An incremental approach to automated protein localisation

Tscherepanow M, Jensen N, Kummert F. An incremental approach to automated protein localisation. BMC Bioinformatics. 2008;9(1): 445.Background: The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected. Results: We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided. Conclusion: We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments

Improvements in Digital Holographic Microscopy

The Ph.D. dissertation consists of developing a series of innovative computational methods for improving digital holographic microscopy (DHM). DHM systems are widely used in quantitative phase imaging for studying micrometer-size biological and non-biological samples. As any imaging technique, DHM systems have limitations that reduce their applicability. Current limitations in DHM systems are: i) the number of holograms (more than three holograms) required in slightly off-axis DHM systems to reconstruct the object phase information without applying complex computational algorithms; ii) the lack of an automatic and robust computation algorithm to compensate for the interference angle and reconstruct the object phase information without phase distortions in off-axis DHM systems operating in telecentric and image plane conditions; iii) the necessity of an automatic computational algorithm to simultaneously compensate for the interference angle and numerically focus out-of-focus holograms on reconstructing the object phase information without phase distortions in off-axis DHM systems operating in telecentric regime; iv) the deficiency of reconstructing phase images without phase distortions at video-rate speed in off-axis DHM operating in telecentric regime, and image plane conditions; v) the lack of an open-source library for any DHM optical configuration; and, finally, vi) the tradeoff between speckle contrast and spatial resolution existing in current computational strategies to reduce the speckle contrast. This Ph.D. dissertation is motivated to overcome or at least reduce the six limitations mentioned above. Each chapter of this dissertation presents and discusses a novel computational method from the theoretical and experimental point of view to address each of these limitations

Fast widefield techniques for fluorescence and phase endomicroscopy

Thesis (Ph.D.)--Boston UniversityEndomicroscopy is a recent development in biomedical optics which gives researchers and physicians microscope-resolution views of intact tissue to complement macroscopic visualization during endoscopy screening. This thesis presents HiLo endomicroscopy and oblique back-illumination endomicroscopy, fast widefield imaging techniques with fluorescence and phase contrast, respectively. Fluorescence imaging in thick tissue is often hampered by strong out-of-focus background signal. Laser scanning confocal endomicroscopy has been developed for optically-sectioned imaging free from background, but reliance on mechanical scanning fundamentally limits the frame rate and represents significant complexity and expense. HiLo is a fast, simple, widefield fluorescence imaging technique which rejects out-of-focus background signal without the need for scanning. It works by acquiring two images of the sample under uniform and structured illumination and synthesizing an optically sectioned result with real-time image processing. Oblique back-illumination microscopy (OBM) is a label-free technique which allows, for the first time, phase gradient imaging of sub-surface morphology in thick scattering tissue with a reflection geometry. OBM works by back-illuminating the sample with the oblique diffuse reflectance from light delivered via off-axis optical fibers. The use of two diametrically opposed illumination fibers allows simultaneous and independent measurement of phase gradients and absorption contrast. Video-rate single-exposure operation using wavelength multiplexing is demonstrated

Submicron-resolution Photoacoustic Microscopy of Endogenous Light-absorbing Biomolecules

