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Do’s And Don’ts Of Clinical Flow Cytometry

Posted on: October 16, 2019

Proteins with Lymphocytes T-Cells

We’ve given you advice about what to consider when planning clinical flow cytometry experiments. Now check out these ‘do’s and don’ts’ to get the most out of your next clinical flow experiment.

Do - Know What Types Of Cells You Want To Identify.

Some cells are very robust and are easy to identify in peripheral blood samples. Other cells die if you shake the tube the wrong way or they behave as if they have their own agenda. Take the time to do pilot experiments with samples that are not precious so you can work out a protocol that is robust and precise enough to identify your cells of interest in your valuable clinical samples.

Don’t - Rush

It is really worth taking the time, which may be weeks or months, to work out a solid protocol for your clinical specimens. This may mean titrating antibodies, optimizing staining panels, or finding the right culture conditions for your cells of interest.  If you assume blindly that a protocol you are given will work the first time, your irreplaceable samples will probably end up down the drain and you will have no useable data.

Do - Consider Your Flow Cytometry Assays In The Context Of Bigger Clinical Protocols

You are ready to work with your clinical samples but you need to think about the logistics of “going live.” It’s better to make decisions during protocol development about ways to streamline or take pauses in your protocol steps without harming your final outcome. It’s rather miserable trying to process 200 samples for intracellular staining by yourself. Set yourself up for success or consider a partnership with a CRO.

Don’t - Assume Clinical Samples Will Always Behave As You Expect

Clinical specimens can be notoriously challenging to study by flow cytometry because the humans who are the source of these samples are extremely unique and variable creatures. You can deal with variations by using good clinical practice (GCP) standards and validation scripts to ensure your data is as robust as possible.


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