The long non-coding RNA ANRIL, antisense to the CDKN2B locus, is transcribed from a gene that encompasses multiple disease-associated polymorphisms. Despite the identification of multiple isoforms of ANRIL, expression of certain transcripts has been found to be tissue-specific and the characterisation of ANRIL transcripts remains incomplete. Several functions have been associated with ANRIL. In our judgement, studies on ANRIL functionality are premature pending a more complete appreciation of the profusion of isoforms. We found differential expression of ANRIL exons, which indicates that multiple isoforms exist in melanoma cells. In addition to linear isoforms, we identified circular forms of ANRIL (circANRIL). Further characterisation of circANRIL in two patient-derived metastatic melanoma cell lines (NZM7 and NZM37) revealed the existence of a rich assortment of circular isoforms. Moreover, in the two melanoma cell lines investigated, the complements of circANRIL isoforms were almost completely different. Novel exons were also discovered. We also found the family of linear ANRIL was enriched in the nucleus, whilst the circular isoforms were enriched in the cytoplasm and they differed markedly in stability. With respect to the variable processing of circANRIL species, bioinformatic analysis indicated that intronic Arthrobacter luteus (Alu) restriction endonuclease inverted repeats and exon skipping were not involved in selection of back-spliced exon junctions. Based on our findings, we hypothesise that "ANRIL" has wholly distinct dual sets of functions in melanoma. This reveals the dynamic nature of the locus and constitutes a basis for investigating the functions of ANRIL in melanoma.

Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data.

MOTIVATION: Because of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy. RESULTS: We conclude that most methods are inaccurate even using idealized data, and that no method is highly accurate once multiple splice forms, polymorphisms, intron signal, sequencing errors, alignment errors, annotation errors and other complicating factors are present. These results point to the pressing need for further algorithm development. AVAILABILITY AND IMPLEMENTATION: Simulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution.

Correctly estimating isoform-specific gene expression is important for understanding complicated biological mechanisms and for mapping disease susceptibility genes. However, estimating isoform-specific gene expression is challenging because various biases present in RNA-Seq (RNA sequencing) data complicate the analysis, and if not appropriately corrected, can affect isoform expression estimation and downstream analysis. In this article, we present PennSeq, a statistical method that allows each isoform to have its own non-uniform read distribution. Instead of making parametric assumptions, we give adequate weight to the underlying data by the use of a non-parametric approach. Our rationale is that regardless what factors lead to non-uniformity, whether it is due to hexamer priming bias, local sequence bias, positional bias, RNA degradation, mapping bias or other unknown reasons, the probability that a fragment is sampled from a particular region will be reflected in the aligned data. This empirical approach thus maximally reflects the true underlying non-uniform read distribution. We evaluate the performance of PennSeq using both simulated data with known ground truth, and using two real Illumina RNA-Seq data sets including one with quantitative real time polymerase chain reaction measurements. Our results indicate superior performance of PennSeq over existing methods, particularly for isoforms demonstrating severe non-uniformity. PennSeq is freely available for download at http://sourceforge.net/projects/pennseq.

Interplay between pre-mRNA splicing and microRNA biogenesis within the supraspliceosome.

MicroRNAs (miRNAs) are central regulators of gene expression, and a large fraction of them are encoded in introns of RNA polymerase II transcripts. Thus, the biogenesis of intronic miRNAs by the microprocessor and the splicing of their host introns by the spliceosome require coordination between these processing events. This cross-talk is addressed here. We show that key microprocessor proteins Drosha and DGCR8 as well as pre-miRNAs cosediment with supraspliceosomes, where nuclear posttranscriptional processing is executed. We further show that inhibition of splicing increases miRNAs expression, whereas knock-down of Drosha increases splicing. We identified a novel splicing event in intron 13 of MCM7, where the miR-106b-25 cluster is located. The unique splice isoform includes a hosted pre-miRNA in the extended exon and excludes its processing. This indicates a possible mechanism of altering the levels of different miRNAs originating from the same transcript. Altogether, our study indicates interplay between the splicing and microprocessor machineries within a supraspliceosome context.

