Biology subjects

Kusumaatmaja, H.

Publications and source records attributed to Kusumaatmaja, H..

6 recordsLinked to original sources

bioRxiv2026

Shapes of condensate droplets containing filaments

The interactions of droplets and filaments can lead to mutual deformations and complex combined behavior. Such interactions also occur within the cell, where biomolecular condensates, distinct liquid phases often composed of proteins, have been observed to structure and affect the organization of the cytoskeleton. In particular, biomolecular condensates have been shown to undergo characteristic deformations when cytoskeletal filaments are fully embedded within them. However, a full understanding of the underlying physical mechanisms is still missing. Here, we combine experiments with coarse-grained molecular dynamics simulations and analytical models to uncover the physical mechanisms that define emerging shapes of droplets containing filaments. We find that the surface tension of the liquid phase and the bending energy of the filament(s) suffice to accurately capture emerging shapes if the length of the filament is small compared to the liquid volume. As the volume fraction of filament(s) increases, wetting effects become increasingly important, setting physical constraints within which surface and bending energies compete to define the droplet shapes. We find that mutual deformations of condensate and filament extend accessible shapes beyond classical stability considerations, leading to structuring and entrapment of contained filaments. Shape deformations may further affect ripening dynamics that favor certain geometries. Our findings provide a physical framework for a better understanding of the possible roles of biomolecular condensates in cytoskeletal organization.

biophysics↗

bioRxiv2025

FlickerPrint: An Analysis Package for Measuring Interfacial Tension and Bending Rigidity of Biomolecular Condensates and Vesicles at Scale

1 In BriefWilliamson, Law et. al. present FlickerPrint, a computational analysis tool which can be used for measuring the interfacial tension and bending rigidity of soft fluctuating bodies, including biomolecular condensates, droplets or vesicles, from confocal microscopy images using flicker spectroscopy. This method is highly scalable so can be used to analyse the properties of whole populations of thousands of such soft bodies. 2 MotivationBiomolecular condensates play fundamental roles in sub-cellular organisation and it is well-known that the composition of condensates can affect their function. Measuring the conden-sates mechanical properties (for example, interfacial tension and bending rigidity) can aid the understanding of their biomolecular composition and cellular functions. However, measuring the properties of individual condensates under physiological conditions is very challenging and cum-bersome to scale to the population level using traditional methods. To overcome these issues, we have developed a software package to run flicker spectroscopy analysis of condensates at scale, to determine their interfacial tension and bending rigidity. At the same time, FlickerPrint can be harnessed to analyse other soft, fluctuating bodies such as lipid vesicles. 3 SummaryAccurate measurement of the mechanical properties of biomolecular condensates is an essential step in understanding their behaviour within cells. We present FlickerPrint, an open-source Python package to determine the interfacial tension and bending rigidity of thousands of biomolecular condensates using flicker spectroscopy by analysing their shape fluctuations in confocal microscopy images. We detail the workflow used by FlickerPrint to scale up these individual measurements to the population level and the computational requirements to run Flicker-Print. We provide examples of experiments in live cells and in vitro which are suitable for analysis with FlickerPrint as well as scenarios where the package cannot be used. Using these examples, we show that the results obtained from the analysis are robust to changes in the imaging setup, including frame rate. This implementation enables a step-change in the capability to measure two key properties of biomolecular condensates, the interfacial tension and bending rigidity. Moreover, the tools in FlickerPrint are also applicable for analysing other soft, fluctuating bodies, which we demonstrate here using vesicles.

cell biology↗

bioRxiv2022

Using Shape Fluctuations to Probe the Mechanics of Stress Granules

Surface tension plays a significant role in many functions of biomolecular condensates, from governing the dynamics of droplet coalescence to determining how condensates interact with and deform lipid membranes and biological filaments. To date, however, there is a lack of accurate methods to measure the surface tension of condensates in living cells. Here, we present a high-throughput flicker spectroscopy technique that is able to analyse the thermal fluctuations of the surfaces of tens of thousands of condensates to extract the distribution of surface tensions. Demonstrating this approach on stress granules, we show for the first time that the measured fluctuation spectra cannot be explained by surface tension alone. It is necessary to include an additional energy contribution, which we attribute to an elastic bending rigidity and suggests the presence of structure at the granule-cytoplasm interface. Our data also show that stress granules do not have a spherical base-shape, but fluctuate around a more irregular geometry. Taken together, these results demonstrate quantitatively that the mechanics of stress granules clearly deviate from those expected for simple liquid droplets.

