What are label-free technologies?

A: Label-free technologies quantitate biomolecules by sensing a change in a physical parameter caused by biomolecular interactions. Typically, the physical parameter is refractive index, optical thickness, energy, or mass. Using this approach, immunoassays based on label-free technologies, i.e. label-free immunoassays (LFIAs), are able to measure antibody-antigen binding in real time, achieving immunometric quantitation without attaching a reporter molecule (enzyme, fluorophore, etc.) to the immunocomplex. Conventional label-free technologies include surface plasmon resonance, isothermal titration calorimetry, and quartz crystal microbalance to name a few.

What are the latest developments with label-free technologies?

In the past decade, label-free technologies have advanced into the era of dip-in-solution sensing probes. The resulting LFIAs are open access, making them similar to plate-format assays without complicated sample delivery or fluidics. This allows for simple experiment workflows and makes new LFIAs well-suited for clinical laboratory applications. A new technology of this kind is thin-film interferometry (TFI), which incorporates a thin glass rod as a sensing probe that transmits light to form thin-film interference on a sensing surface. When biomolecules bind to the sensing surface and change its optical thickness, the interference pattern also changes relative to the number of bound biomolecules. In this way, this method measures the quantity of bound biomolecules in real time.

How might label-free technologies improve detection of therapeutic monoclonal antibody (t-mAb) interference?

The ideal way to detect t-mAb interference in monoclonal gammopathy testing is to change the electrophoretic mobility of the t-mAb and shift it out of the gamma (γ)-region. However, only one product for immunofixation electrophoresis eliminates daratumumab (DARA) interference in this manner, and no product of this kind exists for use with serum protein electrophoresis (SPEP).

As an alternative, our lab conjectured that a clinical laboratory could employ an easy-to-use, rapid immunoassay to measure in serum samples the presence and quantity of t-mAbs known to cause interference with SPEP. This information could then enable labs to easily rule in or rule out interference from t-mAbs when interpreting SPEP results. Working off this hypothesis, we developed an LFIA based on TFI technology that quantitates DARA in serum samples (Clin Chim Acta 2020;502:128-32).

Using mass transport-controlled binding kinetics, this method quantitates DARA in only 10 minutes by measuring the initial binding rate between DARA and its target, CD38. To validate this assay, we measured 37 patient samples submitted for SPEP and found that the LFIA’s positive and negative results agreed 100% with patients’ history of DARA use as documented in their medical records. In addition, we found that the DARA band became visible on the gel between 250 and 500 μg/mL, which falls in the analytical measurement range of the LFIA (10-1,000 μg/mL). This means that the assay’s quantitative results could help in judging the severity of DARA interference.

How can labs integrate a LFIA for t-mAbs into their SPEP workflow?

When we review SPEP results, if a sample has a band in the γ-region that we suspect is DARA, we will analyze the sample using our LFIA. We then use the LFIA result for the final SPEP interpretation. Alternatively, labs could also run the LFIA on all samples with a band in the γ-region, but for our own workflow, we decided this would likely be excessive since patient histories for individuals diagnosed with multiple myeloma are often available at the time of review. We found that the LFIA is most informative if a new band appears in the γ-region for established patients. We hope this workflow serves as a model for other labs trying to detect the interference of emerging t-mAbs with SPEP.

Yiqi Ruben Luo, PhD, is a clinical chemistry fellow at the University of California San Francisco. Prior to this fellowship, he worked with label-free technologies in the clinical diagnostic industry for 9 years. +Email: ruben.luo@ucsf.edu