A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples.

BACKGROUND: Detecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. RESULTS: We propose a new method for solving the isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. CONCLUSION: Our convex formulation to jointly detect and quantify isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop.

Quantification of stochastic noise of splicing and polyadenylation in Entamoeba histolytica.

Alternative splicing and polyadenylation were observed pervasively in eukaryotic messenger RNAs. These alternative isoforms could either be consequences of physiological regulation or stochastic noise of RNA processing. To quantify the extent of stochastic noise in splicing and polyadenylation, we analyzed the alternative usage of splicing and polyadenylation sites in Entamoeba histolytica using RNA-Seq. First, we identified a large number of rarely spliced alternative junctions and then showed that the occurrence of these alternative splicing events is correlated with splicing site sequence, occurrence of constitutive splicing events and messenger RNA abundance. Our results implied the majority of these alternative splicing events are likely to be stochastic error of splicing machineries, and we estimated the corresponding error rates. Second, we observed extensive microheterogeneity of polyadenylation cleavage sites, and the extent of such microheterogeneity is correlated with the occurrence of constitutive cleavage events, suggesting most of such microheterogeneity is likely to be stochastic. Overall, we only observed a small fraction of alternative splicing and polyadenylation isoforms that are unlikely to be solely stochastic, implying the functional relevance of alternative splicing and polyadenylation in E. histolytica is limited. Lastly, we revised the gene models and annotated their 3'UTR in AmoebaDB, providing valuable resources to the community.

Computational approaches for isoform detection and estimation: good and bad news.

BACKGROUND: The main goal of the whole transcriptome analysis is to correctly identify all expressed transcripts within a specific cell/tissue--at a particular stage and condition--to determine their structures and to measure their abundances. RNA-seq data promise to allow identification and quantification of transcriptome at unprecedented level of resolution, accuracy and low cost. Several computational methods have been proposed to achieve such purposes. However, it is still not clear which promises are already met and which challenges are still open and require further methodological developments. RESULTS: We carried out a simulation study to assess the performance of 5 widely used tools, such as: CEM, Cufflinks, iReckon, RSEM, and SLIDE. All of them have been used with default parameters. In particular, we considered the effect of the following three different scenarios: the availability of complete annotation, incomplete annotation, and no annotation at all. Moreover, comparisons were carried out using the methods in three different modes of action. In the first mode, the methods were forced to only deal with those isoforms that are present in the annotation; in the second mode, they were allowed to detect novel isoforms using the annotation as guide; in the third mode, they were operating in fully data driven way (although with the support of the alignment on the reference genome). In the latter modality, precision and recall are quite poor. On the contrary, results are better with the support of the annotation, even though it is not complete. Finally, abundance estimation error often shows a very skewed distribution. The performance strongly depends on the true real abundance of the isoforms. Lowly (and sometimes also moderately) expressed isoforms are poorly detected and estimated. In particular, lowly expressed isoforms are identified mainly if they are provided in the original annotation as potential isoforms. CONCLUSIONS: Both detection and quantification of all isoforms from RNA-seq data are still hard problems and they are affected by many factors. Overall, the performance significantly changes since it depends on the modes of action and on the type of available annotation. Results obtained using complete or partial annotation are able to detect most of the expressed isoforms, even though the number of false positives is often high. Fully data driven approaches require more attention, at least for complex eucaryotic genomes. Improvements are desirable especially for isoform quantification and for isoform detection with low abundance.

Experimental assessment of splicing variants using expression minigenes and comparison with in silico predictions.

