Cell counting is an essential task in cell culture workflows, helping to ensure experimental consistency and repeatability, optimize culture conditions, and support reliable downstream applications. Despite significant technological advances in cell-based research and analytical techniques, many laboratories rely on manual cell counting methods, which can become a bottleneck in otherwise increasingly automated workflows. Addressing this challenge requires a transition from manual counting approaches to automated, data-driven technologies that can deliver rapid, repeatable, and reliable cell measurements while reducing operator-to-operator variability and streamlining laboratory processes.
Why cell counting and viability assessment are critical for experimental success
Cell counting is a critical quality control step in many laboratory workflows, as seeding density directly influences the microenvironment experienced by cells. This environment affects their growth, viability, and phenotype—as well as experimental behaviors and outcomes—as cells do not behave the same way when cultured at very low or very high densities. Inaccurate counts can also impact downstream applications, reducing the reliability and repeatability of many assays.
Importantly, cell number alone does not provide a complete assessment of culture quality. Cell viability is another critical parameter, as the proportion of healthy, functional cells within a population can directly influence experimental performance and repeatability. Even when target cell concentrations are achieved, reduced viability can compromise cell growth, alter cellular responses, and affect the accuracy of downstream assays. Routine evaluation of both cell numbers and viability enables researchers to make informed decisions regarding culture maintenance, assay readiness, and overall sample quality, helping to ensure robust and repeatable experimental outcomes. Unfortunately, many methods to assess cell viability—such as microscopy-based visualization during cell counting—are labor intensive and subjective, creating bottlenecks in cell-based workflows.
Limitations of traditional cell counting methods
There are several methods for cell counting, each with its own advantages and disadvantages. Hemocytometers remain popular, but require users to manually count the number of cells within each chamber of a grid etched on a thick glass slide under a microscope, then mathematically determine the concentration of the original culture. This reliance on manual counting makes the process labor intensive and time consuming, and there is inherent variability between different users. In addition, cells must be stained to allow visualization, increasing the number of processing steps and introducing another source of variability.
Alternative approaches include resistive pulse sensing and automated, image-based cell counting systems, both of which can improve throughput and reduce operator variability but still have their own limitations. Resistive pulse sensing can accurately process large numbers of cells, but viability measurements typically require additional reagents, morphological information is limited, and performance can be affected by debris or other particulate contaminants. Automated, image-based systems can provide cell counts and viability data with reduced user subjectivity, but their performance is influenced by sample characteristics, staining requirements, and the imaging and analysis algorithms employed. As a result, laboratories continue to seek technologies that combine speed, ease of use, repeatability, and the ability to generate richer information about cell populations.
What is digital holographic microscopy?
Digital holographic microscopy (DHM) is an advanced optical imaging technique that creates a three-dimensional image of a specimen by recording and reconstructing a hologram. DHM captures both the phase and amplitude information of light interacting with a sample. This approach can provide consistent, repeatable cell counting measurements without requiring labeling or staining. The automated nature of DHM, combined with the elimination of sample preparation, staining, and incubation, simplifies the cell counting workflow and reduces the number of operator-dependent processing steps. This can improve laboratory efficiency, particularly in time-sensitive workflows, where rapid assessment of cell concentration and viability is required to support downstream experimental decisions. As these measurements are automated, it also eliminates the problem of inter-user variability associated with manual counting methods.
Beyond counting: insights into cell viability
Another advantage of DHM-based cell counters is their ability to provide information on cell viability based on morphological characteristics, without the need for stains or labels. While traditional, staining-based manual counting methods allow users to visually assess cells during analysis, these evaluations are subjective and prone to operator-to-operator variability. In contrast, DHM-based systems combine standardized image acquisition with analytical algorithms trained to evaluate cellular morphology, improving the consistency of viability measurements. This provides a population level overview of cells in the form of a ‘viability map’. Depending on the system configuration, additional analytical features—such as debris filtering and aggregate detection—may further improve the robustness of cell population measurements.
Conclusion
Cell-based research is becoming increasingly complex and data driven, creating demand for more advanced counting technologies that match the needs of evolving workflows. DHM offers a rapid, stain-free and repeatable alternative to traditional methods, allowing researchers to perform routine quality control checks more effectively for greater confidence in their results. Importantly, this approach provides valuable insights into cell viability alongside accurate cell counts, reducing experimental variability and enabling more informed decisions before costly downstream assays.
Combining DHM with machine learning-based analysis represents a significant step forward as laboratories seek to improve productivity and repeatability. It transforms cell counting from a manual process—or one dependent on complex and costly equipment—into a rapid and automated workflow step that enables researchers to generate reliable cell counts while gaining insights into cell viability.
Dr. Hans-Joachim Muhr is Segment Business Development Manager for Life Sciences at METTLER TOLEDO