Exploring the proteomes of the human cerebellum and visual cortex using shotgun mass spectrometry-data independent analyses
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q-Tof Premier, instrument model
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: 
Daniel Martins-de-Souza
PROVIDER: PRD000523 | Pride | 2012-04-04
REPOSITORIES: Pride
Dataset's files
Items per page: 5 1 - 5 of 72 |
Publications
Characterization of the human primary visual cortex and cerebellum proteomes using shotgun mass spectrometry-data-independent analyses.
Martins-de-Souza Daniel D Guest Paul C PC Guest Francesca L FL Bauder Corinna C Rahmoune Hassan H Pietsch Sandra S Roeber Sigrum S Kretzschmar Hans H Mann David D Baborie Atik A Bahn Sabine S
Proteomics 20120110 3
We present the first characterization of the human occipital lobe (primary visual cortex) and cerebellum proteomes. Proteins were identified using a combination of gel electrophoresis and data-independent nanoflow liquid chromatography mass spectrometry (nLC-MS(E) ). The resulting data sets comprised 391 and 330 unique proteins in occipital lobe and cerebellum, respectively, present in at least 75% of the analyzed samples with 297 proteins found in common. These proteins have been associated pre ...[more]
Similar Datasets
Project description:Visual development depends on sensory input during an early developmental critical period. Deviation of the pointing direction of the two eyes (strabismus) or chronic optical blur (anisometropia) separately and together can disrupt the formation of normal binocular interactions and the development of spatial processing, leading to a loss of stereopsis and visual acuity known as amblyopia. To shed new light on how these two different forms of visual deprivation affect the development of visual cortex, we used event-related potentials (ERPs) to study the temporal evolution of visual responses in patients who had experienced either strabismus or anisometropia early in life. To make a specific statement about the locus of deprivation effects, we took advantage of a stimulation paradigm in which we could measure deprivation effects that arise either before or after a configuration-specific response to illusory contours (ICs). Extraction of ICs is known to first occur in extrastriate visual areas. Our ERP measurements indicate that deprivation via strabismus affects both the early part of the evoked response that occurs before ICs are formed as well as the later IC-selective response. Importantly, these effects are found in the normal-acuity nonamblyopic eyes of strabismic amblyopes and in both eyes of strabismic patients without amblyopia. The nonamblyopic eyes of anisometropic amblyopes, by contrast, are normal. Our results indicate that beyond the well-known effects of strabismus on the development of normal binocularity, it also affects the early stages of monocular feature processing in an acuity-independent fashion.
| S-EPMC4121844 | biostudies-other
Project description:The human visual cortex is organized in a hierarchical manner. Although previous evidence supporting this hypothesis has been accumulated, specific details regarding the spatiotemporal information flow remain open. Here we present detailed spatiotemporal correlation profiles of neural activity with low-level and high-level features derived from an eight-layer neural network pretrained for object recognition. These correlation profiles indicate an early-to-late shift from low-level features to high-level features and from low-level regions to higher-level regions along the visual hierarchy, consistent with feedforward information flow. Additionally, we computed three sets of features from the low- and high-level features provided by the neural network: object-category-relevant low-level features (the common components between low-level and high-level features), low-level features roughly orthogonal to high-level features (the residual Layer 1 features), and unique high-level features that were roughly orthogonal to low-level features (the residual Layer 7 features). Contrasting the correlation effects of the common components and the residual Layer 1 features, we observed that the early visual cortex (EVC) exhibited a similar amount of correlation with the two feature sets early in time, but in a later time window, the EVC exhibited a higher and longer correlation effect with the common components (i.e., the low-level object-category-relevant features) than with the low-level residual features-an effect unlikely to arise from purely feedforward information flow. Overall, our results indicate that non-feedforward processes, for example, top-down influences from mental representations of categories, may facilitate differentiation between these two types of low-level features within the EVC.
