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Single-cell transcriptomics examines the gene expression level of individual cells in a given population by simultaneously measuring the Messenger RNA (mRNA) concentration of hundreds to thousands of genes.[1] The unraveling of heterogenous cell populations, reconstruction of cellular developmental trajectories and modelling of transcriptional dynamics all previously masked in bulk transcriptome measurements is made possible through analysis of this transcriptomic data.[2]

Background

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Gene expression analysis has become routine through the development of high-throughput RNA sequencing (RNA-seq) and microarrays. RNA analysis that was previously limited to tracing individual transcripts by Northern blots or quantitative PCR is now used frequently to characterize the expression profiles of populations of thousands of cells.The data produced from the bulk based assays has led to the identification of genes that are differentially expressed in distinct cell populations and biomarker discovery.[3]

These genomic studies are limited as they provide measurements for whole tissues and as a result show an average expression profile for all the constituent cells. In multicellular organisms different cell types within the same population can have distinct roles and form subpopulations with different transcriptional profiles. Correlations in the gene expression of the subpopulations can often be missed due to the lack of subpopulation identification.[4] Moreover, bulk assays fail to identify if a change in the expression profile is due to a change in regulation or composition, in which one cell type arises to dominate the population. Lastly, when examining cellular progression through differentiation, average expression profiles are only able to order cells by time rather than their stage of development and are consequently unable to show trends in gene expression levels specific to certain stages.[5]

Recent advances in biotechnology allow the measurement of gene expression in hundreds to thousands of individual cells simultaneously. Whilst these technological breakthroughs have enabled the generation of single-cell transcriptomic data there are new computational and analytical challenges presented by the data produced. Techniques used for analysing RNA-seq data from bulk cell populations can be used for single-cell data but many new computational approaches have been designed for this data type to facilitate a complete and detailed study of single-cell expression profiles.[6]

Experimental steps

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There is currently no standardized technique to generate single-cell data, all methods must include cell isolation from the population, lysate formation, amplification through reverse transcription and quantification of expression levels. Common techniques for measuring expression are quantitative PCR or RNA-seq.

Isolating single cells

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Fluorescence Assisted Cell Sorting workflow (FACS)

There are several methods available to isolate and amplify cells for single-cell analysis. Low throughput techniques are able to isolate hundreds of cells, are slow and enable selection. These methods include:

High throughput methods are able to quickly isolate hundreds to tens of thousands of cells.[7] Common techniques include:

Quantitative PCR (q-PCR)

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To measure the level of expression of each transcript qPCR can be applied. Gene specific primers are used to amplify the corresponding gene as with regular PCR and as a result data is usually only obtained for sample sizes of less than 100 genes. The inclusion of house keeping genes, whose expression should be constant under the conditions , is used for normalisation. The most commonly used house keeping genes include GAPDH and α-actin, although the reliability of normalisation through this process is questionable as there is evidence that the level of expression can vary significantly.[8]. Fluorescent dyes are used a reporter molecules to detect the PCR product and monitor the progress of the amplification - the increase in fluorescence intensity is proportional to the amplicon concentration. A plot of fluorescence vs. cycle number is made and a threshold fluorescence level is used to find cycle number at which the plot reaches this value. The cycle number at this point is known as the threshold cycle (Ct) and is measured for each gene.[9]

Single-cell RNA-seq

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RNA Seq Experiment

The RNA-seq approach is generally regarded as the most precise method to measure the levels of transcripts and is able to generate data for tens of thousands of genes i.e the whole transcriptome. The basic principal relies on a population of RNA being converted to a library of cDNA fragments. These fragments are sequenced by high-throughput next generation sequencing techniques and the reads are mapped back to the reference genome, providing a count of the number of reads associated with each gene.[10]

Normalisation of RNA-seq data accounts for cell to cell variation in the efficiencies of the cDNA library formation and sequencing. One Generally accepted methods relies on the use of extrinsic RNA spike-ins (RNA sequences of known sequence and quantity) that are added in equal quantities to each cell lysate and used to normalise read count by the number of reads mapped to spike-in mRNA.[11]

A more recently developed control uses Unique molecular identifiers (UMIs)-Short DNA sequences (6–10nt)- that are added to each cDNA before amplification and act as a unique bar code for each cDNA molecule. Normalisation is achieved by using the count number of unique UMIs associated with each gene to account for differences in amplification efficiency.[12]

A combination of both spike-ins, UMIs and other approaches have been combined for more accurate normalisation.

