GSLab: Open-Source Platform for Advanced Phasor Analysis in Fluorescence Microscopy

GSLab: Open-Source Platform for Advanced Phasor Analysis in Fluorescence Microscopy | 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 GSLab: Open-Source Platform for Advanced Phasor Analysis in Fluorescence Microscopy Alexander Vallmitjana , Belén Torrado , Amanda F. Durkin , Alexander Dvornikov , Navid Rajil , Suman Ranjit , Mihaela Balu doi: https://doi.org/10.1101/2025.02.10.637545 Alexander Vallmitjana 1 Beckman Laser Institute and Medical Clinic, University of California , Irvine Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: avallmit{at}uci.edu Belén Torrado 1 Beckman Laser Institute and Medical Clinic, University of California , Irvine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amanda F. Durkin 1 Beckman Laser Institute and Medical Clinic, University of California , Irvine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexander Dvornikov 1 Beckman Laser Institute and Medical Clinic, University of California , Irvine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Navid Rajil 1 Beckman Laser Institute and Medical Clinic, University of California , Irvine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Suman Ranjit 1 Beckman Laser Institute and Medical Clinic, University of California , Irvine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mihaela Balu 1 Beckman Laser Institute and Medical Clinic, University of California , Irvine 2 Department of Dermatology, University of California , Irvine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Preview PDF Abstract GSLab addresses the need for effective image analysis tools in fluorescence microscopy by providing an open-source platform that enhances traditional phasor analysis with advanced features. Key capabilities include machine learning-based clustering, real-time monitoring, and quantitative unmixing of fluorescent species. Designed for both commercial and custom systems, GSLab provides researchers with comprehensive lifetime and spectral phasor image analysis tools to tackle complex biological problems. Main Text Fluorescence lifetime microscopy (FLIM) is a powerful imaging technique that provides quantitative information about the local environment and molecular interactions of fluorophores within biological tissues or other materials. By measuring how long a fluorescent molecule remains in an excited state before emitting a photon, FLIM offers a new dimension for biological imaging, which, combined with spectral imaging, enables researchers to investigate complex cellular processes and interactions. The integration of advanced analytical methods, such as the phasor approach, enables more sophisticated image analysis and interpretation. As a result, this will further enhance the capabilities of FLIM and spectral microscopy. The basis of phasor analysis lies in using the phasor transform to map every pixel of an image onto a two-dimensional space known as phasor space, based on the photon distribution within that pixel across the fluorescence lifetime or spectral dimensions 1 , 2 . The position of each pixel in phasor space is determined by the shape of the photon distribution and is independent of the signal’s intensity. Analysis by means of the phasor representation does not require prior knowledge of the nature of the sample nor fitting of a model. In addition, utilization of the Fast Fourier Transform algorithm enables rapid computation. This analysis simplifies visual inspection and identification of distinct populations of pixels, which can subsequently be mapped back to the original fluorescence image (or set of images) 3 . Furthermore, the mathematical properties of the phasor transform enable researchers to understand the phenomena occurring in the sample by observing changes in the photon distribution represented in phasor space. A brief overview of the mathematics behind the phasor approach for analyzing fluorescence lifetime microscopy images is available in Online Methods. Phasor analysis is widely used in the biophysics and bioimaging fields and has led to a vast number of quantification methods and applications. These include studying cellular metabolic states 4 , molecular interactions 5 , molecular dynamics 6 , drug delivery 7 , chromatin compaction 8 , sensing local polarity 9 , ion concentration 10 , pH 11 , enhancing superresolution 12 and multiplexed imaging 13 . For over a decade, the scientific community has primarily relied on Globals for Images SimFCS software developed by Prof. Enrico Gratton for phasor analysis 14 . However, maintenance and updates for this software were discontinued in 2021, prompting developers to create alternative phasor analysis tools. While several research laboratories have developed