Apoe, Mbl2, and Psp plasma protein levels correlate with diabetic phenotype in NZO mice--an optimized rapid workflow for SRM-based quantification.

Male New Zealand Obese (NZO) mice progress through pathophysiological stages similar to humans developing obesity-associated type 2 diabetes (T2D). The current challenge is to establish quantitative proteomics from small plasma sample amounts. We established an analytical workflow that facilitates a reproducible depletion of high-abundance proteins, has high throughput applicability, and allows absolute quantification of proteins from mouse plasma samples by LC-SRM-MS. The ProteoMiner equalizing technology was adjusted to the small sample amount, and reproducibility of the identifications was monitored by spike proteins. Based on the label-free relative quantification of proteins in depleted plasma of a test set of NZO mice, assays for potential candidates were designed for the setup of a targeted selected reaction monitoring (SRM) approach and absolute quantification. We could demonstrate that apolipoprotein E (Apoe), mannose-binding lectin 2 (Mbl2), and parotid secretory protein (Psp) are present at significantly different quantities in depleted plasma of diabetic NZO mice compared to non-diabetic controls using AQUA peptides. Quantification was validated for Mbl2 using the ELISA technology on non-depleted plasma. We conclude that the depletion technique is applicable to restricted sample amounts and suitable for the identification of T2D signatures in plasma.

A segmentation procedure using colour features applied to images of Arabidopsis thaliana.

In studies of environmental effects on plant growth, the images of plants are often used for non-destructive measurements in phenotyping. In this work, a computational procedure has been developed to segment images of plants allowing an improved separation of plants and other types of objects in the frame such as moss or soil. The proposed procedure is based on colour analysis and image morphology. The red-green-blue (RGB) values are transformed into a colour space as ratios of R, G and B vs the sum of R, G, and B channels. We introduce an approach to render the training set of pixels on a Microsoft Excel two-dimensional graph and a technique to determine the discriminant regions of pixel classes. Two approaches for the classification based on colour analysis are shown: an automatic method using support vector machines and a procedure based on visual inspection. The segmentation procedure is designed to classify more than two object types utilising flexibly curved boundaries of discriminant regions that can also be non-convex. We propose a machine-vision algorithm to detect plant features - leaf anthocyanin accumulation and trichomes. The procedures of segmentation and feature detection are applied to images of Arabidopsis thaliana (L.) Heynh. that grow under either normal or drought stress conditions.