Considerations

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A problem associated with Single-cell data occurs in the form of zero inflated gene expression distributions, known as technical dropouts, that are common due to the low mRNA concentrations of lowly expressed genes not being captured in the reverse transcription process. The percentage of mRNA molecules in the cell lysate that are detected is often low at 10-20%.[13]

When using RNA spike-ins for normalisation the assumption is made that the amplification and sequencing efficiencies for the endogenous and spike-in RNA is the same. Evidence suggests that this is not the case given that there are fundamental differences in the size and features such as the lack of a polyadenylated tail in spike-ins and there shorter length.[14] Additionally, normalisation using UMIs assumes the cDNA library is sequenced to saturation which is not always the case.[15]

Data analysis

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Insights based on single-cell data analysis assumes that the input is a matrix of normalised gene expression counts, generated by the approaches outline above, and can provide opportunities that are not obtainable by bulk.

Three main insights provided:[16]

  1. Identification and characterization of cell types and their spatial organisation in time
  2. Inference of gene regulatory networks and their strength across individual cells
  3. Classification of the stochastic component of transcription

The techniques outlined have been designed to help visualise and explore patterns in the data in order to facilitate the revelation of these three features.

Clustering

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K-Means-Gaussian-data
Iris dendrogram produced using a Hierarchical clustering algorithm

Clustering allows for the formation of subgroups in the cell population. Cells can be clustered by their transcriptomic profile in order to analyse the sub-population structure and identify rare cell types or cell subtypes. Alternatively, genes can be clustered by their expression states in order to identify covarying genes. A combination of both clustering approaches, known as biclustering, has been used to simultaneously cluster by genes and cells to find genes that behave similarly within cell clusters.[17]

Clustering methods applied can be K-means clustering , forming disjoint groups or Hierarchical clustering, forming nested partitions.

Biclustering

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Biclustering provides several advantages by improving the resolution of clustering. Genes that are only informative to a subset of cells and are hence only expressed there can be identified through biclustering. Moreover, similarly behaving genes that differentiate one cell cluster from another can be identified using this method.[18]

Dimensionality reduction

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PCA example of Guinean and other African populations Y chromosome haplogroup frequencies

Dimensionality reduction algorithms such as Principal component analysis (PCA) and t-SNE can be used to simplify data for visualisation and pattern detection by transforming cells from a high to a lower dimensional space. The result of this method produces graphs with each cell as a point in a 2-D or 3-D space. Dimensionality reduction is frequently used before clustering as cells in high dimensions can wrongly appear to be close due to distance metrics behaving non-intuitively.[19]

Principal component analysis

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The most frequently used technique is PCA, which identifies the directions of largest variance principal components and transforms the data so that the first principal component has the largest possible variance, and successive principle components in turn each have the highest variance possible while remaining orthogonal to the preceding components. The contribution each gene makes to each component is used to infer which genes are contributing the most to variance in the population and are involved in differentiating different subpopulations.[20]

Differential expression

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Detecting differences in gene expression level between two populations is used both single-cell and bulk transcriptomic data. Specialised methods have been designed for single-cell data that considers single cell features such as technical dropouts and shape of the distribution e.g. Bimodal vs. unimodal. [21]

Gene ontology enrichment

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Gene ontology terms describe gene functions and the relationships between those functions into three classes:

  1. Molecular function
  2. Cellular component
  3. Biological process

Gene Ontology (GO) term enrichment is a technique used to identify which GO terms are over-represented or under-represented in a given set of genes. In single-cell analysis input list of genes of interest can be selected based on differentially expressed genes or groups of genes generated from biclustering. The number of genes annotated to a GO term in the input list is normalised against the number of genes annotated to a GO term in the background set of all genes in genome to determine statistical significance.[22]

Pseudotemporal ordering

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Graph with minimal spanning tree

As true temporal data is unavailable to study due to the experimental methods destroying cells a new technique,Pseudotemporal ordering, aims to infer gene expression dynamics from snapshot data. Pseudotemporal ordering enables the inference of gene expression dynamics and the ordering of cells by their progression through differentiation or response to external stimuli.[23] The method relies on the assumptions that the cells follow the same path through the process of interest and that their transcriptional state correlates to their progression. The algorithm can be applied to both mixed populations and temporal samples.

