Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis

Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality and time-intensive process of the prevalent frozen section procedure, various intraoperative diagnostic imaging systems have been explored. Microscopy with ultraviolet surface excitation (MUSE) is an inexpensive, maintenance-free, and rapid imaging technique that yields images like thin-sectioned samples without sectioning. However, pathologists find it nearly impossible to assign diagnostic labels to MUSE images of unfixed specimens; thus, AI for intraoperative diagnosis cannot be trained in a supervised learning manner. In this study, we propose a deep-learning pipeline model for lymph node metastasis detection, in which CycleGAN translate MUSE images of unfixed lymph nodes to formalin-fixed paraffin-embedded (FFPE) sample, and diagnostic prediction is performed using deep convolutional neural network trained on FFPE sample images. Our pipeline yielded an average accuracy of 84.6% when using each of the three deep convolutional neural networks, which is a 18.3% increase over the classification-only model without CycleGAN. The modality translation to FFPE sample images using CycleGAN can be applied to various intraoperative diagnostic imaging systems and eliminate the difficulty for pathologists in labeling new modality images in clinical sites. We anticipate our pipeline to be a starting point for accurate rapid intraoperative diagnostic systems for new imaging modalities, leading to healthcare quality improvement.

Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology

BACKGROUND

Several studies have used artificial intelligence (AI) to analyze cytology images, but AI has yet to be adopted in clinical practice. The objective of this study was to demonstrate the accuracy of AI-based image analysis for thyroid fine-needle aspiration cytology (FNAC) and to propose its application in clinical practice.

METHODS

In total, 148,395 microscopic images of FNAC were obtained from 393 thyroid nodules to train and validate the data, and EfficientNetV2-L was used as the image-classification model. The 35 nodules that were classified as atypia of undetermined significance (AUS) were predicted using AI training.

RESULTS

The precision-recall area under the curve (PR AUC) was >0.95, except for poorly differentiated thyroid carcinoma (PR AUC = 0.49) and medullary thyroid carcinoma (PR AUC = 0.91). Poorly differentiated thyroid carcinoma had the lowest recall (35.4%) and was difficult to distinguish from papillary thyroid carcinoma, medullary thyroid carcinoma, and follicular thyroid carcinoma. Follicular adenomas and follicular thyroid carcinomas were distinguished from each other by 86.7% and 93.9% recall, respectively. For two-dimensional mapping of the data using t-distributed stochastic neighbor embedding, the lymphomas, follicular adenomas, and anaplastic thyroid carcinomas were divided into three, two, and two groups, respectively. Analysis of the AUS nodules showed 94.7% sensitivity, 14.4% specificity, 56.3% positive predictive value, and 66.7% negative predictive value.

CONCLUSIONS

The authors developed an AI-based approach to analyze thyroid FNAC cases encountered in routine practice. This analysis could be useful for the clinical management of AUS and follicular neoplasm nodules (e.g., an online AI platform for thyroid cytology consultations).

Automated diagnosis of optical coherence tomography imaging on plaque vulnerability and its relation to clinical outcomes in coronary artery disease

This study sought to develop a deep learning-based diagnostic algorithm for plaque vulnerability by analyzing intravascular optical coherence tomography (OCT) images and to investigate the relation between AI-plaque vulnerability and clinical outcomes in patients with coronary artery disease (CAD). A total of 1791 study patients who underwent OCT examinations were recruited from a multicenter clinical database, and the OCT images were first labeled as either normal, a stable plaque, or a vulnerable plaque by expert cardiologists. A DenseNet-121-based deep learning algorithm for plaque characterization was developed by training with 44,947 prelabeled OCT images, and demonstrated excellent differentiation among normal, stable plaques, and vulnerable plaques. Patients who were diagnosed with vulnerable plaques by the algorithm had a significantly higher rate of both events from the OCT-observed segments and clinical events than the patients with normal and stable plaque (log-rank p < 0.001). On the multivariate logistic regression analyses, the OCT diagnosis of a vulnerable plaque by the algorithm was independently associated with both types of events (p = 0.047 and p < 0.001, respectively). The AI analysis of intracoronary OCT imaging can assist cardiologists in diagnosing plaque vulnerability and identifying CAD patients with a high probability of occurrence of future clinical events.

Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network

OBJECTIVES

To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN).

METHODS

Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared.

RESULTS

Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01).

CONCLUSIONS

The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances.

KEY POINTS

• The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.

Red-Fluorescent Pt Nanoclusters for Detecting and Imaging HER2 in Breast Cancer Cells

Overexpression of human epidermal growth factor receptor 2 (HER2) is associated with more frequent cancer recurrence and metastasis. Sensitive sensing of HER2 in living breast cancer cells is crucial in the early stages of cancer and to further understand its role in cells. Biomedical imaging has become an indispensable tool in the fields of early cancer diagnosis and therapy. In this study, we designed and synthesized platinum (Pt) nanocluster bionanoprobes with red emission (Ex/Em = 535/630 nm) for fluorescence imaging of HER2. Our Pt nanoclusters, which were synthesized using polyamidoamine (PAMAM) dendrimer and preequilibration, exhibited approximately 1% quantum yield and possessed low cytotoxicity, ultrasmall size, and excellent photostability. Furthermore, combined with ProteinA as an adapter protein, we developed Pt bionanoprobes with minimal nonspecific binding and utilized them as fluorescent probes for highly sensitive optical imaging of HER2 at the cellular level. More importantly, molecular probes with long-wavelength emission have allowed visualization of deep anatomical features because of enhanced tissue penetration and a decrease in background noise from tissue scattering. Our Pt nanoclusters are promising fluorescent probes for biomedical applications.

Nerve Segmentation with Deep Learning from Label-Free Endoscopic Images Obtained Using Coherent Anti-Stokes Raman Scattering

Semantic segmentation with deep learning to extract nerves from label-free endoscopic images obtained using coherent anti-Stokes Raman scattering (CARS) for nerve-sparing surgery is described. We developed a CARS rigid endoscope in order to identify the exact location of peripheral nerves in surgery. Myelinated nerves are visualized with a CARS lipid signal in a label-free manner. Because the lipid distribution includes other tissues as well as nerves, nerve segmentation is required to achieve nerve-sparing surgery. We propose using U-Net with a VGG16 encoder as a deep learning model and pre-training with fluorescence images, which visualize the lipid distribution similar to CARS images, before fine-tuning with a small dataset of CARS endoscopy images. For nerve segmentation, we used 24 CARS and 1,818 fluorescence nerve images of three rabbit prostates. We achieved label-free nerve segmentation with a mean accuracy of 0.962 and an F1 value of 0.860. Pre-training on fluorescence images significantly improved the performance of nerve segmentation in terms of the mean accuracy and F1 value (p<0.05). Nerve segmentation of label-free endoscopic images will allow for safer endoscopic surgery, while reducing dysfunction and improving prognosis after surgery.

Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells

It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system.

Excitation of erbium-doped nanoparticles in 1550-nm wavelength region for deep tissue imaging with reduced degradation of spatial resolution

Rare-earth-doped nanoparticles are one of the emerging probes for bioimaging due to their visible-to-near-infrared (NIR) upconversion emission via sequential single-photon absorption at NIR wavelengths. The NIR-excited upconversion property and high photostability make this probe appealing for deep tissue imaging. So far, upconversion nanoparticles include ytterbium ions (Yb3  +  ) codoped with other rare earth ions, such as erbium (Er3  +  ) and thulium (Tm3  +  ). In these types of upconversion nanoparticles, through energy transfer from Yb3  +   excited with continuous wave light at a wavelength of 980 nm, upconversion emission of the other rare earth dopants is induced. We have found that the use of the excitation of Er3  +   in the 1550-nm wavelength region allows us to perform deep tissue imaging with reduced degradation of spatial resolution. In this excitation–emission process, three and four photons of 1550-nm light are sequentially absorbed, and Er3  +   emits photons in the 550- and 660-nm wavelength regions. We demonstrate that, compared with the case using 980-nm wavelength excitation, the use of 1550-nm light enables us to moderate degradation of spatial resolution in deep tissue imaging due to the lower light scattering coefficient compared with 980-nm light. We also demonstrate that live cell imaging is feasible with this 1550 nm excitation.

