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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 5, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

3D flow field estimation and assessment for live cell fluorescence microscopy.

Sandeep Manandhar¹, Patrick Bouthemy¹, Erik Welf²

¹Inria, Centre Rennes-Bretagne Atlantique, Rennes 35042, France.

Bioinformatics (Oxford, England)

|October 22, 2019

Summary

We developed a new 3D motion estimation method for light sheet microscopy data. This approach automatically quantifies cellular dynamics, linking local processes to cellular functions for better biological insights.

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Last Updated: Jan 5, 2026

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Area of Science:

Biophysics
Computational Biology
Microscopy

Background:

Light sheet microscopy allows high-resolution observation of dynamic biological processes.
Interpreting complex multidimensional microscopy data requires automated motion analysis.
Linking observed motions to cellular functions is crucial for understanding biological systems.

Purpose of the Study:

To develop an automated 3D motion estimation method for light sheet microscopy.
To address challenges in analyzing diverse motion types and complex data.
To provide tools for linking local cellular dynamics to overall cellular functions.

Main Methods:

Integration of 3D matching and variational approaches for motion estimation.
Utilizing the Census signature for robust similarity measurement.
Development of intuitive 2D visualization techniques for complex 3D data.
Novel method for assessing flow estimate quality without ground truth.

Main Results:

A novel method for 3D motion estimation in microscopy data was successfully developed.
The method effectively handles diverse motion types without prior object shape assumptions.
The Census signature proved effective for similarity measures in motion estimation.
Intuitive visualization tools and a quality assessment method were presented.

Conclusions:

The developed method enables automated, accurate 3D motion analysis from light sheet microscopy.
This facilitates the interpretation of cellular dynamics and their relation to cellular functions.
The approach offers a valuable tool for researchers studying dynamic biological processes at high resolution.