custom tools to address specific challenges 15 – 24 , and commercial brands such as Leica, PicoQuant, Becker & Hickl, FLIM Labs, and ISS have incorporated phasor analysis into their software suites, there are few reports on open-source software solutions. Existing platforms, based on Phyton (PhasorPy 15 , FLUTE 18 , Phasor Identifier 19 , FLIMPA 25 ), Java (FLIMJ 20 )or MATLAB (PAM 17 ), have successfully incorporated traditional phasor analysis techniques, such as intensity thresholding, image filtering, color-mapping, and cursor analysis (manual clustering). Despite these developments, recent advances in machine learning-clustering for image segmentation 26 and unmixing of fluorescent species present in the same pixel via higher harmonics 27 , are not yet available in open-source formats. To address this gap, our team has developed GSLab, a pioneering, open-source software platform designed to provide researchers with a comprehensive set of tools for advanced lifetime and spectral phasor image analysis. GSLab not only incorporates traditional phasor analysis techniques, but also introduces unique and advanced capabilities, including automated machine learning-based clustering in phasor space for image segmentation, real-time monitoring of both image and phasor space, and quantitative unmixing of multiple fluorescent species from a single pixel, applicable to any imaging system, whether commercial or custom-built. GSLab is developed in MATLAB. It offers robust computational and visualization capabilities, allowing researchers to enhance their phasor analysis workflows effectively. GSLab compiles these tools into a user-friendly program, featuring a graphical user interface that simplifies complex analyses. MATLAB’s extensive library of built-in functions facilitates rapid development, enabling users to tailor GSLab to their specific research needs. While a MATLAB license by Mathworks Inc. may be a barrier for some, the widespread availability in academia ensures that most researchers can benefit from the advanced functionalities of GSLab. We have uploaded GSLab in a public repository 28 under MIT license. Figure 1 provides an overview of GSLab’ capabilities. It highlights its key features across three main areas: Input/Output, Basic Analysis, and Advance Analysis. The Input/Output section illustrates the software’s compatibility with various image formats for lifetime and spectral data, along with its export options for phasor plots and color-coded intensity images. The Basic Analysis section illustrates the commonly available tools such as cursor analysis, phasor filtering and image manipulation, enabling users to interact with and process the data efficiently. The current open-source solutions allow for the implementation of a subset of these analysis tools. The Advanced Analysis section emphasizes the software’s powerful functions for unmixing multiple fluorescence components, machine-learning based clustering, and real-time examination of the reciprocity between image and phasor space, demonstrating its robust analytical capabilities. A more detailed description of GSLab’ capabilities is available in the Supplementary Material. Download figure Open in new tab Figure 1. GSLab capability overview. 1) Input/Output: Supports major image formats for lifetime and spectral data. Allows exporting of phasor plots and color-coded intensity images based on phasor coordinates, component unmixing, or clustering results, with various styling options for both phasor plots and images. 2) Basic Analysis : Offers cursor analysis, phasor filtering, image manipulation, and the ability to create or load masks. Users can measure pixel phasor coordinates and create color gradients on the phasor space, generate lifetime and spectral images based on the color gradients, and calibrate the data using reference files. 3) Advanced Analysis : Enables unmixing of multiple fluorescence components, machine learning-based clustering of phasor distributions, and real time inspection of the reciprocity between image and phasor space. Figure 2 illustrates the advanced capabilities of phasor analysis for automated machine learning, clustering, and unmixing of fluorescent components. We demonstrate these capabilities by analyzing two sets of data. First, label-free FLIM images of a fresh human skin specimen (discarded tissue from surgery), acquired with a custom-built clinical multiphoton microscopy device 29 are used to demonstrate automated clustering and image segmentation ( Figure 2 A-D ). Second, time-resolved fluorescence images of labeled cell cultures, obtained using another custom-built FLIM-based multiphoton microscope for thick tissue imaging 30 are used to demonstrate unmixing of fluorescent components ( Figure 2 E-H ). Download figure