3 – Data analysis

While controls are critical for minimizing the effects of variables in your flow cytometry experiments, choosing the right controls are essential. When your research is published, reviewers need to see that your variables have been analyzed properly. Evaluating strengths and weaknesses will give you information and back up arguments for the case for or against isotype controls. Here’s a review of what isotype controls are and if you need to use them.

Flow cytometry (FCM) datasets that are currently being generated will be two orders of magnitude larger than any that exist today. Reproducibility continues to be a critical area that all researchers need to be aware of and researchers need to keep up on best practices to stay relevant. One area that flow cytometry researchers should be focusing on is the emerging changes in the area of automated data analysis. This brief article explains why.

When sitting down to perform a new analysis of flow cytometry data, the researcher is guided by very particular laws of nature and a very specific method of working through a biological hypothesis to avoid shaping the results to his or her whims. Following these 5 data analysis and gating strategies through the hierarchy described in this article, researchers are provided with several strategies for identifying and displaying the most relevant data from their flow cytometry experiments.

When preparing figures for publication, the scientific question and hypothesis that forms the basis of the paper must be central and all the figures must be in support of that. The flow cytometry data that forms the basis of the conclusions should be presented clearly and concisely. While it provides pretty pictures and colorful layouts, the meat of the data are the numbers ― percentages of populations, fluorescent intensity levels and the like ― are what will convince the reader that the hypothesis tested is valid and well thought out. Here’s how to choose the correct flow figure for presenting…

Cellular proliferation is a critical component in biological systems. While normal cell proliferation keeps the body functioning, abnormal proliferation (such as in cancer) can be a target for therapy. There are several critical components in developing, validating and optimizing an assay to make these measures using flow cytometry. Knowing the steps to optimize these assays and properly interpret the results will help ensure the best data and best opportunities are pursued.

Compensation in flow cytometry is a critical step to ensure accurate interpretation of data. It is also one of the areas that’s steeped in mystery, myths and misinformation. Manually adjusting the compensation values based on how the populations look, or so-called ‘Cowboy Compensation’, is not the correct way to determine proper compensation. The best practices for compensation involve following some very specific rules. Here are 4 steps to correctly compensating 4+ color flow cytometry experiments.

Methods sections in scientific papers are often unable to capture all the critical data necessary to accurately reproduce the results in another lab. Here, information is provided on two specific ways in which flow cytometry researchers are effectively communicating flow cytometry data and metadata to the greater flow cytometry community to improve reproducibility and consistency. These two ways include first, the use of the MIFlowCyt standard and second, sharing data using the Flow Repository.

Reproducibility is a critical component of the scientific process. One cannot publish data if the experiments cannot be replicated. Unfortunately, as Begley and Ellis pointed out in a commentary in Nature that when Amgen attempted to reproduce 53 “landmark” papers in the area of cancer research, only 6 papers were “confirmed.” What does that mean to you, the flow cytometry researcher? To avoid publishing errors that reviews despise, it’s important to follow and promote the best practices in the field, thus ensuring that your data is reproducible to investigators attempting to validate your research. In particular, you must follow these…

There are several methods for analyzing live, dead, and apoptotic cells by flow cytometry. As cells die, the membrane becomes permeable. This allows for antibodies to penetrate the cells, which can now mimic live cells. For this and other reasons, it’s important to remove dead cells from further analysis during your flow cytometry experiments. For example, let’s say you merely need to generate an accurate cell count. If you fail to remove your dead cells first, you might think you’re seeding 10,000 cells, but in reality only 7,000 of your cells are actually viable. Since the dead cells in your…

Flow Cytometry is a remarkably powerful tool for the study of T cells. It has been successfully used for many decades to accurately visualize and enumerate a variety of T cell subsets. With a large sensitivity range for fluorescent probes, >95% sampling efficiency, and the ability to sort populations of interest for further study, fluorescent-based cytometry remains a tool of choice for T cell analysis. The key is to define your T cell populations of interest with correct gating strategies and to back up your T cell subset findings with functional analysis of these subsets. A cell’s actions should guide…

Microvesicles originate from cells and have the same analysis requirements as cells. For these and other reasons, flow cytometry is a popular choice for microvesicle analysis. However, there are pitfalls with small particle flow cytometry that have led to many conflicting publications. The only way to avoid these mistakes is to first identify them and then take measures to prevent them. The following are 4 common mistakes researchers make when preparing microvesicle flow cytometry experiments, as well as how to prevent these mistakes.

All the experiments and experience in the world do not count if you are unable to communicate your results to the scientific community. As part of that communication process, your paper will undergo the dreaded ‘Peer-Review’ process. If you wish your paper to survive this process, you must collect, analyze, and present your flow cytometry data properly—before you submit your paper. A review of the following questions, as well as how to answer them, will help ensure your paper is not rejected. Here are 5 specific questions reviewers will ask when reviewing your flow cytometry data.