Fluorescence lifetime techniques in medical applications

Development of a time-resolved laser-induced fluorescence fingerprinting method for detecting low-level adulteration in extra virgin olive oil

Adulteration identification in extra virgin olive oil (EVOO) is a significant concern in the olive oil industry. This study aimed to detect low-level adulteration of EVOO with other edible oils by using a novel time-resolved laser-induced fluorescence (TRLIF) fingerprinting method. Five EVOO brands were first analyzed to assess its potential for classification based on their differing fluorophore content. The developed method effectively reduces the effects associated with the optical path length and excitation light intensity. Subsequently, three sets of binary mixtures were tested: EVOO adulterated with refined olive oil, soybean oil, and sunflower oil. Quantitative analysis was performed using parallel factor analysis, which achieved a cross-validation coefficient of determination (R2) exceeding 0.90, with a prediction error < 1.40 %. These findings demonstrate that this method has the potential to determine the purity and quality of EVOO.

Enhanced quantification of chlorophyll a and its degradation products in olive oil using time-resolved laser-induced fluorescence fingerprint analysis

Potential errors in the fluorescence analysis of chlorophylls and their degradation products, primarily due to spectral overlap and inner filter, are widely acknowledged. This study aimed to devise a sensitivity-enhanced technique for the concurrent quantification of chlorophyll a and its degradation products while minimizing effects from type-B chlorophylls. Initially, a time-resolved laser-induced fluorescence spectroscopic system was designed and tested on stardard chlorophyll samples. The origins, implications, and mitigation strategies of spectral overlap and the inner filter effect on the measured fluorescence intensity were thoroughly examined. Then, this methodology was proved to be efficacious within complex liquid matrices derived from olive oil. The experimental outcomes not only shed additional light on the mechanisms of chlorophyll fluorescence overlap and the inner filter effect, but also establish a general framework for developing spectrally and timely resolved fluorescence fingerprint analysis for the simultaneous quantification of chlorophylls and their degradation products at high concentrations.

In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning

OBJECTIVES

One of the widely used optical biopsy methods for monitoring cellular and tissue metabolism is time-resolved fluorescence. The use of this method in optical liver biopsy has a high potential for studying the shift in energy-type production from oxidative phosphorylation to glycolysis and changes in the antioxidant defense of malignant cells. On the other hand, machine learning methods have proven to be an excellent solution to classification problems in medical practice, including biomedical optics. We aim to combine time-resolved fluorescence measurements and machine learning to automate the division of liver parenchyma and tumors (primary malignant, metastases and benign tumors) into classes.

MATERIALS AND METHODS

An optical biopsy was performed using a developed setup with a fine-needle optical probe in clinical conditions under ultrasound control. Fluorescence decays were recorded in a conditionally healthy liver and lesions during percutaneous needle biopsy. The labeled data set was created on the basis of the recorded fluorescence results and the histopathological classification of the biopsies obtained. Several machine learning methods were trained using different separation strategies of the training test set, and their respective accuracy was compared.

RESULTS

Our results show that each of the tumor types had its own characteristic metabolic shifts recorded by the time-resolved fluorescence spectroscopy. The application of machine learning demonstrates a reliable separation of the liver and all tumor types into cancer and noncancer classes with sensitivity, specificity and corresponding accuracy greater than 0.91, 0.79 and 0.90, using the random forest method. We also show that our method is capable of giving a preliminary diagnosis of the type of liver tumor (primary malignant, metastases and benign tumors) with a sensitivity, specificity and accuracy of at least 0.80, 0.95 and 0.90.

CONCLUSIONS

These promising results highlight its potential as a key tool in the future development of diagnostic and therapeutic strategies for liver cancers. Lasers Surg. Med. 00:00-00, 2024. 2024 Wiley Periodicals LLC.

Accompanying Hemoglobin Polymerization in Red Blood Cells in Patients with Sickle Cell Disease Using Fluorescence Lifetime Imaging

In recent studies, it has been shown that fluorescence lifetime imaging (FLIM) may reveal intracellular structural details in unstained cytological preparations that are not revealed by standard staining procedures. The aim of our investigation was to examine whether FLIM images could reveal areas suggestive of polymerization in red blood cells (RBCs) of sickle cell disease (SCD) patients. We examined label-free blood films using auto-fluorescence FLIM images of 45 SCD patients and compared the results with those of 27 control persons without hematological disease. All control RBCs revealed homogeneous cytoplasm without any foci. Rounded non-sickled RBCs in SCD showed between zero and three small intensively fluorescent dots with higher lifetime values. In sickled RBCs, we found additionally larger irregularly shaped intensively fluorescent areas with increased FLIM values. These areas were interpreted as equivalent to polymerized hemoglobin. The rounded, non-sickled RBCs of SCD patients with homogeneous cytoplasm were not different from those of the erythrocytes of control patients in light microscopy. Yet, variables from the local binary pattern-transformed matrix of the FLIM values per pixel showed significant differences between non-sickled RBCs and those of control cells. In a linear discriminant analysis, using local binary pattern-transformed texture features (mean and entropy) of the erythrocyte cytoplasm of normal appearing cells, the final model could distinguish between SCD patients and control persons with an accuracy of 84.7% of the patients. When the classification was based on the examination of a single rounded erythrocyte, an accuracy of 68.5% was achieved. Employing the Linear Discriminant Analysis classifier method for machine learning, the accuracy was 68.1%. We believe that our study shows that FLIM is able to disclose the topography of the intracellular polymerization process of hemoglobin in sickle cell disease and that the images are compatible with the theory of the two-step nucleation. Furthermore, we think that the presented technique may be an interesting tool for the investigation of therapeutic inhibition of polymerization.

