Skip to main content
SpringerLink
Log in

Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT)

  • Protocol
  • First Online:
Computational Methods for Single-Cell Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1935))

Abstract

The ability to measure molecular properties (e.g., mRNA expression) at the single-cell level is revolutionizing our understanding of cellular developmental processes and how these are altered in diseases like cancer. The need for computational methods aimed at extracting biological knowledge from such single-cell data has never been greater. Here, we present a detailed protocol for estimating differentiation potency of single cells, based on our Single-Cell ENTropy (SCENT) algorithm. The estimation of differentiation potency is based on an explicit biophysical model that integrates the RNA-Seq profile of a single cell with an interaction network to approximate potency as the entropy of a diffusion process on the network. We here focus on the implementation, providing a step-by-step introduction to the method and illustrating it on a real scRNA-Seq dataset profiling human embryonic stem cells and multipotent progenitors representing the 3 main germ layers. SCENT is aimed particularly at single-cell studies trying to identify novel stem-or-progenitor like phenotypes, and may be particularly valuable for the unbiased identification of cancer stem cells. SCENT is implemented in R, licensed under the GNU General Public Licence v3, and freely available from https://github.com/aet21/SCENT.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Waddington CH (1966) Principles of development and differentiation. Macmillan, London, pp 1905–1975

    Google Scholar 

  2. Moris N, Pina C, Arias AM (2016) Transition states and cell fate decisions in epigenetic landscapes. Nat Rev Genet 17:693–703. https://doi.org/10.1038/nrg.2016.98

    Article  CAS  PubMed  Google Scholar 

  3. Levsky JM (2002) Single-cell gene expression profiling. Science 297:836–840. https://doi.org/10.1126/science.1072241

    Article  CAS  PubMed  Google Scholar 

  4. Laurenti E, Göttgens B (2018) From haematopoietic stem cells to complex differentiation landscapes. Nature 553:418–426. https://doi.org/10.1038/nature25022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Lang AH, Li H, Collins JJ, Mehta P (2014) Epigenetic landscapes explain partially reprogrammed cells and identify key reprogramming genes. PLoS Comput Biol 10:e1003734. https://doi.org/10.1371/journal.pcbi.1003734

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tirosh I, Venteicher AS, Hebert C et al (2016) Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539:309–313. https://doi.org/10.1038/nature20123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tirosh I, Izar B, Prakadan SM et al (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189–196. https://doi.org/10.1126/science.aad0501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63. https://doi.org/10.1038/nrg2484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Grün D, van Oudenaarden A (2015) Design and analysis of single-cell sequencing experiments. Cell 163:799–810. https://doi.org/10.1016/j.cell.2015.10.039

    Article  CAS  PubMed  Google Scholar 

  10. Trapnell C, Cacchiarelli D, Grimsby J et al (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32:381–386. https://doi.org/10.1038/nbt.2859

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Marco E, Karp RL, Guo G et al (2014) Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci U S A 111:E5643–E5650. https://doi.org/10.1073/pnas.1408993111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Setty M, Tadmor MD, Reich-Zeliger S et al (2016) Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol 34:637–645. https://doi.org/10.1038/nbt.3569

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bendall SC, Davis KL, E-AD A et al (2014) Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157:714–725. https://doi.org/10.1016/j.cell.2014.04.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chen J, Schlitzer A, Chakarov S et al (2016) Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development. Nat Commun 7:11988. https://doi.org/10.1038/ncomms11988

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Qiu X, Mao Q, Tang Y et al (2017) Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14:979–982. https://doi.org/10.1038/nmeth.4402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Rizvi AH, Camara PG, Kandror EK et al (2017) Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nat Biotechnol 35:551–560. https://doi.org/10.1038/nbt.3854

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Haghverdi L, Büttner M, Wolf FA et al (2016) Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods 13:845–848. https://doi.org/10.1038/nmeth.3971

    Article  CAS  PubMed  Google Scholar 

  18. Angerer P, Haghverdi L, Büttner M et al (2016) Destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32:1241–1243. https://doi.org/10.1093/bioinformatics/btv715

    Article  CAS  PubMed  Google Scholar 

  19. Chu L-F, Leng N, Zhang J et al (2016) Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol 17:2315. https://doi.org/10.1186/s13059-016-1033-x

    Article  CAS  Google Scholar 

  20. Grün D, Muraro MJ, Boisset J-C et al (2016) De novo prediction of stem cell identity using single-cell Transcriptome data. Cell Stem Cell 19:266–277. https://doi.org/10.1016/j.stem.2016.05.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Guo M, Bao EL, Wagner M et al (2017) SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res 45:e54. https://doi.org/10.1093/nar/gkw1278

    Article  CAS  PubMed  Google Scholar 

  22. Teschendorff AE, Enver T (2017) Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nat Commun 8:15599. https://doi.org/10.1038/ncomms15599

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gómez-Gardeñes J, Latora V (2008) Entropy rate of diffusion processes on complex networks. Phys Rev E Stat Nonlinear Soft Matter Phys 78:114. https://doi.org/10.1103/PhysRevE.78.065102

    Article  CAS  Google Scholar 

  24. Banerji CRS, Miranda-Saavedra D, Severini S et al (2013) Cellular network entropy as the energy potential in Waddington's differentiation landscape. Sci Rep 3:1129. https://doi.org/10.1038/srep03039

    Article  Google Scholar 

  25. Teschendorff AE, Sollich P, Kuehn R (2014) Signalling entropy: a novel network-theoretical framework for systems analysis and interpretation of functional omic data. Methods 67:282–293. https://doi.org/10.1016/j.ymeth.2014.03.013

    Article  CAS  PubMed  Google Scholar 

  26. Banerji CRS, Severini S, Caldas C, Teschendorff AE (2015) Intra-tumour Signalling entropy determines clinical outcome in breast and lung cancer. PLoS Comput Biol 11:e1004115. https://doi.org/10.1371/journal.pcbi.1004115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lun ATL, McCarthy DJ, Marioni JC (2016) A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor. F1000Res 5:2122. https://doi.org/10.12688/f1000research.9501.2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. McCarthy DJ, Campbell KR, Lun ATL, Wills QF (2017) Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 247:btw777. https://doi.org/10.1093/bioinformatics/btw777

    Article  CAS  Google Scholar 

  29. Butler A, Hoffman P, Smibert P et al (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36:411–420. https://doi.org/10.1038/nbt.4096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute of Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Weiyan Chen & Andrew E. Teschendorff

  2. UCL Cancer Institute, University College London, London, UK

    Andrew E. Teschendorff

Authors
  1. Weiyan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew E. Teschendorff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew E. Teschendorff .

Editor information

Editors and Affiliations

  1. Dana–Farber Cancer Institute and Harvard Chan, School of Public Health, Boston, MA, USA

    Guo-Cheng Yuan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Chen, W., Teschendorff, A.E. (2019). Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT). In: Yuan, GC. (eds) Computational Methods for Single-Cell Data Analysis. Methods in Molecular Biology, vol 1935. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9057-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9057-3_9

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9056-6

  • Online ISBN: 978-1-4939-9057-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation