4 – Experimental Design

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…

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…

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…

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…

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.