100 µg/mL stock solutions of two purified antibodies (Ab1 and Ab2) were serially diluted in wells A1-G1 and A2-G2, respectively, of a 96-well plate (threefold dilution factor, six steps). 1X kinetics buffer with no analyte was pipetted into wells H1 and H2 as reference measurements. A sample plate map was constructed and sample information was entered (Figure 1A). The assay time was set at 120 seconds, and the shake speed of the sample plate was set at 400 rpm.
A biosensor tray was loaded with proteins A, G, and L biosensors and a biosensor plate map was prepared using the multi-analyte software feature (software version 7.0 and higher; Figure 1B). Biosensor assignment is semi-automated to facilitate scale-up to higher-throughput applications. Data collection was initiated and monitored in real time.
Results and Discussion
Execution of the multi-analyte experiment acquired data for three different biosensors and two analytes in one 15-minute walk-away experiment, producing 48 individual sensograms (three biosensors x two antibodies x seven dilutions of each antibody + six reference samples).
The raw data, a plot of binding (expressed in nm, the change in distance between the two reflecting surfaces) versus time contains a significant amount of information regarding biosensor-antibody affinity that may be used to select biosensor chemistry for further assay development. Analysis of the data requires consideration of both signal intensity (total nm shift) and the separation between individual traces. Greater signal strength improves signal to noise and the lower limit of detection while greater separation between individual traces improves accuracy and can indicate a greater dynamic range.
Grouping of the raw data by sample and sensor type (grouped view for quantitation, software version 7.0 and higher), enables a clear comparison of biosensor performance for each biosensor-analyte pair (Figure 2). Ab1 bound tightly to protein G, producing large nm shifts of up to 3 nm while smaller responses, due to weaker affinity, were observed with both the protein A and protein L biosensors. The protein G biosensor, therefore, would be selected for further development of a quantitation assay for Ab1.
Ab2 bound to all three biosensors with good affinity, producing maximum nm shifts greater than 4 nm at 100 µg/mL in all examples. At the highest concentration considered, 100 µg/mL, protein L produced the largest signal (approximately 12 nm at 100 µg/mL) with Ab2.
If assay performance at the higher end of the specified concentration range were prioritized, the protein L biosensor would be recommended for further development. However, at lower concentrations of the specified dynamic range, such as between 3.7–11.1 µg/mL, the protein G biosensor produced greater signal strength than the protein L biosensor. Moreover, the protein G biosensor produced greater separation between individual sensograms at lower concentrations than the protein L biosensor. Therefore, if the lower end of the specified dynamic range was prioritized, the protein G biosensor would be recommended for further development of a quantitation assay for Ab2.
In conclusion, the ForteBio Octet instrument and the multi-analyte software tool were used to rapidly evaluate three different biosensor chemistries for development of a quantitation assay of two antibody analytes. Data collection was recorded in less than 15 minutes of instrument time, demonstrating the accelerated pace at which Octet quantitation assay development can proceed. New graphing tools in software version 7.0 enable rapid analysis and visualization of the data. The experimental data highlights the diverse affinities of antibodies-immunoglobulin binding proteins interactions and the benefits of choosing a biosensor empirically.