Rapid fluorescence lifetime estimation with modified phasor approach and Laguerre deconvolution: a comparative study

Artificial Neural Network (ANN)-Based Determination of Fractional Contributions from Mixed Fluorophores using Fluorescence Lifetime Measurements

Here we present an artificial neural network (ANN)-approach to determine the fractional contributions Pi from fluorophores to a multi-exponential fluorescence decay in time-resolved lifetime measurements. Conventionally, Pi are determined by extracting two parameters (amplitude and lifetime) for each underlying mono-exponential decay using non-linear fitting. However, in this case parameter estimation is highly sensitive to initial guesses and weighting. In contrast, the ANN-based approach robustly gives the Pi without knowledge of amplitudes and lifetimes. By experimental measurements and Monte-Carlo simulations, we comprehensively show that accuracy and precision of Pi determination with ANNs and hence the number of distinguishable fluorophores depend on the fluorescence lifetimes' differences. For mixtures of up to five fluorophores, we determined the minimum uniform spacing Δτmin between lifetimes to obtain fractional contributions with a standard deviation of 5%. In example, five lifetimes can be distinguished with a respective minimum uniform spacing of approx. 10 ns even when the fluorophores' emission spectra are overlapping. This study underlines the enormous potential of ANN-based analysis for multi-fluorophore applications in fluorescence lifetime measurements.

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.

Anatomy-Specific Classification Model Using Label-Free FLIm to Aid Intraoperative Surgical Guidance of Head and Neck Cancer

Intraoperative identification of head and neck cancer tissue is essential to achieve complete tumor resection and mitigate tumor recurrence. Mesoscopic fluorescence lifetime imaging (FLIm) of intrinsic tissue fluorophores emission has demonstrated the potential to demarcate the extent of the tumor in patients undergoing surgical procedures of the oral cavity and the oropharynx. Here, we report FLIm-based classification methods using standard machine learning models that account for the diverse anatomical and biochemical composition across the head and neck anatomy to improve tumor region identification. Three anatomy-specific binary classification models were developed (i.e., "base of tongue," "palatine tonsil," and "oral tongue"). FLIm data from patients (N = 85) undergoing upper aerodigestive oncologic surgery were used to train and validate the classification models using a leave-one-patient-out cross-validation method. These models were evaluated for two classification tasks: (1) to discriminate between healthy and cancer tissue, and (2) to apply the binary classification model trained on healthy and cancer to discriminate dysplasia through transfer learning. This approach achieved superior classification performance compared to models that are anatomy-agnostic; specifically, a ROC-AUC of 0.94 was for the first task and 0.92 for the second. Furthermore, the model demonstrated detection of dysplasia, highlighting the generalization of the FLIm-based classifier. Current findings demonstrate that a classifier that accounts for tumor location can improve the ability to accurately identify surgical margins and underscore FLIm's potential as a tool for surgical guidance in head and neck cancer patients, including those subjects of robotic surgery.

Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine

We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM), using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. The results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics

Label-free optical microscopy has matured as a noninvasive tool for biological imaging; yet, it is criticized for its lack of specificity, slow acquisition and processing times, and weak and noisy optical signals that lead to inaccuracies in quantification. We introduce FOCALS (Fast Optical Coherence, Autofluorescence Lifetime imaging, and Second harmonic generation) microscopy capable of generating NAD(P)H fluorescence lifetime, second harmonic generation (SHG), and polarization-sensitive optical coherence microscopy (OCM) images simultaneously. Multimodal imaging generates quantitative metabolic and morphological profiles of biological samples in vitro, ex vivo, and in vivo. Fast analog detection of fluorescence lifetime and real-time processing on a graphical processing unit enables longitudinal imaging of biological dynamics. We detail the effect of optical aberrations on the accuracy of FLIM beyond the context of undistorting image features. To compensate for the sample-induced aberrations, we implemented a closed-loop single-shot sensorless adaptive optics solution, which uses computational adaptive optics of OCM for wavefront estimation within 2 s and improves the quality of quantitative fluorescence imaging in thick tissues. Multimodal imaging with complementary contrasts improves the specificity and enables multidimensional quantification of the optical signatures in vitro, ex vivo, and in vivo, fast acquisition and real-time processing improve imaging speed by 4-40 × while maintaining enough signal for quantitative nonlinear microscopy, and adaptive optics improves the overall versatility, which enable FOCALS microscopy to overcome the limits of traditional label-free imaging techniques.

