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Flow Cytometry

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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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Systematic Design, Generation, and Application of Synthetic Datasets for Flow Cytometry.

Melissa Cheung¹, Jonathan J Campbell², Robert J Thomas³

¹Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom; and M.Cheung@lboro.ac.uk.

PDA Journal of Pharmaceutical Science and Technology

|January 15, 2022

Summary

This summary is machine-generated.

This study introduces a systematic method for creating synthetic flow cytometry datasets with controlled properties. These datasets improve the evaluation of automated cell identification tools, enhancing clinical diagnostics and cell therapy.

Keywords:

Accuracy Clusters Flow cytometry Repeatability Separation Skew Synthetic datasets

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

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Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Area of Science:

Computational Biology
Data Science
Biotechnology

Background:

Synthetic datasets enhance AI tool training but lack standardization in flow cytometry.
Real flow cytometry data is limited by size, parameters, and disease/cell type focus.
Existing synthetic data generation methods are inconsistent and scarce.

Purpose of the Study:

To propose a systematic method for generating controlled synthetic flow cytometry datasets.
To evaluate automated cell population identification software using these synthetic datasets.
To advance the generation and awareness of high-quality synthetic flow cytometry data.

Main Methods:

Developed a systematic approach to design and generate synthetic flow cytometry datasets.
Created datasets with controlled cluster separation and non-normal distributions (skewness, orientation).
Applied synthetic datasets to benchmark the performance of SPADE3 software.

Main Results:

Defined performance limitations of SPADE3 as cell clusters decrease in separation.
Demonstrated SPADE3's capability to process non-normal data.
Achieved robust classification accuracy calculations for SPADE3, exceeding real-world dataset capabilities.

Conclusions:

The proposed method enables the generation of high-quality, controlled synthetic flow cytometry datasets.
Synthetic datasets are crucial for benchmarking cell identification tools and identifying platform inconsistencies.
This work can improve cell characterization workflows in clinical diagnostics and cell therapy manufacturing.