Decoupling diffusion, turnover, and advection in long-term FRAP

Decoupling diffusion, turnover, and advection in long-term FRAP | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Decoupling diffusion, turnover, and advection in long-term FRAP Outa Nakashima , View ORCID Profile Takumi Saito , View ORCID Profile Shinji Deguchi doi: https://doi.org/10.1101/2025.02.04.636435 Outa Nakashima 1 Division of Bioengineering, Graduate School of Engineering Science, The University of Osaka Find this author on Google Scholar Find this author on PubMed Search for this author on this site Takumi Saito 1 Division of Bioengineering, Graduate School of Engineering Science, The University of Osaka Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Takumi Saito Shinji Deguchi 1 Division of Bioengineering, Graduate School of Engineering Science, The University of Osaka 2 3 3 Global Center for Medical Engineering and Informatics, The University of Osaka Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shinji Deguchi For correspondence: deguchi.shinji.es{at}osaka-u.ac.jp Abstract Full Text Info/History Metrics Preview PDF Abstract Intracellular molecular turnover is a dynamic process governed by diffusion, biochemical reactions, and intracellular transport dynamics. While fluorescence recovery after photobleaching (FRAP) has been widely used to quantify fluorescence recovery mechanisms, conventional models primarily focus on diffusion and reaction kinetics, often overlooking the influence of intracellular advection. However, in cytoskeletal structures such as stress fibers, myosin-driven actin retrograde flow generates a significant advective component, which complicates the interpretation of FRAP data, particularly in long-term observations where advective effects become sufficiently pronounced. Here, we develop an analytical framework that extends FRAP modeling to incorporate the coupled effects of diffusion, turnover, and intracellular advection within the photobleached region of interest. By deriving exact solutions to a reaction-diffusion-advection system, we identify three key dimensionless parameters that govern fluorescence recovery dynamics: the turnover-to-diffusion ratio, the monomer-to-filament ratio, and the advection magnitude. Our results demonstrate that, even in the absence of biochemical reactions, fluorescence recovery in a fixed region can occur due to advection, leading to potential misinterpretations of molecular exchange rates. The model provides a theoretical foundation for distinguishing these effects and offers a practical tool for long-term FRAP analysis, where the interplay of diffusion, turnover, and advection becomes increasingly relevant over extended timescales. By systematically characterizing the interplay between molecular diffusion, reaction kinetics, and intracellular transport, our framework provides deeper insight into protein turnover in complex biological environments. Introduction Intracellular structures undergo continuous molecular turnover, enabling cells to adapt to applied stress ( Kaunas & Deguchi, 2011 ; Ueda, Matsunaga, & Deguchi, 2024 ). The mechanisms driving turnover vary depending on the timescale ( Saito, Matsunaga, & Deguchi, 2022b ). Short-term turnover, primarily governed by diffusion and chemical association/dissociation, occurs typically within seconds to minutes. In contrast, long-term turnover involves additional factors such as advection. Often overlooked, advection can significantly influence turnover measurements. Specifically, bulk flow becomes relevant when proteins closely bound to actin cytoskeletal structures are translocated by myosin-driven and actin polymerization-induced retrograde flow ( Gardel et al., 2008 ; Yamashiro et al., 2023 ; Yolland et al., 2019 ). This contribution of advection complicates the interpretation of long-term turnover measurements. Fluorescence recovery after photobleaching (FRAP) is a widely used technique for evaluating protein turnover. While single-exponential fitting is commonly applied to analyze the recovery of fluorescent proteins, more complex scenarios require diffusion-reaction equations to distinguish between the contributions of