Specs are often used for measuring the density of suspension cultures, but the mistake that many people make is to record the OD given by the spec as an absolute value. The OD value represents the amount of light that is absorbed by your sample. But that value is affected by the intensity of the light beam in the spec, and the spec design.
This means that similar samples will give completely different OD values in different specs due to the specs having different bulbs, or even in the same spec over time, as the beam intensity reduces with the age of the bulb.
So recording an OD value in your lab book does not really mean anything as this number is as much dependent on your spec as the density of your culture. What you really want to know from an OD reading is the density of the cells e. And to get this you need a standard curve. In other words, like any other spec-based experiment you will ever perform, you need to calibrate the absorbance value against the number you actually want to know. Light scattering of turbid sample. OD estimations have gotten synonyms with estimations of bacterial concentration C or number N , as per the Beer-Lambert law.
However measurements of OD are actually measurements of turbidity, we can therefore apply the Beer-Lambert law, with certain contemplations, just for microbial colonies of low densities. Difference in OD Readings For turbid samples such as cell cultures, the major contributor for the absorbance measured is light scattering and not the result of molecular absorption following the Beer-Lambert Law.
The measurements are therefore depending on the optical setup of the spectrophotometer distance between the cell holder and instrument exit slit, monochromator optics, slit geometry, etc. Therefore, if results from different spectrophotometers are to be compared, they must be normalized first by either a simple correlation of the OD readings using the same sample on the two different instruments to compare.
A calibration curve can be constructed by comparing measured OD to expected OD over a range of different concentrations. The amount of cells is reflected in the reading and the likelihood of fluctuating amount of cells in a drop from sample to sample can be considered as extremely significant.
It is therefore recommended to use cuvettes since the amount of error in a bigger volume is not as significant. The cuvette measurements provide a bigger average and therefore more reproducible readings. Also, to prevent the suspension settling too quickly and giving an OD reading that changes with time, glycerol should be added to the sample. Lower absorbance reading Linear Range for OD Measurements The linear range of the instrument used for the measurements is critical to cover the entire growth cycle of the culture, an OD limit of at least 2.
Please bear in mind that the sample may not be linear over the entire range if a high amount of dead cells are present, obscuring the result. If samples should still be out of range, dilutions may become necessary; the parameters should be carefully selected to cover the entire desired area without compromising the linearity of the setup.
To reduce the required volume for the OD measurement and to avoid time-consuming and error-prone manual dilutions, special cuvettes are available for the researcher. General calibration of microbial growth in microplate readers.
Beal, J. Time to get serious about measurement in synthetic biology. Trends Biotechnol. Quantification of bacterial fluorescence using independent calibrants. Reproducibility of fluorescent expression from engineered biological constructs in E. Anderson, J. Anderson promoter collection. Biochemical complexity drives log-normal variation in genetic expression. Jarvis, B. Assessment of measurement uncertainty for quantitative methods of analysis: comparative assessment of the precision uncertainty of bacterial colony counts.
Food Microbiol. Hoffman, R. Cytometry Part A 81 , — Download references. This document does not contain technology or technical data controlled under either the U.
International Traffic in Arms Regulations or the U. Export Administration Regulations. Natalie G. Christopher T. Kostopoulos, Stylianos Kotzastratis, Antonios E.
Chalmers University of Technology, Gothenburg, Sweden. Universidad de las Fuerzas Armadas, Sangolqui, Ecuador. Shalini S. Sagar Kittur, Nitish R. University of Lethbridge, Lethbridge, Alberta, Canada. Nanyang Technological University, Singapore, Singapore. Preetha, Khadija Rashid, S. Mohan Kumar. Aristotle University of Thessaloniki, Thessaloniki, Greece. Technische Universitaet Darmstadt, Darmstadt, Germany. Natthawut Adulyanukosol, Theodore A.
Gabriel Byatt, Philippe C. Paulo J. University of Science and Technology, Beijing, China. Universitat Politecnica de Valencia, Valencia, Spain. Ngozi D. Washington University in St. Louis, St. You can also search for this author in PubMed Google Scholar. Conceptualization: J.
Data curation: J. Formal analysis: J. Project administration: J. Resources: T. Software: J. Writing original draft : J. Correspondence to Jacob Beal , Natalie G. Baldwin , Markus Gershater or Christopher T. Reprints and Permissions. Robust estimation of bacterial cell count from optical density.
Commun Biol 3, Download citation. Received : 24 October Accepted : 03 July Published : 17 September Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative. Microbial Cell Factories Biomass Conversion and Biorefinery Cellulose By submitting a comment you agree to abide by our Terms and Community Guidelines.
If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Advanced search. Skip to main content Thank you for visiting nature. Download PDF. Subjects Biological techniques Data acquisition. This article has been updated. Abstract Optical density OD is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count.
Introduction Comparable measurements are a sine qua non for both science and engineering, and one of the most commonly needed measurements of microbes is the number or concentration of cells in a sample. Results To evaluate the three candidate OD calibration protocols, we organized an interlaboratory study as part of the International Genetically Engineered Machine iGEM competition.
Experimental data collection Each contributing team was provided with a set of calibration materials and a collection of eight engineered genetic constructs for constitutive expression of GFP at a variety of levels. Full size image. Discussion Reliably determining the number of cells in a liquid culture has remained a challenge in biology for decades.
Table 1 Summary of the benefits and drawbacks of the three calibration protocols. Full size table. Methods Participating iGEM teams measured OD and fluorescence among the same set of plasmid-based devices, according to standardized protocols. Constructs, culturing, and measurement protocols The genetic constructs supplied to each team for transformation are provided in Supplementary Data 1. Data availability All data generated or analyzed during this study are included in this published article and its Supplementary Information files.
Change history 27 October An amendment to this paper has been published and can be accessed via a link at the top of the paper. References 1. Article Google Scholar 2. Article Google Scholar 3. Article Google Scholar 5. Article Google Scholar 6. Article Google Scholar Article Google Scholar Download references. View author publications. Kostopoulos , Stylianos Kotzastratis , Antonios E. Sagar Kittur , Nitish R. Preetha , Khadija Rashid , S. Ethics declarations Competing interests The authors declare no competing interests.
Supplementary information. Supplementary Information.
0コメント