cell biology↗

bioRxiv2022

Systematic Characterization of Double Emulsion Droplets for Biological Applications

Double emulsion droplets (DEs) are water/oil/water droplets that can be sorted via Fluorescence-Activated Cell Sorting (FACS), allowing for new opportunities in high-throughput cellular analysis, enzymatic screening, and synthetic biology. These applications require stable, uniform droplets with predictable microreactor volumes. However, predicting DE droplet size, shell thickness, and stability as a function of flow rate has remained challenging for monodisperse single core droplets and those containing biologically-relevant buffers, which influence bulk and interfacial properties. As a result, developing novel DE-based bioassays has typically required extensive initial optimization of flow rates to find conditions that produce stable droplets of the desired size and shell thickness. To address this challenge, we conducted systematic size parameterization quantifying how differences in flow rates and buffer properties (viscosity and interfacial tension at water/oil interfaces) alter droplet size and stability, across 6 inner aqueous buffers used across applications such as cellular lysis, microbial growth, and drug delivery, quantifying the size and shell thickness of >22,000 droplets overall. We restricted our study to stable single core droplets generated in a 2-step dripping-dripping formation regime in a straightforward PDMS device. Using data from 138 unique conditions (flow rates and buffer composition), we also demonstrated that a recent physically-derived size law of Wang et al1 can accurately predict double emulsion shell thickness for >95% of observations. Finally, we validated the utility of this size law by using it to accurately predict droplet sizes for a novel bioassay that requires encapsulating growth media for bacteria in droplets. This work has the potential to enable new screening-based biological applications by simplifying novel DE bioassay development.

bioengineering↗

bioRxiv2019

Unrestrained ESCRT-III drives chromosome fragmentation and micronuclear catastrophe

The ESCRT-III membrane fission machinery1,2 restores nuclear envelope integrity during mitotic exit3,4 and interphase5,6. Whereas primary nuclei resealing takes minutes, micronuclear envelope ruptures appear irreversible and result in catastrophic collapse associated with chromosome fragmentation and rearrangements (chromothripsis), thought to be a major driving force in cancer development7-10. Despite its importance11-13, the mechanistic underpinnings of nuclear envelope sealing in primary nuclei and the defects observed in micronuclei remain largely unknown. Here we show that CHMP7, the nucleator of ESCRT-III filaments at the nuclear envelope3,14, and the inner nuclear membrane protein LEMD215 act as a compartmentalization sensor detecting the loss of nuclear integrity. In cells with intact nuclear envelope, CHMP7 is actively excluded from the nucleus to preclude its binding to LEMD2. Nuclear influx of CHMP7 results in stable association with LEMD2 at the inner nuclear membrane that licenses local polymerization of ESCRT-III. Tight control of nuclear CHMP7 levels is critical, as induction of nuclear CHMP7 mutants is sufficient to induce unrestrained growth of ESCRT-III foci at the nuclear envelope, causing dramatic membrane deformation, local DNA torsional stress, single-stranded DNA formation and fragmentation of the underlying chromosomes. At micronuclei, membrane rupture is not associated with repair despite timely recruitment of ESCRT-III. Instead, micronuclei inherently lack the capacity to restrict accumulation of CHMP7 and LEMD2. This drives unrestrained ESCRT-III recruitment, membrane deformation and DNA defects that strikingly resemble those at primary nuclei upon induction of nuclear CHMP7 mutants. Preventing ESCRT-III recruitment suppresses membrane deformation and DNA damage, without restoring nucleocytoplasmic compartmentalization. We propose that the ESCRT-III nuclear integrity surveillance machinery is a double-edged sword, as its exquisite sensitivity ensures rapid repair at primary nuclei while causing unrestrained polymerization at micronuclei, with catastrophic consequences for genome stability16-18.

cell biology↗

bioRxiv2018

Learning dynamical information from static protein and sequencing data

Many complex processes, from protein folding and virus evolution to brain activity and neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. While efficient algorithms for cluster detection and data completion in high-dimensional spaces have been developed and applied over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here, we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. Our approach combines Gaussian mixture approximations and self-consistent dimensionality reduction with minimal-energy path estimation and multi-dimensional transition-state theory. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein folding transitions, gene regulatory network motifs and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations and phylogenetic trees, respectively. The underlying numerical protocol thus allows the recovery of relevant dynamical information from instantaneous ensemble measurements, effectively alleviating the need for time-dependent data in many situations. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein sequencing datasets and future cryo-electron-microscopy data, and can guide the design of new experimental approaches towards studying complex multiphase phenomena.

bioinformatics↗