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Record W2125264414 · doi:10.1373/clinchem.2006.072660

Diffractive Optics Technology: A Novel Detection Technology for Immunoassays

2006· article· en· W2125264414 on OpenAlexaff
Vitali Borisenko, Wei Hu, Pui Lam Tam, Irene Chen, Jean-François Houle, Walter A. Ausserer

Bibliographic record

VenueClinical Chemistry · 2006
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsAxela (Canada)
FundersNational Cancer Institute
KeywordsOpticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

There is an increasing need for high-sensitivity immunoassays that can be used in point-of-care patient testing of complex media. For example, analytes such as the natriuretic peptides and recently discovered sepsis markers are found in blood in very low picomolar concentrations (1)(2). Although advances have been made in the use of fluorescent, chemiluminescent, and other labels to measure markers at lower detection limits, background interference from biological samples and detection instrumentation remains problematic. Optical biosensors offer the promise of label-free real-time measurements, but their application to quantification of analytes in complex media is impaired by higher detection limits and is susceptible to changes in refractive indices or nonspecific surface binding. Moreover, the costliness of these devices has largely prohibited bedside implementation. In this report, we detail the use of a novel diffractive optics technology (dot™) that takes advantage of the inherent properties of diffractive optics to deliver a cost-effective, portable, robust, optical biosensor that detects analytes at picomolar concentrations in complex media. In the dotLab™ System, coherent light striking a nonrandom pattern of capture molecules on the dotLab Sensor creates constructive and destructive interferences that produce a well-defined diffraction image. As molecules bind to the capture molecules, the height of the diffraction pattern is increased, which in turn increases the diffraction efficiency and the diffractive order intensity. A photodiode monitors the intensity of the diffractive order, which is correlated to analyte concentrations. Because diffraction is inherently self-referencing, the transduction of binding events is dependent on the initial pattern, and an increase in diffractive order intensity will occur only if molecules bind exclusively to the patterned capture reagents. Therefore, nonspecific binding to both the patterned and nonpatterned regions will not affect the signal, a characteristic that offers an important advantage over other optical biosensor systems in which any surface-binding event will cause an increase in signal. Previous diffraction-based immunosensors have used silicon wafer chips that were in contact with analyte-containing solutions and required washing and drying before analysis by a simple reader (3). The dotLab system uses a plastic consumable, the dotLab Sensor, with an integrated prism situated below the flow channel so that the light source interrogates the diffraction grating without passing through the bulk solution. Previous studies suggest that this total internal reflection scheme allows for 95% of the laser intensity to be measured, whereas in a nontotal internal reflection set-up only 5% is measured (4). Moreover, with this configuration, our system monitors biomolecule binding in real time and in complex media. We tested the beta prototype of the dotLab instrument, which incorporates the core dot technology in an integrated package intended for in-house and controlled external development work. The instrument includes precision fluidic control of reagents, buffers, and samples—a proprietary integrated optical assembly designed to function with the dotLab Sensor; and software for acquisition, control, and user-interface. A movable stage allows monitoring and potential patterning with different capture reagents for 8 discrete diffraction spots. In addition, each individual spot can be cross-patterned to perform intraspot multiplexing and assays, as we have previously demonstrated (5)(6). We used a single-spot and a streptavidin-patterned sensor with reagents for the detection of N-terminal probrain natriuretic peptide (NT-proBNP) in various matrices at reference concentrations, as summarized in Table 11 . For direct detection of recombinant NT-proBNP, we used a biotinylated monoclonal antibody directed against NT-proBNP immobilized on a streptavidin-patterned dotLab Sensor (Fig. 1A1 ). The binding event can be observed in real time in the trace. The binding of recombinant NT-proBNP is also detected, but at a lower intensity. The recombinant protein has a low relative molecular mass (Mr) of ∼8000, which generates very little signal on its own. At lower concentrations, we used a different format to enhance the diffraction signal. For example, to detect NT-proBNP at nanomolar concentrations, we used a combination of capture and detector antibodies (Fig. 1B1 ), with which the binding of the biotinylated mouse capture antibody was readily detected, whereas the introduction of the recombinant protein did not produce a detectable signal increase. We carried out rinse steps with pulses of phosphate buffered saline (PBS) (137 mmol/L