Quantification of Microtubule Stutters: Dynamic Instability Behaviors that are Strongly Associated with Catastrophe

Abstract

Microtubules (MTs) are dynamic polymers with critical roles in processes ranging from membrane transport to chromosome separation. Central to MT function is dynamic instability (DI), a behavior typically assumed to consist of growth and shortening, with sharp transitions in between. However, this two-state assumption disregards details in MT behavior that are evident in high-resolution data. For example, MTs exhibit growth rate variability, and pinpointing where transitions begin can be difficult when viewed at high spatiotemporal resolution. These observations suggest that MT behavior is more complicated than implied by standard quantification methods. To address these problems, we developed STADIA (Statistical Tool for Automated Dynamic Instability Analysis). STADIA's methods are rooted in machine learning to objectively analyze and quantify macro-level DI behaviors exhibited by MTs. Applying STADIA to MT length-history data revealed a transient, intermediate phase that we term 'stutter', during which the rate of MT length change is smaller in magnitude than growth or shortening phases. Significantly, most catastrophe events in both simulations and experiments are preceded by stutters, suggesting that this newly recognized phase is mechanistically involved in catastrophes. Consistent with this idea, a MT anti-catastrophe factor (CLASP2γ) increases the likelihood of growth following a stutter phase in experiments. We conclude that STADIA enables unbiased identification of DI phases including stutters, producing more complete and accurate DI measurements than possible with classical analysis methods. Identifying stutters as a distinct and quantifiable phase provides a new target for mechanistic studies regarding DI phase transitions and their regulation by MT binding proteins. 5The advent of high-resolution data acquisition has revealed an additional problem with standard DI analysis: it can be difficult to determine with reasonable precision where transitions between phases begin and end (Figure 1 C,E). This observation leaves researchers to make subjective judgments or to use 'in-house' software with non-adaptive criteria to identify the points where phase transitions occur (e.g., (Yenjerla, Lopus, and Wilson 2010; Goodson and Jonasson 2018; Zanic 2016)). To illustrate this problem, consider the zoomed-out length-history plots that are typically used for DI analysis (Figure 1 B,D). Examination of these plots can make the task of determining when transitions occur look trivial. However, the zoomed-in views made possible by high-resolution data acquisition demonstrate that any software or method using the aforementioned two-state behavior framework will have difficulty categorizing ambiguous behavior that often occurs between growth and shortening phases (Figure 1 C,E).Taken together, these issues indicate that there is significant need for an improved method of characterizing MT length-history data that removes the two-state behavior assumption and allows for unbiased, objective quantification of MT behavior a...

Chemomechanical Simulation of Microtubule Dynamics With Explicit Lateral Bond Dynamics

“…The growth paths appear very similar to experimentally observed catastrophe and rescue events. Catastrophes typically feature an initial "transitional" phase of slow shrinking by 50-100 nm as also observed in reference [35]. Moreover, we observe "dips" in the growth paths resembling the "stutter" events from reference [35].…”

“…Figure 10 shows typical MT growth paths featuring two catastrophe events and a rescue event in subfigure (C). Moreover, we observe "dips" in the growth path where a short phase of shrinking appears, which are similar to "stutter" events that have been observed in reference [35]. Videos of these two simulations with two-and threedimensional representations of the MT structure can be found in the Supplementary Material.…”

“…Both catastrophes and the rescue show that the transition from a high probability to stay in the current dynamic state to a high probability to switch into the other dynamic state occurs within a few seconds. In Figures 10A,C, we first observe that catastrophes become practically unavoidable [red color code in (A.C) and (C.C)] after a phase of relatively slow shrinking by 50-100 nm; similar "transitional catastrophe" behavior has been observed in reference [35]. A dip, on the other hand, can only evade a catastrophe [yellow to red color code in (A.D) and (C.D)] if the MT length shrinks by significantly < 50 nm.…”

“…There is, however, a significantly increased roughness of the trajectory during the growth phase, which could be interpreted as increased occurrence of "dips" or "stutter" events. A high probability of stutter events has also been observed in reference [35], which supports the existence of a mechanochemical coupling in hydrolysis. The catastrophe-triggering configuration of a "nucleus" of several neighboring protofilaments shrinking by more than 6 dimers is also similar as snapshots 4 and 5 in Figure 13B show.…”

“…Cracklike catastrophe events are, however, triggered by adjusting chemical rupture rates rather than including MT mechanics. The model by Margolin et al [33] has successfully reproduced features of the experimentally observed MT dynamic instability [35] but relies on a heuristic tuning of simulation parameters.…”