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma: A preliminary study

To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared.No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists (P >.11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P = .98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity (P = .0005), but significantly superior specificity (P = .02).Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists.

[Medical Application of Artificial Intelligence/Deep Learning]

Deep learning is a subset of the medical application of artificial intelligence. Its significant results are garnering attention, particularly in radiographic image interpretation, pathological diagnosis, gene analysis, and prediction of cancer recurrence. In this study, we summarize the concept of deep learning. The human body structure, from the molecule to physical functions, is a complex system. Deep learning is a new way to analyze its complex systems. An essential point of the analysis is the categorization of obstacles. To a certain extent, deep learning approximates a doctor's cognition.

Abstract 2859: Deep learning recognizes FTD-resistant isolated cancer cells of colon cancer

In recent years, innovative technologies that extract feature descriptions from the large volume of data on speech recognition, visual object recognition and detection as well as many other domains, such as drug discovery and DNA sequence annotations by deep learning techniques and applying them to automatic recognition etc. are drawing attention. As cancer research aiming at applying deep learning techniques to cases that are resistant to surgical therapy and drug therapy in metastatic colorectal cancer, we developed a fundamental technology that can predict the resistance of free cancer cells to fluorinated pyrimidine anticancer drugs by deep learning from the morphological image data taken from images. An experimental model was used in our investigation in order to clarify whether or not its image recognition ability can be applied to the determination of drug resistance of free cancer cells circulating in the peripheral blood. That is, a cell line established by inducing a resistance to FTD or 5 FU added to the cell culture solution was prepared over several months and the ability to recognize the tolerance of the drug was examined from a large volume of image data, and it was shown that it can be distinguished dominantly in a short-term culture system. Further, as a result of examination after separation at the single cell level, it was possible to distinguish fluorescent-labeled resistant strains dominantly. In addition, we were able to recognize the drug resistance character well by injecting resistant strains intravenously into the mice to prepare a model of free cancer cells and collecting circulating free cancer cells. Moreover, as a pre-clinical model, resistant strains were mixed with susceptible strains at various ratios and transplanted into mice and experimented. As a result, the nature of the resistance to treatment was predicted by image recognition, and death of the mice due to cancer was well correlated with the malignant trait of drug-resistant cancer cells. Then, by linking the feature expression obtained from the image and the Omics data, a detailed stratification of treatment resistance was possible. From the above, a technique in the mouse that can distinguish free cancer cells collected from the peripheral blood by deep learning of images was constructed, and a foundation to be applied to medical treatment and precision medical care in the future was established.

Citation Format: Kiminori Yanagisawa, Masamitsu Konno, Masayasyu Toratani, Hirohiko Niioka, Ayumu Asai, Jun Koseki, Kenta Tsunekuni, Taroh Satoh, Kazuhiko Ogawa, Jun Miyake, Yuichiro Doki, Masaki Mori, Hideshi Ishii. Deep learning recognizes FTD-resistant isolated cancer cells of colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2859.

Coherent anti-Stokes Raman scattering rigid endoscope toward robot-assisted surgery

Label-free visualization of nerves and nervous plexuses will improve the preservation of neurological functions in nerve-sparing robot-assisted surgery. We have developed a coherent anti-Stokes Raman scattering (CARS) rigid endoscope to distinguish nerves from other tissues during surgery. The developed endoscope, which has a tube with a diameter of 12 mm and a length of 270 mm, achieved 0.91% image distortion and 8.6% non-uniformity of CARS intensity in the whole field of view (650 μm diameter). We demonstrated CARS imaging of a rat sciatic nerve and visualization of the fine structure of nerve fibers.

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

Measurement of DNA Length Changes Upon CpG Hypermethylation by Microfluidic Molecular Stretching

Abnormal DNA methylation in CpG-rich promoters is recognized as a distinct molecular feature of precursor lesions to cancer. Such unintended methylation can occur during in vitro differentiation of stem cells. It takes place in a subset of genes during the differentiation or expansion of stem cell derivatives under general culture conditions, which may need to be monitored in future cell transplantation studies. Here we demonstrate a microfluidic device for investigating morphological length changes in DNA methylation. Arrayed polymer chains of single DNA molecules were fluorescently observed by parallel trapping and stretching in the microfluidic channel. This observational study revealed that the shortened DNA length is due to the increased rigidity of the methylated DNA molecule. The trapping rate of the device for DNA molecules was substantially unaffected by changes in the CpG methylation.

