Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

A Platform for Fast Detection of Let-7 Micro RNA Using Polyaniline Fluorescence and Image Analysis Techniques

The project describes a new strategy for transducing hybridization events through modulating intrinsic properties of the electroconductive polymer polyaniline (PANI). When DNA based probes electrostatically interact with PANI, its fluorescence properties are increased, a phenomenon that can be enhanced by UV irradiation. Hybridization of target nucleic acids results in dissociation of probes causing PANI fluorescence to return to basal levels. By monitoring restoration of base PANI fluorescence as little as 10-11 M (10 pM) of target oligonucleotides could be detected within 15 minutes of hybridization. Detection of complementary oligos was specific, with introduction of a single mismatch failing to form a target-probe duplex that would dissociate from PANI. Furthermore, this approach is robust and is capable of detecting specific RNAs in extracts from animals. This sensor system improves on previously reported strategies by transducing highly specific probe dissociation events through intrinsic properties of a conducting polymer without the need for additional labels. The change in fluorescence property of PANI by oligo immobilization and hybridization with mimic let-7 is measured by fluorescence microscope and the image analyzed by MATLAB. A heuristic algorithm determines color threshold of the fluorescent active image. This image segmentation helps to determine the average pixel intensity representing the active image foreground of PANI fluorescence triggered by DNA immobilization and hybridization process. This would help us to quantify response of PANI based biosensor for detecting micro RNA let-7

WNT-DEPENDENT REGENERATIVE FUNCTION IS INDUCED IN LEUKEMIA-INITIATING AC133BRIGHT CELLS

The Cancer Stem Cell model supported the notion that leukemia was initiated and maintained in vivo by a small fraction of leukemia-initiating cells (LICs). Previous studies have suggested the involvement of Wnt signaling pathway in Acute Myeloid Leukemia (AML) by the ability to sustain the development of LICs. A novel hematopoietic stem and progenitor cell marker, monoclonal antibody AC133, recognizes the CD34bright CD38- subset of human acute myeloid leukemia cells, suggesting that it may be an early marker for the LICs. During the first part of my phD program we previously evaluated the ability of leukemic AC133+ fraction, to perform engraftment following to xenotransplantation in immunodeficient mouse model Rag2-/-\u3b3c-/-. The results showed that the surface marker AC133 is able to enrich for the cell fraction that contains the LICs. In consideration of our previously reported data, derived from the expression profiling analysis performed in normal (n=10) and leukemic (n=33) human long-term reconstituting AC133+ cells, we revealed that the ligand-dependent Wnt signaling is induced in AML through a diffuse expression and release of WNT10B, a hematopoietic stem cells regenerative-associated molecule. In situ detection performed on bone marrow biopsies of AML patients, showed the activation of the Wnt pathway, through the concomitant presence of the ligand WNT10B and of the active dephosphorylated \u3b2-catenin form, suggesting an autocrine / paracrine-type ligand-dependent activation mechanism. In consideration of the link between hematopoietic regeneration and developmental signaling, we transplanted primary AC133+ AML A46 cells into developing zebrafish. This biosensor model revealed the formation of ectopic structures by activation of dorsal organizer markers that act downstream of the Wnt pathway. These results suggested that the misappropriating Wnt associated functions can promote pathological stem cell-like regeneration responsiveness. The analyses performed in situ retained information on the cellular localization, enabling determination of the activity status of individual cells and allowing the tumor environment view. Taking this issue into consideration, during the second part of my phD program, I set up the application of a new in situ method for localized detection and genotyping of individual transcripts directly in cells and tissues. The mRNA in situ detection technique is based on padlock probes ligation and target priming rolling circle amplification allowing the single nucleotide resolution in heterogenous tissues. The mRNA in situ detection performed on bone marrow biopsies derived from AML patients, showed a diffuse localization pattern of WNT10B molecule in the tissue. Conversely, only the AC133bright cell population shows the Wnt signaling activation signature represented by the cytoplasmatic accumulation and nuclear translocation of the active form of \u3b2-catenin. In spite of this, we previously evidenced that the regenerative function of WNT signaling pathway is defined by the up-regulation of WNT10B, WNT10A, WNT2B and WNT6 loci, we identified the WNT10B as a major locus associated with the regenerative function and over-expressed by all AML patients. By the molecular evaluation of the WNT10B transcript, we isolated an aberrant splicing variant (WNT10BIVS1), that identify Non Core-Binding Factor Leukemia (NCBFL) class and whose potential role is discussed. Moreover, we demonstrate that the function of "leukemia stem cell", present in the cell population enriched for the marker AC133bright, is strictly related to regenerative function associated with WNT signaling, defining the key role of WNT10B ligand as a specific molecular marker for leuchemogenesis. This thesis defines the new suitable approaches to characterize the leukemia-initiating cells (LICs) and suggest the role of WNT10B as a new suitable target for AML

