Fluorescence Lifetime Imaging Microscopy Data Research Articles

Pioglitazone Phases and Metabolic Effects in Nanoparticle-Treated Cells Analyzed via Rapid Visualization of FLIM Images.

Fluorescence lifetime imaging microscopy (FLIM) has proven to be a useful method for analyzing various aspects of material science and biology, like the supramolecular organization of (slightly) fluorescent compounds or the metabolic activity in non-labeled cells; in particular, FLIM phasor analysis (phasor-FLIM) has the potential for an intuitive representation of complex fluorescence decays and therefore of the analyzed properties. Here we present and make available tools to fully exploit this potential, in particular by coding via hue, saturation, and intensity the phasor positions and their weights both in the phasor plot and in the microscope image. We apply these tools to analyze FLIM data acquired via two-photon microscopy to visualize: (i) different phases of the drug pioglitazone (PGZ) in solutions and/or crystals, (ii) the position in the phasor plot of non-labelled poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs), and (iii) the effect of PGZ or PGZ-containing NPs on the metabolism of insulinoma (INS-1 E) model cells. PGZ is recognized for its efficacy in addressing insulin resistance and hyperglycemia in type 2 diabetes mellitus, and polymeric nanoparticles offer versatile platforms for drug delivery due to their biocompatibility and controlled release kinetics. This study lays the foundation for a better understanding via phasor-FLIM of the organization and effects of drugs, in particular, PGZ, within NPs, aiming at better control of encapsulation and pharmacokinetics, and potentially at novel anti-diabetics theragnostic nanotools.

Quantifying Fluorescence Lifetime Responsiveness of Environment-Sensitive Probes for Membrane Fluidity Measurements.

The structural diversity of different lipid species within the membrane defines its biophysical properties such as membrane fluidity, phase transition, curvature, charge distribution, and tension. Environment-sensitive probes, which change their spectral properties in response to their surrounding milieu, have greatly contributed to our understanding of such biophysical properties. To realize the full potential of these probes and avoid misinterpretation of their spectral responses, a detailed investigation of their fluorescence characteristics in different environments is necessary. Here, we examined the fluorescence lifetime of two newly developed membrane order probes, NR12S and NR12A, in response to alterations in their environments such as the degree of lipid saturation, cholesterol content, double bond position and configuration, and phospholipid headgroup. As a comparison, we investigated the lifetime sensitivity of the membrane tension probe Flipper in these environments. Applying fluorescence lifetime imaging microscopy (FLIM) in both model membranes and biological membranes, all probes distinguished membrane phases by lifetime but exhibited different lifetime sensitivities to varying membrane biophysical properties (e.g., cholesterol). While the lifetime of Flipper is particularly sensitive to the membrane cholesterol content, the NR12S and NR12A lifetimes are moderately sensitive to both the cholesterol content and lipid acyl chains. Moreover, all of the probes exhibit longer lifetimes at longer emission wavelengths in membranes of any complexity. This emission wavelength dependency results in varying lifetime resolutions at different spectral regions, which are highly relevant for FLIM data acquisition. Our data provide valuable insights on how to perform FLIM with these probes and highlight both their potential and limitations.

Metabolic imaging of human embryos is predictive of ploidy status but is not associated with clinical pregnancy outcomes: a pilot trial.

