IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0021211.html
   My bibliography  Save this article

An Information Theoretic, Microfluidic-Based Single Cell Analysis Permits Identification of Subpopulations among Putatively Homogeneous Stem Cells

Author

Listed:
  • Jason P Glotzbach
  • Michael Januszyk
  • Ivan N Vial
  • Victor W Wong
  • Alexander Gelbard
  • Tomer Kalisky
  • Hariharan Thangarajah
  • Michael T Longaker
  • Stephen R Quake
  • Gilbert Chu
  • Geoffrey C Gurtner

Abstract

An incomplete understanding of the nature of heterogeneity within stem cell populations remains a major impediment to the development of clinically effective cell-based therapies. Transcriptional events within a single cell are inherently stochastic and can produce tremendous variability, even among genetically identical cells. It remains unclear how mammalian cellular systems overcome this intrinsic noisiness of gene expression to produce consequential variations in function, and what impact this has on the biologic and clinical relevance of highly ‘purified’ cell subgroups. To address these questions, we have developed a novel method combining microfluidic-based single cell analysis and information theory to characterize and predict transcriptional programs across hundreds of individual cells. Using this technique, we demonstrate that multiple subpopulations exist within a well-studied and putatively homogeneous stem cell population, murine long-term hematopoietic stem cells (LT-HSCs). These subgroups are defined by nonrandom patterns that are distinguishable from noise and are consistent with known functional properties of these cells. We anticipate that this analytic framework can also be applied to other cell types to elucidate the relationship between transcriptional and phenotypic variation.

Suggested Citation

  • Jason P Glotzbach & Michael Januszyk & Ivan N Vial & Victor W Wong & Alexander Gelbard & Tomer Kalisky & Hariharan Thangarajah & Michael T Longaker & Stephen R Quake & Gilbert Chu & Geoffrey C Gurtner, 2011. "An Information Theoretic, Microfluidic-Based Single Cell Analysis Permits Identification of Subpopulations among Putatively Homogeneous Stem Cells," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-10, June.
  • Handle: RePEc:plo:pone00:0021211
    DOI: 10.1371/journal.pone.0021211
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0021211
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0021211&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0021211?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Johan Paulsson, 2004. "Summing up the noise in gene networks," Nature, Nature, vol. 427(6973), pages 415-418, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin Wang & Bo Huang & Xuefeng Xia & Zhirong Sun, 2006. "Funneled Landscape Leads to Robustness of Cell Networks: Yeast Cell Cycle," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-10, November.
    2. Mayu Sugiyama & Takashi Saitou & Hiroshi Kurokawa & Asako Sakaue-Sawano & Takeshi Imamura & Atsushi Miyawaki & Tadahiro Iimura, 2014. "Live Imaging-Based Model Selection Reveals Periodic Regulation of the Stochastic G1/S Phase Transition in Vertebrate Axial Development," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-16, December.
    3. Lee, Julian, 2023. "Poisson distributions in stochastic dynamics of gene expression: What events do they count?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    4. Kyung H Kim & Herbert M Sauro, 2012. "Adjusting Phenotypes by Noise Control," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-14, January.
    5. Jérémie Bourdon & Damien Eveillard & Anne Siegel, 2011. "Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network," PLOS Computational Biology, Public Library of Science, vol. 7(9), pages 1-11, September.
    6. Ruoyu Luo & Lin Ye & Chenyang Tao & Kankan Wang, 2013. "Simulation of E. coli Gene Regulation including Overlapping Cell Cycles, Growth, Division, Time Delays and Noise," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
    7. Elijah Roberts & Andrew Magis & Julio O Ortiz & Wolfgang Baumeister & Zaida Luthey-Schulten, 2011. "Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-21, March.
    8. Keun-Young Kim & Jin Wang, 2007. "Potential Energy Landscape and Robustness of a Gene Regulatory Network: Toggle Switch," PLOS Computational Biology, Public Library of Science, vol. 3(3), pages 1-13, March.
    9. Luca Cardelli & Rosa D Hernansaiz-Ballesteros & Neil Dalchau & Attila Csikász-Nagy, 2017. "Efficient Switches in Biology and Computer Science," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-16, January.
    10. Chen, Aimin & Tian, Tianhai & Chen, Yiren & Zhou, Tianshou, 2022. "Stochastic analysis of a complex gene-expression model," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    11. Yuval Elhanati & Naama Brenner, 2012. "Metabolic Variability in Micro-Populations," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-9, December.
    12. Cappelletti, Daniele & Pal Majumder, Abhishek & Wiuf, Carsten, 2021. "The dynamics of stochastic mono-molecular reaction systems in stochastic environments," Stochastic Processes and their Applications, Elsevier, vol. 137(C), pages 106-148.
    13. Abhyudai Singh & Mohammad Soltani, 2013. "Quantifying Intrinsic and Extrinsic Variability in Stochastic Gene Expression Models," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    14. Tina Toni & Bruce Tidor, 2013. "Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-17, March.
    15. dos Santos, Renato Vieira & da Silva, Linaena Méricy, 2015. "Discreteness induced extinction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 17-25.
    16. Arjun Raj & Charles S Peskin & Daniel Tranchina & Diana Y Vargas & Sanjay Tyagi, 2006. "Stochastic mRNA Synthesis in Mammalian Cells," PLOS Biology, Public Library of Science, vol. 4(10), pages 1-13, September.
    17. Namiko Mitarai & Ian B Dodd & Michael T Crooks & Kim Sneppen, 2008. "The Generation of Promoter-Mediated Transcriptional Noise in Bacteria," PLOS Computational Biology, Public Library of Science, vol. 4(7), pages 1-9, July.
    18. Wang, Jia-Zeng & Ma, Shu & Ji, Yu & Sun, Qi, 2023. "Response to multiplicative noise: The cross-spectrum of membrane voltage fluctuation and voltage-independent conductance noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    19. Tim Lijster & Christoffer Åberg, 2020. "Asymmetry of nanoparticle inheritance upon cell division: Effect on the coefficient of variation," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-18, November.
    20. Hui Zhang & Yueling Chen & Yong Chen, 2012. "Noise Propagation in Gene Regulation Networks Involving Interlinked Positive and Negative Feedback Loops," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0021211. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.