{"paper_id":"230e9ec4-1462-4a14-86f7-4e28276fabfc","body_text":"1 \n \n \n \n \nMain Manuscript for \nExperimental and theoretical approaches reveal that antimicrobial blue light killing \nefficiency decreases with biofilm growth in a Pseudomonas aeruginosa model. \n \nGiacomo Inseroa,…, Nidia Maldonado-Carmonab,c, …, #, Thomas Panierb, Giovanni Romanoa, *, Nelly \nHenryb,c, * \na University of Florence, Department of Experimental and Clinical Biomedical Sciences “Mario \nSerio”, Florence, Italy, b Sorbonne Université, CNRS, Laboratoire Jean Perrin, F-75005 Paris, \nFrance, cSorbonne Université, CNRS, Inserm, Institut de Biologie Paris-Seine, IBPS, F75005 \nParis, France \n…these authors contributed equally to the manuscript. \n#current affiliation: University of Florence, Department of Experimental and Clinical Biomedical \nSciences “Mario Serio”, Florence, Italy \n*Nelly Henry, Giovanni Romano. \nEmail: nelly.henry@sorbonne-universite.fr, giovanni.romano@unifi.it \nAuthor Contributions: GI: methodology, software, formal analysis, writing - review and editing. \nNM-C: validation, investigation – review and editing, visualization. TP: methodology, validation. GR: \nconceptualization, resources, writing – review and editing, supervision, project administration, \nfunding acquisition. NH: conceptualization, formal analysis, validation, resources, writing – original \ndraft, writing – review and editing, supervision, project administration, funding acquisition.  \nCompeting Interest Statement: The authors have no competi ng interests to declare that are \nrelevant to the content of this article. \nClassification: Biophysics and Computational Biology (Physical Sciences and Engineering) \nKeywords: antimicrobial photoinactivation, blue -light, Pseudomonas aeruginosa biofilm, \nmillifluidics,  video microscopy \n \nThis PDF file includes: \nMain Text \nFigures 1 through 7 \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n2 \n \n \n \nAbstract  \nRecently, the use of antimicrobial blue light (aBL) has gained interest across various applications. \nHowever, a comprehensive framework that addresses  the key fact ors driving bacterial \nphotoinhibition remains lacking —particularly concerning biofilms, the predominant bacterial \nlifestyle. The goal of this w ork was to evaluate the potential of photokilling in this wide -spread \nmicrobial adherent community  type, and to d ecipher the specific mechanisms at stake. To \ninvestigate aBL killing efficiency, we conducted experiments in a Pseudomonas aeruginosa biofilm \nmodel using a well -defined millifluidic device that allows real -time microscopy and quantitative \nanalysis of a living biofilm under local irradiation at a defined light dose. In addition, we developed \na theoretical model for light-biofilm interaction that accounts for the three-dimensional structure of \nthe bacterial biofilm. To inform our model, we examine d the light dose-response in isolated cells \nand found a profile indicative of a multi -target mechanism of lethality . By c omparing the \nexperimental and theoretical results, we identified a loss in killing efficiency as the biofilm grows, \ndue in part to the increase in thickness of the living material inherent to this mode of development. \nOur findings also highlight a reduction in the intrinsic bacterial sensitivity to blue  light as biofilm \ndevelopment progresses, which we attribute to the low oxygen levels typical of densely populated \nbacterial environments. These findings reveal new features of the photokilling mechanism and \nredefine the approa ch to designing effective antimicrobial photoinactivation strategies by \nintegrating the key physical characteristics of bacterial biofilms. \nSignificance Statement  \nAwareness of the bacterial world's global importance is steadily growing in both science and \nsociety. Among the critical challenges, the continuing increase in multidrug resistance to antibiotics \nrepresents a major public  health concern reinforcing the urgency of alternative antimicrobial \ntherapies with photoinactivation as a promising approach. However, its full potential can only be \nachieved through a better understanding of the involved mechanisms in relevant environments. In \nthis study, we combined experimental and theoretical approaches to investigate the \nphotoinactivation of bacteria within a  developing biofilm, the dominant bacterial lifestyle. Our \ncomprehensive analysis sheds light on the mechanisms and limitations of photoinactivation in the \nfight against microbes, which is essential for designing novel antibacterial phototherapies. \nIntrodu ction  \nThe rapid expansion of antimicrobial resistance is now recognized as a serious public health issue \n(1), and unconventional strategies to fight against microbes are actively being researched. \nRecently, antimicrobial blue light (aBL) arose as a potential alternative therapy (2, 3). This approach \nrelies on the presence in microbes of natural photosensitizers such as porphyrins, which are excited \nto the triplet state upon absorption of blue light in the range of 380 - 420 nm. This generates reactive \noxygen species that deactivate locally, damaging a wide range of macromolecules and impairing \nvarious cellular functions (4–9). Due to the lack of a single biological target, it was postulated that \nthe emergence of bacterial resistance should be less probable (10, 11). To date, blue light-induced \nkilling has been demonstrated in a wide range of microorganisms (both bacteria and yeast), and \nseveral studies have reported that irradiation with light doses in the range of 10 to 100 J/cm2, using \nwavelengths between 400 and 470 nm , decrease survival by sever al orders of magnitude  (5, 7, \n12–15). Despite a large variability in results and protocols across different studies (3, 10, 16–18), \nfindings from the last decade generally substantiate the view that blue light reduces bacterial cell \nproliferation. However, the essential factors contributing to the achieved effects remain unclear, as \nshown in a systematic analysis of the literature for Escherichia coli (19). \nIn this context, we focused our investigations on biofilms, the prevailing lifestyle of most pathogens \n(20). These multicellular three-dimensional structures typically adhere to surfaces where cells \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n3 \n \n \n \nmultiply and are confined in a protective, self-secreted extracellular matrix. Within biofilms, cells \nencounter physical and chemical conditions that differ significantly from those experienced in their \nplanktonic state. This i nduces the emergence of specific properties , notably multi-factorial \nresistance to biocides. For now, the question of which physiochemical factors drive the biofilm \nresponse to blue light, and how  this is accomplished , is largely unanswered. Despite previous \nreports of blue light inactivation of pathogenic microorganisms in biofilms (17, 21–26), the absence \nof standardized illumination configurations, biofilm development specifications, and methodological \ndetails for assessing photokilling effic iency have prevented drawing any precise conclusions \nregarding the key parameters that influence the response of bacterial biofilms to blue light. For \ninstance, a popular way to grow biofilms consists of seeding multi-well plates and allowing the cells \nto form the biofilm either directly on the walls of the wells , or on pegs immersed in the wells and \nattached to a removable lid. While these settings facilitate performing numerous experimental \nconditions in parallel, they promote the formation of a biofilm topography that leads to substantial \nheterogeneity in light exposure, making the analysis of the results  challenging. Additionally, \nuncontrolled variations in cellular environmental properties arise when cells are grown in disparate \nsettings. Biofilm architecture is another factor that may impact light delivery, which could affect the \ndesign of an antimicrobial blue light strategy. These obstacles also hinder accurate comparison s \nof the sensitivity of planktonic cells and biofilm-dwelling cells. \nWithin this f ramework, we focus ed our interest on the Pseudomonas aeruginosa PAO1 model \nstrain, an opportunistic pathogen known to colonize the lungs of cystic fibrosis patients, which has \nthe extended potential to develop antibiotic resistance (27, 28) . Our aim was  to devise an \nexperiment that could decipher the main factors involved in biofilm response to blue light irradiation, \nby controlling the biological development of the model, the environment , and light delivery. We \ntherefore opted for a device capable of real-time monitoring of a biofilm grown under flow in a \nmillifluidic channel on a confocal microscope  stage. This setup offers complete control over the \nphysicochemical parameters of biofilm development such as applied shear stress, nutrient supply \nand temperature, and allows for the precise delivery of defined light doses at various stages of \nbiofilm formation within a clear, reproducible and coherent geometry. \nThanks to this device, we can investigate how blue light  impacts the overall development of the \nbacterial community, and how killing efficienc y evolves with biofilm expansion. Specifically, we \ndemonstrate here that the antimicrobial efficacy of blue light decreases as biofilm development \nprogresses, resulting in a rapid recovery with negligible impact in the long term. We quantitatively \nanalyzed the killing efficiency and kinetic s of aBL-induced cell death to identify the underlying \nmechanisms, by comparing biofilm response to single isolated cells. To explain these observations, \nespecially the decline in blue light efficiency as the biofilm develops, we formulate d a physical \nmodel that predicts the dose-dependent bacterial photokilling efficacy by taking into account light \nabsorption in the growing material. Importantly, both experimental evidence and theory consistently \npoint to a decline in aBL efficiency as the biofilm ages. We conclude that thickening of the biofilm \nthroughout its development s ignificantly contributes to the light screening effect . Comparing the \nmodel with the experimental data also revealed a decrease in the cell sensitivity constant, which \ncould be  related to the intense metabolic consumption of oxygen in these cell-concentrated \norganizations. \nOur results reshape the framework for designing a practical antimicrobial photoinactivation \nstrategy, by incorporating the key charact eristics of bacterial biofilms , the dominant bacterial \nlifestyle whose structure and functions impose constraints on photoinactivation. This must therefore \nbe taken into account in any antibacterial phototherapeutic design. \nResults  \nPAO1 biofilm displays a deterministic developmental pattern under flow \nThe formation of PAO1 biofilm in the millifluidic channel was monitored by imaging the internal top \nsurface of the channel where it preferentially forms  (Figure 1a); growth kinetics were then drawn \nfrom time-lapse imaging of the biofilm in transmitted light. The µOD signal (Figure 1b) shows that \nPAO1 biofilm developed deterministically according to three distinct kinetic regimes, as indicated \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n4 \n \n \n \nby the time derivative (Figure 1c). The first phase, lasting approximately 5 hours, follows an \nexponential growth with an apparent growth rate 𝑘! = 1.1\th\"# obtained from adjusting the data \nto \n𝜇𝑂𝐷(𝑡) = 𝐴\t ∙ 𝑒$!%        (Equation 1) \nwhere A is the initial µOD at t = 0 related to the initial number of cells. The biofilm growth then \nexperienced a transition consisting of a transient s lowdown followed by a new increase up to a \nstable value that was reached after approximately 10 h, marking the beginning of the third phase, \ncharacterized by a constant rate. Interestingly, microscopy images show that the first kinetic switch \ncorrelates with the completion of the first cell surface monolayer (Figure 1a, Movie S1  and S2). \nAfter this time point, the cells start to build additional layers and the biofilm thickens.  This \nquantitative description of biofilm development facilitated investigating the biofilm response to blue \nlight in the different developmental stages, from the initial exponential growth to the linear temporal \ndevelopment of the community. \n \n \nFigure 1. PAO1 biofilm develops under flow according to three distinct phases . (a) Representative \nbrightfield images at characteristic biofilm growth times (0, 2, 5 and 20 hours). (b) Biofilm growth reported by \nµOD kinetics taken from time-lapse movies. The experimental curve in black represents the average of five \ndistinct positions in two different channels; the standard deviation is shown in the grey shaded region. The \ninitial part of the curve from t = 0 to 6 h has been adjusted to an exponential growth, represented as the red \ndotted line. (c) The derivative of µOD = f(t)  highlights the three distinct biofilm phases, delineated by the \nmaxima marked by the black arrows at t = 5 h and t = 10 hours. \n \nBlue light killing efficiency declines throughout PAO1 biofilm development \nIn order to evaluate the aBL killing efficiency on the growing PAO1 biofilm, we irradiated defined \npositions at different times following the initiation of the biofilm, using a 2007 J/cm2 light dose. Time-\nlapse imaging of the irradiated zone was recorded in parallel with control non-irradiated positions \nin the same channel (Figure 2a). The resulting growth curves in Figure 2b show that irradiating the \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n5 \n \n \n \nyoung biofilm in its initial exponential growth phase (within the first five hours ) instantaneously \nhinders the biofilm growth in the captured area , inducing a growth delay of several hours (see \nFigure 2c).  \n \nFigure 2: Local irradiation of a PAO1 biofilm affects young biofilm growth. (a) Channel sketch where six \nof the eight positions were illuminated at 405 nm. (b) Local growth curves; with the exception of the controls \n(in black), all positions received the same dose (2007 J/cm2) at different times of biofilm growth. The initial \ntime (t = 0) corresponds to nutrient flow start (0.5 mL/h). Irradiation at 1 h is shown in red, at 3 h in green, at \n4 h in blue , and at 6 h in yellow. Panel (c) (marked by a red dotted line) focuses on the initial exponential \ngrowth phase, with arrows indicating the light dose delivery time of each curve. The curve of each time is the \naverage of at least two independent positions, and is shown shaded with the standard deviation of the data \nset. \n \nThe irradiated zones subsequently recover and essentially match the biomass and growth rate of \nthe controls, demonstrating a limited long -term effect on global biofilm development. To better \nassess the damage induced by the different irradiations, we measure d the local cell mortality \nthrough the use of propidium iodide (PI), the cell death marker, in the nutrient flow and collected \ntime-lapse transmission and red fluorescence images throughout biofilm growth, both before and \nafter illumination (Figure 3a). Upon irradiation, PI fluorescence intensity ( FPI) suddenly increased \nup to a stable plateau, according to a first order kinetic (Figure 3b) as follows: \n𝐹\"#(𝑡̅) = 𝐹$%,'() (1 − 𝑒*+!