RNA is vastly underestimated by the constraints of this ideology, and the growing field of RNA-seq seeks to elucidate the many roles of RNA in the modulation of cellular processes, not only as the intermediate molecule but its other functions in the cell as well. RNA-seq provides researchers a window into the RNA environment of a cell during different physiological or pathological states or during different stages of development to determine cellular responses to these changes.
RNA-seq allows for high throughput NGS, providing both qualitative and quantitative information about the different RNA species present in a given sample. There are many different types of RNA-seq. Single-cell RNA-seq provides an expression profile on the single cell level to avoid potential biases from sequencing mixed groups of cells.
Transcriptomics looks at the mRNA species within a sample. There are still more less common technologies available for RNA-seq. The large variety of techniques can be attributed to the fact that RNA-seq technology can be adapted to answer many different types of research questions. Prior to RNA-seq, the best technology for detection of gene expression was microarrays. Microarrays consist of thousands of defined spots on a slide that contained known sequences which would fluoresce when the samples were bound to the known constructs.
RNA-seq is a more versatile and robust technology. It is not limited to known genomic sequences. RNA-seq does not rely on specific probes, so non-model or novel organisms can be sequenced without having a reference genome. The lack of probes and primers also reduces the bias of an RNA-seq run as compared to the probe reliant microarrays.
RNA-seq is also becoming increasingly inexpensive as the technology continues to develop. In addition, RNA-seq has less background signal compared to microarrays because reads can be mapped to regions of the genome.
RNA-seq can be used to determine RNA expression levels more accurately than microarrays, which rely on relative quantities rather than absolute quantities which are possible with RNA-seq. Absolute quantification allows for comparisons between experiments for RNA-seq, whereas the relative quantification of microarrays makes this impossible.
As previously mentioned, there is a wide variety of RNA-seq techniques to answer different questions. Some common applications for RNA-seq include differential gene expression analysis, novel gene identification, and splice variant analysis. To accommodate the variety of applications, RNA-seq workflows can differ significantly, but there are three main steps to all RNA-seq: library preparation, sequencing, and analysis. Now that we have cDNA, we can undergo an optional process of selection.
Selection will consist of either enrichment of target molecules or depletion of overly abundant molecules. This step is important for downstream efficiency. Overly abundant transcripts, such as rRNA and globin, will take up the vast majority of reads during a sequencing run, which is a waste of money, reagents, and read depth.
For an optimized RNA sequencing run, it is in your best interest to remove these overly abundant transcripts. There are three main methods of obtaining high numbers of sequencing reads from targets of interest and removing transcripts of low importance: target enrichment, probe-based depletion, and enzymatic depletion. One method to increase the number of sequencing reads for transcripts of interest is to enrich the samples.
A popular method of target enrichment is mRNA selection, often done through poly d T magnetic beads. Chains of thymine T molecules are covalently bound to magnetic beads.
See figure below for step by step pictorial. Step 1 the beads with their oligos are added to a total RNA sample. Step 2 the mRNA transiently binds to the oligo d T chains attached to the beads. Step 3 the beads are reserved by collecting them against the side of the tube with a magnet while the rest of the sample is washed away.
This process can bias the pool of transcripts in the sample. RNA sequencing: advances, challenges and opportunities. Genet ,; 12 2 , 87— RNA-Seq based genetic variant discovery provides new insights into controlling fat deposition in the tail of sheep. Sci Rep 10, Advanced applications of RNA sequencing and challenges. Insights , ;9 Suppl 1 , 29— Schuster SC. Methods , ;5 1 , 16— Genome sequencing: Defining your experiment.
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Comparison of RNA-seq and microarray gene expression platforms for the toxicogenomic evaluation of liver from short-term rat toxicity studies. RNA sequencing and analysis. Cold Spring Harb Protoc. The Cresko Lab of the University of Oregon. Other considerations are whether single cells have actually been isolated or whether indeed two or more cells have been mistakenly assessed in a particular sample.
This can sometimes be assessed at the time of single-cell isolation, but, depending on the chosen technique, this might not always be possible. Once the scRNA-seq data are filtered for poor samples, they can be interpreted by an ever-increasing range of bio-informatic and computational methods, which have been reviewed extensively elsewhere [ 74 , 82 ]. The crux of the issue is how to examine tens of thousands of genes possibly being expressed in one cell, and provide a meaningful comparison to another cell expressing the same large number of genes, but in a very different manner.