Does fluorescence lifetime imaging microscopy (FLIM)-based metabolic imaging assessment of human blastocysts prior to frozen transfer correlate with pregnancy outcomes? FLIM failed to distinguish consistent patterns in mitochondrial metabolism between blastocysts leading to pregnancy compared to those that did not. FLIM measurements provide quantitative information on NAD(P)H and flavin adenine dinucleotide (FAD+) concentrations. The metabolism of embryos has long been linked to their viability, suggesting the potential utility of metabolic measurements to aid in selection. This was a pilot trial enrolling 121 IVF couples who consented to have their frozen blastocyst measured using non-invasive metabolic imaging. After being warmed, 105 couples' good-quality blastocysts underwent a 6-min scan in a controlled temperature and gas environment. FLIM-assessed blastocysts were then transferred without any intervention in management. Eight metabolic parameters were obtained from each blastocyst (4 for NAD(P)H and 4 for FAD): short and long fluorescence lifetime, fluorescence intensity, and fraction of the molecule engaged with enzyme. The redox ratio (intensity of NAD(P)H)/(intensity of FAD) was also calculated. FLIM data were combined with known metadata and analyzed to quantify the ability of metabolic imaging to differentiate embryos that resulted in pregnancy from embryos that did not. De-identified discarded aneuploid human embryos (n = 158) were also measured to quantify correlations with ploidy status and other factors. Statistical comparisons were performed using logistic regression and receiver operating characteristic (ROC) curves with 5-fold cross-validation averaged over 100 repeats with random sampling. AUC values were used to quantify the ability to distinguish between classes. No metabolic imaging parameters showed significant differences between good-quality blastocysts resulting in pregnancy versus those that did not. A logistic regression using metabolic data and metadata produced an ROC AUC of 0.58. In contrast, robust AUCs were obtained when classifying other factors such as comparison of Day 5 (n = 64) versus Day 6 (n = 41) blastocysts (AUC = 0.78), inner cell mass versus trophectoderm (n = 105: AUC = 0.88) and aneuploid (n = 158) versus euploid and positive pregnancy embryos (n = 108) (AUC = 0.82). The study protocol did not select which embryo to transfer and the cohort of 105 included blastocysts were all high quality. The study was also limited in number of participants and study sites. Increased power and performing the trial in more sites may have provided a stronger conclusion regarding the merits of the use of FLIM clinically. FLIM failed to distinguish consistent patterns in mitochondrial metabolism between good-quality blastocysts leading to pregnancy compared to those that did not. Blastocyst ploidy status was, however, highly distinguishable. In addition, embryo regions and embryo day were consistently revealed by FLIM. While metabolic imaging detects mitochondrial metabolic features in human blastocysts, this pilot trial indicates it does not have the potential to serve as an effective embryo viability detection tool. This may be because mitochondrial metabolism plays an alternative role post-implantation. This study was sponsored by Optiva Fertility, Inc. Boston IVF contributed to the clinical site and services. Becker Hickl, GmbH, provided the FLIM system on loan. T.S. was the founder and held stock in Optiva Fertility, Inc., and D.S. and E.S. had options with Optiva Fertility, Inc., during this study. The study was approved by WCG Connexus IRB (Study Number 1298156).

Analysis of individual nanoscale block copolymer vesicles by atomic force microscopy combined with time-resolved fluorescence microscopy

Abstract We report on the analysis of individual dye loaded block copolymer (BCP) vesicles via a combination of confocal laser scanning microscopy (CSLM)/fluorescence lifetime imaging microscopy (FLIM) on the one hand and atomic force microscopy (AFM) on the other hand. AFM measurements on ATTO 647N-loaded poly(styrene-block-poly(acrylic acid)) (PS115-b-PAA15) vesicles were carried out with high spatial resolution and afforded morphology and dimensions of BCP vesicles on solid substrates. By contrast the CSLM and FLIM data are diffraction limited, but from the time resolved fluorescence data information on the local vicinity of the reporter dye can be extracted. In the combined experiment individual dye-loaded vesicles and vesicle aggregates were discerned, analyzed metrologically by AFM and in more detail by CSLM and FLIM. On the basis of FLIM data the partitioning of the reporter dye was analyzed. The dye resides preferentially in the hydrophilic corona inside the vesicles. The dye concentration in the polymersome was about 90 times higher than in the solution used for encapsulation. These results underline that the combination of AFM with sensitive optical techniques, especially FLIM, is a promising approach for obtaining a deeper understanding of molecular interactions and nanoenvironments in supramolecular macromolecular structures and beyond.

Fluorescence lifetime imaging microscopy as a tool to characterize spice powder variations for quality and authenticity purposes: A ginger case study

Spices are usually ground for applications and the resulting particle size of the powders is an important product attribute in view of the release of flavour. However, inhomogeneity of the original material may lead to variations in the physicochemical characteristics of the particles. This variation and its linkage to particle size may be examined by particular imaging techniques. This study aimed to explore the potential of Fluorescence Lifetime Imaging Microscopy (FLIM) to characterize spice powders according to particle size variations and correlation with their pigment contents to reveal the chemical information contained within the FLIM data. Ginger powder was used as a representative powder model. The FLIM profiles of the individual samples and populations revealed that FLIM coupled with the phasor approach has the capacity to characterize spice powder according to particle size. Meanwhile, Principal Component Analysis of pre-processed FLIM data revealed clustering of particle size groups. Further correlation analysis between the pigment compound contents and FLIM data of the ginger powders indicated that FLIM reflected chemical information of ginger powder and was able to visualize endogenous fluorophores. The current study revealed the potential of FLIM to characterize ginger powder particles. This approach may be extrapolated to other spice powder products. The new knowledge is a step further in paving the way for the application of innovative techniques, already prevalent in other domains, to food quality and authentication.

