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bioRxiv2019

Critical analysis of the mosquito repellency evoked by the 10-34 kHz recorded animal sounds: The case of the African female A. gambiae s.s

Animals sounds have been mimicked in electronic mosquito repellents (EMRs) and exploited as a tool in the control of malaria by targeting the vector, the female Anopheles gambiae s.s. The claimed mosquito repellency of 30.3 % due to Anti-Pic(R), an electronic mosquito repellent, had failed to be confirmed in subsequent studies. However, studies on mosquito startle based on initial behavioural activities without an attractant yielded 34.12 % repellency elicited by the 10-34 kHz recorded sound of O. tormota. Other malaria intervention measures involving the use of chemicals have been impeded by the pathogen and vector resistance hence slowing down the rate of decline of malaria morbidity and mortality. The research thus focused on the analytical study of the African female A. gambiae s.s repellency evoked by the 10-34 kHz recorded animal sound of male mosquito, Anopheles gambiae and Delphinapterus leucas. Landing rates and behavioural startle responses of the mated female A. gambiae on food attractant evoked by the individual sound of the male mosquito, A. gambiae, O. tormota and D. leucas were determined and analysed. The male and female A. gambiae were bred and reared under controlled laboratory conditions of 60-80 % humidity, 25{+/-}2 {degrees}C temperature with equal light-darkness hour cycle in KEMRI, entomology laboratories. Isolation of the male and female mosquitoes from a swarm was based on physical features and affinity to blood meal. The sounds of O. tormota and D. leucas were acquired and the sound of the male A. gambiae were recorded from the Kenya Medical Research Institute (KEMRI) entomology laboratory, Kisumu. The sounds were filtered into 10-34 kHz frequency band and analysed using Avisoft-SAS LAB Pro version 5.2 and Raven Pro 1.5 software. The sound of O. tormota was also studied. A fighto-Y glass cage well designed into control, neutral and treatment chambers was used in the study. Both control and treatment chambers were connected to blood meal maintained at 38.60{degrees}C. The treatment cage was also connected to the source of sound and a swarm of 50 female mosquitoes into the neutral cage and observed for 1,200 s. The sounds of the A. gambiae, O. tormota and D. leucas yielded 2.10, 2.20 and 3.00 landings/minute respectively associated with adverse behaviour. The protection index (PI) anchored on the number of mosquitoes that landed, probed and fed on the blood meal in the treatment and neutral cage for the sounds of the A. gambiae, O. tormota and D. leucas was 42.73 %, 40.24 % and 10.64 % respectively. The sound of the A. gambiae was characterised by steady and minimally dipped pulsate acoustic power with wide bandwidth. The protection index achieved by the sound of the male A. gambiae did not differ significantly from the sound of O. tormota (0.1740 > 0.05), though differed significantly from the sound emitted from the Anti-Pic(R) EMR (p = 5.3440 x 10-5). The author summaryPhilip Amuyunzu Mangare is a PhD. Physics student in Egerton University. He has authored many papers and books. He is currently a Lecturer of Physics (Electronics), Masinde Muliro University of Science and Technology. He is a member of the Biophysical Society and the current President of Biophysical society (Kenya). Prof. Ndiritu F. Gichuki, is a Professor of Physics Egerton University. Currently he is the Registrar Academic Affairs in Chuka University. His vast experience has seen him supervise many postgraduate students who have taken key positions in the society. Prof. Samwel Rotich is a Profesor of Physics in Moi University specialising in Electronics. He has a wide experience in Physics and Biophysics. He is a registered member of the Biophysical Society and the Patron of Biophysical Society Kenya Chapter. He has published many papers and supervised many postgraduate students. Dr. Makatiani Kubochi is a Lecturer in Moi University with vast experience in entomology. She has published many papers and supervised many postgraduate students. Dr. Rapando Bernard Wakhu is a renown theoretical Physicist with experience in acoustics and Fourier analysis based in Masinde Muliro University of Science and Technology. He has supervised many postgraduate students and published many papers.