\t-̅)      (Equation 2) \nwhere FPI,max is the maximum intensity at the kinetic plateau, related to  the number of dead cells  \nNd; 𝑡̅ is the time elapsed from irradiation triggering; and kd is the kinetic constant that provides the \ncharacteristic time for the emergence of PI -labelled cells. The PI-DNA interaction occurs in less \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n6 \n \n \n \nthan two minutes in pre-permeabilized cells, indicating that here the full cell permeabilization rate-\nlimiting step, in which the detrimental effects of blue light result in cell death, has been identified. \nFPI,max and kd were obtained by fitting time-lapse data to Equation 2, and are plotted as a function \nof biofilm age at the time of irradiation (Figure 3 c-d). kd drastically decreases for irradiations \nperformed after 5 hours of biofilm development, revealing that light-induced killing takes more time \nto complete as the biofilm matures beyond the initial phase. FPI,max, which reflects the absolute \nnumber of dead cells, displays a non-monotonic profile that can be understood only relative to the \ntotal biomass at the irradiation time. \n \n \n \nFigure 3: Blue light toxicity during biofilm development . (a) Transmission (upper row) and red \nfluorescence (lower row) microscopy images were recorded just before (left column) and after (right column) \nirradiation (2007J/cm2) of the growing biofilm supplied with PI (3 µM). Fluorescence images were taken at the \nPI fluorescence plateau; the white scale bar represents 25 µm. (b) Measurements of PI rise kinetics (purple \ndots) were adjusted to the first order equation (Equation 2; red dashed line). The evolution of kd (c), the kinetic \nconstant for PI increase in the biofilm, and (d) FPI,max as a function of the time of irradiation.  \n \nWe therefore defined the killing efficiency KE as KE = Nd/N0, where Nd is the number of dead cells \nand N0 is the total number of cells at the start of the irradiation given by the µOD signal calibrated \nby GFP fluorescence at a short time scale (see Materials and Methods). The results in Figure 4 \nindicate that KE remains stable and near a value of 1 for irradiations applied within the first five \nhours of development and then declines gradually and consistently, similar to the trend observed \nfor kd. Interestingly, the shift in the curve at about 5 h coincides with the end of the biofilm’s initial \ngrowth phase and the point at which the community begins to expand three -dimensionally after \ncompleting the first monolayer.  These results led us to hypothesize that the 3D structure of the \nbacterial biofilm and its thickening during growth could be responsible for the observed reduction \nin blue light efficiency. To test this idea, we devised a simple mathematical model to describe the \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n7 \n \n \n \nlight screening effect and the potential light dose reduction induced by the addition of new cell \nlayers as the biofilm grows.  \n \nFigure 4: Killing efficiency decreases with biofilm aging.  To examine killing efficiency,  KE = \nNd/N0 was derived from FPI,max and biofilm µOD for irradiations at increasing biofilm development \ntimes. Irradiations were performed at 2007 J/cm2 under constant nutrient flow (0.5 mL/h) and in the \npresence of PI (3 µM). \n \nModeling light dose attenuation induced by biofilm thickening \nTo e valuate the light dose attenuation caused by biofilm thickening, we con sidered the light \nabsorption process as described by the Beer-Lambert law, which states that the transmitted light \nintensity impinging orthogonally on the biofilm decreases exponentially with the optical path length \nand concentration of absorbing compounds, yielding: \n𝐷 = 𝐷/𝑒*0∙2        (Equation 3) \nwhere D is the light dose corresponding to the energy fluence impinging on the unit surface \northogonal to the illumination direction at a given depth z inside the biofilm; D0 is the light dose at \nz = 0; and μ represents the effective absorption coefficient, an intrinsic invariant of the cell. This \ncoefficient is related to the presence of various absorbers composed of cellular material, including \nthe membrane and the intracellular medium packed with proteins and DNA, and it can be expressed \nby the formula μ = ∑i·εi·ci, in which εi and ci respectively represent the molar extinction coefficient \nand the concentration of each individual absorber.  To facilitate comparisons between the model \nand the experimental data, we chose to express z in terms of the number of cell layers,  with μ \nrepresenting the effective absorption coefficient per unit cell layer. To evaluate this, we measured \nlight transmission through small patches o f PAO1 cell monolayers confined in an agar pad \nilluminated by a 405 nm LED and obtained a value of μ = (0.22 ± 0.1) per cell layer (Supplementary \nInformation I and Figure S1. In order to relate this to the killing efficiency in the biofilm, we must \nalso presume a theoretical light dose dependence of the blue light intrinsic killing efficiency for a \npopulation of isolated bacteria. \n \nModeling light dose-dependence of aBL intrinsic killing efficiency  \nDespite the large number of reports dedicated to aBL ( 1-19, 21-26), studies on the light dose-\ndependence of the killing efficiency are sparse and poorly generalizable (15). In order to infer the \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n8 \n \n \n \ndependence of the killing efficiency on the light dose, we devised an experiment aimed at \nspecifically measuring the dose-response of a population of isolated cells that do not form a biofilm \nunder our defined conditions. For this, we employed cells deposited in agar pads with added PI, \nwhich allows monitoring  light irradiation toxicity at the different applied light doses. This \nconfiguration places all of the cells in a unique optical plane, which enables defining a single light \ndose value for all of the cells and precisely monitoring cell death. The photokilling efficiency as a \nfunction of the delivered light dose 𝐷/ is reported in Figure 5. The curve shows a pronounced \ninflection at low light doses, suggesting the presence of a threshold effect.  \n \nFigure 5: Isolated cells display an S-shaped sensitivity to blue light. Killing efficiency is illustrated as a \nfunction of light dose (405 nm) in isolated cells immobilized in an agar pad. Experimental data (blue dots) are \nshown with their adjustment to Equation 5 (\t𝐾! = (1 − 𝑒\"#!$)%\t) (dashed red line), providing ks = (0.021 ± \n0.003) (J/cm2)-1 and n = (10 ± 2). \n \nTo account for this result, we presumed a dose-dependence similar to what has been proposed for \nmodeling ionizing radiation activity on living cells, assuming a multi-target mechanism (29). This \nmodel considers radiation as a sequence of projectiles — here, impinging photons — that produce \nhits with a Poisson law probability, and assumes that a single hit damages a single target per cell \nwith a probability P1. The single-target damage is assumed to be sublethal with a probability equal \nto \n𝑃3 = (1 − 𝑒*+\"4)       (Equation 4) \nwhere D is the energy per unit surface expressed in J/cm2 that is related to the number of photons \nper unit surface during the whole illumination time, and ks is a sensitivity constant  that is \nindependent of D. Thus, assuming that cell death is induced by hitting n targets per cell, its \nprobability is Pn = (P1)n. The killing efficiency is therefore:  \n𝐾5 = 𝑃6 = (1 − 𝑒*+\"4)6      (Equation 5) \nBy fitting the isolated cell killing efficiency data (Figure 5) with Eq. 5, the best agreement between \nthe data and the model is obtained for ks = (0.021 ± 0.003) (J/cm2)-1 and n = (10 ± 2). \nNotably, the multi-target model is compatible with the living cell population surviving a dose D that \nexponentially decreases with D (29), in which the ks and n parameters are independent of D.  \n \nModeling aBL killing efficiency in a thickening biofilm \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n9 \n \n \n \nTo take into account the exponential decrease in light dose with the optical path length and the \nconsequent decrease in photokilling efficacy, we generalized the expression of the killing efficiency \nestablished for isolated cells (Eq. 5) to a growing biofilm of thickness H to obtain the following \nformula: \nK5 =\n3\n7 ∫ \t(1 − 𝑒*+\"∙4#∙8$%.'\n)6\td𝑧\n7\n/      (Equation 6) \nin which killed cells have been integrated over H, assuming constant cell volume density. Numerical \nintegration of this equation was used to simulate the model and examine the dependence of the \nkilling efficiency on the key system parameters , ks, D0 , n, and μ (Figure 6a,b,c and d). \n \n \n \nFigure 6: Killing efficiency calculated from the model for different sets of parameter values. (A) 0.001 \n< ks < 0.02 (J/cm2)-1 with D = 2 kJ/cm2, µ = 0.2 per layer, and n = 10. (B) 0.5 < D < 2.5 kJ/cm2 with ks = 0.01 \n(J/cm2)-1, µ = 0.2 per layer, and n = 10. (C) 5 < n < 40 with D = 2 kJ/cm2, µ = 0.2 per layer, and ks = 0.01 \n(J/cm2)-1. (D) 0.1 < µ < 0.8 with D = 2 kJ/cm2, ks = 0.01 (J/cm2), and n = 10. (E) The experimental killing \nefficiency is shown as a function of the number of cell layers, obtained using a light dose of 2 007J/cm2. \n \nFigure 6 shows the behavior of the model according to a series of parameters selected for their \nexperimental significance, in the range of one to about ten cell layers. The trend is qualitatively \nconsistent with experimental observations showing that killing efficiency tends to decrease as the \nbiofilm ages, and thus as it thickens (Figure 6e). These simulations illustrate the different \nparameters that can affect the killing efficiency. Logically, the loss of killing efficiency intensifies \nwith increasing extinction coefficients, but declines with stronger doses and higher sensitivities. \nAdditionally, a greater number of targets needed to induce lethality are associated with an amplified \nloss of efficiency during biofilm development. \nTo quantitatively compare the model with the data, we converted the experimental data giving the \nkilling efficiency as a function of the biofilm development time, i.e. KE = f(t), into data giving the \nvariation of the killing efficiency with H, i.e. KE = f(H) (Figure 6e), in which H denotes biofilm \nthickness expressed in number of cell layers. The conversion law used to change the variable t into \nvariable H is provided by the biofilm µOD, assuming that the absorbance increases in proportion \nto the number of layers (Supplementary Information II and Figure S2). We therefore quantified the \nagreement of the theoretical killing efficiency function (Equation 6) with the experimental data using \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n10 \n \n \n \nthe Chi-squared (𝜒2) test as an estimator of goodness-of-fit (see Supplementary Information III and \nFigure S3 for a detailed explanation of the numerical procedure).  \nWe constrained the minimization by setting D0 to an experimentally determined value (D0 = 2007 \nJ/cm2) and varied n and ks for a series of µ values between 0.2 and 0.3 per cell layer, as indicated \nby the experimental determination (µ = 0.22 per layer). We thus observed a well-defined, crescent-\nshaped area in which the 𝜒2 value is minimal and outlines a parameter set domain consistent with \nthe experimental results (Figure 7a). A minimal 𝜒2 value of 1.75 was obtained for µ = 0.26 per cell \nlayer, which returned a pair of ks and n values equal to 0.04 (J/cm2)-1 and 14, respectively. Using \nthis parameter set, we compare d the time dependence of the killing efficiency derived from the \nmodel with that given by the experimental data (Figure 7b) and obtained a good agreement within \nthe limits of the error associated with the experimental measurements. \n \n \n \nFigure 7: 𝜒2 contour map. (a) 𝜒2 values reported for the n and ks parameters ranging between 1—10 and \n1—10\t×\t10-3 (J/cm2)-1, respectively calculated for µ = 0.26 per cell layer and a light dose of 2007 J/cm². The \nblack star indicates the parameter set corresponding to the minimum 𝜒2 value, i.e. ks = 0.004 (J/cm2)-1 and n \n= 14 . (b) The e xperimental data with associated errors ( blue markers) vs. the theoretical model (yellow \nmarkers), calculated with parameters corresponding to the black star in (a). \n \nInterestingly, parameters µ and n, which were filtered out by 𝜒² minimization, are in reasonable \nalignment with the experimental determination. Our experimental value of µ = 0.22 per cell layer is \nsufficiently comparable to the value of 0.26 returned by the model. Moreover, the n value of 10 \nobtained in the isolated  cells experiment is on the same order of magnitude as the value of 14 \ndetermined by the 𝜒² minimization. By contrast, the model revealed a significantly lower sensitivity \nconstant (by a factor of 5) than the value determined in the isolated cell experiments. This result \nsuggests that, in addition to the light dose attenuation caused by biofilm thickening, a decrease in \ncell intrinsic sensitivity occurs as the biofilm grows, participating in the loss of efficiency. This is in \nline with the view that the biofilm lifestyle induces significant environmental changes. \nDiscussion  \nWe report here a quantitative analysis of the antibacterial effect of blue  light on a P. aeruginosa \nbiofilm gro wn under constant flow of growth medium in a millifluidic device , which en abled \ncharacterizing the spatiotemporal development of the community  in real  time. Our results  \ndemonstrate that localized irradiation of the biofilm induces a transient delay in biofilm development \ndue to photokilling, which is eventually outpaced by the g rowth of neighboring untreated \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n11 \n \n \n \npopulations. Significantly, this demonstrates that biofilms ofte n exhibit complex topographies, \nmaking it unrealistic to irradiate the entire bacterial population. \nBy l ooking more closely at the induced phototoxicity, we prov ide evidence that aBL efficiency \ndecreases throughout biofilm development. In order to clarify the factors behind this decrease, we \nhave proposed a theoretical model based on the cell intrinsic aBL dose -response and material \nabsorption laws.  \nIrradiating isolated cells in a configuration where the cells receiving a defined dose of photons could \nbe monitored in real time revealed a killing mechanism in which n targets must be damaged to \nachieve irreversible toxicity. This view, which exquisitely fits the experimental data, is in line with \npreviously established radiation theories that modeled the effect of X-rays on E. coli (30). Even if \nthis theory was developed in relation to ionizing radiation, we considered extending its applicability \nto the optical radiation field, as the model is ultimately based on cell -localized damage resulting \nfrom single-photon interaction effects. The model that considers light as quanta of projectiles better \ndescribes the experimental behavior than previous modeling dedicated to phototoxicity , which \nmainly focuses on aBL inactivation kinetics (31, 32). To the best of our knowledge, few studies \nhave addressed the question of light dose-dependence of the killing efficiency by also incorporating \nthe key characteristics of bacterial biofilms  such as their th ree-dimensional nature and specific \nphysico-chemistry. \nPrevious works reported by Kumar and collaborators, have transposed the kinetic models to fit the \nkilling efficiency data , using for instance a modification of the Gompertz growth equation (15). \nHowever, these models do not intrinsically extrapolate to zero and the function must be forced to \nthe origin by subtracting a constant that has no physical meaning. Therefore, although a satisfying \nadjustment of the experimental data can be obtained , the modeling does not support any \nmechanism of action or predictive viewpoint. The model we propose here i ndicates a minimum \nlethal number of targets per cell, on the order of 10 for isolated cells.  \nIn our experiments, we determined a cutoff dose on the order of 50 J/cm2, which corresponds to a \nphoton flux of approximately 1010 photons/s per cell provided during about 100 s. This indicates an \nextremely unfavorable ratio of photons/killed cell, which likely arises from photosensitizer-limited \navailability, oxygen-limited concentration, antioxidant processes , and ROS production qu antum \nyield. In fact , it can be calculated from a previous study (6) that about 160 molecules of \nphotosensitizer are expected per cell in P. aeruginosa. It is also known that bacteria poss ess \nantioxidant defenses that may reduce the light damaging efficacy by deactivating oxygen radicals \n(3, 6). These numbers should be considered in relation to the mean intensity of the solar illumination \nat the Earth’s surface, e.g. 140 mW/cm2 according to previous work (33). This indicates that the \nlethality threshold could be reached within approximately 6  minutes of sunlight exposure, \nsuggesting that dark habitats as well as the biofilm life mode should be more favorable to these \nbacteria.  \nWe determined that light attenuation due to the thickening of the biofilm during its growth partially \nexplains the loss in efficiency observed as the biofilm ages. The sets of parameters returned by \nfitting the experimental data with the model are compatible with the idea that bacterial development \nunder a biofilm lifestyle also induces a decrease in the bacteria photosensitivity constant. This result \ncan be related to the rapid decrease in oxygen levels that occur s in a biofilm, due to the high \nconsumption in a densely populated cellular environment, as previously demonstrated in an E. coli \nbiofilm through the evolution of GFP fluorescence (34). This hypothesis is supported by our work, \nsince we also observed in our P. aeruginosa biofilm that the collinearity between GFP and biomass \nbegins to diverge as the biofilm ente rs the multilayer stage . Such a decrease is very likely to \nnegatively impact the aBL killing efficiency by impairing ROS formation , consistent with the ks \ndecrease revealed by the data modelization. \nThe model also returned a µ value slightly higher than the measured one. This might be due to the \nfact that we approximate d the biofilm to an ideal material composed of purely ordered stack s of \nabsorbing cells, whereas a real biofilm possesses an extracellular polymer matrix and likely exhibits \nsome disorder in the piling of the different layers. However, the significantly lower molecular density \nof the extracellular matrix compared to the cells justifies  neglecting its contribution to the overall \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n12 \n \n \n \nbiofilm absorption. We also estimated that structural disorder was not expected to significantly \nchange our estimated absorption. Indeed, previous numerical work (35) shows that switching the \norder parameter value from 0 to 1 causes a reflectance increase of about 20%, which might explain \nthe observed discrepancy. \nWe conclude that two simultaneously occurring hurdles affect bacterial photokilling in biofilms as \nthey grow: the increasing thickness o f biological material, which screens the impinging photons; \nand the potential onset of hypoxia, which hinders ROS formation — the primary mechanism of \nphotokilling in bacteria. In this context, effective phototherapy strategies should focus on targeting \nyoung monolayer biofilms to avoid any screening effects, and also to mitigate hypoxia by ensuring \ndirect access to environmental O2, so as to balance consumption. Nonetheless, even suboptimal \nlight-induced killing of a mature biofilm could be valuable for future clinical applications, especially \nwhen combined with antibiotics. Moreover, collecting information about bacterial target -specific \noptical properties will undoubtedly help in designing more efficient phototherapy protocols. \nOur approach can easily be extended to any irradiation wavelength other than blue , and also to \nphotokilling in the presence of an external photosensitizer, provided that certain changes are made \n(e.g. measuring the biofilm absorption that corresponds to the irradiation wavelength, etc.). This \nwill greatly expand the applicability of both the experimental methods and the theoretical model.  \nFinally, our study  demonstrates the depth and effectiveness of combining experimental and \ntheoretical approaches to generate new insights and pract ical strategies  for antibacterial \nphototherapies. \nMaterials a nd Methods  \nChemical reagents, bacterial strains and culture media \nPropidium iodide (Sigma-Aldrich) stock solutions were prepared from powder in distilled water for \na stock concentration of 3 mM. M9 medium (Na2HPO4 12.8 g/L; KH2PO4 3 g/L; NaCl 0.5 g/L; NH4Cl \n1 g/L; MgSO4 20 mM; CaCl2 0.1 mM; glucose 4 g/ L) was supplemented with casamino  acids (2 \ng/L) to prepare M9CA medium, which was used in all experiments.  \nPseudomonas aeruginosa  (PA01) and its g reen fluorescent variant (PA01 -GFP) were kindly \nprovided by the University of Liverpool. PA01 -GFP constitutively expressed GFP, as previously \ndescribed (36). Overnight cultures were obtained by inoculating a smear from the frozen stock in 5 \nmL of M9CA medium and incubating at 37°C, under constant agitation (500 rpm). For biofilm \nexperiments, the overnight culture was diluted in 5 mL of fresh M9CA medium to an optical density \nof 0.1, then incubated for 2 hours under the same conditions (37°C, 500 rpm). This exp onential \nculture was then diluted to an optical density of 0.1, corresponding to ~107 CFU/mL. \nAgar pad preparation \nA 1.5% low-gelling agarose solution in M9CA medium, supplemented with propidium iodide (PI) at \na final concentration of 3 µM when needed, was added to a spacer-delimited cavity fixed to a glass \nslide (125 µL frame, 1.7 cm by 2.8 cm; Thermo Fisher Scientific), which was smoothed by carefully \nsliding a coverslip on the free surface before depositing bacterial cells (2 µL of an exponentially \ngrowing suspension with an OD of about 0.1) and closing the device by sticking a coverslip on the \nspacer. \n \nMillifluidic device \nMillifluidic channels molded in PDMS were microf abricated as previously detailed (37). Growth \nmedium was continuously supplied using syringe pumps. For connections, we used stainless steel \nconnectors (0.013” ID and 0.025” OD) and microbore Tygon tubing (0.020” ID and 0.06” OD) \nsupplied by Phymep (France). Channel dimensions were 30 mm x 1 mm x 0.25 mm, providing an \ninner volume of 7.5 µL. \nBiofilm formation \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n13 \n \n \n \nAn exponentially growing culture diluted in M9CA medium at an OD of 0.1 was injected by syringe \ndirectly into the PDMS channels before connecting the tubing. The cells were then incubated inside \nthe channel for 30 minutes at 37°C. M9CA was then continuously pushed into the channels at a \nrate of 0.5 mL/h throughout the entire duration of the experiment. The start of the flow defined time \nzero (t = 0) for all experiments. \nMicroscopy imaging \nWe used an inverted NIKON TE300 microscope equipped with a motorized x, y, and z stage. \nImages were collected using different objectives: Nikon 4X Plan (NA 0.10 WD 30 mm), 40X S Fluor \n(NA 0.90 WD 0.11-0.23 mm), and 40X Plan Fluo (NA 0.90 WD 0.11-0.23 mm). Brightfield images \nwere collected in direct illumination (no phas e). Fluorescence acquisitions were performed using \neither the green channel filters for GFP (Ex. 482/35, DM 506 Em. FF01-536/40) or the red channel \nfilter for propidium iodide (PI) (Ex 562/40nm DM 593 Em. 641/75). Excitation was performed using \nLED lines (CoolLed pE-4000) at 490 nm or 555 nm (50% power level), and exposure times of 500 \nms or 100 ms for the green and red emissions, respectively. A Hamamatsu ORCA -R2 EMCCD \ncamera was used for time-lapse acquisitions of 1344 x 1024 pixel images to capture an xy field of \nview of 215 µm x 165 µm. Brightfield and fluorescence images were typically collected for 24 hours \nat a frequency of 6 frames per hour. \nIrradiation and light fluence measurements  \nIn situ irradiations of the biofilm at different developmental times were performed on the microscope \nstage using a 405 nm laser diode (LDI-7, 89 North, VT, USA) and a Nikon Plan Fluor 40x/0.60 WD \n3.7-2.7 mm  objective. The light power was measured with a photodiode (S130VC, ThorLabs) \nconnected to a digital power meter (P M100D, ThorLabs) at the objective focal plane level. The \nbiofilm imaged area received a mean irradiance of ~18 W/cm2, measured at 100% of the laser \ncapacity and according to the method detailed in Supplementary Information IV and Figure S4. The \ndose was adjusted by varying the irradiation time. \nMicroscopy quantitative descriptors  \nMicro-optical density (µOD) was measured from transmitted light images according to µOD = ln \n(I0/I), by analogy with  the Beer-Lambert law where I0 and I indicate the incident and tra nsmitted \nlight, respectively. I0 and I are given here by the intensity per pixel averaged over the entire image \nrecorded in a channel containing only medium or the growing biofilm, respectively. As previously \nreported, this quantity proportionally reports the bacterial biomass developing in the channel as \nlong as no signal saturation occurs, i.e. µOD < 0.7 (37). \nThe GFP fluorescence signal (FGFP) was recorded in parallel with µOD on PAO1-GFP biofilms. In \nthe early stage of biofilm development, constitutive expression of the protein provides a \nfluorescence signal proportional to the number of cells N (34). The GFP images acquired during \nthe first 3 to 4 hours of the experiments were used to delineate single cells and determine their \ncount, N (number of cells per image). Then, we calculated the image mean fluorescence per pixel \nfluorescence, FGFP, by subtracting the background bg, obtained by recording an image in the \nabsence of cells from the raw image intensity I, giving FGFP = I - bg. The fluorescence weight wi, GFP \nwas deduced as wi, GFP = FGFP/N (see details in Supplementary Information V, Figure S5 to S7). The \nabsolute number of cells, N, was used to calibrate the µOD signal. \nKilling efficiency evaluation \nTo evaluate cell death induced by irradiation, the growth medium was supplemented with propidium \niodide (PI) at a final concentration of 3 µM continuously since the initial step of the biofilm formation. \nOnly cells with compromised membranes permeable to PI could be labeled and detected using the \nred fluorescence path (38). We verified that the exposure of cells to PI did not affect the viability or \ndevelopment of the PA01 and PA01-GFP biofilms (Supplementary Information VI and Figure S8).  \nThe PI fluorescence signal (FPI) was recorded throughout the entire duration of biofilm development \nusing time-lapse imaging to evaluate the number of dead cells. Similar to GFP fluorescence, the \nsingle-cell PI unit fluorescence weight wi, PI was derived from images in which the cell population \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n14 \n \n \n \nwas low enough to delineate and count individual cells. The number of dead cells Nd in larger biofilm \npopulations can therefore be deduced from PI intensity (FPI = IPI - br), with IPI representing the raw \nimage intensity and br the red background intensity in the absence of dead cells, according to wi,PI \n= FPI/Nd (Supplementary Figure S4). \n \nAcknowledgments  \nThis work was supported  by the project “Light4Lungs”, H2020 -FETOPEN-2018-2020, Grant \nAgreement n.863102, by the International Emerging Action program of CNRS and by the European \nUnion – Next Generation EU - National Recovery and Resilience Plan, Mission 4 Component 2 - \nInvestment 1.5 - THE - Tuscany Health Ecosystem - ECS00000017 - CUP B83C22003920001.  \nReferences  \n1.  C. J. L. Murray, et al., Global burden of bacterial antimicrobial resistance in 2019: a systematic \nanalysis. The Lancet 399, 629-655 (2022). \n2.  T. Dai, Y.-Y. Huang, M. R. Hamblin, Photodynamic therapy for localized infections—State of \nthe art. 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Shi, et al., Limits of propidium iodide as a cell viability indicator for environmental bacteria. \nCytometry A 71, 592–598 (2007). \n \nLegends to Movies S1 and S2 \n \nMovie S1: Biofilm growth without irradiation.  \nTypical movie from transmitted light time -lapse microscopy of a non -irradiated PAO1 biofilm \ngrowing in a PDMS-glass channel (1mmx0.25mmmx30mm) supplied with M9CA medium. Focus \nis on top surface of the channel using 40x objective (NA 0.6). Acquisition frequency is 6 images \nper hour. \n \nMovie S2: same as movie S1 for a different position. \n \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n17 \n \n \n \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n18 \n \n \n \nSupplementary Information for \n \nExperimental and theoretical approaches reveal that antimicrobial blue light killing \nefficiency decreases with biofilm growth in a Pseudomonas aeruginosa model. \n \nGiacomo Inseroa,…, Nidia Maldonado-Carmonab,c, …, #, Thomas Panierb, Giovanni Romanoa, *, Nelly \nHenryb,c, * \na University of Florence, Department of Experimental and Clinical Biomedical Sciences “Mario \nSerio”, Florence, Italy, b Sorbonne Université, CNRS, Laboratoire Jean Perrin, LJP, F-75005 \nParis, France, cSorbonne Université, CNRS, Inserm, Institut de Biologie Paris-Seine, IBPS, \nF75005 Paris, France \n…these authors contributed equally to the manuscript. \n#current affiliation: University of Florence, Department of Experimental an d Clinical Biomedical \nSciences “Mario Serio”, Florence, Italy \n*Nelly Henry, Giovanni Romano. \nEmail: nelly.henry@sorbonne-universite.fr, giovanni.romano@unifi.it \n \nThis PDF file includes: \n \nSupplementary information I to VI \nFigures S1 to S6  \nTables S1 to S3 \nLegends for Movies S1 and S2 \n \nSupplementary material References  \n \nOther supplementary materials for this manuscript include the following: 1 \n \nMovies S1 to S2 \n \n \n \n \n \n \n \n \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n19 \n \n \n \nSupplementary Information I: Single cell absorption coefficient. \nCell absorption coefficient, 𝜇, has been measured by considering transmission microscopy images \n(Figure S6). PAO1 cells were deposited on a thin agar layer in between a glass slide and a \ncoverslip, then imaged in brightfield (63x, Nikon). Images are collected in ten different fields of view \nand the intensity per pixel of the small cell patches are analyzed individually (as shown in the yellow \nbox). The background intensity (I0) is measured on cell-free regions of 10 x 10 pixels, while the light \nintensity transmitted through one cell diam eter (I) is considered to be equivalent to what is \ntransmitted through a cell monolayer. Three spots of 3 to 4 pixels are measured over a single cell \nas shown on the right insert. The results are shown in Table S1. The value of 𝜇 is deduced from \nthese measurements from 𝑙𝑛 𝑙𝑛\t\n%#\n% = 𝜇\t𝑙 . Using 𝑙 = 1, we obtain 𝜇 = 0.22 ± 0.06\tper cell layer. \n \n \nFigure S1: Cellular absorption coefficient (𝜇). Transmitted light images of exponentially growing PA01 cells \n(left). Inset is represented in the right image (see text for details).  \n \nTable S1: Microscopy measurement of the absorption coefficient of one cell height. \nField of view I0 (SD)a  I (SD) 𝜀9 b \n#1 812 (39) 673 (32) 0.18 ±0.02 \n#2 340 (12) 286 (6) 0.17 ±0.01 \n#3 113 (4) 86 (6) 0.27±0.1 \n#4 707 (23) 595 (23) 0.17 ±0.012 \n#5 676 (33) 535 (39) 0.23 ±0.018 \n#6 1607 (26) 1279 (59) 0.23±0.062 \n#7 958 (35) 777 (24) 0.21±0.066 \n#8 702 (29) 510 (36) 0.32±0.12 \n#9 566(23) 456 (26) 0.22±0.1 \n#10 2589(67) 2084(144) 0.22±0.093 \n   µ =0.22±0.06 \naThe measurement were performed at different intensities of incident light to rule out possible bias \ndue to illumination. \nbSD on µ is calculated from the sum of the relative SDs on I0 and I. \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n20 \n \n \n \nSupplementary Information II: Conversion of  biofilm development time into biofilm \nthickness. \nIn order to compare the experimental data with the model we transposed the time-dependent curve 𝐾! = 𝑓(𝑡) \nto a biofilm thickness-dependent curve 𝐾! = 𝑓(𝐻) where 𝐻 is the thickness of the biofilm expressed in number \nof cell layers. To build time-thickness correspondence, we rely on the Beer -Lambert law which states that \noptical density grows linearly with light path length, or equivalently that µOD proportionally varies with the \nnumber of cell layers (Figure S2).   \n \n \n \nFigure S2: Conversion features. The Beer-Lambert law can be used to relate the µOD with the number of \nlayers n as explained in panel (a). Knowing the monolayer stage (n=1) corresponds to the kink of the biofilm \ngrowth curve, we derive the conversion factor that links µOD and 𝐻, resulting to be 0.045±0.005 µOD units \nper biofilm layer (average of three biological replicates) and allowing to relate µOD,  t and n as marked by the \nblack arrows  (c) Then the correspondence between the KE value at the specific time and the corresponding \ncell layer thickness is transitively established as shown in panel c, providing the data to plot 𝐾! = 𝑓(𝐻) curve \n(Figure 6e of the main text). \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n21 \n \n \n \nSupplementary Information III: 𝜒2 test, Numerical procedure. \nThe experimental data report the killing efficiency 𝐾5 : , with associated absolute error 𝛥𝐾5:, relative \nto a set of 𝑁 different 𝐻: values. For each of these 𝐻: values, the theoretical killing efficiency \n𝐾5\n-;8<(𝐻:)was also calculated using the following formula:  \n𝐾5\n-;8<(𝐻:, 𝑛, 𝜇, 𝑘=\t) =\n3\n7(\n∫ B1 − 𝑒*+\"∙4#∙8$%'\nC\n67(\n/ \t𝑑𝑧\t\nat a specific light dose, with the other parameters assuming values from any of the possible \ncombinations obtained by varying the parameters within the following ranges: \n𝑛 : varying between 1 and 12 (in steps of 1)  \n𝜇 : varying between 0.1 and 1.0 (in steps of 0.1)  \n𝑘= ∶ varying between 0.002 and 0.030 (in steps of 0.002 up to 0.012 and then in steps of 0.005 \nstarting from 0.015) \nThe Chi-squared (𝜒>) value was obtained by the following formula: \n𝜒>(\t𝑛, 𝜇, 𝑘=) = ∑ (@)\n*+,-(7(,6,0,+\"\t)*@)(\t)\t.\nB@)(\n.\nC\n:D3 \t\nfor each combination of the  𝑛, 𝜇, 𝑘= parameters. \nA typical result of this calculation is shown in Figure S8, showing the 𝜒>(\t𝑛, 𝜇, 𝑘=) values obtained \nfor a light dose of 2007 J/cm2 and for 𝑛 = 1 and 𝑛 = 10.  \n   \n \nFigure S3: 𝜒&(\t𝑛, 𝜇, 𝑘') results for a light dose of 2007 J/cm², shown for 𝑛 = 1 (a) and 𝑛 = 10 (b). \nThe other parameters vary according to the axes in the figure. The black star indicates the \nparameter set corresponding to the minimum Chi-squared value. The white crosses represent \nthe parameter values used to evaluate the Chi-Squared. \n \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n22 \n \n \n \nSupplementary Information IV: Irradiance determination. \nThe mean irradiance and dose values corresponding to 405 nm irradiation were estimated by the \nfollowing methodology. A fluorescent plastic slide (Chroma, ref. 92001) with a uniform fluorophore \ndensity was imaged by a 40x, NA = 0.6 objective (Nikon) with ORCA -Fusion BT Digital CMOS \ncamera (Hamamatsu), taking care to avoid image saturation and photobleaching of the fluorophore. \nThe corresponding image was calibrated to obtain the µm/pixel ratio value by imaging a stage \nmicrometer in the same conditions. The numerical matrix corresponding to the spot image (370 x \n370 µm, 2072 x 2072 pixels, 16 -bit gray levels) was analyzed by the Origin® software and fitted \nwith the following function: \n𝑧(𝑥, 𝑦) = \t 𝑧/ + 𝐴 𝑒𝑥𝑝 𝑒𝑥𝑝\t(\t\t−\n() *) #).\n>E. − \t\n(F*F#).\n>E. )     \nobtained by adding a constant background z_0 to a bi-dimensional Gaussian function with \namplitude A, centroid coordinate (x0, y0) and standard deviation w. Following this, we derive the \nbackground- free and normalized function (over the whole x-y plane): \n𝑓(𝑥, 𝑦) =\n3\n>GE. 𝑒𝑥𝑝 𝑒𝑥𝑝\t(\t\t−\n() *) #).\n>E. − \t\n(F*F#).\n>E. )     \nTo properly quantify the amount of power received by the area imaged in the experiments (region \nB, area S(B) = 215 µm x 165 µm, Figure S4), we calculate the integral of f(x,y) over the imaged \narea. Knowing that the illumination spot is centered with respect to the imaged region B (meaning \nx0 = 0 and y0 = 0), we evaluate the following integral over B:  \n𝐼 = ∬H\n\t\n\t𝑓(𝑥, 𝑦, 𝑥/ = 0, 𝑦/ = 0)\t𝑑𝑥\t𝑑𝑦 = 0.425\t\nThis means that the biofilm imaged region receives 42.5% of the whole spot power P. This last was \nmeasured by a power meter at the exit of the microscope objective (40x) and corresponds to 15 \nmW. The mean dose was then calculated by  D = 0.425 x P x tirr / S(B), being tirr the irradiation time \nand S(B) the imaged region area. For tirr =111 s we obtain D = 2007 J/cm2. \n \n \n \nFigure S4. Fluorescent image corresponding to 405 nm excitation (A, 370 µm x 370 µm); the dotted line \nrectangle indicates the biofilm imaged region (B, 215 µm x 165 µm). \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n23 \n \n \n \nSupplementary Information V: Image Analysis.  \nTransmitted light and fluorescence movies provide optical\tsignals as a function of time. In order to \nmove from these raw data to biofilm parameters, we developed an image treatment and analysis \nprocedure enabling to convert the micro -optical density and fluorescence intensities in cell \nnumbers. From previous work (1, 2), we already know that the micro -optical density (µOD) is \nproportional to the number of cells building the biofilm up to a value of 0.7 after which saturation \noccurs. Similarly, GFP fluorescence intensity— when gfp gene is under the control of a constitutive \npromoter as it is the case here — also reports the number of cells at the lowest cell densities at \nwhich the consumption of oxygen is not too high. In the linear regime, GFP and µOD signals are \ncolinear. \n \nGFP images analysis for µOD calibration. \nTo calibrate the GFP signals in terms of µOD, we first analyze the low density GFP images as \nfollows:  \n(i) We first determine 𝐹IJ$ = 𝐼IJ$ − 𝑏K, where 𝐼IJ$ is the mean fluorescence intensity per pixel of a \nraw image and 𝑏K the fluorescence background which is the mean intensity per pixel in regions \nwithout cells. \n(ii) Then we determine the number of cells, 𝑁, contributing to 𝐹IJ$ using the following M atlab® \ncounting algorithm that binarizes the image taking into account the inhomogeneity of the \nbackground by applying a local filter: \n \nfilename = 'raw_image’.tif';  \na=double(imread(fullfile(folder,filename))); \nb=conv2(a,ones(3),'same'); \nc=imtophat(b,ones(20)); \nd=double(c>72); \ne=imopen(d,ones(4)); \nf=imreconstruct(e,d); \n%N