Classification of fluorescent anisotropy decay based on the distance approach in the frequency domain

Frequency-domain (FD) fluorometry is a widely utilized tool to probe unique features of complex biological structures, which may serve medical diagnostic purposes. The conventional data analysis approaches used today to extract the fluorescence intensity or fluorescence anisotropy (FA) decay data suffer from several drawbacks and are inherently limited by the characteristics and complexity of the decay models. This paper presents the squared distance (D²) technique, which categorized samples based on the direct frequency response data (FRD) of the FA decay. As such, it improves the classification ability of the FD measurements of the FA decay as it avoids any distortion that results from the challenged translation into time domain data. This paper discusses the potential use of the D² approach to classify biological systems. Mathematical formulation of D² technique adjusted to the FRD of the FA decay is described. In addition, it validates the D² approach using 2 simulated data sets of 6 groups with similar widely and closely spaced FA decay data as well as in experimental data of 4 samples of a fluorophore-solvent (fluorescein-glycerol) system. In the simulations, the classification accuracy was above 95% for all 6 groups. In the experimental data, the classification accuracy was 100%. The D² approach can help classify samples whose FA decay data are difficult to extract making FA in the FD a realistic diagnostic tool. The D² approach offers an advanced method for sorting biological samples with differences beyond the practical temporal resolution limit in a reliable and efficient manner based on the FRD of their time-resolved fluorescence measurements thereby achieving better diagnostic quality in a shorter time.

End-to-End Neural Network for Feature Extraction and Cancer Diagnosis of In Vivo Fluorescence Lifetime Images of Oral Lesions

In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural network classification method able to accommodate lesion heterogeneity for improved oral cancer diagnosis using multispectral autofluorescence lifetime imaging (maFLIM) endoscopy. Our method uses an autoencoder framework jointly trained with a classifier designed to handle overfitting problems with reduced databases, which is often the case in healthcare applications. The autoencoder guides the feature extraction process through the reconstruction loss and enables the potential use of unsupervised data for domain adaptation and improved generalization. The classifier ensures the features extracted are task-specific, providing discriminative information for the classification task. The data-driven feature extraction method automatically generates task-specific features directly from fluorescence decays, eliminating the need for iterative signal reconstruction. We validate our proposed neural network method against support vector machine (SVM) baselines, with our method showing a 6.5%-8.3% increase in sensitivity. Our results show that neural networks that implement data-driven feature extraction provide superior results and enable the capacity needed to target specific issues, such as inter-patient variability and the heterogeneity of oral lesions.Clinical relevance- We improve standard classification algorithms for in vivo diagnosis of oral cancer lesions from maFLIm for clinical use in cancer screening, reducing unnecessary biopsies and facilitating early detection of oral cancer.

Development and characterization of phasor-based analysis for FLIM to evaluate the metabolic and epigenetic impact of HER2 inhibition on squamous cell carcinoma cultures

SIGNIFICANCE

Deranged metabolism and dysregulated growth factor signaling are closely associated with abnormal levels of proliferation, a recognized hallmark in tumorigenesis. Fluorescence lifetime imaging microscopy (FLIM) of endogenous nicotinamide adenine dinucleotide (NADH), a key metabolic coenzyme, offers a non-invasive, diagnostic indicator of disease progression, and treatment response. The model-independent phasor analysis approach leverages FLIM to rapidly evaluate cancer metabolism in response to targeted therapy.

AIM

We combined lifetime and phasor FLIM analysis to evaluate the influence of human epidermal growth factor receptor 2 (HER2) inhibition, a prevalent cancer biomarker, on both nuclear and cytoplasmic NAD(P)H of two squamous cell carcinoma (SCC) cultures. While better established, the standard lifetime analysis approach is relatively slow and potentially subject to intrinsic fitting errors and model assumptions. Phasor FLIM analysis offers a rapid, model-independent alternative, but the sensitivity of the bound NAD(P)H fraction to growth factor signaling must also be firmly established.

APPROACH

Two SCC cultures with low- and high-HER2 expression, were imaged using multiphoton-excited NAD(P)H FLIM, with and without treatment of the HER2 inhibitor AG825. Cells were challenged with mitochondrial inhibition and uncoupling to investigate AG825's impact on the overall metabolic capacity. Phasor FLIM and lifetime fitting analyses were compared within nuclear and cytoplasmic compartments to investigate epigenetic and metabolic impacts of HER2 inhibition.