animal behavior and cognition↗
bioRxiv2026

How simple physics drives the earliest stages of embryogenesis

The initial stages of mammalian embryo development involve a single fertilised egg that repeatedly divides to create a solid ball of cells called a morula. Despite the apparent simplicity of this process, which involves only one cell type and a few tens of cells, there are still a host of unanswered questions, particularly around the underlying biophysical mechanisms that are at play. To address this, we here develop a novel type of vertex model that includes cortical tension, cell-to-cell adhesion, membrane curvature and cell volume forces, along with a zona pellucida, cell division and the effect of noise. We fit our model to both mouse and human experimental data, which allows us to address a number of key questions including the relative roles of adhesion and tension, how the cortical tension varies around the cell, the purpose of the zona pellucida, and the rules governing the first few cell divisions. We also determine the biophysical effects responsible for compaction and internalisation, including addressing why the morula does not typically decompact during internalisation. Next, we investigate the position-versus-polarisation debate during trophectoderm differentiation, how the division axis is determined during later divisions, and the role of noise. Finally, we compare human and mouse, focussing on the key similarities that may span all mammals. Our use of a force-based computational model allows us to address fundamental questions relating to mammalian development, particularly the underlying biophysical rules governing early embryogenesis, with important applications to stem cell models such as blastoids, conservation efforts of endangered species and embryo grading during IVF. Significance StatementThe very first stages of embryogenesis are a vital but still poorly understood part of development, with important applications to fertility, fertility lifespan and assisted conception such as IVF. Most research in this area has focussed on experimental approaches, ignoring the potential for biophysical modelling. Here, we address this by developing a novel computer simulation of the first thee-to-four cell divisions of the fertilised egg, resulting in a compact bunch of cells called a morula. In particular, we develop a new type of vertex model for the early embryo that for the first time includes contributions from a range of realistic biophysical forces. By analysing real mouse and human embryo data through our model, we reveal how simple physics drives crucial early developmental processes, including compaction, internalisation, cleavage, noise and species-specific differences.

developmental biology↗
bioRxiv2026

Characterizing Physicochemical Selection in Protein Evolution with Property-Informed Models (PRIME)

AbstractStandard probabilistic models of coding sequence evolution effectively identify where and when selection acts but remain agnostic to the mechanistic realization of these forces. We introduce PRIME (PRoperty Informed Models of Evolution), a framework of codon-level maximum likelihood methods--including global (G-PRIME), episodic (E-PRIME), and site-specific (S-PRIME) implementations--that explicitly model amino acid exchange-ability as a function of physicochemical properties. By parameterizing attributes such as molecular volume, hydropathy, and secondary structure propensities, PRIME resolves the biophysical basis of selective constraint across both the sequence and the phylogeny. At the site level, S-PRIME leverages an explicit biophysical taxon-omy to precisely categorize residues as conserved, neutral, or changing for specific properties, resolving selective signals that remain invisible to traditional rate-based metrics. Our analysis of a benchmark of 24 diverse datasets and a genome-wide screen of 18,944 mammalian genes demonstrates that biophysical realism yields substantial improvements in model fit, acting synergistically with rate variation to explain complex evolutionary patterns. We find that power to detect physicochemical constraints at individual sites is fundamentally governed by simple informational redundancy (substitutions per unique amino acid; AUC = 0.91), with sensitivity exceeding 90% in data-rich alignments. E-PRIME reveals a distinct biophysical hierarchy: while core packing and beta-sheet scaffolds are rigidly conserved, alpha-helix propensity and surface electrostatics serve as the primary substrates for adaptive tuning. Furthermore, PRIME importance weights align with aspects of the primary semantic axes of deep learning representations (ESM-2) and capture key features of experimental fitness landscapes. By trans-forming abstract evolutionary rates into interpretable biophysical rules, PRIME provides a useful framework for characterizing the mechanistic drivers of protein diversity.

