4 Gating Controls Your Flow Cytometry Experiment Needs To Improve Reproducibility
Written by Tim Bushnell, PhD
To reproduce reliably in flow cytometry, one must control the gate.
The identification of the target cells of an experiment is the critical first step to performing the secondary analysis that will be used to judge the biological hypothesis and is done by peeling away the layers of cells that do not meet the criteria.
This involves the data reduction method of ‘gating’ with the researcher as gatekeeper, controlling what may pass and what shall not pass, based on the controls designed for the specific experiment.
It is disappointing to realize that in the paper, Maecker et al., the authors evaluated different models for conducting clinical trials and found that individual labs experienced a ~20% CV in the data analysis whereas a central lab showed only a ~4% variance in data analysis.
One of the best ways to improve gating is to ensure the most appropriate controls are identified and collected in the experiment.
How these controls are used to identify the population of interest is also critical to improving this process. There are 4 common gating contr ...Read More
How To Improve Reproducibility Through The Automated Analysis Of Flow Cytometry Data
Written by Ryan Brinkman, Ph.D.
Editor’s Note: Reproducibility continues to be a critical area that all researchers need to be aware of. From the NIH’s focus on reproducibility in grant applications, to a renewed focus by reviewers on the way data has been analyzed and presented, it is imperative that researchers keep up on best practices to ensure they pass these hurdles.
One area that flow cytometry researchers should be focusing on is the emerging changes in the area of automated data analysis. Over the last five years there have been dramatic changes and improvements in these programs and workflows. As Dr. Brinkman discusses below, the automated analysis of flow cytometry data is coming into its own.
Flow cytometry (FCM) datasets that are currently being generated will be two orders of magnitude larger than any that exist today, and new instruments, both flow and mass cytometry, have increased the number of parameters measured for each single cell by 50% (to 30).
Even in 14 dimensional datasets there are 16,384 possible cell populations of interest pre-sample (1). ...Read More
What Is Fluorescent Activated Cell Sorting And 4 Other Questions About FACS Data Analysis
Written By Tim Bushnell, PhD
Prior to the mid-1960’s, the ability to study a defined cell type was severely limited.
Researchers had to use centrifugation methods, such as differential centrifugation, rate zonal centrifugation, or isopycnic centrifugation, to define cell types.
All of these methods would allow separation of cells based on the property of the particles within different separation medias, but didn’t allow for very fine resolution of the cell populations.
That all changed starting in the mid-1960’s, when Mack Fulwyler published the first true cell sorter, which combined the power of cell characterization by the Coulter principle with the electrostatic separation of droplets developed by Richard Sweet (and used in inkjet printers).
For the first time, researchers could rapidly isolate individual cells based on more precise physical characteristics.
4 Common Questions About FACS Analysis
Early cell sorting technology eventually found its way into the Herzenberg lab at Stanford University, where a talented research group added lasers and developed what is no ...Read More
4 Biggest Mistakes Scientists Make During Multicolor Flow Cytometry Cell Sorting Experiments
Written By Mike Kissner
Multicolor cell sorting is a complicated process and certain scientific errors can be common.
Unsuccessful multicolor sorts can result in erroneous data and inconclusive results. Successful multicolor sorts, on the other hand, can give excellent results and lead to dynamic conclusions.
Successful multicolor cell sorting requires special attention to planning.
Using specific setup strategies for your experiment can create a streamlined system for an otherwise complicated process. For example, these critical steps and strategies for multicolor sorting experiments can save you time and maximize your results.
When setting up a multicolor experiment, the most common mistakes are failing to set PMT voltages properly, failing to use a viability dye, failing to address doublet discrimination properly, and failing to set the right sort regions and gates. Eliminating these 4 mistakes is important for any kind of flow cytometry experiment, but particularly for flow cytometry cell sorting experiments.
The following 4 mistakes should be avoided prior to the setup phase, wh ...Read More
5 Gating Strategies To Get Your Flow Cytometry Data Published In Peer-Reviewed Scientific Journals
Written by Tim Bushnell, PhD
“Every block of stone has a statue inside it and it is the task of the sculptor to discover it.” — Michelangelo
When sitting down to perform a new analysis of flow cytometry data, it is much like Michelangelo staring at a piece of marble. There is a story inside the data, and it is the job of the researcher to unravel it.
The critical difference between sculptor and scientist is that while the sculptor is guided by a creative vision, the researcher is guided by very particular laws of nature and a specific method of working through a biological hypothesis to avoid shaping the results to his or her whims.
Science must be objective, or it is simply an exercise in creative sculpting, which does nothing to move science forward.
Thankfully, there are many ways to avoid shaping the results, and instead sifting for the real and actual data that is relevant to the flow cytometry experiment at hand.
Communicating the results of a flow cytometry experiment is where the researcher has the power to make new or subtle findings instantly comprehensible to the aud ...Read More