What is compensation and when should you do it?
In this first of 3 blog articles, we will discuss the principles of compensation, as well as why it is important, and how to perform compensation. Subsequent articles will discuss the rules that must be followed for proper compensation and some of the common compensation myths that permeate the field. It all begins with an understanding of the process of fluorescence.
After excitation, a fluorescent molecule emits a photon. This photon has an emission maximum — that is, the most probable photon wavelength that will be emitted. However, this emission is not so specific, and there are a range of photons that can be released from the molecule. This can be modeled with a variety of software. A typical emission profile for a common fluorochrome, fluorescein, is shown in Figure 1.
Figure 1: Fluorescein emission profile.
As can be seen from this spectra, Fluorescein has a maximal emission of about 524 nm. However, it has a very long tail, and there is a chance (albeit small) that a photon of over 600 nm can be emitted by this molecule. Flu ...Read More
Speed is a highly touted metric in flow cytometry. Look at any vendor’s website and you will see the highlights on how many events per second their instrument can acquire, how many cells can be sorted per second, and more. The limitations are imposed by the physics of flow cytometry, the speed of pulse processing, and more. With cell sorters, Poisson statistics dominate the speed calculation. As has been discussed before, the optimal sort rate is ¼ the frequency of droplet generation. Sorting faster will impact purity of the final product.
One of the trends in flow cytometry is pushing the limit of the number of parameters that can be measured at one time. The CyTOF threw the gauntlet down to start this new race by changing how the signal was detected. It didn’t take long for fluorescence-based cytometers to begin pushing past the 18-fluorochrome limit, and now instruments that can do 24 or more fluorescent parameters at the same time are available. Spectral cytometry may push this limit to 50 parameters or more in the near future.
With all these parameters, the data files bec ...Read More
The NIH has released a series of reproducibility guidelines that scientists must address. These guidelines have been introduced because there’s a lot of data showing that reproducibility in science is frustratingly lacking in certain experiments.
Non-reproducible experiments waste time, money, and resources. As Begley and Ioannidis cite in their 2015 article :
“The estimates for irreproducibility based on these empirical observations range from 75% to 90%. These estimates fit remarkably well with estimates of 85% for the proportion of biomedical research that is wasted at-large.”
Reproducibility is a mindset, and involves an overall analysis of the scientific process to identify the areas that can be improved. In this article by Bruce Booth, he reviews “Begley’s six rules”. Two of these rules focus on the controls and reagents used in the experiment.
Of equal importance are the instruments used to make measurements. For example, how often are the pipettes calibrated? Are all lab members adequately trained in technique? This chart from Gilson is a useful one to have i ...Read More
With the increased focus on reproducibility of scientific data, it is important to look at how data is interpreted. To assist in data interpretation, the scientific method requires that controls are built into the experimental workflow. These controls are essential to minimize the effects of variables in the experiment so that changes caused by the independent variable can be properly elucidated. In fact, one of Begley’s 6 rules, as described by Bruce Booth, asks if the positive and negative controls were both shown.
What types of controls should be considered when designing a flow cytometry experiment?
Focus controls to minimize confounding variability. Sample processing, for example, can be controlled using a reference control. Where to properly set gates can be addressed using the FMO control. Controls for treatment can include Unstimulated and Stimulated controls. Reagent controls ensure that the reagents are working, and are at the correct concentration. Compensation controls are critical — these have been discussed in detail elsewhere. Of course, there are some controls th ...Read More
To conclude our series on rare event analysis, it is time to discuss the statistics behind rare event analysis. The first 2 parts of this series covered the hardware aspects of measuring rare events and some specific recommendations for gating/analysis of rare events.
It is necessary to sort through hundreds of thousands or millions of cells to find the few events of interest.
With such low event numbers, we move away from the comfortable domain of the Gaussian distribution and move into the realm of Poisson statistics.
There are 3 points to consider to build confidence in the data that the events being counted are truly events of interest and not random events that just happen to fall into the gates of interest.
1. How do you know if an event is real?
How do you know that your rare event is real? When subsetting the population, you might have an occurrence rate of 0.1% or lower. This means that for every 100,000 cells, 100 cells or fewer will be in the final gate of interest.
How can you confirm and be comfortable they are real?
In Poisson statistics, the number of positive events ...Read More