Multispectral Emissions of Lanthanide-Doped Gadolinium Oxide Nanophosphors for Cathodoluminescence and Near-Infrared Upconversion/Downconversion Imaging

Comprehensive imaging of a biological individual can be achieved by utilizing the variation in spatial resolution, the scale of cathodoluminescence (CL), and near-infrared (NIR), as favored by imaging probe Gd₂O₃ co-doped lanthanide nanophosphors (NPPs). A series of Gd₂O₃:Ln3+/Yb3+ (Ln3+: Tm3+, Ho3+, Er3+) NPPs with multispectral emission are prepared by the sol-gel method. The NPPs show a wide range of emissions spanning from the visible to the NIR region under 980 nm excitation. The dependence of the upconverting (UC)/downconverting (DC) emission intensity on the dopant ratio is investigated. The optimum ratios of dopants obtained for emissions in the NIR regions at 810 nm, 1200 nm, and 1530 nm are applied to produce nanoparticles by the homogeneous precipitation (HP) method. The nanoparticles produced from the HP method are used to investigate the dual NIR and CL imaging modalities. The results indicate the possibility of using Gd₂O₃ co-doped Ln3+/Yb3+ (Ln3+: Tm3+, Ho3+, Er3+) in correlation with NIR and CL imaging. The use of Gd₂O₃ promises an extension of the object dimension to the whole-body level by employing magnetic resonance imaging (MRI).

Correlative near-infrared light and cathodoluminescence microscopy using Y2O3:Ln, Yb (Ln = Tm, Er) nanophosphors for multiscale, multicolour bioimaging

This paper presents a new correlative bioimaging technique using Y₂O₃:Tm, Yb and Y₂O₃:Er, Yb nanophosphors (NPs) as imaging probes that emit luminescence excited by both near-infrared (NIR) light and an electron beam. Under 980 nm NIR light irradiation, the Y₂O₃:Tm, Yb and Y₂O₃:Er, Yb NPs emitted NIR luminescence (NIRL) around 810 nm and 1530 nm, respectively, and cathodoluminescence at 455 nm and 660 nm under excitation of accelerated electrons, respectively. Multimodalities of the NPs were confirmed in correlative NIRL/CL imaging and their locations were visualized at the same observation area in both NIRL and CL images. Using CL microscopy, the NPs were visualized at the single-particle level and with multicolour. Multiscale NIRL/CL bioimaging was demonstrated through in vivo and in vitro NIRL deep-tissue observations, cellular NIRL imaging, and high-spatial resolution CL imaging of the NPs inside cells. The location of a cell sheet transplanted onto the back muscle fascia of a hairy rat was visualized through NIRL imaging of the Y₂O₃:Er, Yb NPs. Accurate positions of cells through the thickness (1.5 mm) of a tissue phantom were detected by NIRL from the Y₂O₃:Tm, Yb NPs. Further, locations of the two types of NPs inside cells were observed using CL microscopy.

Rare-earth-doped nanophosphors for multicolor cathodoluminescence nanobioimaging using scanning transmission electron microscopy

We describe rare-earth-doped nanophosphors (RE-NPs) for biological imaging using cathodoluminescence(CL) microscopy based on scanning transmission electron microscopy (STEM). We report the first demonstration of multicolor CL nanobioimaging using STEM with nanophosphors. The CL spectra of the synthesized nanophosphors (Y2O3∶Eu, Y2O3∶Tb) were sufficiently narrow to be distinguished. From CL images of RE-NPs on an elastic carbon-coated copper grid, the spatial resolution was beyond the diffraction limit of light.Y2O3∶Tb and Y2O3∶Eu RE-NPs showed a remarkable resistance against electron beam exposure even at high acceleration voltage (80 kV) and retained a CL intensity of more than 97% compared with the initial intensity for 1 min. In biological CL imaging with STEM, heavy-metal-stained cell sections containing the RE-NPs were prepared,and both the CL images of RE-NPs and cellular structures, such as mitochondria, were clearly observed from STEM images with high contrast. The cellular CL imaging using RE-NPs also had high spatial resolution even though heavy-metal-stained cells are normally regarded as highly scattering media. Moreover, since theRE-NPs exhibit photoluminescence (PL) excited by UV light, they are useful for multimodal correlative imaging using CL and PL.

