Multiphoton Imaging Research Articles

Repeated Measurement of Microglia-Dendritic Spine Interactions Using Multi-Photon Imaging.

In recent decades, mounting evidence has shown that microglia play a vital role in maintaining synapses throughout life. This maintenance is done via numerous microglial processes, which are long, thin, and highly motile protrusions from the cell body that monitor their environment. However, due to the brevity of the contacts and the potentially transient nature of synaptic structures, establishing the underlying dynamics of this relationship has proven difficult. This article describes a method of using rapidly acquired multiphoton microscopy images to track microglial dynamics and microglia:synapse interactions and the fate of the synaptic structures following those interactions. First, we detail a method for capturing multiphoton images at 1-min intervals for approximately 1 hr and how that process can be done at multiple time points. We then discuss how best to prevent and account for any drifting of the region of interest that can occur during the imaging session and how to remove excessive background noise from those images. Finally, we detail the annotation process for dendritic spines and microglial processes using plugins in MATLAB and Fiji, respectively. These semi-automated plugins allow tracking of individual cell structures, even if both microglia and neurons are imaged in the same fluorescent channel. This protocol presents a method of tracking both microglial dynamics and synaptic structures, in the same animal, at multiple time points, giving the user information on process speed, branching, tip size, location, and dwell time, as well as any dendritic spine gains, losses, and size changes. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Rapid multiphoton image capture Basic Protocol 2: Image preparation using MATLAB and Fiji Basic Protocol 3: Dendritic spine and microglial processes annotation using ScanImage and TrackMate.

TOF MLEM Adaptation for the Total-Body J-PET With a Realistic Analytical System Response Matrix

We report a study of the original image reconstruction algorithm based on the time-of-flight maximum likelihood expectation maximisation (TOF MLEM), developed for the total-body (TB) Jagiellonian PET (J-PET) scanners. The method is applicable to generic cylindrical or modular multi-layer layouts and is extendable to multi-photon imaging. The system response matrix (SRM) is represented as a set of analytical functions, uniquely defined for each pair of plastic scintillator strips used for the detection. A realistic resolution model (RM) in detector space is derived from fitting the Monte Carlo simulated emissions and detections of annihilation photons on oblique transverse planes. Additional kernels embedded in SRM account for TOF, parallax effect and axial smearing. The algorithm was tested on datasets, simulated in GATE for the NEMA IEC and static XCAT phantoms inside a 24-module 2-layer TB J-PET. Compared to the reference TOF MLEM with none or a shift-invariant RM, an improvement was observed, as evaluated by the analysis of image quality, difference images and ground truth metrics. We also reconstructed the data with additive contributions, pre-filtered geometrically and with non-TOF scatter correction applied. Despite some deterioration, the obtained results still capitalise on the realistic RM with better edge preservation and superior ground truth metrics. The envisioned prospects of the TOF MLEM with analytical SRM include its application in multi-photon imaging and further upgrade to account for the non-collinearity, positron range and other factors.

Immunomodulation under the lens of real-time in vivo imaging.

Modulation of cells and molecules of the immune system not only represents a major opportunity to treat a variety of diseases including infections, cancer, autoimmune, and inflammatory disorders but could also help understand the intricacies of immune responses. A detailed mechanistic understanding of how a specific immune intervention may provide clinical benefit is essential for the rational design of efficient immunomodulators. Visualizing the impact of immunomodulation in real-time and in vivo has emerged as an important approach to achieve this goal. In this review, we aim to illustrate how multiphoton intravital imaging has helped clarify the mode of action of immunomodulatory strategies such as antibodies or cell therapies. We also discuss how optogenetics combined with imaging will further help manipulate and precisely understand immunomodulatory pathways. Combined with other single-cell technologies, in vivo dynamic imaging has therefore a major potential for guiding preclinical development of immunomodulatory drugs.

NeuroWRAP: integrating, validating, and sharing neurodata analysis workflows.

Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP (www.neurowrap.org), which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.

Probing the deep brain: Enhanced multi-photon imaging by aggregation-induced emission luminogens via nanocrystallization

The latest requirement of optical imaging is to visualize vital, thick, and complex biological tissues in real-time and minimum-invasive mode. Among diverse available diagnostic tools, multi-photon imaging (MPI) with long wavelength excitation and exceptional lateral resolution has been a perfect option. However, the existing palette of MPI contrast agents either suffers from sub-optimal imaging performance or non-negligible risk in biosafety, exploration of suitable probes is thus of great significance. To address these concerns, in this work, pure organic nanocrystals (NCs) were developed based on aggregation-induced emission luminogens (AIEgens) (DPTA-BT) with an ultrasound-assisted method. Compared to the amorphous DPTA-BT nanoparticles (NPs), NCs with orderly arranged molecules exhibited highly enhanced nonlinear optical signals, including up to 15.35 folds of two-photon action cross-section (2PACS) and up to 4.11 folds of three-photon action cross-section (3PACS). The combination of MPI and DPTA-BT NCs has contributed to a 1900 μm imaging depth of vasculature after craniotomy and a 765 μm imaging depth through the intact skull. The safe use of DPTA-BT NCs was verified by the in vitro and in vivo biosafety assessment. This work is thus expected to encourage more innovation in the development of organic contrast agents for MPI.

High resolution recording of local field currents simultaneously with sound-evoked calcium signals by a photometric patch electrode in the auditory cortex field L of the chick

BackgroundFunctioning of the brain is based on both electrical and metabolic activity of neural ensembles. Accordingly, it would be useful to measure intracellular metabolic signaling simultaneously with electrical activity in the brain in vivo. New methodWe innovated a PhotoMetric-patch-Electrode (PME) recording system that has a high temporal resolution incorporating a photomultiplier tube as a light detector. The PME is fabricated from a quartz glass capillary to transmit light as a light guide, and it can detect electrical signals as a patch electrode simultaneously with a fluorescence signal. ResultsWe measured the sound-evoked Local Field Current (LFC) and fluorescence Ca2+ signal from neurons labeled with Ca2+-sensitive dye Oregon Green BAPTA1 in field L, the avian auditory cortex. Sound stimulation evoked multi-unit spike bursts and Ca2+ signals, and enhanced the fluctuation of LFC. After a brief sound stimulation, the cross-correlation between LFC and Ca2+ signal was prolonged. D-AP5 (antagonist for NMDA receptors) suppressed the sound-evoked Ca2+ signal when applied locally by pressure from the tip of PME. Comparison with existing methodsIn contrast to existing multiphoton imaging or optical fiber recording methods, the PME is a patch electrode pulled simply from a quartz glass capillary and can measure fluorescence signals at the tip simultaneously with electrical signal at any depth of the brain structure. ConclusionThe PME is devised to record electrical and optical signals simultaneously with high temporal resolution. Moreover, it can inject chemical agents dissolved in the tip-filling medium locally by pressure, allowing manipulation of neural activity pharmacologically.

Multispectral Imaging for Skin Diseases Assessment-State of the Art and Perspectives.

Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists. Therefore, a comprehensive method for skin analysis has not yet been established. Multispectral imaging (MSI) is based on light-tissue interaction properties due to radiation wavelength variation. An MSI device collects the reflected radiation after illumination of the lesion with light of different wavelengths and provides a set of spectral images. The concentration maps of the main light-absorbing molecules in the skin, the chromophores, can be retrieved using the intensity values from those images, sometimes even for deeper-located tissues, due to interaction with near-infrared light. Recent studies have shown that portable and cost-efficient MSI systems can be used for extracting skin lesion characteristics useful for early melanoma diagnoses. This review aims to describe the efforts that have been made to develop MSI systems for skin lesions evaluation in the last decade. We examined the hardware characteristics of the produced devices and identified the typical structure of an MSI device for dermatology. The analyzed prototypes showed the possibility of improving the specificity of classification between the melanoma and benign nevi. Currently, however, they are rather adjuvants tools for skin lesion assessment, and efforts are needed towards a fully fledged diagnostic MSI device.

Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning.

Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions. Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning. Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT). The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively. The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.

Abstract 2389: In vivo multiphoton autofluorescence imaging is sensitive to CD8 T cell and tumor cell metabolic changes during immunotherapy in a murine melanoma model

Abstract Introduction: In vivo multiphoton autofluorescence microscopy provides live, label free, single cell imaging of metabolic changes. These metabolic changes are quantified via the metabolic coenzymes NAD(P)H and FAD which are autofluorescent molecules endogenous to all cells. Metabolic reprogramming of tumor and immune cells is closely associated with cancer progression and cell phenotype. We aim to study metabolic changes during administration of an effective, triple-combination immunotherapy within murine melanoma tumors. This therapy includes external beam radiation, intratumoral hu14.18-IL2 immunocytokine (anti-GD2 mAb fused to IL2, provided by Anyxis Immuno-Oncology GmbH of Vienna, Austria), and intraperitoneal anti-CTLA-4 leading to in situ vaccination and cure of GD2+ murine tumors. Previous work has shown that a T cell response is critical to the efficacy of this therapy, so we created an mCherry-labeled T cell mouse model to study this response. Methods: We implanted syngeneic B78 (GD2+) melanoma cells into the flanks of mCherry-labeled CD8 T cell reporter mice (C57Bl/6 background) to induce tumors. Under anesthesia, skin flap surgery was performed and tumors were imaged at several time points during therapy. Multiphoton imaging was performed to collect NAD(P)H, FAD, mCherry, and collagen signal with a 40X objective. Fluorescence lifetime data was collected using time correlated single photon counting electronics. Tissues were harvested and analyzed via flow cytometry and multiplex immunofluorescence to corroborate intravital imaging findings and characterize the immune infiltrate. Results: Here we demonstrate that our in vivo imaging is sensitive to metabolic changes in both B78 melanoma and CD8 T cells from immunotherapy treated versus control tumors. These metabolic differences include changes in protein binding, redox balance, and fluorescence lifetime. We also show remodeling of collagen, a major component of the extracellular matrix, during immunotherapy that may be explained by an observed decrease in macrophages. Flow cytometry and multiplex immunofluorescence illustrate changes in the immune infiltrate composition, activation, cytotoxicity, and spatial distribution during therapy. We are also currently investigating metabolic changes in an MC38 mouse model - a hot tumor model - that we anticipate will provide excellent metabolic contrast to our B78 immunologically cold tumor model. Conclusions: These results show that in vivo metabolic imaging enables single cell quantification of metabolic changes in tumor and immune cells during therapy. Combined with other traditional assays, we can elucidate key immune cell populations and the crucial timepoints during therapy where changes are occurring. With continued efforts, this imaging platform may be leveraged to develop new combinations of immunotherapies. Citation Format: Alexa R. Heaton, Anna Hoefges, Peter R. Rehani, Nathaniel J. Burkard, Arika S. Feils, Dan V. Spiegelman, Noah W. Tsarovsky, Alina A. Hampton, Amy K. Erbe Gurel, Alexander L. Rakhmilevich, Paul M. Sondel, Melissa C. Skala. In vivo multiphoton autofluorescence imaging is sensitive to CD8 T cell and tumor cell metabolic changes during immunotherapy in a murine melanoma model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2389.

