Label-Free Optical Metabolic Imaging in Cells and Tissues

More than double the fun with two-photon excitation microscopy

For generations researchers have been observing the dynamic processes of life through the lens of a microscope. This has offered tremendous insights into biological phenomena that span multiple orders of time- and length-scales ranging from the pure magic of molecular reorganization at the membrane of immune cells, to cell migration and differentiation during development or wound healing. Standard fluorescence microscopy techniques offer glimpses at such processes in vitro, however, when applied in intact systems, they are challenged by reduced signal strengths and signal-to-noise ratios that result from deeper imaging. As a remedy, two-photon excitation (TPE) microscopy takes a special place, because it allows us to investigate processes in vivo, in their natural environment, even in a living animal. Here, we review the fundamental principles underlying TPE aimed at basic and advanced microscopy users interested in adopting TPE for intravital imaging. We focus on applications in neurobiology, present current trends towards faster, wider and deeper imaging, discuss the combination with photon counting technologies for metabolic imaging and spectroscopy, as well as highlight outstanding issues and drawbacks in development and application of these methodologies.

Fluorescence spectral analysis and logistic regression modeling for diagnosing basal cell carcinoma on head and neck

The optical fluorescence method is distinguished by key features such as non-invasiveness, high sensitivity, and resolution, which are superior to traditional diagnostic approaches. Unlike histopathological examinations and biochemical analyses, optical diagnostic methods obviate the need for tissue sampling, enabling the analysis of virtually unlimited material. The research aims to examine the effectiveness of emission spectra analysis in the diagnosis of basal cell carcinoma (BCC) of the scalp and neck. The analysis was based on data provided by Specialized Hospital No. 2 in Bytom comprising a study sample of 10 patients. For each patient, fluorescence emission spectra were recorded from each of 512 points along a 5 mm line. The results obtained from the histopathological examination, the analysis and morphological evaluation of the tissue, and the diagnosis through microscopic observation were used to define a dichotomous variable (presence or absence of a cancerous lesion), adopted in the study as the modeled variable. The next step of the presented study involved constructing a logistic regression model for identifying cancerous lesions depending on the biochemical indicator's relative fluorescence value (RFV) and emission wavelength (ELW) within the 620 nm to 730 nm range. This wavelength range is often used in fluorescence diagnostics to detect various pathologies, including cancerous lesions. The resulting binary logistic regression model, logit(p)=-33.17+0.04ELW+0.01RFV, indicates a statistically significant relationship between wavelength and relative fluorescence values with the probability of detecting cancer. The estimated model exhibits a good fit and high predictive accuracy. The overall model accuracy is 84.8 %, with the correct classification rates at approximately 96 % for healthy individuals and 74 % for individuals with cancer. These findings underscore the potential of photodynamic diagnostics in cancer detection and monitoring.

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the β-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

Emerging Functional Connections Between Metabolism and Epigenetic Remodeling in Neural Differentiation

Stem cells possess extraordinary capacities for self-renewal and differentiation, making them highly valuable in regenerative medicine. Among these, neural stem cells (NSCs) play a fundamental role in neural development and repair processes. NSC characteristics and fate are intricately regulated by the microenvironment and intracellular signaling. Interestingly, metabolism plays a pivotal role in orchestrating the epigenome dynamics during neural differentiation, facilitating the transition from undifferentiated NSC to specialized neuronal and glial cell types. This intricate interplay between metabolism and the epigenome is essential for precisely regulating gene expression patterns and ensuring proper neural development. This review highlights the mechanisms behind metabolic regulation of NSC fate and their connections with epigenetic regulation to shape transcriptional programs of stemness and neural differentiation. A comprehensive understanding of these molecular gears appears fundamental for translational applications in regenerative medicine and personalized therapies for neurological conditions.

Multiphoton Imaging of Maturation in Tissue Engineering

Donor cell-specific tissue-engineered (TE) implants are a promising therapy for personalized treatment of cardiovascular diseases, but current development protocols lack a stable longitudinal assessment of tissue development at subcellular resolution. As a first step toward such an assessment approach, in this study we establish a generalized labeling and imaging protocol to obtain quantified maturation parameters of TE constructs in three dimensions (3D) without the need of histological slicing, thus leaving the tissue intact. Focusing on intracellular matrix (ICM) and extracellular matrix (ECM) networks, multiphoton laser scanning microscopy (MPLSM) was used to investigate TE patches of different conditioning durations of up to 21 days. We show here that with a straightforward labeling procedure of whole-mount samples (so without slicing into thin histological sections), followed by an easy-to-use multiphoton imaging process, we obtained high-quality images of the tissue in 3D at various time points during development. The stacks of images could then be further analyzed to visualize and quantify the volume of cell coverage as well as the volume fraction and network of structural proteins. We showed that collagen and alpha-smooth muscle actin (α-SMA) volume fractions increased as normalized to full tissue volume and proportional to the cell count, with a converging trend to the final density of (4.0% ± 0.6%) and (7.6% ± 0.7%), respectively. The image analysis of ICM and ECM revealed a developing and widely branched interconnected matrix. We are currently working on the second step, that is, to integrate MPLSM endoscopy into a dynamic bioreactor system to monitor the maturation of intact TE constructs over time, thus without the need to take them out.