diffusion and chemical association/dissociation. Traditional FRAP models assume spatially uniform and immobile chemical equilibrium distributions, limiting their applicability to short-term observations ( McGrath, Tardy, Dewey, Meister, & Hartwig, 1998 ; Tardy, McGrath, Hartwig, & Dewey, 1995 ). For long-term FRAP analyses, where single-exponential fitting can become inadequate, double-exponential models are often employed ( Campbell & Knight, 2007 ; Sakurai-Yageta, Maruyama, Suzuki, Ichikawa, & Murakami, 2015 ). However, the interpretation of the two independent time constants in double-exponential models remains unclear particularly concerning the actual physicochemical factors such as diffusion, chemical association/dissociation, and advection. To address these complexities, we previously developed a numerical tool capable of automatically tracking the region of interest (ROI) during FRAP measurements ( Saito, Matsunaga, & Deguchi, 2022a ). This tool enables the separate analysis of diffusion, chemical equilibrium constants, and intracellular flow velocity. Despite its effectiveness, automatic ROI tracking is not always feasible for researchers who conventionally fix the ROI position. Fixed ROI measurements, while widely used in many studies ( Hotulainen & Lappalainen, 2006 ; Katsuta et al., 2023 ), are susceptible to errors introduced by advection, leading to changes in fluorescence signals even when diffusion and association/dissociation are minimal. Consequently, developing a model that accurately interprets conventional FRAP data with a fixed ROI is crucial, especially for long-term observations of actin-based structures. Here, we present a FRAP analytical model developed for fixed ROI analysis, accounting for myosin-driven actin retrograde flow, namely intracellular advection. 2. Analytical model 2.1. Governing equations The system of interest within a cell is represented as a rectangular structure with constant thickness and uniform properties ( Fig. 1 ). The spatial coordinate x denotes the position along the system of length L , with the photobleached band of width w . Considering a sufficiently elongated cell, where intracellular flow is predominantly in the longitudinal direction, the problem can thus be effectively reduced to one dimension. Monomeric actin is modeled as belonging either to the filamentous meshwork or to the diffusive pool. The model assumes no net change in the total amounts of fluorescent and non-fluorescent actin molecules, nor in the relative proportions of filamentous meshwork and diffusive pool, with polymerization and depolymerization balanced throughout the system over time t . Under these conditions, the time-series changes in the concentrations of fluorescent monomers, categorized into diffusive monomers ( c m ) and those incorporated into the filamentous meshwork ( c f ), are governed by diffusion with a diffusion coefficient 𝒟, turnover at a rate k , and advection with a velocity of u : Download figure Open in new tab Fig. 1. Schematic representation of the cellular system, consisting of monomeric actin (red particles) and a filamentous meshwork (polymerized actin filaments), with the fixed bleached region advected by a flow of velocity u . and where c m _ total and c f _ total are the total concentrations of fluorescent monomers within the diffusive pool and filamentous meshwork, respectively. The rationale behind the reaction terms of the exchange process in the above reaction-diffusion-advection equations is as follows. The probability of monomers departing from the filamentous meshwork is expressed as c f / c f _ total , while that from the diffusive pool is given by c m / c m _ total , and the two groups exchange at a constant rate. The initial conditions are given by and where x 0 is the center of the bleached region, and c m _ ext and c f _ ext are the concentrations of fluorescent actin in the diffusive pool and filamentous meshwork outside of the bleached region, respectively. 