Abstract 6613: Label-free multimodal multiphoton microscopy for predicting DNA damage response in patient derived non-small cell lung cancer organoids

Abstract Background: DNA damage response (DDR) is a fundamental mechanism for evaluating efficacy of cancer therapeutics, many of which target DNA or associated processes (e.g., etoposide: inhibits DNA synthesis, cisplatin: crosslinks DNA). Current techniques to evaluate DDR rely on immunostaining for gamma H2AX foci (γH2AX), which is an indicator of DNA double-strand break formation. While γH2AX immunostaining can provide a snapshot of DDR in fixed cellular and tissue samples, this method is technically cumbersome as: 1) temporal monitoring of DDR requires timepoint replicates; 2) extensive assay development efforts for 3D samples such as organoids, and 3) protocols for γH2AX immunostaining and its evaluation are time-consuming. The goal of this project is to reduce overall burden on assay duration and development in NSCLC organoids by leveraging label-free multiphoton imaging. Method: To predict DDR in 3D organoid samples, label-free multimodal multiphoton techniques such as simultaneous label-free autofluorescence multiharmonic (SLAM) imaging can provide intracellular information based on endogenous contrast mechanisms. SLAM makes use of a single laser to excite autofluorescence in multiple cellular components including FAD and NAD(P)H, enabling the calculation of the optical redox ratio, which estimates the equilibrium of electron donating versus accepting species in cells. Moreover, label-free, multiphoton systems enable imaging of live samples without the need to add labels or generate sacrificial samples and have improved image acquisition in 3D space over conventional confocal microscopy. Using SLAM, we propose to predict DDR in non-small cell lung cancer (NSCLC) organoids. NSCLC organoids recapitulate histological and genetic features of their derived patient tissue, retaining tumor variability. Results: Predictive modeling via gradient boost regressor between handcrafted SLAM image features and γH2AX immunostained images on weakly-paired images confirmed strong correlation between SLAM image features and γH2AX speckles/nuclei. Across multiple patient derived NSCLC organoid lines and multiple DNA targeting chemotherapeutics, the optical redox ratio was demonstrated to robustly predict DDR. Conclusion: Label-free imaging via SLAM can be used to predict DDR in live 3D NSCLC organoids with minimal sample handling and without the need to add labelled reporters, enabling quicker and less technically involved DDR assaying. Citation Format: Terrence T. Roh, Aneesh Alex, Janet E. Sorrells, Prasanna Chandramouleeswaran, Marina Marjanovic, Steve R. Hood, BanuPriya Sridharan, Stephen A. Boppart. Label-free multimodal multiphoton microscopy for predicting DNA damage response in patient derived non-small cell lung cancer organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6613.

Combining the guidelines-method and multiphoton imaging method to improve the prognostic value of tumor-infiltrating lymphocytes in breast cancer.

Multiphoton microscopy (MPM) was introduced to label-freely obtain tumor-infiltrating lymphocytes (TILs) images from a total of 611 patients, and the prognostic value of TILs in breast cancer was assessed by the MPM method (TILs-MPM) and guidelines-method proposed by the International Immuno-Oncology Biomarker Working Group (TILs-WG), respectively. Moreover, the clinical (CLI) model, TILs-WG+TILs-MPM model, and full model (CLI+TILs-WG+TILs-MPM) were developed to investigate the prognostic value of TILs. The results show that TILs-WG performs better in ER-negative subgroup, and TILs-MPM is comparable with TILs-WG in the ER-negative subgroup, but has the best performance in the ER-positives subgroup. Furthermore, the TILs-WG+TILs-MPM model can significantly improve the prognostic power compared with the TILs-WG model, and the full model has excellent performance, with high AUC and HR in both ER-positive, ER-negative subgroups, and the complete cohort. Our results suggest that the combination of TILs-WG with TILs-MPM model can greatly improve the prognostic value of TILs.

Changes in the microstructure of the human aortic adventitia under biaxial loading investigated by multi-photon microscopy.

