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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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Related Experiment Video

Updated: Jan 1, 2026

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Published on: August 16, 2015

Model-based cell clustering and population tracking for time-series flow cytometry data.

Kodai Minoura^1,2, Ko Abe¹, Yuka Maeda³

¹Division of Systems Biology, Graduate School of Medicine, Nagoya University, 65 Trumumai-cho, Showa-ku, Nagoya, 4668550, Japan.

BMC Bioinformatics

|December 29, 2019

Summary

This summary is machine-generated.

CYBERTRACK is a new statistical framework for analyzing time-series flow cytometry data. It enables systematic tracking of cell populations and provides better insights into cell dynamics over time.

Keywords:

Baysian inference Flow cytometry Time-series Topic model

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

Published on: March 24, 2023

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Last Updated: Jan 1, 2026

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

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Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

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Published on: December 13, 2012

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

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

Published on: March 24, 2023

Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Flow cytometry enables multi-marker single-cell analysis, crucial in diverse research fields.
Manual gating is a traditional method for cell population detection, but it is subjective and time-consuming.
Existing automated tools are not optimized for time-series flow cytometry data, limiting dynamic cell population analysis.

Purpose of the Study:

To develop a statistical framework for automated clustering and tracking of cell populations in time-series flow cytometry data.
To address the need for systematic analysis of dynamic cell population changes over time.

Main Methods:

Proposed CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), a statistical framework for time-series flow cytometry data.
CYBERTRACK models data using a multivariate Gaussian mixture distribution with time-dependent mixture proportions.
Evaluated performance using simulation data for parameter estimation, mixture proportion tracking, and change-point detection.

Main Results:

CYBERTRACK effectively estimates parameters of multivariate Gaussian mixture distributions.
The framework accurately tracks time-dependent transitions in mixture proportions.
Validated on real flow cytometry datasets, demonstrating consistency with known lymphocyte dynamics.

Conclusions:

CYBERTRACK provides a systematic approach to analyze time-series flow cytometry data.
Enhances understanding of time-dependent cell population dynamics for cytometry users.