2.2. Nondimensionalization The above governing equations and initial conditions are nondimensionalized using the following dimensionless numbers marked with an asterisk and the resulting equations are and 2.3. Analytical solutions Assuming periodic boundary conditions, the variables are expressed using Fourier coefficients for the wavenumber ξ , and Substitution of Eqs. (10) and (11) into Eq. (6) and (7) yields Multiplying these equations by exp(− i 2 l π x ∗ ) and integrating from 0 to 1 extract the component for the wavenumber l as follows: and The resulting equations are transformed using the Laplace transform expressed in the s -domain and are organized into a matrix form and hence the solutions are Applying the inverse Laplace transform yields and where and Equations (18) and (19) are rewritten with alternative variables to simplify their notations as follows and where S 0 and C 0 are even and odd functions of the wavenumber l , respectively, and and and and a 1 and a 2 are the real parts of s 1 and s 2 , respectively, and Considering the properties of odd and even functions, the final solutions are obtained as follows: These analytical solutions, describing the time evolution of fluorescence recovery for actin monomers in the diffusive pool and filamentous meshwork, respectively, capture the effects of diffusion, turnover, and advection as a function of the three parameters: β (turnover-to-diffusion ratio), γ (monomer-to-filament ratio), and U (advection magnitude). Note that these equations satisfy the periodic boundary conditions: 2.4. Net FRAP dynamics The weighted average F , obtained from the proportions of molecules belonging to each group, is normalized by the maximum fluorescence F 0 to provide the overall fluorescence recovery: By integrating this expression over the bleached region and determining the mean intensity, the time evolution of fluorescence recovery within the fixed bleached area is obtained The integrated analytical solutions for the diffusive monomer group and the filamentous meshwork group are shown by and 3. Results and discussion The present study aimed to address challenges in extracting long-term intracellular protein turnover from conventional FRAP data. While short-term FRAP behavior within cells is primarily governed by diffusion and turnover, long-term turnover involves an additional influential factor, advection, driven by myosin-actin retrograde flow. Most existing FRAP models assume immobile equilibrium distributions ( Campbell & Knight, 2007 ; Ciocanel, Kreiling, Gagnon, Mowry, & Sandstede, 2017 ; Hallen, Liang, & Endow, 2008 ; Saito, Kikuchi, & Ishikawa, 2024 ; Saito, Matsunaga, Matsui, Noi, & Deguchi, 2021 ; Sakurai-Yageta et al., 2015 ; Sprague et al., 2006 ), limiting their applicability to short-term analyses. Specifically, under sufficient advection, apparent fluorescence recovery in a fixed ROI can occur even in the absence of diffusion or turnover ( Saito, Matsunaga, & Deguchi, 2022b ). This phenomenon arises when non-bleached fluorescent structures from adjacent regions flow into the ROI, resulting in an increase in fluorescence intensity. Although unrelated to diffusion or turnover, it can lead to the misleading interpretation that turnover has occurred. To address this issue, we previously developed a dynamic ROI-tracking model capable of isolating each contributing factor ( Saito, Matsunaga, & Deguchi, 2022a ). However, many FRAP users may prefer measurements with a fixed ROI due to its simplicity and widespread usage. Here, we developed a fixed ROI-based model, providing a framework for simultaneously detecting the individual effects of diffusion, turnover, and advection from a single FRAP measurement. The recovery behavior is analytically derived, enabling the interpretation of complex multi-physicochemical phenomena through three dimensionless parameters, β (turnover-to-diffusion ratio), γ (monomer-to-filament ratio), and U (advection magnitude), which quantify the relative contributions of each process. While intracellular dynamics has been theoretically interpreted through analytical models ( Kang, Day, Di Benedetto, & Kenworthy, 2010 ; Kubitscheck, Wedekind, & Peters, 1994 ; Lele & Ingber, 2006 ; Lopez, Dupou, Altibelli, Trotard, & Tocanne, 1988 ; Sbalzarini, Mezzacasa, Helenius, & Koumoutsakos, 2005 ; Seiffert & Oppermann, 2005 ; Tardy et al., 1995 ; Wedekind, Kubitscheck, Heinrich, & Peters, 