Among the three layers of the aortic wall, the media is primarily responsible for its mechanical properties, but the adventitia prevents the aorta from overstretching and rupturing. The role of the adventitia is therefore crucial with regard to aortic wall failure, and understanding the load-induced changes in tissue microstructure is of high importance. Specifically, the focus of this study is on the changes in collagen and elastin microstructure in response to macroscopic equibiaxial loading applied to the aortic adventitia. To observe these changes, multi-photon microscopy imaging and biaxial extension tests were performed simultaneously. In particular, microscopy images were recorded at 0.02 stretch intervals. The microstructural changes of collagen fiber bundles and elastin fibers were quantified with the parameters of orientation, dispersion, diameter, and waviness. The results showed that the adventitial collagen was divided from one into two fiber families under equibiaxial loading conditions. The almost diagonal orientation of the adventitial collagen fiber bundles remained unchanged, but the dispersion was substantially reduced. No clear orientation of the adventitial elastin fibers was observed at any stretch level. The waviness of the adventitial collagen fiber bundles decreased under stretch, but the adventitial elastin fibers showed no change. These original findings highlight differences between the medial and adventitial layers and provide insight into the stretching process of the aortic wall. STATEMENT OF SIGNIFICANCE: To provide accurate and reliable material models, it is essential to understand the mechanical behavior of the material and its microstructure. Such understanding can be enhanced with tracking of the microstructural changes caused by mechanical loading of the tissue. This study provides therefore a unique dataset of structural parameters of the human aortic adventitia obtained under equibiaxial loading. The structural parameters describe orientation, dispersion, diameter, and waviness of collagen fiber bundles and elastin fibers. Eventually, the microstructural changes in the human aortic adventitia are compared with the microstructural changes in the human aortic media from a previous study. This comparison reveals the cutting-edge findings on the differences in the response to the loading between these two human aortic layers.

A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

We present MiniVess, the first annotated dataset of rodent cerebrovasculature, acquired using two-photon fluorescence microscopy. MiniVess consists of 70 3D image volumes with segmented ground truths. Segmentations were created using traditional image processing operations, a U-Net, and manual proofreading. Code for image preprocessing steps and the U-Net are provided. Supervised machine learning methods have been widely used for automated image processing of biomedical images. While much emphasis has been placed on the development of new network architectures and loss functions, there has been an increased emphasis on the need for publicly available annotated, or segmented, datasets. Annotated datasets are necessary during model training and validation. In particular, datasets that are collected from different labs are necessary to test the generalizability of models. We hope this dataset will be helpful in testing the reliability of machine learning tools for analyzing biomedical images.

Broadband supercontinuum generation in cascaded tapered liquid core fiber

Most recent broad supercontinuum sources have used nonlinear fibers with small core diameters, resulting in low coupling efficiency, limiting power level launching into the fiber, and low spectral power density. In this work, we numerically investigate tapered liquid-core fibers for supercontinuum generation that have a potential for both high coupling efficiency (with a standard SMF-28 fiber) and high nonlinear effects. Carbon disulfide is selected to fill into the fibers because of its high nonlinear refractive index and high transparency from the visible to the mid-infrared range. We consider cascaded fibers including SMF-28 and the tapered liquid-core fibers for all-normal and anomalous dispersion supercontinuum generation pumped by C-band femtosecond lasers (1560 nm, 90 fs). In such a case, we obtain all-normal dispersion supercontinuum generation with flat-top, broad bandwidth (1.0–2.5μm) via an input peak power of 27 kW. By changing the core diameter of the tapered fiber, we also yield anomalous dispersion supercontinuum generation with broad spectral bandwidth (1.0–3.3μm). In addition, the use of the cascaded tapered fibers is possible to mitigate fiber damage (via thermal effects of a high-power laser on the selected liquids) and increase stability of output supercontinuum spectra. In this context, supercontinuum generation in the cascaded fibers by using a picosecond laser with high average power (≈ 800 mW) is considered towards applications requiring high spectral power density (e.g., multiphoton imaging techniques).

Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning.

