Mitochondrial respiration and glycolysis are two highly important parameters for understanding energy metabolism and cellular functions. Cellular metabolism can also be a key indicator of tumorigenesis, aging, stress, and other diseases
The Seahorse™ XF Analyzer has been utilized to measure the:
- Oxygen Consumption Rate (OCR)
- Extracellular Acidification Rate (ECAR)/ Proton Production Rate (PPR) for assessing mitochondrial respiration and glycolysis, respectively 
One of the important steps in performing Seahorse™ XF cell metabolism analysis is normalization of the resultant data. This minimizes inconsistency and variations from well-to-well. This variation is due to potential non-uniform cell seeding across the microplate. Normalization is commonly performed using total protein content in each well which is time-consuming and requires multiple steps such as cell lysing, incubation, and transferring lysed content for analysis. This BCA (bicinchoninic acid) assay can take up to ~45 min for the entire 96-well plate. Washing and transfer procedures can also introduce risks, such as cell loss or pipetting error, thus skewing the final normalized results .
The normalized ECAR time-dependent data for protein and cell count normalization is compared in Figure 1. It is obvious that measurements with protein normalization cannot distinguish between the glycolytic function of DCIS and HCT116 cell lines. In contrast, the cell count normalization method showed a reduction in standard deviation as well as a clear separation between the ECAR values of DCIS and HCT116 cell lines.
Figure 1 Improved result normalization using direct cell counting compared to protein based normalization.
An alternate method: Direct cell counting improves Seahorse data
A major requirement for obtaining reproducible and reliable data with the Seahorse™ XF Analyzers is working with a consistently uniform population of target cells that are evenly distributed and firmly attached to the culture dish. Plate imagers provide a fast, effective, and cell stress-free technique to count cells plated onto uncoated or coated culture plates. Furthermore, plate imagers have a large dynamic range of counting cells in whole well, i.e. counting 1 to ~120,000 cells per well for suspension cells and 1 to ~90,000 cells for adherent cells in a 96-well plate.
Figure 2 Direct cell counting by Nexcelom, Celigo Image Cytometer in a Seahorse™ XF plate
Improve your Seahorse™ XF results using direct cell counting
An alternative method to protein content analysis, that can improve SeahorseTM XF normalization results is performing direct cell counting per well prior to the metabolic stress tests. Cell counting is a non-destructive method to generate data for Seahorse™ XF normalization, which can take account of non-uniform seeding density across the well and plate (Fig 3). Since cell counting is non-destructive, downstream assays are still possible after normalization. Finally, direct cell counting can significantly reduce the amount of time required to perform normalization [1, 2]
Figure 3 Example of non-uniform seeding within the critical area of Seahorse™ XF Analyzer, imaged using Celigo Image Cytometer from Nexcelom
Figure 4 Normalized endpoint data for protein and cell count normalization, showing direct cell results in more accurate and reproducible data from Seahorse™
Conclusion: Direct cell counting improves Seahorse™ data
The ability to employ a high-throughput and high-speed direct cell counting method for Seahorse™ XF normalization enables improvement in final OCR and ECAR results as well as a reduction in time required from the BCA protein analysis.
For more information on direct cell counting for Seahorse normalization, download our application note.
- “Normalizing XF metabolic data to cellular or mitochondrial parameters”, Application Note, Seahorse Bioscience
- “Normalization of Agilent Seahorse XF Data by In-situ Cell Counting Using a BioTek Cytation 5”, Application, Seahorse Bioscience
- Riddle et al., “Expansion capacity of human muscle progenitor cells differs by age, sex, and metabolic fuel preference,” Am J Physiol Cell Physiol, doi:10.1152/ajpcell.00135, 2018