1996 ), our approach overcomes the challenges associated with fixed ROI-based FRAP experiments, offering a practical tool for analyzing intracellular turnover over extended timescales. Stress fibers are dynamic structures that continuously undergo assembly and disassembly, playing critical roles in adaptive cellular remodeling ( Kassianidou & Kumar, 2015 ; Ueda, Matsunaga, & Deguchi, 2022 ; Ueda et al., 2024 ), the regulation of pro-inflammatory responses ( Huang et al., 2021 ; J. D. Humphrey, 2007 ; Jay D. Humphrey, Dufresne, & Schwartz, 2014 ; Kaunas & Deguchi, 2011 ; Saito, Huang, et al., 2021 ), and the progression of senescence ( Chantachotikul, Liu, Furukawa, & Deguchi, 2025 ; Liu, Matsui, Kang, & Deguchi, 2022 ). These processes are driven by the coordinated activity of signaling and structural molecules. Understanding the mechanisms behind these processes requires precise analysis of molecular turnover, with a clear distinction between the contributions of diffusion, turnover, and advection. However, as these phenomena often occur simultaneously, the lack of FRAP models that adequately account for the influence of advection has made their differentiation challenging. For example, stress fibers, primarily composed of actin, myosin, and various associated proteins, are subject to retrograde flow. This advection influences apparent turnover in fixed ROI-based FRAP analysis, complicating efforts to accurately resolve the true dynamics of these structures. Further studies should integrate detailed analyses with experimental approaches to refine our understanding of intracellular turnover mechanisms. Competing interests The authors declare no competing interests. Funding This study was partly supported by JSPS KAKENHI grants (21H03796 and 22J00060). References ↵ Campbell , J. J. , & Knight , M. M. ( 2007 ). An improved confocal FRAP technique for the measurement of long-term actin dynamics in individual stress fibers . Microscopy Research and Technique , 70 ( 12 ), 1034 – 1040 . doi: 10.1002/JEMT.20513 OpenUrl CrossRef PubMed ↵ Chantachotikul , P. , Liu , S. , Furukawa , K. , & Deguchi , S. ( 2025 ). AP2A1 modulates cell states between senescence and rejuvenation . Cellular Signalling , 127 , 111616 . doi: 10.1016/J.CELLSIG.2025.111616 OpenUrl CrossRef ↵ Ciocanel , M. V. , Kreiling , J. A. , Gagnon , J. A. , Mowry , K. L. , & Sandstede , B. ( 2017 ). Analysis of Active Transport by Fluorescence Recovery after Photobleaching . Biophysical Journal , 112 ( 8 ), 1714 – 1725 . doi: 10.1016/j.bpj.2017.02.042 OpenUrl CrossRef ↵ Gardel , M. L. , Sabass , B. , Ji , L. , Danuser , G. , Schwarz , U. S. , & Waterman , C. M. ( 2008 ). Traction stress in focal adhesions correlates biphasically with actin retrograde fl ow speed . Journal of Cell Biology , 183 ( 6 ), 999 – 1005 . doi: 10.1083/JCB.200810060/VIDEO-10 OpenUrl Abstract / FREE Full Text ↵ Hallen , M. A. , Liang , Z. Y. , & Endow , S. A. ( 2008 ). Ncd motor binding and transport in the spindle . Journal of Cell Science , 121 ( 22 ), 3834 – 3841 . doi: 10.1242/JCS.038497 OpenUrl Abstract / FREE Full Text ↵ Hotulainen , P. , & Lappalainen , P. ( 2006 ). Stress fibers are generated by two distinct actin assembly mechanisms in motile cells . Journal of Cell Biology , 173 ( 3 ), 383 – 394 . doi: 10.1083/jcb.200511093 OpenUrl Abstract / FREE Full Text ↵ Huang , W. , Matsui , T. S. , Saito , T. , Kuragano , M. , Takahashi , M. , Kawahara , T. , … Deguchi , S. ( 2021 ). Mechanosensitive myosin II but not cofilin primarily contributes to cyclic cell stretch-induced selective disassembly of actin stress fibers . American Journal of Physiology - Cell Physiology , 320 ( 6 ), C1153 – C1163 . doi: 10.1152/AJPCELL.00225.2020/ASSET/IMAGES/LARGE/AJPCELL.00225.2020_F007.JPEG OpenUrl CrossRef ↵ Humphrey , J. D. ( 2007 ). Vascular Adaptation and Mechanical Homeostasis at Tissue, Cellular, and Sub-cellular Levels . Cell Biochemistry and Biophysics 2007 50:2 , 50 ( 2 ), 53 – 78 . doi: 10.1007/S12013-007-9002-3 OpenUrl CrossRef PubMed ↵ Humphrey , Jay D. , Dufresne , E. R. , & Schwartz , M. A. ( 2014 ). Mechanotransduction and extracellular matrix homeostasis . Nature Reviews Molecular