Prostate carcinoma, a slow-growing and often indolent tumour, is the second most commonly diagnosed cancer among men worldwide. The prognosis is mainly based on the Gleason system through prostate biopsy analysis. However, new treatment and monitoring strategies depend on a more precise diagnosis. Here, we present results by multiphoton imaging for prostate tumour samples from 120 patients that allow to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis was developed to recognise and quantify stromal fibre and neoplastic cell regions in each image. The set of metrics was able to distinguish between non-neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with accuracy of 89% ± 3%, but between Gleason groups of only 46% ± 6%. The reactive stroma analysis improved the accuracy to 65% ± 5%, clearly demonstrating that stromal parameters should be considered as additional criteria for a more accurate diagnosis.

Nrf2 regulates glucose uptake and metabolism in neurons and astrocytes

The transcription factor Nrf2 and its repressor Keap1 mediate cell stress adaptation by inducing expression of genes regulating cellular detoxification, antioxidant defence and energy metabolism. Energy production and antioxidant defence employ NADH and NADPH respectively as essential metabolic cofactors; both are generated in distinct pathways of glucose metabolism, and both pathways are enhanced by Nrf2 activation. Here, we examined the role of Nrf2 on glucose distribution and the interrelation between NADH production in energy metabolism and NADPH homeostasis using glio-neuronal cultures isolated from wild-type, Nrf2-knockout and Keap1-knockdown mice. Employing advanced microscopy imaging of single live cells, including multiphoton fluorescence lifetime imaging microscopy (FLIM) to discriminate between NADH and NADPH, we found that Nrf2 activation increases glucose uptake into neurons and astrocytes. Glucose consumption is prioritized in brain cells for mitochondrial NADH and energy production, with a smaller contribution to NADPH synthesis in the pentose phosphate pathway for redox reactions. As Nrf2 is suppressed during neuronal development, this strategy leaves neurons reliant on astrocytic Nrf2 to maintain redox balance and energy homeostasis.

Label-free photothermal optical coherence microscopy to locate desired regions of interest in multiphoton imaging of volumetric specimens

Biochip-based research is currently evolving into a three-dimensional and large-scale basis similar to the in vivo microenvironment. For the long-term live and high-resolution imaging in these specimens, nonlinear microscopy capable of label-free and multiscale imaging is becoming increasingly important. Combination with non-destructive contrast imaging will be useful for effectively locating regions of interest (ROI) in large specimens and consequently minimizing photodamage. In this study, a label-free photothermal optical coherence microscopy (OCM) serves as a new approach to locate the desired ROI within biological samples which are under investigation by multiphoton microscopy (MPM). The weak photothermal perturbation in sample by the MPM laser with reduced power was detected at the endogenous photothermal particles within the ROI using the highly sensitive phase-differentiated photothermal (PD–PT) OCM. By monitoring the temporal change of the photothermal response signal of the PD–PT OCM, the hotspot generated within the sample focused by the MPM laser was located on the ROI. Combined with automated sample movement in the x–y axis, the focal plane of MPM could be effectively navigated to the desired portion of a volumetric sample for high-resolution targeted MPM imaging. We demonstrated the feasibility of the proposed method in second harmonic generation microscopy using two phantom samples and a biological sample, a fixed insect on microscope slide, with dimensions of 4 mm wide, 4 mm long, and 1 mm thick.

Label-free multiphoton imaging flow cytometry.