Measurement, optimisation and control of particle properties in pharmaceutical manufacturing processes

Previously held under moratorium from 2 June 2020 until 6 June 2022.The understanding and optimisation of particle properties connected to their structure and morphology is a common objective for particle engineering applications either to improve materialhandling in the manufacturing process or to influence Critical Quality Attributes (CQAs) linked to product performance. This work aims to demonstrate experimental means to support a rational development approach for pharmaceutical particulate systems with a specific focus on droplet drying platforms such as spray drying. Micro-X-ray tomography (micro-XRT) is widely applied in areas such as geo- and biomedical sciences to enable a three dimensional investigation of the specimens. Chapter 4 elaborates on practical aspects of micro-XRT for a quantitative analysis of pharmaceutical solid products with an emphasis on implemented image processing and analysis methodologies. Potential applications of micro-XRT in the pharmaceutical manufacturing process can range from the characterisation of single crystals to fully formulated oral dosage forms. Extracted quantitative information can be utilised to directly inform product design and production for process development or optimisation. The non-destructive nature of the micro-XRT analysis can be further employed to investigate structure-performance relationships which might provide valuable insights for modelling approaches. Chapter 5 further demonstrates the applicability of micro-XRT for the analysis of ibuprofen capsules as a multi-particulate system each with a population of approximately 300 pellets. The in-depth analysis of collected micro-XRT image data allowed the extraction of more than 200 features quantifying aspects of the pellets’ size, shape, porosity, surface and orientation. Employed feature selection and machine learning methods enabled the detection of broken pellets within a classification model. The classification model has an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets from three capsules. The combination of single droplet drying (SDD) experiments with a subsequent micro-XRT analysis was used for a quantitative investigation of the particle design space and is described in Chapter 6. The implemented platform was applied to investigate the solidification of formulated metformin hydrochloride particles using D-mannitol and hydroxypropyl methylcellulose within a selected, pragmatic particle design space. The results indicate a significant impact of hydroxypropyl methylcellulose reducing liquid evaporation rates and particle drying kinetics. The morphology and internal structure of the formulated particles after drying are dominated by a crystalline core of D-mannitol partially suppressed with increasing hydroxypropyl methylcellulose additions. The characterisation of formulated metformin hydrochloride particles with increasing polymer content demonstrated the importance of an early-stage quantitative assessment of formulation-related particle properties. A reliable and rational spray drying development approach needs to assess parameters of the compound system as well as of the process itself in order to define a well-controlled and robust operational design space. Chapter 7 presents strategies for process implementation to produce peptide-based formulations via spray drying demonstrated using s-glucagon as a model peptide. The process implementation was supported by an initial characterisation of the lab-scale spray dryer assessing a range of relevant independent process variables including drying temperature and feed rate. The platform response was captured with available and in-house developed Process Analytical Technology. A B-290 Mini-Spray Dryer was used to verify the development approach and to implement the pre-designed spray drying process. Information on the particle formation mechanism observed in SDD experiments were utilised to interpret the characteristics of the spray dried material.The understanding and optimisation of particle properties connected to their structure and morphology is a common objective for particle engineering applications either to improve materialhandling in the manufacturing process or to influence Critical Quality Attributes (CQAs) linked to product performance. This work aims to demonstrate experimental means to support a rational development approach for pharmaceutical particulate systems with a specific focus on droplet drying platforms such as spray drying. Micro-X-ray tomography (micro-XRT) is widely applied in areas such as geo- and biomedical sciences to enable a three dimensional investigation of the specimens. Chapter 4 elaborates on practical aspects of micro-XRT for a quantitative analysis of pharmaceutical solid products with an emphasis on implemented image processing and analysis methodologies. Potential applications of micro-XRT in the pharmaceutical manufacturing process can range from the characterisation of single crystals to fully formulated oral dosage forms. Extracted quantitative information can be utilised to directly inform product design and production for process development or optimisation. The non-destructive nature of the micro-XRT analysis can be further employed to investigate structure-performance relationships which might provide valuable insights for modelling approaches. Chapter 5 further demonstrates the applicability of micro-XRT for the analysis of ibuprofen capsules as a multi-particulate system each with a population of approximately 300 pellets. The in-depth analysis of collected micro-XRT image data allowed the extraction of more than 200 features quantifying aspects of the pellets’ size, shape, porosity, surface and orientation. Employed feature selection and machine learning methods enabled the detection of broken pellets within a classification model. The classification model has an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets from three capsules. The combination of single droplet drying (SDD) experiments with a subsequent micro-XRT analysis was used for a quantitative investigation of the particle design space and is described in Chapter 6. The implemented platform was applied to investigate the solidification of formulated metformin hydrochloride particles using D-mannitol and hydroxypropyl methylcellulose within a selected, pragmatic particle design space. The results indicate a significant impact of hydroxypropyl methylcellulose reducing liquid evaporation rates and particle drying kinetics. The morphology and internal structure of the formulated particles after drying are dominated by a crystalline core of D-mannitol partially suppressed with increasing hydroxypropyl methylcellulose additions. The characterisation of formulated metformin hydrochloride particles with increasing polymer content demonstrated the importance of an early-stage quantitative assessment of formulation-related particle properties. A reliable and rational spray drying development approach needs to assess parameters of the compound system as well as of the process itself in order to define a well-controlled and robust operational design space. Chapter 7 presents strategies for process implementation to produce peptide-based formulations via spray drying demonstrated using s-glucagon as a model peptide. The process implementation was supported by an initial characterisation of the lab-scale spray dryer assessing a range of relevant independent process variables including drying temperature and feed rate. The platform response was captured with available and in-house developed Process Analytical Technology. A B-290 Mini-Spray Dryer was used to verify the development approach and to implement the pre-designed spray drying process. Information on the particle formation mechanism observed in SDD experiments were utilised to interpret the characteristics of the spray dried material

