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

Automatiseeritud meetodi arendamine bakuloviiruste kvantifitseerimiseks

Käesolevas bakalaureusetöös on välja töötatud meetod läbiva valguse mikroskoobi piltidelt rakkude suuruste mõõtmiseks ning selle abil bakuloviiruste tiitri määramiseks. Töös optimeeriti rakkude suuruse mõõtmiseks mikroskoobiga pildistamise parameetrid ja töötati välja automaatsed mõõtmisprotokollid. Samuti programmeeriti töörist ICSE (ingl. k. Image-based cell size estimation) Tools, mille abil on mikroskoobi piltide analüüs lihtne ja kiire ning loodi võimalus mõõtmistulemusi regressioonianalüüsiprogrammiga lihtsasti analüüsida. Töö tulemusena loodud bakuloviiruste tiitrimise meetod on kiirem, nõuab vähem manuaalset tööd ja kogemust kui varasemad meetodid. Uue meetodiga saadavad tulemused langevad hästi kokku varem kasutusel olnutega

Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images

Tscherepanow M, Zöllner F, Hillebrand M, Kummert F. Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images. In: Perner P, Salvetti O, eds. Proceedings of the International Conference on Mass-Data Analysis of Images and Signals (MDA). Berlin: Springer; 2008: 158-172.The automatic subcellular localisation of proteins in living cells is a critical step in determining their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to recognise these cells in bright-field microscope images taken in parallel with the regarded fluorescence micrographs. Unfortunately, currently available cell recognition methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can only be employed with too low magnifications. Therefore, this article introduces a novel approach to the robust automatic recognition of unstained living cells in bright-field microscope images. Here, the focus is on the automatic segmentation of cells

Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images

Tscherepanow M, Zöllner F, Kummert F. Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images. In: Perner P, ed. Proceedings of the Workshop on Mass-Data Analysis of Images and Signals (MDA). Leipzig: IBaI CD-Report; 2006: 86-95.The automatic subcellular localisation of proteins in living cells is a critical step to determine their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to locate and segment these cells in bright-field microscope images taken in parallel with the fluorescence micrographs. Unfortunately, currently available cell segmentation methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can merely be employed with too small magnifications. Therefore, this article introduces a novel approach for the robust automatic segmentation of unstained living cells in bright-field microscope images

Methodology for extensive evaluation of semiautomatic and interactive segmentation algorithms using simulated Interaction models

Performance of semiautomatic and interactive segmentation(SIS) algorithms are usually evaluated by employing a small number of human operators to segment the images. The human operators typically provide the approximate location of objects of interest and their boundaries in an interactive phase, which is followed by an automatic phase where the segmentation is performed under the constraints of the operator-provided guidance. The segmentation results produced from this small set of interactions do not represent the true capability and potential of the algorithm being evaluated. For example, due to inter-operator variability, human operators may make choices that may provide either overestimated or underestimated results. As well, their choices may not be realistic when compared to how the algorithm is used in the field, since interaction may be influenced by operator fatigue and lapses in judgement. Other drawbacks to using human operators to assess SIS algorithms, include: human error, the lack of available expert users, and the expense. A methodology for evaluating segmentation performance is proposed here which uses simulated Interaction models to programmatically generate large numbers of interactions to ensure the presence of interactions throughout the object region. These interactions are used to segment the objects of interest and the resulting segmentations are then analysed using statistical methods. The large number of interactions generated by simulated interaction models capture the variabilities existing in the set of user interactions by considering each and every pixel inside the entire region of the object as a potential location for an interaction to be placed with equal probability. Due to the practical limitation imposed by the enormous amount of computation for the enormous number of possible interactions, uniform sampling of interactions at regular intervals is used to generate the subset of all possible interactions which still can represent the diverse pattern of the entire set of interactions. Categorization of interactions into different groups, based on the position of the interaction inside the object region and texture properties of the image region where the interaction is located, provides the opportunity for fine-grained algorithm performance analysis based on these two criteria. Application of statistical hypothesis testing make the analysis more accurate, scientific and reliable in comparison to conventional evaluation of semiautomatic segmentation algorithms. The proposed methodology has been demonstrated by two case studies through implementation of seven different algorithms using three different types of interaction modes making a total of nine segmentation applications to assess the efficacy of the methodology. Application of this methodology has revealed in-depth, fine details about the performance of the segmentation algorithms which currently existing methods could not achieve due to the absence of a large, unbiased set of interactions. Practical application of the methodology for a number of algorithms and diverse interaction modes have shown its feasibility and generality for it to be established as an appropriate methodology. Development of this methodology to be used as a potential application for automatic evaluation of the performance of SIS algorithms looks very promising for users of image segmentation

