Subcellular Scale Research Articles

Ultrastructure and cardiac impulse propagation: scaling up from microscopic to macroscopic conduction.

The standard conception of cardiac conduction is based on the cable theory of nerve conduction, which treats cardiac tissue as a continuous syncytium described by the Hodgkin-Huxley equations. However, cardiac tissue is composed of discretized cells with microscopic and macroscopic heterogeneities and discontinuities, such as subcellular localizations of sodium channels and connexins. In addition to this, there are heterogeneities in the distribution of sympathetic and parasympathetic nerves, which powerfully regulate impulse propagation. In the continuous models, the ultrastructural details, i.e. the microscopic heterogeneities and discontinuities, are ignored by 'coarse graining' or 'smoothing'. However, these ultrastructural components may play crucial roles in cardiac conduction and arrhythmogenesis, particularly in disease states. We discuss the current progress of modelling the effects of ultrastructural components on electrical conduction, the issues and challenges faced by the cardiac modelling community, and how to scale up conduction properties at the subcellular (microscopic) scale to the tissue and whole-heart (macroscopic) scale in future modelling and experimental studies, i.e. how to link the ultrastructure at different scales to impulse conduction and arrhythmogenesis in the heart.

Concave/Convex Curvature of Anisotropic Grooves Differentially Alters Cellular Morphology, Adhesion, and Proliferation.

Curvature is an integral part of the complex in vivo tissue architecture across various length scales. Therefore, several in vitro models with a patterned curvature in different length scales have been developed to understand the role of this in cellular behavior. At the subcellular scale, wavy patterns have been reported wherein concave and convex grooves are adjacently present. However, the independent effect of continuous subcellular concave and convex shapes has not been reported, mainly owing to the limitations in fabricating such patterns. In this study, we developed continuous concave and convex grooves on polydimethylsiloxane (PDMS) using a Dracaena sanderiana (bamboo) leaf as a template. The first (negative) replica from the abaxial side of the bamboo leaf, which imparted concave grooves on PDMS, was subsequently used as a template to fabricate a positive replica of the leaf, resulting in convex grooves of the same size and arrangement as the concave grooves. We examined the influence of the groove curvature on the morphology of bone marrow-derived human mesenchymal stem cells (BM-hMSCs) and skeletal muscle cells (C2C12). BM-hMSCs and C2C12 cells aligned on both concave and convex grooves as compared to the random orientation on a flat substrate. The significant difference was observed in the morphology of both cells, in terms of area, aspect ratio, number, and length of protrusions on concave and convex patterns. We found that the number of protrusions was also dependent on the ratio of cell to pattern length scale for convex-shaped grooves but independent of length scale for concave-shaped grooves. The proliferation of BM-hMSCs was also found to be different on concave and convex shapes. Therefore, this study shows the importance of (1) convex and concave curvatures of the subcellular length scale in cellular response, (2) dependence on the ratio of cell and curvature length scale, and (3) use of natural templates for overcoming fabrication challenges.

A two-dimensional vertex model for curvy cell-cell interfaces at the subcellular scale.

Cross-sections of cell shapes in a tissue monolayer typically resemble a tiling of convex polygons. Yet, examples exist where the polygons are not convex with curved cell-cell interfaces, as seen in the adaxial epidermis. To date, two-dimensional vertex models predicting the structure and mechanics of cell monolayers have been mostly limited to convex polygons. To overcome this limitation, we introduce a framework to study curvy cell-cell interfaces at the subcellular scale within vertex models by using a parametrized curve between vertices that is expanded in a Fourier series and whose coefficients represent additional degrees of freedom. This extension to non-convex polygons allows for cells with the same shape index, or dimensionless perimeter, to be, for example, either elongated or globular with lobes. In the presence of applied, anisotropic stresses, we find that local, subcellular curvature or buckling can be energetically more favourable than larger scale deformations involving groups of cells. Inspired by recent experiments, we also find that local, subcellular curvature at cell-cell interfaces emerges in a group of cells in response to the swelling of additional cells surrounding the group. Our framework, therefore, can account for a wider array of multicellular responses to constraints in the tissue environment.

