| 1. |
- Johansson, Carolina, et al.
(författare)
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Differential expression analysis of Escherichia coli proteins using a novel software for relative quantitation of LC-MS/MS data
- 2006
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 6:16, s. 4475-4485
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Tidskriftsartikel (refereegranskat)abstract
- The study of changes in protein levels between samples derived from cells representing different biological conditions is a key to the understanding of cellular function. There are two main methods available that allow both for global scanning for significantly varying proteins and targeted profiling of proteins of interest. One method is based on 2-D gel electrophoresis and image analysis of labelled proteins. The other method is based on LC-MS/MS analysis of either unlabelled peptides or peptides derived from isotopically labelled proteins or peptides. In this study, the non-labelling approach was used involving a new software, DeCydei (TM) S Differential Analysis Software (DeCyder MS) intended for automated detection and relative quantitation of unlabelled peptides in LC-MS/MS data. Total protein extracts of E. coli strains expressing varying levels of dihydrofolate reductase and integron integrase were digested with trypsin and analyzed using a nanoscale liquid chromatography system, Ettan (TM) MDLC, online connected to an LTQ (TM) linear ion-trap mass spectrometer fitted with a nanospray interface. Acquired NIS data were subjected to DeCyder NIS analysis where 2-D representations of the peptide patterns from individual LC-MS/MS analyses were matched and compared. This approach to unlabelled quantitative analysis of the E. coli proteome resulted in relative protein abundances that were in good agreement with results obtained from traditional methods for measuring protein levels.
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| 2. |
- Kaplan, Anders, et al.
(författare)
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An Automated Method for Scanning LC−MS Data Sets for Significant Peptides and Proteins, Including Quantitative Profiling and Interactive Confirmation : An Automated Method for Scanning LC−MS Data Sets for Significant Peptides and Proteins, Including Quantitative Profiling and Interactive Confirmation
- 2007
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 6:7, s. 2888-2895
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Tidskriftsartikel (refereegranskat)abstract
- Differential quantification of proteins and peptides by LC-MS is a promising method to acquire knowledge about biological processes, and for finding drug targets and biomarkers. However, differential protein analysis using LC-MS has been held back by the lack of suitable software tools. Large amounts of experimental data are easily generated in protein and peptide profiling experiments, but data analysis is time-consuming and labor-intensive. Here, we present a fully automated method for scanning LC-MS/MS data for biologically significant peptides and proteins, including support for interactive confirmation and further profiling. By studying peptide mixtures of known composition, we demonstrate that peptides present in different amounts in different groups of samples can be automatically screened for using statistical tests. A linear response can be obtained over almost 3 orders of magnitude, facilitating further profiling of peptides and proteins of interest. Furthermore, we apply the method to study the changes of endogenous peptide levels in mouse brain striatum after administration of reserpine, a classical model drug for inducing Parkinson disease symptoms.
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| 6. |
- Wählby, Carolina, et al.
(författare)
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Algorithms for cytoplasm segmentation of fluorescence labeled cells
- 2002
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Ingår i: Analytical Cellular Pathology. - 0921-8912 .- 1878-3651. ; 24:2-3, s. 101-111
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Tidskriftsartikel (refereegranskat)abstract
- Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre-processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO-cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.
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