Skip to main content

Systems Biology in Single Cells

  • Chapter
  • First Online:
Essentials of Single-Cell Analysis

Part of the book series: Series in BioEngineering ((SERBIOENG))

  • 2228 Accesses

Abstract

From the beginning of the twenty-first century, there has been a shift towards studying biological processes using a holistic rather than a reductionist scientific paradigm thus establishing the approach now named “systems biology” or “systomics”. This method of biological investigation represents a synergy where life sciences, systems engineering, and information technology examine the interactions between biological pathways, rather than solely focusing on individual pathways in an isolated manner. To date, systems biology has often studied population averages rather than individual characteristics of cells which might display a significant spread. However, as a single cell is the smallest operational biological unit that encompasses all metabolites necessary for maintaining a viable living entity, the application of systems biology approaches to the study of distinct cells is fast becoming a goal of many research groups. In this chapter we will describe some of the technologies that enable the isolation of individual cells in a form that accommodates systomics studies, the biological methods that are then deployed on such isolated cells to generate system-level information, and finally describe some of the bioinformatics that is specifically directed towards single-cell studies.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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. Westerhoff HV, Alberghina L (2005) Systems biology: did we know it all along? Anonymous Syst Biol Springer:3–9

    Google Scholar 

  2. Mast FD, Ratushny AV, Aitchison JD (2014) Systems cell biology. J Cell Biol 206(6):695–706. doi:10.1083/jcb.201405027

    Google Scholar 

  3. Short B (2009) Cell biologists expand their networks. J Cell Biol 186(3):305–311. doi:10.1083/jcb.200907093

    Google Scholar 

  4. Tay S, Hughey JJ, Lee TK et al (2010) Single-cell NF-[kgr] B dynamics reveal digital activation and analogue information processing. Nature 466(7303):267–271

    Article  Google Scholar 

  5. Cheong R, Bergmann A, Werner SL et al (2006) Transient Ikappa B kinase activity mediates temporal NF-kappaB dynamics in response to a wide range of tumor necrosis factor-alpha doses. J Biol Chem 281(5):2945–2950. M510085200 [pii]

    Google Scholar 

  6. Lahav G, Rosenfeld N, Sigal A et al (2004) Dynamics of the p53-Mdm2 feedback loop in individual cells. Nat Genet 36(2):147–150

    Article  Google Scholar 

  7. Di Carlo D, Wu LY, Lee LP (2006) Dynamic single cell culture array. Lab Chip 6(11):1445–1449

    Article  Google Scholar 

  8. Bonetta L (2005) Flow cytometry smaller and better. Nat Methods 2(10):785–795. doi:10.1038/nmeth1005-785

    Google Scholar 

  9. Kaczmarczyk A, Sullivan KF (2014) CENP-W plays a role in maintaining bipolar spindle structure. PLoS One 9(10):e106464. doi:10.1371/journal.pone.0106464

    Google Scholar 

  10. Yivgi-Ohana N, Eifer M, Addadi Y et al (2011) Utilizing mitochondrial events as biomarkers for imaging apoptosis. Cell Death Dis 2(6):e166. doi:10.1038/cddis.2011.47

    Google Scholar 

  11. Breslauer DN, Lee PJ, Lee LP (2006) Microfluidics-based systems biology. Mol Biosyst 2(2):97–112. doi:10.1039/B515632G

    Google Scholar 

  12. Lee PJ, Hung PJ, Rao VM et al (2006) Nanoliter scale microbioreactor array for quantitative cell biology. Biotechnol Bioeng 94(1):5–14

    Article  Google Scholar 

  13. Takayama S, Ostuni E, LeDuc P et al (2001) Subcellular positioning of small molecules. Nature 411(6841):1016. doi:10.1038/35082637

    Google Scholar 

  14. Lucchetta EM, Lee JH, Fu LA et al (2005) Dynamics of drosophila embryonic patterning network perturbed in space and time using microfluidics. Nature 434(7037):1134–1138

    Article  Google Scholar 

  15. Rowat AC, Bird JC, Agresti JJ et al (2009) Tracking lineages of single cells in lines using a microfluidic device. Proc Natl Acad Sci U S A 106(43):18149–18154. doi:10.1073/pnas.0903163106

    Google Scholar 

  16. Wu H, Wheeler A, Zare RN (2004) Chemical cytometry on a picoliter-scale integrated microfluidic chip. Proc Natl Acad Sci U S A 101(35):12809–12813. doi:10.1073/pnas.0405299101

