Application Note | TURBIDI.T™
How to quantify cell concentration with TURBIDI.T™?
by Gloria Pinilla and Nora Kasaplar,
Application Scientists, Rheolution Inc.
In colaboration with
École Nationale Supérieure de Technologie des Biomolécules de Bordeaux (ENSTBB).
SUMMARY
- Growing demands for quicker cell quantification methods push beyond traditional cell counting chambers, prompting innovative solutions that facilitate accurate counting while diminishing time.
- Turbidity, reflecting solution cloudiness in the presence of light scatterers, emerges as an effective means to accelerate cell quantification.
- In this application note, a strong correlation was established between turbidity (FTU) of a S. cerevisiae culture solution and cell numbers (cells/mL) counted with a conventional cell counting chamber.
- Utilizing TURBIDI.T™ for accurate turbidity measurements offers a user-friendly and rapid approach to estimating cell numbers, saving time and resources in routine cell counting.
INTRODUCTION
In the context of cell culture analysis, the demand for efficient cell quantification methods has led to the search for alternatives to the time-consuming traditional technique of cell counting chambers. This conventional approach, while reliable, poses challenges in terms of speed and practicality, particularly in the face of growing research and industrial requirements.
Turbidity, a measure of cloudiness in a solution, emerges as an interesting approach for accelerating cell quantification. The level of turbidity in a medium has been linked to cell concentration within a cell culture [1]. Exploiting this connection, this application note introduces a new approach to rapidly determine cell numbers. This method leverages the capabilities of TURBIDI.T™, an IoT-enabled instrument designed for accurate turbidity measurement. By utilizing this tool, we present a practical and efficient technique to estimate cell numbers based on turbidity measurements, marking a significant step forward in cell quantification methodologies.
Figure 1: Relationship between turbidity and cell concentration. Low cell concentrations lead to high transmission of light through the sample, which is detected as low turbidity (left), while high cell concentration leads to lower light transmission, detected as high turbidity values in the sample. Blue arrows indicate the direction of the light.
MATERIALS AND METHODS
Saccharomyces cerevisiae cells (5-10 µm) were grown in a bioreactor (BIOSTAT B; Sartorius Stedim Biotech, Göttingen, Germany) as explained in our previous application note (Figure 2).
Figure 2: Bioreactor setup for yeast growing in sterile conditions.
Yeast was inoculated into the bioreactor from a yeast pre-culture previously grown overnight. A volume of 100 mL was inoculated in order to have an initial turbidity value of 200 FTU. After inoculation, sterile aliquots were taken every two hours and the number of cells (i.e., cell concentration) was determined by the methylene blue staining method. This method is based on a dye, methylene blue, which anchors to cell components and stains them in blue. This dye reacts differently in viable and in non-viable cells since it is transformed by living cells (which remains colorless) while dead cells remain stained in blue. The stained cells were placed on a hemocytometer (a.k.a. cell counting or Malassez chamber) and observed under a microscope (x40). The hemocytometer consists of nine 1 x 1 mm (1 mm2) squares. A volume of 1 µL was used for each measurement.
Turbidity of the solutions was determined with the use of TURBIDI.T™. The instrument was calibrated with formazin standards (1-4000 FTU). Light emission and reception were enabled using Emitt.805 cartridges (850 nm) and Receiv.ViS cartridges (wavelength ranging from 400 nm to 1000 nm), respectively. A wavelength of 850 nm was used since the effect of light absorbance by the sample was lower (data not shown). In this work, we chose to use the 10 mL vials where samples were aliquoted and measured on the TURBIDI.T™. Data was collected and displayed on the tablet operating the Soft Matter Analytics App™.
Both cell counting and turbidity measurements were done in triplicate (n=3). Data is represented as the average ± standard deviation. Variability is defined as the ratio between standard deviation and mean value.
RESULTS AND DISCUSSION
Cell aliquots at the growing phase of the yeast culture were analyzed by means of turbidity and cell counting with a cell counting chamber. Figure 3 shows the time evolution of both parameters, which increase over time. We see from the graph that the turbidity and cell concentration evolution follow the same trend, this being exponential overall. The increase of cell concentration is directly affecting the turbidity of the culture. More cells are scattering light and affecting the overall turbidity of the medium.