Image Processing and Simulation Toolboxes of Microscopy Images of Bacterial Cells

Recent advances in microscopy imaging technology have allowed the characterization of the dynamics of cellular processes at the single-cell and single-molecule level. Particularly in bacterial cell studies, and using the E. coli as a case study, these techniques have been used to detect and track internal cell structures such as the Nucleoid and the Cell Wall and fluorescently tagged molecular aggregates such as FtsZ proteins, Min system proteins, inclusion bodies and all the different types of RNA molecules. These studies have been performed with using multi-modal, multi-process, time-lapse microscopy, producing both morphological and functional images. To facilitate the finding of relationships between cellular processes, from small-scale, such as gene expression, to large-scale, such as cell division, an image processing toolbox was implemented with several automatic and/or manual features such as, cell segmentation and tracking, intra-modal and intra-modal image registration, as well as the detection, counting and characterization of several cellular components. Two segmentation algorithms of cellular component were implemented, the first one based on the Gaussian Distribution and the second based on Thresholding and morphological structuring functions. These algorithms were used to perform the segmentation of Nucleoids and to identify the different stages of FtsZ Ring formation (allied with the use of machine learning algorithms), which allowed to understand how the temperature influences the physical properties of the Nucleoid and correlated those properties with the exclusion of protein aggregates from the center of the cell. Another study used the segmentation algorithms to study how the temperature affects the formation of the FtsZ Ring. The validation of the developed image processing methods and techniques has been based on benchmark databases manually produced and curated by experts. When dealing with thousands of cells and hundreds of images, these manually generated datasets can become the biggest cost in a research project. To expedite these studies in terms of time and lower the cost of the manual labour, an image simulation was implemented to generate realistic artificial images. The proposed image simulation toolbox can generate biologically inspired objects that mimic the spatial and temporal organization of bacterial cells and their processes, such as cell growth and division and cell motility, and cell morphology (shape, size and cluster organization). The image simulation toolbox was shown to be useful in the validation of three cell tracking algorithms: Simple Nearest-Neighbour, Nearest-Neighbour with Morphology and DBSCAN cluster identification algorithm. It was shown that the Simple Nearest-Neighbour still performed with great reliability when simulating objects with small velocities, while the other algorithms performed better for higher velocities and when there were larger clusters present