evolutionary biology↗
bioRxiv2016

NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor

NODDI-DTI is a simplification of the NODDI model that, when its underlying assumptions are met, allows extraction of biophysical parameters from standard single-shell DTI data, permitting biophysical analysis of DTI datasets.\n\nIn contrast to the NODDI signal model, the NODDI-DTI signal model contains no CSF compartment, restricting its application to voxels with very low CSF partial-volume contamination. This simplification allowed derivation of simple analytical relations between parameters representing axon density, v and dispersion,{tau} , and DTI invariants (MD and FA) through moment expansion of the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. It should be emphasised that the NODDI-DTI model inherits the strong assumptions of the NODDI model, and so is nominally restricted to analysis of healthy brains.\n\nNODDI-DTI parameter estimates were computed by applying the proposed analytical relations to DTI parameters extracted from the first shell of data, and compared to parameters extracted by fitting the NODDI-DTI model to (i) both shells (recommended) and (ii) the first shell (as for DTI) of data in the white matter of three different in vivo diffusion datasets. NODDI-DTI parameters estimated from DTI and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model it was found that diffusional kurtosis strongly biased DTI-based MD values, and so a novel heuristic correction requiring only DTI data was derived and used to account for this bias.\n\nOur results demonstrate that NODDI-DTI is a promising model and technique to interpret restricted datasets acquired for DTI analysis with greater biophysical specificity, though its limitations must be borne in mind.

Biophysics↗
bioRxiv2021

Lipid fingerprints are similar between SLC6 transporters in the neuronal membrane

We use molecular dynamics simulations to characterise the local lipid annulus, or "fingerprint", of three SLC6 transporters (dDAT, hSERT, and GlyT2) embedded into a complex neuronal membrane. New membrane analysis tools were created to improve leaflet detection and leaflet-dependent properties. Overall, lipid fingerprints are comprised of similar lipids when grouped by headgroup or tail saturation. The enrichment and depletion of specific lipids, including sites of cholesterol contacts, varies between transporters. The subtle differences in lipid fingerprints results in varying membrane biophysical properties near the transporter. Through comparisons to previous literature, we highlight that the lipid-fingerprint in complex membranes is highly dependent on membrane composition. Furthermore, through embedding these transporters in a simplified model membrane, we show that the simplified membrane is not able to capture the biophysical properties of the complex membrane. Our results further characterise how the presence and identity of membrane proteins affects the complex interplay of lipid-protein interactions, including the local lipid environment and membrane biophysical properties. HIGHLIGHTSO_LILipid fingerprints are comprised of similar lipid classes C_LIO_LISites of specific lipid contacts, including CHOL, varies between transporters C_LIO_LIChanges in lipid annulus result in variable local membrane biophysical properties C_LIO_LIMembrane composition, including that of complex membranes, affects lipid annulus C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/427530v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@ae6ee9org.highwire.dtl.DTLVardef@1f39af0org.highwire.dtl.DTLVardef@412256org.highwire.dtl.DTLVardef@355f1c_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗
bioRxiv2021

Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments

The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. We argue that answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a stochastic transcription rate coupled to a discrete stochastic RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. Although biophysically distinct, these models are mathematically similar, and we show they are hard to distinguish without comparing whole predicted probability distributions. Our work illustrates the importance of theory-guided data collection, and introduces a general framework for constructing and solving mathematically nontrivial continuous-discrete stochastic models. Significance StatementThe interpretation of transcriptomic observations requires detailed models of biophysical noise that can be compared and fit to experimental data. Models of intrinsic noise, describing stochasticity in molecular reactions, and extrinsic noise, describing cell-to-cell variation, are particularly common. However, integrating and solving them is challenging, and previous results are largely limited to summary statistics. We examine two mechanistically grounded stochastic models of transcriptional variation and demonstrate that (1) well-known regimes naturally emerge in limiting cases, and (2) the choice of noise model significantly affects the RNA distributions, but not the lower moments, offering a route to model identification and inference. This approach provides a simple and biophysically interpretable means to construct and unify models of transcriptional variation.