| S-EPMC6865718 | biostudies-literature
Project description:Background: Early screening for colorectal cancer (CRC) is essential to improve its prognosis. Liquid biopsies are increasingly being considered for diagnosing cancer due to low invasiveness and high reproducibility. In addition, circulating extracellular vesicles (crEVs, extracellular vesicles isolated from plasma) expressing tumour-specific proteins are potential biomarkers for various cancers. Here, we present a data-independent acquisition (DIA)-mass spectrometry (MS)-based diagnostic method for liquid biopsies. Methods: Extracellular vesicles (EVs) were isolated from culture supernatants of human CRC cell lines, and plasma of patients with CRC at different tumour stages, by overnight ultracentrifugation coupled with sucrose density gradient centrifugation. Tumour-specific EV proteins were prioritized using Tandem Mass Tag (TMT)-based shotgun proteomics and phosphoproteomics. The results were verified in a second independent cohort and a mouse tumour-bearing model using Western blotting (WB). The candidate biomarkers were further validated in a third cohort by DIA-MS. Finally, the DIA-MS methodology was accelerated to permit high-throughput detection of EV biomarkers in another independent cohort of patients with CRC and healthy controls. Results: High levels of total and phosphorylated fibronectin 1 (FN1) in crEVs, haptoglobin (HP), S100A9 and fibrinogen ? chain (FGA) were significantly associated with cancer progression. FGA was the most dominant biomarker candidate. Analysis of the human CRC cell lines and the mouse model indicated that FGA+ crEVs were likely released by CRC cells. Furthermore, fast DIA-MS and parallel reaction monitoring (PRM)-MS both confirmed that FGA+ crEVs could distinguish colon adenoma with an area of curve (AUC) in the receiver operating characteristic (ROC) curve of 0.949 and patients with CRC (AUC of ROC is 1.000) from healthy individuals. The performance outperformed conventional tumour biomarkers. The DIA-MS quantification of FGA+ crEVs among three groups agreed with that from PRM-MS. Conclusion: DIA-MS detection of FGA+ crEVs is a potential rapid and non-invasive screening tool to identify early stage CRC. Abbreviations: FGA: fibrinogen ? chain; CRC: colorectal cancer; crEVs: circulating extracellular vesicles; EV: extracellular vesicles;MS: mass spectrometry; WB: Western blotting; ROC: receiver operating characteristic; PRM: Parallel Reaction Monitoring; GPC1: Glypican-1; GO: Gene ontology; TEM: transmission electron microscopy; FN1: Fibronectin 1; HP: haptoglobin; TMT: Tandem Mass Tag; LC-MS/MS: liquid chromatography coupled to tandem mass spectrometry; DIA: data-independent acquisition; DDA: data-dependent acquisition; CiRT: Common internal Retention Time standards;AGC: Automatic gain control; AUC: area under curve.
| S-EPMC7178829 | biostudies-literature
Project description:Shotgun proteomics is the inferential analysis of proteoforms using peptide proxies produced by enzyme-catalyzed hydrolysis of entire proteomes. Such peptides are usually identified by nanoflow liquid chromatography coupled to tandem mass spectrometry analysis (nLC-MS/MS). Traditionally, MS/MS analysis is performed in data-dependent acquisition (DDA) mode, which usually produces a pattern of fragment masses unique to a single peptide's fragmentation. Here, I describe a statistically rigorous qualitative and quantitative computational analysis for shotgun proteomics DDA analysis using free open-source software tools. MS/MS data are used to identify peptides, and the area of peptide mass/charge over chromatographic elution is used to quantify peptides. All peptides that uniquely map to a protein sequence predicted from the genome are combined into a single protein quantity, which can then be compared across experimental conditions. Statistically significant protein changes can be summarized using gene ontology or pathway term enrichment analysis.
| S-EPMC8583964 | biostudies-literature
Project description:Database search programs for peptide identification by tandem mass spectrometry ask their users to set various parameters, including precursor and fragment mass tolerances, digestion specificity, and allowed types of modifications. Even proteomics experts with detailed knowledge of their samples may find it difficult to make these choices without significant investigation, and poor choices can lead to missed identifications and misleading results. Here we describe a program called Preview that analyzes a set of mass spectra for mass errors, digestion specificity, and known and unknown modifications, thereby facilitating parameter selection. Moreover, Preview optionally recalibrates mass over charge measurements, leading to further improvement in identification results. In a study of Bruton's tyrosine kinase, we find that the use of Preview improved the number of confidently identified mass spectra and phosphorylation sites by about 50%.
| S-EPMC3134881 | biostudies-literature
Project description:Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies peptides by their mass-to-charge ratio, retention time, and intensity, is particularly challenging due to the overlapping nature of peptides and weak signals that are often indistinguishable from noises, thus creating a reliance on rigid mathematical structures and heuristics. In this study, we developed a deep-learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct MS2 feature detection. Experimental results show that our model can produce more accurate values and identifications than existing feature detection tools, as well as a high rate of true positive features quantified. Therefore, we believe that our model illustrates the advantages of deep learning techniques applied towards computational proteomics.
