1. Single-cell RNA-sequencing
Núria Farrús Bartolo

2. INDEX Introduction Workflow -Isolation of single cells -Sequencing
Applications of single-cell RNA-seq to basic research
Future challenges
Bibliography

3. INTRODUCTION
Single cell sequencing examines the sequence information using DNA or RNA from individual cells with NGS technologies, providing a higher resolution of cellular differences and a better understanding of the function in its microenvironment.
Reveal the heterogeneity and subpopulation expression variability
More challenging to perform than sequencing from cells in bulk
Low level of nucleic acids Heavy amplification is often needed, resulting in uneven coverage, noise and inaccurate quantification of sequencing data
Figure 1. Single-cell RNA-seq reveals cellular heterogeneity that is masked by bulk RNA-seq methods. Reprinted from 10XGenomics, n.d., Retrieved November 23, 2018, from

4. WORKFLOW
Figure 2. Single cell RNA sequencing workflow. Reprinted from Hemberg-lab, n.d., Retrieved November 30, 2018, from

5. 1. ISOLATION OF SINGLE CELLS
Single-cell isolation from dissociated cell suspensions
-Flow-activated cell sorting (FACS):
Popular Wide availability of robust platforms, ‘user-friendly’ interfaces, efficient data visualization tools and low running costs
Need to use antibodies
Large starting volume
Not useful to the isolation of extremely rare cells neither for environmental samples containing heterogeneous cell sizes
It can’t combine morphological and transcriptomic analyses
Figure 3. Flow-activated sorting. Reprinted from “Single-cell RNA-seq: advances and future challenges”, by A. E. Saliba, A. J. Westermann, S.A. Gorski and J. Vogel, 2014, Oxford University Press, 14 (42), p

6. 1. ISOLATION OF SINGLE CELLS
Single-cell isolation from tissue samples
Long-term passaging of cells can cause dramatic genomic rearrangements and mutations
Gene expression changes comparing 2D monolayers and 3D cultures
Mechanical forces within tissues have an effect on the expression of many genes
Solution Laser-capture microdissection: it works without prior dissociation of the cells and thus preserves their 3D structure.
Figure 4. Laser-capture microdissection. Reprinted from “Single-cell RNA-seq: advances and future challenges”, by A. E. Saliba, A. J. Westermann, S.A. Gorski and J. Vogel, 2014, Oxford University Press, 14 (42), p

7. 2. SEQUENCING: WORKFLOW
Figure 5. Single cell RNA sequencing workflow. Reprinted from Hemberg-lab, n.d., Retrieved November 30, 2018, from

8. 2. SEQUENCING: RNA REVERSE TRANSCRIPTION
Template switching
Poly(A) tailing
In vitro transcription
Currently used single-cell RNA-seq methods
Rolling circle amplification
3’ selection
5’ selection
Methods based on attach poly(dT) primer on the polyadenylated RNA.
Capture the most mRNAs and lncRNAs
Certain non-polyadenylated informative RNAs and non-polyadenylated lncRNAs will be lost
Not compatible from prokaryotic cells
 Solution: an exonuclease is added to reduce tRNA and rRNA but this treatment will also deplete mRNA or small RNA species

9. 2. SEQUENCING: RNA REVERSE TRANSCRIPTION
-PCR amplification:
Figure 6. Existing methods to prepare sequencing libraries from a single cell. Reprinted from “Single-cell RNA-seq: advances and future challenges”, by A. E. Saliba, A. J. Westermann, S.A. Gorski and J. Vogel, 2014, Oxford University Press, 14 (42), p

10. 2. SEQUENCING: RNA REVERSE TRANSCRIPTION
Figure 7. Existing methods to prepare sequencing libraries from a single cell. Reprinted from “Single-cell RNA-seq: advances and future challenges”, by A. E. Saliba, A. J. Westermann, S.A. Gorski and J. Vogel, 2014, Oxford University Press, 14 (42), p