Real-time open-source FLIM analysis.

Fluorescence lifetime imaging microscopy (FLIM) provides valuable quantitative insights into fluorophores' chemical microenvironment. Due to long computation times and the lack of accessible, open-source real-time analysis toolkits, traditional analysis of FLIM data, particularly with the widely used time-correlated single-photon counting (TCSPC) approach, typically occurs after acquisition. As a result, uncertainties about the quality of FLIM data persist even after collection, frequently necessitating the extension of imaging sessions. Unfortunately, prolonged sessions not only risk missing important biological events but also cause photobleaching and photodamage. We present the first open-source program designed for real-time FLIM analysis during specimen scanning to address these challenges. Our approach combines acquisition with real-time computational and visualization capabilities, allowing us to assess FLIM data quality on the fly. Our open-source real-time FLIM viewer, integrated as a Napari plugin, displays phasor analysis and rapid lifetime determination (RLD) results computed from real-time data transmitted by acquisition software such as the open-source Micro-Manager-based OpenScan package. Our method facilitates early identification of FLIM signatures and data quality assessment by providing preliminary analysis during acquisition. This not only speeds up the imaging process, but it is especially useful when imaging sensitive live biological samples.

Phasor identifier: A cloud-based analysis of phasor-FLIM data on Python notebooks

This paper introduces an innovative approach utilizing Google Colaboratory for the versatile analysis of phasor fluorescence lifetime imaging microscopy (FLIM) data collected from various samples (e.g., cuvette, cells, tissues) and in various input file formats. In fact, phasor-FLIM widespread adoption has been hampered by complex instrumentation and data analysis requirements. We mean to make advanced FLIM analysis more accessible to researchers through a cloud-based solution that 1) harnesses robust computational resources, 2) eliminates hardware limitations, and 3) supports both CPU and GPU processing. We envision a paradigm shift in FLIM data accessibility and potential, aligning with the evolving field of artificial intelligence-driven FLIM analysis. This approach simplifies FLIM data handling and opens doors for diverse applications, from studying cellular metabolism to investigating drug encapsulation, benefiting researchers across multiple domains. The comparative analysis of freely distributed FLIM tools highlights the unique advantages of this approach in terms of adaptability, scalability, and open-source nature.

Review of Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis Using Machine Learning

Fluorescence lifetime imaging microscopy (FLIM) has emerged as a promising tool for all scientific studies in recent years. However, the utilization of FLIM data requires complex data modeling techniques, such as curve-fitting procedures. These conventional curve-fitting procedures are not only computationally intensive but also time-consuming. To address this limitation, machine learning (ML), particularly deep learning (DL), can be employed. This review aims to focus on the ML and DL methods for FLIM data analysis. Subsequently, ML and DL strategies for evaluating FLIM data are discussed, consisting of preprocessing, data modeling, and inverse modeling. Additionally, the advantages of the reviewed methods are deliberated alongside future implications. Furthermore, several freely available software packages for analyzing the FLIM data are highlighted.

Kernelizing: A way to increase accuracy in trilinear decomposition analysis of multiexponential signals