Turbidity measurements were more consistent (showing 3.8% of variability) compared to the cell counting technique (with a variability of 17.6%). This divergence can be attributed to inaccuracies in counting due to human perception, but also to the dissimilarity in sample volume between the two methods. While cell counting employs 1µL of sample, turbidity assessment is conducted in this study using 10mL aliquots. Particularly in scenarios involving larger bioreactor volumes (as in our case, 2L), a substantial sample volume contributes to a more comprehensive representation of the ongoing processes within the cell culture, consequently providing a more accurate estimation.
Figure 3: S. cerevisiae culture time evolution of (A) turbidity (FTU) and (B) cell concentration (cells/mL) counted with a cell counting chamber.
Figure 4 demonstrates the linear correlation between turbidity measurements and microscopy cell counting. The curve is represented by the equation: y=32 345x-4 894 248 and has a coefficient of determination R²=0.998.
Figure 4: Correlation between the cell number counted with a cell counting chamber and turbidity using TURBIDI.T™
It is interesting to notice that similar graphs can be created using different culture media and microbial cell types to capture this correlation. The obtained graph can then be used to estimate the cell count in a sample by simply correlating the turbidity value measured with TURBIDI.T™ to the associated number of cells. This inferring method consist in four successive steps (Figure 5):
- A sample containing the unknown concentration of cells is aliquoted in (a) vial(s).
- The vial is placed in the TURBIDI.T™ chamber and a measurement is taken. Results are displayed by the Soft Matter Analytics™ App.
- With the use of the correlation curve (cells/mL vs. FTUs), FTUs are associated with a cell density.
- Cell concentration is calculated using the curve equation.
Figure 5: A method to estimate the cell concentration in liquid culture medium with turbidity.
CONCLUSIONS AND PRESPECTIVES
- The turbidity of yeast cells in a liquid culture medium can be easily measured with the user-friendly TURBIDI.T™ instrument.
- The user can choose different wavelengths of light emission in the TURBIDI.T™ to better fit the type and size of particles or cells. In the case of coloured solutions, 850 nm has shown to be the most precise wavelength since sample absorbance does not affect the results.
- Cell counting analysis with a conventional cell counting chamber is highly correlated with turbidity values (R²=0.998), demonstrating that an inference method to estimate cell number in a culture medium can be easily developed and effectively used.
- The fast acquisition features of TURBIDI.T™ combined with the easy-to-use Soft Matter Analytics™ App help to rapidly quantify cells using turbidity and ultimately save time and resources in the lab when routine cell counting measurements are needed.
$8,500 per system
REFERENCES
INTRODUCTION
In the context of cell culture analysis, the demand for efficient cell quantification methods has led to the search for alternatives to the time-consuming traditional technique of cell counting chambers. This conventional approach, while reliable, poses challenges in terms of speed and practicality, particularly in the face of growing research and industrial requirements.
Turbidity, a measure of cloudiness in a solution, emerges as an interesting approach for accelerating cell quantification. The level of turbidity in a medium has been linked to cell concentration within a cell culture [1]. Exploiting this connection, this application note introduces a new approach to rapidly determine cell numbers. This method leverages the capabilities of TURBIDI.T™, an IoT-enabled instrument designed for accurate turbidity measurement. By utilizing this tool, we present a practical and efficient technique to estimate cell numbers based on turbidity measurements, marking a significant step forward in cell quantification methodologies.
Figure 1: Relationship between turbidity and cell concentration. Low cell concentrations lead to high transmission of light through the sample, which is detected as low turbidity (left), while high cell concentration leads to lower light transmission, detected as high turbidity values in the sample. Blue arrows indicate the direction of the light.
MATERIALS AND METHODS
Saccharomyces cerevisiae cells (5-10 µm) were grown in a bioreactor (BIOSTAT B; Sartorius Stedim Biotech, Göttingen, Germany) as explained in our previous application note (Figure 2).
Figure 2: Bioreactor setup for yeast growing in sterile conditions.
