Project description:The molecular networks underlying Alzheimer’s disease (AD) are not well-defined. We present temporal profiling of >14,000 proteins and >34,000 phosphosites at the asymptomatic and symptomatic stages of AD, deep proteomics analysis of transgenic mouse models.
Project description:The molecular networks underlying Alzheimer’s disease (AD) are not well-defined. We present temporal profiling of >14,000 proteins and >34,000 phosphosites at the asymptomatic and symptomatic stages of AD, deep proteomics analysis of transgenic mouse models.
Project description:inSPIRE is an open-source tool for spectral rescoring of mass spectrometry search results. For this project, inSPIRE was applied to MaxQuant, PEAKS DB, and Mascot search results from a tryptic digestion of the K562 proteome. Here we provide the RAW files and search results using MaxQuant, PEAKS DB, and Mascot. We also reprocessed RAW data from the PXD031709 and PXD031812 repositories for which we provide the search result files. Additionally, we provide PEAKS search results from RAW files from the PXD015489 repository which was used as training data for a predictor used within inSPIRE. Michele Mishto, Head of the research group Molecular Immunology at King’s College London and the Francis Crick Institute, London (UK). Email: michele.mishto@kcl.ac.uk,
Project description:The molecular networks underlying Alzheimer’s disease (AD) are not well-defined. We present temporal profiling of >14,000 proteins and >34,000 phosphosites at the asymptomatic and symptomatic stages of AD, deep proteomics analysis of transgenic mouse models.
Project description:Active protein translation can be assessed and measured using ribosome profiling sequencing strategies. Existing approaches make use of sequence fragment length or frame occupancy to differentiate between active translation and background noise, however they do not consider additional characteristics inherent to the technology which limits their overall accuracy. Here, we present an analytical tool that models the overall tri-nucleotide periodicity of ribosomal occupancy using a classifier based on spectral coherence. Our software, SPECtre, examines the relationship of normalized ribosome profiling read coverage over a rolling series of windows along a transcript against an idealized reference signal. A comparison of SPECtre against current methods on existing and new data shows a marked improvement in accuracy for detecting active translation and exhibits overall high sensitivity at a low false discovery rate. Classification of actively translated transcripts in ribosome profiling data derived from human neuroblastoma SH-SY5Y cells, and data previously published derived from mouse embryonic stem cells and zebrafish embryos.
Project description:The molecular networks underlying Alzheimer’s disease (AD) are not well-defined. We present temporal profiling of >14,000 proteins and >34,000 phosphosites at the asymptomatic and symptomatic stages of AD, deep proteomics analysis of transgenic mouse models.
Project description:The retina plays a crucial role in processing and decoding visual information, both in normal development and during myopia progression. Recent advancements have introduced a library-independent approach for data-independent acquisition (DIA) analyses. This study demonstrates deep proteome identification and quantification in individual mice retinas during myopia development, with an average of 6,263 ± 86 unique protein groups. We anticipate that this robust quantification and in-depth retinal-specific spectral library will contribute to a better understanding of the proteome complexity of retina. Furthermore, a comprehensive mice retinal-specific spectral library was generated, encompassing a total identification of 9,401 protein groups, 70,041 peptides, 95,339 precursors, and 761,868 transitions acquired using SWATH-MS acquisition on a ZenoTOF 7600 mass spectrometer. This dataset surpasses the spectral library generated through high-pH reversed-phase fractionation by data-dependent acquisition (DDA). The data is available via ProteomeXchange with the identifier PXD046983. It will also serve as an indispensable reference for investigations in myopia research and other retinal or neurological diseases.
Project description:Data-independent acquisition (DIA) proteomics allows systematic and unbiased measurement of protein samples and enables fast quantitative analysis of large cohorts of samples. However, sample-specific spectral libraries are usually required prior to perform DIA experiments. The libraries are built by data-dependent acquisition (DDA) proteomic analysis on the same samples normally with pre-fractionation, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning based approach to generate in silico spectral libraries for DIA analysis. We benchmarked DeepDIA with HeLa and mixed proteome data sets, showing that the quality of in silico spectral libraries is comparable to that of experimental spectral libraries. We further demonstrated that DeepDIA can be performed on human cell lines and human serum without pre-knowledge of peptides lists by DDA experiments. Compared to the state-of-the-art protocol using DDA-based spectral library with high abundance protein depletion and pre-fractionation, the number of identified and quantified proteins from human serum was increased by >100% using DeepDIA. Accuracy of the identification results was validated using a standard mixture containing >800 stable isotope labelled reference peptides from >500 proteins in human plasma. We expect this work contributing to the studies of quantitative proteomics and especially blood proteomics, whereas expanding the toolbox for DIA proteomics.