Open in new tab Figure 2. Advanced phasor analysis: Automated machine-learning clustering of phasor distributions and fluorescent component unmixing. A ) Intensity obtained through time-resolved two-photon excited fluorescence signal detection from a human skin specimen. B ) Corresponding fluorescence lifetime phasor plot with gradient color distribution representing tau-phase values. The plot demonstrates automated machine learning clustering into four populations with ellipses outlining 88% of pixels within each Gaussian component. C ) The intensity image from (A) shown as color-coded lifetime image, based on tau-phase values from the phasor plot. D ) The intensity image from (A) displayed as color-coded image highlighting the four populations identified through automatic clustering. These populations correspond to known skin structures: keratin in epidermal keratinocytes (green), melanin in pigmented keratinocytes (red), elastin in the dermis (blue), and other structures characterized by a mixed fluorescence lifetime distribution, representing a linear combination of the other three clusters. E ) Intensity images obtained through time-resolved two-photon excitation signal detection from five cell cultures: four of them stained independently with one of the nuclear dyes Ethidium Bromide, Acridine Orange, NucBlue, and Rose Bengal, and the fifth with a mixture of all four dyes. F ) Corresponding fluorescence lifetime phasor plot with gradient color distribution representing tau-modulation values. The plot shows the fluorescence lifetime signatures for each dye and the signature of the mixed sample as an elongated distribution in the center. The theoretical lifetime of the four dyes is used to compute the coordinates for each of the four components, depicted by the vertices of a quadrilateral. Inset displays the same data in the 2nd harmonic required for the fluorescent component unmixing. G ) Intensity images from (E) shown as color-coded lifetime images, based on tau-modulation values from the phasor plot. H) Results of fluorescent component unmixing: H1-H4 ) Unmixed photon fraction images for each component (rows) and each measurement (columns). Inset violin plots illustrate pixel value distribution for each image. H5 ) Merged images for each measurement using linear addition of panels ( H1-H4 ). Phasor analysis of the skin specimen’s FLIM image ( Figure 2 B ) shows automated machine-learning-based clustering into four populations, which are subsequently illustrated in the corresponding color-coded FLIM image revealing well-known skin structures. Phasor analysis of the fluorescence images of stained cell cultures highlights the ability to unmix four fluorescent components by clearly resolving the fluorescence lifetime signatures of the dyes and computing the pixel fractions of the components in the mixed sample. These examples demonstrate the powerful advanced capabilities of GSLab for resolving complex fluorescence signatures within a single sample. Online Methods / Supplementary Material Phasor Analysis for Fluorescence Lifetime and Hyperspectral Microscopy In fluorescence lifetime microscopy and spectral imaging, each pixel within the image represents a measured photon distribution, either spectral or temporal, that is determined by the fluorescent molecules present at that location. In fluorescence lifetime imaging, the characteristic times the fluorophores spend in their excited states prior to transitioning back to the ground state define the shape of this curve. In spectral imaging, the wavelength associated with the energy differences between these transitions determine the curve’s profile. The phasor approach is a technique that transforms the photon distribution curve from each pixel into a point in a two-dimensional phasor space using the first two terms of the Fourier discrete decomposition 31 – 33 . The two dimensions of the phasor space are usually labeled S and G, and are obtained for each pixel using the following expressions, for lifetime imaging: where the integrals are taken over the effective pixel dwell time t, I(t) is the accumulated distribution of the number of photons as a function of arrival time with respect to the excitation time, ω=n2πT -1 is the angular frequency such that the trigonometric functions fit n whole periods in the excitation period T , with n being the harmonic number of the phasor transform. Analogously for spectral imaging: where the integrals are taken over the detection spectral bandwidth [ λ 0 λ 1 ], I(λ) is the photon distribution as a function of the spectral dimension, ω=n2π(λ 1 -λ 0 ) -1 is the angular frequency such that the trigonometric functions fit n whole periods in the spectral band [ λ 0 λ 1 ], with n being the harmonic number of the phasor transform. In