Advanced optical imaging for the rational design of nanomedicines

Despite the enormous potential of nanomedicines to shape the future of medicine, their clinical translation remains suboptimal. Translational challenges are present in every step of the development pipeline, from a lack of understanding of patient heterogeneity to insufficient insights on nanoparticle properties and their impact on material-cell interactions. Here, we discuss how the adoption of advanced optical microscopy techniques, such as super-resolution optical microscopies, correlative techniques, and high-content modalities, could aid the rational design of nanocarriers, by characterizing the cell, the nanomaterial, and their interaction with unprecedented spatial and/or temporal detail. In this nanomedicine arena, we will discuss how the implementation of these techniques, with their versatility and specificity, can yield high volumes of multi-parametric data; and how machine learning can aid the rapid advances in microscopy: from image acquisition to data interpretation.</p

Advanced optical imaging for the rational design of nanomedicines

Despite the enormous potential of nanomedicines to shape the future of medicine, their clinical translation remains suboptimal. Translational challenges are present in every step of the development pipeline, from a lack of understanding of patient heterogeneity to insufficient insights on nanoparticle properties and their impact on material-cell interactions. Here, we discuss how the adoption of advanced optical microscopy techniques, such as super-resolution optical microscopies, correlative techniques, and high-content modalities, could aid the rational design of nanocarriers, by characterizing the cell, the nanomaterial, and their interaction with unprecedented spatial and/or temporal detail. In this nanomedicine arena, we will discuss how the implementation of these techniques, with their versatility and specificity, can yield high volumes of multi-parametric data; and how machine learning can aid the rapid advances in microscopy: from image acquisition to data interpretation.</p

Analysis of Alterations in Cellular Architecture by cryo-Soft X-ray Tomography and Ultrastructure Expansion Microscopy

The central aim of this thesis was to visualize changes in cellular architecture induced by environmental factors such as drug treatment or viral infection. High-resolution microscopic analysis of such alterations is crucial to elucidate the mechanisms of disease, as well as to develop potential therapies. The importance thereof was clearly underscored by the COVID 19 pandemic, whose causative agent, the novel severe acute respiratory syndrome coronavirus 2 (SARS CoV 2), infected and killed millions of people across the globe. By using high-resolution 3D imaging by cryo soft X ray tomography (cryo SXT), rapid changes in the cellular ultrastructure upon infection with SARS CoV 2 or feline infectious peritonitis virus (FIPV) could be visualized. The changes induced by viral infection were affected by treatment with FDA-approved drugs, which had previously emerged as candidates from drug-repurposing screens to combat SARS CoV 2. Cryo SXT analysis revealed formation of lysosome-associated dark-rimmed vesicles (DRVs), which were demonstrated to be multilamellar lipid deposits by transmission electron microscopy. The observed striking interplay of drug- and virus-induced alterations on the level of size and number of lysosome-associated DRVs suggests involvement of lysosomal function in the inhibition of the viruses by the drugs. It is likely that this inhibition is either due to impairment of lysosomal escape or due to reduced lipid availability for membrane remodeling essential for virus replication. To comprehensively test these models, complementary screening tools are required, which allow ultrastructural analysis at a higher throughput than cryo SXT. For that, the recently developed ultrastructure expansion microscopy (U ExM) is a promising technique. Comparing the structural preservation of cell organelles after the U ExM sample preparation to that of cells acquired under near-native conditions in cryo SXT, it emerged that the morphology of large organelles such as nuclei and plasma membrane can be visualized efficiently by U ExM, while very small or lipid-dense structures were more difficult to preserve accurately. These results highlight the benefits of cryo SXT, but also the potential of U ExM to complement cryo SXT with its higher throughput of ultrastructure visualization, even if its resolution is lower than that of cryo SXT. Therefore, U ExM can be used for experimental characterization for cryo SXT, as one step towards more efficiency of cryo SXT. Another step is to automate the segmentation of cryo SXT tomograms. To that end, a deep learning platform was trained and validated on a large pool of cryo SXT data acquired in this work. This convoluted neural network performs full annotation of cellular features in cryo SXT tomograms within less than half an hour on consumer-grade GPUs, thereby significantly reducing the time required for data analysis. Taken together, these results illustrate how cryo SXT, U ExM and deep learning can be used complementarily to address highly relevant biological questions

