Counting is a simple concept that we grasp at an early age, yet counting the number of cells accurately and reproducibly for cell-based biotechnology has been exceedingly challenging. The difficulties arise from the complex biological origin and dynamic nature of cells as well as the sheer number (thousands of cells in a small droplet of solution to billions of cells in several liter batches). Cell counting (or cell enumeration) is one of the most fundamental measurements in biotechnology, from biomanufacturing to medical diagnosis to advanced therapy. For example, many cell-based bioassays, including activity and potency, must be normalized to the cell number to allow data inter-comparability. The number of cells within a bioreactor may serve as a quality assurance metric in a manufacturing process. Cell number is critical for determining the proper dose of a cell-based therapy. The NIST program addresses this critical need by establishing confidence in cell counting measurements.
Advances in instrumentation and cell sourcing have led to a wide range of cell counting methods and conditions. Although there are many technologies to choose from, it can be difficult to select the most appropriate cell counting process since different methods can produce different results, and characteristics of the biological sample (such as clumping) may preclude some methods.
Because of the diversity of cell types and counting methods it is unlikely that there is a single method that is appropriate under all conditions. Given this limitation, we have recently developed an approach that combines experimental design and statistical analysis to allow the users to evaluate the confidence of his/her cell counting measurement process through a statistical performance metric for cell counting. This approach is particularly useful in the absence of a "ground truth", reference method or reference material. Our current research is focused on testing the robustness of this statistical method, through both experiment and in silico modeling, and its application to differential counting (e.g., counting of a subpopulation of cells).
Contact Sumona Sarkar at sumona.sarkar [at] nist.gov to learn more about this project and ways to collaborate with the Biosystems and Biomaterials Division.