Yeast was inoculated into the bioreactor from a yeast pre-culture previously grown overnight. A volume of 100 mL was inoculated in order to have an initial turbidity value of 200 FTU. After inoculation, sterile aliquots were taken every two hours and the number of cells (i.e., cell concentration) was determined by the methylene blue staining method. This method is based on a dye, methylene blue, which anchors to cell components and stains them in blue. This dye reacts differently in viable and in non-viable cells since it is transformed by living cells (which remains colorless) while dead cells remain stained in blue. The stained cells were placed on a hemocytometer (a.k.a. cell counting or Malassez chamber) and observed under a microscope (x40). The hemocytometer consists of nine 1 x 1 mm (1 mm2) squares. A volume of 1 µL was used for each measurement.
Turbidity of the solutions was determined with the use of TURBIDI.T™. The instrument was calibrated with formazin standards (1-4000 FTU). Light emission and reception were enabled using Emitt.805 cartridges (850 nm) and Receiv.ViS cartridges (wavelength ranging from 400 nm to 1000 nm), respectively. A wavelength of 850 nm was used since the effect of light absorbance by the sample was lower (data not shown). In this work, we chose to use the 10 mL vials where samples were aliquoted and measured on the TURBIDI.T™. Data was collected and displayed on the tablet operating the Soft Matter Analytics App™.
Both cell counting and turbidity measurements were done in triplicate (n=3). Data is represented as the average ± standard deviation. Variability is defined as the ratio between standard deviation and mean value.
RESULTS AND DISCUSSION
Cell aliquots at the growing phase of the yeast culture were analyzed by means of turbidity and cell counting with a cell counting chamber. Figure 3 shows the time evolution of both parameters, which increase over time. We see from the graph that the turbidity and cell concentration evolution follow the same trend, this being exponential overall. The increase of cell concentration is directly affecting the turbidity of the culture. More cells are scattering light and affecting the overall turbidity of the medium.
Turbidity measurements were more consistent (showing 3.8% of variability) compared to the cell counting technique (with a variability of 17.6%). This divergence can be attributed to inaccuracies in counting due to human perception, but also to the dissimilarity in sample volume between the two methods. While cell counting employs 1µL of sample, turbidity assessment is conducted in this study using 10mL aliquots. Particularly in scenarios involving larger bioreactor volumes (as in our case, 2L), a substantial sample volume contributes to a more comprehensive representation of the ongoing processes within the cell culture, consequently providing a more accurate estimation.
Figure 3: S. cerevisiae culture time evolution of (A) turbidity (FTU) and (B) cell concentration (cells/mL) counted with a cell counting chamber.
Figure 4 demonstrates the linear correlation between turbidity measurements and microscopy cell counting. The curve is represented by the equation: y=32 345x-4 894 248 and has a coefficient of determination R2=0.998.
Figure 4: Correlation between the cell number counted with a cell counting chamber and turbidity using TURBIDI.T™
It is interesting to notice that similar graphs can be created using different culture media and microbial cell types to capture this correlation. The obtained graph can then be used to estimate the cell count in a sample by simply correlating the turbidity value measured with TURBIDI.T™ to the associated number of cells. This inferring method consist in four successive steps (Figure 5):
- A sample containing the unknown concentration of cells is aliquoted in (a) vial(s).
- The vial is placed in the TURBIDI.T™ chamber and a measurement is taken. Results are displayed by the Soft Matter Analytics™ App.
- With the use of the correlation curve (cells/mL vs. FTUs), FTUs are associated with a cell density.
- Cell concentration is calculated using the curve equation.
Figure 5: A method to estimate the cell concentration in liquid culture medium with turbidity.
CONCLUSIONS AND PRESPECTIVES
- The turbidity of yeast cells in a liquid culture medium can be easily measured with the user-friendly TURBIDI.T™ instrument.
- The user can choose different wavelengths of light emission in the TURBIDI.T™ to better fit the type and size of particles or cells. In the case of coloured solutions, 850 nm has shown to be the most precise wavelength since sample absorbance does not affect the results.
- Cell counting analysis with a conventional cell counting chamber is highly correlated with turbidity values (R2=0.998), demonstrating that an inference method to estimate cell number in a culture medium can be easily developed and effectively used.
- The fast acquisition features of TURBIDI.T™ combined with the easy-to-use Soft Matter Analytics™ App help to rapidly quantify cells using turbidity and ultimately save time and resources in the lab when routine cell counting measurements are needed.
$8,500 per system
REFERENCES
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