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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
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In a beam of charged particles created by a heated cathode, the particles move at different speeds. However, many applications need a beam with uniform particle speeds. An arrangement known as a velocity selector uses electric and magnetic fields to pick particles with a particular speed from the beam.
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When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
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Assessing sub-cellular resolution in spatial proteomics experiments.

Laurent Gatto1, Lisa M Breckels2, Kathryn S Lilley3

  • 1Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK; Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK; de Duve Institute, UCLouvain, Avenue Hippocrate 75, 1200 Brussels, Belgium.

Current Opinion in Chemical Biology
|February 4, 2019
PubMed
Summary
This summary is machine-generated.

This study evaluates the sub-cellular resolution of 29 mass spectrometry-based spatial proteomics experiments. A new tool, QSep, quantifies how well these methods resolve cellular niches, aiding researchers in selecting appropriate techniques.

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Area of Science:

  • Proteomics
  • Cell Biology
  • Biochemistry

Background:

  • Protein sub-cellular localization is crucial for function; mis-localization can cause disease.
  • Numerous experimental techniques, including mass spectrometry, aim to determine protein localization.
  • Spatial proteomics datasets vary in their ability to resolve cellular compartments.

Purpose of the Study:

  • To quantitatively assess and compare the sub-cellular resolution of 29 mass spectrometry-based spatial proteomics experiments.
  • To introduce and utilize a novel tool, QSep, for evaluating spatial proteomics data.
  • To provide guidance for developers and users of spatial proteomics methods.

Main Methods:

  • Meta-analysis of 29 spatial proteomics datasets.
  • Application of a newly developed tool, QSep, for quantitative resolution assessment.
  • Comparison of sub-cellular niche resolution across different experimental approaches.

Main Results:

  • Quantitative data on the resolution capabilities of various spatial proteomics experiments.
  • Identification of strengths and limitations of different methods in resolving cellular locations.
  • Benchmarking of spatial proteomics techniques using the QSep tool.

Conclusions:

  • Spatial proteomics methods exhibit varying degrees of sub-cellular resolution.
  • The QSep tool offers a standardized approach to evaluate and compare spatial proteomics data.
  • This meta-analysis informs the selection and development of more precise protein localization techniques.