Correlative cathodoluminescence and near-infrared fluorescence imaging for bridging from nanometer to millimeter scale bioimaging

Correlative light and electron microscopy (CLEM) is one attractive method of observing biological specimens because it combines the advantages of both light microscopy (LM) and electron microscopy (EM). In LM, specimens are fully hydrated, and molecular species are distinguished based on the fluorescence colors of probes. EM provides both high-spatial-resolution images superior to those obtained with LM and ultrastructural information of cellular components. The combination of LM and EM gives much more information than either method alone, which helps us to analyze cellular function in more detail.We propose a Y2O3:Tm,Yb phosphor nanoparticle which allows upconversion luminescence (UCL) imaging with near-infrared (NIR) light excitation and cathodoluminescence (CL) imaging [1], where the light emission induced by an electron beam is called cathodoluminescence (CL). Due to electron beam excitation, the spatial resolution of CL microscopy is on the order of nanometers [2,3]. Upconversion is a process in which lower energy, longer wavelength excitation light is transduced to higher energy, shorter wavelength emission light. So far, in LM observation for CLEM, ultraviolet (UV) or visible light has been used for excitation. However, UV and visible light have limited ability to observe deep tissue regions due to absorption, scattering, and autofluorescence. On the other hand, NIR light does not suffer from these problems. Rare-earth-doped upconversion nanophosphors have been applied to biological imaging because of the advantages of NIR excitation [4].We investigated the UCL and CL spectra of Y2O3:Tm,Yb nanophosphors. Y2O3:Tm,Yb nanophosphors that emit visible and near-infrared UCL under 980nm irradiation and blue CL via electron beam excitation. To confirm bimodality of our nanophosphors, correlative UCL/CL images of the nanophosphors were obtained for the same region. The nanophosphors were poured onto a P doped Si substrate (Fig. 1(a)) and were irradiated with 980 nm NIR CW laser light or an electron beam. Fig. 1(b) shows the UCL image of the nanophosphors under 980 nm NIR CW laser irradiation, UCL spots were observed, but the individual nanophosphors in each spot were difficult to distinguish in the UCL image. On the other hand, the edges and the gap between the nanophosphors were clearly distinguished in the CL image (Fig. 1(c)), showing that the spatial-resolution of CL imaging was enough higher than that of UCL image. We believe that upconversion phosphors of the type described here will allow the realization of new CLEM imaging techniques covering the nanometer to millimeter scale, i.e., the molecular to in vivo scale.jmicro;63/suppl_1/i29/DFU073F1F1DFU073F1Fig. 1.(a) SEM and correlative (b) UCL (intensity of 980 nm NIR CW laser 8 mW) and (c) CL images of Y2O3:Tm,Yb nanophosphors in same region (accelerating voltage 3 kV, exposure time 100 ms/pixel).

Laser-targeted photofabrication of gold nanoparticles inside cells

Nanoparticle manipulation is of increasing interest, since they can report single molecule-level measurements of the cellular environment. Until now, however, intracellular nanoparticle locations have been essentially uncontrollable. Here we show that by infusing a gold ion solution, focused laser light-induced photoreduction allows in situ fabrication of gold nanoparticles at precise locations. The resulting particles are pure gold nanocrystals, distributed throughout the laser focus at sizes ranging from 2 to 20 nm, and remain in place even after removing the gold solution. We demonstrate the spatial control by scanning a laser beam to write characters in gold inside a cell. Plasmonically enhanced molecular signals could be detected from nanoparticles, allowing their use as nano-chemical probes at targeted locations inside the cell, with intracellular molecular feedback. Such light-based control of the intracellular particle generation reaction also offers avenues for in situ plasmonic device creation in organic targets, and may eventually link optical and electron microscopy.

