If you study human disease, you will likely handle a pre-analytical sample or two (or hundreds). For example, you could handle whole blood, serum or plasma, tissue biopsies, urine, fecal samples, cerebrospinal fluid, or synovial fluid—to name a few. You will probably use these samples to look for specific metabolites, proteins, or nucleic acids that provide important information about the condition from which they have been isolated. No matter the analyte of interest, your major requirement from the clinical material is the same—it should resemble its in vivo status until the time of analysis.
But a quick scan through studies using human clinical material shows a wide variability in data—across and within studies. Apparently, almost 70% of errors in diagnostic laboratory measurements are due to errors incorporated during the pre-analytical phase of diagnostic testing!!1 The quest to get tight replicates and consistency in laboratory measurements is historical. This lack of 100% consensus among studies was also partly the reason why the Cochrane reviews were initiated.
What can you do to ensure tight error bars, reproducibility, and measurements that reflect “real” (or almost real) values that exist in vivo? Well, the journey begins much before laboratory processing of clinical materials, hence the name pre-analytical sample processing.
Let’s look at the steps you can take to maximize your pre-analytical sample handling potential—so that your error bars are tight and your data are more true to the in vivo situation.
Minimize Time-To-Processing of Your Pre-Analytical Sample…
…Or at Least Keep It Constant across All Samples in a Study
One of the most important pre-analytical sample variables is the time between sample collection and processing—this is especially important in a bioassay. Standard protocols include:
Prepare serum and plasma within 2-4 hrs of blood collection and store them at -80°C until further use.
If you are working with cells from whole blood, such as RBCs or WBCs, your approach will be a bit different. Store RBCs for about 5-6 weeks in a slightly hypertonic solution, generally SAGM (sodium, adenine, glucose, mannitol, 376 mOsm/L). Prolong their survival to 42 days by removing leukocytes.2 Store frozen red cells in 40% W/V glycerol at -80°C for several years!3
Keep platelets at 20-25°C for about 5 days with gentle agitation and at -20°C for about 8-9 months.
Store all cellular components, such as protein preparations, organelle preparations, and so on, in liquid nitrogen for long-term storage and at -80°C for up to a week.4
Keep DNA and RNA samples at -80°C for long-term storage.
Since a large part of pre-analytical sample processing happens outside the lab, sometimes you cannot limit the amount of time a sample spends in non-ideal storage conditions.3 In this case, the best approach is to treat all samples within a study with the same non-ideal conditions. For example, if the serum from patients with the disease of interest experiences room temperature for 10 hours, then keep all sera samples (those from the diseased group and from healthy controls) at room temperature for 10 hours before analysis. Most experiments compare clinical measurements between study groups; minimize variability due to non-ideal conditions by handling samples and storage conditions uniformly.
Preserve the Integrity of the Analyte-Of-Interest
Once you obtain a pre-analytical sample, you need to preserve its integrity. If you’re working with proteins, add protease inhibitors to all buffers and keep samples at 4°C, beginning from the very first step. Know where you can take a break in the protocol and store your samples without compromising the integrity of your protein. You might have to work this out for each individual sample or protein of interest.
If you’re working with metabolites, then you will need to ensure the stability of your metabolite of interest.
Ideally, store sera and plasma -80°C.
When working with plasma, perform an extra spin at 1,000 x g to get rid of platelets.
Many times, samples contain cells that might use up or contribute to the levels of certain metabolites. If this is the case, you can do one of two things: 1) carry out a quenching procedure that will stop any metabolic activity or 2) extract the metabolites (this will get rid of proteins as well as other cellular material).
For DNA or RNA preps, make sure all your plasticware (e.g., tips, pipette tips) and work surfaces used in subsequent steps are DNase and RNase free.
Make Sure Your Samples Are Collected in the Appropriate Manner
How you collect your pre-analytical sample can be as important as how you process them. The method you choose will depend on the end use of your sample.
Are you going to do PCR? Avoid using heparin for blood collection.
Are you using your samples for RNA prep? Immediately add trizol or RNAlater. In some cases one works better than the other, so test it out with your samples first.
Are you working with tissue samples that need to be processed later? This will require detailed thinking. You will want to maintain the tissue in a metabolically active form outside the body until it is subjected to your procedure. Many times, you can collect tissues in media or in custodial HTK (Histidine-Tryptophan-Ketoglutarate) to allow them to remain active after excision for a brief period of time.
Improve Your Study Design
The design of your study is crucial in determining the variability of measurements. Make your study groups tighter and define inclusion and exclusion criteria strictly.5 Many studies relax their criteria in order to increase the sample size and improve statistical significance. However, for proteomics, metabolomics, and genomics studies, a smaller but strictly defined population can make all the difference.
In spite of all this care, there will be factors that you cannot control. In scientific parlance these are called “uncontrollable pre-analytical variables.”6 These include age, sex, ethnicity, circadian rhythms—all of which are known to have measurable effects on biological parameters but on which you have no control. The best way to deal with these is to make a note of these parameters and account for these during statistical analyses of data. However, enough care with controllable pre-analytical variables will allow you to ensure that you get the most meaningful data possible for your experiments.
Genomics, transcriptomics, proteomics, metabolomics – words that in 2015 sound very familiar even to a freshman in any biology field. Although most have heard those words before, I keep encountering students or even post-graduates who find it difficult to explain what they are. So, to make things easier here is a peek behind the curtains […]
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