Biophysical Reports

Fluorescence recovery after photobleaching (FRAP) is widely used to characterize diffusion in cells, but quantitative interpretation of the data in small prokaryotes requires explicitly accounting for cell geometry. While this has been successfully achieved for spherical and rod-shaped bacteria, analytical approaches developed in these cases are not directly applicable to cells with more complex morphologies. Here, we explore the application of FRAP to helical bacteria using simulations. We show that half-compartment FRAP experiments, where one-half of the cell is photobleached, provide a robust means of characterizing fast protein diffusion. To help with the practical implementation of this technique, we established the relationship between the diffusion coefficient and characteristic fluorescence recovery time as a function of cell length and helical parameters, and for two different ways of estimating the recovery time. As a first application, we report measurements of the diffusion coefficient of the fluorescent protein, mNeonGreen, in the helical bacterium Paramagnetospirillum magneticum AMB-1. We find it to be D = 4.9 {+/-} 2.2 {micro}m2 s-1 in isosmotic conditions, not significantly different from the value measured in Escherichia coli. Although developed for helical bacteria, including spirilla, spirochetes, and vibrios, our framework can readily be extended to cells or compartments with other geometries.

Fluorescent reporters such as fluorescent proteins or chemigenetic indicators are indispensable tools for studying biological processes using light microscopy. Choosing an appropriate fluorescent tag is a crucial step in experimental design not only for imaging but also for quantitative measurements such as fluorescence fluctuation spectroscopy. Two key parameters should be considered: Fluorescent brightness and photo-bleaching. Change to fluorescence intensity due to photobleaching is relatively easy to assess in different biological environments, while brightness is more elusive. Here, we develop and employ a fluorescence correlation spectroscopy (FCS) based excitation scan assay that determines fluorescent protein performance and validate it in tissue culture and zebrafish embryos. We employ our FCS pipeline to compare a set of 10 established fluorescent proteins as well as HALO and SNAP tags for both cellular imaging and measurements of diffusion dynamics with FCS. We show that mNeonGreen outperforms mEGFP in tissue culture and zebrafish embryos. We also compare StayGold variants against other green fluorescent proteins and chemigenetic reporters in tissue culture. Overall, we present a broadly applicable approach for determining fluorescent reporter brightness in the living system of interest.

Understanding protein oligomerization in living cells is essential for elucidating cellular signaling and regulation, yet quantitative analysis remains challenging due to heterogeneous expression levels, dynamic interactions, and limited access to absolute protein concentrations. Here, we present a standardized, open-source framework for quantifying protein assemblies in living cells by integrating fluorescence lifetime and anisotropy imaging (heteroFRET and homoFRET) with molecular brightness-based concentration estimation and image analysis. Using natural variants of a vertebrate GPCR, the melanocortin-4 receptor (MC4R-A and MC4R-B2), as a model system, we demonstrate how to discriminate monomers, dimers, and higher-order oligomers, extract inter-fluorophore distance distributions, and determine association constants under physiologically relevant conditions in living cells. Standard fluorescent protein tags report on proximity and oligomerization via Homo- and HeteroFRET. Association constants are quantified using the variable protein expression in living cells and the spectroscopy readouts. By high-content imaging we overcome the biological noise and attain data qualities comparable to conventional biochemical in vitro assays. Intensity- and fluctuation-based segmentation further extends the accessible concentration range within individual cells, improving affinity analysis robustness. Our results establish quantitative image spectroscopy on living cells as quantitative tool for investigating protein-protein interactions under physiologically relevant conditions. All computational workflows are implemented in open-source software and are accompanied by detailed protocols and analysis scripts, enabling reproducible application and adaptation. Beyond GPCRs, this framework provides a practical and transferable methodology for quantitative studies on protein-protein interactions, mechanistic studies and drug discovery in complex cellular environments.

