Fluoresoivien partikkeleiden havaitseminen kolibakteereissa

Escherichia coli are one of the most commonly used bacteria to study important biolog-ical processes such as transcription and translation. This is due to its simple structure and gene expression system, as well as the easiness to maintain live cultures in a laboratory environment. Due to recent developments in fluorescence microscopy and fluorescence labeling, it is now possible to study such biological processes in live cells at single cell and single molecule level. When analyzing such biological processes, the detection of fluorescent objects and subcellular particles is usually one of the first tasks providing important information for subsequent data analysis. Although many algorithms have been proposed for the task, it still remains a challenge due to the limitations of image acquisition when imaging live cells. For example, the intensity of the illumination light and the exposure time is usually minimized to prevent damage to the cells, resulting in images with low signal-to-noise ratio. Due to this and the large amount of data typically used for these studies, automated, high quality parti-cle detection algorithms are needed. In this thesis, we present a novel method for detecting fluorescently labeled subcellular particles in Escherichia coli. The proposed method is tested in both synthetic and em-pirical images and is compared to previous, commonly used methods using standard performance evaluation metrics. The results indicate that the proposed algorithm has a good performance with all image types tested and that it outperforms the previous methods. It is also able to achieve good results with other types of cells than E. coli. Moreover, it allows a robust detection of particles from low signal-to-noise ratio images with good accuracy, thus providing accurate and unbiased results for subsequent analy-sis

ZebIAT, an image analysis tool for registering zebrafish embryos and quantifying cancer metastasis

BACKGROUND: Zebrafish embryos have recently been established as a xenotransplantation model of the metastatic behaviour of primary human tumours. Current tools for automated data extraction from the microscope images are restrictive concerning the developmental stage of the embryos, usually require laborious manual image preprocessing, and, in general, cannot characterize the metastasis as a function of the internal organs. METHODS: We present a tool, ZebIAT, that allows both automatic or semi-automatic registration of the outer contour and inner organs of zebrafish embryos. ZebIAT provides a registration at different stages of development and an automatic analysis of cancer metastasis per organ, thus allowing to study cancer progression. The semi-automation relies on a graphical user interface. RESULTS: We quantified the performance of the registration method, and found it to be accurate, except in some of the smallest organs. Our results show that the accuracy of registering small organs can be improved by introducing few manual corrections. We also demonstrate the applicability of the tool to studies of cancer progression. CONCLUSIONS: ZebIAT offers major improvement relative to previous tools by allowing for an analysis on a per-organ or region basis. It should be of use in high-throughput studies of cancer metastasis in zebrafish embryos.Work supported by the Academy of Finland (ASR), Emil Aaltonen Foundation (EL), and the Finnish Funding Agency for Technology and Innovation (ASR,TA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.S

Fluoresoivien partikkeleiden havaitseminen kolibakteereissa

Escherichia coli are one of the most commonly used bacteria to study important biolog-ical processes such as transcription and translation. This is due to its simple structure and gene expression system, as well as the easiness to maintain live cultures in a laboratory environment. Due to recent developments in fluorescence microscopy and fluorescence labeling, it is now possible to study such biological processes in live cells at single cell and single molecule level. When analyzing such biological processes, the detection of fluorescent objects and subcellular particles is usually one of the first tasks providing important information for subsequent data analysis. Although many algorithms have been proposed for the task, it still remains a challenge due to the limitations of image acquisition when imaging live cells. For example, the intensity of the illumination light and the exposure time is usually minimized to prevent damage to the cells, resulting in images with low signal-to-noise ratio. Due to this and the large amount of data typically used for these studies, automated, high quality parti-cle detection algorithms are needed. In this thesis, we present a novel method for detecting fluorescently labeled subcellular particles in Escherichia coli. The proposed method is tested in both synthetic and em-pirical images and is compared to previous, commonly used methods using standard performance evaluation metrics. The results indicate that the proposed algorithm has a good performance with all image types tested and that it outperforms the previous methods. It is also able to achieve good results with other types of cells than E. coli. Moreover, it allows a robust detection of particles from low signal-to-noise ratio images with good accuracy, thus providing accurate and unbiased results for subsequent analy-sis

Keskustelua

  ELIAS ANNILA & PETTERI MUUKKONEN Alhaisimman kustannuksen reitin määrittäminen tarkasti ja tehokkaasti luokitellusta paikkatietoaineistosta MAIJA TOIVAKKA, TEPPO REPO, AAPELI LEMINEN, MIKKO PYYKÖNEN, TIINA LAATIKAINEN & MARKKU TYKKYLÄINEN Potilastieto ja paikkatieto kohtaavat LAURA MONONEN, PETTERI VIHERVAARA, LEENA KOPPEROINEN & ARTO VIINIKKA Ekosysteemipalvelujen kartoittamisella kohti kestävämpää ympäristönsuunnittelua ja -käyttöä &nbsp