Introduction why analyze single cells

Accurate characterization of samples with high cellular heterogeneity may only be achieved by analyzing single cells. In this chapter, we discuss the rationale for performing analyses on individual cells in more depth, cover the fields of study in which single-cell behavior is yielding new insights into biological and clinical questions, and speculate on how single-cell analysis will be critical in the future.

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Reprints and Permissions. Jun, S. Commun Biol 3, Download citation. Received : 05 February Accepted : 12 March Published : 02 April Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.

Nature Cell Biology Nature Reviews Drug Discovery By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Advanced search. Skip to main content Thank you for visiting nature. Download PDF. Subjects Genomics Molecular engineering. Results Overview of the system To facilitate the generation and tracking of point mutations, lentivirus- and piggyBac -based delivery systems are needed for integration of single-copy sgRNAs in each cell and stable expression of the CRISPR RNA-guided deaminase, respectively.

Full size image. Discussion In the present study, we established a platform combining CRISPR RNA-guided deaminase and scRNA-seq technologies that enables the introduction of substitution mutations into multiple genes and facilitates their functional screening and measurement of perturbations in single cells.

Analysis of targeted capture sequencing data From the NGS data, reads were aligned to hg19 using bwa v0. Deep sequencing of genomic DNA samples To validate whether the genome was edited by specific sgRNAs, the targeted region was amplified from the genome and sequenced.

References 1. PubMed Google Scholar PubMed Article Google Scholar Google Scholar View author publications. Ethics declarations Competing interests The authors declare no competing interests. Supplementary information. However, as we shall see in this course, it is possible to alleviate some of these issues through proper normalization and corrections.

Figure 2. Development of new methods and protocols for scRNA-seq is currently a very active area of research, and several protocols have been published over the last few years. A non-comprehensive list includes:. The methods can be categorized in different ways, but the two most important aspects are quantification and capture. For quantification, there are two types, full-length and tag-based. The former tries to achieve a uniform read coverage of each transcript.

The choice of quantification method has important implications for what types of analyses the data can be used for. In theory, full-length protocols should provide an even coverage of transcripts, but as we shall see, there are often biases in the coverage.

The main advantage of tag-based protocol is that they can be combined with unique molecular identifiers UMIs which can help improve the quantification see chapter 4. On the other hand, being restricted to one end of the transcript may reduce the mappability and it also makes it harder to distinguish different isoforms Archer et al.

The strategy used for capture determines throughput, how the cells can be selected as well as what kind of additional information besides the sequencing that can be obtained. The three most widely used options are microwell- , microfluidic- and droplet- based.

For well-based platforms, cells are isolated using for example pipette or laser capture and placed in microfluidic wells. One advantage of well-based methods is that they can be combined with fluorescent activated cell sorting FACS , making it possible to select cells based on surface markers.

However, the precise functionalities that need to be offered continue to be an area of active development. In summary, an understanding of the bioinformatic and computational issues involved in scRNA-seq studies is needed, and specialist support for biomedical researchers and clinicians from bio-informaticians who are comfortable with handling scRNA-seq datasets would be beneficial. Before further analyses, scRNA-seq data typically require a number of bio-informatic QC checks, where poor-quality data from single cells arising as a result of many possible reasons, including poor cell viability at the time of lysis, poor mRNA recovery and low efficiency of cDNA production can be justifiably excluded from subsequent analysis.

Currently, there is no consensus on exact filtering strategies, but most widely used criteria include relative library size, number of detected genes and fraction of reads mapping to mitochondria-encoded genes or synthetic spike-in RNAs [ 76 , 77 ]. Recently, sophisticated computational tools for identifying low-quality cells have also been introduced [ 78 , 79 , 80 , 81 ]. 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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