Sectioning techniques based on patterned illumination have been widely used to obtain well-contrasted images of thick samples using widefield imaging setups. While their application to fluorescence microscopy has been extensively demonstrated and studied, their application to reflection imaging is scarcer and their performance has only been partly characterized. In this paper, we study numerically and analytically two such sectioning techniques, line confocal (LC) and structured illumination (SI), in the context of their application to reflection interference contrast microscopy (RICM), an imaging technique widely use in soft matter and biophysics studies to monitor object-surface interactions, or quantify surface functionalization. Our derivation, however, should provide insight into their use with other reflection methods such as optical coherence tomography (OCT) or scanning laser ophtalmoscope (SLO). We derive approximate analytical equations to relate the performance of sectioning to the optical setup parameters, allowing straightforward understanding of their influence on the achieved image intensity and depth of focus, and we systematically compare our prediction with experimental data. Finally, we quantify the precision and accuracy of each method in typical practical cases, providing guidelines to choose the most appropriate (LC, SI, or a simple background subtraction on a widefield image) for the sample under study.

Phasor analysis is a well-established tool in hyperspectral and lifetime microscopy, providing a powerful, fit-free approach for interpreting complex fluorescence. However, its application has remained largely restricted to imaging-based modalities. Spectral flow cytometry (SFC) enables acquisition of full emission spectra from large numbers of independent single-cell events, offering superior statistical power compared to microscopy, albeit at the expense of spatial and temporal information. Here, we present the first implementation of spectral phasor analysis for SFC (phSFC), establishing a unified analytical framework that preserves interpretative continuity with hyperspectral microscopy while extending phasor-based analysis to high-throughput, single-cell measurements. Using the membrane-sensitive probe LAURDAN as a benchmark, we demonstrate that SFC reproduces phasor signatures of membrane order previously reported by hyperspectral confocal microscopy (HSI). We performed comparative analyses using multilamellar lipid vesicles (MLVs) prepared from known physical order compositions. Both modalities, SFC and HSI, accurately resolved MLVs with fluid, gel and liquid-ordered and liquid-disorder membrane phases, capturing cholesterol-dependent spectral shifts, including trajectories associated with mixtures of the different lipid phase behavior. Although absolute phasor coordinates differed between modalities due to distinct spectral sampling and detector configurations, the relative organization of membrane physical states was preserved. Notably, SFC produced more compact phasor distributions, consistent with larger sample size and enhanced statistical robustness. To further extend phSFC, we first evaluated its capacity to resolve membrane changes in live cultured cells following cholesterol depletion, establishing consistency between HSI and SFC measurements. We then applied phSFC to detect membrane dynamics in primary leukocytes isolated from bronchoalveolar lavage of mice with inflammation-associated lung pathology. LAURDAN fluorescence in the presence of autofluorescence and antibody-derived signals is quantified and discussed with simple solution by n-harmonic phasor analysis unmixing. Together, these results establish SFC as a robust and complementary extension of LAURDAN phasor analysis, bridging HSI and high-throughput flow cytometry measurements.

It is an essential element of mechanobiology to measure the forces of biological cells. In microparticle traction force microscopy, they are inferred from the deformation of elastic microparticles. Two complementary variants have been introduced before: the volume method, which reconstructs surface stresses from the displacements of fiducial markers embedded inside the particles, and the surface method, which infers stresses directly from the deformation of the particle surface. However, a systematic comparison of the two methods has been lacking. Here, we quantitatively compare both approaches using simulated traction fields representing biologically relevant loading scenarios. We find that the surface method consistently reconstructs traction profiles with substantially lower errors than the volume method, which suffers from displacement tracking and stress calculation at the surface. At high noise levels, however, the performance gap becomes smaller. To compare the performance of the two methods in a realistic experimental setting, we developed DNA-based hydrogel microparticles equipped with both fluorescent surface labels and embedded fluorescent nanoparticles, enabling the direct comparison of the two methods within the same system. Compression experiments produced traction profiles consistent with Hertzian contact mechanics and confirmed the trends observed in the simulations. While our computational workflow establishes a framework to apply both methods, our experimental workflow establishes DNA microparticles as versatile and biocompatible probes for measuring cellular forces.