A novel application of electrostatic separation technology on green enrichment for protein from Enteromorpha prolifera

Electrostatic separation technology had been applied for the first time to the separation of algal proteins. The results indicated that grinding to a subcellular scale (<20 μm) effectively achieved the physical dispersion of Enteromorpha prolifera (E. prolifera) protein nuclei from other matrices. Compared to E. prolifera polysaccharides, proteins exhibited superior tribocharging properties, allowing them to acquire more positive charges during friction with polytetrafluoroethylene (PTFE) tubes. The electrostatic separation of E. prolifera ultrafine powder was optimized by adjusting the friction tube length and airflow velocity. It was found that with a friction tube length of 200 cm, an airflow velocity of 5 m/s, and an electric field strength of 1.2 kV/cm, the content of obtained protein increased from 4.29 ± 0.19% to 9.38 ± 0.44%, with a separation efficiency of 67.67 ± 3.14%. These findings suggest the potential application of electrostatic separation technology for the green enrichment of algal proteins.

Modelling Cellular Response to Ionizing Radiation: Mechanistic, Semi-Mechanistic, and Phenomenological Approaches - A Historical Perspective.

Radiation research is a multidisciplinary field, and among its many branches, mathematical and computational modelers have played a significant role in advancing boundaries of knowledge. A fundamental contribution is modelling cellular response to ionizing radiation as that is the key to not only understanding how radiation can kill cancer cells, but also cause cancer and other health issues. The invention of microdosimetry in the 1950s by Harold Rossi paved the way for brilliant scientists to study the mechanism of radiation at cellular and sub-cellular scales. This paper reviews some snippets of ingenious mathematical and computational models published in microdosimetry symposium proceedings and publications of the radiation research community. Among these are simulations of radiation tracks at atomic and molecular levels using Monte Carlo methods, models of cell survival, quantification of the amount of energy required to create a single strand break, and models of DNA-damage-repair. These models can broadly be categorized into mechanistic, semi-mechanistic, and phenomenological approaches, and this review seeks to provide historical context of their development. We salute pioneers of the field and great teachers who supported and educated the younger members of the community and showed them how to build upon their work.

A modular framework for multi-scale tissue imaging and neuronal segmentation.

The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries.

Machine learning techniques for predicting microvascular network hemodynamics

Existing techniques to image microvascular networks in vivo do not allow a direct and accurate measurement of several hemodynamic variables such as the wall shear stress (WSS). They cannot also provide a full 3D characterization of blood velocity and red blood cell (RBC) concentration profiles in every vessel in a microvascular network. Such physiological parameters however play critical roles in blood flow regulation, disease progression, angiogenesis, and hemostasis. Theoretical models of network blood flow, often used for hemodynamic predictions in experimentally acquired images of the microvascular network, cannot provide the full 3D blood velocity, RBC concentration and WSS profiles as these models assume one-dimensional flow governed by Poiseuille’s law. In contrast, high-fidelity computational models that simulate flow of blood as a suspension of deformable RBCs and model the 3D vessel geometry can readily predict such physiological variables with high accuracy. Such high-fidelity computational models have recently been applied to predict hemodynamic variables in microvascular networks in silico composed of many vessels and vascular junctions. Such models, however, tend to become computationally expensive with increasing size of the network; therefore, they are not feasible for use in large networks at organ scale. To overcome this limitation, we developed a machine learning (ML) approach that can predict hemodynamic variables in 3D and with high accuracy. The ML models are based on artificial neural network and convolution-based U-net models. Using experimentally acquired images of the microvascular networks, inputs at a few inlets, and a “bank” of database that we have developed, these ML models can predict flow and RBC distribution in all vessels in the network, 3D blood velocity and RBC concentration profiles over the cross-section of every vessel, 3D WSS profiles, and time-dependent variations in blood flow in any vessel. The prediction from the ML models compare very well against the true data. Also, the prediction time using ML is significantly less than that of the high-fidelity models. The current ML models, which to our knowledge are the first of their kind, are highly promising for image-based predictions of sub-cellular resolved capillary hemodynamics in organ-scale networks. This study paves the way for AI to make hemodynamic predictions in organ-scale capillary vessel networks while retaining the sub-cellular scale details and overcoming the limitations of the in vivo imaging techniques, with potential applications in hematological and microvascular disorders, angiogenesis, and vascular-mediated drug delivery. Funded by NIH (R01-EY033003) and NSF (CBET 2302212). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Bento: a toolkit for subcellular analysis of spatial transcriptomics data