Metode za restauraciju i segmentaciju digitalne slike zasnovane naminimizaciji funkcije energije koja favorizuje retke reprezentacijesignala

Energy minimization approach is widely used in image processing applications. Many image processing problems can be modelled in a form of a minimization problem. This thesis deals with two crucial tasks of image analysis workflows: image restoration and segmentation of images corrupted by blur and noise. Both image restoration and segmentation are modelled as energy minimization problems, where energy function is composed of two parts: data fidelity term and regularization term. The main contribution of this thesis is development of new data fidelity and regularization terms for both image restoration and segmentation tasks. Image restoration methods (non-blind and blind deconvolution and superresolution reconstruction) developed within this thesis are suited for mixed Poisson-Gaussian noise which is encountered in many realistic imaging conditions. We use generalized Anscombe variance stabilization transformation for removing signal-dependency of noise. We propose novel data fidelity term which incorporates variance stabilization transformation process into account. Turning our attention to the regularization term for image restoration, we investigate how sparsity promoting regularization in the gradient domain formulated as Total Variation, can be improved in the presence of blur and mixed Poisson-Gaussian noise. We found that Huber potential function leads to significant improvement of restoration performance. In this thesis we propose new segmentation method, the so called coverage segmentation, which estimates the relative coverage of each pixel in a sensed image by each image component. Its data fidelity term takes into account blurring and down-sampling processes and in that way it provides robust segmentation in the presence of blur, allowing at the same time segmentation at increased spatial resolution. In addition, new sparsity promoting regularization terms are suggested: (i) Huberized Total Variation which provides smooth object boundaries and noise removal, and (ii) non-edge image fuzziness, which responds to an assumption that imaged objects are crisp and that fuzziness is mainly due to the imaging and digitization process. The applicability of here proposed restoration and coverage segmentation methods is demonstrated for Transmission Electron Microscopy image enhancement and segmentation of micro-computed tomography and hyperspectral images.Поступак минимизације функције енергије је често коришћен за решавање проблема у обради дигиталне слике. Предмет истраживања тезе су два круцијална задатка дигиталне обраде слике: рестаурација и сегментација слика деградираних шумом и замагљењем. И рестaурација и сегментација су моделовани као проблеми минимизације функције енергије која представља збир две функције: функције фитовања података и регуларизационе функције. Главни допринос тезе је развој нових функција фитовања података и нових регуларизационих функција за рестаурацију и сегментацију. Методе за рестаурацију (оне код којих је функција замагљења позната и код којих је функцију замагљења потребно оценити на основу датих података као и методе за реконструкцију слике у супер-резолуцији) развијене у оквиру ове тезе третирају мешавину Поасоновог и Гаусовог шума који се појављује у многобројним реалистичним сценаријима. За третирање такве врсте шума користили смо нелинеарну трансформацију и предложили смо нову функцију фитовања података која узима у обзир такву трансформацију. У вези са регуларизационим функцијама смо тестирали хипотезу да се функција Тоталне Варијације која промовише ретку слику у градијентном домену може побољшати уколико се користе тзв. потенцијалне функције. Показали смо да се употребом Хуберове потенцијалне функције може значајно побољшати квалитет рестауриране слике која је деградирана замагљењем и мешавином Поасоновог и Гаусовог шума. У оквиру тезе смо предложили нову методу сегментације која допушта делимичну покривеност пиксела објектом. Функција фитовања података ове методе укључује и модел замагљења и смањења резолуције. На тај начин је постигнута робустност сегментације у присуству замагљења и добијена могућност сегментирања слике у супер-резолуцији. Додатно, нове регуларизационе функције које промовишу ретке репрезентације слике су предложене. Предложене методе рестаурације и сегментације која допушта делимичну покривеност пиксела објектом су примењене на слике добијене помоћу електронског микроскопа, хиперспектралне слике и медицинске ЦТ слике.Postupak minimizacije funkcije energije je često korišćen za rešavanje problema u obradi digitalne slike. Predmet istraživanja teze su dva krucijalna zadatka digitalne obrade slike: restauracija i segmentacija slika degradiranih šumom i zamagljenjem. I restauracija i segmentacija su modelovani kao problemi minimizacije funkcije energije koja predstavlja zbir dve funkcije: funkcije fitovanja podataka i regularizacione funkcije. Glavni doprinos teze je razvoj novih funkcija fitovanja podataka i novih regularizacionih funkcija za restauraciju i segmentaciju. Metode za restauraciju (one kod kojih je funkcija zamagljenja poznata i kod kojih je funkciju zamagljenja potrebno oceniti na osnovu datih podataka kao i metode za rekonstrukciju slike u super-rezoluciji) razvijene u okviru ove teze tretiraju mešavinu Poasonovog i Gausovog šuma koji se pojavljuje u mnogobrojnim realističnim scenarijima. Za tretiranje takve vrste šuma koristili smo nelinearnu transformaciju i predložili smo novu funkciju fitovanja podataka koja uzima u obzir takvu transformaciju. U vezi sa regularizacionim funkcijama smo testirali hipotezu da se funkcija Totalne Varijacije koja promoviše retku sliku u gradijentnom domenu može poboljšati ukoliko se koriste tzv. potencijalne funkcije. Pokazali smo da se upotrebom Huberove potencijalne funkcije može značajno poboljšati kvalitet restaurirane slike koja je degradirana zamagljenjem i mešavinom Poasonovog i Gausovog šuma. U okviru teze smo predložili novu metodu segmentacije koja dopušta delimičnu pokrivenost piksela objektom. Funkcija fitovanja podataka ove metode uključuje i model zamagljenja i smanjenja rezolucije. Na taj način je postignuta robustnost segmentacije u prisustvu zamagljenja i dobijena mogućnost segmentiranja slike u super-rezoluciji. Dodatno, nove regularizacione funkcije koje promovišu retke reprezentacije slike su predložene. Predložene metode restauracije i segmentacije koja dopušta delimičnu pokrivenost piksela objektom su primenjene na slike dobijene pomoću elektronskog mikroskopa, hiperspektralne slike i medicinske CT slike