Interactions between RNA and lipids are fundamental for biological processes and are increasingly exploited for RNA delivery by lipid nanoparticles. However, RNA-lipid interactions remain challenging to characterize at the molecular level. Here, we address the modeling of RNA at lipid/water interfaces using coarse-grained (CG) simulations, experimental validation using scattering data and prediction of neutron (NR) and X-ray reflectivity (XRR) profiles from the simulations. Using neutral DOPC and cationic DOTAP bilayers, we show that lipid-RNA interactions depend strongly on RNA secondary structure, with single-stranded regions exhibiting higher interfacial affinity than double-stranded segments. We validate the CG lipid simulations, showing that while they reproduce experimental X-ray scattering data only qualitatively, the agreement improves markedly after backmap-ping to atomistic resolution followed by energy minimization and short all-atom molecular dynamics simulations. We further simulated distinct tRNA conformations and analyzed the influence of RNA secondary structure, concentration, solvent contrast, and lipid deuteration on NR and XRR signals identifying the conditions under which such experiments probe RNA adsorption and discriminate between different RNA conformations. Together, these results demonstrate that CG simulations combined with reflectivity data provide a powerful approach to probe RNA adsorption and structure at lipid/water interfaces and support the design and interpretation of scattering experiments. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/689668v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@9a1297org.highwire.dtl.DTLVardef@13aa90dorg.highwire.dtl.DTLVardef@30a68corg.highwire.dtl.DTLVardef@662aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

Fluorescence Lifetime Imaging Microscopy (FLIM) enables quantitative mapping of molecular environments in living systems with high biochemical specificity. However, spatial overlap dictated by the diffraction-limited point spread function (PSF) causes a mixing of temporal signals: photons from neighboring emitters collected within the same pixel yield composite decay profiles, generating apparent intermediate lifetimes that can be mistaken for variations in the local molecular environment. We introduce a workflow that applies Mean-Shift Super-Resolution (MSSR) to raw intensity data to generate intensity-derived spatial masks prior to phasor-based lifetime analysis. The method is computationally efficient and preserves decay kinetics because it operates on intensity-derived spatial information rather than modifying temporal data. In U2OS cells labeled with spectrally-overlapping fluorophores, phasor analysis reveals an intermediate lifetime population localized at PSF-overlap interfaces, consistent with optical mixing rather than intrinsic lifetime heterogeneity. MSSR-derived masking suppressed this mixed population while preserving stable phasor cluster centers -i.e. the distribution of similar phasor coordinates in the phasor plane- for each fluorophore. Simulations of strictly monoexponential fluorescence decay emitters further show that blended lifetime decay profiles are present at separations up to 4{sigma} and becomes maximal near [~]1.6{sigma}, indicating that conventional spatial resolution criteria can underestimate lifetime cross-talk. Application of this workflow to three-component FLIM showed also a reduced overlap of pixel distributions in phasor plots while maintaining distinct lifetime signatures. Overall, MSSR-based spatial refinement provides an accessible strategy to improve the spatial resolution while maintaining accuracy of FLIM measurements.

Ultraweak photon emission is the spontaneous emission of extremely low levels of light from a broad range of biological systems. Recent studies have reported that UPE measured extracranially can serve as a potential non-invasive biomarker of brain activity. Here, we show that this interpretation suffers from serious problems. First, when observed under properly dark conditions, the UPE from the head is much weaker than what is reported in certain papers on brain UPE from human heads. Signals detected in these studies are overwhelmingly dominated by background light. Second, photons at wavelengths < 600 nm are strongly attenuated by scalp and skull tissues, and longer wavelengths fall largely outside the effective spectral sensitivity of the photomultiplier tubes (PMTs) used. As a consequence, even if UPE from the head is detected under properly background-free conditions, it is likely to be dominated by emission from the scalp rather than from the brain, certainly as long as PMTs are used. Our results emphasize the importance of careful experimental design to make genuine progress on this important question.