    Google Scholar 

  17. Cai L, Friedman N, Xie XS (2006) Stochastic protein expression in individual cells at the single molecule level. Nature 440(7082):358–362

    Article  Google Scholar 

  18. Di Carlo D, Aghdam N, Lee LP (2006) Single-cell enzyme concentrations, kinetics, and inhibition analysis using high-density hydrodynamic cell isolation arrays. Anal Chem 78(14):4925–4930

    Article  Google Scholar 

  19. Burger R, Kurzbuch D, Gorkin R et al (2015) An integrated centrifugo-opto-microfluidic platform for arraying, analysis, identification and manipulation of individual cells. Lab Chip 15(2):378–381

    Article  Google Scholar 

  20. Eriksson E, Sott K, Lundqvist F et al (2010) A microfluidic device for reversible environmental changes around single cells using optical tweezers for cell selection and positioning. Lab Chip 10(5):617–625

    Article  Google Scholar 

  21. Konry T, Golberg A, Yarmush M (2013) Live single cell functional phenotyping in droplet nano-liter reactors. Sci Rep:3. doi:10.1038/srep03179

  22. Mazutis L, Gilbert J, Ung WL et al (2013) Single-cell analysis and sorting using droplet-based microfluidics. Nat Protoc 8(5):870–891

    Article  Google Scholar 

  23. Gautier A, Juillerat A, Heinis C et al (2008) An engineered protein tag for multiprotein labeling in living cells. Chem Biol 15(2):128–136

    Article  Google Scholar 

  24. Prendergast L, Van Vuuren C, Kaczmarczyk A et al (2011) Premitotic assembly of human CENPs-T and-W switches centromeric chromatin to a mitotic state. PLoS Biol 9(6):e1001082

    Article  Google Scholar 

  25. Bodor DL, Valente LP, Mata JF et al (2013) Assembly in G1 phase and long-term stability are unique intrinsic features of CENP-A nucleosomes. Mol Biol Cell 24(7):923–932. doi:10.1091/mbc.E13-01-0034

    Google Scholar 

  26. Lubeck E, Cai L (2012) Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat Methods 9(7):743–748

    Article  Google Scholar 

  27. Hell SW (2007) Far-field optical nanoscopy. Science 316(5828):1153–1158. 316/5828/1153 [pii]

    Google Scholar 

  28. Shapiro HM (2005) Practical flow cytometry. Wiley, Hoboken

    Google Scholar 

  29. Groner W, Simson E (1995) Practical guide to modern hematology analyzers. Wiley, England

    Google Scholar 

  30. Rust MJ, Bates M, Zhuang X (2006) Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods 3(10):793–796

    Article  Google Scholar 

  31. Dempsey GT, Vaughan JC, Chen KH et al (2011) Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nat Methods 8(12):1027–1036

    Article  Google Scholar 

  32. Schermelleh L, Heintzmann R, Leonhardt H (2010) A guide to super-resolution fluorescence microscopy. J Cell Biol 190(2):165–175. doi:10.1083/jcb.201002018

    Google Scholar 

  33. Kantor AB, Alters SE, Cheal K et al (2004) Immune systems biology: immunoprofiling of cells and molecules. Biotechniques 36(3):520–525

    Google Scholar 

  34. Zuck P, Lao Z, Skwish S et al (1999) Ligand-receptor binding measured by laser-scanning imaging. Proc Natl Acad Sci U S A 96(20):11122–11127

    Article  Google Scholar 

  35. Kamentsky LA (2000) Laser scanning cytometry. Methods Cell Biol 2001(63):51–88

    Google Scholar 

  36. Tibbe AG, de Grooth BG, Greve J et al (1999) Optical tracking and detection of immuno magnetically selected and aligned cells. Nat Biotechnol 17(12):1210–1213

    Article  Google Scholar 

  37. Amnis Corporation, Seattle, WA, U.S.A. https://www.amnis.com/documents/brochures/ISX-MKII%20Brochure_Final_Web.pdf

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jens Ducrée .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Glynn, M., King, D., Ducrée, J. (2016). Systems Biology in Single Cells. In: Tseng, FG., Santra, T. (eds) Essentials of Single-Cell Analysis. Series in BioEngineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49118-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-49118-8_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49116-4

  • Online ISBN: 978-3-662-49118-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics