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No matter how advanced or cutting-edge your study is, good science is profoundly dependent on the fundamentals. In fact, no matter your experience level, it is always good to revisit the fundamentals now and again. After all, if flow cytometry were easy, anyone could don a lab coat and get published. In reality, science is as challenging to conduct as it is exciting – that means it’s beneficial for virtually all scientists to take some time and refresh themselves on best practices. You might be surprised to realize what an impact they have on experimental quality… 

No researcher wants to discover that the results of a long, careful experiment are confounded by an uncontrolled variable. To assist in data interpretation, you must build careful controls into your experimental workflow. These controls minimize the effects of confounding variables in the experiment while helping to identify the changes related to the independent variable. When designing a flow cytometry experiment, what controls should you consider? Below are a few experimental controls that can dramatically enhance reproducibility in your flow cytometry experiments.

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.

Imaging deep into tissues has always been difficult, whether challenges derive from working distance, light absorption by natural chromophores, or light scattering by mismatched refractive indexes. Cells are full of mismatched refractive indexes, which is great for DIC but bad for fluorescence. These fluids have a refractive index of 1.35 while lipids and membranes like the golgi and mitochondria have a refractive index of 1.45. Structural proteins such as actin or microtubules can have a refractive index greater than 1.5 – this all means that light will not follow a straight line as it enters and exits the samples. Thus, it is immensely helpful to use a sample preparation method like optical tissue clearing.

The most common flow assay is undoubtedly immunophenotyping, in which fluorescently tagged antibodies are used to bind to cellular proteins. This allows you to determine the types of cells present. As long as there is a fluorescent reporter available, it is possible to measure biological processes using flow cytometry – especially in a phenotypically defined manner. Probably the most common of these assays is the calcium flux assay. And that is just the tip of the iceberg. In addition to calcium, it is possible to measure magnesium and zinc concentrations, reactive oxygen species, and even membrane potential using flow. Today, we’ll cover 4 assays that use a fluorescent reporter to measure their target, allowing researchers to challenge the cells and measure their response in real time.

What is the single-most important feature of a flow cytometry experiment? Arguably, it’s the stained cells that gather data about biological processes of interest. However, a flow cytometer can measure cell-like particles as well as cells, which opens the realm of cytometry to the use of microspheres. Most researchers are familiar with the 4-Cs that beads can be used for: Control, Calibration, Compensation, and Counting. Beyond the 4-Cs, many are familiar with the multiplex bead assays for measuring analytes. Today, we will take a look beyond these well-known uses and discover the myriad applications of the “Mighty Microspheres.”

Have you ever noticed how painful it can be to purchase a new microscope? It would be hard to miss – this can be a frustrating process. A lot of scientists and students consider the new microscope hunt quite scary for a variety of reasons. It might be that you’re worried you won’t get the right microscope and that you’ll regret it, or you may find that dealing with salespeople, in general, makes you kind of uncomfortable. But remember, salespeople are just human beings like you and me, and if we can treat them as such, the whole process of…

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. It should go without saying that image reuse/manipulation represents profound dishonesty in science – a field intended to uphold the most stringent possible standards of truthful inquiry! But what about studies with a sloppy or stunted capacity for reproduction? These, too, plague science and hinder our ability to seamlessly move…

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.

There are 7 different common “artifacts” that may be affecting the quality of your imaging. Before digging into the details, let’s begin by defining an artifact: Essentially, it is any error introduced through sample preparation, the equipment or post-processing methods. This is an important concept to grasp because the effects can cause false positives or negatives, and they can physically distort your data. This is, of course, at odds with your mission to obtain reliable quantitative data. So what can you do to stop these artifacts? The problems can range from dirty objectives to bigger issues like light path aberrations.

In the flow cytometry community, SPADE (Spanning-tree Progression Analysis of Density-normalized Events) is a favored algorithm for dealing with highly multidimensional or otherwise complex datasets. Like tSNE, SPADE extracts information across events in your data unsupervised and presents the result in a unique visual format. Given the growing popularity of this kind of algorithm for dealing with complex datasets, we decided to test the SPADE algorithm in 5 software packages, including Cytobank, FCS Express, FlowJo, R, and the original, free software made available by the author of SPADE. Which was the fastest?

Because mass cytometry allows users to characterize masses so effectively, data can be normalized much more efficiently than what traditional fluorescent flow will permit. If there is no working CyTof at your institution, you can still partner with CyTof-friendly research institutions that have the technology on hand. And because the samples are fixed, you can ship them overnight. This way, they will be analyzed for you. Today’s article will summarize the functionality of mass cytometry technology. This tech has been commercialized largely by Fluidigm in the CyTof systems. There are 5 key points to cover, or takeaways, that cytometrists should keep in mind as they perform their research. The 5 points include how mass cytometry works, panel design, proper sample preparation, data analysis, and imaging mass cytometry.