Principal component analysis PCA is a mathematical algorithm that reduces the dimensionality of data, and is a basic and very useful tool for examining heterogeneity in scRNA-seq data. This has been augmented by a number of methods involving different machine-learning algorithms, including for example t-distributed stochastic neighbour embedding t-SNE and Gaussian process latent variable modelling GPLVM , which have been reviewed in detail elsewhere [ 74 , 82 , 83 ].
Dimensionality reduction and visualization are, in many cases, followed by clustering of cells into subpopulations that represent biologically meaningful trends in the data, such as functional similarity or developmental relationship.
Owing to the high dimensionality of scRNA-seq data, clustering often requires special consideration [ 84 ], and a number of bespoke methods have been developed [ 45 , 86 , 87 ,, 85 — 88 ]. Likewise, a variety of methods exist for identifying differentially expressed genes across cell populations [ 89 ]. An increasing number of algorithms and computational approaches are being published to help researchers define the molecular relationships between single cells characterized by scRNA-seq and thus extend the insights gained by simple clustering.
These trajectory-inference methods are conceptually based on identification of intermediate cell states, and the most recent tools are able to trace both linear differentiation processes as well as multipronged fate decisions [ 22 , 91 , 92 , 93 , 94 ,, 24 , 90 — 95 ].
While these approaches currently require at least elementary programming skills, the source codes for these methods are usually freely available for bio-informaticians to download and use. This reinforces the need to cultivate a good working relationship with bio-informaticians if scRNA-seq data are to be analysed effectively. Over the past 6 or so years, there has been an explosion of interest in using scRNA-seq to provide answers to biologically and medically related questions, both in experimental animals and in humans.
Many of the studies from this period either pioneered new wet-lab scRNA-seq protocols and methodologies or reported novel bio-informatic and computational approaches for quality-controlling and interpreting these unique datasets. Some studies also provided tantalizing glimpses of new biological phenomena that could not have been easily observed without scRNA-seq.
Here, we consider what the next 5 years might hold for scRNA-seq from the perspective of clinical and experimental researchers looking to use this technology for the first time. Given that the field of single-cell genomics is experiencing rapid growth, aside from being confident that numerous advances will be made, exactly what these will be remains difficult to predict. Nevertheless, we point towards various areas in which we hope and expect numerous advances to be made.
First, most scRNA-seq studies have tended to examine freshly isolated cells. We expect many more studies will explore cryopreserved and fixed tissue samples using scRNA-seq, which will further open up this technology to clinical studies. As isolation of single cells is of paramount importance to this approach, we expect more advances in wet-lab procedures that rapidly dissociate tissue into individual cells without perturbing their transcriptomes.
In addition, while many scRNA-seq studies have employed expensive hardware, including microfluidic and droplet-based platforms, future studies will reduce costs by further reducing reaction volumes, and perhaps also by avoiding the need for bespoke pieces of equipment [ 38 ].
Given ongoing trends for decreasing sequencing costs, we anticipate that these cost benefits will also make scRNA-seq more affordable on a per-cell basis. This will likely drive another trend—the ever-increasing number of cells examined in a given study. While early studies examined a few hundred cells, with reduced costs and the widespread adoption of newer droplet-based technologies, we anticipate that analysis of millions to billions of cells will become commonplace within the next 5 years [ 96 ].
The Human Cell Atlas project [ 51 ], with the ultimate goal of profiling all human cell states and types, is evidence of this trend. With the accumulation of such enormous datasets, the issue arises regarding how to use them to their full potential. Many researchers would without doubt benefit from centralized repositories where data could be easily accessed at the cellular level instead of just sequence level [ 97 ]. We expect that mRNA capture rates will continue to improve over the next 5 years, to an extent where perhaps almost all mRNA molecules will be captured and detected.
This will permit more-sensitive analysis of gene expression in individual cells and might also serve to reduce the number of cells required in any given study. Given the unique analytical challenges posed by scRNA-seq datasets, we expect great advances in bioinformatic and computational approaches in the coming years. In particular, user-friendly, web-browser-like interfaces will emerge as gold-standard packages for dealing with scRNA-seq data.