biophysics↗
bioRxiv2023

3D Cell Cultures Amplify Diffusion Signals

Biophysical models can predict the behavior of cell cultures including 3D cell aggregates (3DCAs), thereby reducing the need for costly and time-consuming experiments. Specifically, mass transfer models enable studying the transport of nutrients, oxygen, signaling molecules, and drugs in 3DCA. These models require the defining of boundary conditions (BC) between the 3DCA and surrounding medium. However, accurately modeling the BC that relates the inner and outer boundary concentrations at the border between the 3DCA and the medium remains a challenge that this paper addresses using both theoretical and experimental methods. The provided biophysical analysis indicates that the concentration of molecules inside boundary is higher than that at the outer boundary, revealing an amplification factor that is confirmed by a particle-based simulator (PBS). Due to the amplification factor, the PBS confirms that when a 3DCA with a low concentration of target molecules is introduced to a culture medium with a higher concentration, the molecule concentration in the medium rapidly decreases. The theoretical model and PBS simulations were used to design a pilot experiment with liver spheroids as the 3DCA and glucose as the target molecule. Experimental results agree with the proposed theory and derived properties. Author summaryThe primary objective of our research was to enable the development of reliable biophysical models for three-dimensional cell aggregates (3DCAs). To achieve this goal, we employed a combination of theoretical and experimental methods to derive and characterize the amplification boundary condition (BC), which represents the relation of inner and outer boundary concentrations at the border between a 3DCA and its surrounding medium. By understanding the amplificaiton BC, we can better comprehend the transport and diffusion processes that occur within 3DCAs. The significance of our research lies in its potential to advance the understanding of 3DCAs and their underlying biophysical processes. This knowledge is crucial for a wide range of applications, including drug design and analysis of drug dosages within tissues. This factor may provide insight into the mechanisms behind tumor development and morphogenesis. In particular, the packed structure of cancer tumors enables them to receive and trap a higher concentration of nutrients and oxygen molecules based on the amplification factor. Thus, this study could contribute to the development of novel approaches to manage and treat cancerous tissues.

biophysics↗
bioRxiv2023

Electrostatic encoding of genome organization principles within single native nucleosomes

The eukaryotic genome, first packed into nucleosomes of about 150 bp around the histone core, is organized into euchromatin and heterochromatin, corresponding to the A and B compartments, respectively. Here, we asked if individual nucleosomes in vivo know where to go. That is, do mono-nucleosomes by themselves contain A/B compartment information, associated with transcription activity, in their biophysical properties? We purified native mono-nucleosomes to high monodispersity and used physiological concentrations of biological polyamines to determine their condensability. The chromosomal regions known to partition into A compartments have low condensability and vice versa. In silico chromatin polymer simulations using condensability as the only input showed that biophysical information needed to form compartments is all contained in single native nucleosomes and no other factors are needed. Condensability is also strongly anticorrelated with gene expression, and especially so near the promoter region and in a cell type dependent manner. Therefore, individual nucleosomes in the promoter know whether the gene is on or off, and that information is contained in their biophysical properties. Comparison with genetic and epigenetic features suggest that nucleosome condensability is a very meaningful axis onto which to project the high dimensional cellular chromatin state. Analysis of condensability using various condensing agents including those that are protein-based suggests that genome organization principle encoded into individual nucleosomes is electrostatic in nature. Polyamine depletion in mouse T cells, by either knocking out ornithine decarboxylase (ODC) or inhibiting ODC, results in hyperpolarized condensability, suggesting that when cells cannot rely on polyamines to translate biophysical properties of nucleosomes to control gene expression and 3D genome organization, they accentuate condensability contrast, which may explain dysfunction known to occur with polyamine deficiency.

biophysics↗
bioRxiv2024

ComboPath: An ML system for predicting drug combination effects with superior model specification

Drug combinations have been shown to be an effective strategy for cancer therapy, but identifying beneficial combinations through experiments is labor-intensive and expensive [Mokhtari et al., 2017]. Machine learning (ML) systems that can propose novel and effective drug combinations have the potential to dramatically improve the efficiency of combinatoric drug design. However, the biophysical parameters of drug combinations are degenerate, making it difficult to identify the ground truth of drug interactions even given experimental data of the highest quality available. Existing ML models are highly underspecified to meet this challenge, leaving them vulnerable to producing parameters that are not biophysically realistic and harming generalization. We have developed a new ML model, "ComboPath", aimed at a novel ML task: to predict interpretable cellular dose response surface of a two-drug combination based on each drugs interactions with their known protein targets. ComboPath incorporates a biophysically-motivated intermediate parameterization with prior information used to improve model specification. This is the first ML model to nominate beneficial drug combinations while simultaneously reconstructing the dose response surface, providing insight on both the potential of a drug combination and its optimal dosing for therapeutic development. We show that our models were able to accurately reconstruct 2D dose response surfaces across held out combination samples from the largest available combinatoric screening dataset while substantially improving model specification for key biophysical parameters.

