4 – Experimental Design

There are 4 major ways to sort cells. The first way can use magnetic beads coupled to antibodies and pass the cells through a magnetic field. The labeled cells will stick, and the unlabeled cells will remain in the supernatant. The second way is to use some sort of mechanical force like a flapper or air stream that separates the target cells from the bulk population. The third way is the recently introduced microfluidics sorter, which uses microfluidics channels to isolate the target cells. The last method, which is the most common––based on Fuwyler’s work––is the electrostatic cell sorter. This blog will focus on recommendations for electrostatic sorters.

There are 4 major ways to sort cells. The first way can use magnetic beads coupled to antibodies and pass the cells through a magnetic field. The labeled cells will stick, and the unlabeled cells will remain in the supernatant. The second way is to use some sort of mechanical force like a flapper or air stream that separates the target cells from the bulk population. The third way is the recently introduced microfluidics sorter, which uses microfluidics channels to isolate the target cells. The last method, which is the most common––based on Fuwyler’s work––is the electrostatic cell sorter. This blog will focus on recommendations for electrostatic sorters.

There are 4 major ways to sort cells. The first way can use magnetic beads coupled to antibodies and pass the cells through a magnetic field. The labeled cells will stick, and the unlabeled cells will remain in the supernatant. The second way is to use some sort of mechanical force like a flapper or air stream that separates the target cells from the bulk population. The third way is the recently introduced microfluidics sorter, which uses microfluidics channels to isolate the target cells. The last method, which is the most common––based on Fuwyler’s work––is the electrostatic cell sorter. This blog will focus on recommendations for electrostatic sorters.

There are 4 major ways to sort cells. The first way can use magnetic beads coupled to antibodies and pass the cells through a magnetic field. The labeled cells will stick, and the unlabeled cells will remain in the supernatant. The second way is to use some sort of mechanical force like a flapper or air stream that separates the target cells from the bulk population. The third way is the recently introduced microfluidics sorter, which uses microfluidics channels to isolate the target cells. The last method, which is the most common––based on Fuwyler’s work––is the electrostatic cell sorter. This blog will focus on recommendations for electrostatic sorters.

Written By: Heather Brown-Harding, PhD It’s not easy to improve reproducibility in your experiments. Image manipulation has become a major problem in science, whether intentional or accidental. This has exploded with the advent of digital imaging and software like Photoshop. There are even mobile applications like Instagram filters that can be used for imaging trickery.…

Compensation is necessary due to the physics of fluorescence. Basically, compensation is the mathematical process of correcting spectral spillover from a fluorochrome into a secondary detector so that it is possible to identify single positive events in the context of a multidimensional panel. Good compensation requires that your controls tightly adhere to three rules. If the controls don’t meet this criteria, it will lead to faulty compensation resulting in false conclusions and poorly reproducible data. Even among flow cytometry veterans, a strong foundation is occasionally in need of a tune-up. And in a topic as complex as flow cytometry, it’s important that we review the fundamentals on a regular basis. In fact, it is the longtime cytometry expert who must check themselves for any sort of faith in older compensation practices. Science is ever a work in progress, and traditional methods are not always the right methods.

Reproducibility is key to the scientific method. After the results of a study are published, the community validates the findings and extends them. If the findings are not reproducible, the second step is impossible. With performable experiments increasing in complexity, and the concurrent increase in the cost of equipment and reagents to perform these experiments, it is important to find the best way to maximize the money spent on advancing research. In flow cytometry, there are many places where improvements can be made to increase the consistency and reproducibility of an experiment. The most obvious place is in the instrument, but today’s focus is on the reagents we use to identify cells of interest: Antibodies and fluorochromes.

All flow cytometer instruments have a certain 3 components, and the way they are put together will dictate the performance of the system. As a user, you’ll be interacting heavily with these components, so you need to know both what they are and how they work. There are fluidics, optics, and electronics. The fluidics allow you to interact at the right flow rate so that your data keep a tight CV. Then you can run the same flow rate for all your samples, and you won’t have different CVs for different samples. There are also different optics you can use, like PMTs, APDs, and PDs. It’s important to remember the bandpass filters because they indicate the detector on which your signal will be measured. And with a newer generation of instruments, you can actually change out bandpass filters and design the flow cytometer to your specifications – just make sure you cite the specific bandpass filter that you use. Finally, there are electronics, which process the photon into an electronic signal that is ultimately digitized and stored in a file known as the “FCS file.” An analysis can be performed on this file at a later time.

Did you know that tissues can be measured by flow cytometry? Flow cytometry is the measurement of cellular processes at the whole-cell level. This definition is useful because it includes not only flow cytometry, but any technique that measures at the level of the whole cell. Microscopy, for instance, is a great example of cytometry. But, what can be measured by flow cytometry? For one, tissues with lots of cells. When flow cytometry is practiced, the cells are broken up. Therefore, any cellular interactions within the sample are also broken up. This includes tissues, cell-to-cell contacts in tissues, and virtually any information about the microenvironment. As we continue to discover, the microenvironment can play a dramatic role in cell development, influencing how cells grow and change. This article will discuss how to analyze tissues and microenvironments by flow cytometry.

It has been said that “a picture is worth a thousand words.” We are visual creatures, and we seek to capture and describe the world around us. Some of the earliest evidence for this comes from very old cave paintings found around the world, like this painting of a horse found in the caves in Lascaux, France.

With the development of reliable microscopes, such as those developed by the dutch draper Antonie van Leeuwenhoek, we were able to see what was previously invisible, probing the unseen and learning in great detail how organisms worked.

Over time, the field of cytometry (the analysis of biological processes at the whole-cell level) has expanded in many different directions. Flow cytometry can be thought of as a microscope with very poor resolution. The power of flow cytometry lies in its ability to analyze thousands of cells through many dimensions, providing an amazingly detailed understanding of the cell. However, due to the resolution, it is not possible to tell where these signals are located.

Quality control is the hallmark of improving reproducibility. QC programs are designed to help determine when the process in question goes off the expected path. Depending on the deviation from the established acceptance criteria will dictate the level of intervention that needs to occur. This can be as easy as cleaning the instrument and rerunning the QC, or as extreme as removing the data from the final analysis. Since there is documentation as to the deviations, this provides the rationale for excluding data.

To get the best flow cytometry data you need to be thinking about all the steps in your experiment to ensure that you have high-quality data to analyze. To improve the quality of your analysis make sure you’re adding keywords at the beginning of your experimental setup, develop a quality control program, trust but verify any software wizards, use proper controls, and make sure you extract the correct data.

Reproducibility is a state of mind. It’s not one simple thing that you do that will make all your data more reproducible, it a shift in the way one thinks about and perform experiments. With the emphasis on rigor and reproducibility in science, it’s very important that researchers start putting into place everything they can do to help improve the quality and reproducibility of there data. Learn 3 action steps that can be taken to enhance experimental reproducibility.

The way you set your gates in flow cytometry is a key part of your experiment. You need to know that you can find the populations that you’re interested in so you can extract the appropriate data. This is how you can do your secondary or statistical analysis confidently. Learn what gates you should be using and how to define them properly.

Cell death is a natural part of the lifecycle of a cell. In cases of development, it is critical for the shaping of fingers during human development. The processes of ordered cell death, or Apoptosis, are so important that in 2002, Sidney Brenner, Robert Horvitz, and John Sulston received the Nobel Prize in Medicine for their work on understanding this process. There are many different ways to measure cell death and flow cytometry is an ideal tool for this technique. Whether you are just assessing the viability of your cells or you are interested in the exact stage of cell death your sample is in, there are a variety of ways that you can measure cell death. Learn 3 ways you can use flow cytometry to measure cell death.

Reproducibility is a state of mind. It’s not one simple thing that you do that will make all your data more reproducible, it a shift in the way one thinks about and perform experiments. With the emphasis on rigor and reproducibility in science, it’s very important that researchers start putting into place everything they can do to help improve the quality and reproducibility of there data. Learn 3 action steps that can be taken to enhance experimental reproducibility.

There are so many downstream applications of cell sorting, but if you don’t take the time to do you cell sort the right way your downstream experiments won’t work. In order to have the most success with your cell sort be sure you consider these 3 things, size dictates almost everything you are going to do, sample preparation is key, and think about what type of tube you are collecting your cells in. If you account for those 3 things you will set yourself up for a successful cell sort and successful downstream applications.

Controls are an incredibly important part of your flow cytometry experiments. If not done correctly, poor controls will waste time and money. But with proper care, high-quality controls will result in high-quality data. Just be sure to ask yourself these key questions, should you be using isotype controls, do you have a quality control procedure in place, and are you following the 3 cardinal rules of compensation.

Cell sorting is a combination of a numbers game (Recovery), quality of output (Purity) and speed. For any experiment, the end goal is going to be measured by these three characteristics, and as soon as one of these measures is more heavily favored, the other two must be compromised in some manner.
When designing a sorting experiment, start with the question of what will the cells be used for after sorting, and how many cells will you need for those experiments? That will set the minimum recovery that is needed. The second question is how pure do you need the cells? The requirements of the downstream assay will also dictate the purity needed.

The cell type being used will, in part, dictate the speed of sorting. Smaller cells can be sorted faster because a smaller nozzle can be used.

When you start a cell sort it’s important that you are aware of the downstream analysis and assays that you want to run. This will determine how you perform the sort and how you determine if your sort was successful or not.
Successful cell sorting involves balancing recovery, yield and speed. What do these three terms mean and what influences each of these factors?

