scholarly journals Proteomic insights into synaptic signaling in the brain: the past, present and future

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yalan Xu ◽  
Xiuyue Song ◽  
Dong Wang ◽  
Yin Wang ◽  
Peifeng Li ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Complexes ◽  
Synaptic Signaling ◽  
Brain Functions ◽  
The Past ◽  
Data Independent Acquisition ◽  
Technological Advances ◽  
Neural Communication ◽  
Chemical Synapses ◽  
The Brain

AbstractChemical synapses in the brain connect neurons to form neural circuits, providing the structural and functional bases for neural communication. Disrupted synaptic signaling is closely related to a variety of neurological and psychiatric disorders. In the past two decades, proteomics has blossomed as a versatile tool in biological and biomedical research, rendering a wealth of information toward decoding the molecular machinery of life. There is enormous interest in employing proteomic approaches for the study of synapses, and substantial progress has been made. Here, we review the findings of proteomic studies of chemical synapses in the brain, with special attention paid to the key players in synaptic signaling, i.e., the synaptic protein complexes and their post-translational modifications. Looking toward the future, we discuss the technological advances in proteomics such as data-independent acquisition mass spectrometry (DIA-MS), cross-linking in combination with mass spectrometry (CXMS), and proximity proteomics, along with their potential to untangle the mystery of how the brain functions at the molecular level. Last but not least, we introduce the newly developed synaptomic methods. These methods and their successful applications marked the beginnings of the synaptomics era.

scholarly journals RNA-Centric Approaches to Profile the RNA–Protein Interaction Landscape on Selected RNAs

2021 ◽  
Vol 7 (1) ◽  
pp. 11 ◽  
Author(s):  
André P. Gerber
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Cell Protein ◽  
Transcriptional Regulatory Networks ◽  
Technological Advances

RNA–protein interactions frame post-transcriptional regulatory networks and modulate transcription and epigenetics. While the technological advances in RNA sequencing have significantly expanded the repertoire of RNAs, recently developed biochemical approaches combined with sensitive mass-spectrometry have revealed hundreds of previously unrecognized and potentially novel RNA-binding proteins. Nevertheless, a major challenge remains to understand how the thousands of RNA molecules and their interacting proteins assemble and control the fate of each individual RNA in a cell. Here, I review recent methodological advances to approach this problem through systematic identification of proteins that interact with particular RNAs in living cells. Thereby, a specific focus is given to in vivo approaches that involve crosslinking of RNA–protein interactions through ultraviolet irradiation or treatment of cells with chemicals, followed by capture of the RNA under study with antisense-oligonucleotides and identification of bound proteins with mass-spectrometry. Several recent studies defining interactomes of long non-coding RNAs, viral RNAs, as well as mRNAs are highlighted, and short reference is given to recent in-cell protein labeling techniques. These recent experimental improvements could open the door for broader applications and to study the remodeling of RNA–protein complexes upon different environmental cues and in disease.

On the Limits of Time in the Brain

KronoScope ◽  
2013 ◽  
Vol 13 (2) ◽  
pp. 228-239
Author(s):  
Rémy Lestienne
Keyword(s):  
Human Brain ◽  
Brain Functions ◽  
Inner Sense ◽  
The Past ◽  
Intellectual Abilities ◽  
The Future ◽  
Flow Of Time ◽  
Nested Hierarchy ◽  
Future Outcomes ◽  
The Brain

Abstract J.T. Fraser used to emphasize the uniqueness of the human brain in its capacity for apprehending the various dimensions of “nootemporality” (Fraser 1982 and 1987). Indeed, our brain allows us to sense the flow of time, to measure delays, to remember past events or to predict future outcomes. In these achievements, the human brain reveals itself far superior to its animal counterpart. Women and men are the only beings, I believe, who are able to think about what they will do the next day. This is because such a thought implies three intellectual abilities that are proper to mankind: the capacity to take their own thoughts as objects of their thinking, the ability of mental time travels—to the past thanks to their episodic memory or to the future—and the possibility to project very far into the future, as a consequence of their enlarged and complexified forebrain. But there are severe limits to our timing abilities of which we are often unaware. Our sensibility to the passing time, like other of our intellectual abilities, is often competing with other brain functions, because they use at least in part the same neural networks. This is particularly the case regarding attention. The deeper the level of attention required, the looser is our perception of the flow of time. When we pay attention to something, when we fix our attention, then our inner sense of the flux of time freezes. This limitation should not sound too unfamiliar to the reader of J.T. Fraser who wrote in his book Time, Conflict, and Human Values (1999) about “time as a nested hierarchy of unresolvable conflicts.”

scholarly journals NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

2020 ◽  
Vol 48 (14) ◽  
pp. e83-e83 ◽  
Author(s):  
Shisheng Wang ◽  
Wenxue Li ◽  
Liqiang Hu ◽  
Jingqiu Cheng ◽  
Hao Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Missing Values ◽  
Protein Complexes ◽  
Label Free ◽  
Missing Value ◽  
Imputation Methods ◽  
Data Independent Acquisition ◽  
Low Performance ◽  
User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

Resolving Missing Protein Problems Using Functional Class Scoring

2022 ◽  
Author(s):  
Bertrand Jernhan Wong ◽  
Weijia Kong ◽  
Limsoon Wong ◽  
Wilson Wen Bin Goh
Keyword(s):  
Protein Complexes ◽  
Functional Class ◽  
P Value ◽  
Proteomics Data ◽  
Individual Protein ◽  
Data Independent Acquisition ◽  
Technological Advances ◽  
Incomplete Coverage ◽  
Protein Components ◽  
Using Data

Abstract Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in “data holes”. These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples. This hinders biomarker and drug discovery from proteomics data. Network-based approaches are powerful: The Functional Class Scoring (FCS) method using protein complexes was able to easily recover missed proteins with weak or partial support. However, there are limitations: The verification approach (in determining missing protein recovery) is potentially biased as the test data was based on relatively outdated Data-Dependent Acquisition (DDA) proteomics and FCS does not provide a scoring scheme for individual protein components (in significant complexes). To address these issues: First, we devised a more rigorous evaluation of FCS based on same-sample technical replicates. And second, we evaluate using data from more recent Data-Independent Acquisition (DIA) technologies (viz. SWATH).Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved. This confirms FCS as a viable method that selects biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p-values. Although cross-replicate verification rates are not spectacular, the predicted missing proteins as a whole, have higher peptide support than non-predicted proteins. FCS also has the capacity to predict missing proteins that are often lost due to weak specific peptide support. As a yet unresolved limitation, we find that FCS cannot assign meaningful probabilities to individual protein components (no relationship between actual probability of verification and FCS-assigned probability) as it only provides a p-value at the level of complexes.

Evaluation of Acquisition Modes for the Quantitative Analysis of Cross-Linked Peptides by Targeted and Untargeted Mass Spectrometry

2020 ◽  
Author(s):  
Hannah Britt ◽  
Tristan Cragnolini ◽  
Suniya Khatun ◽  
Abubakar Hatimy ◽  
Juliette James ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Dynamic Range ◽  
Temporal Dynamics ◽  
Protein Complexes ◽  
Isotopic Labeling ◽  
Single Experiment ◽  
Data Independent Acquisition ◽  
Quantitative Metrics ◽  
Parallel Reaction Monitoring ◽  
Further Development

<div> <div> <p>Cross-linking mass spectrometry (XL-MS) is a structural biology technique that can provide insights into the structure and interactions of proteins and their complexes, especially those that cannot be easily assessed by other methods. Quantitative XL-MS has the potential to probe the structural and temporal dynamics of protein complexes; however, it requires further development. Until recently, quantitative XL-MS has largely relied upon isotopic labeling and data dependent acquisition (DDA) methods, limiting the number of biological samples that can be studied in a single experiment. Here, the acquisition modes available on an ion mobility (IM) enabled QToF mass spectrometer are evaluated for the quantitation of cross-linked peptides, eliminating the need for isotopic labels and thus expanding the number of comparable studies that can be conducted in parallel. Workflows were optimized using metabolite and peptide standards analyzed in biological matrices, facilitating modelling of the data and addressing linearity issues, which allow for significant increases in dynamic range. Evaluation of the DDA acquisition method commonly used in XL-MS studies indicated consistency issues between technical replicates and reduced performance in quantitative metrics. On the contrary, data independent acquisition (DIA) and parallel reaction monitoring (PRM) modes proved more robust for analyte quantitation. Mobility enabled modes exhibited an improvement in sensitivity due to the added dimension of separation, and a simultaneous reduction in dynamic range, which was largely recovered by correction methods. Hi[3] and probabilistic quantitation methods were successfully applied to the DIA data, determining the molar amounts of cross-linked peptides relative to their linear counterparts.</p></div></div>

CURRENT VIEWS ON NEUROPLASTICITY: WHAT IS NEW AND WHAT IS OLD?

2020 ◽  
Vol 18 (1) ◽  
pp. 1-14
Author(s):  
Bożydar L.J. Kaczmarek
Keyword(s):  
Neuronal Plasticity ◽  
Brain Plasticity ◽  
Imaging Techniques ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Hard Data ◽  
Brain Functions ◽  
Technological Advances ◽  
Treatment Of Depression ◽  
The Brain

The main aim of the paper is to show that many previously forgotten discoveries within the field of neuroscience own their rediscovery and renaissance to the refinement of tools provided by the technological advances. Most spectacular is the advancement of brain imaging techniques, which provide hard data that support for evidence for previously neglected presumptions and ideas. Neuroplasticity is an example of such a long ignored historical discovery. One reason for that neglect is that it stood in contradiction to beliefs and theories prevailing at the first half of the twenties century. The idea of neuronal plasticity is not disputed any longer since it has found confirmation not only in a dramatic development of neuroimaging but also in the advancement of neurobiology. Most authors concentrate upon neuronal plasticity, recent studies, however, have produced a wealth of information regarding neurogenesis, in which astrocytes have proved to play a significant role. The significance of adult neurogenesis for learning and memory and for treatment of depression is outlined. Moreover, it was observed that neuroplasticity benefits patients suffering from obsessive-compulsive disorder (OCD) who undergo effective, evidence-based treatment. Convincing examples of brain plasticity brings also clinical practice, which often unveils the appearance of hitherto hidden artistic abilities in people who have suffered from brain damage. In addition, the possibilities of altering the brain functions by mental force alone are discussed. Thus, the paper reveals that many “controversial” ideas were confirmed by contemporary studies forcing changes in a traditional view on brain works.

Mass Spectrometry for Structural Biology: Determining the Composition and Architecture of Protein Complexes

2011 ◽  
Vol 64 (6) ◽  
pp. 681 ◽  
Author(s):  
Tara L. Pukala
Keyword(s):  
Mass Spectrometry ◽  
Structural Biology ◽  
Protein Interactions ◽  
Structural Investigation ◽  
Protein Complexes ◽  
Individual Species ◽  
Protein Protein Interactions ◽  
Identify Protein Complex ◽  
Technological Advances ◽  
Recent Developments

Knowledge of protein structure and protein–protein interactions is vital for appreciating the elaborate biochemical pathways that underlie cellular function. While many techniques exist to probe the structure and complex interplay between functional proteins, none currently offer a complete picture. Mass spectrometry and associated methods provide complementary information to established structural biology tools, and with rapidly evolving technological advances, can in some cases even exceed other techniques by its diversity in application and information content. This is primarily because of the ability of mass spectrometry to precisely identify protein complex stoichiometry, detect individual species present in a mixture, and concomitantly offer conformational information. This review describes the attributes of mass spectrometry for the structural investigation of multiprotein assemblies in the context of recent developments and highlights in the field.

Evaluation of Acquisition Modes for the Quantitative Analysis of Cross-Linked Peptides by Targeted and Untargeted Mass Spectrometry

2020 ◽  
Author(s):  
Hannah Britt ◽  
Tristan Cragnolini ◽  
Suniya Khatun ◽  
Abubakar Hatimy ◽  
Juliette James ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Dynamic Range ◽  
Temporal Dynamics ◽  
Protein Complexes ◽  
Isotopic Labeling ◽  
Single Experiment ◽  
Data Independent Acquisition ◽  
Quantitative Metrics ◽  
Parallel Reaction Monitoring ◽  
Further Development

<div> <div> <p>Cross-linking mass spectrometry (XL-MS) is a structural biology technique that can provide insights into the structure and interactions of proteins and their complexes, especially those that cannot be easily assessed by other methods. Quantitative XL-MS has the potential to probe the structural and temporal dynamics of protein complexes; however, it requires further development. Until recently, quantitative XL-MS has largely relied upon isotopic labeling and data dependent acquisition (DDA) methods, limiting the number of biological samples that can be studied in a single experiment. Here, the acquisition modes available on an ion mobility (IM) enabled QToF mass spectrometer are evaluated for the quantitation of cross-linked peptides, eliminating the need for isotopic labels and thus expanding the number of comparable studies that can be conducted in parallel. Workflows were optimized using metabolite and peptide standards analyzed in biological matrices, facilitating modelling of the data and addressing linearity issues, which allow for significant increases in dynamic range. Evaluation of the DDA acquisition method commonly used in XL-MS studies indicated consistency issues between technical replicates and reduced performance in quantitative metrics. On the contrary, data independent acquisition (DIA) and parallel reaction monitoring (PRM) modes proved more robust for analyte quantitation. Mobility enabled modes exhibited an improvement in sensitivity due to the added dimension of separation, and a simultaneous reduction in dynamic range, which was largely recovered by correction methods. Hi[3] and probabilistic quantitation methods were successfully applied to the DIA data, determining the molar amounts of cross-linked peptides relative to their linear counterparts.</p></div></div>

scholarly journals Technological Advances in the University of Washington Accelerator Mass Spectrometry System

Radiocarbon ◽  
1983 ◽  
Vol 25 (2) ◽  
pp. 755-760 ◽  
Author(s):  
G W Farwell ◽  
P M Grootes ◽  
D D Leach ◽  
F H Schmidt
Keyword(s):  
Mass Spectrometry ◽  
Accelerator Mass Spectrometry ◽  
Ion Source ◽  
The Past ◽  
Technological Advances ◽  
Recent Developments ◽  
Mass Spectrometry System ◽  
The University ◽  
Preparation Techniques ◽  
Accelerator System

During the past year we have continued to work toward greater stability and flexibility in nearly all elements of our accelerator mass spectrometry (AMS) system, which is based upon an FN tandem Van de Graaff accelerator, and have carried out measurements of 14C/12C and 10Be/9Be isotopic abundance ratios in natural samples. The principal recent developments and improvements in the accelerator system and in our sample preparation techniques for carbon and beryllium are discussed, and the results of a study of 10Be cross-contamination of beryllium samples in the sputter ion source are presented.

scholarly journals Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0

2019 ◽  
Author(s):  
Eric W. Deutsch ◽  
Lydie Lane ◽  
Christopher M. Overall ◽  
Nuno Bandeira ◽  
Mark S. Baker ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Interpretation ◽  
Human Proteome ◽  
Mass Spectrometry Data ◽  
Data Sets ◽  
The Past ◽  
Data Independent Acquisition ◽  
Human Proteins ◽  
Universal Spectrum ◽  
Human Proteome Project

AbstractThe Human Proteome Organization’s (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20,000 human proteins encoded by the human genome.Abstract Figure

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