practice, we operate with digital measurements; both temporal and spectral dimensions are divided into bins and each pixel is assigned a photon count for each bin. Both spectral and lifetime phasor transforms are expressed as summations over the number of bins: with the summations being taken over all bins with I(b) representing the count values at each bin b , and ω=n2πB -1 denoting the angular frequency such that the trigonometric functions fit n whole periods across the B bins. In FLIM, if we model the intensity as an exponential decay , we now have a definition for lifetime: the characteristic time τ that molecules spend in the excited state. By inserting this exponential model into the equations in (1), if we solve the integrals and join the equations, we obtain the expression for the universal circle S 2 = G − G 2 . Lifetime measurements on the phasor plot only make sense on this circle, any pixel that is not on the circle has a combination of lifetimes. When generating so-called lifetime images, one needs to project the phasor coordinates on to the universal circle. Using polar coordinates, we can define the phase φ and modulation M of a measurement based on the cartesian phasor coordinates: A measurement by phase is to assign the lifetime of the exponential decay such that it has the same phase as the measurement. A measurement by modulation corresponds to assigning the lifetime of the exponential decay such that it has the same modulation as the measurement. Geometrically speaking, the first is to find the point on the universal circle that has the same phase as the measurement, the second is to find the point on the universal circle that has the same modulation as the measurement (see Figure S1 ). We name these two lifetime measurements tau-phase and tau-modulation. The first is more precise when measuring fast lifetimes relative to the excitation frequency, the latter is more precise when measuring slow lifetimes relative to the excitation frequency. For this reason, a third method is sometimes used, namely tau-normalized 34 , which consists in projecting from the center of the universal circle. Given the phasor coordinates of a measurement (G , S) , by imposing the afore-mentioned geometrical considerations, one can derive the expressions for the three different projections, namely tau-phase, tau-modulation and tau-normalized: with ω = 2π fn being the angular frequency of the excitation source with frequency f (in Hertz to obtain lifetimes in seconds), and n the harmonic number of the transform. The parameter corresponds to the G coordinate of the normalized projection (see Figure S1 ), with the inverse tangent function returning values in the first and second quadrant. Download figure Open in new tab Figure S1. Lifetime of a measurement. Geometrical interpretation of tau -phase, -modulation and -normalized GSLab Capabilities Input/Output Image Formats We developed our software for image analysis of lifetime and spectral measurements. We initially developed it for our home-built microscope (FLAME) 35 operated by ScanImage (MBF Bioscience). However, we have since expanded its capabilities to support other types of image file formats for FLIM data generated by commercial microscopes such as Leica-Falcon LAS X (S and G phasor exports) and for home-built microscopes using SimFCS. Additionally, we designed the software to load and analyze hyperspectral images obtained with Zeiss ZEN (.lsm). Data Export GSLab has flexibility to export phasor plots, phasor-colored images and spreadsheets of computed values in different formats. Intensity images can be rendered using a logarithmic intensity scale. A pseudo-high dynamic range routine is implemented to compute the local dynamic range for each pixel, enabling the export of images with an extended dynamic range. Raw grayscale images can be exported or color-coded in various ways: based on cursor positions, phasor coordinates, or the results of clustering or unmixing algorithms. Colors can be applied to the image space using two methods: hard masking, which assigns a solid color to each pixel and soft masking, which creates an intensity-weighted color image. Additionally, built-in options for exporting phasor plots are available, such as the ability to adjust the binning resolution of phasor space histogram and choose between the linear or logarithmic phasor plot representation. Phasor distributions can be normalized to each loaded file to represent multiple files at the same time. Users can select different colormaps to depict the phasor density representations and for phasor measurement images. Contour lines can also be added to the phasor plots. Basic Analysis Calibration To allow for an appropriate calibration and referencing of phasor coordinates, GSLab allows loading