Label-free imaging flow cytometry is a powerful tool for biological and medical research as it overcomes technical challenges in conventional fluorescence-based imaging flow cytometry that predominantly relies on fluorescent labeling. To date, two distinct types of label-free imaging flow cytometry have been developed, namely optofluidic time-stretch quantitative phase imaging flow cytometry and stimulated Raman scattering (SRS) imaging flow cytometry. Unfortunately, these two methods are incapable of probing some important molecules such as starch and collagen. Here, we present another type of label-free imaging flow cytometry, namely multiphoton imaging flow cytometry, for visualizing starch and collagen in live cells with high throughput. Our multiphoton imaging flow cytometer is based on nonlinear optical imaging whose image contrast is provided by two optical nonlinear effects: four-wave mixing (FWM) and second-harmonic generation (SHG). It is composed of a microfluidic chip with an acoustic focuser, a lab-made laser scanning SHG-FWM microscope, and a high-speed image acquisition circuit to simultaneously acquire FWM and SHG images of flowing cells. As a result, it acquires FWM and SHG images (100 × 100 pixels) with a spatial resolution of 500 nm and a field of view of 50 μm × 50 μm at a high event rate of four to five events per second, corresponding to a high throughput of 560-700 kb/s, where the event is defined by the passage of a cell or a cell-like particle. To show the utility of our multiphoton imaging flow cytometer, we used it to characterize Chromochloris zofingiensis (NIES-2175), a unicellular green alga that has recently attracted attention from the industrial sector for its ability to efficiently produce valuable materials for bioplastics, food, and biofuel. Our statistical image analysis found that starch was distributed at the center of the cells at the early cell cycle stage and became delocalized at the later stage. Multiphoton imaging flow cytometry is expected to be an effective tool for statistical high-content studies of biological functions and optimizing the evolution of highly productive cell strains.

In Vivo Deep-Brain 3- and 4-Photon Fluorescence Imaging of Subcortical Structures Labeled by Quantum Dots Excited at the 2200 nm Window.

Multiphoton microscopy (MPM) is an enabling technology for visualizing deep-brain structures at high spatial resolution in vivo. Within the low tissue absorption window, shifting to longer excitation wavelengths reduces tissue scattering and boosts penetration depth. Recently, the 2200 nm excitation window has emerged as the last and longest window suitable for deep-brain MPM. However, multiphoton fluorescence imaging at this window has not been demonstrated, due to the lack of characterization of multiphoton properties of fluorescent labels. Here we demonstrate technologies for measuring both the multiphoton excitation and emission properties of fluorescent labels at the 2200 nm window, using (1) 3-photon (ησ3) and 4-photon action cross sections (ησ4) and (2) 3-photon and 4-photon emission spectra both ex vivo and in vivo of quantum dots. Our results show that quantum dots have exceptionally large ησ3 and ησ4 for efficient generation of multiphoton fluorescence. Besides, the 3-photon and 4-photon emission spectra of quantum dots are essentially identical to those of one-photon emission, which change negligibly subject to the local environment of circulating blood. Based on these characterization results, we further demonstrate deep-brain vasculature imaging in vivo. Due to the superb multiphoton properties of quantum dots, 3-photon and 4-photon fluorescence imaging reaches a maximum brain imaging depth of 1060 and 940 μm below the surface of a mouse brain, respectively, which enables the imaging of subcortical structures. We thus fill the last gap in multiphoton fluorescence imaging in terms of wavelength selection.

Super-resolution SRS microscopy with A-PoD.

Stimulated Raman scattering (SRS) offers the ability to image metabolic dynamics with high signal-to-noise ratio. However, its spatial resolution is limited by the numerical aperture of the imaging objective and the scattering cross-section of molecules. To achieve super-resolved SRS imaging, we developed a deconvolution algorithm, adaptive moment estimation (Adam) optimization-based pointillism deconvolution (A-PoD) and demonstrated a spatial resolution of lower than 59 nm on the membrane of a single lipid droplet (LD). We applied A-PoD to spatially correlated multiphoton fluorescence imaging and deuterium oxide (D2O)-probed SRS (DO-SRS) imaging from diverse samples to compare nanoscopic distributions of proteins and lipids in cells and subcellular organelles. We successfully differentiated newly synthesized lipids in LDs using A-PoD-coupled DO-SRS. The A-PoD-enhanced DO-SRS imaging method was also applied to reveal metabolic changes in brain samples from Drosophila on different diets. This new approach allows us to quantitatively measure the nanoscopic colocalization of biomolecules and metabolic dynamics in organelles.