Cell counting underpins numerous applications, spanning basic research through to the development and production of cell therapies. In recent years, manual cell counting methods have largely been replaced by the use of automated cell counters, which are both faster and more accurate, especially for complex sample types. This article suggests factors to consider when choosing an automated cell counter and shares tips for instrument use to ensure accurate results.
While cell-based research has traditionally relied on immortalized cell lines, it is increasingly common for more complex sample types to be used. These include primary cells, peripheral blood cells, stem cells, dissociated tumor cells, and even engineered T cells, which vary in terms of size, shape, and aggregation properties. To improve cell counting accuracy, particularly for these newer entities, many researchers have switched from using manual, hemocytometer-based methods to counting cells with automated platforms. However, automated cell counters can only provide accurate results if they feature the right specifications for the cell type in question. Although an automated cell counter equipped with brightfield microscopy optics and a low-magnification objective represents a budget-friendly option for counting well-isolated, homogenous cell lines, an instrument capable of measuring fluorescence is often a better choice for counting clumpy samples or smaller cell types such as peripheral blood mononuclear cells (PBMCs).
So, what factors should researchers consider when using an automated cell counter? A primary concern is the optimal dilution range, typically provided as cells/mL. This differs among instruments based on the size of the field of view (FOV), the magnification, and the image sensor size. For counting accuracy, the sample should be diluted such that it falls within this range—if too dilute, counts will be inaccurate; if too concentrated, the instrument software will struggle to distinguish individual cells.
Most automated cell counters allow viability measurements to be performed using trypan blue staining and brightfield imaging. But, while this approach provides accurate results for homogenous samples like cancer cell lines, it is less reliable for more diverse sample types. For example, primary cells are often contaminated with large numbers of red blood cells, which will be mistakenly classified as dead cells after staining with trypan blue. Cellular debris and non-cellular particles can also be misidentified in this way, leading to data being artificially skewed. Fluorescence-based methods such as acridine orange/propidium iodide (AO/PI) staining provide greater accuracy than trypan blue, regardless of cell type, and are fast becoming a preferred method for measuring cell viability.
Once samples have been prepared for counting, they are loaded onto a chamber slide; this functions to provide a fixed volume measurement based on the chamber height. Because the chamber height is set by the slide manufacturer, scanning multiple FOVs is the best way of increasing cell counting volume to achieve greater accuracy and precision. Modern instruments equipped with an automated scanning stage can count volumes of up to 5.1 µL, which is 10-fold higher than a conventional hemocytometer measurement.
The most common error when counting cells is incorrect focusing, which, for brightfield microscopy, can lead to poor discrimination of live cells from dead cells. If an automated cell counter offers only manual focusing, it is recommended that researchers obtain a bright spot at the center of each live cell for brightfield imaging to produce an accurate count. Fortunately, most modern automated cell counters feature an autofocusing function, which can minimize focus-related issues when running in brightfield mode. Alternatively, since fluorescence imaging is relatively insensitive to focus, using a fluorescence cell counter with autofocus function will eliminate this problem.
Image shows autofocus, dual fluorescent counting of the LUNA-FX7 using AO/PI staining; living cells (green) and dead cells (red).
Another widespread mistake is failure to optimize the cell counting algorithm, also known as the counting protocol. This is proprietary to each automated cell counter and is used for image pre-processing, object finding, and object classification. Because it would be impossible for a single counting protocol to cover every cell type, most algorithms allow the end user to perform further optimization. Factors to consider here include the cell size, spot brightness, cell detection sensitivity, roundness, noise reduction, and fluorescence intensity, all of which should be carefully tailored to the diversity of cell types within the sample.
The LUNA™ automated cell counter family from Logos Biosystems provides fast, accurate counting for a broad range of cell types, including PBMC and CAR T cells. To learn more, visit logosbio.com
Higher throughput. Offering a variety of slide options, the LUNA-FX7™ utilizes a counting volume of up to 5.1 µl, lowering error and CV for each count.