Assessment of the functional consequences of variants near splice sites is a major challenge in the diagnostic laboratory. To address this issue, we created expression minigenes (EMGs) to determine the RNA and protein products generated by splice site variants (n = 10) implicated in cystic fibrosis (CF). Experimental results were compared with the splicing predictions of eight in silico tools. EMGs containing the full-length Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) coding sequence and flanking intron sequences generated wild-type transcript and fully processed protein in Human Embryonic Kidney (HEK293) and CF bronchial epithelial (CFBE41o-) cells. Quantification of variant induced aberrant mRNA isoforms was concordant using fragment analysis and pyrosequencing. The splicing patterns of c.1585-1G>A and c.2657+5G>A were comparable to those reported in primary cells from individuals bearing these variants. Bioinformatics predictions were consistent with experimental results for 9/10 variants (MES), 8/10 variants (NNSplice), and 7/10 variants (SSAT and Sroogle). Programs that estimate the consequences of mis-splicing predicted 11/16 (HSF and ASSEDA) and 10/16 (Fsplice and SplicePort) experimentally observed mRNA isoforms. EMGs provide a robust experimental approach for clinical interpretation of splice site variants and refinement of in silico tools.

IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data.

BACKGROUND: Most genes in mammals generate several transcript isoforms that differ in stability and translational efficiency through alternative splicing. Such alternative splicing can be tissue- and developmental stage-specific, and such specificity is sometimes associated with disease. Thus, detecting differential isoform usage for a gene between tissues or cell lines/types (differences in the fraction of total expression of a gene represented by the expression of each of its isoforms) is potentially important for cell and developmental biology. RESULTS: We present a new method IUTA that is designed to test each gene in the genome for differential isoform usage between two groups of samples. IUTA also estimates isoform usage for each gene in each sample as well as averaged across samples within each group. IUTA is the first method to formulate the testing problem as testing for equal means of two probability distributions under the Aitchison geometry, which is widely recognized as the most appropriate geometry for compositional data (vectors that contain the relative amount of each component comprising the whole). Evaluation using simulated data showed that IUTA was able to provide test results for many more genes than was Cuffdiff2 (version 2.2.0, released in Mar. 2014), and IUTA performed better than Cuffdiff2 for the limited number of genes that Cuffdiff2 did analyze. When applied to actual mouse RNA-Seq datasets from six tissues, IUTA identified 2,073 significant genes with clear patterns of differential isoform usage between a pair of tissues. IUTA is implemented as an R package and is available at http://www.niehs.nih.gov/research/resources/software/biostatistics/iuta/index.cfm. CONCLUSIONS: Both simulation and real-data results suggest that IUTA accurately detects differential isoform usage. We believe that our analysis of RNA-seq data from six mouse tissues represents the first comprehensive characterization of isoform usage in these tissues. IUTA will be a valuable resource for those who study the roles of alternative transcripts in cell development and disease.

Updating RNA-Seq analyses after re-annotation.

UNLABELLED: The estimation of isoform abundances from RNA-Seq data requires a time-intensive step of mapping reads to either an assembled or previously annotated transcriptome, followed by an optimization procedure for deconvolution of multi-mapping reads. These procedures are essential for downstream analysis such as differential expression. In cases where it is desirable to adjust the underlying annotation, for example, on the discovery of novel isoforms or errors in existing annotations, current pipelines must be rerun from scratch. This makes it difficult to update abundance estimates after re-annotation, or to explore the effect of changes in the transcriptome on analyses. We present a novel efficient algorithm for updating abundance estimates from RNA-Seq experiments on re-annotation that does not require re-analysis of the entire dataset. Our approach is based on a fast partitioning algorithm for identifying transcripts whose abundances may depend on the added or deleted isoforms, and on a fast follow-up approach to re-estimating abundances for all transcripts. We demonstrate the effectiveness of our methods by showing how to synchronize RNA-Seq abundance estimates with the daily RefSeq incremental updates. Thus, we provide a practical approach to maintaining relevant databases of RNA-Seq derived abundance estimates even as annotations are being constantly revised. AVAILABILITY AND IMPLEMENTATION: Our methods are implemented in software called ReXpress and are freely available, together with source code, at http://bio.math.berkeley.edu/ReXpress/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference.