| S-EPMC10457098 | biostudies-literature
Project description:The problem of identifying proteins from a shotgun proteomics experiment has not been definitively solved. Identifying the proteins in a sample requires ranking them, ideally with interpretable scores. In particular, "degenerate" peptides, which map to multiple proteins, have made such a ranking difficult to compute. The problem of computing posterior probabilities for the proteins, which can be interpreted as confidence in a protein's presence, has been especially daunting. Previous approaches have either ignored the peptide degeneracy problem completely, addressed it by computing a heuristic set of proteins or heuristic posterior probabilities, or estimated the posterior probabilities with sampling methods. We present a probabilistic model for protein identification in tandem mass spectrometry that recognizes peptide degeneracy. We then introduce graph-transforming algorithms that facilitate efficient computation of protein probabilities, even for large data sets. We evaluate our identification procedure on five different well-characterized data sets and demonstrate our ability to efficiently compute high-quality protein posteriors.
| S-EPMC2948606 | biostudies-literature
Project description:Atom probe tomography (APT) can theoretically deliver accurate chemical and isotopic analyses at a high level of sensitivity, precision, and spatial resolution. However, empirical APT data often contain significant biases that lead to erroneous chemical concentration and isotopic abundance measurements. The present study explores the accuracy of quantitative isotopic analyses performed via atom probe mass spectrometry. A machine learning-based adaptive peak fitting algorithm was developed to provide a reproducible and mathematically defensible means to determine peak shapes and intensities in the mass spectrum for specific ion species. The isotopic abundance measurements made with the atom probe are compared directly with the known isotopic abundance values for each of the materials. Even in the presence of exceedingly high numbers of multi-hit detection events (up to 80%), and in the absence of any deadtime corrections, our approach produced isotopic abundance measurements having an accuracy consistent with values limited predominantly by counting statistics.
| S-EPMC7717065 | biostudies-literature
Project description:MotivationTandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a 'spectral library'), but this approach is expensive because the libraries do not typically generalize well across laboratories.ResultsHere, we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool.Availability and implementationThe open source, Apache licensed source code is available as part of the Crux mass spectrometry analysis toolkit (http://crux.ms).Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8686675 | biostudies-literature
Project description:Hallucinations, a hallmark of psychosis, can be induced by the psychotomimetic N-methyl-D-aspartic acid (NMDA) receptor antagonists, ketamine and phencyclidine (PCP), and are associated with hypersynchronization in the ?-frequency band, but it is unknown how reduced interneuron activation associated with NMDA receptor hypofunction can cause hypersynchronization or distorted perception. Low-frequency ?-oscillations (LF?) and high-frequency ?-oscillations (HF?) serve different aspects of perception. In this study, we test whether ketamine and PCP affect the interactions between HF? and LF? in the rat visual cortex in vitro. In slices of the rat visual cortex, kainate and carbachol induced LF? (? 34 Hz at 32°C) in layer V and HF? (? 54 Hz) in layer III of the same cortical column. In controls, HF? and LF? were independent, and pyramidal neurons recorded in layer III were entrained by HF?, but not by LF?. Sub-anesthetic concentrations of ketamine selectively decelerated HF? by 22 Hz (EC(50)=2.7 ?M), to match the frequency of LF? in layer V. This caused phase coupling of the two ?-oscillations, increased spatial coherence in layer III, and entrained the firing of layer III pyramidal neurons by LF? in layer V. PCP similarly decelerated HF? by 22 Hz (EC(50)=0.16 ?M), causing cross-layer phase coupling of ?-oscillations. Selective NMDA receptor antagonism, selective NR2B subunit-containing receptor antagonism, and reduced D-serine levels caused a similar selective deceleration of HF?, whereas increasing NMDA receptor activation through exogenous NMDA, D-serine, or mGluR group 1 agonism selectively accelerated HF?. The NMDA receptor hypofunction-induced phase coupling of the normally independent ?-generating networks is likely to cause abnormal cross-layer interactions, which may distort perceptions due to aberrant matching of top-down information with bottom-up information. If decelerated HF? and subsequent cross-layer synchronization also underlie pathological psychosis, acceleration of HF? could be the target for improved antipsychotic therapy.
| S-EPMC3055671 | biostudies-literature