NaCl; 2.7 mmol/L KCl; 10 mmol/L Na2HPO4/KH2PO4, pH 7.4; OmniPur) and PBS-Tween (10 mmol/L phosphate buffer, pH 7.4; 140 mmol/L NaCl; 3 mmol/L KCl; 0.025% (w/v) Tween-20; Calbiochem). When we introduced a polyclonal goat antibody directed against NT-proBNP, the diffraction signal increased. For internal calibration purposes and to minimize any impact of intersensor variability, we calculated the ratio of the signal increase for the detector-binding event to that for the capture-antibody binding event. With this approach we generated a calibration curve for recombinant NT-proBNP spanning 10 to 1000 μg/L, or ∼1.25 to 125 nmol/L in a PBS matrix (Fig. 1C1 ). For detection at picomolar concentrations, we added a 3rd antibody, donkey antigoat horseradish peroxidase conjugate, to the assay format. To investigate the compatibility of this system with a typical clinical matrix, we ran these assays in a human plasma matrix stripped of endogenous NT-proBNP. For these experiments, we incubated the recombinant NT-proBNP with all 3 antibodies (see Table 11 ) in a single mixture before loading onto the sensor surface. The 90-min preincubation was necessary because of the relatively low on-rate of 1 antibody in the mixture. The binding event detected after loading the mixture reflects the binding of free and complexed biotinylated monoclonal antibody. After being washed briefly in both PBS-Tween and PBS, the complexes were detected by incubation with a precipitating form of 3,3′,5,5′ tetramethylbenzidine (TMB) [1-component TMB membrane peroxidase substrate (Kirkegaard & Perry Laboratories)]. The specific and localized precipitation of TMB on the diffraction pattern causes a signal increase. We again performed internal calibration by use of the ratio of the signal generated by the TMB precipitation to the signal generated by the binding events (Fig. 1D1 ); alternatively, a ratio of binding rates can be used, obviating the need to observe reactions until saturation. We repeated this experiment over a range of calibrator concentrations and generated the calibration curve shown in Fig. 1E1 . Each point is the mean of a duplicate or triplicate determination. The curve spans 31.25 to 2500 ng/L (∼4–300 pmol/L). The lowest value reported is 2 SDs above the mean determination for the zero analyte control. Several clinically relevant decision points are highlighted in Fig. 1E1 . In conclusion, we demonstrated that the dotLab system can be used to detect a clinically relevant analyte over a wide range of concentrations in complex media. The observation of real-time binding can reduce the time required to obtain results, because quantification based on rate measurement is inherently more rapid than end-point determinations. For the assay developer, real-time binding observations help identify unwanted reagent interactions that may be sources of background interference or noise. The detection and capture molecules can be modified to quantify other biomolecules such as DNA; we have detected the hybridization of cDNA strands (not shown). Thus, our method lends itself to the detection of many types of analytes. The simple, robust optics technology at the core of the current compact bench-top platform can be readily deployed, as in the current stand-alone system, or incorporated into various other platforms in the central laboratory and for bedside patient testing. Experimental components and assay format. N/A, not applicable Experimental components and assay format. N/A, not applicable Detection and quantification of NT-proBNP over a range of concentrations. (A), real-time trace from the dotLab system, truncated because of space limitations. The dashed box is an enhanced view of the NT-proBNP binding to the immobilized capture antibody. Unprocessed voltage signal from the photodiode reflects the intensity of the diffractive order measured in diffraction units (DU). Arrows indicate introduction of capture antibody (CA) and NT-proBNP calibrator (NT). (B), a real–time trace from the 2 component assays. Bars indicate the approximate location where delta values were derived and used to establish the ratiometric value for each concentration of calibrator. Arrows indicate introduction of CA, NT, detector antibody (DT), and rinse step with PBS or PBS-Tween (R). (C), relationship of delta ratio to NT-proBNP calibrator concentrations. (D), real-time trace from the multicomponent assay. Arrows indicate introduction of mixture (CK) (see Table 11 ). TMB and R. The large DU increase during the R phase indicates a change in bulk refractive index of the solution from plasma to the PBS washing matrix that did not affect our ability to monitor the subsequent TMB precipitation reaction. Bars indicate the approximate location of the delta values used in the ratiometric determination. (E), relationship of delta ratio to NT-proBNP calibrator concentrations in plasma. Clinical decision points: ①, the clinical cutoff for congestive heart failure (CHF) (1), ② and ③, age-related cut-offs for acute CHF (7), ④, recently suggested cutoff for short-term adverse outcome (8).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.297
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations20
Published2006
Admission routes1
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