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Related Concept Videos

Quality Control01:05

Quality Control

198
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
198
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

168
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
168
Quality Assurance01:19

Quality Assurance

159
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
159
Data Validation01:15

Data Validation

187
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
187
Instrument Calibration01:12

Instrument Calibration

220
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
220
Sampling Plans01:23

Sampling Plans

214
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
214

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Updated: Jul 23, 2025

Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging cPILOT
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Evaluating cPILOT Data toward Quality Control Implementation.

Bailey L Bowser1, Khiry L Patterson1, Renã As Robinson1,2,3,4,5

  • 1Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.

Journal of the American Society for Mass Spectrometry
|July 17, 2023
PubMed
Summary
This summary is machine-generated.

Quality control metrics improve high-throughput quantitative proteomics. Implementing these metrics in combined precursor isotopic labeling and isobaric tagging (cPILOT) experiments minimizes missing data for thousands of proteins.

Keywords:
TMTcPILOTmultiplexingquality controlquantitative proteomics

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Multiplexing in quantitative proteomics enhances sample throughput by enabling simultaneous analysis of hundreds to thousands of proteins.
  • Common multiplexing techniques include precursor isotopic labeling and isobaric tagging, with combined precursor isotopic labeling and isobaribg tagging (cPILOT) offering higher throughput.
  • Optimizing analytical performance and minimizing missing data are crucial for enhanced multiplexing strategies.

Purpose of the Study:

  • To evaluate the implementation of quality control (QC) metrics in a large-scale 36-plex cPILOT experiment.
  • To assess the utility of a sample pool for monitoring instrument performance and normalizing data across batches.
  • To determine the impact of QC metrics on data quality and the minimization of missing values in complex proteomic analyses.

Main Methods:

  • Applied previously developed QC metrics to a 36-plex cPILOT experiment involving 144 mouse samples.
  • Utilized a pooled sample derived from all experimental samples to track daily instrument performance.
  • Employed the pooled sample for inter-batch data normalization.

Main Results:

  • QC metric tracking facilitated the quantification of approximately 7000 proteins per sample batch.
  • Approximately 70% of quantified proteins exhibited minimal missing values across up to 36 multiplexed sample channels.
  • The pooled sample effectively monitored instrument performance and enabled data normalization.

Conclusions:

  • Implementation of QC metrics is essential for ensuring optimal analytical performance in enhanced multiplexing proteomics.
  • QC metrics significantly reduce missing data, maximizing the usability of quantitative proteomic datasets.
  • These QC strategies are valuable for cPILOT and other advanced multiplexing techniques, leading to high-quality data generation.