Beyond conventional wisdom: unveiling quantitative insights in fluorescence lifetime imaging via realistic simulation of biological systems

Fluorescence lifetime imaging microscopy (FLIM) and photometry (FLiP) are illuminating the dynamics of biological signals. Because fluorescence lifetime is an intensive property of a fluorophore that is insensitive to sensor expression levels, it excels over fluorescence intensity measurements by allowing comparison across animals, over chronic time periods, and quantitation of the absolute levels of biological signals. However, the insensitivity of lifetime to sensor expression level does not always hold true in biological experiments where autofluorescence, ambient light, dark currents and afterpulses of the detectors are present. To quantitatively evaluate the potential and limitations of fluorescence lifetime measurements, we introduce FLiSimBA, a flexible platform enabling realistic F luorescence Li fetime Sim ulation for B iological A pplications. FLiSimBA accurately recapitulates experimental data and provides quantitative analyses. Using FLiSimBA, we determine the photons required for minimum detectable differences in lifetime and quantify the impact of hardware innovation. Furthermore, we challenge the conventional view that fluorescence lifetime is insensitive to sensor expression levels and define the conditions in which sensor express levels do not result in statistically significant difference in biological experiments. Thus, we introduce an adaptable simulation tool that allows systematic exploration of parameters to define experimental advantages and limitations in biological applications. Moreover, we provide a statistical framework and quantitative insights into the impact of key experimental parameters on signal-to-noise ratio and fluorescence lifetime responses. Our tool and results will enable the growing community of FLIM users and developers to optimize FLIM experiments, expose limitations, and identify opportunities for future innovation of fluorescence lifetime technologies.

UVA Hyperspectral Light-Sheet Microscopy for Volumetric Metabolic Imaging: Application to Preimplantation Embryo Development

Cellular metabolism is a key regulator of energetics, cell growth, regeneration, and homeostasis. Spatially mapping the heterogeneity of cellular metabolic activity is of great importance for unraveling the overall cell and tissue health. In this regard, imaging the endogenous metabolic cofactors, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), with subcellular resolution and in a noninvasive manner would be useful to determine tissue and cell viability in a clinical environment, but practical use is limited by current imaging techniques. In this paper, we demonstrate the use of phasor-based hyperspectral light-sheet (HS-LS) microscopy using a single UVA excitation wavelength as a route to mapping metabolism in three dimensions. We show that excitation solely at a UVA wavelength of 375 nm can simultaneously excite NAD(P)H and FAD autofluorescence, while their relative contributions can be readily quantified using a hardware-based spectral phasor analysis. We demonstrate the potential of our HS-LS system by capturing dynamic changes in metabolic activity during preimplantation embryo development. To validate our approach, we delineate metabolic changes during preimplantation embryo development from volumetric maps of metabolic activity. Importantly, our approach overcomes the need for multiple excitation wavelengths, two-photon imaging, or significant postprocessing of data, paving the way toward clinical translation, such as in situ, noninvasive assessment of embryo viability.

Restoration of metabolic functional metrics from label-free, two-photon human tissue images using multiscale deep-learning-based denoising algorithms

Significance

Label-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these images severely complicate the extraction of biologically useful information.

Aim

We aim to employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-signal-to-noise ratio (SNR), TPEF images.

Approach

TPEF images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used to assess the impact of various denoising models, preprocessing methods, and data on metrics of image quality and the recovery of six metrics of metabolic function from the images relative to ground truth images.

Results

Optimized recovery of the redox ratio and mitochondrial organization is achieved using a novel algorithm based on deep denoising in the wavelet transform domain. This algorithm also leads to significant improvements in peak-SNR (PSNR) and structural similarity index measure (SSIM) for all images. Interestingly, other models yield even higher PSNR and SSIM improvements, but they are not optimal for recovery of metabolic function metrics.

Conclusions

Denoising algorithms can recover diagnostically useful information from low SNR label-free TPEF images and will be useful for the clinical translation of such imaging.