Detection and characterization of colorectal cancer by autofluorescence lifetime imaging on surgical specimens

Colorectal cancer (CRC) ranks among the most prevalent malignancies worldwide, driving a quest for comprehensive characterization methods. We report a characterization of the ex vivo autofluorescence lifetime fingerprint of colorectal tissues obtained from 73 patients that underwent surgical resection. We specifically target the autofluorescence characteristics of collagens, reduced nicotine adenine (phosphate) dinucleotide (NAD(P)H), and flavins employing a fiber-based dual excitation (375 nm and 445 nm) optical imaging system. Autofluorescence-derived parameters obtained from normal tissues, adenomatous lesions, and adenocarcinomas were analyzed considering the underlying clinicopathological features. Our results indicate that differences between tissues are primarily driven by collagen and flavins autofluorescence parameters. We also report changes in the autofluorescence parameters associated with NAD(P)H that we tentatively attribute to intratumoral heterogeneity, potentially associated to the presence of distinct metabolic subpopulations. Changes in autofluorescence signatures of malignant tumors were also observed with lymphatic and venous invasion, differentiation grade, and microsatellite instability. Finally, we characterized the impact of radiative treatment in the autofluorescence fingerprints of rectal tissues and observed a generalized increase in the mean lifetime of radiated adenocarcinomas, which is suggestive of altered metabolism and structural remodeling. Overall, our preliminary findings indicate that multiparametric autofluorescence lifetime measurements have the potential to significantly enhance clinical decision-making in CRC, spanning from initial diagnosis to ongoing management. We believe that our results will provide a foundational framework for future investigations to further understand and combat CRC exploiting autofluorescence measurements.

Light-field tomographic fluorescence lifetime imaging microscopy

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging technique that enables the visualization of biological samples at the molecular level by measuring the fluorescence decay rate of fluorescent probes. This provides critical information about molecular interactions, environmental changes, and localization within biological systems. However, creating high-resolution lifetime maps using conventional FLIM systems can be challenging, as it often requires extensive scanning that can significantly lengthen acquisition times. This issue is further compounded in three-dimensional (3D) imaging because it demands additional scanning along the depth axis. To tackle this challenge, we developed a computational imaging technique called light-field tomographic FLIM (LIFT-FLIM). Our approach allows for the acquisition of volumetric fluorescence lifetime images in a highly data-efficient manner, significantly reducing the number of scanning steps required compared to conventional point-scanning or line-scanning FLIM imagers. Moreover, LIFT-FLIM enables the measurement of high-dimensional data using low-dimensional detectors, which are typically low cost and feature a higher temporal bandwidth. We demonstrated LIFT-FLIM using a linear single-photon avalanche diode array on various biological systems, showcasing unparalleled single-photon detection sensitivity. Additionally, we expanded the functionality of our method to spectral FLIM and demonstrated its application in high-content multiplexed imaging of lung organoids. LIFT-FLIM has the potential to open up broad avenues in both basic and translational biomedical research.

Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging

There is no clinical tool available to primary care physicians or dermatologists that could provide objective identification of suspicious skin cancer lesions. Multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy enables label-free biochemical and metabolic imaging of skin lesions. This study investigated the use of pixel-level maFLIM dermoscopy features for objective discrimination of malignant from visually similar benign pigmented skin lesions. Clinical maFLIM dermoscopy images were acquired from 60 pigmented skin lesions before undergoing a biopsy examination. Random forest and deep neural networks classification models were explored, as they do not require explicit feature selection. Feature pools with either spectral intensity or bi-exponential maFLIM features, and a combined feature pool, were independently evaluated with each classification model. A rigorous cross-validation strategy tailored for small-size datasets was adopted to estimate classification performance. Time-resolved bi-exponential autofluorescence features were found to be critical for accurate detection of malignant pigmented skin lesions. The deep neural network model produced the best lesion-level classification, with sensitivity and specificity of 76.84%±12.49% and 78.29%±5.50%, respectively, while the random forest classifier produced sensitivity and specificity of 74.73%±14.66% and 76.83%±9.58%, respectively. Results from this study indicate that machine-learning driven maFLIM dermoscopy has the potential to assist doctors with identifying patients in real need of biopsy examination, thus facilitating early detection while reducing the rate of unnecessary biopsies.

Mass spectrometry for metabolomics analysis: Applications in neonatal and cancer screening

Chemical analysis by analytical instrumentation has played a major role in disease diagnosis, which is a necessary step for disease treatment. While the treatment process often targets specific organs or compounds, the diagnostic step can occur through various means, including physical or chemical examination. Chemically, the genome may be evaluated to give information about potential genetic outcomes, the transcriptome to provide information about expression actively occurring, the proteome to offer insight on functions causing metabolite expression, or the metabolome to provide a picture of both past and ongoing physiological function in the body. Mass spectrometry (MS) has been elevated among other analytical instrumentation because it can be used to evaluate all four biological machineries of the body. In addition, MS provides enhanced sensitivity, selectivity, versatility, and speed for rapid turnaround time, qualities that are important for instance in clinical procedures involving the diagnosis of a pediatric patient in intensive care or a cancer patient undergoing surgery. In this review, we provide a summary of the use of MS to evaluate biomarkers for newborn screening and cancer diagnosis. As many reviews have recently appeared focusing on MS methods and instrumentation for metabolite analysis, we sought to describe the biological basis for many metabolomic and additional omics biomarkers used in newborn screening and how tandem MS methods have recently been applied, in comparison to traditional methods. Similar comparison is done for cancer screening, with emphasis on emerging MS approaches that allow biological fluids, tissues, and breath to be analyzed for the presence of diagnostic metabolites yielding insight for treatment options based on the understanding of prior and current physiological functions of the body.

Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images

Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.

Advancements in fluorescence lifetime imaging microscopy Instrumentation: Towards high speed and 3D

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging tool offering molecular specific insights into samples through the measurement of fluorescence decay time, with promising applications in diverse research fields. However, to acquire two-dimensional lifetime images, conventional FLIM relies on extensive scanning in both the spatial and temporal domain, resulting in much slower acquisition rates compared to intensity-based approaches. This problem is further magnified in three-dimensional imaging, as it necessitates additional scanning along the depth axis. Recent advancements have aimed to enhance the speed and three-dimensional imaging capabilities of FLIM. This review explores the progress made in addressing these challenges and discusses potential directions for future developments in FLIM instrumentation.