Automated Spore Analysis using Bright-Field Imaging and Raman Microscopy

After the discovery of inadvertent shipments of viable B. anthracis spores by the United States Department of Defense in 2015, alternative and orthogonal methods were investigated to analyze spores to determine their viability. In this thesis we demonstrate a novel analysis technique that combines bright-field microscopy imaging with Raman chemical microscopy. We first developed an image segmentation routine based on the watershed method to locate individual spores within bright-field images. This routine was able to effectively demarcate 97.4% of the Bacillus spores within the bright-field images with minimal over-segmentation. Size and shape measurements, to include major and minor axis and area, were then extracted for 4048 viable spores which showed very good agreement with previously published values. When similar measurements were taken on 3627 gamma-irradiated spores, a statistically significant difference was noted for the minor axis length, ratio of major to minor axis, and total area when compared to the non-irradiated spores. Classification results show the ability to correctly classify 67% of viable spores with an 18% misclassification rate using the bright-field image by thresholding the minimum classification length. Raman chemical imaging microscopy (RCIM) was then used to measure populations of viable, gamma irradiated, and autoclaved spores of B. anthracis Sterne, B. atrophaeus. B. megaterium, and B. thuringiensis kurstaki. Significant spectral differences were observed between viable and inactivated spores due to the disappearance of features associated with calcium dipicolinate after irradiation. Principal component analysis was used which showed the ability to distinguish viable spores of B. anthracis Sterne and B. atrophaeus from each other and the other two Bacillus species. Finally, Raman microscopy was used to classify mixtures of viable and gamma inactivated spores. A technique was developed that fuses the size and shape characteristics obtained from the bright-field image to preferentially target viable spores. Simulating a scenario of a A practical demonstration of the technique was performed on a field of view containing approximately 7,000 total spores of which are only 12 were viable to simulate a sample that was not fully irradiated. Ten of these spores are properly classified while interrogating just 25% of the total spores

Mikrosystembasierte Zellkultivierung und Zellmanipulation zur Applikation mechanischer Reize auf Zellen

Die übergeordnete Fragestellung der vorliegenden Arbeit ist biomedizinischer Art und seit mehr als 150 Jahren anhängig: Wie verhalten sich Zellen unter definierter Belastung? In vivo ist die Beobachtung zellulärer Prozesse bisher nicht ohne invasive Methoden möglich. Das verlangt nach einer Lösung in vitro, welche die natürlichen Bedingungen adäquat nachahmt und gleichzeitig optimale Bedingungen für die Beobachtung und Beeinflussung der Prozesse bietet. Dass sich dafür Mikrosysteme mit einer angepassten Peripherie eignen, wird in dieser Arbeit nachgewiesen.The motivating question underlying this work is generated by life sciences, pending for more than 150 years: How do cells behave under defined load? In vivo it is not possible to monitor subsiding cellular processes without the use of invasive methods. This demands for a solution in vitro, which mimics natural conditions adequately and offers optimized conditions for observation and manipulation at the same time. For this purpose BioMEMS (Bio Micro Electro Mechanical Systems) for cell are suitable. By means of analysis of state of the art for conventional macro and for micro system based cell cultivation and manipulation, requirements from cells and from users of such systems are defined. A micro system with a cultivation camber, tube connectors, an integrated scaffold, an optical and a mechanical access and other components forms the backbone of the entire system. It is completed by peripheral modules for supply of cells under adequate environmental conditions, observation of cells and processes and manipulation of cells and technical components. This configuration is explained in detail by exemplary realizations. Cell cultivation outside an incubator is feasible, securing biocompatibility. Considerations of the application of stimuli on cells are founded on this newly developed infrastructure. Existing macroscopic and microscopic methods may be adapted to the system, realizations are suggested. The performance of the entire system is discussed with reference to results of technical and biological tests. As result of the documented developmental process now a system exists, which after integration of cell-specific loading methods can be used by life scientists conduce to answer questions on cellular behavior.Zusätzliche Dateien: - Tabelle 5: Mikrozellkultivierungssysteme - Anhang A5: Literatur Zelldetektio

Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.

Automated tracking of cells across timelapse microscopy image sequences typically employs complex segmentation routines and/or bio-staining of the tracking objective. Often accurate identification of a cell's morphology is not of interest and the accurate segmentation of cells in pursuit of non-morphological parameters is complex and time consuming. This thesis explores the potential of internalized quantum dot nanoparticles as alternative, bio- and photo-stable optical markers for tracking the motions of cells through time. CdTe/ZnS core-shell quantum dots act as nodes in moving light display networks within A549, epithelial, lung cancer cells over a 40 hour time period. These quantum dot fluorescence sources are identified and interpreted using simplistic algorithms to find consistent, non-subjective centroids that represent cell centre locations. The presented tracking protocols yield an approximate 91% success rate over 24 hours and 78% over the full 40 hours. The nanoparticle moving light displays also provide simultaneous collection of cell motility data, resolution of mitotic traversal dynamics and identification of familial relationships enabling the construction of multi-parameter lineage trees. This principle is then developed further through inclusion of 3 different coloured quantum dots to create cell specific colour barcodes and reduce the number of time points necessary to successfully track cells through time. The tracking software and identification of parameters without detailed morphological knowledge is also demonstrated through automated extraction of DOX accumulation profiles and Cobalt agglomeration accruement statistics from two separate toxicology assays without the need for cell segmentation