Sub-microscopic analysis of t-tubule geometry in living cardiac ventricular myocytes using a shape-based analysis method

- Living cardiomyocytes were dual-labeled with fluorescent surface and volume probes.
- A novel 3D image processing strategy enabled calculation of t-tubule diameter.
- The method shows rabbit and mouse t-tubules have quite different morphologies.
- Mean diameters of rabbit and mouse t-tubules were 360 and 170 nm, respectively.
- Estimated electrical space constants are sufficient to ensure electrical uniformity.

Transverse-axial tubules (TTs) are key structures involved in cardiac excitation-contraction coupling and can become deranged in disease. Although optical measurement of TTs is frequently employed to assess TT abundance and regularity, TT dimensions are generally below the diffraction limit of optical microscopy so determination of tubule size is problematic. TT diameter was measured by labeling both local surface membrane area and volume with fluorescent probes (FM4-64 and calcein, respectively), correcting image asymmetry by image processing and using the relationship between surface area and volume for a geometric primitive. This method shows that TTs have a mean (± SEM) diameter of 356 ± 18 nm in rabbit and 169 ± 15 nm in mouse (p < 0.001). Rabbit TT diameters were more variable than those of mouse (p < 0.01) and the smallest TT detected was 41 nm in mouse and the largest 695 nm in rabbit. These estimates are consistent with TT diameters derived from the more limited sampling of high-pressure frozen samples by electron tomography (which examines only a small fraction of the cell volume). Other measures of TT abundance and geometry (such as volume, membrane fractions and direction) were also derived. On the physiological time scale of E-C coupling (milliseconds), the average TT electrical space constant is ~ 175 μm in rabbit and ~ 120 μm in mouse and is ~ 50% of the steady-state space constant. This is sufficient to ensure reasonable electrical uniformity across normal cells. The image processing strategy and shape-based 3D approach to feature quantification is also generally applicable to other problems in quantification of sub-cellular anatomy.