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Detektion des Kartoffelspindelknollen Viroids mit Hilfe der Loop Mediated Isothermal Amplification
(2011)
PSTV ist ein hoch infektiöses Viroid, das in Kartoffeln verkleinerte und spindelähnliche Knollen verursacht. Um Ernteverlusten vorzubeugen, ist eine Detektion in frühen Infektionsstadien von großer Bedeutung. Aufgrund ihrer hohen Sensitivität und Spezifität wird die PCR als Standardnachweisverfahren für PSTV verwendet. Nachteilig an dieser Methode sind der apparative Aufwand und die zeitaufwändige Durchführung. Als viel versprechende Alternative konnte der Nachweis von PSTV mit Hilfe der Reverse Transcription Loop Mediated Isothermal Amplification (RT-LAMP) gezeigt werden. Hierbei handelt es sich um eine einfache und schnelle Methode, für die wenig aufwändige Laborausrüstung benötigt wird. Dabei ermöglichte eine an den Amplifikationsprozess gekoppelte Fluoreszenzreaktion die Detektion von Produkten direkt nach der Nachweisreaktion mit dem bloßen Auge (bzw. unter UV-Licht). Die Ergebnisse konnten mit Hilfe einer Real Time Detektion des auftretenden Fluoreszenzsignals bestätigt werden.
In this work we describe and test high-throughput methods with the model organism Synechocystis sp. PCC6803. The required technical automation for these processes was achieved with a Tecan Freedom Evo 200 pipetting robot. The cultivation was performed in 2.2 ml deepwell microtiter plates within a cultivation chamber outfitted with programmable shaking conditions, variable illumination, variable temperature, and an adjustable CO2 atmosphere. Each microtiter-well within the chamber functions as a separate cultivation vessel with reproducible conditions. The automated measurement of various parameters such as growth, full absorption spectrum, chlorophyll concentration, MALDI-TOF-MS, as well as a novel vitality measurement protocol, have already been established and can be monitored during cultivation. Measurement of growth parameters can be used as inputs for the system to allow for periodic automatic dilutions and therefore a semi-continuous cultivation of hundreds of cultures in parallel. The system also allows the automatic generation of mid and long term backups of cultures to repeat experiments or to retrieve strains of interest.
Random mutagenesis is a useful tool to genetically modify organisms for various purposes, such as adaptation to cultivation conditions, the induction of tolerances, or increased yield of valuable substances. This is especially attractive for systems where it is not obvious which genes require modifications. Random mutagenesis has been extensively used to modify crop plants, but even with the renewed interest in microalgae and cyanobacteria for biofuel applications, there is relatively limited current research available on the application of random mutagenesis for these organisms, especially for cyanobacteria. In the presented work we characterized the lethality and rate of non-lethal point mutations for ultraviolet radiation and methyl methanesulphonate on the model cyanobacteria Synechocystis sp. PCC6803. Based on these results an optimal dosage of 10–50 J/m2 for UV and either 0.1 or 1 v% for MMS was determined. A Synechocystis wildtype culture was then mutagenized and selected for increased temperature tolerance in vivo. During the second round of mutagenesis the viability of the culture was monitored on a cell by cell level from the treatment of the cells up to the growth at an increased temperature. After four distinct rounds of treatment (two with each mutagen) the temperature tolerance of the strain was effectively raised by about 2°C. Coupled with an appropriate in vivo screening, the described methods should be applicable to induce a variety of desirable characteristics in various strains. Coupling random mutagenesis with high-throughput screening methods would additionally allow to select for important characteristics for biofuel production, which do not yield a higher fitness and can not be selected for in vivo, such as fatty acid concentration. In a combined approach with full genome sequencing random mutagenesis could be used to determine suitable target-genes for more focused methods.
Currently established methods to identify viable and non-viable cells of cyanobacteria are either time-consuming (eg. plating) or preparation-intensive (eg. fluorescent staining). In this paper we present a new and fast viability assay for unicellular cyanobacteria, which uses red chlorophyll fluorescence and an unspecific green autofluorescence for the differentiation of viable and non-viable cells without the need of sample preparation.
Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.
Ethanol production via genetically engineered cyanobacteria is a promising solution for the production of biofuels. Through the introduction of a pyruvate decarboxylase and alcohol dehydrogenase direct ethanol production becomes possible within the cells. However, during cultivation genetic instability can lead to mutations and thus loss of ethanol production. Cells then revert back to the wild type phenotype.
A method for a rapid and simple detection of these non-producing revertant cells in an ethanol producing cell population is an important quality control measure in order to predict genetic stability and the longevity of a producing culture. Several comparable cultivation experiments revealed a difference in the pigmentation for non-producing and producing cells: the accessory pigment phycocyanin (PC) is reduced in case of the ethanol producer, resulting in a yellowish appearance of the culture. Microarray and western blot studies of Synechocystis sp. PCC6803 and Synechococcus sp. PCC7002 confirmed this PC reduction on the level of RNA and protein.
Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.
The analysis of blood is one of the best possibilities to diagnose and control diseases and deficiency symptoms. Common blood tests that are performed in medical laboratories are time-consuming and work-intensive. In under-developed areas, there is often also a lack of specialised staff and facilities. The development of a mobile microscopic system that contains an automated image analysis and that can be used via a smartphone, could represent a valuable help to improve the diagnostic care, especially in those areas. it aims to enable a very fast, cheap, location- and knowledge-independent application for many use cases.