The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell–cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene–gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

A multiscale modeling study of nanoparticle-based targeting therapy against atherosclerosis

Although researches on nanoparticle-based (NP-based) drug delivery system for atherosclerosis treatment have grown rapidly in recent years, there are limited studies in quantifying the effects of targeting drugs on plaque components and microenvironment. The purpose of the present study was to quantitatively assess the targeting therapeutic effects against atherosclerosis by establishing a multiscale mathematical model.The multiscale model involved subcellular, cellular and microenvironmental scales to simulate lipid catabolism, macrophage behaviors and dynamics of microenvironmental components, respectively. In vitro and in vivo experimental data were integrated into the mathematical model according to Bayesian statistics, in order to evaluate the therapeutic effects of a proposed NP-based platform for macrophage-specific delivery to simultaneously deliver SR-A siRNA (to reduce LDL uptake) and LXR-L (to stimulate cholesterol efflux). Dosage variation analysis was then performed to investigate the drug efficacy under varied dosage combinations of SR-A siRNA and LXR-L.The simulation results demonstrated that the dynamics of the microenvironmental components presented different developments in Untreated and Treated groups. We also found that the balance of lipid metabolism between uptake and efflux resulted in the improvement of lipid and inflammatory microenvironment, consequently in the plaque regression. In addition, the model predicted optimized dosage combinations according to the co-effect analysis of the two drugs on the lipid microenvironment.This study suggests that multiscale modeling can be a powerful quantitative tool for estimating the therapeutic effects of targeting drugs for plaque regression and designing the enhanced treatment strategies against atherosclerosis.

Comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo using highly responsive biosensors.

As a key glycolytic metabolite, lactate has a central role in diverse physiological and pathological processes. However, comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo has remained an unsolved problem until now owing to the lack of a high-performance tool. We recently developed a series of genetically encoded fluorescent sensors for lactate, named FiLa, which illuminate lactate metabolism in cells, subcellular organelles, animals, and human serum and urine. In this protocol, we first describe the FiLa sensor-based strategies for real-time subcellular bioenergetic flux analysis by profiling the lactate metabolic response to different nutritional and pharmacological conditions, which provides a systematic-level view of cellular metabolic function at the subcellular scale for the first time. We also report detailed procedures for imaging lactate dynamics in live mice through a cell microcapsule system or recombinant adeno-associated virus and for the rapid and simple assay of lactate in human body fluids. This comprehensive multiscale metabolic analysis strategy may also be applied to other metabolite biosensors using various analytic platforms, further expanding its usability. The protocol is suited for users with expertise in biochemistry, molecular biology and cell biology. Typically, the preparation of FiLa-expressing cells or mice takes 2 days to 4 weeks, and live-cell and in vivo imaging can be performed within 1-2 hours. For the FiLa-based assay of body fluids, the whole measuring procedure generally takes ~1 min for one sample in a manual assay or ~3 min for 96 samples in an automatic microplate assay.

Brillouin microscopy.

The field of Brillouin microscopy and imaging was established approximately 20 years ago, thanks to the development of non-scanning high-resolution optical spectrometers. Since then, the field has experienced rapid expansion, incorporating technologies from telecommunications, astrophotonics, multiplexed microscopy, quantum optics and machine learning. Consequently, these advancements have led to much-needed improvements in imaging speed, spectral resolution and sensitivity. The progress in Brillouin microscopy is driven by a strong demand for label-free and contact-free methods to characterize the mechanical properties of biomaterials at the cellular and subcellular scales. Understanding the local biomechanics of cells and tissues has become crucial in predicting cellular fate and tissue pathogenesis. This Primer aims to provide a comprehensive overview of the methods and applications of Brillouin microscopy. It includes key demonstrations of Brillouin microscopy and imaging that can serve as a reference for the existing research community and new adopters of this technology. The article concludes with an outlook, presenting the authors' vision for future developments in this vibrant field. The Primer also highlights specific examples where Brillouin microscopy can have a transformative impact on biology and biomedicine.