Photoluminescent Characterization of Metal Nanoclusters: Basic Parameters, Methods, and Applications

Metal nanoclusters (MNCs) can be synthesized with atomically precise structures and molecule formulae due to the rapid development of nanocluster science in recent decades. The ultrasmall size range (normally < 2 nm) endows MNCs with plenty of molecular-like properties, among which photoluminescent properties have aroused extensive attention. Tracing the research and development processes of luminescent nanoclusters, various photoluminescent analysis and characterization methods play a significant role in elucidating luminescent mechanism and analyzing luminescent properties. In this review, it is aimed to systematically summarize the normally used photoluminescent characterizations in MNCs including basic parameters and methods, such as excitation/emission wavelength, quantum yield, and lifetime. For each key parameter, first its definition and meaning is introduced and then the relevant characterization methods including measuring principles and the revelation of luminescent properties from the collected data are discussed. Then, it is discussed in details how to explore the luminescent mechanism of MNCs and construct NC-based applications based on the measured data. By means of these characterization strategies, the luminescent properties of MNCs and NC-based designs can be explained quantitatively and qualitatively. Hence, this review is expected to provide clear guidance for researchers to characterize luminescent MNCs and better understand the luminescent mechanism from the measured results.

Fibre-based fluorescence-lifetime imaging microscopy: a real-time biopsy guidance tool for suspected lung cancer

Lung cancer is the most common cause of cancer-related deaths worldwide. Early detection improves outcomes, however, existing sampling techniques are associated with suboptimal diagnostic yield and procedure-related complications. Autofluorescence-based fluorescence-lifetime imaging microscopy (FLIM), a technique which measures endogenous fluorophore decay rates, may aid identification of optimal biopsy sites in suspected lung cancer. Our fibre-based fluorescence-lifetime imaging system, utilising 488 nm excitation, which is deliverable via existing diagnostic platforms, enables real-time visualisation and lifetime analysis of distal alveolar lung structure. We evaluated the diagnostic accuracy of the fibre-based fluorescence-lifetime imaging system to detect changes in fluorescence lifetime in freshly resected ex vivo lung cancer and adjacent healthy tissue as a first step towards future translation. The study compares paired non-small cell lung cancer (NSCLC) and non-cancerous tissues with gold standard diagnostic pathology to assess the performance of the technique. Paired NSCLC and non-cancerous lung tissues were obtained from thoracic resection patients (N=21). A clinically compatible 488 nm fluorescence-lifetime endomicroscopy platform was used to acquire simultaneous fluorescence intensity and lifetime images. Fluorescence lifetimes were calculated using a computationally-lightweight, rapid lifetime determination method. Fluorescence lifetime was significantly reduced in ex vivo lung cancer, compared with non-cancerous lung tissue [mean ± standard deviation (SD), 1.79±0.40 vs. 2.15±0.26 ns, P<0.0001], and fluorescence intensity images demonstrated distortion of alveolar elastin autofluorescence structure. Fibre-based fluorescence-lifetime imaging demonstrated good performance characteristics for distinguishing lung cancer, from adjacent non-cancerous tissue, with 81.0% sensitivity and 71.4% specificity. Our novel fibre-based fluorescence-lifetime imaging system, which enables label-free imaging and quantitative lifetime analysis, discriminates ex vivo lung cancer from adjacent healthy tissue. This minimally invasive technique has potential to be translated as a real-time biopsy guidance tool, capable of optimising diagnostic accuracy in lung cancer.

Compact source of energetic sub-nanosecond red pulses based on cascaded quasi-phase-matching in aperiodically poled lithium niobate

We present a simple, compact source of sub-nanosecond pulsed red radiation, based on cascaded nonlinear optical processes-degenerate optical parametric generation and sum-frequency generation-performed with a sample of aperiodically poled lithium niobate pumped by passively Q-switched 1.064 µm Nd:YAG laser. This system does not require feedback from an optical cavity; a single pass of the short pump is all that is required to obtain the cascaded processes, which shortens the output pulse. When pumped with a 1.2 ns, 75 µJ pulse, we obtain 670 ps pulses centered around 709 nm with an energy of 2.8 µJ, corresponding to a peak power of over 4 kW. A numerical model that predicts qualitatively the main characteristics of this source is also presented.

Applications of machine learning in time-domain fluorescence lifetime imaging: a review

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

Fibre-optic based exploration of lung cancer autofluorescence using spectral fluorescence lifetime

Fibre-optic based time-resolved fluorescence spectroscopy (TRFS) is an advanced spectroscopy technique that generates sample-specific spectral-temporal signature, characterising variations in fluorescence in real-time. As such, it can be used to interrogate tissue autofluorescence. Recent advancements in TRFS technology, including the development of devices that simultaneously measure high-resolution spectral and temporal fluorescence, paired with novel analysis methods extracting information from these multidimensional measurements effectively, provide additional insight into the underlying autofluorescence features of a sample. This study demonstrates, using both simulated data and endogenous fluorophores measured bench-side, that the shape of the spectral fluorescence lifetime, or fluorescence lifetimes estimated over high-resolution spectral channels across a broad range, is influenced by the relative abundance of underlying fluorophores in mixed systems and their respective environment. This study, furthermore, explores the properties of the spectral fluorescence lifetime in paired lung tissue deemed either abnormal or normal by pathologists. We observe that, on average, the shape of the spectral fluorescence lifetime at multiple locations sampled on 14 abnormal lung tissue, compared to multiple locations sampled on the respective paired normal lung tissue, shows more variability; and, while not statistically significant, the average spectral fluorescence lifetime in abnormal tissue is consistently lower over every wavelength than the normal tissue.

A blinded study using laser induced endogenous fluorescence spectroscopy to differentiate ex vivo spine tumor, healthy muscle, and healthy bone