Direct Visualization of Single Molecule Amyloid Beta Peptides in Action by High-Speed Atomic Force Microscopy

Alzheimer’s disease (AD) has recently been labelled the ‘twenty-first century plaque’ and it has been estimated that more than 40 million people around the world suffer from this progressive disease. Within the amyloid hypothesis in Alzheimer’s disease, current focus has shifted to earlier stages of amyloid beta (Aβ) peptide assembly, involving soluble oligomers and smaller aggregates that are more toxic to cells compared to their morphological distinct fibril forms. Critical to the Aβ field is unlocking the molecular-level kinetic pathways or mechanisms of oligomerization, leading to the culprit subset or specific species of Aβ oligomer populations responsible for the disease etiology. However, since the protein aggregation is highly dynamic, involving temporally and kinetically controlled processes, and also very difficult to monitor in the early stages, a key challenge is understanding dynamic Aβ at the single molecule level under physiologically relevant conditions such as liquid. Therefore, to probe the combined structural-dynamics of Aβ peptides, High-Speed Atomic Force Microscope (HS-AFM) was used, as it enables direct observation of single molecules at sub-molecular spatial resolution and with time resolution of up to 50milliseconds. For example, the main inventors of HS-AFM, Toshio Ando and his co-workers, produced realtime videos of the walking mechanism of a single Myosin V along an actin filament. Such movies existed before only as animations, but have now become reality due to the advent of HS-AFM. The thesis comprises 5 chapters, including introduction, 3 experimental and conclusion/future work..

Single atom imaging with time-resolved electron microscopy

Developments in scanning transmission electron microscopy (STEM) have opened up new possibilities for time-resolved imaging at the atomic scale. However, rapid imaging of single atom dynamics brings with it a new set of challenges, particularly regarding noise and the interaction between the electron beam and the specimen. This thesis develops a set of analytical tools for capturing atomic motion and analyzing the dynamic behaviour of materials at the atomic scale. Machine learning is increasingly playing an important role in the analysis of electron microscopy data. In this light, new unsupervised learning tools are developed here for noise removal under low-dose imaging conditions and for identifying the motion of surface atoms. The scope for real-time processing and analysis is also explored, which is of rising importance as electron microscopy datasets grow in size and complexity. These advances in image processing and analysis are combined with computational modelling to uncover new chemical and physical insights into the motion of atoms adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis, where the catalytic activity can depend intimately on the atomic environment. The study of Cu atoms on a graphene oxide support reveals that the atoms undergo anomalous diffusion as a result of spatial and energetic disorder present in the substrate. The investigation is extended to examine the structure and stability of small Cu clusters on graphene oxide, with atomistic modelling used to understand the significant role played by the substrate. Finally, the analytical methods are used to study the surface reconstruction of silicon alongside the electron beam-induced motion of adatoms on the surface. Taken together, these studies demonstrate the materials insights that can be obtained with time-resolved STEM imaging, and highlight the importance of combining state-ofthe- art imaging with computational analysis and atomistic modelling to quantitatively characterize the behaviour of materials with atomic resolution.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement 291522–3DIMAGE, as well as from the European Union Seventh Framework Programme under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3)

2015 IMSAloquium, Student Investigation Showcase

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