biophysics↗
bioRxiv2024

Predicting the macrovascular contribution to resting-state fMRI functional connectivity at 3 Tesla: A model-informed approach

Macrovascular biases have been a long-standing challenge for fMRI, limiting its ability to detect spatially specific neural activity. Recent experimental studies, including our own (Huck et al., 2023; Zhong et al., 2023), found substantial resting-state macrovascular BOLD fMRI contributions from large veins and arteries, extending into the perivascular tissue at 3 T and 7 T. The objective of this study is to demonstrate the feasibility of predicting, using a biophysical model, the experimental resting-state BOLD fluctuation amplitude (RSFA) and associated functional connectivity (FC) values at 3 Tesla. We investigated the feasibility of both 2D and 3D infinite-cylinder models as well as macrovascular anatomical networks (mVANs) derived from angiograms. Our results demonstrate that: 1) with the availability of mVANs, it is feasible to model macrovascular BOLD FC using both the mVAN-based model and 3D infinite-cylinder models, though the former performed better; 2) biophysical modelling can accurately predict the BOLD pairwise correlation near to large veins (with R2 ranging from 0.53 to 0.93 across different subjects), but not near to large arteries; 3) compared with FC, biophysical modelling provided less accurate predictions for RSFA; 4) modelling of perivascular BOLD connectivity was feasible at close distances from veins (with R2 ranging from 0.08 to 0.57), but not arteries, with performance deteriorating with increasing distance. While our current study demonstrates the feasibility of simulating macrovascular BOLD in the resting state, our methodology may also apply to understanding task-based BOLD. Furthermore, these results suggest the possibility of correcting for macrovascular bias in resting-state fMRI and other types of fMRI using biophysical modelling based on vascular anatomy.

biophysics↗
bioRxiv2024

Tracing Cellular Senescence in Bone: Time-Dependent Changes in Osteocyte Cytoskeleton Mechanics and Morphology

Aging-related bone loss significantly impacts the growing elderly population globally, leading to debilitating conditions such as osteoporosis. Senescent osteocytes play a crucial role in the aging process of bone. This longitudinal study examines the impact of continuous local and paracrine exposure to senescence-associated secretory phenotype (SASP) factors on senescence-associated biophysical and biomolecular markers in osteocytes. We found significant cytoskeletal stiffening in irradiated osteocytes, accompanied by expansion of F-actin areas and a decline in dendritic integrity. These changes, correlating with alterations in pro-inflammatory cytokine levels and osteocyte-specific gene expression, support the reliability of biophysical markers for identifying senescent osteocytes. Notably, local accumulation of SASP factors had a more pronounced impact on osteocyte properties than paracrine effects, suggesting that the interplay between local and paracrine exposure could substantially influence cellular aging. This study underscores the importance of osteocyte mechanical and morphological properties as biophysical markers of senescence, highlighting their time-dependence and differential effects of local and paracrine SASP exposure. Collectively, our investigation into biophysical senescence markers offer unique and reliable functional hallmarks for non-invasive identification of senescent osteocytes, providing insights that could inform therapeutic strategies to mitigate aging-related bone loss.

biophysics↗
bioRxiv2024

Elastic analysis of structural ensemble reveals the energetic basis of hand-over-hand in a AAA+ motor

