Normalisation assays in biomarker studies – all’s well that starts well

The normalisation of quantitative assay data is critical when interpreting effective biological system status. With cells grown in culture and lysed, a simple total protein determination such as the Bradford assay (developed by Marion Bradford at the University of Georgia in 1976) can be enough by giving an estimate of the total cellular proteins. However, this type of measurement, along with Lowry and other dye binding assays, can be prone to errirs due to various factors such as detergent, chelators… Standardization also involves the use of a protein of interest. Here again, the protein used is crucial for the accuracy of the overall assay.

Discovery of new biomarkers… 3 tips regarding controls

One of the recurrent questions that we get at the Biomarkers team at tebu-bio is on what controls should be included in a given experiment. Either if the experiment is done by researchers in their lab, or if we collect their samples and perform the analysis in our lab, a good design starts by using the most convenient controls.

One of the controls is related to the study itself. In this sense, definition of what a control population is, and how we want to study it vs. a cohort of patients has been discussed elsewhere in a proteomics post. Today, we will put our spotlight on the “technical” controls, i.e. those related to the technique itself.

Control # 1 – positive control

Obvious. We need to check that the technology we are using is able to detect what we want to detect. And before starting with unknown samples, we need to check that it works in samples we know well.

Ideally, a positive control should be as similar as the samples we want to analyse. In this sense, samples for a given health state (be it with a disease or not), are commercially available, or they can be found if not yet available. tebu-bio has a network of collaborations with private companies that can provide validated samples, fulfilling all ethical and clinical criteria.

Background can be an issue…only if you are not able to detect it. If you can see it, then you can either modify your protocol, or discard that sample. Picture shows an example of what can be seen with slightly-hemolysed plasma.

Alternatively, we can use recombinant or chemically synthesised controls. If we take the example of recombinant proteins (either in pure form or spiked in biological samples), there are many which are commercially available, or they can be made upon demand. Here it is important that the recombinant protein is very similar to the one found in an organism, including glycosylations and other post-translational changes. In this sense, for many control proteins, HEK293 is preferred over E.coli as an expression system.

Control #2 – negative control

Water. Or PBS. Or not?

Ideally, a negative sample should be as similar as to our case samples as possible. Meaning that it has the same clinical and biological parameters than our samples of interest…

Commercially available samples mentioned previously can be a good approach. For immunological studies involving cell culture supernatants, it is important to include a control with the culture medium only, as FBS can affect the specificity of the assay and can render false-negatives due to background.

Background due to FBS is not detected by technologies such as ELISAs or bead-based, whereas it is detected in optical-based technologies such as arrays and Q-plex.

Control #3 – technical replicates

Every technology has an inherent coefficient of variation (CV).

Genomic technologies usually are under 5 % CV. Immunoassays are around 10 to 25 % (or even more). This means that, for some biomarkers where the difference between healthy vs. disease is small, CV may hide the relevance of these biomarkers. This is especially dramatic in studies related to signal transduction, where differences are usually very small.

6-plicates in an antibody array.

A way to make sure about whether a result comes from real biology or artificial CV is the performance of replicates.

Triplicates (or even 4-plicates) have been popular with ELISA users. Nowadays, however, most researchers perform duplicates, and repeat the analysis of the sample if the results are very discordant. This approach is quite practical, and still allows to have accurate results in a sensible way (i.e. not doing 4-plicates for every sample!).

Antibody arrays in the market usually have replicates spotted onto the same slide (from duplicates to 8-plicates), which can be considered as semi-independent technical replicates. Therefore, there is no need, in most cases, to perform additional technical replicates.

In any case, every project is different, so we are continously advising our customers on what is the best approach for one given study. From its design to the technology best suited to get the best results, we are glad to contribute to the advance of the understanding of biomarkers in several diseases.

Wondering what controls to include in your experiment? Don’t hesitate to contact us!

Biomarker profiling: Diluting samples case studies (II)

Back to dilution of samples. After our post on whether samples should be diluted or not, let’s discuss today about how much they should be diluted.

Case # 1 – ELISA or simplex assays

Most ELISA manufacturers indicate the dilution range for different types of samples (serum, plasma, cell culture supernatants, etc.) to be used in their products. However, the dilution range can sometimes be from 1:2 to 1:2,000. How to choose the right dilution factor?