The apparent pKa of ionizable lipids in lipid nanoparticles (LNPs) is a key determinant of RNA encapsulation during formulation and endosomal release after cellular uptake. However, it is difficult to predict the effective pKa of a given ionizable lipid solely from its solution pKa, because it is sensitive to the membranes composition, as well as solution conditions such as the salt concentration. We developed a simple continuum electrostatics model, based on Gouy-Chapman theory, to predict the shift in effective pKa for ionizable lipids in lipid bilayers as a function of salt concentration and membrane composition. We derive equations for the surface potential and fraction of lipids charged, which are solved self-consistently as a function of solution pH to extract the titration curve and effective pKa. The model shows that the shift in effective pKa is largest when the concentration of titratable lipid is high, and the effect is diminished by increasing salt concentration. We provide a python implementation of the model and an interactive notebook that will allow users to further easily explore the predicted pKa shifts as a function of formulation variables.

Single-molecule localization microscopy (SMLM) methods enable fluorescence imaging of biological specimens with nanometer-scale resolution. Although fluorophore localization precision is theoretically limited only by photon statistics, in practice the resolution of SMLM images is often degraded by physical drift of the sample and/or the microscope during data acquisition. At present, correcting this effect requires either specialized stabilization systems or computationally intensive post-processing, and established drift correction algorithms based on image cross-correlation suffer from limited temporal resolution. In this study we introduce COMET, a new method for SMLM drift estimation which achieves a substantially higher precision, accuracy, and temporal resolution compared with existing algorithmic approaches. We demonstrate that improved drift estimation translates directly into higher SMLM image resolution, limited by localization precision rather than drift artifacts. COMET is applicable to all types of SMLM data, operating directly on 2D or 3D localization datasets, and is readily integrated into analysis workflows. We benchmark its performance using both simulations and experiments, including STORM, MINFLUX, and Sequential OligoSTORM measurements, where long acquisition times make drift correction particularly challenging. COMET is published as an open-source, Python-based software project and is also available on open cloud-computing platforms.

Fluorescence resonance energy transfer (FRET) is the highly distance dependent (3-10 nm) transfer of energy from a donor to an acceptor fluorophore, with transfer efficiency inversely proportional to the distance between the fluorophores. Consequently FRET serves as a powerful spectroscopic ruler for probing molecular interactions. Whilst cell based FRET assays report bulk relative changes in FRET efficiency in a population, single molecule FRET (smFRET) is capable of deconvoluting these population averages into distinct structural states. However, the lack of universal benchmarks prevents the direct translation of in vitro distance measurements to the intracellular environment and vice versa. Here, we present a modular protein ladder designed to harmonize FRET data across diverse platforms. Using an engineered repeating TPR motif and self-labeling enzymes, we demonstrate that our standards yield consistent FRET efficiencies across expression systems (mammalian and bacterial) and labelling strategies (self labelling enzymes and click chemistry with non-canonical amino acids). By providing a predictable calibration curve, the ladder enables interpolation between different experimental FRET modalities, including confocal smFRET, flow cytometry based-FRET and Fluorescence Lifetime Imaging Microscopy FRET (FLIM-FRET). This is the necessary infrastructure to relate molecular distances from the test tube to the cell.

