Cell Counting 101 – Tip 6: The Importance of Data Validation: Finding the Sources of Error in Cell Counting

Tip 6 of 6

Read the previous blog post that covers what cell counting variance is and how to calculate it.

When manually cell counting, you’ll typically experience some counting errors. When performing manual cell counts with hemocytometers, the error range can be as high as 20-30%1. This is often likely due to human errors such as imprecise pipetting, errors in dilution preparation or overloading the hemocytometer chambers.

In addition, the process of cell counting can be highly subjective when determining whether a certain particle is defined as a cell, or whether it is within the counting grid. Therefore, it is highly advantageous to perform duplicates or triplicates of the cell count to obtain data that is as representative as possible of the cells in your sample.

You could consider an automated cell count to decrease or eliminate the source of some of these errors. Here, human errors, for example, pipetting imprecisions and subjectivity as to what defines a cell, are minimized, or even eliminated, as the process is fully automated and uses the same counting standards every time.

With automated cell counting, there are still some error-generating parameters such as whether the dilutions are prepared correctly or if the sample is representative of the entire cell population2. When using automated cell counters that require you to set the right focus on the cells, you could also introduce errors to the system if the setting is sub-optimal.

Use Standard Deviation as a Measurement of Consistency

When performing a cell count from your experiment in duplicates, triplicates or more, the standard deviation is an important parameter used to determine by how much the different counts can deviate from each other. The standard deviation is denoted σ and can be calculated using the following formula: NOTE: x = the individual total cell count, whereas x̅ = the mean of all the values.

The mean can be calculated by dividing the sum of the individual cell counts by the total number of counts, represented by n. You divide by n-1 because you are calculating the standard deviation of a sample. If you want to calculate the standard deviation of the whole population, divide by n3. The standard deviation is also equal to the square root of the variance. The variance reflects the average of each cell counts difference from the mean. For more on calculating the variance, click here.

A low standard deviation means that the cell counts do not vary much from the mean, i.e. you have repeatedly consistent cell counts. You ideally want a low standard deviation when repeating the experiments.

How Does the Standard Deviation Differ from the Variance?

Remember in the last blog post of the Cell Counting 101 series when we explained the variance? Now you might be wondering what the difference is between the standard deviation (σ) and the variance (σ2)? Well, in fact the two are closely related! The standard deviation is equal to the square root of the variance. This means that their units differ: The standard deviation shares the same unit as that of the data, whereas for the variance, the unit is squared. Even though squared units may be less straightforward to interpret, both parameters are of equal importance.

The Only Tool You’ll Need for Cell Counting

Errors can also occur when you perform the final calculations after a cell count. But we are offering you a tool to help you prevent this: A downloadable all-in-one spreadsheet to copy and use for your counts!

By using this sheet, you can eliminate errors in calculations or formula choice. It’s your handy guide for when you need to determine the total cell count, viability, variance and dilution factor.

At ChemoMetec, we strive towards making cell counting easier and more efficient. We hope you like the tool, and if you have any questions or suggestions for improvement, don’t hesitate to leave a comment below or contact us for more information.

If you want to know all about manual cell counting using a hemocytometer, here’s the overview of the Cell Counting 101 blog series.

References

1. Electron Microscopy Sciences: Neubauer Haemocytometry.
2. Vembadi A, Menachery A, Qasaimeh MA.: Cell Cytometry: Review and Perspective on Biotechnological Advances. Front Bioeng Biotechnol. 2019; 7:147. 2019 Jun 18.
3. Ross, S. M. (2014). Introduction to Probability and Statistics for Engineers and Scientists (Fifth Edition) Chapter 2. Academic Press.

By Christina Psaradaki, Student Assistant at ChemoMetec
Christina Psaradaki studies Human Life Science Engineering at the Technical University of Denmark. At ChemoMetec, she writes for the Cell Counting Blog.

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1. B Russo | PhD student, Milano Statale |