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Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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FTflow: An Open-Source Python GUI for FT-IM-MS Experiments.

Elvin R Cabrera1, Arthur Laganowsky2, Brian H Clowers1

  • 1Department of Chemistry, Washington State University, Pullman, Washington 99164, United States.

Journal of the American Society for Mass Spectrometry
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

A new web-based tool, FTflow, simplifies Fourier-transform ion mobility mass spectrometry (FT-IM-MS) data analysis. It reconstructs arrival time distributions and extracts mobility information, aiding researchers in data interpretation.

Keywords:
Fourier transformgraphical user interface (GUI)ion mobilityion trapmass spectrometrymultiplexingsignal processing

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Chemistry

Background:

  • Fourier-transform ion mobility mass spectrometry (FT-IM-MS) generates complex datasets.
  • Analyzing FT-IM-MS data requires specialized tools for extracting mobility information.
  • Seamless integration of ion mobility with mass spectrometry is crucial for advanced analysis.

Purpose of the Study:

  • To develop a user-friendly, all-in-one graphical user interface (GUI) tool for FT-IM-MS data analysis.
  • To facilitate the integration of Fourier-transform ion mobility (FT-IM) data with mass spectrometry (MS) data.
  • To provide researchers with a flexible platform for exploratory FT-IM-MS data analysis.

Main Methods:

  • Development of a web-browser-based GUI tool named FTflow.
  • Utilizing Python for both the GUI interface and processing scripts for flexibility.
  • Accepting mzML file formats for data input.
  • Reconstructing arrival time distributions and extracting mobility-related information (Ko, CCS).

Main Results:

  • FTflow successfully reconstructs arrival time distributions from FT-IM-MS data.
  • The tool extracts key mobility parameters such as Ko and collision cross-section (CCS).
  • The GUI provides clear input fields for experimental conditions, enhancing ease-of-use.

Conclusions:

  • FTflow offers a valuable starting point for exploratory analysis of FT-IM-MS data.
  • The tool's Python-based architecture allows for user modification and adaptation.
  • FTflow can be integrated into automated data processing pipelines via its core routines.