Photoacoustic imaging in biomedicine has the unique advantage of probing endogenous light absorbers at various length scales with a 100% relative sensitivity. Among the several modalities of photoacoustic imaging, optical-resolution photoacoustic microscopy (OR-PAM) can achieve high spatial resolution, on the order of optical wavelength, at \u3c1 mm depth in biological tissue (the optical ballistic regime). OR-PAM has been applied successfully to structural and functional imaging of blood vasculature and red blood cells in vivo. Any molecules which absorb sufficient light at certain wavelengths can potentially be imaged by PAM. Compared with pure optical imaging, which typically targets fluorescent markers, label-free PAM avoids the major concerns that the fluorescent labeling probes may disturb the function of biomolecules and may have an insufficient density. This dissertation aims to advance label-free OR-PAM to the subcellular scale. The first part of this dissertation describes the technological advancement of PAM yielding high spatial resolution in 3D. The lateral resolution was improved by using optical objectives with high numerical apertures for optical focusing. The axial resolution was improved by using broadband ultrasonic transducers for ultrasound detection. We achieved 220 nm lateral resolution in transmission mode, 0.43 µm lateral resolution in reflection mode, 7.6 µm axial resolution in normal tissue, and 5.8 µm axial resolution with silicone oil immersion/injection. The achieved lateral resolution and axial resolution were the finest reported at the time. With high-resolution in 3D, PAM was demonstrated to resolve cellular and subcellular structures in vivo, such as red blood cells and melanosomes in melanoma cells. Compared with previous PAM systems, our high-resolution PAM could resolve capillaries in mouse ears more clearly. As an example application, we demonstrated intracellular temperature imaging, assisted by fluorescence signal detection, with sub-degree temperature resolution and sub-micron lateral resolution. The second part of this dissertation describes the exploration of endogenous light-absorbing biomolecules for PAM. We demonstrated cytochromes and myoglobin as new absorption contrasts for PAM and identified the corresponding optimal wavelengths for imaging. Fixed fibroblasts on slides and mouse ear sections were imaged by PAM at 422 nm and 250 nm wavelengths to reveal cytoplasms and nuclei, respectively, as confirmed by standard hematoxylin and eosin (H&E) histology. By imaging a blood-perfused mouse heart at 532 nm down to 150 µm in depth, we derived the myocardial sheet thickness and the cleavage height from an undehydrated heart for the first time. The findings promote PAM at new wavelengths and open up new possibilities for characterizing biological tissue. Of particular interest, dual-wavelength PAM around 250 nm and 420 nm wavelengths is analogous to H&E histology. The last part of this dissertation describes the development of sectioning photoacoustic microscopy (SPAM), based on the advancement in spatial resolution and new contrasts for PAM, with applications in brain histology. Label-free SPAM, assisted by a microtome, acquires serial distortion-free images of a specimen on the surface. By exciting cell nuclei at 266 nm wavelength with high resolution, SPAM could pinpoint cell nuclei sensitively and specifically in the mouse brain section, as confirmed by H&E histology. SPAM was demonstrated to generate high-resolution 3D images, highlighting cell nuclei, of formalin-fixed paraffin-embedded mouse brains without tissue staining or clearing. SPAM can potentially serve as a high-throughput and minimal-artifact substitute for histology, probe many other biomolecules and cells, and become a universal tool for animal or human whole-organ microscopy, with diverse applications in life sciences

Virtual Histology with Photoacoustic Remote Sensing

Histopathology plays a central role in cancer screening, surgical margin analysis, cancer classification, and understanding disease progression. The vast majority of biopsies or surgical excisions are examined via transmission-mode bright-field microscopy. However, bright-field microscopy requires thin stained tissue samples as it is unable to visualize contrast on thick tissues. Consequently, biopsies and surgically excised specimens undergo extensive tissue processing to prepare histology slides. This tissue processing can take up to two weeks for complex cases before a diagnosis can be presented, potentially resulting in poorer patient outcomes. Surgical margins are commonly analyzed intraoperatively using frozen sectional analysis. While this technique has improved patient outcomes, the quality of frozen sections is often lower than post-operative histologic analysis. This lower quality leads to significant variability in diagnosis. Ultimately, both frozen section analysis and standard histologic analysis are limiting because of the need to process tissues to cater to bright-field microscopy. It would be desirable to forego creating thin tissue sections and instead visualize tissue morphology directly on biopsies and surgical specimens or even directly on the patient’s body (in-situ). Photoacoustic remote sensing (PARSTM) is an emerging non-contact imaging technique. PARS microscopy is an all-optical photoacoustic imaging modality that takes advantage of endogenous optical absorption present within tissues to provide contrast to enable non- contact label-free imaging. PARS has demonstrated excellent resolution and contrast in various applications, such as in-vivo imaging, functional imaging, and deep imaging, while operating in a reflection-mode architecture. This non-contact label-free reflection-mode design lends itself well to imaging unprocessed tissue specimens or in-situ morphological assessment. Using PARS microscopy, this thesis takes preliminary steps towards an in-situ surgical microscope. These steps take the form of developing a PARS system that can recover contrast from DNA and visualize the resulting nuclear morphology in real-time and on arbitrarily sized specimens. Later, this system was expanded to image additional contrasts from hemoglobin to approach the diagnostic information provided by standard histopathology. This research imaged a variety of human tissue types, including breast, gastrointestinal, and skin. These specimens were in the form of thin unstained slides and thick tissue blocks. The tissue blocks serve as an analog to visualization of contrast fresh tissues and in-situ imaging. Adjacent sections of each tissue type were prepared using standard histopathology and compared against the PARS images for experimental validation. These results represent the first reports of imaging human tissues with a non-contact label-free reflection-mode modality. The author believes this research takes vital steps towards an imaging technique that may one day reveal cancer in-situ