Ten patients undergoing surgical resection for spinal tumors were selected. Samples of tumor, muscle, and bone were resected, de-identified by the treating surgeon, and then scanned with the TumorID technology ex vivo. This study investigates whether TumorID technology is able to differentiate three different human clinical fresh tissue specimens: spine tumor, normal muscle, and normal bone. The TumorID technology utilizes a 405 nm excitation laser to target endogenous fluorophores, thereby allowing for the detection of tissue based on emission spectra. Metabolic profiles of tumor and healthy tissue vary, namely NADH (bound and free emission peak, respectively: 487 nm, 501 nm) and FAD (emission peak: 544) are endogenous fluorophores with distinct concentrations in tumor and healthy tissue. Emission spectra analyzed consisted of 74 scans of spine tumor, 150 scans of healthy normal bone, and 111 scans of healthy normal muscle. An excitation wavelength of 405 nm was used to obtain emission spectra from tissue as previously described. Emission spectra consisted of approximately 1400 wavelength intensity pairs between 450 and 750 nm. Kruskal-Wallis tests were conducted comparing AUC distributions for each treatment group, α = 0.05. Spectral signatures varied amongst the three different tissue types. All pairwise comparisons among tissues for Free NADH were statistically significant (Tumor vs. Muscle: p = 0.0006, Tumor vs. Bone: p < 0.0001, Bone vs. Muscle: p = 0.0357). The overall comparison of tissues for FAD (506.5-581.5 nm) was also statistically significant (p < 0.0001), with two pairwise comparisons being statistically significant (Tumor vs. Muscle: p < 0.0001, Tumor vs. Bone: p = 0.0045, Bone vs. Muscle: p = 0.249). These statistically significant differences were maintained when stratifying tumor into metastatic carcinoma (N = 57) and meningioma (N = 17). TumorID differentiates tumor tissue from normal bone and normal muscle providing further clinical evidence of its efficacy as a tissue identification tool. Future studies should evaluate TumorID's ability to serve as an adjunctive tool for intraoperative assessment of surgical margins and surgical decision-making.

Combined fluorescence lifetime and surface topographical imaging of biological tissue

In this work a combined fluorescence lifetime and surface topographical imaging system is demonstrated. Based around a 126 × 192 time resolved single photon avalanche diode (SPAD) array operating in time correlated single-photon counting (TCSPC) mode, both the fluorescence lifetime and time of flight (ToF) can be calculated on a pixel by pixel basis. Initial tests on fluorescent samples show it is able to provide 4 mm resolution in distance and 0.4 ns resolution in lifetime. This combined modality has potential biomedical applications such as surgical guidance, endoscopy, and diagnostic imaging. The system is demonstrated on both ovine and human pulmonary tissue samples, where it offers excellent fluorescence lifetime contrast whilst also giving a measure of the distance to the sample surface.

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.

Robust Optical Detection of Ga3+ by a Rhodamine- and Coumarin-Based Proficient Probe: Theoretical Investigations and Biological Applications

The present investigation highlights a rhodamine-B- and coumarin-based efficient probe that selectively detects Ga3+ over other metal ions. The active pocket of the ligand for trapping the metal ions and the binding stoichiometry of its Ga3+ complex were discovered by single-crystal X-ray diffraction (SC-XRD) analysis. This binding stoichiometry was further confirmed in the solution state by mass spectrometry and Job's plot. The detection limit was found to be at the nanomolar level. Pyrophosphate being a well-known quencher could easily quench the fluorescence intensity of the RC in the presence of Ga3+ and reversibly recognize Ga3+ in the solution. The spiro ring opening of the ligand after Ga3+ insertion is proposed to be the principal mechanism for the turn-on fluorescence response. This ring opening was confirmed by SC-XRD data and nuclear magnetic resonance (NMR) titration experiments. Both ground- and excited-state calculations of the ligand and complex have been carried out to obtain information about their energy levels and to obtain the theoretical electronic spectra. Furthermore, the live-cell imaging of the probe only and the probe after the addition of Ga3+ have been carried out in HaCaT cells and satisfactory responses were observed. Interestingly, with the help of this probe, Ga3+ can be tracked inside the intracellular organelle such as lysosomes along with other regions of the cell. The article highlights a rhodamine-coumarin-based probe for the detection of Ga3+ over other metal ions with a nanomolar level detection limit. Structural characterization of the ligand and its Ga3+ complex was investigated by SC-XRD. Density functional theory (DFT) and time-dependent DFT (TD-DFT) studies were carried out to explore the excited-state energies and electronic spectra. The application of the probe for the detection of Ga3+ in live cells has been explored, and positive responses were observed.

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.

High-resolution multi-spectral snapshot 3D imaging with a SPAD array camera

Currently, mainstream light detection and ranging (LiDAR) systems usually involve a mechanical scanner component, which enables large-scale, high-resolution and multi-spectral imaging, but is difficult to assemble and has a larger system size. Furthermore, the mechanical wear on the moving parts of the scanner reduces its usage lifetime. Here, we propose a high-resolution scan-less multi-spectral three-dimensional (3D) imaging system, which improves the resolution with a four-times increase in the pixel number and can achieve multi-spectral imaging in a single snapshot. This system utilizes a specially designed multiple field-of-view (multi-FOV) system to separate four-wavelength echoes carrying depth and spectral reflectance information with predetermined temporal intervals, such that one single pixel of the SPAD array can sample four adjacent positions through the four channels' FOVs with subpixel offset. The positions and reflectivity are thus mapped to wavelengths in different time-bins. Our results show that the system can achieve high-resolution multi-spectral 3D imaging in a single exposure without scanning component. This scheme is the first to realize scan-less single-exposure high-resolution and multi-spectral imaging with a SPAD array sensor.

Self-sensing intelligent microrobots for noninvasive and wireless monitoring systems

Microrobots have garnered tremendous attention due to their small size, flexible movement, and potential for various in situ treatments. However, functional modification of microrobots has become crucial for their interaction with the environment, except for precise motion control. Here, a novel artificial intelligence (AI) microrobot is designed that can respond to changes in the external environment without an onboard energy supply and transmit signals wirelessly in real time. The AI microrobot can cooperate with external electromagnetic imaging equipment and enhance the local radiofrequency (RF) magnetic field to achieve a large penetration sensing depth and a high spatial resolution. The working ranges are determined by the structure of the sensor circuit, and the corresponding enhancement effect can be modulated by the conductivity and permittivity of the surrounding environment, reaching ~560 times at most. Under the control of an external magnetic field, the magnetic tail can actuate the microrobotic agent to move accurately, with great potential to realize in situ monitoring in different places in the human body, almost noninvasively, especially around potential diseases, which is of great significance for early disease discovery and accurate diagnosis. In addition, the compatible fabrication process can produce swarms of functional microrobots. The findings highlight the feasibility of the self-sensing AI microrobots for the development of in situ diagnosis or even treatment according to sensing signals.