calibration samples (measurement of a fluorophore solution of known single-exponential lifetime). The user can associate each calibration file with a file name tag to identify physical detectors and/or spectral channels in the instrument. We have incorporated the use of ref file format, allowing us to save image phasor coordinates in multiple harmonics, which adds compatibility with legacy SimFCS. Quantification Phase and modulation are classical parameters for characterizing fluorescence lifetime in the frequency domain 36 . They have a direct representation on the phasor plot and are used as a way to render lifetime images. Researchers report tau-phase or tau-modulation images or a combination of both, which correspond to the lifetime associated with a measurement based on phase difference or modulation factor of the fluorescence signal with respect to the excitation signal (see Figure S1 ). We implement a color-coded gradient representation of these parameters, allowing users to assign each pixel a specific lifetime value and export color-coded images depending on the assigned lifetime values. The software also allows the use of custom projections on the universal circle, by setting the coordinates of the origin and selecting if projecting either radially or angularly, which would be a generalization for lifetime measurements. Image manipulation Inspired by SimFCS, our software can perform multi-image phasor analysis, enabling users to load any number of images and display them in one panel while viewing their corresponding phasor distribution in another, allowing for real time interaction between the image and phasor spaces. Users can select or deselect files from the set to display the phasor or to use the phasor transform data with the other available tools. Additionally, the dynamic range of the intensity image can be adjusted to enhance certain regions. Image Masking Phasor representation with image masking via thresholding involves selecting pixels in the image space based on their intensity to represent them on the phasor space. This functionality allows users to select or remove very bright populations or very dim/noisy pixels, which may be disguising relevant information. In combination with the previous methods, it allows users to create an image mask which can be used to exclude specific regions from analysis. Additionally, users can load image masks created in other software to pre-segment the images for phasor analysis. Phasor Filtering We have included various options to facilitate phasor analysis, including phasor filtering. Users can define a kernel size and the number of times the filter needs to be applied. The kernel is convolved over the phasor space coordinates to generate the density histogram. Additionally, users can set the resolution of the phasor space, along with the harmonic number of the phasor transform. The filtering is performed for all harmonics simultaneously. Cursor Analysis Traditional cursor analysis can be seen as manual clustering in the phasor space. GSLab implements this by allowing users to point and click to create circles (cursors) of any size on the phasor space and assign colors to these cursors. The software highlights the pixels in the image space whose phasor coordinates fall within the cursors based on the assigned color of these cursors. We have implemented these capabilities inspired by SimFCS with improvements towards ease of use. Advanced Analysis Inspection Mode GSLab has a real-time built in functionality that makes use of the phasor reciprocity principle 37 for image content exploration. The reciprocity principle connects the phasor space to the image space, allowing the selection of a set of coordinates in one space to be highlighted in the other. The cursor analysis described above uses one direction of this reciprocity principle; however, it does not operate in real time. GSLab implements an inspection mode allowing users to hover the mouse over the image space to see the corresponding pixels in the phasor space in real time, and vice versa, hover the mouse over the phasor space to see where the pixels fall on the image space. Multiple images can be loaded simultaneously which may carry some computational burden. The software addresses this by down sampling the loaded images thus reducing the computation time for all other functionalities. When exporting data, users can select between a fast, low-resolution version using the down-sampled images or a high-resolution version based on the original dimensions of the images. Users can also adjust the level of down-sampling as needed. Clustering in the Phasor Space Gaussian mixture models are the preferred machine learning algorithm for automated clustering of populations in the phasor space 27 . Automatic