An example algorithm is the Monocle algorithm that carries out dimensionality reduction of the data, builds a minimal spanning tree using the transformed data, orders cells in pseudo-time by following the longest connected path of the tree and consequently labels cells by type.[24]

Network inference

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Gene regulatory network inference is a technique that aims to construct a network, shown as a graph, in which the nodes represent the genes and edges indicate co-regulatory interactions. The method relies on the assumption that a strong statistical relationship between the expression of genes is an indication of a potential functional relationship.[25] The most commonly used method to measure the strength of a statistical relationship is correlation. However, correlation fails to identify non-linear relationships and mutual information is used as an alternative. Gene clusters linked in a network signify genes that undergo coordinated changes in expression.[26]

References

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  1. ^ Kanter, Itamar; Kalisky, Tomer (1 January 2015). "Single Cell Transcriptomics: Methods and Applications". Frontiers in Oncology. 5. doi:10.3389/fonc.2015.00053. ISSN 2234-943X.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  2. ^ Liu, Serena; Trapnell, Cole (17 February 2016). "Single-cell transcriptome sequencing: recent advances and remaining challenges". F1000Research. 5. doi:10.12688/f1000research.7223.1. ISSN 2046-1402.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  3. ^ Szabo, David T. "Chapter 62 - Transcriptomic biomarkers in safety and risk assessment of chemicals". Biomarkers in Toxicology. Academic Press. pp. 1033–1038. ISBN 9780124046306.
  4. ^ Kanter, Itamar; Kalisky, Tomer (10 March 2015). "Single Cell Transcriptomics: Methods and Applications". Frontiers in Oncology. 5. doi:10.3389/fonc.2015.00053. ISSN 2234-943X.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  5. ^ Trapnell, Cole (1 October 2015). "Defining cell types and states with single-cell genomics". Genome Research. 25 (10): 1491–1498. doi:10.1101/gr.190595.115. ISSN 1088-9051.
  6. ^ Stegle, Oliver; Teichmann, Sarah A.; Marioni, John C. (1 March 2015). "Computational and analytical challenges in single-cell transcriptomics". Nature Reviews Genetics. 16 (3): 133–145. doi:10.1038/nrg3833. ISSN 1471-0056.
  7. ^ Poulin, Jean-Francois; Tasic, Bosiljka; Hjerling-Leffler, Jens; Trimarchi, Jeffrey M.; Awatramani, Rajeshwar (1 September 2016). "Disentangling neural cell diversity using single-cell transcriptomics". Nature Neuroscience. 19 (9): 1131–1141. doi:10.1038/nn.4366. ISSN 1097-6256.
  8. ^ Radonić, Aleksandar; Thulke, Stefanie; Mackay, Ian M.; Landt, Olfert; Siegert, Wolfgang; Nitsche, Andreas (23 January 2004). "Guideline to reference gene selection for quantitative real-time PCR". Biochemical and Biophysical Research Communications. 313 (4): 856–862. ISSN 0006-291X.
  9. ^ Wildsmith, S. E.; Archer, G. E.; Winkley, A. J.; Lane, P. W.; Bugelski, P. J. (1 January 2001). "Maximization of signal derived from cDNA microarrays". BioTechniques. 30 (1): 202–206, 208. ISSN 0736-6205.
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  11. ^ Jiang, Lichun; Schlesinger, Felix; Davis, Carrie A.; Zhang, Yu; Li, Renhua; Salit, Marc; Gingeras, Thomas R.; Oliver, Brian (23 March 2017). "Synthetic spike-in standards for RNA-seq experiments". Genome Research. 21 (9): 1543–1551. doi:10.1101/gr.121095.111. ISSN 1088-9051.
  12. ^ Islam, Saiful; Zeisel, Amit; Joost, Simon; La Manno, Gioele; Zajac, Pawel; Kasper, Maria; Lönnerberg, Peter; Linnarsson, Sten (1 February 2014). "Quantitative single-cell RNA-seq with unique molecular identifiers". Nature Methods. 11 (2): 163–166. doi:10.1038/nmeth.2772. ISSN 1548-7091.
  13. ^ Kharchenko, Peter V.; Silberstein, Lev; Scadden, David T. (1 July 2014). "Bayesian approach to single-cell differential expression analysis". Nature Methods. 11 (7): 740–742. doi:10.1038/nmeth.2967. ISSN 1548-7091.
