SINGLECELL RNASEQUENCING Nria Farrs Bartolo INDEX Introduction Workflow
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 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. ü 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 10 XGenomics, n. d. , Retrieved November 23, 2018, from https: //community. 10 xgenomics. com/t 5/10 x-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: //hemberglab. github. io/sc. RNA. 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): ü 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. 8847.
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 2 D monolayers and 3 D 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 3 D 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. 8847.
2. SEQUENCING: WORKFLOW Figure 5. Single cell RNA sequencing workflow. Reprinted from Hemberg-lab, n. d. , Retrieved November 30, 2018, from https: //hemberglab. github. io/sc. RNA. seq. course/introduction-to-single-cell-rna-seq. html
2. SEQUENCING: RNA REVERSE TRANSCRIPTION Methods based on attach poly(d. T) primer on the polyadenylated RNA. ü Capture the most m. RNAs and lnc. RNAs Certain non-polyadenylated informative RNAs and non-polyadenylated lnc. RNAs will be lost Not compatible from prokaryotic cells Poly(A) tailing Currently used single-cell RNAseq methods Template switching In vitro transcription Rolling circle amplification Solution: an exonuclease is added to reduce t. RNA and r. RNA but this treatment will also deplete m. RNA or small RNA species 5’ selection 3’ selection
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. 8849.
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. 8849.
2. SEQUENCING: RNA REVERSE TRANSCRIPTION • Use of barcodes Figure 8. Existing methods to prepare sequencing libraries from a single cell. Reprinted from “Single-cell RNAseq: advances and future challenges”, by A. E. Saliba, A. J. Westermann, S. A. Gorski and J. Vogel, 2014, Oxford University Press, 14 (42), p. 8852.
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 templateswitching oligonucleotide or in the oligo(d. T) 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 c. DNA samples. Reprinted from Stiftung für Innovative Medizin, n. d. , Retrieved December 2, 2018, from https: //www. youtube. com/watch? v=0 uuvnxtts 1 s
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 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.
FUTURE CHALLENGES • Detect transcripts without poly(A): use ‘not-sorandom’ primers • Improve sensitivity: difficult to distinguish between technical noise and biological variability for lowabundance transcripts loss of information • Study the transcriptome of whole organism: difficulties in maintaining the 3 D 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. 8855.
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 singlecell RNA-seq to the level that all types of cellular transcripts are analyzed in parallel As c. DNA 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 usingle-molecule sequencing. Nature, 517(7536), 608 -611. http: //doi. org/10. 1038/nature 13907 • Hwang, B. , Lee, J. H. , & Bang, D. (2018). Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine. http: //doi. org/10. 1038/s 12276 -0180071 -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 future challenges. Oxford University Press, 42(14), 8845 -8860. http: //doi. org/10. 1093/nar/gku 555
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