Optical Microscopy of Soft Matter Systems

Colloids, polymers, gels, and biological materials are widely used for numerous technological applications. The design, fabrication, and understanding of the physico-chemical properties of such (bio)materials, however, represent a challenge for scientists and technologists. This review is a concise update of the latest achievements in surface and bulk analytical techniques applied to biomaterials and soft matter systems. Specific emphasis is devoted to their structural, mechanical, surface, and chemical properties. The review introduces the basics of atomic force microscopy (AFM) and discusses its combination with optical microscopies and spectroscopic techniques. In particular, we report the AFM combination with fluorescence, confocal microscopy, stimulated emission depletion, Raman, and infrared spectroscopy. The review highlights the impact and the potential applications of the combination of such experimental techniques on current problems and questions related to material and polymer science, cell biology, biochemical engineering, and protein chemistry. This review is an accessible introduction to AFM for the newcomer and a source of new experimental information for the experienced researcher.

The field of optical nanoscopy , a paradigm referring to the recent cutting-edge developments aimed at surpassing the widely acknowledged 200nm-diffraction limit in traditional optical microscopy, has gained recent prominence & traction in the $21^{\mathrm {st}}$ century. Numerous optical implementations allowing for a new frontier in traditional confocal laser scanning fluorescence microscopy to be explored (termed super-resolution fluorescence microscopy ) have been realized through the development of techniques such as stimulated emission and depletion (STED) microscopy, photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), amongst others. Nonetheless, it would be apt to mention at this juncture that optical nanoscopy has been explored since the mid-late $20^{\mathrm {th}}$ century, through several computational techniques such as deblurring and deconvolution algorithms. In this review, we take a step back in the field, evaluating the various in silico methods used to achieve optical nanoscopy today, ranging from traditional deconvolution algorithms (such as the Nearest Neighbors algorithm) to the latest developments in the field of computational nanoscopy, founded on artificial intelligence (AI). An insight is provided into some of the commercial applications of AI-based super-resolution imaging, prior to delving into the potentially promising future implications of computational nanoscopy. This is facilitated by recent advancements in the field of AI, deep learning (DL) and convolutional neural network (CNN) architectures, coupled with the growing size of data sources and rapid improvements in computing hardware, such as multi-core CPUs & GPUs, low-latency RAM and hard-drive capacities.

Microsphere-aided optical microscopy and its applications for super-resolution imaging

Bead encoding is a key problem central to all multiplexed bead-based microarrays. Most existing bead-based microarrays require complicated and costly bead fabrication and/or sophisticated bead decoding hardware to achieve high multiplexing. Recently, the development of spatial bead encoding techniques have opened up the possibility of using pattern matching and image processing to develop highly multiplexed, high-throughput bead-based arrays. In this thesis, we improve the existing spatial bead encoding scheme, and develop a pipeline of computational methods, which allows for automated spatial bead encoding. Six novel computational methods, that automate the proposed bead encoding scheme, are developed in this thesis. The proposed scheme improves the previously reported spatial encoding schemes by making them better suited for use in real world scenarios. In the spatial bead encoding scheme, a sequential bead deposition method is used to capture the identities of the beads in bright or dark field images of the array. While, a second fluorescence image of the array is used to quantify the target analyte concentrations associated with the beads. By aligning the two images using the patterns formed by the beads, both the bead identities (i.e. the target analyte associate with the bead) and the analyte concentrations are decoded. The six computational methods developed in this thesis, are grouped into three categories: bright and dark field image processing, fluorescence image processing, and bead pattern matching. These methods are developed such that they require minimal parameter tuning, and are able to deal with noisy images acquired in uncontrolled environments As part of the bright and dark field image processing we have developed two novel methods: a fully automatic method for detecting the underlying micro-well grid structure in the images, and an unsupervised learning based method to classify the micro-wells as either empty or containing a bead. We show the ability of the proposed methods to deal with extreme amounts of noise and distortions using multiple datasets. While, microarray image gridding is a well-studied problem in the context of fluorescence images of planar microarrays, to the best of our knowledge no methods have yet been developed for processing either bright or dark field images of bead-based microarrays. Unlike in bright field images, where all the beads are visible, in fluorescence images only those beads that are expressed in the sample are visible. Therefore, in fluorescence image analysis the first step is to detect the beads in the image. To this end, we have developed an unsupervised, simultaneous bead detection and segmentation method which uses the coherence in shape and size of the beads to circumvent the need for parameter optimization. The main challenge in this segmentation is the large dynamic range across which the beads are expressed. We have also developed a probabilistic non-linear gridding method that attempts to establish the micro-well grid structure from the detected beads. While similar gridding techniques have been developed in the past, we make two contributions in this regard: first, we put the gridding method in a probabilistic framework, and second, we show that in certain cases it is impossible to ascertain if the correct grid has been detected without using additional information about the physical properties of the array. The final step in the proposed bead encoding scheme is to establishing one-to-one correspondences between the beads detected in the bright field and fluorescence images. We have developed two methods for this matching. When the micro-well grid structure in the fluorescence image is successfully established, the grid information can be used to reduce the matching problem to a binary grid alignment problem. A grid matching method that is very fast and can handle a large number of beads (>50,000) has been developed for this purpose. Finally, to perform matching when the fluorescence image grid is not available, a novel point pattern matching method that is affine invariant and robust to small non-linear distortions has been developed. The point pattern matching method addresses the problem of matching large point sets in the presence of large amounts of outliers and occlusion. The proposed method is evaluated using several real and simulated datasets.