  14. ^ Svensson, Valentine; Natarajan, Kedar Nath; Ly, Lam-Ha; Miragaia, Ricardo J.; Labalette, Charlotte; Macaulay, Iain C.; Cvejic, Ana; Teichmann, Sarah A. (6 March 2017). "Power analysis of single-cell RNA-sequencing experiments". Nature Methods. advance online publication. doi:10.1038/nmeth.4220. ISSN 1548-7105.
  15. ^ Islam, Saiful; Zeisel, Amit; Joost, Simon; La Manno, Gioele; Zajac, Pawel; Kasper, Maria; Lönnerberg, Peter; Linnarsson, Sten (1 February 2014). "Quantitative single-cell RNA-seq with unique molecular identifiers". Nature Methods. 11 (2): 163–166. doi:10.1038/nmeth.2772. ISSN 1548-7091.
  16. ^ Stegle, Oliver; Teichmann, Sarah A.; Marioni, John C. (1 March 2015). "Computational and analytical challenges in single-cell transcriptomics". Nature Reviews Genetics. 16 (3): 133–145. doi:10.1038/nrg3833. ISSN 1471-0056.
  17. ^ Buettner, Florian; Natarajan, Kedar N.; Casale, F. Paolo; Proserpio, Valentina; Scialdone, Antonio; Theis, Fabian J.; Teichmann, Sarah A.; Marioni, John C.; Stegle, Oliver (1 February 2015). "Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells". Nature Biotechnology. 33 (2): 155–160. doi:10.1038/nbt.3102. ISSN 1087-0156.
  18. ^ Ntranos, Vasilis; Kamath, Govinda M.; Zhang, Jesse M.; Pachter, Lior; Tse, David N. (26 May 2016). "Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts". Genome Biology. 17. doi:10.1186/s13059-016-0970-8. ISSN 1474-7596.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  19. ^ Pierson, Emma; Yau, Christopher (1 January 2015). "ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis". Genome Biology. 16: 241. doi:10.1186/s13059-015-0805-z. ISSN 1474-760X.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  20. ^ Treutlein, Barbara; Brownfield, Doug G.; Wu, Angela R.; Neff, Norma F.; Mantalas, Gary L.; Espinoza, F. Hernan; Desai, Tushar J.; Krasnow, Mark A.; Quake, Stephen R. (15 May 2014). "Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq". Nature. 509 (7500): 371–375. doi:10.1038/nature13173.
  21. ^ Korthauer, Keegan D.; Chu, Li-Fang; Newton, Michael A.; Li, Yuan; Thomson, James; Stewart, Ron; Kendziorski, Christina (1 January 2016). "A statistical approach for identifying differential distributions in single-cell RNA-seq experiments". Genome Biology. 17: 222. doi:10.1186/s13059-016-1077-y. ISSN 1474-760X.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  22. ^ Haghverdi, Laleh; Büttner, Maren; Wolf, F. Alexander; Buettner, Florian; Theis, Fabian J. (1 October 2016). "Diffusion pseudotime robustly reconstructs lineage branching". Nature Methods. 13 (10): 845–848. doi:10.1038/nmeth.3971. ISSN 1548-7091.
  23. ^ Trapnell, Cole; Cacchiarelli, Davide; Grimsby, Jonna; Pokharel, Prapti; Li, Shuqiang; Morse, Michael; Lennon, Niall J.; Livak, Kenneth J.; Mikkelsen, Tarjei S.; Rinn, John L. (23 March 2017). "Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions". Nature biotechnology. 32 (4): 381–386. doi:10.1038/nbt.2859. ISSN 1087-0156.
  24. ^ Haghverdi, Laleh; Büttner, Maren; Wolf, F. Alexander; Buettner, Florian; Theis, Fabian J. (1 October 2016). "Diffusion pseudotime robustly reconstructs lineage branching". Nature Methods. 13 (10): 845–848. doi:10.1038/nmeth.3971. ISSN 1548-7091.
  25. ^ Wei, J.; Hu, X.; Zou, X.; Tian, T. (1 December 2016). "Inference of genetic regulatory network for stem cell using single cells expression data". 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM): 217–222. doi:10.1109/BIBM.2016.7822521.
  26. ^ Moignard, Victoria; Macaulay, Iain C.; Swiers, Gemma; Buettner, Florian; Schütte, Judith; Calero-Nieto, Fernando J.; Kinston, Sarah; Joshi, Anagha; Hannah, Rebecca; Theis, Fabian J.; Jacobsen, Sten Eirik; de Bruijn, Marella F.; Göttgens, Berthold (1 April 2013). "Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis". Nature Cell Biology. 15 (4): 363–372. doi:10.1038/ncb2709. ISSN 1465-7392.
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