Light-field tomographic fluorescence lifetime imaging microscopy

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging technique that enables the visualization of biological samples at the molecular level by measuring the fluorescence decay rate of fluorescent probes. This provides critical information about molecular interactions, environmental changes, and localization within biological systems. However, creating high-resolution lifetime maps using conventional FLIM systems can be challenging, as it often requires extensive scanning that can significantly lengthen acquisition times. This issue is further compounded in three-dimensional (3D) imaging because it demands additional scanning along the depth axis. To tackle this challenge, we developed a novel computational imaging technique called light field tomographic FLIM (LIFT-FLIM). Our approach allows for the acquisition of volumetric fluorescence lifetime images in a highly data-efficient manner, significantly reducing the number of scanning steps required compared to conventional point-scanning or line-scanning FLIM imagers. Moreover, LIFT-FLIM enables the measurement of high-dimensional data using low-dimensional detectors, which are typically low-cost and feature a higher temporal bandwidth. We demonstrated LIFT-FLIM using a linear single-photon avalanche diode array on various biological systems, showcasing unparalleled single-photon detection sensitivity. Additionally, we expanded the functionality of our method to spectral FLIM and demonstrated its application in high-content multiplexed imaging of lung organoids. LIFT-FLIM has the potential to open up new avenues in both basic and translational biomedical research.

Single-Shot Reconfigurable Femtosecond Imaging of Ultrafast Optical Dynamics

Understanding ultrafast dynamics in the femtosecond timescale plays a pivotal role in fundamental research and technology innovation. Spatiotemporal observation of those events in real-time requires imaging speeds greater than 1012 frames per second (fps), far beyond the fundamental speed limits of the ubiquitous semiconductor sensor technologies. In addition, a majority of femtosecond events are non-repeatable or difficult-to-repeat since they either work in a highly unstable nonlinear regime or require extreme or rare conditions to initiate. Therefore, the traditional pump-probe imaging approach fails since it heavily depends on precise event repetition. Single-shot ultrafast imaging emerges as the only solution; however, existing techniques cannot reach more than 15×1012 fps, and they only record an insufficient number of frames. Compressed ultrafast spectral photography (CUSP) is proposed to overcome these limitations. Here, CUSP's full design space is explored by manipulating the ultrashort optical pulse in the active illumination. Via parameter optimization, an extraordinarily fast frame rate of 219×1012 fps is achieved. This implementation of CUSP is also highly flexible, allowing various combinations of imaging speeds and numbers of frames (several hundred up to 1000) to be readily deployed in diverse scientific studies, such as laser-induced transient birefringence, self-focusing, and filaments in dielectric media.

Fluorescence microscopic platforms imaging mitochondrial abnormalities in neurodegenerative diseases

Neurodegenerative diseases (NDs) are progressive disorders that cause the degeneration of neurons. Mitochondrial dysfunction is a common symptom in NDs and plays a crucial role in neuronal loss. Mitochondrial abnormalities can be observed in the early stages of NDs and evolve throughout disease progression. Visualizing mitochondrial abnormalities can help understand ND progression and develop new therapeutic strategies. Fluorescence microscopy is a powerful tool for dynamically imaging mitochondria due to its high sensitivity and spatiotemporal resolution. This review discusses the relationship between mitochondrial dysfunction and ND progression, potential biomarkers for imaging dysfunctional mitochondria, advances in fluorescence microscopy for detecting organelles, the performance of fluorescence probes in visualizing ND-associated mitochondria, and the challenges and opportunities for developing new generations of fluorescence imaging platforms for monitoring mitochondria in NDs.

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner.

Reconstruction of fluorophore absorption and fluorescence lifetime using early photon mesoscopic fluorescence molecular tomography: a phantom study

SIGNIFICANCE

Fluorescence molecular lifetime tomography (FMLT) plays an increasingly important role in experimental oncology. The article presents and experimentally verifies an original method of mesoscopic time domain FMLT, based on an asymptotic approximation to the fluorescence source function, which is valid for early arriving photons.

AIM

The aim was to justify the efficiency of the method by experimental scanning and reconstruction of a phantom with a fluorophore. The experimental facility included the TCSPC system, the pulsed supercontinuum Fianium laser, and a three-channel fiber probe. Phantom scanning was done in mesoscopic regime for three-dimensional (3D) reflectance geometry.

APPROACH

The sensitivity functions were simulated with a Monte Carlo method. A compressed-sensing-like reconstruction algorithm was used to solve the inverse problem for the fluorescence parameter distribution function, which included the fluorophore absorption coefficient and fluorescence lifetime distributions. The distributions were separated directly in the time domain with the QR-factorization least square method.

RESULTS

3D tomograms of fluorescence parameters were obtained and analyzed using two strategies for the formation of measurement data arrays and sensitivity matrices. An algorithm is developed for the flexible choice of optimal strategy in view of attaining better reconstruction quality. Variants on how to improve the method are proposed, specifically, through stepped extraction and further use of a posteriori information about the object.

CONCLUSIONS

Even if measurement data are limited, the proposed method is capable of giving adequate reconstructions but their quality depends on available a priori (or a posteriori) information. Further research aims to improve the method by implementing the variants proposed.

Computational macroscopic lifetime imaging and concentration unmixing of autofluorescence

Single-pixel computational imaging can leverage highly sensitive detectors that concurrently acquire data across spectral and temporal domains. For molecular imaging, such methodology enables to collect rich intensity and lifetime multiplexed fluorescence datasets. Herein we report on the application of a single-pixel structured light-based platform for macroscopic imaging of tissue autofluorescence. The super-continuum visible excitation and hyperspectral single-pixel detection allow for parallel characterization of autofluorescence intensity and lifetime. Furthermore, we exploit a deep learning based data processing pipeline, to perform autofluorescence unmixing while yielding the autofluorophores' concentrations. The full scheme (setup and processing) is validated in silico and in vitro with clinically relevant autofluorophores flavin adenine dinucleotide, riboflavin, and protoporphyrin. The presented results demonstrate the potential of the methodology for macroscopically quantifying the intensity and lifetime of autofluorophores, with higher specificity for cases of mixed emissions, which are ubiquitous in autofluorescence and multiplexed in vivo imaging.