The honest answer is: nobody knows. That is,  nobody knows what is the right dilution factor in your specific research project, for your specific biomarker.

Biomarkers of micronutrient malnutrition

Unfortunately, and still in the XXIst century, micronutrient (MN) malnutrition is a problem in all countries, in all segments of society. While part of the world starves, another part is obese. And a balanced diet is still far from being real for many people. Something to think about…

This is a real public health concern, and policies in this area are aimed at preventing, among others, mild, moderate an severe MN malnutrition. Deficiencies in MNs such as iron, vitamin A (VA), iodine, zinc and folic acid are associated with adverse health outcomes, especially in pregnant women and children. For example, VA is critical for embryonic development, adult growth and development, cellular differentiation, immune function, reproduction and vision. On the other hand, low iron levels lead to reduced physical activity in adults and impaired brain development in children.

Diluting samples for proteomics – biomarker profiling case studies (I)

To dilute or not to dilute biological samples? And if yes, how much? That’s the question!

Following our post on whether samples should be pooled or not when performing studies for Biomarker discovery, it’s now time to discuss sample dilution (yes or no) and dilution factor (how much) when performing proteomic analysis of Biomarkers.

Novel biomarkers in gastric cancer

The word “cancer” groups a series of diseases of different origin and different prognosis, with differentiated molecular mechanisms causing their onset and progression. Hence, discovery of novel biomarkers for each type of cancer, allowing to understand their underlying mechanisms and provide a specific (or even personalised) treatment is a fast-moving area of research.

In previous posts, we have described the finding of novel biomarkers for several types of cancer (e.g. esophageal, glioblastoma, pancreatic cancer, etc).  Let’s take a look today at gastric cancer.

An antibody array has recently been launched, allowing to detect and quantify the levels of 5 biomarkers associated to gastric cancer (CA19-9, CA72-4, CEA, Pepsinogen 1 and Pepsinogen 2). The array is not intended for clinical use, but rather, as a valuable tool to study cohorts of patients and see the levels of these biomarkers in serum and other types of samples, in order to validate them as future clinical markers.

CA19-9 is a glycosylated protein that has traditionally been considered as a tumour biomarker. It is not specific to gastric cancer, as it can also be present in pancreas cancer. It is useful, however, to monitor disease progression in some cases.

CA72-4 is another glyosylated protein, a bit more specific for gastric cancer than CA19-9, though it can also be present in some types of ovarian cancer.

Detection of CA19-9 and CA72-4 can be done adding CEA to the pannel, which increases the sensitivity and specificity from 42 % to 57 %. CEA (Carcinoembryonic antigen) groups a series of very similar glycoproteins involved in cell adhesion.

Finally, pepsinogens are clear indicators of gastric disease, and ratio between pepsinogen I and II can have a clinical significance for many gastric disorders. Combination of all these 5 biomarkers can be a good way to study gastric cancer in a more specific way.

Which model are you working on? If you’re interested this antibody array, or in any other arrays for your biomarkers of interest, get in touch!

 

 

Novel biomarkers for esophageal cancer

As discussed in previous posts, use of new tools allows the finding of new disease-related biomarkers. Today, we want to put our spotlight on esophageal cancer.

A recent alliance between Quansys Biosciences and Allegheny Health Network has allowed the development of a novel 4-plex assay for use in the early diagnosis of esophageal cancer. Though esophageal cancer is rare, it often has deadly outcomes.

New tools to fight against Pneumococcus

Pneumococcus, or scientifically speaking, Streptococcus pneumoniae, is a pathogen responsible for a number of illnesses, including pneumonia, ear infections, sinus infections, meningitis and bacteremia. Some of these illnesses can be life-threatening, and therefore, an early and accurate identification of the pathogen causing them is crucial.

Adipokine monitoring during Hunger and Satiety balance

Cytokines and growth factors that are produced by or have an effect on adipocytes and related peptide neuro-transmitters are central players in hunger vs. satiety balance and in eating behavior (1). Recent evidence shows that adipokines, and more generally mediators or indicators of inflammation (2), play roles in the development of insulin resistance (3), diabetes (4) and many other concomittant health problems associated with obesity, including hypertension, dyslipidemia and atherosclerosis (5).