November 15, 2008 (Vol. 28, No. 20)

Anna A. Fisher
James C. Nelson
David Ure

Surface Technology Created to Provide Greater Sensitivity, Consistency, and Robustness

In recent years, the measurement of multiple proteins as disease signatures or biomarkers from patient samples has risen rapidly as an enabling tool for the pharmaceutical and diagnostics industries. However, because of the fragile and complex composition and nature of the protein, applications that enable large numbers of proteins to be measured in a precise, consistent, and cost-effective way are limited.

i-Slide™, Inanovate’s core biochip technology, offers a potential solution to these problems by controlling the interaction of capture probes through the use of a nano-structured surface (Figure 1). The i-Slide consists of a surface patterned with many thousands of nanoscale structures, each of which anchors a protein of choice.

The nanoscale structures are used to control the spacing and influence orientation of the proteins; both optimizing and controlling the activity/responsiveness of proteins on the biochip surface. The objective of this tutorial is to demonstrate the advantages of using i-Slide to perform a multiplexed quantitative sandwich immunoassay with a set of five human cytokine assay systems.

Inanovate’s nano-particle substrate (the i-Slide) with attachment of protein

Experimental Methods

In this article we demonstrate the use of a nano-structured biochip substrate in combination with fluorescent imaging to quantitatively measure the concentration of multiple protein biomarkers. A series of assay optimization experiments was performed with a starting set of seven cytokine systems.

Each system was composed of a monoclonal-capture antibody, recombinant antigen, biotinylated monoclonal secondary antibody, and dye-labeled streptavidin. The assay process involved first dispensing multiple-capture antibodies in an array format using a PerkinElmer Piezorray dispenser, followed by incubation and subsequent drying of the arrayed slides.

The arrayed slides were subdivided into 16 wells per slide using a well-separator device. Blocking solution was added to each well, followed by removal and incubation with antigen solutions. The wells were washed using an automated plate washer and treated and washed sequentially with biotinylated secondary antibody and dye-labeled streptavidin. After the final wash step, the slides were removed from separator and imaged with a ScanArray HT slide scanner.

During assay optimization the following assay parameters were examined: capture-antibody concentration, post-arraying incubation, blocking method, dynamic range of eight-point standard curve, reagent concentration/incubation times, and wash method. Previously, buffer-optimization experiments were performed to develop i-Slide specific formulations for arraying, blocking, and washing (now sold as i-Array, i-Block, and i-Wash).

These buffer formulations were used without further optimization. Full sandwich assay experiments were performed and image data was analyzed for signal to noise at lowest (nonzero) concentration, curve-fit-based detection and quantitation limits, and variation analysis between replicate array spots, wells, and slides. Following optimization, the best specific methods for each system and/or the best collective methods for all systems were used to select five of the seven test systems for use in a final three-day performance evaluation.

Results and Discussion

Assay Optimization. Capture antibody concentrations were found to be critical during optimization capture. Three concentrations were studied (100, 200, and 400 ug/mL). In general, the higher concentration gave better signal to noise as well as reduced variability. In all but one of the seven systems, 400 ug/mL was best, with 200 ug/mL giving slightly inferior performance. One of the seven systems was reversed, with 200 ug/mL the best and 400 ug/mL a close second. In all cases, 100 ug/mL resulted in reduced signal to noise, increased variability, and observance of spot heterogeneity.

Post arraying incubation, blocking method, and wash method were not found to be critical, with all methods giving essentially the same result. In the case of the blocking method, the i-Slide was found to be more process compatible as compared with thin nitrocellulose giving acceptable results with manual well addition without the need for rapid immersion (Figure 2).

Incubation times were found to be most optimal at two hours for antigen, one hour for secondary antibody at 1 ug/mL, and 30 min for streptavidin at 0.1 ug/mL. Lastly, a reduced background was observed when FBS or BSA was added to the antigen sample during incubation, indicating the potential importance of the sample diluent in the assay process.

Dynamic Range. Following assay optimization, the dynamic range was examined for all seven systems. The approach taken was to run separate assays using a broad 5-log and a focused 3-log antigen range over an eight-point standard dilution curve. To collect the 5-log image data required a reduced PMT setting on the ScanArray HT.

The degree of biological (nonscanner-related) saturation was indicated by a signal plateau with higher concentrations. This plateu point, or lack thereof, was used to estimate the high limit of detection (HLOD).

In all but one case, the HLOD was found to be above 10,000 pg/mL, the exception (IFNg) saturated between 2,000 and 10,000 pg/mL. The 3-log curve was focused on the low concentration range and used to determine the lower limits of detection (LOD).

For all seven systems LODs below 1 pg/mL were observed, with the best case (IL-10) at 0.02 pg/mL. The dynamic range observed using both 5-log and 3-log data and two PMT settings was between 4–5 log (0.1 pg/mL–10,000 pg/mL). Higher HLOD could potentially be achieved (albeit at the cost of the lower end) through sample dilution and/or reduced antigen incubation time. Increasing antigen incubation time above two hours is unlikely to improve LODs significantly.

Final Performance Evaluation. The final performance evaluation was accomplished by selecting the best five of seven systems for a three-consecutive-day assay run. Two types of assay layouts were utilized in order to measure both variation as well as percent spike and recovery from mock unknown samples. The variation experiment was essentially two replicate eight-point curves per slide with two replicate slides per day (Figure 3).

The recovery experiment utilized a duplicate eight-point curve over two slides with 16 mock unknown samples. Detection limits and variation results from the final evaluation are shown in Tables 1 and 2. Detection limit data shown are for the best day of the three-day run. The data presented for variation is for all replicates over the three-day trial. Additionally, the spike and recovery performance showed less than +/-15% error for all mock unknown samples tested, indicating the suitability of the i-Slide for use in quantitative protein assays (the FDA’s criteria for quantitative assays is +/-20%).

Underpinning and amplifying the problems of a protein biochip-based application is the lack of a high performance and cost-effective substrate. Current protein biochip substrates are mostly made from nitrocellulose membranes or chemically treated glass. However, these approaches have failed to deliver a substrate with the sensitivity, consistency, robustness, and dynamic range levels required for many applications. We have presented application data on i-Slide, which shows promise toward solving the surface attachment issues related to protein biochip applications.

By using the nano-structured biochip, we were able to achieve limits of detection well below 1 pg/mL and as low as 0.02 pg/mL. Additionally, the variation across consecutive day runs was less than 8.5% with intraday variation as low at 2%.

Post-assay comparison of spot morphology and blocking effects observed for the i-Slide and an available thin nitrocellulose substrate

Example of curve fit standardization and detection limit analysis

Anna A. Fisher, Ph.D., is vice president of assay development, James C. Nelson, Ph.D. ([email protected]), is CTO, and David Ure is CEO at Inanovate. Web:

Previous articlemiRNA Implicated in Inflammation Integral to Alzheimer’s Disease
Next articleUnited Therapeutics Pays $150M for Rights to Lily’s Tadalafil