Label-Free Optical Spectroscopy for Early Detection of Oral Cancer

Oral cancer is the 16th most common cancer worldwide. It commonly arises from painless white or red plaques within the oral cavity. Clinical outcome is highly related to the stage when diagnosed. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Therefore, there is a need to develop a non-invasive cost-effective diagnostic technique to differentiate non-malignant and early-stage malignant lesions. Optical spectroscopy may provide an appropriate solution to facilitate early detection of these lesions. It has many advantages over traditional approaches including cost, speed, objectivity, sensitivity, painlessness, and ease-of use in clinical setting for real-time diagnosis. This review consists of a comprehensive overview of optical spectroscopy for oral cancer diagnosis, epidemiology, and recent improvements in this field for diagnostic purposes. It summarizes major developments in label-free optical spectroscopy, including Raman, fluorescence, and diffuse reflectance spectroscopy during recent years. Among the wide range of optical techniques available, we chose these three for this review because they have the ability to provide biochemical information and show great potential for real-time deep-tissue point-based in vivo analysis. This review also highlights the importance of saliva-based potential biomarkers for non-invasive early-stage diagnosis. It concludes with the discussion on the scope of development and future demands from a clinical point of view.

Deep learning-assisted co-registration of full-spectral autofluorescence lifetime microscopic images with H&E-stained histology images

Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsupervised image-to-image translation network that significantly improves the success of the co-registration using a conventional optimisation-based regression network, applicable to autofluorescence lifetime images at different emission wavelengths. A preliminary blind comparison by experienced researchers shows the superiority of our method on co-registration. The results also indicate that the approach is applicable to various image formats, like fluorescence in-tensity images. With the registration, stitching outcomes illustrate the distinct differences of the spectral lifetime across an unstained tissue, enabling macro-level rapid visual identification of lung cancer and cellular-level characterisation of cell variants and common types. The approach could be effortlessly extended to lifetime images beyond this range and other staining technologies.

Compressed fluorescence lifetime imaging via combined TV-based and deep priors

Compressed fluorescence lifetime imaging (Compressed-FLIM) is a novel Snapshot compressive imaging (SCI) method for single-shot widefield FLIM. This approach has the advantages of high temporal resolution and deep frame sequences, allowing for the analysis of FLIM signals that follow complex decay models. However, the precision of Compressed-FLIM is limited by reconstruction algorithms. To improve the reconstruction accuracy of Compressed-FLIM in dealing with large-scale FLIM problem, we developed a more effective combined prior model 3DTGp V_net, based on the Plug and Play (PnP) framework. Extensive numerical simulations indicate the proposed method eliminates reconstruction artifacts caused by the Deep denoiser networks. Moreover, it improves the reconstructed accuracy by around 4dB (peak signal-to-noise ratio; PSNR) over the state-of-the-art TV+FFDNet in test data sets. We conducted the single-shot FLIM experiment with different Rhodamine reagents and the results show that in practice, the proposed algorithm has promising reconstruction performance and more negligible lifetime bias.

Fluorescence lifetime imaging and electron microscopy: a correlative approach

Fluorescence lifetime imaging microscopy (FLIM) allows the characterization of cellular metabolism by quantifying the rate of free and unbound nicotinamide adenine dinucleotide hydrogen (NADH). This study delineates the correlative imaging of cells with FLIM and electron microscopy (EM). Human fibroblasts were cultivated in a microscopy slide bearing a coordinate system and FLIM measurement was conducted. Following chemical fixation, embedding in Epon and cutting with an ultramicrotome, tomograms of selected cells were acquired with a scanning transmission electron microscope (STEM). Correlative imaging of antimycin A-treated fibroblasts shows a decrease in fluorescence lifetime as well as swollen mitochondria with large cavities in STEM tomography. To our knowledge, this is the first correlative FLIM and EM workflow. Combining the high sensitivity of FLIM with the high spatial resolution of EM could boost the research of pathophysiological processes involving cell metabolism, such as cancer, neurodegenerative disorders, and viral infection.

Spatial resolution improved fluorescence lifetime imaging via deep learning

We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from fluorescence lifetime imaging (FLIM) systems. We first proposed a theoretical method for training neural networks to generate massive semi-synthetic FLIM data with various cellular morphologies, a sizeable dynamic lifetime range, and complex decay components. We then developed a degrading model to obtain LR-HR pairs and created a hybrid neural network, the spatial resolution improved FLIM net (SRI-FLIMnet) to simultaneously estimate fluorescence lifetimes and realize the nonlinear transformation from LR to HR images. The evaluative results demonstrate SRI-FLIMnet's superior performance in reconstructing spatial information from limited pixel resolution. We also verified SRI-FLIMnet using experimental images of bacterial infected mouse raw macrophage cells. Results show that the proposed data generation method and SRI-FLIMnet efficiently achieve superior spatial resolution for FLIM applications. Our study provides a solution for fast obtaining HR FLIM images.

Picosecond-resolved fluorescence resonance energy transfer (FRET) in diffuse reflectance spectroscopy explores biologically relevant hidden molecular contacts in a non-invasive way

The potentiality of Förster resonance energy transfer (FRET) for studying molecular interactions inside biological tissues with improved spatial (Angström) and temporal (picosecond) resolution is well established. On the other hand, the efficacy of diffuse reflectance spectroscopy (DRS) that uses optical radiation in order to determine physiological parameters including haemoglobin, and oxygen saturation is well known. Here we have made an attempt to combine diffuse reflectance spectroscopy (DRS) with picosecond-resolved FRET in order to show improvement in the exploration of molecular contacts in biological tissue models. We define the technique as ultrafast time-resolved diffuse reflectance spectroscopy (UTRDRS). The illuminated photon of the fluorophore from the surface of the tissue-mimicking layers carries the hidden information of the molecular contact. In order to investigate the validation of the Kubelka-Munk (KM) formulism for the developed UTRDRS technique in tissue phantoms, we have studied the propagation of incandescent and picosecond-laser light through several layers of cellulose membranes. While picosecond-resolved FRET in the diffuse reflected light confirms the hidden nano-contact (4.6 nm) of two different dye layers (8-anilino-1-naphthalenesulfonic acid and Nile blue), high-resolution optical microscopy on the cross-section of the layers reveals the proximity and contacts of the layers with limited spatial resolution (∼300 nm). We have also investigated two biologically relevant molecules, namely carboxyfluorescein and haemoglobin, in tissue phantom layers in order to show the efficacy of the UTRDRS technique. Overall, our studies based on UTRDRS in tissue mimicking layers may have potential applications in non-invasive biomedical diagnosis for patients suffering from skin diseases.

