Automated REcognition of tissue-associated erythrocytes (ARETE)-a new tool in tissue cytometry.

Automated microscopic image analysis of immunofluorescence-stained targets on tissue sections is challenged by autofluorescent elements such as erythrocytes, which might interfere with target segmentation and quantification. Therefore, we developed an automated system (Automated REcognition of Tissue-associated Erythrocytes; ARETE) for in silico exclusion of erythrocytes. To detect erythrocytes in transmission images, a cascade of boosted decision trees of Haar-like features was trained on 8,640/4,000 areas (15 × 15 pixels) with/without erythrocytes from images of placental sections (4 µm). Ground truth data were generated on 28 transmission images. At least two human experts labelled the area covered by erythrocytes. For validation, output masks of human experts and ARETE were compared pixel-wise against a mask obtained from majority voting of human experts. F1 score, specificity, and Cohen's κ coefficients were calculated. To study the influence of erythrocyte-derived autofluorescence, we investigated the expression levels of a protein (receptor for advanced glycated end products; RAGE) in placenta and number of Ki-67-positive/cytokeratin 8-positive epithelial cells in colon sections. ARETE exhibited high sensitivity (99.87%) and specificity (99.81%) on a training-subset and processed transmission images (1,392 × 1,024 pixels) within 4 sec. ARETE and human expert's F1-scores were 0.55 versus 0.76, specificities 0.85 versus 0.92 and Cohen's κ coefficients 0.41 versus 0.68. A ranking of Cohen's κ coefficient by the scale of Fleiss certified "good agreement" between ARETE and the human experts. Applying ARETE, we demonstrated 4-14% false-positive RAGE-expression in placenta, and 18% falsely detected proliferative epithelial cells in colon, caused by erythrocyte-autofluorescence. ARETE is a fast system for in silico reduction of erythrocytes, which improves automated image analysis in research and diagnostic pathology.

Towards a versatile automated cell-detection system for science and diagnostics.

Analyzing in-situ tissue structures with complex shapes and textures such as multinuclear cells or cells without nuclei is still a challenge for currently available imageprocessing software. This work aims to provide a versatile system to solve such tasks provided that the structures of interests were detected by immunofluorescence microscopy. Images were automatically acquired using slide-based microscopy. Human domain-experts manually marked up tissue samples to evaluate the performance of the computer generated masks. From precision and recall a balanced F-score was computed to measure the correlation between experts and algorithm output. Exhaustive parameter optimization was conducted to ensure that the optimal input parameters were applied during evaluation of the developed algorithm. This procedure significantly increased the performance compared to manually chosen input parameters. We present an approach that can handle huge tissue areas and does not rely on nuclei detection. Once a markup has been created, the algorithm can be parameter-optimized on ground-truth data for the chosen tissue sample. Thereafter, the resulting settings could be applied automatically to the respective stitched image. Concluding, we provide new insights in physiological and pathopysiological cellular mechanisms by automating the in-situ analysis of proteins in intact tissues.

Quantifying phenotypic variation in isogenic Caenorhabditis elegans expressing Phsp-16.2::gfp by clustering 2D expression patterns.

Isogenic populations of animals still show a surprisingly large amount of phenotypic variation between individuals. Using a GFP reporter that has been shown to predict longevity and resistance to stress in isogenic populations of the nematode Caenorhabditis elegans, we examined residual variation in expression of this GFP reporter. We found that when we separated the populations into brightest 3% and dimmest 3% we also saw variation in relative expression patterns that distinguished the bright and dim worms. Using a novel image processing method which is capable of directly analyzing worm images, we found that bright worms (after normalization to remove variation between bright and dim worms) had expression patterns that correlated with other bright worms but that dim worms fell into two distinct expression patterns. We have analysed a small set of worms with confocal microscopy to validate these findings, and found that the activity loci in these clusters are caused by extremely bright intestine cells. We also found that the vast majority of the fluorescent signal for all worms came from intestinal cells as well, which may indicate that the activity of intestinal cells is responsible for the observed patterns. Phenotypic variation in C. elegans is still not well understood but our proposed novel method to analyze complex expression patterns offers a way to enable a better understanding.

GPSDB: a new database for synonyms expansion of gene and protein names.

UNLABELLED: We present a new database, GPSDB (Gene and Protein Synonyms DataBase) which collects gene/protein names, in a species specific way, from 14 main biological resources. A web-based search interface gives access to the database: given a gene/protein name, it retrieves all synonyms for this entity and queries Medline with a set of user-selected terms. AVAILABILITY: GPSDB is freely available from http://biomint.oefai.at/ CONTACT: johann@oefai.at.