Subcellular Nanobionic Liposome with High Zeta Potential Enhances Intravesical Adhesion and Drug Delivery.

The administration of drugs resident to counteract fluid washout has received considerable attention. However, the fabrication of a biocompatible system with adequate adhesion and tissue penetration capability remains challenging. This study presents a cell membrane-inspired carrier at the subcellular scale that facilitates interfacial adhesion and tissue penetration to improve drug delivery efficiency. Both chitosan oligosaccharide (COS) and oleic acid (OA) modified membranes exhibit a high affinity for interacting with the negatively charged glycosaminoglycan layer, demonstrating that the zeta potential of the carrier is the key to determining spontaneous penetration and accumulation within the bladder tissue. In vivo modeling has shown that a high surface charge significantly improves the retention of the drug carrier in the presence of urine washout. Possibly due to charge distribution, electric field gradients, and lipid membrane softening, the high positive surface charge enabled the carriers to penetrate the urinary bladder barrier and/or enter the cell interior. Overall, this study represents a practical and effective delivery strategy for tissue binders.

Near-Native Imaging of Label-Free Silver Nanoparticles-Triggered 3D Subcellular Ultrastructural Reorganization in Microalgae.

Understanding the spatial orientation of nanoparticles and the corresponding subcellular architecture events favors uncovering fundamental toxic mechanisms and predicting response pathways of organisms toward environmental stressors. Herein, we map the spatial location of label-free citrate-coated Ag nanoparticles (Cit-AgNPs) and the corresponding subcellular reorganization in microalgae by a noninvasive 3D imaging approach, cryo-soft X-ray tomography (cryo-SXT). Cryo-SXT near-natively displays the 3D maps of Cit-AgNPs presenting in rarely identified sites, namely, extracellular polymeric substances (EPS) and the cytoplasm. By comparative 3D morphological assay, we observe that Cit-AgNPs disrupt the cellular ultrastructural homeostasis, triggering a severe malformation of cytoplasmic organelles with energy-producing and stress-regulating functions. AgNPs exposure causes evident disruption of the chloroplast membrane, significant attenuation of the pyrenoid matrix and starch sheath, extreme swelling of starch granules and lipid droplets, and shrinkage of the nucleolus. In accompaniment, the number and volume occupancy of starch granules are significantly increased. Meanwhile, the spatial topology of starch granules extends from the chloroplast to the cytoplasm with a dispersed distribution. Linking the dynamics of the internal structure and the alteration of physiological properties, we derive a comprehensive cytotoxic and response pathway of microalgae exposed to AgNPs. This work provides a perspective for assessing the toxicity at subcellular scales to achieve label-free nanoparticle-caused ultrastructure remodeling of phytoplankton.

Sensitive subcellular scale and real-time detection of hydrogen peroxide by a W-doped Pt microelectrode.

A W-doped Pt modified graphene oxide (Pt-W-GO) electrochemical microelectrode was developed to detect hydrogen peroxide (H2O2) in real time at a subcellular scale. Interestingly, results showed that the concentration of H2O2 in the nucleus of HeLa cells was 2.68 times and 0.51 times that in the extracellular membrane and cytoplasm, respectively.

Chondroitin sulfate, dermatan sulfate, and hyaluronic acid differentially modify the biophysical properties of collagen-based hydrogels