Mitochondrial membrane potential ({Delta}{Psi}m) is central to ATP production, ion homeostasis, and cell survival, reflecting the functional state of the inner mitochondrial membrane and oxidative phosphorylation. Accurate assessment of {Delta}{Psi}m is therefore essential for understanding mitochondrial physiology and dysfunction in health, ageing, and disease. Lipophilic cationic fluorescent dyes, such as TMRM and TMRE, are widely used to monitor {Delta}{Psi}m in live cells, enabling high-temporal-resolution imaging of both steady-state membrane potential and dynamic fluctuations. Beyond stable bioenergetic measurements, live-cell imaging reveals transient, reversible depolarisation events, known as mitochondrial "flickers." These events, observed across multiple cell types and imaging platforms, are often associated with brief openings of the mitochondrial permeability transition pore (mPTP) and may represent regulated mitochondrial excitability, rather than irreversible damage. While excessive or synchronised depolarisations may signal mitochondrial injury, transient flickers are increasingly viewed as potential signalling mechanisms within the mitochondrial network. This work discusses methodological considerations for {Delta}{Psi}m imaging, the biological significance of mitochondrial flickers, and the importance of distinguishing physiological events from probe- and light-induced artefacts, highlighting the emerging concept of mitochondria as dynamic and communicative bioenergetic networks.

High-throughput screening and optimization of high-value protein formulations requires intensified measurements to extract a wide range of properties using a small number of measurement techniques, small sample volumes, and short measurement times. We demonstrate how differential dynamic microscopy (DDM) can fill this need by measuring a broad range of key biophysical properties relevant to protein formulations from a single workflow on microliter-scale samples using label-free video optical microscopy. We show that the use of phase contrast imaging dramatically enhances measurement resolution for protein solutions at dilute and semidilute concentrations, enabling measurement of colloidal properties such as protein-protein interactions, protein size, aggregation, and solution viscosity from a single set of measurements. DDM measurements on a representative human immunoglobulin (IgG) system yield estimates for the hydrodynamic radius (Rh), second osmotic virial coefficient (B2), and hydrodynamic interaction (kd) that are consistent with independently measured values, validating the ability of DDM to extract these parameters from a single set of measurements. Observed trends in B2 with pH and ionic strength are consistent with the antibodys charge and screened electrostatics, demonstrating the ability of DDM to provide insight on protein-protein interactions. To show the utility of DDM as a "multitool" for quantifying multiple formulation properties from a single measurement, we use the results to test a predictive colloidal model for the solution viscosity, which is in fair agreement with measurements obtained using DDM-based microrheology. Combined with low sample requirements and short measurement times, DDM thus offers a high-throughput and efficient route to accelerate protein biophysics and formulation development. SIGNIFICANCE STATEMENTThe formulation of stable, high-concentration antibodies and other protein solutions requires extensive biophysical measurements that are often material- and time-intensive. We demonstrate that differential dynamic microscopy (DDM) provides a powerful alternative by providing rapid access to a broad range of industrially relevant colloidal properties from a single measurement on microliter-scale samples using conventional video optical microscopy. This capability makes DDM an attractive, low-resource approach for routine biomolecular formulation screening and optimization.

Mechanical forces transmitted through focal adhesions regulate cell behavior and disease progression, yet remain difficult to quantify at the molecular level. Genetically encoded FRET-based tension probes enable measurements of piconewton-scale forces across specific proteins in living cells, but their quantitative interpretation is highly sensitive to probe design and measurement modality. Here, we systematically compared vinculin tension sensors under identical experimental conditions, evaluating unloaded reference constructs, fluorophore pairs, mechanical sensor modules, and circularly permuted variants. Unloaded controls established a common no-force baseline and validated force-dependent readout. Among the fluorophore pairs tested, the green-red combination Clover-mScarlet-I yielded a higher unloaded FRET efficiency and hence a broader measurable dynamic range. Comparison of six mechanical sensor modules identified the binary-response sensors FL and CC-S2 as the most responsive, showing the largest force-dependent FRET changes and broadest FRET distributions. At the sub-focal adhesion level, CC-S2 reported the steepest proximal-to-distal tension gradient, indicating that vinculin tension increases sharply along peripheral adhesions and exceeds 10 piconewton. Circular permutation experiments revealed that fluorophore orientation has a strong, module-dependent influence on the measured FRET readout. Together, these results establish a comparative framework for interpreting FLIM-based vinculin tension measurements and provide practical design principles for selecting and engineering molecular tension probes.

