Currently, there are several approaches like 3D-SIM, PALM, STORM, and others that are able to reveal highly detailed information on the interior of cells.2,3,19
These methods yield images with detail down to 5–10 nm resolution; however, obtaining combined high temporal and
spatial resolution in live cells remains an objective that is yet to be achieved. Although such innovative methods can obtain nanometer-scale information, they require fixing the specimen (3D-SIM, PALM) and they take ~minutes to hours to image (STORM), so if live cell imaging were to ever be implemented with such methods, the ability to resolve temporal details that are especially important when dealing with such dynamic structures as MTs would be lost. By utilizing live cell imaging with the tip tracking algorithm presented here, we can avoid such obstacles.
The goal of this work was to improve the quality of MT tip tracking using conventional digital fluorescence microscopy combined with semi-automated image analysis. While developing the algorithm, we discovered a relationship between the accuracy of the program and the state of MT tips. As a result, the final version of the tip tracking algorithm allowed us to track MT tips at single time points with sub-pixel precision (~36 nm RMS error in live LLCPK1-a cells), surpassing the typical Rayleigh resolution limit on eGFP-tubulin imaging by 6-fold. In addition, the algorithm allowed us to estimate the conformational dynamics of MT tips. Thus, our tip tracking algorithm allows for near-molecular measurement accuracy using conventional imaging systems. This method compares favorably with the methods described earlier, and is suitable for studying dynamic and temporally-evolving MT tips. For example, in the case of manual hand-tracking with a mouse-driven cursor overlaid onto video monitor projection of DIC images, the precision was found to be <500 nm7
and ~160 nm.11
Neither study used model-convolution or fixed MTs to assess the accuracy of the measurements. Subsequently, manual tracking has been used to analyze fluorescence images of MTs, including for GFP-MTs in LLCPK1α cells.17
Rusan et al
. did not report the accuracy and precision, although our own visual assessment of their time series trajectories suggests submicrometer precision. The method of Hadjidemetriou et al
. used high curvature of the isointensity contours of tip points to track MT tips and is described in detail in section 2.6 of their work.12
They estimated the accuracy to be ~260 nm, which is comparable to the previous hand-tracking approaches but has the advantage of being semi-automated. Our present study describes a semi-automated method that has accuracy of ~36 nm, improving on the previous methods by several-fold. In the future it will be important to assess the accuracy under less ideal conditions, including cases where MTs do not lie in the focal plane.
After we found that we could evaluate MT tip structure with our algorithm, we tested whether MT tips in living cells are blunt. When we compared σPF+PSF
distributions from simulated live-cell blunt MTs and the MTs of experimental live cells, the p
-value from a two-sample t-test was <10−69
. Thus, we conclude that not all MTs in our LLCPK1α cells have blunt-ended tips. Rather, we estimate that they have an average standard deviation in their protofilament lengths of ~171 nm. This result gives us insight into the magnitude of single tubulin dimer bonding energies, an issue addressed in works by VanBuren et al
A strong lateral bond relative to the longitudinal bond will result in tubulin dimers forming stronger associations with their lateral neighbors, thereby making the MT tip relatively blunt. Alternatively, if longitudinal bonds are much stronger that the lateral ones, individual protofilaments would favor longitudinal rather than lateral additions, which would result in dynamic growth with non-blunt tips predominating. This second case is what seems to be the situation with live LLC-PK1α cell MTs since blunt tips are relatively rare compared to tips with dispersed protofilament lengths. The extent to which various MT-associated proteins are responsible for the relative strength of the longitudinal bonds will be an important area of future investigation.
Finally, it must be mentioned that there is always a possibility of MTs being affected by cell fixation. We used fixed cell MTs to compare their tip tracking to the tip tracking on the simulated images. Our MT simulations did not include MT dynamics so in order to truly compare “apples to apples” we needed to have non-dynamic experimental MTs which we achieved with cell fixation. We applied our algorithm to simulated as well as experimental fixed cell MTs and found that their step change distributions were nearly identical, which indicated that our model convolution approach for tip tracking validation is adequate. However, if one needs to qualitatively determine effects of fixation altered MT tip structures in any way, one can compare σPF+PSF distributions from live and fixed cells. In our case, cell fixation did not have significant effect on MT tips since the distributions were similar (see ).