RESULTS

NAD(P)H fluorescence lifetime and bound fraction consistently decreased following HER2 inhibition in both cell lines. High-HER2 SCC74B cells displayed a more significant response than low-HER2 SCC74A in both techniques. HER2 inhibition induced greater changes in nuclear than cytoplasmic compartments, leading to an increase in NAD(P)H intensity and concentration.

CONCLUSIONS

The use of both, complementary FLIM analysis techniques together with quantitative fluorescence intensity revealed consistent, quantitative changes in NAD(P)H metabolism associated with inhibition of growth factor signaling in SCC cell lines. HER2 inhibition promoted increased reliance on oxidative phosphorylation in both cell lines.

Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions

Fluorescence lifetime imaging (FLIm) is an optical spectroscopic imaging technique capable of real-time assessments of tissue properties in clinical settings. Label-free FLIm is sensitive to changes in tissue structure and biochemistry resulting from pathological conditions, thus providing optical contrast to identify and monitor the progression of disease. Technical and methodological advances over the last two decades have enabled the development of FLIm instrumentation for real-time, in situ, mesoscopic imaging compatible with standard clinical workflows. Herein, we review the fundamental working principles of mesoscopic FLIm, discuss the technical characteristics of current clinical FLIm instrumentation, highlight the most commonly used analytical methods to interpret fluorescence lifetime data and discuss the recent applications of FLIm in surgical oncology and cardiovascular diagnostics. Finally, we conclude with an outlook on the future directions of clinical FLIm.

Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications

SIGNIFICANCE

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to distinguish the unique molecular environment of fluorophores. FLIM measures the time a fluorophore remains in an excited state before emitting a photon, and detects molecular variations of fluorophores that are not apparent with spectral techniques alone. FLIM is sensitive to multiple biomedical processes including disease progression and drug efficacy.

AIM

We provide an overview of FLIM principles, instrumentation, and analysis while highlighting the latest developments and biological applications.

APPROACH

This review covers FLIM principles and theory, including advantages over intensity-based fluorescence measurements. Fundamentals of FLIM instrumentation in time- and frequency-domains are summarized, along with recent developments. Image segmentation and analysis strategies that quantify spatial and molecular features of cellular heterogeneity are reviewed. Finally, representative applications are provided including high-resolution FLIM of cell- and organelle-level molecular changes, use of exogenous and endogenous fluorophores, and imaging protein-protein interactions with Förster resonance energy transfer (FRET). Advantages and limitations of FLIM are also discussed.

CONCLUSIONS

FLIM is advantageous for probing molecular environments of fluorophores to inform on fluorophore behavior that cannot be elucidated with intensity measurements alone. Development of FLIM technologies, analysis, and applications will further advance biological research and clinical assessments.

Exploring protein-protein interactions with large differences in protein expression levels using FLIM-FRET

Many molecular processes within a cell are carried out by molecular machines built from a large number of proteins organized by their protein-protein interactions (PPIs). Exploring PPIs in their cellular context is critical to better understand the proteins functions. Förster resonance energy transfer measured by fluorescence lifetime imaging (FLIM-FRET) enables to monitor PPIs and to map their spatial organization in a living cell with high spatial and temporal specificity. But both the accurate measurement and the interpretation of multi-exponential FLIM-FRET data associated to mixtures of interacting and non-interacting proteins are difficult. Here we show that a simple diagram plot can find interesting visualization properties by clustering pixels with similar decay signatures. FLIM diagram plot can be used to provide valuable information about stoichiometry and binding mode in PPIs, even in the presence of large differences in protein expression levels of the different interacting partners. The proposed FLIM diagram plot is a useful visual approach for a more straightforward interpretation of complex lifetime data. This approach was applied for revealing critical features of PPIs in live Pseudomonas aeruginosa.

In vitro and in vivo phasor analysis of stoichiometry and pharmacokinetics using short-lifetime near-infrared dyes and time-gated imaging

We introduce a simple new approach for time-resolved multiplexed analysis of complex systems using near-infrared (NIR) dyes, applicable to in vitro and in vivo studies. We show that fast and precise in vitro quantification of NIR fluorophores' short (subnanosecond) lifetime and stoichiometry can be done using phasor analysis, a computationally efficient and user-friendly representation of complex fluorescence intensity decays obtained with pulsed laser excitation and time-gated camera imaging. We apply this approach to the study of binding equilibria by Förster resonant energy transfer using two different model systems: primary/secondary antibody binding in vitro and ligand/receptor binding in cell cultures. We then extend it to dynamic imaging of the pharmacokinetics of transferrin engagement with the transferrin receptor in live mice, elucidating the kinetics of differential transferrin accumulation in specific organs, straightforwardly differentiating specific from nonspecific binding. Our method, implemented in a freely-available software, has the advantage of time-resolved NIR imaging, including better tissue penetration and background-free imaging, but simplifies and considerably speeds up data processing and interpretation, while remaining quantitative. These advances make this method attractive and of broad applicability for in vitro and in vivo molecular imaging and could be extended to applications as diverse as image-guided surgery or optical tomography.

