Methods for Processing and Analyzing Single-Channel Data

  • Masahiro Sokabe
Part of the Springer Protocols Handbooks book series (SPH)


Analysis of data from single-channel studies can provide us with insights into the detailed physicochemical features of channel proteins, such as ion permeation rate, ion selectivity, and gating. However, single-channel analysis, particularly gating analysis, is sometimes time-consuming, unconvincing, and unproductive. Notwithstanding, if it is combined with structure information and site-directed mutagenesis of channel proteins, it can prove to be a powerful tool that moves us toward the ultimate understanding of structure–function of channel proteins. This chapter is composed of roughly two parts. The first part describes the technical aspect how to set up the hardware for correct acquisition of the tiny, long-lasting single-channel currents against background noises and drifts. The second part deals with the statistical estimation of the current amplitude and dwell times of the open and closed states and how to fit the estimated data to an appropriate reaction model. Each section is described in a step-by-step fashion so even beginners can understand and experience the course of single-channel study, from data acquisition to model fitting.


Dwell Time Reaction Model Closed State Open Probability Closed Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Miller C (ed) (1986) Ion channel reconstitution. Plenum, New YorkGoogle Scholar
  2. 2.
    Sakmann B, Neher E (eds) (1995) Single-channel recording, 2nd edn. New York, PlenumGoogle Scholar
  3. 3.
    Patlak JB (1988) Sodium channel subconductance levels measured with a new variance-mean analysis. J Gen Physiol 92:413–430PubMedCrossRefGoogle Scholar
  4. 4.
    Hille B (1992) Ionic channels of excitable membranes, 2nd edn. Sinauer, SunderlandGoogle Scholar
  5. 5.
    Colquhoun D, Hawkes AG (1983) The principles of the stochastic interpretation of ion-channel mechanisms. In: Sakmann B, Neher E (eds) Single-channel recording. Plenum, New York, pp 135–189CrossRefGoogle Scholar
  6. 6.
    Sachs F, Neil J, Barkakati N (1983) The automated analysis of data from single ionic channels. Pflugers Arch 395:331–340CrossRefGoogle Scholar
  7. 7.
    Dempster J (1993) Computer analysis of electrophysiological signals. Academic Press, London, p167Google Scholar
  8. 8.
    Colquhoun D (1987) Practical analysis of single channel records. In: Standen NB, Gray PTA, Whitaker MJ (eds) Microelectrode techniques. The Plymus workshop handbook. Company of Biologist Ltd, Cambridge, pp 83–104Google Scholar
  9. 9.
    Sigworth FJ, Sine SM (1987) Data transformations for improved display and fitting of single channel dwell time histograms. Biophys J 48:149–158Google Scholar
  10. 10.
    Jackson MB (1992) Stationary single-channel analysis. Method Enzymol 207:729–746CrossRefGoogle Scholar
  11. 11.
    Nakagawa T, Koyanagi Y (1982) Analysis of experimental data by least square method (in Japanese). University of Tokyo Press, TokyoGoogle Scholar
  12. 12.
    Awaya T (1991) Data analysis (2nd edition, in Japanese). Japan Scientific Societies Press, TokyoGoogle Scholar
  13. 13.
    French RJ, Wonderli WF (1992) Software for acquision and analysis of ion channel data:choices, tasks, and strategies. Method Enzymol 207:711–728CrossRefGoogle Scholar
  14. 14.
    Colquhoun D, Hawkes AG (1981) On the stochastic properties of single ion channels. Proc R Soc Lond B 211:205–235PubMedCrossRefGoogle Scholar
  15. 15.
    Colquhoun D, Hawkes AG (1983) On the stochastic properties of bursts of single ion channel openings and clusters of bursts. Phil Trans R Soc Lond B 300:1–59CrossRefGoogle Scholar

Copyright information

© Springer 2012

Authors and Affiliations

  1. 1.Department of PhysiologyNagoya University Graduate School of MedicineNagoyaJapan

Personalised recommendations