3D microscopy imaging is important for understanding complex biological assemblies. Computers and digital image acquisition systems have made possible the 3D visualization of images obtained from optical microscopes. This paper discussed a morphological approach for 3D blood cell visualization. The 3D plotted image is obtained from 2D image by combining 2D image space with the intensity value presented as third axis. 2D image detection techniques are commonly apply for blood cell analysis on image taken from microscopy or scanner sample which normally could be affected by noise. In order to enhance the detection of blood cell for 2D image and to obtain the clearest view of the blood cell, 3D image detection, which is more flexible and accurate, is proposed. This approach helps to obtain a clear image for disease analysis. A color image taken from microscopy or scanner has to gone through such image analysis using intensity, and edge operator to detect the blood cell disease, before 3D visualization can be done. INTRODUCTION In blood analysis component of the blood are distinguished by their dimensions and color. Blood cell classification and analysis is an important diagnostic technique in the detection of many illnesses [1]. The importance of traceability in the medical diagnostics is a technique that can be determined from a microscope slide or from a set of images. An accurate diagnosis could aid in diagnoses treatment disease, therefore the accurate details of the cells has to be analytical. In malarial blood, the red corpuscles of vertebrates are infected by malarial parasites [2]. The morphological blood cell image analysis includes calculation of geometrical features for recognition of pathology [3]. The goal of this paper is to enhance the detection by using or viewing blood cells visualization to provide a clear view to pathologies in order to reduce noise of the image for more accuracy diagnoses. BLOOD IMAGE DETECTION TECHNIQUES There are automated technologies for cell analysis, such as impendence analysis and flow cytometry, these method lack the ability to visualize each of the cell used in there determinations, and visually inspect them to ensure that they were classified appropriately [4]. In edge image detection acquired from stained blood, smears are suitable for measuring and characterizing properties of individual cells [5]. Edge detection is essentially the operation of detection significantly local changes in image. In one dimension, a step edge is associated with local peak in the first derivative. The gradient is a measure of change in function, and an image can be considered to be an array of sample of some continues function of image intensity. The gradient is the two-dimensional equivalent of the first derivative and is defined as the vector. 3D Visualization For Blood Cells Analysis Versus Edge Detection

Fast, nondestructive three-dimensional (3D) imaging of live suspension cells remains challenging without substrate treatment or fixation, precluding scalable single-cell morphometry with minimal alterations. While optical sectioning techniques achieve 3D live cell imaging, lateral versus depth resolution differences further complicate analysis. We present a scalable microfluidic method capable of 3D fluorescent isotropic imaging of live, nonadherent cells suspended inside picoliter droplets with high-speed single-cell volumetric readout (800 to 1,200 slices in 5 to 8 s) and near-diffraction limit resolution (~216 nm). The platform features a droplet trap array that leverages flow-induced droplet interfacial shear to generate intradroplet microvortices, which rotate single cells on their axis to enable optical projection tomography (OPT)-based imaging. This allows gentle (~1 mPa shear stress) observation of cells encapsulated inside nontoxic isotonic buffer droplets, facilitating scalable OPT acquisition by simultaneous spinning of hundreds of cells. We demonstrate 3D imaging of live myeloid and lymphoid cells in suspension, including K562 cells, as well as naive and activated T cells-small cells prone to movement in their suspended phenotype. Our fully suspended, orientation-independent cell morphometry, driven by isotropic imaging and spherical harmonic analysis, enabled the study of primary T cells across various immunological activation states. This approach unveiled six distinct nuclear content distributions, contrasting with conventional 2D images that typically portray spheroid and bean-like nuclear shapes associated with lymphocytes. Our arrayed-droplet OPT technology is capable of isotropic, single live-cell 3D imaging, with the potential to perform large-scale morphometry of immune cell effector function states while providing compatibility with microfluidic droplet operations.