Membrane mimetics such as lipid bicelles and nanodiscs have become indispensable platforms for high-resolution structural, dynamical, and functional studies of membrane-associated systems by NMR spectroscopy, cryo-electron microscopy, and X-ray crystallography. In particular, magnetically aligned bicelles and nanodiscs uniquely enable the measurement of anisotropic NMR interactions, providing direct access to membrane geometry, lipid order, thickness, and molecular dynamics. However, the quantitative interpretation of such anisotropic NMR spectra has been hindered by the absence of physically rigorous dynamic models that properly account for the coupled effects of molecular diffusion, orientational distribution, and membrane deformation. Here, we present a comprehensive theoretical framework for the dynamic simulation of 31P chemical shift anisotropy and 14N quadrupolar NMR lineshapes in bicelles and nanodiscs. The model explicitly incorporates lipid diffusion, orientational distributions on curved membrane geometries, and membrane thinning, enabling physically consistent and quantitatively accurate reproduction of experimentally observed anisotropic lineshapes. Using this framework, we simulate dynamic 31P and 14N NMR spectra of DMPC/DHPC bicelles and nanodiscs and demonstrate how membrane thinning and lipid diffusion govern the apparent reduction of anisotropic interactions commonly observed upon peptide or protein association. This approach establishes a general physical basis for interpreting anisotropic NMR spectra of aligned membrane mimetics and provides a unified platform for quantitative investigation of membrane structure, dynamics, and membrane-active biomolecular interactions.

Developing active transport systems for microcargo delivery is challenging and requires overcoming the low Reynolds number constraints. We developed a bio-hybrid micro-swimmer, "chlamylipo" consisting of the green alga Chlamydomonas reinhardtii, encapsulated within a giant liposome. Although internal encapsulation offers cargo protection, it requires a mechanism to transmit the propulsion force across a closed membrane. We demonstrated that chlamylipo exhibited forward swimming and phototactic directional control. High-speed imaging of membrane shape and fluid flow revealed that the driving force originated from periodic membrane deformations and was accompanied by characteristic fluid dynamics. Flow analysis showed rapid oscillations at tens of hertz corresponding to flagellar beating, superimposed on slower axial migration at approximately 4 Hz associated with cell rotation. Corresponding flow signatures were also detected in the external fluid, indicating mechanical coupling across the lipid bilayer. Membrane domain tracking further showed that fluid motions inside and outside the membrane were coupled through viscous friction and membrane deformation, generating a characteristic four-vortex flow field consistent with a two-point force model. Together, these results suggest that membrane flow mainly reflects force transmission across the bilayer, whereas forward propulsion is primarily driven by periodic membrane deformation. This study elucidates the physical mechanism of force transmission in encapsulated swimmers, demonstrating that internal hydrodynamic power can effectively drive the motion of macroscopic containers. SignificanceThe development of autonomous micro-swimmers for targeted drug delivery is a major challenge in biophysics. We present "chlamylipo," a hybrid system in which a swimming alga is encapsulated inside a lipid vesicle. This study is significant because it demonstrates that an enclosed swimmer can propel a macroscopic container solely via hydrodynamic coupling across a closed membrane without direct external mechanical links. Furthermore, we achieved external directional control using phototaxis. This study provides physical insights into fluid-membrane interactions and proposes a novel strategy for designing light-guided active transport carriers.