Fibrillar collagens and glycosaminoglycans (GAGs) are structural biomolecules that are natively abundant to the extracellular matrix (ECM). Prior studies have quantified the effects of GAGs on the bulk mechanical properties of the ECM. However, there remains a lack of experimental studies on how GAGs alter other biophysical properties of the ECM, including ones that operate at the length scales of individual cells such as mass transport efficiency and matrix microstructure. This study focuses on the GAG molecules chondroitin sulfate (CS), dermatan sulfate (DS), and hyaluronic acid (HA). CS and DS are stereoisomers while HA is the only non-sulfated GAG. We characterized and decoupled the effects of these GAG molecules on the stiffness, transport, and matrix microarchitecture properties of type I collagen hydrogels using mechanical indentation testing, microfluidics, and confocal reflectance imaging, respectively. We complement these biophysical measurements with turbidity assays to profile collagen aggregate formation. Surprisingly, only HA enhanced the ECM indentation modulus, while all three GAGs had no effect on hydraulic permeability. Strikingly, we show that CS, DS, and HA differentially regulate the matrix microarchitecture of hydrogels due to their alterations to the kinetics of collagen self-assembly. In addition to providing information on how GAGs define key physical properties of the ECM, this work shows new ways in which stiffness measurements, microfluidics, microscopy, and turbidity kinetics can be used complementarily to reveal details of collagen self-assembly and structure. Statement of SignificanceCollagen and glycosaminoglycans (GAGs) are integral to the structure, function, and bioactivity of the extracellular matrix (ECM). Despite widespread interest in collagen-GAG composite hydrogels, there is a lack of quantitative understanding of how different GAGs alter the biophysical properties of the ECM across tissue, cellular, and subcellular length scales. Here we show using mechanical, microfluidic, microscopy, and analytical methods and measurements that the GAG molecules chondroitin sulfate, dermatan sulfate, and hyaluronic acid differentially regulate the mechanical, transport, and microstructural properties of hydrogels due to their alterations to the kinetics of collagen self-assembly. As such, these results will inform improved design and utilization of collagen-based scaffolds of tailored composition, mechanical properties, molecular availability due to mass transport, and microarchitecture.

A paradigm for ethanol consumption in head-fixed mice during prefrontal cortical two-photon calcium imaging

The prefrontal cortex (PFC) is a hub for cognitive behaviors and is a key target for neuroadaptations in alcohol use disorders. Recent advances in genetically encoded sensors and functional microscopy allow multimodal in vivo PFC activity recordings at subcellular and cellular scales. While these methods could enable a deeper understanding of the relationship between alcohol and PFC function/dysfunction, they typically require animals to be head-fixed. Here, we present a method in mice for binge-like ethanol consumption during head-fixation. Male and female mice were first acclimated to ethanol by providing home cage access to 20% ethanol (v/v) for 4 or 8 days. After home cage drinking, mice consumed ethanol from a lick spout during head-fixation. We used two-photon calcium imaging during the head-fixed drinking paradigm to record from a large population of PFC neurons (>1000) to explore how acute ethanol affects their activity. Drinking exerted temporally heterogeneous effects on PFC activity at single neuron and population levels. Intoxication modulated the tonic activity of some neurons while others showed phasic responses around ethanol receipt. Population level activity did not show tonic or phasic modulation but tracked ethanol consumption over the minute-timescale. Network level interactions assessed through between-neuron pairwise correlations were largely resilient to intoxication at the population level while neurons with increased tonic activity showed higher synchrony by the end of the drinking period. By establishing a method for binge-like drinking in head-fixed mice, we lay the groundwork for leveraging advanced microscopy technologies to study alcohol-induced neuroadaptations in PFC and other brain circuits.This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".

A multiscale theory for spreading and migration of adhesion-reinforced mesenchymal cells.

We present a chemomechanical whole-cell theory for the spreading and migration dynamics of mesenchymal cells that can actively reinforce their adhesion to an underlying viscoelastic substrate as a function of its stiffness. Our multiscale model couples the adhesion reinforcement effect at the subcellular scale with the nonlinear mechanics of the nucleus-cytoskeletal network complex at the cellular scale to explain the concurrent monotonic area-stiffness and non-monotonic speed-stiffness relationships observed in experiments: we consider that large cell spreading on stiff substrates flattens the nucleus, increasing the viscous drag force on it. The resulting force balance dictates a reduction in the migration speed on stiff substrates. We also reproduce the experimental influence of the substrate viscosity on the cell spreading area and migration speed by elucidating how the viscosity may either maintain adhesion reinforcement or prevent it depending on the substrate stiffness. Additionally, our model captures the experimental directed migration behaviour of the adhesion-reinforced cells along a stiffness gradient, known as durotaxis, as well as up or down a viscosity gradient (viscotaxis or anti-viscotaxis), the cell moving towards an optimal viscosity in either case. Overall, our theory explains the intertwined mechanics of the cell spreading, migration speed and direction in the presence of the molecular adhesion reinforcement mechanism.