Proteins carry out cellular functions by changing their structure among a few conformations, each characterised by a different energy level. Therefore, structural changes, energy transformations, and protein function are intimately related. Despite its central importance, this relationship remains elusive. For example, while many hexameric ATPase motors are known to function using a hand-over-hand alternation of subunits, how energy transduction throughout the assembly structure drives the hand-over-hand mechanism is not known. In this work, we unravel the energetic basis of hand-over-hand in a model AAA+ motor, RuvB. To do so, we develop a general method to compute the residue-scale elastic pseudoenergy due to structure changes and apply it to RuvB structures, recently resolved through cryo-EM. This allows us to quantify how progression through RuvBs mechanochemical cycle translates into residue-scale energy transduction. In particular, we find that DNA binding is associated with overcoming a high energy barrier. This is possible through inter-subunit transmission of energy, and ultimately driven by nucleotide exchange. Furthermore, we show how this structure-inferred energetic quantification can be integrated into a non-equilibrium model of AAA+ assembly dynamics, consistent with single-molecule biophysics measurements. Overall, our work elucidates the energetic basis for the hand-over-hand mechanism in RuvBs cycle. Furthermore, it presents a generally applicable methodology for studying the energetics of conformational cycles in other proteins, allowing to quantitatively bridge data from structural biology and single-molecule biophysics. Author SummaryMolecular motors are proteins that transform chemical energy into motion. To do so, they change their shape through a highly organised cycle. How exactly these changes in shape translate into energy transformations that ultimately result in motion is still not fully understood. In our work, we combined tools from structural biology and biophysics to better quantify and model this process. We focused on RuvB, a motor made of six identical parts that work together to move DNA through its central pore. We first tracked how RuvBs shape changes and how much energy these changes involve, and then built a theoretical model that simulates its operational cycle. More broadly, our approach shows how studying protein energetics can connect the perspectives of structural biology and single-molecule biophysics, thus providing new ways to understand how molecular machines work.

biophysics↗
bioRxiv2025

Structure and dynamics of a multidomain ligand-gated ion channel revealed under acidic conditions

Pentameric ligand-gated ion channels are critical mediators of electrochemical signal transduction across evolution, including key targets of biophysical studies and therapeutic development. However, the intrinsically allosteric, polymodal, modular nature of gating in this protein family presents persistent challenges to biophysical characterization. The bacterial channel DeCLIC constitutes a provocative model system for structure, function, and dynamics in this family, including a modulatory N-terminal domain (NTD). Previous closed structures of DeCLIC support a rationale for its inhibition by calcium via a site of conserved relevance in other family members; however, mechanisms of gating in DeCLIC and properties of its open state have remained unclear. Here we integrated structure-function methods including cryogenic electron microscopy (cryo-EM), molecular dynamics simulations and small-angle neutron scattering under acidic conditions to characterize a previously unreported conformation of DeCLIC with a fully hydrated pore. In contrast to previous structures, the low-pH open conformation captured by cryo-EM was stable and permeable in simulations, and consistent with solution-phase behavior as measured by small-angle scattering. We further captured an alternative closed state of the channel, evidently promoted by depletion of modulatory calcium at low pH, exhibiting dynamic rearrangements in the N-terninal domain. The expanded-pore structure evidently corresponds to a functional open state of DeCLIC, while calcium-site and NTD dynamics drive channel closure, providing a detailed template for biophysical characterization of modulatory mechanisms in ligand-gated ion channels and related systems. Significance StatementThe bacterial protein DeCLIC is a provocative representative of the pentameric ligand-gated ion-channel family. Like its eukaryotic relatives, DeCLIC is sensitive to external calcium, and incorporates a possibly disordered modulatory domain. But as for many ion channels, the details of DeCLIC activation have remained unclear, as no stable open state has been characterized. Combining biophysical methods, including cryogenic electron microscopy, molecular dynamics simulations, and small-angle neutron scattering, allows us to capture, validate, and characterize an evidently open state of DeCLIC, as well as a closed state with a disordered modulatory region. Details of these structures, their function, and their dynamics allow us to propose a mechanism for DeCLIC gating, as well as a generalized scheme for the larger protein family.

biophysics↗
bioRxiv2025

Transient volume stabilization reveals the key role a PM permeability in pyroptotic cell swelling.