selection of populations enables the segmentation of structures in the image space based on their phasor signatures, facilitating further analysis based on the segmentation. This process can be seen as an automated version of the cursor analysis described above. We have implemented the gaussian mixture model algorithm, allowing users to train a model using a set of data and then apply it to other datasets. Users can define starting points for the clusters and assign colors to export segmented images based on the probability of assignment to each cluster, as determined by the posterior of the model. Phasor Unmixing Phasor unmixing in lifetime imaging is a method that allows users to compute the photon fraction for each individual species contributing to the fluorescence in a pixel 26 . Similar to phasor analysis, phasor unmixing is model-free. The user needs to input only the lifetime values of the components for unmixing. We have implemented the computation and representation of N-harmonics, allowing users to perform phasor unmixing for up to 2N+1 components. Users define a set of pure components, assign a lifetime value to each of them and solve the unmixing at each pixel. Component fractions can be exported in spreadsheets and unmixed images can be generated, both as independent fractions and as component color-coded images. Additionally, component ratio-metric images can be generated. Furthermore, users can take empirical measurements and select them as pure components, which is particularly useful for unmixing spectral components. Author Contributions A.V. and S.R conceived the project. A.V. designed and wrote the code for GSLab. A.V., B.T. and S.R. troubleshot the successive implementations. B.T., A.F.D., A.D. and N.R. performed the test experiments. A.V. and M.B wrote the manuscript with the input of all authors. M.B. supervised the project. Ethics Declarations MB is a coauthor of a patent owned by the University of California, Irvine (UCI) related to the development of clinical multiphoton microscopy technology. Additionally, MB is a cofounder of Infraderm, LLC, a startup spin-off from UCI focused on commercializing clinical multiphoton microscopy imaging platforms that may benefit from the use of advanced analysis tools. The Institutional Review Board and Conflict of Interest Office of UCI have reviewed patent disclosures and found no concerns. The other authors declare no competing interests. Acknowledgements We wish to recognize the impactful career of Prof. Enrico Gratton, whose contributions have significantly influenced our work. We also acknowledge the funding support for this project from the NIBIB (R01EB026705), NIAMS (R21AR082648) and NCI (R01CA259019), as well as the Skin Biology Resource-Based Center at the University of California, Irvine (P30AR075047). References 1. ↵ Digman , M. A. , Caiolfa , V. R. , Zamai , M. & Gratton , E. The Phasor Approach to Fluorescence Lifetime Imaging Analysis . Biophys J 94 , L14 – L16 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 2. ↵ Fereidouni , F. , Bader , A. N. & Gerritsen , H. C. Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images . Opt Express 20 , 12729 – 12741 ( 2012 ). OpenUrl CrossRef PubMed 3. ↵ Malacrida , L. , Ranjit , S. , Jameson , D. M. & Gratton , E. The Phasor Plot: A Universal Circle to Advance Fluorescence Lifetime Analysis and Interpretation . Annu Rev Biophys 50 , 575 – 593 ( 2021 ). OpenUrl CrossRef PubMed 4. ↵ Stringari , C. et al. Phasor approach to fluorescence lifetime microscopy distinguishes different metabolic states of germ cells in a live tissue . Proc Natl Acad Sci U S A 108 , 13582 – 13587 ( 2011 ). OpenUrl Abstract / FREE Full Text 5. ↵ Guaglianone , G. et al. Elucidating the Oligomerization and Cellular Interactions of a Trimer Derived from Aβ through Fluorescence and Mass Spectrometric Studies . ACS Chem Neurosci 13 , 2473 – 2482 ( 2022 ). OpenUrl CrossRef PubMed 6. ↵ Rossetta , A. The bright future of fluorescence lifetime analysis . 1239805 , 26 ( 2023 ). OpenUrl 7. ↵ Jeong , S. et al. Time-resolved fluorescence microscopy with phasor analysis for visualizing multicomponent topical drug distribution within human skin . Sci Rep 10 , ( 2020 ). 8. ↵ Pelicci , S. , Diaspro , A. & Lanzanò , L. Chromatin nanoscale compaction in live cells visualized by acceptor-to-donor ratio corrected Förster resonance energy transfer between DNA dyes . J Biophotonics 12 , ( 2019 ). 9. ↵ Malacrida , L. & Gratton , E. LAURDAN fluorescence and phasor plots reveal the effects of a H2O2 bolus in NIH-3T3 fibroblast membranes dynamics and hydration . Free Radic Biol Med 128 , 144 – 156 ( 2018 ). OpenUrl CrossRef PubMed 10. ↵ Celli , A. et al. The epidermal Ca2+ gradient: Measurement using the phasor representation of fluorescent lifetime imaging . Biophys J 98 , 911 – 921 ( 2010 ). OpenUrl CrossRef PubMed 11. ↵ Rennick , J. J. , Nowell , C. J. , Pouton , C. W. & Johnston , A. P. R. Resolving subcellular pH with a quantitative fluorescent lifetime biosensor . Nat Commun 13 , ( 2022 ). 