Formalin fixation and paraffin embedding interfere with the preservation of optical metabolic assessments based on endogenous NAD(P)H and FAD two-photon excited fluorescence

Endogenous NAD(P)H and FAD two-photon excited fluorescence (TPEF) images provide functional metabolic information with high spatial resolution for a wide range of living specimens. Preservation of metabolic function optical metrics upon fixation would facilitate studies which assess the impact of metabolic changes in the context of numerous diseases. However, robust assessments of the impact of formalin fixation, paraffin embedding, and sectioning on the preservation of optical metabolic readouts are lacking. Here, we evaluate intensity and lifetime images at excitation/emission settings optimized for NAD(P)H and FAD TPEF detection from freshly excised murine oral epithelia and corresponding bulk and sectioned fixed tissues. We find that fixation impacts the overall intensity as well as the intensity fluctuations of the images acquired. Accordingly, the depth-dependent variations of the optical redox ratio (defined as FAD/(NAD(P)H + FAD)) across squamous epithelia are not preserved following fixation. This is consistent with significant changes in the 755 nm excited spectra, which reveal broadening upon fixation and additional distortions upon paraffin embedding and sectioning. Analysis of fluorescence lifetime images acquired for excitation/emission settings optimized for NAD(P)H TPEF detection indicate that fixation alters the long lifetime of the observed fluorescence and the long lifetime intensity fraction. These parameters as well as the short TPEF lifetime are significantly modified upon embedding and sectioning. Thus, our studies highlight that the autofluorescence products formed during formalin fixation, paraffin embedding and sectioning overlap highly with NAD(P)H and FAD emission and limit the potential to utilize such tissues to assess metabolic activity.

Non-invasive Imaging Techniques for Monitoring Cellular Response to Treatment in Stable Vitiligo

Punch grafting procedures, where small pieces of normal skin are transplanted into stable vitiligo patches, results in repigmentation in only half of patients treated, yet the factors that determine whether a patient responds to treatment or not are still unknown. Reflectance confocal microscopy (RCM) is adept at visualizing melanocyte migration and epidermal changes over large areas while multiphoton microscopy (MPM) can capture metabolic changes in keratinocytes. With the overall goal of identifying optical biomarkers for early treatment response, we followed 12 vitiligo lesions undergoing punch grafting. Dendritic melanocytes adjacent to the graft site were observed before clinical evidence of repigmentation in treatment responsive patients but not in treatment non-responsive patients, suggesting that the early visualization of melanocytes is indicative of a therapeutic response. Keratinocyte metabolic changes in vitiligo skin adjacent to the graft site also correlated with treatment response, indicating that a keratinocyte microenvironment that more closely resembles normal skin is more hospitable for migrating melanocytes. Taken together, these studies suggest that successful melanocyte transplantation requires both the introduction of new melanocytes and modulation of the local tissue microenvironment.

Formalin fixation and paraffin embedding interfere with preservation of optical metabolic assessments based on endogenous NAD(P)H and FAD two photon excited fluorescence

Endogenous NAD(P)H and FAD two-photon excited fluorescence (TPEF) images provide functional metabolic information with high spatial resolution for a wide range of living specimens. Preservation of metabolic function optical metrics upon fixation would facilitate studies which assess the impact of metabolic changes in the context of numerous diseases. However, robust assessments of the impact of formalin fixation, paraffin embedding, and sectioning on the preservation of optical metabolic readouts are lacking. Here, we evaluate intensity and lifetime images at excitation/emission settings optimized for NAD(P)H and FAD TPEF detection from freshly excised murine oral epithelia and corresponding bulk and sectioned fixed tissues. We find that fixation impacts the overall intensity as well as the intensity fluctuations of the images acquired. Accordingly, the depth-dependent variations of the optical redox ratio (defined as FAD/(NAD(P)H + FAD)) across squamous epithelia are not preserved following fixation. This is consistent with significant changes in the 755 nm excited spectra, which reveal broadening upon fixation and additional distortions upon paraffin embedding and sectioning. Analysis of fluorescence lifetime images acquired for excitation/emission settings optimized for NAD(P)H TPEF detection indicate that fixation alters the long lifetime of the observed fluorescence and the long lifetime intensity fraction. These parameters as well as the short TPEF lifetime are significantly modified upon embedding and sectioning. Thus, our studies highlight that the autofluorescence products formed during formalin fixation, paraffin embedding and sectioning overlap highly with NAD(P)H and FAD emission and limit the potential to utilize such tissues to assess metabolic activity.

Restoration of metabolic functional metrics from label-free, two-photon cervical tissue images using multiscale deep-learning-based denoising algorithms

Label-free, two-photon imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, this modality suffers from low signal arising from limitations imposed by the maximum permissible dose of illumination and the need for rapid image acquisition to avoid motion artifacts. Recently, deep learning methods have been developed to facilitate the extraction of quantitative information from such images. Here, we employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-SNR, two-photon images. Two-photon excited fluorescence (TPEF) images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used. We assess the impact of the specific denoising model, loss function, data transformation, and training dataset on established metrics of image restoration when comparing denoised single frame images with corresponding six frame averages, considered as the ground truth. We further assess the restoration accuracy of six metrics of metabolic function from the denoised images relative to ground truth images. Using a novel algorithm based on deep denoising in the wavelet transform domain, we demonstrate optimal recovery of metabolic function metrics. Our results highlight the promise of denoising algorithms to recover diagnostically useful information from low SNR label-free two-photon images and their potential importance in the clinical translation of such imaging.