Pyroptosis, an inflammatory form of cell death, is characterized by massive cell swelling and plasma membrane rupture. Although swelling was recently shown to occur in two steps, the molecular and biophysical mechanisms driving this process remained unclear. Using fast quantitative microscopy, we reveal that between the two swelling phases, cell volume transiently stabilizes despite sustained plasma membrane permeability to ions and small molecules. From a biophysical perspective, the existence of such a plateau is puzzling, as ion pumps should not be able to regulate cell volume under these conditions. To address this, we developed a physical model based on an ion pump and leak framework that incorporates the dynamics of non-selective pore formation. Experimentally, we demonstrate that the plateau phase is controlled by the dynamics of the GSDMD pore enlargement, which is modulated by Ninj1 activation, possibly through intracellular calcium. Ninj1-mediated lesions are also required for the second swelling phase. We further show that fully opened GSDMD pores display an effective hydrodynamic radius slightly above 1.9 nm, providing an insitu upper bound for pore size. Together, our findings demonstrate that pyroptotic volume dysregulation emerges from the successive and interdependent actions of GSDMD and Ninj1, each imparting distinct permeability regimes associated with increased water filtration and decreased ion selectivity due to pore opening. These insights bridge molecular and biophysical perspectives on lytic cell death and may inform the broader understanding of membrane rupture in inflammatory and pathological contexts. Significance StatementAmong programmed modes of lytic cell death, pyroptosis mediated by gasdermin D (GSDMD) and ninjurin-1 (Ninj1) involves dramatic changes in cell shape and large volume fluctuations, fundamentally altering the cells physical properties. By combining optogenetics, quantitative microscopy, and modeling, we show that a progressive increase in plasma membrane pore size drives cell swelling and membrane lysis through successive and interdependent actions of GSDMD and Ninj1, each imparting distinct permeability regimes associated with increased water filtration and decreased ion selectivity. A deeper understanding of these dynamic cell modifications will shed light on the molecular and biophysical mechanisms driving different forms of cell death.

biophysics↗
bioRxiv2025

Dielectrophoresis Reveals Stimulus-Induced Remodeling of Insulin Granule Subpopulations

The pancreatic {beta}-cell contains several functional subpopulations of insulin secretory granules (ISGs). These subpopulations vary in maturity, age, and secretory capacity. Differences in protein and lipid composition of ISGs are correlated with disease but require further study to understand how ISG remodeling regulates normal biology. Due to limitations in traditional separation methods, the extent of these subpopulations, any overlap between them, and how they are affected by insulinotropic signals have not been determined. In this work, we adapted direct current insulator-based dielectrophoresis (DC-iDEP) to separate ISGs isolated from INS-1E cells, an immortalized rat insulinoma cell line model, according to their electrokinetic mobility ratio (EKMr). We were able to separate ISG subpopulations from unstimulated cells to determine a baseline distribution and identify characteristic profiles for immature, young, and old ISGs. We then analyzed distributions of subpopulations from cells stimulated with insulin secretion signals known to induce biophysical remodeling and maturation. We found significant changes in each subpopulation studied in response to stimulation, consistent with the increases in maturation, crystallization, and changes in size reported in the literature. This work provides new insights into how the cell controls ISG remodeling and may drive future development of more effective therapies. SIGNIFICANCEUnderstanding insulin secretory granule (ISG) heterogeneity and the functional role of subpopulations is a critical step towards unraveling the mechanisms of insulin secretion. We adapted direct current insulator-based dielectrophoresis (DC-iDEP) to resolve immature, young, and old ISG subpopulations based on their biophysical properties. We also explored the biophysical remodeling of ISGs under insulinotropic stimuli to further probe how the cell controls remodeling and secretion. Our work provides a new framework for quantifying granule heterogeneity by linking biophysical features to functional subtypes and assessing how environmental stimuli remodel ISGs. This methodology establishes a broadly applicable platform to interrogate organelle and vesicle diversity in complex biological systems.

