Nanomolar ATP binding to single myosin cross-bridges in rigor: a molecular approach to studying myosin ATP kinetics using single human cardiomyocytes

Abstract

Our knowledge in the field of cardiac muscle and associated cardiomyopathies has been evolving incrementally over the past 60 years and all was possible due to the parallel progress in techniques and methods allowing to take a fresh glimpse at an old problem. Here, we describe an exciting tool used to examine the various states of the human cardiac myosin at the single molecule level. By imaging single Alexa₆₄₇-ATP binding to permeabilised cardiomyocytes using total internal reflection fluorescence (TIRF) microscopy, we are able to acquire large populations of events in a short timeframe (~ 5000 sites in ~ 10 min) and measure each binding event with high spatio-temporal resolution. The applied algorithm decomposes the point pattern of single molecule binding events into individually distinct binding sites that enables us to recover kinetic parameters, such as bound or free time per site. This single molecule binding approach is a useful tool used to examine muscle contractility. Of particular importance is its application to probing the dynamic lifetimes and proportion of myosins in the super-relaxed state in human cardiomyopathies.

Keywords: ATP analogues; ATP-binding kinetics; Human heart cardiomyocytes; SMLM; Single molecule imaging of ATP; TIRF microscopy.

Fig. 1

Example of ATP-Alexa-647 binding to fibril time series acquired using TIRF microscope. a A diagrammatic illustration of the cardiac sarcomere showing the Z discs (orange), the M line (dark green), the thin filaments (purple), myosin filaments (blue) and titin (light green). The nine red bars represent the location of cMyBP-C in the C-zone where it makes contact with the LMM portion of myosin and titin. The A band is central to the sarcomere but the I band, defined by the region containing only thin filament proteins, spans both sides of the Z disc and extends into the next sarcomere. b Schematic of TIRF setup with ATP-Alexa-647 binding and unbinding to the cardiomyocytes, while evanescent field excites only those within ~ 100–200 nm from the surface of the coverslip. Panels c, d, and e show 3 different frames from a 10 000 frames time series. At each frame, molecules (white circles) were detected above threshold signal-to-noise ratio of 6. f A single molecule localisation map obtained after combining centroids of all molecules detected in 10,000 frames

Fig. 2

Workflow for converting the SMLM into a cluster map, defining binding sites for ATP-Alexa-647. Bottom row show the insets defined by dashed lines rectangles area of the images in top row. a SMLM point pattern obtained by TIRF imaging of ATP-Alexa-647 binding. b 2D histogram of molecular localisations, convolved with LoG filter with 30 nm sigma. Peaks are shown as higher amplitudes values (bright features). Red dots represent the locations of peaks detected and representing local clusters central point. c Inverted and mean thresholded (areas outside bright bands of fibrils set to zero) image from b ready to be used in watershed algorithm for clusters segmentation. d Segmented clusters are coloured with different colours and superimposed as black dots is SMLM point pattern. Scale bar in a is equal to 5 μm

Fig. 3

Workflow for extracting a cluster’s ATP-Alexa-647 binding kinetics. a Segmented cluster example zooms in from Fig. 2d, showing each cluster colour coded differently. The black dots are showing the single molecule localisations. b Red circle shows the cluster for which the example of molecular occupancy is shown. When cluster occupancy is equal to 1, it reflects that a molecule is bound to this cluster and 0 signifies that cluster was not occupied for these frames. c The zoom in to the frames 1200 to 1300 of occupancy diagram shown in b). The episodes for which cluster was occupied (green arrows) are defined as bound time, t_bound, and frames for which cluster was not occupied (red arrows) are grouped in free time, t_free. d Examples of histograms of average bound and free times extracted for one field of view (FOV) imaged. For each cluster, we obtain one value of average bound and free time. Since there are several thousand of clusters per FOV, histograms like the ones shown are obtained per FOV. e Comparison of average free and f bound time between a donor heart fibril before (blue) and after (red) blebbistatin treatment. Error bars represent standard error of the mean value for 7–10 FOVs recorded

Fig. 4

Flow diagram of steps taken between obtaining a heart sample and image analysis