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1. Using STADIA to quantify dynamic instability in microtubules

   https://doi.org/10.1016/bs.mcb.2020.03.002  

   Patel, RJ; Murray, KS; Martin, PO; Sinclair, M; Scripture, JP; Goodson, HV; Mahserejian, SM. (April 2020, Methods in cell biology)

   Quantification of microtubule (MT) dynamic instability (DI) is essential to mechanistic dissection of MT assembly and the activities of MT binding proteins. Typical methods for quantifying MT dynamics assume that MT behavior consists of growth and shortening phases, with instantaneous transitions (rescues and catastrophes) in between. However, examination of DI data at high temporal and spatial resolution reveals the presence of ambiguous behaviors that cannot easily fit into these categories. Failure to objectively recognize and quantify these behaviors could reduce the reproducibility of DI data and impact attempts to dissect mechanisms. To address these problems, we recently developed STADIA (Statistical Tool for Automated Dynamic Instability Analysis), a MT analysis software package that uses length-history data as input and is (presently) implemented in MATLAB. STADIA uses machine learning methods to objectively analyze and quantify macro-level DI behaviors exhibited by MTs, including variable rates of growth and shortening and a newly quantified DI phase: stutter. Here we overview the process of using STADIA to quantify MT dynamics and provide a set of concrete protocols for using STADIA to process and analyze MT length history data. 

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2. Siamese neural network improves the performance of a convolutional neural network in colloidal self-assembly state classification

   https://doi.org/10.1063/5.0244337  

   Lizano-Villalobos, Andres; Namikas, Benjamin; Tang, Xun (November 2024, The Journal of Chemical Physics)

   Identifying the state of the colloidal self-assembly process is critical to monitoring and controlling the system into desired configurations. Recent application of convolutional neural networks with unsupervised clustering has shown a comparable performance to conventional approaches, in representing and classifying the states of a simulated 2D colloidal batch assembly system. Despite the early success, capturing the subtle differences among similar configurations still presents a challenge. To address this issue, we leverage a Siamese neural network to improve the accuracy of the state classification. Results from a Brownian dynamics-simulated electric field-mediated colloidal self-assembly system and a magnetic field-mediated colloidal self-assembly system demonstrate significant improvement from the original convolutional neural network-based approach. We anticipate the proposed improvement to further pave the way for automated monitoring and control of colloidal self-assembly processes in real time and real space. 

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3. Programmable Crowding and Tunable Phases in a Binary Mixture of Colloidal Particles under Light-Driven Thermal Convection

   https://doi.org/10.1021/acs.jpcb.4c02301  

   Lopez-Ceja, Jose; Flores, Vanessa; Juliano, Shirlaine; Machler, Sean; Smith, Stephen; Mansingh, Gargi; Shen, Meng; Tanjeem, Nabila (September 2024, The Journal of Physical Chemistry B)

   We employ photothermally driven self-assembly of colloidal particles to design microscopic structures with programmable size and tunable order. The experimental system is based on a binary mixture of “plasmonic heater” gold nanoparticles and “assembly building block” microparticles. Photothermal heating of the gold nanoparticles under visible light causes a natural convection flow that efficiently assembles the microscale building block particles (diameter 1–10 μm) into a monolayer. We identify the onset of active Brownian motion of colloidal particles under this convective flow by varying the conditions of light intensity, gold nanoparticle concentration, and sample height. We realize a crowded assembly of microparticles around the center of illumination and show that the size of the particle crowd can be programmed using patterned light illumination. In a binary mixture of gold nanoparticles and polystyrene microparticles, we demonstrate the formation of rapid and large-scale crystalline monolayers, covering an area of 0.88 mm2 within 10 min. We find that the structural order of the assembly can be tuned by varying the surface charge of the nanoparticles and the size of the microparticles, giving rise to the formation of different phases–colloidal crystals, crowds, and gels. Using Monte Carlo simulations, we explain how the phases emerge from the interplay between hydrodynamic and electrostatic interactions, as well as the assembly kinetics. Our study demonstrates the promise of self-assembly with programmable shapes and structural order under nonequilibrium conditions using an accessible setup comprising only binary mixtures and LED light. 

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4. Quantification of microtubule stutters: dynamic instability behaviors that are strongly associated with catastrophe

   https://doi.org/10.1091/mbc.E20-06-0348  

   Mahserejian, Shant M.; Scripture, Jared P.; Mauro, Ava J.; Lawrence, Elizabeth J.; Jonasson, Erin M.; Murray, Kristopher S.; Li, Jun; Gardner, Melissa; Alber, Mark; Zanic, Marija; et al (March 2022, Molecular Biology of the Cell)

   Walczak, Claire (Ed.)

   Microtubules (MTs) are cytoskeletal fibers that undergo dynamic instability (DI), a remarkable process involving phases of growth and shortening separated by stochastic transitions called catastrophe and rescue. Dissecting DI mechanism(s) requires first characterizing and quantifying these dynamics, a subjective process that often ignores complexity in MT behavior. We present a Statistical Tool for Automated Dynamic Instability Analysis (STADIA) that identifies and quantifies not only growth and shortening, but also a category of intermediate behaviors that we term “stutters.” During stutters, the rate of MT length change tends to be smaller in magnitude than during typical growth or shortening phases. Quantifying stutters and other behaviors with STADIA demonstrates that stutters precede most catastrophes in our in vitro experiments and dimer-scale MT simulations, suggesting that stutters are mechanistically involved in catastrophes. Related to this idea, we show that the anticatastrophe factor CLASP2γ works by promoting the return of stuttering MTs to growth. STADIA enables more comprehensive and data-driven analysis of MT dynamics compared with previous methods. The treatment of stutters as distinct and quantifiable DI behaviors provides new opportunities for analyzing mechanisms of MT dynamics and their regulation by binding proteins. 

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5. Controlling the shape and topology of two-component colloidal membranes

   https://doi.org/10.1073/pnas.2204453119  

   Khanra, Ayantika; Jia, Leroy L.; Mitchell, Noah P.; Balchunas, Andrew; Pelcovits, Robert A.; Powers, Thomas R.; Dogic, Zvonimir; Sharma, Prerna (August 2022, Proceedings of the National Academy of Sciences)

   Changes in the geometry and topology of self-assembled membranes underlie diverse processes across cellular biology and engineering. Similar to lipid bilayers, monolayer colloidal membranes have in-plane fluid-like dynamics and out-of-plane bending elasticity. Their open edges and micrometer-length scale provide a tractable system to study the equilibrium energetics and dynamic pathways of membrane assembly and reconfiguration. Here, we find that doping colloidal membranes with short miscible rods transforms disk-shaped membranes into saddle-shaped surfaces with complex edge structures. The saddle-shaped membranes are well approximated by Enneper’s minimal surfaces. Theoretical modeling demonstrates that their formation is driven by increasing the positive Gaussian modulus, which in turn, is controlled by the fraction of short rods. Further coalescence of saddle-shaped surfaces leads to diverse topologically distinct structures, including shapes similar to catenoids, trinoids, four-noids, and higher-order structures. At long timescales, we observe the formation of a system-spanning, sponge-like phase. The unique features of colloidal membranes reveal the topological transformations that accompany coalescence pathways in real time. We enhance the functionality of these membranes by making their shape responsive to external stimuli. Our results demonstrate a pathway toward control of thin elastic sheets’ shape and topology—a pathway driven by the emergent elasticity induced by compositional heterogeneity. 

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