Therapeutic Fluorescent Hybrid Nanoparticles for Traceable Delivery of Glucocorticoids to Inflammatory Sites

Treatment of inflammatory disorders with glucocorticoids (GCs) is often accompanied by severe adverse effects. Application of GCs via nanoparticles (NPs), especially those using simple formulations, could possibly improve their delivery to sites of inflammation and therefore their efficacy, minimising the required dose and thus reducing side effects. Here, we present the evaluation of NPs composed of GC betamethasone phosphate (BMP) and the fluorescent dye DY-647 (BMP-IOH-NPs) for improved treatment of inflammation with simultaneous in vivo monitoring of NP delivery.

Methods: BMP-IOH-NP uptake by MH-S macrophages was analysed by fluorescence and electron microscopy. Lipopolysaccharide (LPS)-stimulated cells were treated for 48 h with BMP-IOH-NPs (1×10^-5-1×10^-9 M), BMP or dexamethasone (Dexa). Drug efficacy was assessed by measurement of interleukin 6. Mice with Zymosan-A-induced paw inflammation were intraperitoneally treated with BMP-IOH-NPs (10 mg/kg) and mice with ovalbumin (OVA)-induced allergic airway inflammation (AAI) were treated intranasally with BMP-IOH-NPs, BMP or Dexa (each 2.5 mg/kg). Efficacy was assessed in vivo by paw volume measurements with µCT and ex vivo by measurement of paw weight for Zymosan-A-treated mice, or in the AAI model by in vivo x-ray-based lung function assessment and by cell counts in the bronchoalveolar lavage (BAL) fluid and histology. Delivery of BMP-IOH-NPs to the lungs of AAI mice was monitored by in vivo optical imaging and by fluorescence microscopy.

Results: Uptake of BMP-IOH-NPs by MH-S cells was observed during the first 10 min of incubation, with the NP load increasing over time. The anti-inflammatory effect of BMP-IOH-NPs in vitro was dose dependent and higher than that of Dexa or free BMP, confirming efficient release of the drug. In vivo, Zymosan-A-induced paw inflammation was significantly reduced in mice treated with BMP-IOH-NPs. AAI mice that received BMP-IOH-NPs or Dexa but not BMP revealed significantly decreased eosinophil numbers in BALs and reduced immune cell infiltration in lungs. Correspondingly, lung function parameters, which were strongly affected in non-treated AAI mice, were unaffected in AAI mice treated with BMP-IOH-NPs and resembled those of healthy animals. Accumulation of BMP-IOH-NPs within the lungs of AAI mice was detectable by optical imaging for at least 4 h in vivo, where they were preferentially taken up by peribronchial and alveolar M2 macrophages.

Conclusion: Our results show that BMP-IOH-NPs can effectively be applied in therapy of inflammatory diseases with at least equal efficacy as the gold standard Dexa, while their delivery can be simultaneously tracked in vivo by fluorescence imaging. BMP-IOH-NPs thus have the potential to reach clinical applications.

Label-free fluorescence lifetime spectroscopy detects radiation-induced necrotic changes in live brain in real-time

Current clinical imaging modalities do not reliably identify brain tissue regions with necrosis following radiotherapy. This creates challenges for stereotaxic biopsies and surgical-decision making. Time-resolved fluorescence spectroscopy (TRFS) provides a means to rapidly identify necrotic tissue by its distinct autofluorescence signature resulting from tissue breakdown and altered metabolic profiles in regions with radiation damage. Studies conducted in a live animal model of radiation necrosis demonstrated that necrotic tissue is characterized by respective increases of 27% and 108% in average lifetime and redox ratio, when compared with healthy tissue. Moreover, radiation-damaged tissue not visible by MRI but confirmed by histopathology, was detected by TRFS. Current results demonstrate the ability of TRFS to identify radiation-damaged brain tissue in real-time and indicates its potential to assist with surgical guidance and MRI-guided biopsy procedures.