Single-molecule microscopy step detection algorithms: kinesin motor proteins and the cellulose synthesis complex

Open Access
Deffenbaugh, Nathan C
Graduate Program:
Master of Science
Document Type:
Master Thesis
Date of Defense:
April 03, 2015
Committee Members:
  • William O Hancock, Thesis Advisor
  • single-molecule microscopy
  • step detection
  • kinesin
  • motor proteins
  • cellulose synthesis complex
  • hidden Markov models
Single-molecule microscopy is a versatile tool that can be used to investigate the stepping mechanism of motor proteins such as kinesin, and to determine the copy number of subunits within membrane bound proteins such as the cellulose synthesis complex. Step detection algorithms provide a means for uncovering critical information within single-molecule microscopy data collected from these systems. Kinesin proteins are intracellular molecular motors that utilize energy from adenosine triphosphate (ATP) in order to transmit force and transport cellular cargo along microtubule tracks. Despite the current wealth of knowledge regarding these proteins, many unresolved mechanisms of the kinesin stepping cycle remain. Step detection algorithms that recover underlying piecewise-constant signals within noise-corrupted, single-molecule time series position data provide a strategy for resolving these mechanisms. The work presented in this thesis shows that by treating a positional time series as an observation sequence from a hidden Markov model, we can apply the model-dependent, continuous Viterbi algorithm in order to determine the most-likely hidden state sequence of the tracked motor protein. This approach has the critical capability of keeping “phase” of plateaus within a given time series, which allows for more accurate determination of kinetic rates and motor domain displacements associated with state transitions during stepping. In growing plant cells, cellulose synthesis complexes (CSCs) exist in the plasma membrane as six-lobed rosettes that contain different cellulose synthase (CESA) isoforms, but the number and stoichiometry of CESAs in each CSC are unknown. To begin to address this question, we performed photobleaching of GFP-tagged AtCESA3-containing particles in living Arabidopsis thaliana cells followed by step detection analysis to estimate copy number. The step detection algorithms introduced in this work account for changes in signal variance due to changing numbers of fluorophores in order to avoid overfitting. These procedures can be applied to photobleaching data for any complex with large numbers of fluorescently tagged subunits, providing a new analytical tool with which to probe complex composition and stoichiometry.