Fluorescence lifetime needle optical biopsy discriminates hepatocellular carcinoma

This work presents results of in vivo and in situ measurements of hepatocellular carcinoma by a developed optical biopsy system. Here, we describe the technical details of the implementation of fluorescence lifetime and diffuse reflectance measurements by the system, equipped with an original needle optical probe, compatible with the 17.5G biopsy needle standard. The fluorescence lifetime measurements observed by the setup were verified in fresh solutions of NADH and FAD⁺⁺, and then applied in a murine model for the characterisation of inoculated hepatocellular carcinoma (HCC) and adjacent liver tissue. The technique, applied in vivo and in situ and supplemented by measurements of blood oxygen saturation, made it possible to reveal statistically significant transformation in the set of measured parameters linked with the cellular pools of NADH and NADPH. In the animal model, we demonstrate that the characteristic changes in registered fluorescent parameters can be used to reliably distinguish the HCC tissue, liver tissue in the control, and the metabolically changed liver tissues of animals with the developed HCC tumour. For further transition to clinical applications, the optical biopsy system was tested during the routing procedure of the PNB in humans with suspected HCC. The comparison of the data from murine and human HCC tissues suggests that the tested animal model is generally representative in the sense of the registered fluorescence lifetime parameters, while statistically significant differences between their absolute values can still be observed.

Aberration Correction to Optimize the Performance of Two-Photon Fluorescence Microscopy Using the Genetic Algorithm

Due to less light scattering and a better signal-to-noise ratio in deep imaging, two-photon fluorescence microscopy (TPFM) has been widely used in biomedical photonics since its advent. However, optical aberrations degrade the performance of TPFM in terms of the signal intensity and the imaging depth and therefore restrict its application. Here, we introduce adaptive optics based on the genetic algorithm to detect the distorted wavefront of the excitation laser beam and then perform aberration correction to optimize the performance of TPFM. By using a spatial light modulator as the wavefront controller, the correction phase is obtained through a signal feedback loop and a process of natural selection. The experimental results show that the signal intensity and imaging depth of TPFM are improved after aberration correction. Finally, the method was applied to two-photon fluorescence lifetime imaging, which helps to improve the signal-to-noise ratio and the accuracy of lifetime analysis. Furthermore, the method can also be implemented in other experiments, such as three-photon microscopy, light-sheet microscopy, and super-resolution microscopy.

Compressed sensing in fluorescence microscopy

Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy.

Multi-Scale Aggregated-Dilation Network for ex-vivo Lung Cancer Detection with Fluorescence Lifetime Imaging Endomicroscopy

Multi-scale architectures at a granular level are characterised by separating input features into groups and applying multi-scale feature extractions to the split input features, and thus the correlations among the input features as global information are no longer retained. Moreover, they usually require more input features due to the separation, and therefore, more complexity is introduced. To retain the global information while utilising the advantages of feature-level hierarchical multi-scale architectures, we propose a multi-scale aggregated-dilation architecture (MSAD) to perform hierarchical fusion of features at a layer level, with the integration of dilated convolutions to overcome these issues. To evaluate the model, we integrate it into ResNet, and apply it to a unique dataset, containing over 60,000 fluorescence lifetime endomicroscopic images (FLIM) collected on ex-vivo lung normal/cancerous tissues from 14 patients, by a custom fibre-based FLIM system. To evaluate the performance of our proposal, we use accuracy, precision, recall, and AUC. We first compare our MSAD model with eight networks achieving a superiority over 6%. To illustrate the advantages and disadvantages of multi-scale architectures at layer and feature-level, we thoroughly compare our MSAD model with the state-of-the-art feature-level multiscale network, namely Res2Net, in terms of parameters, scales, and effective convolutions.

Deep Learning in Biomedical Optics

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

Flavor classification and year prediction of Chinese Baijiu by time-resolved fluorescence

Baijiu is a traditional and popular Chinese liquor with enormous sale potential, which is affected by factors such as flavor and storage time. Chinese Baijiu is a complex and transparent mixture that makes analyzing difficult. The utility of time-resolved fluorescence helped to develop a new method to analyze Baijiu. Forty-two Baijiu samples among six brands with three flavors were prepared, and their fluorescence spectra were analyzed with an excitation light of 374.2 nm. Hexanoic acid and ethyl butyrate were found to have an impact on Baijiu fluorescence. The properties of lifetimes in Baijiu were investigated, and its mechanism was studied by calculations through density function theory. Using parameters of fluorescence lifetimes, Baijiu samples were classified according to their flavors. Additionally, the correlations between fluorescence lifetimes and storage time of Baijiu in Luzhou flavor were obtained, leading to a reliable and efficient method to establish the year forecast model of Chinese Baijiu with a mean error of 2.79 months. It also provides an important reference of the utility of time-resolved fluorescence for quantitative research of multi-component systems.

Real-time pixelwise phasor analysis for video-rate two-photon fluorescence lifetime imaging microscopy

Two-photon fluorescence lifetime imaging microscopy (FLIM) is a widely used technique in biomedical optical imaging. Presently, many two-photon time-domain FLIM setups are limited by long acquisition and postprocessing times that decrease data throughput and inhibit the ability to image fast sub-second processes. Here, we present a versatile two-photon FLIM setup capable of video-rate (up to 25 fps) imaging with graphics processing unit (GPU)-accelerated pixelwise phasor analysis displayed and saved simultaneously with acquisition. The system uses an analog output photomultiplier tube in conjunction with 12-bit digitization at 3.2 GHz to overcome the limited maximum acceptable photon rate associated with the photon counting electronics in many FLIM systems. This allows for higher throughput FLIM acquisition and analysis, and additionally enables the user to assess sample fluorescence lifetime in real-time. We further explore the capabilities of the system to examine the kinetics of Rhodamine B uptake by human breast cancer cells and characterize the effect of pixel dwell time on the reduced nicotinamide adenine dinucleotide and reduced nicotinamide adenine dinucleotide phosphate (NAD(P)H) autofluorescence lifetime estimation accuracy.