Targeted Nanodiamonds for Identification of Subcellular Protein Assemblies in Mammalian Cells

Transmission electron microscopy (TEM) can be used to successfully determine the structures of proteins. However, such studies are typically done ex situ after extraction of the protein from the cellular environment. Here we describe an application for nanodiamonds as targeted intensity contrast labels in biological TEM, using the nuclear pore complex (NPC) as a model macroassembly. We demonstrate that delivery of antibody-conjugated nanodiamonds to live mammalian cells using maltotriose-conjugated polypropylenimine dendrimers results in efficient localization of nanodiamonds to the intended cellular target. We further identify signatures of nanodiamonds under TEM that allow for unambiguous identification of individual nanodiamonds from a resin-embedded, OsO4-stained environment. This is the first demonstration of nanodiamonds as labels for nanoscale TEM-based identification of subcellular protein assemblies. These results, combined with the unique fluorescence properties and biocompatibility of nanodiamonds, represent an important step toward the use of nanodiamonds as markers for correlated optical/electron bioimaging.Comment: 38 pages, 6 figures, SI section with 3 figure

Computational Imaging for Phase Retrieval and Biomedical Applications

In conventional imaging, optimizing hardware is prioritized to enhance image quality directly. Digital signal processing is viewed as supplementary. Computational imaging intentionally distorts images through modulation schemes in illumination or sensing. Then its reconstruction algorithms extract desired object information from raw data afterwards. Co-designing hardware and algorithms reduces demands on hardware and achieves the same or even better image quality. Algorithm design is at the heart of computational imaging, with model-based inverse problem or data-driven deep learning methods as approaches. This thesis presents research work from both perspectives, with a primary focus on the phase retrieval issue in computational microscopy and the application of deep learning techniques to address biomedical imaging challenges. The first half of the thesis begins with Fourier ptychography, which was employed to overcome chromatic aberration problems in multispectral imaging. Then, we proposed a novel computational coherent imaging modality based on Kramers-Kronig relations, aiming to replace Fourier ptychography as a non-iterative method. While this approach showed promise, it lacks certain essential characteristics of the original Fourier ptychography. To address this limitation, we introduced two additional algorithms to form a whole package scheme. Through comprehensive evaluation, we demonstrated that the combined scheme outperforms Fourier ptychography in achieving high-resolution, large field-of-view, aberration-free coherent imaging. The second half of the thesis shifts focus to deep-learning-based methods. In one project, we optimized the scanning strategy and image processing pipeline of an epifluorescence microscope to address focus issues. Additionally, we leveraged deep-learning-based object detection models to automate cell analysis tasks. In another project, we predicted the polarity status of mouse embryos from bright field images using adapted deep learning models. These findings highlight the capability of computational imaging to automate labor-intensive processes, and even outperform humans in challenging tasks.</p

Digital image processing for prognostic and diagnostic clinical pathology

When digital imaging and image processing methods are applied to clinical diagnostic and prognostic needs, the methods can be seen to increase human understanding and provide objective measurements. Most current clinical applications are limited to providing subjective information to healthcare professionals rather than providing objective measures. This Thesis provides detail of methods and systems that have been developed both for objective and subjective microscopy applications. A system framework is presented that provides a base for the development of microscopy imaging systems. This practical framework is based on currently available hardware and developed with standard software development tools. Image processing methods are applied to counter optical limitations of the bright field microscope, automating the system and allowing for unsupervised image capture and analysis. Current literature provides evidence that 3D visualisation has provided increased insight and application in many clinical areas. There have been recent advancements in the use of 3D visualisation for the study of soft tissue structures, but its clinical application within histology remains limited. Methods and applications have been researched and further developed which allow for the 3D reconstruction and visualisation of soft tissue structures using microtomed serial histological sections specimens. A system has been developed suitable for this need is presented giving considerations to image capture, data registration and 3D visualisation, requirements. The developed system has been used to explore and increase 3D insight on clinical samples. The area of automated objective image quantification of microscope slides presents the allure of providing objective methods replacing existing objective and subjective methods, increasing accuracy and rsducinq manual burden. One such existing objective test is DNA Image Ploidy which seeks to characterise cancer by the measurement of DNA content within individual cell nuclei, an accepted but manually burdensome method. The main novelty of the work completed lies in the development of an automated system for DNA Image Ploidy measurement, combining methods for automatic specimen focus, segmentation, parametric extraction and the implementation of an automated cell type classification system. A consideration for any clinical image processing system is the correct sampling of the tissue under study. VVhile the image capture requirements for both objective systems and subjective systems are similar there is also an important link between the 3D structures of the tissue. 3D understanding can aid in decisions regarding the sampling criteria of objective tests for as although many tests are completed in the 2D realm the clinical samples are 3D objects. Cancers such as Prostate and Breast cancer are known to be multi-focal, with areas of seeming physically, independent areas of disease within a single site. It is not possible to understand the true 3D nature of the samples using 2D micro-tomed sections in isolation from each other. The 3D systems described in this report provide a platform of the exploration of the true multi focal nature of disease soft tissue structures allowing for the sampling criteria of objective tests such as DNA Image Ploidy to be correctly set. For the Automated DNA Image Ploidy and the 3D reconstruction and visualisation systems, clinical review has been completed to test the increased insights provided. Datasets which have been reconstructed from microtomed serial sections and visualised with the developed 3D system area presented. For the automated DNA Image Ploidy system, the developed system is compared with the existing manual method to qualify the quality of data capture, operational speed and correctness of nuclei classification. Conclusions are presented for the work that has been completed and discussion given as to future areas of research that could be undertaken, extending the areas of study, increasing both clinical insight and practical application