The unmixing of multiexponential decay signals into monoexponential components using soft modelling approaches is a challenging task due to the strong correlation and complete window overlap of the profiles. To solve this problem, slicing methodologies, such as PowerSlicing, tensorize the original data matrix into a three-way data array that can be decomposed based on trilinear models providing unique solutions. Satisfactory results have been reported for different types of data, e.g., nuclear magnetic resonance or time-resolved fluorescence spectra. However, when decay signals are described by only a few sampling (time) points, a significant degradation of the results can be observed in terms of accuracy and precision of the recovered profiles.In this work, we propose a methodology called Kernelizing that provides a more efficient way to tensorize data matrices of multiexponential decays. Kernelizing relies on the invariance of exponential decays, i.e., when convolving a monoexponential decaying function with any positive function of finite width (hereafter called “kernel”), the shape of the decay (determined by the characteristic decay constant) remains unchanged and only the preexponential factor varies. The way preexponential factors are affected across the sample and time modes is linear, and it only depends on the kernel used. Thus, using kernels of different shapes, a set of convolved curves can be obtained for every sample, and a three-way data array generated, for which the modes are sample, time and kernelizing effect. This three-way array can be afterwards analyzed by a trilinear decomposition method, such as PARAFAC-ALS, to resolve the underlying monoexponential profiles. To validate this new approach and assess its performance, we applied Kernelizing to simulated datasets, real time-resolved fluorescence spectra collected on mixtures of fluorophores and fluorescence-lifetime imaging microscopy data. When the measured multiexponential decays feature few sampling points (down to fifteen), more accurate trilinear model estimates are obtained than when using slicing methodologies.

Measurement of Patient-Derived Glioblastoma Cell Response to Temozolomide Using Fluorescence Lifetime Imaging of NAD(P)H.

Personalized strategies in glioblastoma treatment are highly necessary. One of the possible approaches is drug screening using patient-derived tumor cells. However, this requires reliable methods for assessment of the response of tumor cells to treatment. Fluorescence lifetime imaging microscopy (FLIM) is a promising instrument to detect early cellular response to chemotherapy using the autofluorescence of metabolic cofactors. Here, we explored FLIM of NAD(P)H to evaluate the sensitivity of patient-derived glioma cells to temozolomide (TMZ) in vitro. Our results demonstrate that the more-responsive cell cultures displayed the longest mean fluorescence lifetime τm after TMZ treatment due to an increase in the protein-bound NAD(P)H fraction α2 associated with a shift to oxidative phosphorylation. The cell cultures that responded poorly to TMZ had generally shorter τm, i.e., were more glycolytic, and showed no or insignificant changes after treatment. The FLIM data correlate well with standard measurements of cellular drug response-cell viability and proliferation index and clinical response in patients. Therefore, FLIM of NAD(P)H provides a highly sensitive, label-free assay of treatment response directly on patient-derived glioblastoma cells and can become an innovative platform for individual drug screening for patients.

Reaction-Induced structural and compositional heterogeneity in amine-cured epoxy/epoxy thermosets: Visualization of heterogeneity using fluorescence lifetime imaging microscopy (FLIM)

The strategic integration of a rhodamine-spirolactam (RS) mechanophore into transparent epoxy-amine (E-A) networks has revealed using fluorescence lifetime imaging microscopy (FLIM), multiple domains in the cured epoxies that are different (compositional and structural heterogeneity) even though only a single glass transition temperature (Tg) is observed in the solid rheology data. For the E-A networks investigated in this study, the FLIM data suggests that heterogeneity exists at two levels (molecular and macroscopic). Heterogeneity in the network composed of diglycidyl ether of bisphenol-A (DGEBA) epoxy cured with meta-phenylenediamine (m-PDA) is associated with the network being under-cured. In the network composed of the diglycidyl ether of 1,4-butanediol (DGEBD) cured with m-PDA, heterogeneity is linked to the oligomers that likely self-associate in the 60 mass % DGEBD medium, thereby creating discrete regions of low crosslink density in the cured network. In the DGEBA (80 mass %)/DGEBD (20 mass %) epoxy blend cured with m-PDA, the heterogeneous morphology in this fully cured network is caused by reaction kinetics differences of the two miscible bis-epoxides with m-PDA at the molecular level and the self-association of the oligomers found in the 60 mass % DGEBD medium at the macroscopic level.

