Qualifying ELISA data: combining information

New approaches for biomarker stability determination in regulated bioanalysis: trending, bridging and incurred samples

Aim: Determining the stability of biomarkers continues to present challenges. Disease states, complex matrices and differences between recombinant and endogenous analytes require new approaches to maintain stability and measure it. In this report, we determine stability for two assays using trending and statistical analysis. Methodology & results: Monitoring trends helps identify out of specification measurements and determine whether concerns are due to the stability of the analyte. We also describe challenges presented when measuring arginase activity in human sputum, a complex matrix, for respiratory diseases. We controlled preanalytical protease activity and collection heterogeneity and monitored incurred sample stability to improve stability of arginine. Conclusion: These new approaches to achieving and determining biomarker stability may provide solutions for increasingly complex biomarker measurements.

A computational solution to improve biomarker reproducibility during long-term projects

Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a computational solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor “S” is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1–5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this computational solution maximizes biomarker reproducibility.

Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder

Several studies have reported an association between traumatic stress and telomere length suggesting that traumatic stress has an impact on ageing at the cellular level. A newly derived tool provides an additional means to investigate cellular ageing by estimating epigenetic age based on DNA methylation profiles. We therefore hypothesise that in a longitudinal study of traumatic stress both indicators of cellular ageing will show increased ageing. We expect that particularly in individuals that developed symptoms of post-traumatic stress disorder (PTSD) increases in these ageing parameters would stand out. From an existing longitudinal cohort study, ninety-six male soldiers were selected based on trauma exposure and the presence of symptoms of PTSD. All military personnel were deployed in a combat zone in Afghanistan and assessed before and 6 months after deployment. The Self-Rating Inventory for PTSD was used to measure the presence of PTSD symptoms, while exposure to combat trauma during deployment was measured with a 19-item deployment experiences checklist. These groups did not differ for age, gender, alcohol consumption, cigarette smoking, military rank, length, weight, or medication use. In DNA from whole blood telomere length was measured and DNA methylation levels were assessed using the Illumina 450K DNA methylation arrays. Epigenetic ageing was estimated using the DNAm age estimator procedure. The association of trauma with telomere length was in the expected direction but not significant (B=-10.2, p=0.52). However, contrary to our expectations, development of PTSD symptoms was associated with the reverse process, telomere lengthening (B=1.91, p=0.018). In concordance, trauma significantly accelerated epigenetic ageing (B=1.97, p=0.032) and similar to the findings in telomeres, development of PTSD symptoms was inversely associated with epigenetic ageing (B=-0.10, p=0.044). Blood cell count, medication and premorbid early life trauma exposure did not confound the results. Overall, in this longitudinal study of military personnel deployed to Afghanistan we show an acceleration of ageing by trauma. However, development of PTSD symptoms was associated with telomere lengthening and reversed epigenetic ageing. These findings warrant further study of a perhaps dysfunctional compensatory cellular ageing reversal in PTSD.

Treatment of batch in the detection, calibration, and quantification of immunoassays in large-scale epidemiologic studies

Many laboratory assays measure biomarkers via a 2-stage process. Direct measurement yields relative measures that are subsequently transformed to the unit of interest by using a calibration experiment. The calibration experiment is performed within the main experiment and uses a validation set for which true values are known and relative values are measured by assays to estimate the relation between relative and absolute values. Immunoassays, polymerase chain reaction, and chromatographic approaches are among assays performed in this manner.

METHODS

For studies with multiple batches, data from more than a single calibration experiment are available. Conventionally, calibration of assays based on the standard curve is performed specific to each batch; the calibration experiment from each batch is used to calibrate each batch independently. This batch-specific approach incorporates batch variability but, due to the small number of calibration measurements in each batch, may not be best suited for this purpose.

RESULTS

Mixed-effects models have been described to address interassay variability and to provide a measure of quality assurance. Conversely, when interbatch variability is negligible, a model that does not incorporate batch effect may be used to estimate an overall calibration curve.

CONCLUSION

We explore approaches for use of calibration data in studies with many batches. Using a real data example with biomarker and outcome information, we show that risk estimates may vary depending on the calibration approach used. We demonstrate the potential for bias when using simulations. Under minimal interbatch variability, as seen in our data, conventional batch-specific calibration does not best use information available in the data and results in attenuated risk estimates.

A linear mixed-effects calibration in qualifying experiments

In many applications, controls are used to monitor the process or experiment and to assess whether the process is in control or the experiment is valid. In this case, the traditional fixed-effects calibration is usually not adequate, but a mixed-effects model is appropriate. In this article, a linear mixed-effects calibration model is considered to qualify an experiment. Two estimating methods for the controls based on maximum likelihood and restricted maximum likelihood are proposed. The bias and mean squared error performances are studied by simulation. Five different methods to construct confidence intervals for the controls are compared. A dataset is used to demonstrate the advantages of the mixed-effects model.