12. ↵ Lanzanò , L. et al. Encoding and decoding spatio-temporal information for super-resolution microscopy . Nat Commun 6 , 1 – 9 ( 2015 ). OpenUrl CrossRef PubMed 13. ↵ Yao , Z. et al. Multiplexed bioluminescence microscopy via phasor analysis . Nat Methods 19 , 893 – 898 ( 2022 ). OpenUrl CrossRef PubMed 14. ↵ Enrico Gratton . Globals for Images SimFCS . https://www.lfd.uci.edu/globals/ ( 2021 ). 15. ↵ Gohlke , C. & Malacrida , L. PhasorPy . https://www.phasorpy.org/stable/ ( 2024 ). 16. Tan , K. K. D. , Tsuchida , M. A. , Chacko , J. V. , Gahm , N. A. & Eliceiri , K. W. Real-time open-source FLIM analysis . Frontiers in Bioinformatics 3 , ( 2023 ). 17. ↵ Schrimpf , W. , Barth , A. , Hendrix , J. & Lamb , D. C. PAM: A Framework for Integrated Analysis of Imaging, Single-Molecule, and Ensemble Fluorescence Data . Biophys J 114 , 1518 – 1528 ( 2018 ). OpenUrl CrossRef PubMed 18. ↵ Gottlieb , D. , Asadipour , B. , Kostina , P. , Ung , T. P. L. & Stringari , C. FLUTE: A Python GUI for interactive phasor analysis of FLIM data . Biological Imaging 3 , ( 2023 ). 19. ↵ Bernardi , M. & Cardarelli , F. Phasor identifier: A cloud-based analysis of phasor-FLIM data on Python notebooks . Biophysical Reports 3 , ( 2023 ). 20. ↵ Gao , D. et al. FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis . PLoS One 15 , ( 2020 ). 21. Zoccoler , M. , Iarovenko , S. & Wetzker , C. FLIM phasor plotter . https://doi.org/10.5281/zenodo.12620956 ( 2024 ) x doi: 10.5281/zenodo.12620956 . OpenUrl CrossRef 22. Castello , M. et al. A robust and versatile platform for image scanning microscopy enabling super-resolution FLIM . Nat Methods 16 , 175 – 178 ( 2019 ). OpenUrl CrossRef PubMed 23. Stowers Institute . Spectral Image Analysis Plugins . https://research.stowers.org/imagejplugins/spectral_imaging.html ( 2017 ). 24. ↵ Farzad Fereidouni . SPECHRON . http://www.spechron.com/Spectral%20Phasor-Download.aspx ( 2024 ). 25. ↵ Kapsiani , S. et al. FLIMPA: A versatile software for Fluorescence Lifetime Imaging Microscopy Phasor Analysis . bioRxiv ( 2024 ) doi: 10.1101/2024.09.13.612802 . OpenUrl Abstract / FREE Full Text 26. ↵ Vallmitjana , A. et al. Resolution of 4 components in the same pixel in FLIM images using the phasor approach . Methods Appl Fluoresc 8 , 035001 ( 2020 ). OpenUrl CrossRef PubMed 27. ↵ Vallmitjana , A. , Torrado , B. & Gratton , E. Phasor-based image segmentation: machine learning clustering techniques . Biomed Opt Express 12 , 3410 ( 2021 ). OpenUrl CrossRef PubMed 28. ↵ Vallmitjana , A. GSLab: Open-Source Platform for Advanced Phasor Analysis in Fluorescence Microscopy . doi: 10.6084/m9.figshare.27921966 ( 2024 ). OpenUrl CrossRef 29. ↵ Fast , A. et al. Fast, large area multiphoton exoscope (FLAME) for macroscopic imaging with microscopic resolution of human skin . Sci Rep 10 , 1 – 14 ( 2020 ). OpenUrl CrossRef PubMed 30. ↵ Dvornikov , A. , Malacrida , L. & Gratton , E. The diver microscope for imaging in scattering media . Methods Protoc 2 , 1 – 12 ( 2019 ). OpenUrl References 31. ↵ Clayton , A. H. A. , Hanley , Q. S. & Verveer , P. J. Graphical representation and multicomponent analysis of single-frequency fluorescence lifetime imaging microscopy data . J Microsc 213 , 1 – 5 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 32. Datta , R. , Heaster , T. M. , Sharick , J. T. , Gillette , A. A. & Skala , M. C. Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications . J Biomed Opt 25 , 1 ( 2020 ). OpenUrl CrossRef PubMed 33. ↵ Malacrida , L. , Ranjit , S. , Jameson , D. M. & Gratton , E. The Phasor Plot: A Universal Circle to Advance Fluorescence Lifetime Analysis and Interpretation . 88 , 49 ( 2024 ). OpenUrl 34. ↵ Silberberg , M. & Grecco , H. E. pawFLIM: reducing bias and uncertainty to enable lower photon count in FLIM experiments . Methods Appl Fluoresc 5 , 024016 ( 2017 ). OpenUrl CrossRef PubMed 35. ↵ Fast , A. et al. Fast, large area multiphoton exoscope (FLAME) for macroscopic imaging with microscopic resolution of human skin . Sci Rep 10 , 1 – 14 ( 2020 ). OpenUrl CrossRef PubMed 36. ↵ Torrado , B. , Pannunzio , B. , Malacrida , L. & Digman , M. A. Fluorescence lifetime imaging microscopy . Nature Reviews Methods Primers 4 , 80 ( 2024 ). OpenUrl CrossRef 37. ↵ Ranjit , S. , Malacrida , L. , Jameson , D. M. & Gratton , E. Fit-free analysis of fluorescence lifetime imaging data using the phasor approach . Nat Protoc 13 , 1979 – 2004 ( 2018 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted February 15, 2025. 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