What does coincidence refer to in automated blood cell counting instruments that use impedance as a technique to count cells?

Coincidence in Automated Blood Cell Counting Using Impedance Technique

In automated blood cell counting instruments that use impedance as a technique to count cells, coincidence refers to two or more cells passing through the detection area at the same time.

Understanding Coincidence in Impedance-Based Cell Counting

Impedance-based cell counting (also known as the Coulter principle) works by measuring the change in electrical impedance when cells pass through a small aperture. When two or more cells pass through this sensing zone simultaneously, this creates a "coincidence event" that can affect the accuracy of cell counts.

How Impedance Cell Counting Works

• The Coulter technique involves applying an electric field across a micron-sized aperture
• Cells are hydrodynamically focused to pass through the aperture one at a time
• Each cell causes a measurable change in electrical impedance proportional to its volume
• This allows for precise sizing and counting of cells in three dimensions 1

Impact of Coincidence Events

• Coincidence events reduce the observed number of particles counted
• They can lead to errors in measured cell properties
• Without correction, coincidence causes undercounting of cells
• This becomes more significant at higher cell concentrations 2

Types of Coincidence Events

Research has characterized coincidence detection into three main types based on the time delay between cells passing through the detection area:

1. Complete overlap - cells pass through simultaneously
2. Partial overlap - cells pass with a small time delay
3. Sequential passage - cells pass with a larger but still significant time delay 3

Each type creates different electrical signal patterns that must be correctly interpreted by the instrument.

Coincidence Correction Methods

Modern automated cell counters employ several methods to address coincidence:

• Mathematical correction formulas - Statistical algorithms to estimate the true count based on observed count 4
• Dilution techniques - Using sequential dilutions to extrapolate the true count 4
• Signal processing algorithms - Proprietary curve-fitting and software algorithms to identify and correct for coincidence events 1
• Bayesian approaches - Advanced statistical methods to decompose signals from coinciding particles into individual contributions 2

Clinical Significance

Coincidence detection and correction are particularly important in:

• Counting specific cell populations (e.g., CD4+ or CD8+ T cells in HIV/AIDS patients)
• Ensuring accurate complete blood count (CBC) results
• Applications requiring precise cell enumeration over a broad range of concentrations 3

Common Pitfalls and Considerations

• Coincidence correction becomes more critical at higher cell concentrations
• Different instruments may use different correction algorithms
• Proper sample dilution is essential to minimize coincidence events
• Quality control procedures should include verification of coincidence correction 5

Accurate cell counting requires understanding the limitations of impedance-based methods and ensuring proper instrument calibration and maintenance to minimize counting errors due to coincidence events.