biophysics↗
bioRxiv2019

Multiplexed Nanometric 3D Tracking of Microbeads using a FFT-Phasor Algorithm

Many single-molecule biophysical techniques rely on nanometric tracking of microbeads to obtain quantitative information about the mechanical properties of biomolecules such as chromatin fibers. Their three-dimensional position can be resolved by holographic analysis of the diffraction pattern in wide-field imaging. Fitting this diffraction pattern to Lorentz Mie scattering theory yields the bead position with nanometer accuracy in three dimensions but is computationally expensive. Real-time multiplexed bead tracking therefore requires a more efficient tracking method. Here, we introduce 3D phasor tracking, a fast and robust bead tracking algorithm with nanometric localization accuracy in a z-range of over 10 {micro}m. The algorithm is based on a 2D cross-correlation using Fast Fourier Transforms with computer-generated reference images, yielding a processing rate of up to 10.000 regions of interest per second. We implemented the technique in magnetic tweezers and tracked the 3D position of over 100 beads in real-time on a generic CPU. Its easy implementation, efficiency, and robustness can improve multiplexed biophysical bead tracking applications, especially where high throughput is required.\n\nSignificanceMicrobeads are often used in biophysical single-molecule manipulation experiments and accurately tracking their position in 3 dimensions is key for quantitative analysis. Holographic imaging of these beads allows for multiplexing bead tracking but image analysis can be a limiting factor. Here we present a 3D tracking algorithm based on Fast Fourier Transforms that is fast, has nanometric precision, is robust against common artifacts and is accurate over 10s of micrometers. We show its real-time application for magnetic tweezers based force spectroscopy on more than 100 chromatin fibers in parallel, but we anticipate that many other bead-based biophysical essays can benefit from this simple and robust 3 phasor algorithm.

biophysics↗
bioRxiv2015

A literature-based meta-analysis of brain-wide electrophysiological diversity

For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literature-based database of electrophysiological properties (www.neuroelectro.org), to better understand neuronal diversity - both within and across neuron types - and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across labs than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into 6-9 super-classes. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at theta-band frequencies.

Neuroscience↗
bioRxiv2017

Reactive astrocytes - comprehending when neurons play 4’33”

Homeostatic regulation is a powerful tool utilized by virtually all biological systems, brain included. Broadly speaking, each homeostatic process embodies two components: the sensing component, whereby a deviation is detected and quantified, and the effecting component that executes the homeostatic adjustment with the goal of alleviating the deviation. In the central nervous system, homeostatic plasticity has been suggested to play an important role in shaping the dynamics of single neurons and neuronal networks. However, existing \"biophysical\" models of homeostatic plasticity are exceedingly simplistic. These models usually describe the sensor component in terms of simple averaging over neuronal activity and offer no explanation of the relevant biochemical pathways. Here, we attempt to fill this gap in our understanding of homeostatic plasticity by proposing a biophysical framework to explain detection of prolonged synaptic inactivity that may occur in some scenarios of brain injury. We propose that sensing of, and response to, synaptic inactivity involves detection of the extracellular glutamate level and occurs via the activation of metabotropic glutamate receptors (mGluRs), while the inactivity-induced synthesis of one of the homeostatic plasticity effectors, tumor necrosis factor alpha (TNF), serves as an effecting component of the system. This model can help to explain the experimental observations linking prolonged neuronal inactivity to TNF signaling. Importantly, the proposed signaling scheme is not limited to mGluRs and astrocytes, but rather is potentially applicable to any cells expressing receptors that activate the relevant G protein units. The proposed signaling scheme is likely to be useful for developing pharmacological interventions targeting homeostatic plasticity pathways.\n\nAuthor summaryHomeostatic regulation refers to the self-regulation ability of a system aimed at remaining in the same (or nearly the same) state. Homeostatic plasticity, a form of homeostatic regulation that arises in the context of neural dynamics, has been shown to shape neuronal and network dynamics by attempting to maintain physiological levels of neuronal activity in brain networks. Thus, it changes the excitation-inhibition balance in a network when sufficiently large and long-lasting deviations from physiological levels of activity are detected. However, the biophysical mechanisms of homeostatic plasticity and in particular those related to the ability to sense neuronal inactivity, remain elusive. We propose a feasible biophysical model of a homeostatic sensor, in which the sensing of inactivity depends on the presence and activation of G-protein-coupled receptors with different activation thresholds. In this model, the activation of the higher-threshold receptor suppresses the detection of inactivity, while the activation of the lower-threshold receptor promotes the detection of inactivity. The proposed model helps to explain a growing body of experimental data relating synaptic inactivity to production of homeostatic plasticity effectors such as tumor necrosis factor alpha. Although we consider a specific case study of metabotropic glutamate receptors on astrocytes, the model conclusions are likely to be applicable to any cells expressing receptors that activate the relevant G protein units.

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