Optical microscopy is suitable for numerous applications ranging from various fields of research such as mineralogy, entomology, botany, medicine, surgery, and various sectors of industrial production. As far as forensic applications are concerned, it maintains a prominent position as an important tool for the examination of the evidence. Actually, optical microscopy has always been a key application in judicial investigations and, until the early 1900s, it has been the forensic scientists’ main tool. At present, although highly advanced analytical techniques support forensic scientists for characterizing and identifying traces linked to a particular offence, optical microscopy continues to play an important role, especially in the early stages of laboratory investigation. Optical microscopy can provide direct information when there are comparative materials associated with suspects, or indirect information when compared with databases dedicated to forensic science applications. The most used optical microscopy tools for forensic purposes on a wide range of materials are the stereoscopic microscope and the polarized light microscope (PLM).

Purpose 3 D imaging of the lung is not a trivial undertaking as during preparation the lung may collapse. Also serial sections and scans followed by 3 D reconstruction may lead to artifacts. The present study aims to figure out the best way to perform 3 D imaging in lung research. Materials and Methods We applied an optical tissue clearing (OTC) method, which uses ethyl cinnamate (ECi) as a fast, nontoxic and cheap clearing solvent, for 3 D imaging of retrograde perfused lungs by laser confocal fluorescence microscopy and light sheet fluorescence microscopy. We also introduced expansion microscopy (ExM), a cutting-edge technique, in 3 D imaging of lungs. We examined and compared the usefulness of these techniques for 3 D lung imaging. The ExM protocol was further extended to paraffin-embedded lung metastases blocks. Results The MHI148-PEI labeled lung vasculature was visualized by retrograde perfusion combined with trachea ligation and ECi based OTC. As compared with trans-cardiac perfusion, the retrograde perfusion results in a better maintenance of lung morphology. 3 D structure of alveoli, vascular branches and cilia in lung were revealed by immunofluorescence staining after ExM. 3 D distribution of microvasculature and neutrophil cells in 10 years old paraffin-embedded lung metastases were analyzed by ExM. Conclusions The retrograde perfusion combined with trachea ligation technique could be applied in the lung research in mice. 3 D structure of lung vasculature can be visualized by MHI148-PEI perfusion and ECi based OTC in an efficient way. ExM and immunofluorescence staining protocol is highly recommended to perform 3 D imaging of fresh fixed lung as well as paraffin-embedded lung blocks.

Intensity-based 3D local image registration

BackgroundNonlinear optical (NLO) microscopy techniques have potential to improve the early detection of epithelial ovarian cancer. In this study we showed that multimodal NLO microscopies, including two-photon excitation fluorescence (TPEF), second-harmonic generation (SHG), third-harmonic generation (THG) and fluorescence lifetime imaging microscopy (FLIM) can detect morphological and metabolic changes associated with ovarian cancer progression.Methodology/Principal FindingsWe obtained strong TPEF + SHG + THG signals from fixed samples stained with Hematoxylin & Eosin (H&E) and robust FLIM signal from fixed unstained samples. Particularly, we imaged 34 ovarian biopsies from different patients (median age, 49 years) including 5 normal ovarian tissue, 18 serous tumors and 11 mucinous tumors with the multimodal NLO platform developed in our laboratory. We have been able to distinguish adenomas, borderline, and adenocarcinomas specimens. Using a complete set of scoring methods we found significant differences in the content, distribution and organization of collagen fibrils in the stroma as well as in the morphology and fluorescence lifetime from epithelial ovarian cells.Conclusions/SignificanceNLO microscopes provide complementary information about tissue microstructure, showing distinctive patterns for serous and mucinous ovarian tumors. The results provide a basis to interpret future NLO images of ovarian tissue and lay the foundation for future in vivo optical evaluation of premature ovarian lesions.