Microscopy Conference 2021 (MC 2021) - Proceedings

Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2021"

Compact realizations of optical super-resolution microscopy for the life sciences

Sandmeyer A. Compact realizations of optical super-resolution microscopy for the life sciences. Bielefeld: Universität Bielefeld; 2019

Electron tomography of cells

The electron microscope has contributed deep insights into biological structure since its invention nearly 80 years ago. Advances in instrumentation and methodology in recent decades have now enabled electron tomography to become the highest resolution three-dimensional (3D) imaging technique available for unique objects such as cells. Cells can be imaged either plastic-embedded or frozen-hydrated. Then the series of projection images are aligned and back-projected to generate a 3D reconstruction or ‘tomogram’. Here, we review how electron tomography has begun to reveal the molecular organization of cells and how the existing and upcoming technologies promise even greater insights into structural cell biology

Using light and electron microscopy to understand the replication of aphthoviruses

Aphthovirus is a genus of the family Picornaviridae which includes Foot-and-mouth-disease virus (FMDV) and Equine rhinitis A virus (ERAV). FMDV is a highly contagious pathogen infecting cloven-hoofed animals and is hence economically important. FMDV replication takes place in the cytoplasm and induces massive rearrangement of the host cell membranes to facilitate virus replication. Rearranged membranes form structures providing the site of viral genome replication known as the replication organelle (RO). The understanding of the RO, viral proteins and site of virus assembly is not well established. This project applies various microscopy approaches to investigate details of aphthovirus replication in cells. FMDV 3A protein is known to play a key role in viral replication machinery. We generated recombinant viruses of FMDV with various tags fused to this protein, subsequently allowing 3A to be detected in confocal microscopy. We developed a split-GFP system to study the dynamics of 3A protein in vitro. We showed that 3A signals appeared contiguous to the Golgi membrane signals suggesting that it potentially serve as a main source of membrane associated with viral replication. This approach was taken with the aim of facilitating the development of a correlative light electron microscopy (CLEM) system to unravel the localisation of virus proteins and their link to RO in cells. ERAV was used as a surrogate model to study FMDV replication in a lower containment laboratory using cryo-electron tomography (cryo-ET). Virus particles were observed associated with membrane structures with single membrane vesicles being more predominant than double membrane vesicles in infected cells. By sub-tomogram averaging, we reconstructed 3-dimensional (3D) models of intracellular ERAV full and empty particles which were compared with structures obtained for the virus purified from tissue culture and crystallized. Additional density was identified in the ERAV empty particles potentially corresponding to RNA contact sites inside the capsid