These will contain all the necessary functionality to allow researchers first to QC their data and then to extract biological information relating to heterogeneity, the existence of rare populations, lineage tracing, gene—gene co-regulation and other parameters. Recent studies are providing exciting possibilities for combining scRNA-seq with other modalities.
We expect that many new combination approaches will emerge using proteomics, epigenomics and analysis of non-coding RNA species alongside scRNA-seq reviewed in [ ]. We speculate that the next decade will take us closer to a truly holistic examination of single cells, which takes into account not only mRNA, but also the genome, epigenome, proteome and metabolome. Finally, we believe that several clinical applications will emerge for scRNA-seq in the next 5 or so years.
For example, resected tumours might be routinely assessed for the presence of rare malignant and chemo-resistant cancer cells. This information will provide crucial diagnostic information and will guide decisions regarding treatment. Next, as an extension to a full blood count, scRNA-seq assessments will provide in-depth information on the response of immune cells, which again will inform diagnoses and the choice of therapy.
Finally, the relatively small numbers of cells present in a range of other tissue biopsies, for example from the skin and gut mucosal surfaces, will be ideal for providing molecular data that informs on diagnosis, disease progression and appropriate treatments. Thus, scRNA-seq will progress out of specialist research laboratories and will become an established tool for both basic scientists and clinicians alike.
This decade has marked tremendous maturation of the field of single-cell transcriptomics. This has spurred the launch of numerous easily accessible commercial solutions, increasingly being accompanied by dedicated bioinformatics data-analysis suites. With the recent advances in microfluidics and cellular barcoding, the throughput of scRNA-seq experiments has also increased substantially. At the same time, protocols compatible with fixation and freezing have started to emerge.
These developments have made scRNA-seq much better suited for biomedical research and for clinical applications. For example, the ability to study thousands of cells in a single run has greatly facilitated prospective studies of highly heterogeneous clinical samples. This can be expected to have a profound impact on both translational applications as well as our understanding of basic tissue architecture and physiology.
With these increasing opportunities for single-cell transcriptome characterization, we have witnessed remarkable diversification of experimental protocols, each coming with characteristic strengths and weaknesses.
Researchers therefore face decisions such as whether to prioritize cell throughput or sequencing depth, whether full-length transcript information is required, and whether protein-level or epigenomic measurements are to be performed from the same cells.
Having clearly defined biological objectives and a rational experimental design are often vital for making an informed decision about the optimal approach. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry.
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Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis.
Cell Rep. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.
Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. T cell fate and clonality inference from single-cell transcriptomes. Defining the three cell lineages of the human blastocyst by single-cell RNA-seq.
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Sci Immunol. Deterministic and stochastic allele specific gene expression in single mouse blastomeres. PLoS One. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq.
Nat Genet. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat Commun. Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression. Liu S, Trapnell C. RNA-seq provides digital data in the form of aligned read-counts, resulting in a very wide dynamic range, improving the sensitivity of detection for rare transcripts. It is also very cost-competitive to microarrays, as today, between samples can be multiplexed in a single Illumina sequencing lane.
Lastly, you can reanalyze an RNA-seq dataset as more information about the transcriptome becomes available. There are many methods for performing an RNA-seq experiment. In fact, the techniques are evolving so rapidly it can be difficult to decide which one to use. Most people use the first method and then need to make a further choice between a strand-specific protocol and one that is not. Once you have a sequencing library, it is sequenced to a specified depth, which is dependent on what you want to do with the data.
These reads are aligned to the genome or transcriptome and are counted to determine differential gene expression or further analyzed to determine splicing and isoform expression. Most people are sequencing RNA using paired-end bp methods.
The exception is microRNA sequencing, as this only requires single-end 36bp sequencing in most cases. The resulting double-strand cDNA is used as the input to a standard Illumina library prep which includes end-repair, adapter ligation and PCR amplification to give you a library ready for sequencing. There has been a lot of discussion about anti-sense transcription and its biological relevance.
If you are interested in simple differential gene expression, then strand information will not add much to your experiment, but will make your protocol more complex.
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