determination taking into account the systematic 20 \n% underestimation due to the  \n%presence of aggregates in images exhibiting approx. in between 200 and 1000 cells  \nN=size(regionprops(bwconncomp(f)),1)*1.2;  \n \nA typical example is shown in Figure S 5 a and b. A few images randomly picked are “manually” \ncounted using imageJ counter tool and compared with the automatized determinations. The \nmanually and automatized counting results are in good agreement with an average relative error \nof 3.5%, confirming the accuracy of the counting routine (Table S2) \n \nTable S2: Counting algorithm validation. Images from independent positions and channels are \nrandomly picked up and counted both manually and automatically \n \nAlgorithm \ncount \nGround truth \n(Manual count) \nRelative \ndifference \n987 950 3,8% \n768 772 0,5% \n531 530 0,1% \n586 547 7% \n518 494 4,9% \n979 1010 3,0% \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n24 \n \n \n \n389 378 2.9% \n887 841 5.4% \n mean 3.5% \n \n \nFigure S5. Cell counting from GFP fluorescence images. Images raw (a), substracted background (b) and \nbinarized (c) are shown from left to right, respectively. Image details from the raw ( d) and binarized images \n(e), focus on the presence of doublets or at most triplets responsible for the  20% underestimation of the \nautomatized counting (see Table S3).  \n(iii) Next, in the GFP linear regime, i.e. t < 4.5 hours, we calculate the GFP unit cell fluorescence \nweight, 𝑤:,IJ$, as follows: \n𝑤:,IJ$ =\nJ/01\nC          \nwhere 𝑤:,IJ$ includes the in-plane emission of the cells but also the contributions of the scattered \nand reflected light which increase linearly with the number of cells as can be shown by analyzing \nthe mean fluorescence of empty spaces in all the images in which cell clusters can be delineated. \nThese contributions can be viewed as a cell-dependent additional background associated with each \ncell.  \n \nTable S3: Cell fluorescent weight. Eight determinations on distinct positions in one experimental channel \nproviding a 𝑤:,IJ$ value of 0.013±0.003 (obtained by using the automatized evaluation of N). \n \n𝐼IJ$ 𝐹IJ$ \n𝐼IJ$ − 𝑏K \nN (Ground \nTruth) \nN \n(Algorithm) \n𝑤:,IJ$ \n141.1 11.1 950 986 0.011 \n139.4 9.4 772 768 0.012 \n136.3 6.3 530 530 0.012 \n137.2 7.2 547 586 0.012 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n25 \n \n \n \n137.1 7.1 494 518 0.014 \n140.7 10.7 1020 979 0.011 \n137.3 7.3 389 378 0.019 \n139.7 9.7 887 841 0.012 \n   mean, sdt = 0.013±0.003 \n \n(iv) Finally, given the linearity between 𝐹IJ$ and the µOD signal (Figure S6), we determine the \nconversion factor 𝑓IJ$ that relates the GFP with the µOD by means of the following formula: \n𝐹IJ$ = 𝑓IJ$. 𝜇𝑂𝐷        \n \n \nFigure S6. Initial GFP vs µOD collinearity. GFP (green curve) exhibits collinearity with the µOD \ncurve (blue curve) up to approximately- 4.5 hours. In this time range, both signals increase \naccording to an exponential function of the type 𝑓(𝑡) = 𝑓(𝑡/). 𝑒+- with 𝑘\t= 1.05 h-1 (purple dashed \nline). \nThen, knowing that the number of cell N depends on µOD through the parameter p defined as: \n𝑁 = 𝑝. 𝜇𝑂𝐷         \nwe can combine the previous two equations to define the p function as: \n𝑁(𝑡) = 𝐹IJ$\n𝑤:,IJ$\n= 𝑓IJ$. 𝜇𝑂𝐷\n𝑤:,IJ$\n⟹ 𝑝 = 𝑓IJ$\n𝑤:,IJ$\n\t\nThis allows to measure the value of p which  allows for the determination of N(t) as a function of \nμOD. \nWe stress the fact that the proportionality factors are linked to the acquisition conditions such as \nthe excitation intensity, acquisition time or objective numerical aperture, and must be redetermined \nfor any change in the experimental settings.   \n \nPI fluorescence images analysis for cell death evaluation  \nTo derive the number of dead cells from PI fluorescence images, we apply the same cell counting \nalgorithm to red fluorescence images which enables accurate delineation of the cells and provide \ndead cells fluorescence\t𝐹$% = 𝐼$% − 𝑏L,\twith\t𝐼$% the mean fluorescence intensity per pixel of a raw \nimage (Figure S7a) and 𝑏L the red fluorescence background which is the mean intensity per pixel \nin the absence of dead cells (before irradiation).  After background subtraction (Figure S7b), the \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n26 \n \n \n \nbinarization (Fig. S7c) allows to determine 𝑁M, the number of dead cells and derive 𝑤:,$%, the cell \nfluorescence weight: \n𝑤:,$% = 𝐹$%\n𝑁M\n\t\nThat will be used to deduce 𝑁M from any image of irradiated biofilm supplied with PI and recorded \nin the same conditions. \n \n \nFigure S7. PI fluorescence image analysis . Typical analysis of a red fluorescence image of a biofilm \nsupplied with PI 3 µM and irradiated after 2 hours of growth. Raw image (a) is taken at PI fluorescence intensity \nplateau which establishes within the few hours following the irradiation as shown in the main text. (b) is the \nbackground subtracted image and (c) is the binarized image (here the image corresponds to 𝑁( = 514\t). \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n27 \n \n \n \nSupplementary Information VI: Propidium iodide innocuity test. \nIn order to check the innocuity of propidium iodide to PA 01 PA01-GFP cells, a wide range of PI \nconcentrations was tested against planktonic cells. \nPA01 and PA01-GFP cultures were diluted in PI-enriched M9CA media to an OD of 0.05 and with \nPI concentration resulting in the following values: 0, 0.1875, 0.375, 0.750, 1.5, 3, 6, 12, 24, 48, 96 \nµM. The experiments were performed in 3 replicates per strain and concentration, while 2 controls, \nwithout bacteria but with added PI, were prepared and worked as blanks and sterility controls. \nThe growth was followed using the plate reader Tecan Infinite 200 Pro (Tecan, Switzerland), which \nkept the plate at 37 °C (Figure S8). Every 10 minutes, the plate was stirred (orbital shaking, 2 mm) \nand absorbance lectures were made at 600 nm. Blank absorbance was subtracted from the raw \ndata.   \n \n \nFigure S8. PI toxicity. Optical density at 600 nm (OD600) of PA01 (A) and PA01-GFP (B) in the presence of \ndifferent PI concentrations during 24 hours. \nWhen comparing the growing curves and the growth at 24 hours, both the final OD and the growth \nrate were not affected by the presence of PI into the media even at the highest PI concentration. \nNo negative effect was found with the tested PI conditions against the control conditions by \nemploying the one-way ANOVA, multiple comparisons test p > 0.05 for the growth rate and final \nOD at 24 hours. \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n28 \n \n \n \nLegends to Movies S1 and S2 \n \nMovie S1: Biofilm growth without irradiation.  \nTypical movie from transmitted light time -lapse microscopy of a non -irradiated PAO1 biofilm \ngrowing in a PDMS-glass channel (1mmx0.25mmmx30mm) supplied with M9CA medium. Focus \nis on top surface of the channel using 40x objective (NA 0.6). Acquisition frequency is 6 images \nper hour. \n \nMovie S2: same as movie S1 for a different position. \n \n \n  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint \n\n \n \n \n \n29 \n \n \n \n   \nSupplementary Material References \n \n1.  A. Monmeyran, et al., The inducible chemical -genetic fluorescent marker FAST outperforms \nclassical fluorescent proteins in the quantitative reporting of bacterial biofilm dynamics. Sci Rep \n8, 10336 (2018). \n2.  P. Thomen, et al., Bacterial biofilm under flow: First a physical struggle to stay, then a matter \nof breathing. PLoS ONE 12, e0175197 (2017). \n \n \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted February 26, 2025. ; https://doi.org/10.1101/2025.02.26.636652doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}