Fluorescence imaging with spectrally variant fluorophores allows the spatial mapping of biological structures with exquisite cellular and molecular specificity. However, the ability to robustly discriminate multiple fluorophores in any single imaging experiment is greatly hindered by the broad emission spectra of bio-compatible fluorophores and the large contribution of noise in low-energy regime fluorescence microscopy. In this study, we propose a novel machine learning framework, Bleaching-Excitation-Emission Photodynamics (BEEP) learning, that exploits multiple discriminatory features of fluorescent dyes to greatly expand the number of distinguishable objects in an image by integrating emission spectra, excitation variability, and bleaching dynamics into a unified multi-view, fluorescence unmixing approach. Our method is built upon a rank-one-tensor-based generalized linear model and leverages two biophysically grounded assumptions: consistent spectral and bleaching behaviors under fixed excitation, and invariant fluorophore abundances across excitations. We first extract excitation-specific spectral and bleaching signatures from reference images, and then use them to estimate abundances in complex mixtures. Experimental results on both simulated and real images of microbial populations demonstrate that our approach significantly outperforms conventional and partially multi-view methods, offering improved robustness and accuracy in highly multiplexed fluorescence imaging.

Fluorescent proteins (FPs) are essential tools for biological imaging but are limited by photobleaching, a light-induced loss of fluorescence intensity that reduces spatial and temporal resolution. Despite extensive use, the molecular mechanisms underlying FP photobleaching remain poorly understood due to the diversity of FPs and the complexity of their photochemistry. Existing approaches either monitor fluorescence decay in live cells, reflecting imaging conditions but lacking molecular detail, or rely on in vitro spectroscopy of purified proteins, providing mechanistic insight but often limited to individual FPs. We introduce a quantitative workflow bridging these approaches by combining live-cell measurements with in vitro spectroscopy. In vitro measurements are performed on a dedicated setup that simultaneously monitors absorption, emission, and fluorescence decay during photobleaching. Applied to six FPs spanning different chromophores, emission ranges and sequences, this approach reveals that photobleaching strongly depends on FP. It involves multiple chemical pathways, including oxidation, dimerization, and backbone cleavage. Spectroscopic analysis uncovers a heterogeneous ensemble of photoproducts with distinct photophysical properties that can remain optically active during irradiation, including shortened fluorescence lifetimes or altered absorption spectra. These findings demonstrate that FP photobleaching cannot be described as a simple ON-OFF process but involves complex transformations affecting both fluorescence intensity and lifetime. Such transformations can introduce significant biases in quantitative imaging, particularly in advanced techniques such as FLIM and FRET. Finally, we introduce quantitative indicators enabling robust comparison of FP photostability across experimental conditions. This framework provides a comprehensive approach for understanding and quantifying photobleaching and its implications for fluorescence imaging.

We present a fast, unconditionally energy-stable numerical scheme for simulating vesicle deformation under osmotic pressure using a phase-field approach. The model couples an Allen-Cahn equation for the biomembrane interface with a variable-mobility Cahn-Hilliard equation governing mass exchange across the membrane. Classical approaches, including nonlinear multigrid and Multiple Scalar Auxiliary Variable (MSAV) methods, require iterative solution of variable-coefficient systems at each time step, resulting in substantial computational cost. We introduce a constant-coefficient MSAV (CC-MSAV) scheme that incorporates stabilization directly into the Cahn-Hilliard evolution equation rather than the chemical potential. This reformulation yields fully decoupled constant-coefficient elliptic problems solvable via fast discrete cosine transform (DCT), eliminating iterative solvers entirely. The method achieves O(N2 log N) complexity per time step while preserving unconditional energy stability and discrete mass conservation. Numerical experiments verify second-order temporal and spatial accuracy, mass conservation to relative errors below 5 x 10-11, and close agreement with nonlinear multigrid benchmarks. On grids with N [&ge;] 2048, CC-MSAV achieves 6-15x overall speedup compared to classical MSAV with optimized preconditioning, while the dominant Cahn-Hilliard subsystem is accelerated by up to two orders of magnitude. These efficiency gains, achieved without sacrificing accuracy, make CC-MSAV particularly well-suited for large-scale simulations of vesicle dynamics.