We investigate three-dimensional (3D) imaging properties in confocal two-photon (2-p) fluorescence microscopy with a finite-sized detector. The 3D intensity point spread function, the 3D optical transfer function (OTF), the strength of optical sectioning and the signal levels for point, planar and volume objects are calculated in order to reveal their relationships to the detector size. It is shown that confocal 2-p fluorescence imaging with a point detector gives super-resolution, compared with conventional 2-p imaging without a confocal pinhole. Although optical sectioning can be attained in the latter case, its strength is inferior to that in the confocal case. In particular, a finite-sized detector can result in negative values in the 3D OTF and reduce the effective cut-off spatial frequencies, thus degrading the capability of confocal 2-p fluorescence microscopy for 3D imaging. Unlike the single-photon fluorescence microscope, there is no missing cone of spatial frequencies in the 3D OTF for confocal 2-p fluorescence imaging when the detector size becomes very large

A simultaneous multimodal optical imaging system with OCT-guided dynamic focusing is developed in a single platform. The imaging system integrates photoacoustic microscopy, optical coherence tomography (OCT), optical Doppler tomography, OCT angiography, and confocal fluorescence microscopy.

Carrier-based dry powder inhaler (DPI) formulations need to be accurately characterised for their particle size distributions, surface roughnesses, fines contents and flow properties. Understanding the micro-structure of the powder formulation is crucial, yet current characterisation methods give incomplete information. Commonly used techniques like laser diffraction (LD) and optical microscopy (OM) are limited due to the assumption of sphericity and can give variable results depending on particle orientation and dispersion. The aim of this work was to develop new three dimensional (3D) powder analytical techniques using X-ray computed tomography (XCT) that could be employed for non-destructive metrology of inhaled formulations. α-lactose monohydrate powders with different characteristics have been analysed, and their size and shape (sphericity/aspect ratio) distributions compared with results from LD and OM. The three techniques were shown to produce comparable size distributions, while the different shape distributions from XCT and OM highlight the difference between 2D and 3D imaging. The effect of micro-structure on flowability was also analysed through 3D measurements of void volume and tap density. This study has demonstrated for the first time that XCT provides an invaluable, non-destructive and analytical approach to obtain number- and volume-based particle size distributions of DPI formulations in 3D space, and for unique 3D characterisation of powder micro-structure.

ABSTRACTBackground:Temporomandibular Joint (TMJ) disorders are a common cause of facial pain, and accurate diagnosis is crucial for effective management. Imaging techniques, such as 2D radiography and 3D imaging (cone beam computed tomography [CBCT] and magnetic resonance imaging [MRI]), play a significant role in diagnosing TMJ disorders.Materials and Methods:A comparative analysis was conducted on 100 patients diagnosed with TMJ disorders. Fifty patients underwent 2D imaging (panoramic radiographs), while the remaining 50 patients were examined using 3D imaging (CBCT and MRI). Diagnostic parameters, such as joint space narrowing, condylar morphology, and disc displacement, were evaluated. The data were statistically analyzed using a Chi-square test to compare the diagnostic accuracy between the two imaging modalities.Results:The diagnostic accuracy for detecting joint space narrowing was 70% in the 2D group and 90% in the 3D group. In identifying changes in condylar morphology, 3D imaging demonstrated superior resolution, with an accuracy of 85%, compared to 60% in 2D imaging. Furthermore, disc displacement was better visualized using MRI in the 3D imaging group, with a detection rate of 92% versus 50% in the 2D group. The overall clinical applicability of 3D imaging was rated higher by practitioners due to its enhanced diagnostic clarity.Conclusion:3D imaging techniques, particularly CBCT and MRI, offer superior diagnostic accuracy and resolution compared to 2D radiography in the diagnosis of TMJ disorders.

A key requirement for performing three-dimensional (3D) imaging using optical microscopes is that they be capable of optical sectioning by distinguishing in-focus signal from out-of-focus background. Common techniques for fluorescence optical sectioning are confocal laser scanning microscopy and two-photon microscopy. But there is increasing interest in alternative optical sectioning techniques, particularly for applications involving high speeds, large fields of view or long-term imaging. In this Review, I examine two such techniques, based on planar illumination or structured illumination. The goal is to describe the advantages and disadvantages of these techniques.