FLUTE: A Python GUI for interactive phasor analysis of FLIM data.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. The fit-free phasor approach to FLIM data is increasingly being used due to its ease of interpretation. To date, no open-source graphical user interface (GUI) for phasor analysis of FLIM data is available in Python, thus limiting the widespread use of phasor analysis in biomedical research. Here, we present Fluorescence Lifetime Ultimate Explorer (FLUTE), a Python GUI that is designed to fill this gap. FLUTE simplifies and automates many aspects of the analysis of FLIM data acquired in the time domain, such as calibrating the FLIM data, performing interactive exploration of the phasor plot, displaying phasor plots and FLIM images with different lifetime contrasts simultaneously, and calculating the distance from known molecular species. After applying desired filters and thresholds, the final edited datasets can be exported for further user-specific analysis. FLUTE has been tested using several FLIM datasets including autofluorescence of zebrafish embryos and in vitro cells. In summary, our user-friendly GUI extends the advantages of phasor plotting by making the data visualization and analysis easy and interactive, allows for analysis of large FLIM datasets, and accelerates FLIM analysis for non-specialized labs.

Effect of UV light illumination in humid air on the optical and electronic properties of the orthorhombic α-MoO3 and monoclinic β-MoO3

We present reflectivity measurements on monoclinic MoO2, orthorhombic α-MoO3, and monoclinic β-MoO3 in a wide frequency range of 190–2500 nm. The extracted optical conductivity [σ(ω)] showed that MoO2 has a metallic character while α-MoO3 and β-MoO3 have an insulating behavior. In addition, the photochromic properties of both α-MoO3 and β-MoO3 have been studied. The σ(ω) spectra for both samples showed a different spectral weight of the optical transition due to the formation of color center bands, which formed as a result of UV exposure. The spectral weight of optical transition from the bulk sixfold cations Mob5+ to Mo6+ cations is higher in case of the illuminated β-MoO3 sample than the illuminated α-MoO3 sample. The XRD results showed that both α-MoO3 and β-MoO3 were transformed to monoclinic molybdenum oxide dihydrate (H4MoO5) after exposure to UV irradiation in humid air. The σ(ω) spectra revealed that photoinjection of hydrogen into the β-MoO3 film is higher than in the case of the α-MoO3 film. In addition, the time domain fluorescence lifetime imaging microscopy data showed that the lifetime due to the optical transition from surface fourfold cations Mos5+ to Mo6+ cations in the case of illuminated β-MoO3 is higher than that for the illuminated α-MoO3 for the same optical transition. Meaning that, in the case of illuminated β-MoO3, the surface Mos5+ cations disperse and penetrate into the bulk, lowering the spectral weight of the [Mos5+ Mos5+] dimers and enhancing the spectral weight of the bulk centers.

High resolution fluorescence lifetime maps from minimal photon counts

Fluorescence lifetime imaging microscopy (FLIM) can be used to probe concentration profiles of key marco-molecules within sub-cellular environments. However, current analyses for FLIM require a large number of photons per pixel to provide such a quantitative picture of life. In order to acquire such large number of photons, we must either increase data acquisition time, which limits temporal resolution, or increase laser intensity, which causes greater photo damage to the sample, or both. Here, we propose to analyze FLIM data by leveraging tools from the Bayesian paradigm.

Single-photon peak event detection (SPEED): a computational method for fast photon counting in fluorescence lifetime imaging microscopy.

Fluorescence lifetime imaging microscopy (FLIM) characterizes samples by examining the temporal properties of fluorescence emission, providing useful contrast within samples based on the local physical and biochemical environment of fluorophores. Despite this, FLIM applications have been limited in scope by either poor accuracy or long acquisition times. Here, we present a method for computational single-photon counting of directly sampled time-domain FLIM data that is capable of accurate fluorescence lifetime and intensity measurements while acquiring over 160 Mega-counts-per-second with sub-nanosecond time resolution between consecutive photon counts. We demonstrate that our novel method of Single-photon PEak Event Detection (SPEED) is more accurate than direct pulse sampling and faster than established photon counting FLIM methods. We further show that SPEED can be implemented for imaging and quantifying samples that benefit from higher -throughput and -dynamic range imaging with real-time GPU-accelerated processing and use this capability to examine the NAD(P)H-related metabolic dynamics of apoptosis in human breast cancer cells. Computational methods for photon counting such as SPEED open up more opportunities for fast and accurate FLIM imaging and additionally provide a basis for future innovation into alternative FLIM techniques.

Toxicological Analysis of Hepatocytes Using FLIM Technique: In Vitro versus Ex Vivo Models.