11. 2. SEQUENCING: RNA REVERSE TRANSCRIPTION
Use of barcodes
Figure 8. Existing methods to prepare sequencing libraries from a single cell. Reprinted from “Single-cell RNA-seq: advances and future challenges”, by A. E. Saliba, A. J. Westermann, S.A. Gorski and J. Vogel, 2014, Oxford University Press, 14 (42), p

12. 2. SEQUENCING: RNA REVERSE TRANSCRIPTION
Barcoding strategies
Single-cell RNA-seq analysis of a whole tissue profiling of millions of representative individual cells
Incorporation of a unique cellular identifier in the template-switching oligonucleotide or in the oligo(dT) primer has made it possible to pool up cells for simultaneous sequencing  each read could be assigned to its original cell.
Barcoding strategies can also be used to perform absolute quantification of each transcript in a single cell.
Figure 9. Pooled cDNA samples. Reprinted from Stiftung für Innovative Medizin, n.d., Retrieved December 2, 2018, from

13. 2. SEQUENCING: RNA REVERSE TRANSCRIPTION
Table 1. Principal characteristics of currently used single-cell RNA-seq methods. Reprinted from “Single-cell RNA-seq: advances and future challenges”, by A. E. Saliba, A. J. Westermann, S.A. Gorski and J. Vogel, 2014, Oxford University Press, 14 (42), p

14. APPLICATIONS OF SINGLE-CELL RNA-SEQ TO BASIC RESEARCH
Stem cell differentiation: useful to study the molecular basis of single-cell decision process
Embryogenesis: it’s the differentiation transition from the cellular to the whole-organism level
Whole-tissue analysis: if we analyze the transcriptome of all the cells from a tissue, we can know the lineage hierarchy
Whole-organism studies: to understand how single cells divide and differentiate to build up an entire organism.
 We can expected single-cell RNA-seq to soon enter the clinics to facilitate more personalized therapeutic decisions for patients.

15. FUTURE CHALLENGES
Detect transcripts without poly(A): use ‘not-so- random’ primers
Improve sensitivity: difficult to distinguish between technical noise and biological variability for low- abundance transcripts  loss of information
Study the transcriptome of whole organism: difficulties in maintaining the 3D information of tissue architecture at the same time as sequencing
Study of single-cell transcriptomics of prokaryotic cells: general lack of poly(A) tails in prokaryotes
Figure 10. Envisioned strategies for nanopore-based RNA-seq. Reprinted from “Single-cell RNA-seq: advances and future challenges”, by A. E. Saliba, A. J. Westermann, S.A. Gorski and J. Vogel, 2014, Oxford University Press, 14 (42), p

16. CONCLUSIONS
RNA-seq has revolutionized transcriptomics and rapidly become the method of choice to address both quantitative and qualitative aspects of gene expression
Most studies have analyzed the average transcriptome of a whole population of cells. However, many important cellular aspects can only be assessed with the help of single-cell approaches
A major future challenge will be to go beyond the poly(A) transcriptome of eukaryotes and bring single-cell RNA-seq to the level that all types of cellular transcripts are analyzed in parallel
As cDNA synthesis dictates the transcript classes to be captured and represents the material-limiting, the long-term goal must be to directly sequence full-length RNA molecules

17. BIBLIOGRAPHY
Chaisson, M. J. P., Huddleston, J., Dennis, M. Y., Sudmant, P. H., Malig, M., Hormozdiari, F., … Eichler, E. E. (2014). Resolving the complexity of the human genome using single-molecule sequencing. Nature, 517(7536),
Hwang, B., Lee, J. H., & Bang, D. (2018). Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine
Oldham, M. C., & Kreitzer, A. C. (2018). Sequencing Diversity One Cell at a Time. Cell, 174(4),
Saliba, A., Westermann, A. J., & Gorski, S. A. (2014). Single-cell RNA-seq: advances and future challenges. Oxford University Press, 42(14),