Cell Biology 2014 15:12 , 15 ( 12 ), 802 – 812 . doi: 10.1038/nrm3896 OpenUrl CrossRef PubMed ↵ Kang , M. , Day , C. A. , Di Benedetto , E. , & Kenworthy , A. K. ( 2010 ). A quantitative approach to analyze binding diffusion kinetics by confocal FRAP . Biophysical Journal , 99 ( 9 ), 2737 – 2747 . doi: 10.1016/j.bpj.2010.09.013 OpenUrl CrossRef PubMed Web of Science ↵ Kassianidou , E. , & Kumar , S. ( 2015 ). A biomechanical perspective on stress fiber structure and function . Biochimica et Biophysica Acta (BBA) - Molecular Cell Research , 1853 ( 11 ), 3065 – 3074 . doi: 10.1016/J.BBAMCR.2015.04.006 OpenUrl CrossRef ↵ Katsuta , H. , Okuda , S. , Nagayama , K. , Machiyama , H. , Kidoaki , S. , Kato , M. , … Hirata , H. ( 2023 ). Actin crosslinking by α-actinin averts viscous dissipation of myosin force transmission in stress fibers . IScience , 26 ( 3 ), 106090 . doi: 10.1016/J.ISCI.2023.106090 OpenUrl CrossRef PubMed ↵ Kaunas , R. , & Deguchi , S. ( 2011 , April ). Multiple roles for myosin II in tensional homeostasis under mechanical loading . Cellular and Molecular Bioengineering , Vol. 4 , pp. 182 – 191 . doi: 10.1007/s12195-011-0175-x OpenUrl CrossRef ↵ Kubitscheck , U. , Wedekind , P. , & Peters , R. ( 1994 ). Lateral diffusion measurement at high spatial resolution by scanning microphotolysis in a confocal microscope . Biophysical Journal , 67 ( 3 ), 948 – 956 . doi: 10.1016/S0006-3495(94)80596-X OpenUrl CrossRef PubMed Web of Science ↵ Lele , T. P. , & Ingber , D. E. ( 2006 ). A mathematical model to determine molecular kinetic rate constants under non-steady state conditions using fluorescence recovery after photobleaching (FRAP) . Biophysical Chemistry , 120 ( 1 ), 32 – 35 . doi: 10.1016/j.bpc.2005.10.007 OpenUrl CrossRef PubMed ↵ Liu , S. , Matsui , T. S. , Kang , N. , & Deguchi , S. ( 2022 ). Analysis of senescence-responsive stress fiber proteome reveals reorganization of stress fibers mediated by elongation factor eEF2 in HFF-1 cells . Molecular Biology of the Cell , 33 ( 1 ). doi: 10.1091/MBC.E21-05-0229/ASSET/IMAGES/LARGE/MBC-33-AR10-G007.JPEG OpenUrl CrossRef ↵ Lopez , A. , Dupou , L. , Altibelli , A. , Trotard , J. , & Tocanne , J. F. ( 1988 ). Fluorescence recovery after photobleaching (FRAP) experiments under conditions of uniform disk illumination. Critical comparison of analytical solutions, and a new mathematical method for calculation of diffusion coefficient D . Biophysical Journal , 53 ( 6 ), 963 – 970 . doi: 10.1016/S0006-3495(88)83177-1 OpenUrl CrossRef PubMed Web of Science ↵ McGrath , J. L. , Tardy , Y. , Dewey , C. F. , Meister , J. J. , & Hartwig , J. H. ( 1998 ). Simultaneous measurements of actin filament turnover, filament fraction, and monomer diffusion in endothelial cells . Biophysical Journal , 75 ( 4 ), 2070 – 2078 . doi: 10.1016/S0006-3495(98)77649-0 OpenUrl CrossRef PubMed Web of Science ↵ Saito , T. , Huang , W. , Matsui , T. S. , Kuragano , M. , Takahashi , M. , & Deguchi , S. ( 2021 ). What factors determine the number of nonmuscle myosin II in the sarcomeric unit of stress fibers? Biomechanics and Modeling in Mechanobiology , 20 ( 1 ), 155 – 166 . doi: 10.1007/s10237-020-01375-8 OpenUrl CrossRef PubMed ↵ Saito , T. , Kikuchi , K. , & Ishikawa , T. ( 2024 ). Glucose stockpile in the intestinal apical brush border in C. elegans . Biochemical and Biophysical Research Communications , 706 ( March ), 149762 . doi: 10.1016/j.bbrc.2024.149762 OpenUrl CrossRef PubMed ↵ Saito , T. , Matsunaga , D. , & Deguchi , S. ( 2022a ). Analysis of chemomechanical behavior of stress fibers by continuum mechanics-based FRAP . Biophysical Journal , 121 ( 15 ), 2921 – 2930 . doi: 10.1016/j.bpj.2022.06.032 OpenUrl CrossRef PubMed ↵ Saito , T. , Matsunaga , D. , & Deguchi , S. ( 2022b ). Long-term molecular turnover of actin stress fibers revealed by advection-reaction analysis in fluorescence recovery after photobleaching . PLOS ONE , 17 ( 11 ), e0276909 . doi: 10.1371/journal.pone.0276909 OpenUrl CrossRef ↵ Saito , T. , Matsunaga , D. , Matsui , T. S. , Noi , K. , & Deguchi , S. ( 2021 ). Determining the domain-level reaction-diffusion properties of an actin-binding protein transgelin-2 within cells . Experimental Cell Research , 404 ( 1 ). doi: 10.1016/j.yexcr.2021.112619 OpenUrl CrossRef ↵ Sakurai-Yageta , M. , Maruyama , T. , Suzuki , T. , Ichikawa , K. , & Murakami , Y. ( 2015 ). Dynamic regulation of a cell adhesion protein complex including CADM1 by combinatorial analysis of FRAP with exponential curve-fitting . PLoS ONE , 10 ( 3 ). doi: 10.1371/journal.pone.0116637 OpenUrl CrossRef PubMed ↵ Sbalzarini , I. F. , Mezzacasa , A. , Helenius , A. , & Koumoutsakos , P. ( 2005 ). Effects of Organelle Shape on Fluorescence Recovery after Photobleaching . Biophysical Journal , 89 ( 3 ), 1482 – 1492 . doi: 10.1529/BIOPHYSJ.104.057885 OpenUrl CrossRef PubMed Web of Science ↵ Seiffert , S. , & Oppermann , W. ( 2005 ). Systematic evaluation of FRAP experiments performed in a confocal laser scanning microscope . Journal of Microscopy , 220 ( 1 ), 20 – 30 . doi: 10.1111/J.1365-2818.2005.01512.X OpenUrl CrossRef PubMed Web of Science ↵ Sprague , B. L. , Müller , F. , Pego , R. L. , Bungay , P. M. , Stavreva , D. A. , & McNally , J. G. ( 2006 ). Analysis of binding at a single spatially localized cluster of binding sites by fluorescence recovery after photobleaching . Biophysical Journal , 91 ( 4 ), 1169 – 1191 . doi: 10.1529/biophysj.105.073676 OpenUrl CrossRef PubMed Web of Science ↵ Tardy , Y. , McGrath , J. L. , Hartwig , J. H. , & Dewey , C. F. ( 1995 ). Interpreting photoactivated fluorescence microscopy measurements of steady-state actin dynamics . Biophysical Journal , 69 ( 5 ), 1674 – 1682 . doi: 10.1016/S0006-3495(95)80085-8 OpenUrl CrossRef PubMed Web of Science ↵ Ueda , Y. , Matsunaga , D. , & Deguchi , S. ( 2022 ). A statistical mechanics model for determining the length distribution of actin filaments under cellular tensional homeostasis . Scientific Reports 2022 12:1 , 12 ( 1 ), 1 – 10 . doi: 10.1038/s41598-022-18833-1 OpenUrl CrossRef PubMed ↵ Ueda , Y. , Matsunaga , D. , & Deguchi , S. ( 2024 ). Asymmetric response emerges between creation and disintegration of force-bearing subcellular structures as revealed by percolation analysis . Integrative Biology , 16 . doi: 10.1093/INTBIO/ZYAE012 OpenUrl CrossRef ↵ Wedekind , P. , Kubitscheck , U. , Heinrich , O. , & Peters , R. ( 1996 ). Line-scanning microphotolysis for diffraction-limited measurements of lateral diffusion . Biophysical Journal , 71 ( 3 ), 1621 – 1632 . doi: 10.1016/S0006-3495(96)79366-9 OpenUrl CrossRef PubMed ↵ Yamashiro , S. , Rutkowski , D. M. , Lynch , K. A. , Liu , Y. , Vavylonis , D. , & Watanabe , N. ( 2023 ). Force transmission by retrograde actin flow-induced dynamic molecular stretching of Talin . Nature Communications 2023 14:1 , 14 ( 1 ), 1 – 14 . doi: 10.1038/s41467-023-44018-z OpenUrl CrossRef PubMed ↵ Yolland , L. , Burki , M. , Marcotti , S. , Luchici , A. , Kenny , F. N. , Davis , J. R. , … Stramer , B. M. ( 2019 ). Persistent and polarized global actin flow is essential for directionality during cell migration . Nature Cell Biology 2019 21:11 , 21 ( 11 ), 1370 – 1381 . doi: 10.1038/s41556-019-0411-5 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted February 04, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Decoupling diffusion, turnover, and advection in long-term FRAP Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Decoupling diffusion, turnover, and advection in long-term FRAP Outa Nakashima , Takumi Saito , Shinji Deguchi bioRxiv 2025.02.04.636435; doi: https://doi.org/10.1101/2025.02.04.636435 Share This Article: Copy Citation Tools Decoupling diffusion, turnover, and advection in long-term FRAP Outa Nakashima , Takumi Saito , Shinji Deguchi bioRxiv 2025.02.04.636435; doi: https://doi.org/10.1101/2025.02.04.636435 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Biophysics Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17697) Bioengineering (13894) Bioinformatics (41951) Biophysics (21455) Cancer Biology (18593) Cell Biology (25509) Clinical Trials (138) Developmental Biology (13380) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24322) Genetics (15611) Genomics (22509) Immunology (17737) Microbiology (40398) Molecular Biology (17183) Neuroscience (88619) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)