The search for new criteria indicating acute or chronic pathological processes resulting from exposure to toxic agents, testing of drugs for potential hepatotoxicity, and fundamental study of the mechanisms of hepatotoxicity at a molecular level still represents a challenging issue that requires the selection of adequate research models and tools. Microfluidic chips (MFCs) offer a promising in vitro model for express analysis and are easy to implement. However, to obtain comprehensive information, more complex models are needed. A fundamentally new label-free approach for studying liver pathology is fluorescence-lifetime imaging microscopy (FLIM). We obtained FLIM data on both the free and bound forms of NAD(P)H, which is associated with different metabolic pathways. In clinical cases, liver pathology resulting from overdoses is most often as a result of acetaminophen (APAP) or alcohol (ethanol). Therefore, we have studied and compared the metabolic state of hepatocytes in various experimental models of APAP and ethanol hepatotoxicity. We have determined the potential diagnostic criteria including the pathologically altered metabolism of the hepatocytes in the early stages of toxic damage, including pronounced changes in the contribution from the bound form of NAD(P)H. In contrast to the MFCs, the changes in the metabolic state of hepatocytes in the ex vivo models are, to a greater extent, associated with compensatory processes. Thus, MFCs in combination with FLIM can be applied as an effective tool set for the express modeling and diagnosis of hepatotoxicity in clinics.

New Extensibility and Scripting Tools in the ImageJ Ecosystem.

ImageJ provides a framework for image processing across scientific domains while being fully open source. Over the years ImageJ has been substantially extended to support novel applications in scientific imaging as they emerge, particularly in the area of biological microscopy, with functionality made more accessible via the Fiji distribution of ImageJ. Within this software ecosystem, work has been done to extend the accessibility of ImageJ to utilize scripting, macros, and plugins in a variety of programming scenarios, e.g., from Groovy and Python and in Jupyter notebooks and cloud computing. We provide five protocols that demonstrate the extensibility of ImageJ for various workflows in image processing. We focus first on Fluorescence Lifetime Imaging Microscopy (FLIM) data, since this requires significant processing to provide quantitative insights into the microenvironments of cells. Second, we show how ImageJ can now be utilized for common image processing techniques, specifically image deconvolution and inversion, while highlighting the new, built-in features of ImageJ-particularly its capacity to run completely headless and the Ops matching feature that selects the optimal algorithm for a given function and data input, thereby enabling processing speedup. Collectively, these protocols can be used as a basis for automating biological image processing workflows. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Using PyImageJ for FLIM data processing Alternate Protocol: Groovy FLIMJ in Jupyter Notebooks Basic Protocol 2: Using ImageJ Ops for image deconvolution Support Protocol 1: Using ImageJ Ops matching feature for image inversion Support Protocol 2: Headless ImageJ deconvolution.

Fluorescence Lifetime Imaging Microscopy Maps Fluorophore Concentrations, a Gaussian Process Approach

Fluorescence microscopy techniques have made many contributions to unravel rules of biological processes in the past few decades. These procedures make use of different properties, such as life-time, emission spectrum and intensity of fluorophores to learn about sub-cellular environments. In particular, fluorescence lifetime imaging microscopy (FLIM) makes use of lifetimes of fluorescent dyes to map sub-cellular environment variations, such as temperature, pH, molecule concentrations, and protein-protein interactions. In FLIM, the number of photons collected by the detector depends on the lifetimes and concentrations of the fluorescent dyes present in the sample. Therefore, FLIM equips us with a powerful tool to learn the fluorophore concentrations with high spatial resolution. FLIM analysis can be performed in either time or frequency domain. In time domain, photon arrival times are fitted to a mixture of decay profiles, which are convolutions of exponential components and the instrument response function. The current time domain approaches group the input data into multiple bins which leads to loss of information. Moreover, these techniques report sub-cellular heterogeneity with a resolution limited to the pixel size. We developed a time domain FLIM data analysis technique in a Bayesian paradigm that uses Gaussian processes to map the fluorophore concentrations with sub-pixel resolution. This method efficiently uses all the available photon arrival time information and, as such, requires a minimum number of photons to infer accurate fluorophore concentrations. The approach was validated using both empirical and simulated data.

FLIM data analysis based on Laguerre polynomial decomposition and machine-learning.

Significance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis.Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML).Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker.Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components.Conclusions: The ML-based approach shows great performance in FLIM data analysis.

FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis.

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ's routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.