High-resolution microscopy for biological specimens via cathodoluminescence of Eu- and Zn-doped Y2O3 nanophosphors

High-resolution microscopy for biological specimens was performed using cathodoluminescence (CL) of Y(2)O(3):Eu, Zn nanophosphors, which have high CL intensity due to the incorporation of Zn. The intensity of Y(2)O(3):Eu nanophosphors at low acceleration voltage (3 kV) was increased by adding Zn. The CL intensity was high enough for imaging even with a phosphor size as small as about 30 nm. The results show the possibility of using CL microscopy for biological specimens at single-protein-scale resolution. CL imaging of HeLa cells containing laser-ablated Y(2)O(3):Eu, Zn nanophosphors achieved a spatial resolution of a few tens of nanometers. Y(2)O(3):Eu, Zn nanophosphors in HeLa cells were also imaged with 254 nm ultraviolet light excitation. The results suggest that correlative microscopy using CL, secondary electrons and fluorescence imaging could enable multi-scale investigation of molecular localization from the nanoscale to the microscale.

Fast spectral coherent anti-Stokes Raman scattering microscopy with high-speed tunable picosecond laser

We develop a coherent anti-Stokes Raman scattering (CARS) microscopy system equipped with a tunable picosecond laser for high-speed wavelength scanning. An acousto-optic tunable filter (AOTF) is integrated in the laser cavity to enable wavelength scanning by varying the radio frequency waves applied to the AOTF crystal. An end mirror attached on a piezoelectric actuator and a pair of parallel plates driven by galvanometer motors are also introduced into the cavity to compensate for changes in the cavity length during wavelength scanning to allow synchronization with another picosecond laser. We demonstrate fast spectral imaging of 3T3-L1 adipocytes every 5  cm-1 in the Raman spectral region around 2850  cm-1 with an image acquisition time of 120 ms. We also demonstrate fast switching of Raman shifts between 2100 and 2850  cm-1, corresponding to CD2 symmetric stretching and CH2 symmetric stretching vibrations, respectively. The fast-switching CARS images reveal different locations of recrystallized deuterated and nondeuterated stearic acid.

Real-time imaging of laser-induced membrane disruption of a living cell observed with multifocus coherent anti-Stokes Raman scattering microscopy

We demonstrate the real-time imaging of laser-induced disruption of the cellular membrane in a living HeLa cell and its cellular response with a multifocus coherent anti-Stokes Raman scattering (CARS) microscope. A near-infrared pulsed laser beam tightly focused on the cellular membrane of a living cell induces ablation at the focal point causing a local disruption of the cellular membrane. After the membrane disruption a dark spot decreasing CARS intensity of 2840 cm(-1) Raman shift at the disrupted site appears. This dark spot immediately disappears and a strong CARS signal is observed around the disrupted site. This increase of the CARS signal might be caused by resealing of the disrupted site via aggregation of the patch lipid vesicles in the cytoplasm. The accumulation of lipids around the disrupted site is also confirmed with three-dimensional CARS images of a cell before and after membrane disruption. The temporal behavior of the CARS signal at the disrupted site is observed to detect the fusion dynamics of patch vesicles.

Femtosecond laser nano-ablation in fixed and non-fixed cultured cells

To understand the onset and morphology of femtosecond laser submicron ablation in cells and to study physical evidence of intracellular laser irradiation, we used transmission electron microscopy (TEM). The use of partial fixation before laser irradiation provides for clear images of sub-micron intracellular laser ablation, and we observed clear evidence of bubble-type physical changes induced by femtosecond laser irradiation at pulse energies as low as 0.48 nJ in the nucleus and cytoplasm. By taking ultrathin sliced sections, we reconstructed the laser affected subcellular region, and found it to be comparable to the point spread function of the laser irradiation. Laser-induced bubbles were observed to be confined by the surrounding intracellular structure, and bubbles were only observed with the use of partial pre-fixation. Without partial pre-fixation, laser irradiation of the nucleus was found to produce observable aggregation of nanoscale electron dense material, while irradiation of cytosolic regions produced swollen mitochondria but residual local physical effects were not observed. This was attributed to the rapid collapse of bubbles and/or the diffusion of any observable physical effects from the irradiation site following the laser exposure.