Imaging glycosylated RNAs at the subcellular scale.

Imaging glycosylated RNAs at the subcellular scale.

Opto-RhoGEFs, an optimized optogenetic toolbox to reversibly control Rho GTPase activity on a global to subcellular scale, enabling precise control over vascular endothelial barrier strength.

The inner layer of blood vessels consists of endothelial cells, which form the physical barrier between blood and tissue. This vascular barrier is tightly regulated and is defined by cell-cell contacts through adherens and tight junctions. To investigate the signaling that regulates vascular barrier strength, we focused on Rho GTPases, regulators of the actin cytoskeleton and known to control junction integrity. To manipulate Rho GTPase signaling in a temporal and spatial manner we applied optogenetics. Guanine-nucleotide exchange factor (GEF) domains from ITSN1, TIAM1, and p63RhoGEF, activating Cdc42, Rac, and Rho, respectively, were integrated into the optogenetic recruitment tool improved light-induced dimer (iLID). This tool allows for Rho GTPase activation at the subcellular level in a reversible and non-invasive manner by recruiting a GEF to a specific area at the plasma membrane, The membrane tag of iLID was optimized and a HaloTag was applied to gain more flexibility for multiplex imaging. The resulting optogenetically recruitable RhoGEFs (Opto-RhoGEFs) were tested in an endothelial cell monolayer and demonstrated precise temporal control of vascular barrier strength by a cell-cell overlap-dependent, VE-cadherin-independent, mechanism. Furthermore, Opto-RhoGEFs enabled precise optogenetic control in endothelial cells over morphological features such as cell size, cell roundness, local extension, and cell contraction. In conclusion, we have optimized and applied the optogenetic iLID GEF recruitment tool, that is Opto-RhoGEFs, to study the role of Rho GTPases in the vascular barrier of the endothelium and found that membrane protrusions at the junction region can rapidly increase barrier integrity independent of VE-cadherin.

Opto-RhoGEFs, an optimized optogenetic toolbox to reversibly control Rho GTPase activity on a global to subcellular scale, enabling precise control over vascular endothelial barrier strength

The inner layer of blood vessels consists of endothelial cells, which form the physical barrier between blood and tissue. This vascular barrier is tightly regulated and is defined by cell-cell contacts through adherens and tight junctions. To investigate the signaling that regulates vascular barrier strength, we focused on Rho GTPases, regulators of the actin cytoskeleton and known to control junction integrity. To manipulate Rho GTPase signaling in a temporal and spatial manner we applied optogenetics. Guanine-nucleotide exchange factor (GEF) domains from ITSN1, TIAM1, and p63RhoGEF, activating Cdc42, Rac, and Rho, respectively, were integrated into the optogenetic recruitment tool improved light-induced dimer (iLID). This tool allows for Rho GTPase activation at the subcellular level in a reversible and non-invasive manner by recruiting a GEF to a specific area at the plasma membrane, The membrane tag of iLID was optimized and a HaloTag was applied to gain more flexibility for multiplex imaging. The resulting optogenetically recruitable RhoGEFs (Opto-RhoGEFs) were tested in an endothelial cell monolayer and demonstrated precise temporal control of vascular barrier strength by a cell-cell overlap-dependent, VE-cadherin-independent, mechanism. Furthermore, Opto-RhoGEFs enabled precise optogenetic control in endothelial cells over morphological features such as cell size, cell roundness, local extension, and cell contraction. In conclusion, we have optimized and applied the optogenetic iLID GEF recruitment tool, that is Opto-RhoGEFs, to study the role of Rho GTPases in the vascular barrier of the endothelium and found that membrane protrusions at the junction region can rapidly increase barrier integrity independent of VE-cadherin.