Project description:Brain insulin action plays an important role in metabolic and cognitive health but so far, there is no biomarker available to assess brain insulin resistance in humans. Here, we developed a machine learning framework based on blood DNA methylation profiles of participants who did not have Type 2 Diabetes with and without brain insulin resistance and detailed metabolic phenotyping.
Project description:Brain insulin action plays an important role in metabolic and cognitive health but so far, there is no biomarker available to assess brain insulin resistance in humans. Here, we developed a machine learning framework based on blood DNA methylation profiles of participants who did not have Type 2 Diabetes with and without brain insulin resistance and detailed metabolic phenotyping.
Project description:cytoplasmic extract of Bacillus subtilis wild type control (untreated) separated by non-reducing SDS-PAGE, lane cut into 10 fractions, fraction 1 in-gel-digested
Project description:Different sample preparation methods were tested for HeLa proteome analysis. A sample obtained using sodium deoxycholate-based lysis allowed identification of the highest number of proteins. For this sample, a dilution series was acquired in triplicates ranging from 0.2ng to 200ng. All measurements were performed on Bruker timsTOF Pro 2 operated in dia-PASEF mode and analysed library-free using DIA-NN 1.8.
Project description:Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set, a small pilot data set, and a larger proteomic data set.