Tuning the Surface Chemistry of Second-Harmonic-Active Lithium Niobate Nanoprobes Using a Silanol-Alcohol Condensation Reaction

The surface functionalization of nanoparticles (NPs) is of great interest for improving the use of NPs in, for example, therapeutic and diagnostic applications. The conjugation of specific molecules with NPs through the formation of covalent linkages is often sought to provide a high degree of colloidal stability and biocompatibility, as well as to provide functional groups for further surface modification. NPs of lithium niobate (LiNbO₃) have been explored for use in second-harmonic-generation (SHG)-based bioimaging, expanding the applications of SHG-based microscopy techniques. The efficient use of SHG-active LiNbO₃ NPs as probes will, however, require the functionalization of their surfaces with molecular reagents such as polyethylene glycol and fluorescent molecules to enhance their colloidal and chemical stability and to enable a correlative imaging platform. Herein, we demonstrate the surface functionalization of LiNbO₃ NPs through the covalent attachment of alcohol-based reagents through a silanol-alcohol condensation reaction. Alcohol-based reagents are widely available and can have a range of terminal functional groups such as carboxylic acids, amines, and aldehydes. Attaching these molecules to NPs through the silanol-alcohol condensation reaction could diversify the reagents available to modify NPs, but this reaction pathway must first be established as a viable route to modifying NPs. This study focuses on the attachment of a linear alcohol functionalized with carboxylic acid and its use as a reactive group to further tune the surface chemistry of LiNbO₃ NPs. These carboxylic acid groups were reacted to covalently attach other molecules to the NPs using copper-free click chemistry. This derivatization of the NPs provided a means to covalently attach polyethylene glycols and fluorescent probes to the NPs, reducing NP aggregation and enabling multimodal tracking of SHG nanoprobes, respectively. This extension of the silanol-alcohol condensation reaction to functionalize the surfaces of LiNbO₃ NPs can be extended to other types of nanoprobes for use in bioimaging, biosensing, and photodynamic therapies.

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.

High-speed compressed-sensing fluorescence lifetime imaging microscopy of live cells

We present high-resolution, high-speed fluorescence lifetime imaging microscopy (FLIM) of live cells based on a compressed sensing scheme. By leveraging the compressibility of biological scenes in a specific domain, we simultaneously record the time-lapse fluorescence decay upon pulsed laser excitation within a large field of view. The resultant system, referred to as compressed FLIM, can acquire a widefield fluorescence lifetime image within a single camera exposure, eliminating the motion artifact and minimizing the photobleaching and phototoxicity. The imaging speed, limited only by the readout speed of the camera, is up to 100 Hz. We demonstrated the utility of compressed FLIM in imaging various transient dynamics at the microscopic scale.

Extracellular pH affects the fluorescence lifetimes of metabolic co-factors

SIGNIFICANCE

Autofluorescence measurements of the metabolic cofactors NADH and flavin adenine dinucleotide (FAD) provide a label-free method to quantify cellular metabolism. However, the effect of extracellular pH on flavin lifetimes is currently unknown.

AIM

To quantify the relationship between extracellular pH and the fluorescence lifetimes of FAD, flavin mononucleotide (FMN), and reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H].

APPROACH

Human breast cancer (BT474) and HeLa cells were placed in pH-adjusted media. Images of an intracellular pH indicator or endogenous fluorescence were acquired using two-photon fluorescence lifetime imaging. Fluorescence lifetimes of FAD and FMN in solutions were quantified over the same pH range.

RESULTS

The relationship between intracellular and extracellular pH was linear in both cell lines. Between extracellular pH 4 to 9, FAD mean lifetimes increased with increasing pH. NAD(P)H mean lifetimes decreased with increasing pH between extracellular pH 5 to 9. The relationship between NAD(P)H lifetime and extracellular pH differed between the two cell lines. Fluorescence lifetimes of FAD, FAD-cholesterol oxidase, and FMN solutions decreased, showed no trend, and showed no trend, respectively, with increasing pH.

CONCLUSIONS

Changes in endogenous fluorescence lifetimes with extracellular pH are mostly due to indirect changes within the cell rather than direct pH quenching of the endogenous molecules.

Fluorescence lifetime imaging is able to recognize different hematopoietic precursors in unstained routine bone marrow films

Fluorescence lifetime imaging (FLIM) has been used in living cells to measure metabolic activity and demonstrate cell differentiation. The aim of this study was to investigate whether the FLIM technique could be able to demonstrate cell maturation during myelopoiesis and erythropoiesis in unlabeled routine bone marrow (BM) preparations. Air-dried, unstained smears of BM aspiration samples of 32 patients without BM disease and a normal morphology on May-Grünwald-Giemsa (MGG) stained smears entered the study. FLIM images were captured with a Zeiss LSM 780 NLO multiphoton microscope equipped with a Becker & Hickl SPC-830 TCSPC FLIM module and HPM-100-40 hybrid detector. The samples were irradiated by two-photon excitation at 800 nm with a titanium-sapphire laser of the LSM 780 NLO. FLIM images were compared with those obtained by autofluorescence high resolution imaging. FLIM images of unstained smears were highly contrasted. Different cell types could be easily recognized as they were similar to those seen in MGG stained preparations. Cytoplasm of cells from the erythroid lineage revealed relatively short fluorescence lifetimes due to the presence of hemoglobin, and therefore could easily be distinguished from granulocytic precursors. Nuclear fluorescence lifetimes of all cell types were higher than those of the corresponding cytoplasm. So, FLIM of unstained BM smears obtained under routine real-life conditions permits an easy identification of BM cells, by highlighting differences of their physicochemical properties.

Combined autofluorescence and diffuse reflectance for brain tumour surgical guidance: initial ex vivo study results

This ex vivo study was conducted to assess the potential of using a fibre optic probe system based on autofluorescence and diffuse reflectance for tissue differentiation in the brain. A total of 180 optical measurements were acquired from 28 brain specimens (five patients) with eight excitation and emission wavelengths spanning from 300 to 700 nm. Partial least square-linear discriminant analysis (PLS-LDA) was used for tissue discrimination. Leave-one-out cross validation (LOOCV) was then used to evaluate the performance of the classification model. Grey matter was differentiated from tumour tissue with sensitivity of 89.3% and specificity of 92.5%. The variable importance in projection (VIP) derived from the PLS regression was applied to wavelengths selection, and identified the biochemical sources of the detected signals. The initial results of the study were promising and point the way towards a cost-effective, miniaturized hand-held probe for real time and label-free surgical guidance.