What is Cytometry?
Cytometry is the quantitative analysis of cells and cell systems. A wide range of cutting edge techniques are used to perform cytometry which plays a crucial role in advancing the frontiers of biology, medicine, and technology.
Most cytometric techniques measure the molecular properties of cells by employing fluorescent labeling to detect specific antigens using antibodies, intracellular ions using indicator dyes, fluorescent reporter molecules such as green fluorescent protein (GFP), and DNA and RNA using nucleic acid-specific probes.
Other optical signals can also be measured, including light scatter. Cells may be live or fixed, depending on the application, and individual cells can often be physically sorted using a cytometer.
Although "cytometry" can apply to any method used to extract quantitative information from individual cells, the most common examples are flow cytometry
and image cytometry
, which are primarily optical methods.
Flow cytometry is generally used for analyzing individual cells in a suspension, and is common in immunology and haematopathology. Hallmarks of flow cytometry are analysis speed, detection sensitivity, the ability to measure many parameters simultaneously, and the ability to sort individual cells.
Recent trends in flow cytometry technology include single cell spectroscopy, single cell mass spectrometry, imaging of individual cells in flow, and the development of small inexpensive flow cytometers and sorters.
Image cytometry is generally used for the multiparameter analysis of individual adherent cells, and is used to measure many of the same parameters as flow cytometry. Image cytometry, however, carries the added ability of three-dimensional imaging. "High content analysis" is a term often applied to image cytometry in the context of high throughput screening, while "tissue cytometry" refers to the application of these techniques to cells in situ. Image cytometry is generally performed using automated microscopy and computational image processing and analysis.
Recent trends in image cytometry include high speed imaging, super-resolution imaging, kinetic image cytometry, and machine learning for image analysis and interpretation.