Cell sorting is a combination of a numbers game (Recovery), quality of output (Purity) and speed. For any experiment, the end goal is going to be measured by these three characteristics, and as soon as one of these measures is more heavily favored, the other two must be compromised in some manner.
When designing a sorting experiment, start with the question of what will the cells be used for after sorting, and how many cells will you need for those experiments? That will set the minimum recovery that is needed. The second question is how pure do you need the cells? The requirements of the downstream assay will also dictate the purity needed.

The cell type being used will, in part, dictate the speed of sorting. Smaller cells can be sorted faster because a smaller nozzle can be used.

When you start a cell sort it’s important that you are aware of the downstream analysis and assays that you want to run. This will determine how you perform the sort and how you determine if your sort was successful or not.
Successful cell sorting involves balancing recovery, yield and speed. What do these three terms mean and what influences each of these factors?

As a researcher, you want to achieve the best cell sorting possible. So, how can you achieve that? There are clear strategies you can use to achieve great cell sorting results, including finding your ideal sample concentration, using magnetic sorting to enrich your population, suspending cells in the right buffer to avoid cell clumps, changing your instrument settings when sorting small cells, and optimizing your sample preparation and instrument when sorting large cells.

The topic of compensation is a critical one for the cytometrist to understand. It requires adherence to some specific rules, an understanding of how the instrument works, and how fluorescence occurs. Poor or incorrect compensation can easily lead to incorrect conclusions, and decreases the reliability and robustness of the data generated.
It is critical to question the wisdom of the “Protocol’s Book” and understand that the “truths” in this book are not always correct anymore. The new user doesn’t necessarily know any differently, and for this reason there are suboptimal practices that permeate flow cytometry experiments to this day.

Understanding compensation, and being armed with the knowledge, allows the researcher to combat those fairytales that continue to make their rounds in science. It is time to put them to bed and move forward with a full understanding of the process.

Understanding the 3 rules of compensation, and applying them to your everyday workflows, is an essential step in good, consistent, and reproducible flow cytometry data. Making sure the controls are bright, and treated the same way, is essential. Don’t bring unfixed controls when your samples are fixed, as the controls will not reflect the spectra from the fixed samples. Make sure not to rely on the “Universal Negative”, use a single sample to set background, and collect enough events to make sure an accurate measurement is made, as this will further improve the quality of your control and therefore the data.

3 different theories on compensation are discussed. The first, non-pensaton, is not recommended, and only possible under a narrowly defined instrument. The second, manual compensation, is also not recommended for anything more than 2 fluorochromes. It is error prone and subject to the researcher’s judgement, unless statistics are invoked and then it becomes a tedious and difficult exercise in algebra. For polychromatic flow cytometry, best practices in flow cytometry is to use the automated compensation methodologies. This will ensure consistent and accurate compensation, if some rules are followed.

Why is the speed of the algorithm so important? Why worry when you can just set up the analysis and go for lunch? If you’re like me, when I’m analyzing data, I like to stay in that mindset. Distractions, like a long break, can impact the train of thought about the analysis. Additionally, with long run-times, it is depressing to return to the data and see the calculation stopped prematurely because of an incorrect parameter or some other error.

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. Getting into the mindset to improve the reproducibility of flow cytometry experiments requires a hard look at the appropriate controls to use in each experiment.

Stem cells, circulating tumor cells, and minimal residual disease in cancer patients were all discovered through the power of rare event flow cytometry. When preparing for rare event analysis, sample preparation and data analysis must be taken into account at the beginning. How will we stain our cells? How will we analyze our cells? What controls will we use to help us identify our rare events? What statistical methods do we use to analyze our results? Here are 5 procedural limitations that impact the quality of rare event flow cytometry data and how to optimize your assay to get the best results possible.

One of the most common assays in flow cytometry is the surface labeling of cells with antibodies. Often termed “immunophenotyping”, it allows the researcher to identify, count, and isolate cells of interest in a mix of input cells. Every lab has their own favorite protocol to move from sample to cytometer, handed down from some hallowed, chemical-stained notebook, and followed as exactly as making a souffle. The real questions are, which of those steps are critical, and what other factors should be considered when staining cells? This article will focus on staining immune cells, but the principles apply in general, and specific issues for a specific sample type can be optimized in a similar way.

There are several areas that researchers can focus on to improve the reproducibility of their flow cytometry experiments. From instrument quality control, through validation of reagents, to reporting out the findings, a little effort will go a long way to ensure that flow cytometry data is robust, reproducible, and accurately reported to the greater scientific community. Initiatives by ISAC have further offered additional levels of standards to support these initiatives, which were developed even before the Reproducibility Crisis came to a head in both scientific and popular literature.