Bacterial image analysis based on time-lapse microscopy

H time-lapse μικροσκοπία επιτρέπει πλέον τη λεπτομερή δημιουργία δεδομένων από δυναμικές κυτταρικές διεργασίες σε επίπεδο μεμονωμένων κυττάρων (single cell level). Πρόσφατες μελέτες έχουν τονίσει τη χρήση και τη σημασία αυτής της τεχνολογίας για τη διερεύνηση του βιολογικού θορύβου στην έκφραση γονιδίων και των βιολογικών μονοπατιών. Μαθηματικά και στατιστικά μοντέλα για την περιγραφή και τον έλεγχο υποθέσεων σχετικά με τη δυναμική των βακτηριακών κοινοτήτων βασίζονται στην ανάλυση των &quot;ταινιών&quot; time-lapse εικόνων. Ωστόσο, η ανάλυση των ακολουθιών εικόνας είναι πολύ χρονοβόρα και επιρρεπής σε λάθη, δεδομένου ότι απαιτεί ακόμα και σήμερα σε μεγάλο βαθμό τη συμμετοχή ενός ανθρώπου εμπειρογνώμονα. Έχουμε αναπτύξει μία σωλήνωση (pipeline) αλγορίθμων που προσδιορίζει τα όρια των επιμέρους κυττάρων (βακτηριακή κατάτμηση) και τα ανιχνεύει στην πάροδο του χρόνου (ανίχνευση βακτηρίων), ακόμη και σε μεγάλου μεγέθους αποικίες μικροβίων, όπου υπάρχει μεγάλη δυσκολία στον εντοπισμό των ορίων μεμονωμένων κυττάρων. Η μεθοδολογία μας συνδυάζει προηγμένες τεχνικές από την επεξεργασία εικόνας και τη μηχανική μάθηση για την κατάτμηση των βακτηρίων και την παρακολούθηση της ανάπτυξης μιας αποικίας (κατασκευή δέντρου γενεαλογίας). Επιπλέον, είναι πλήρως αυτοματοποιημένη, υπολογιστικά αποδοτική και κατάλληλη για υψηλής ρυθμαπόδοσης ανάλυση χωρίς να απαιτείται παρέμβαση του χρήστη.Time-lapse microscopy now enables the continuous monitoring of dynamic cellular processes at the single cell level. Recent studies have highlighted the use and importance of this technology for investigating biological noise in genes expression and the behavior of biological pathways. Mathematical and statistical models for describing and testing hypotheses regarding the dynamics of bacterial communities rely on the analysis of time-lapse image &quot;movies&quot;. However, the analysis of image sequences is very time consuming and error prone since it currently requires the heavy involvement of a human expert. We developed a pipeline of algorithms for determining the boundaries of individual cells (bacterial segmentation) and follow them over time (bacterial tracking), even in large-size microbial colonies, where there is great difficulty in identifying individual cell boundaries. Our methodology combines advanced image processing and machine learning techniques for segmentation of bacteria and monitoring the development of the colony (construction of the lineage tree). Furthermore it is fully automated, computationally efficient and suitable for high throughput image analysis without requiring any user intervention