SINGLE-CELL RNA-SEQUENCING

Núria Farrús Bartolo

INDEX

• Introduction

• Workflow

-Isolation of single cells

-Sequencing

• Applications of single-cell RNA-seq to basic research

• Future challenges

• Bibliography

INTRODUCTION

Reveal the heterogeneity and subpopulationexpression variability

More challenging to perform than sequencingfrom cells in bulk

Low level of nucleic acids Heavy amplification isoften needed, resulting in uneven coverage, noiseand inaccurate quantification of sequencing data

Single cell sequencing examines the sequence information using DNA or RNA from individual cellswith NGS technologies, providing a higher resolution of cellular differences and a betterunderstanding of the function in its microenvironment.

Figure 1. Single-cell RNA-seq reveals cellular heterogeneity that is masked by bulk RNA-seq methods. Reprinted from10XGenomics, n.d., Retrieved November 23, 2018, from https://community.10xgenomics.com/t5/10x-Blog/Single-Cell-RNA-Seq-An-Introductory-Overview-and-Tools-for/ba-p/547

WORKFLOW

Figure 2. Single cell RNA sequencing workflow. Reprinted from Hemberg-lab, n.d., Retrieved November 30, 2018, from https://hemberg-lab.github.io/scRNA.seq.course/introduction-to-single-cell-rna-seq.html

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 environmentalsamples 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. 8847.

1. ISOLATION OF SINGLE CELLS

Single-cell isolation from tissue samples

Long-term passaging of cells can cause dramatic genomic rearrangementsand mutations

Gene expression changes comparing 2D monolayers and 3D cultures

Mechanical forces within tissues have an effect on the expression of manygenes

Solution Laser-capture microdissection: it works without prior dissociationof the cells and thus preserves their 3D structure.

Figure 4. Laser-capture microdissection.Reprinted from “Single-cell RNA-seq: advancesand future challenges”, by A. E. Saliba, A. J.Westermann, S.A. Gorski and J. Vogel, 2014,Oxford University Press, 14 (42), p. 8847.

2. SEQUENCING: WORKFLOW

Figure 5. Single cell RNA sequencing workflow. Reprinted from Hemberg-lab, n.d., Retrieved November 30, 2018, from https://hemberg-lab.github.io/scRNA.seq.course/introduction-to-single-cell-rna-seq.html

2. SEQUENCING: RNA REVERSE TRANSCRIPTION

Methods based on attach poly(dT) primer on thepolyadenylated RNA.

Capture the most mRNAs and lncRNAs

Certain non-polyadenylated informative RNAs andnon-polyadenylated lncRNAs will be lost

Not compatible from prokaryotic cells

Solution: an exonuclease is added to reducetRNA and rRNA but this treatment will also depletemRNA or small RNA species

Templateswitching

Poly(A) tailing

In vitro transcription

Currently usedsingle-cell RNA-

seq methods

Rolling circleamplification

3’ selection

5’ selection

2. SEQUENCING: RNA REVERSE TRANSCRIPTION

-PCR amplification:

Figure 6. Existing methods to prepare sequencinglibraries from a single cell. Reprinted from “Single-cellRNA-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. 8849.

2. SEQUENCING: RNA REVERSE TRANSCRIPTION

Figure 7. Existing methods to prepare sequencing libraries from a singlecell. Reprinted from “Single-cell RNA-seq: advances and futurechallenges”, by A. E. Saliba, A. J. Westermann, S.A. Gorski and J. Vogel,2014, Oxford University Press, 14 (42), p. 8849.

2. SEQUENCING: RNA REVERSE TRANSCRIPTION

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, OxfordUniversity Press, 14 (42), p. 8852.

• Use of barcodes

2. SEQUENCING: RNA REVERSE TRANSCRIPTION

Barcoding strategies

• Single-cell RNA-seq analysis of a whole tissue profiling ofmillions of representative individual cells

• Incorporation of a unique cellular identifier in the template-switching oligonucleotide or in the oligo(dT) primer has madeit 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 absolutequantification of each transcript in a single cell.

Figure 9. Pooled cDNA samples. Reprinted from Stiftung für Innovative Medizin,n.d., Retrieved December 2, 2018, fromhttps://www.youtube.com/watch?v=0uuvnxtts1s

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. 8851.

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 knowthe lineage hierarchy

• Whole-organism studies: to understand how single cells divide and differentiate to build up anentire organism.

We can expected single-cell RNA-seq to soon enter the clinics to facilitate more personalizedtherapeutic decisions for patients.

FUTURE CHALLENGES

• Detect transcripts without poly(A): use ‘not-so-random’ primers

• Improve sensitivity: difficult to distinguish betweentechnical noise and biological variability for low-abundance transcripts loss of information

• Study the transcriptome of whole organism:difficulties in maintaining the 3D information of tissuearchitecture at the same time as sequencing

• Study of single-cell transcriptomics of prokaryoticcells: 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. 8855.

CONCLUSIONS

RNA-seq has revolutionized transcriptomics and rapidly become the method of choice to address bothquantitative and qualitative aspects of gene expression

Most studies have analyzed the average transcriptome of a whole population of cells. However, manyimportant 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

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-moleculesequencing. Nature, 517(7536), 608-611. http://doi.org/10.1038/nature13907

• Hwang, B., Lee, J. H., & Bang, D. (2018). Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine. http://doi.org/10.1038/s12276-018-0071-8

• Oldham, M. C., & Kreitzer, A. C. (2018). Sequencing Diversity One Cell at a Time. Cell, 174(4), 777-779. http://doi.org/10.1016/j.cell.2018.07.024

• Saliba, A., Westermann, A. J., & Gorski, S. A. (2014). Single-cell RNA-seq: advances and futurechallenges. Oxford University Press, 42(14), 8845-8860. http://doi.org/10.1093/nar/gku555