MOTIVATION: Many human genes express multiple transcript isoforms through alternative splicing, which greatly increases diversity of protein function. Although RNA sequencing (RNA-Seq) technologies have been widely used in measuring amounts of transcribed mRNA, accurate estimation of transcript isoform abundances from RNA-Seq data is challenging because reads often map to more than one transcript isoforms or paralogs whose sequences are similar to each other. RESULTS: We propose a statistical method to estimate transcript isoform abundances from RNA-Seq data. Our method can handle gapped alignments of reads against reference sequences so that it allows insertion or deletion errors within reads. The proposed method optimizes the number of transcript isoforms by variational Bayesian inference through an iterative procedure, and its convergence is guaranteed under a stopping criterion. On simulated datasets, our method outperformed the comparable quantification methods in inferring transcript isoform abundances, and at the same time its rate of convergence was faster than that of the expectation maximization algorithm. We also applied our method to RNA-Seq data of human cell line samples, and showed that our prediction result was more consistent among technical replicates than those of other methods. AVAILABILITY: An implementation of our method is available at http://github.com/nariai/tigar CONTACT: nariai@megabank.tohoku.ac.jp SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Novel transcripts from a distinct promoter that encode the full-length AKT1 in human breast cancer cells.

BACKGROUND: The serine-threonine kinase AKT1 plays essential roles during normal mammary gland development as well as the initiation and progression of breast cancer. AKT1 is generally considered a ubiquitously expressed gene, and its persistent activation is transcriptionally controlled by regulatory elements characteristic of housekeeping gene promoters. We recently identified a novel Akt1 transcript in mice (Akt1m), which is induced by growth factors and their signal transducers of transcription from a previously unknown promoter. The purpose of this study was to examine whether normal and neoplastic human breast epithelial cells express an orthologous AKT1m transcript and whether its expression is deregulated in cancer cells. METHODS: Initial sequence analyses were performed using the UCSC Genome Browser and GenBank to assess the potential occurrence of an AKT1m transcript variant in human cells and to identify conserved promoter sequences that are orthologous to the murine Akt1m. Quantitative RT-PCR was used to determine the transcriptional activation of AKT1m in mouse mammary tumors as well as 41 normal and neoplastic human breast epithelial cell lines and selected primary breast cancers. RESULTS: We identified four new AKT1 transcript variants in human breast cancer cells that are orthologous to the murine Akt1m and that encode the full-length kinase. These transcripts originate from an alternative promoter that is conserved between humans and mice. Akt1m is upregulated in the majority of luminal-type and basal-type mammary cancers in four different genetically engineered mouse models. Similarly, a subset of human breast cancer cell lines and primary breast cancers exhibited a higher expression of orthologous AKT1m transcripts. CONCLUSIONS: The existence of an alternative promoter that drives the expression of the unique AKT1m transcript may provide a mechanism by which the levels of AKT1 can be temporally and spatially regulated at particular physiological states, such as cancer, where a heightened activity of this kinase is required.

SSP: an interval integer linear programming for de novo transcriptome assembly and isoform discovery of RNA-seq reads.

Recent advances in the sequencing technologies have provided a handful of RNA-seq datasets for transcriptome analysis. However, reconstruction of full-length isoforms and estimation of the expression level of transcripts with a low cost are challenging tasks. We propose a novel de novo method named SSP that incorporates interval integer linear programming to resolve alternatively spliced isoforms and reconstruct the whole transcriptome from short reads. Experimental results show that SSP is fast and precise in determining different alternatively spliced isoforms along with the estimation of reconstructed transcript abundances. The SSP software package is available at http://www.bioinf.cs.ipm.ir/software/ssp.

In Silico Analysis of Micro-RNA Sequencing Data.

High-throughput sequencing for micro-RNAs (miRNAs) to obtain expression estimates is a central method of molecular biology. Surprisingly, there are a number of different approaches to converting sequencing output into micro-RNA counts. Each has their own strengths and biases that impact on the final data that can be obtained from a sequencing run. This chapter serves to make the reader aware of the trade-offs one must consider in analyzing small RNA sequencing data. It then compares two methods, miRge2.0 and the sRNAbench and the steps utilized to output data from their tools.

Obstacles to detecting isoforms using full-length scRNA-seq data.

BACKGROUND: Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses. RESULTS: In this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results. CONCLUSIONS: To accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq.