the immuassay handbook parte30

223© 2013 David G. Wild. Published by Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/B978-0-08-097037-0.00016-6

The scientists who develop immunoassays are constantly striving for greater analytical sensitivity to proteins. Greater sensitivity not only allows measurement of lower concentrations of proteins and earlier detection of dis-ease but can also improve precision, allow interferences to be minimized (by dilution), enable faster assays, and allow smaller volume samples to be tested. As a result, much of the innovation in immunoassay technology is focused on improving sensitivity. From an analytical chemist’s perspective, the ability to detect down to a single protein molecule would provide the ultimate ana-lytical sensitivity in immunoassays. Striving to develop methods for resolving single protein molecules, there-fore, represents the future for immunoassays. Given recent developments in single molecule detection, we think it likely that single molecule methods will have a major impact on the field of clinical immunodiagnostics and start to compete with traditional protein detection methods that measure the ensemble response from many protein molecules.

Several techniques have emerged in recent years that make it possible to detect single protein molecules, includ-ing laser-induced fluorescence methods (Todd et al., 2007; Nalefski et al., 2006), total internal reflection fluorescence microscopy (Tessler et al., 2009), and whispering gallery mode micro-cavities (Armani et al., 2007). Researchers have used these methods to demonstrate significant improvements in the sensitivity of immunoassays com-pared to standard, ensemble methods, such as the enzyme-linked immunosorbent assay (ELISA), that have typically had limits of detection (LODs) around picomolar concen-trations (Davies, 2005), While improvements in sensitivity have been demonstrated, single molecule methods face several challenges in order to replace reliable (if less sensi-tive) ensemble methods that are widely used both in research and in vitro diagnostics (IVDs). First, the data generated by single molecule methods must maintain the same standards in precision and accuracy that are expected from immunoassays, i.e., they must be reliable. Second, the methods must be simple enough to be amenable to the development of automated instrumentation that has become standard practice in clinical laboratories. Third, the method must use low-cost reagents and consumables that fit within the budget of modern testing practices. A highly sensitive, single molecule protein detection tech-nology that is also expensive, complex, and unreliable will, of course, fail as a commercial product.

In this chapter, we will describe in detail a single mole-cule immunoassay developed in our laboratory—which we call digital ELISA (Rissin et al., 2010)—that can detect subfemtomolar concentrations of proteins in serum and also addresses the challenges that novel immunoassay plat-forms face to become widely used both in research and clinical diagnostics. Digital ELISA, as well as having the potential to become a powerful single molecule immuno-assay platform, also has the unique property that it quanti-fies concentration by “counting the number of molecules.” This ability to literally count the number of protein mol-ecules detected in an assay makes it possible to test some of the theoretical models presented in earlier chapters in this book.

The Digital ELISA ApproachThe genesis of digital ELISA lies in the work of Walt et al. (Rissin and Walt, 2006a, 2006b) and Noiji et al. (Rondelez et al., 2005) who used arrays of microwells to trap a solu-tion of enzymes, most often β-galactosidase, in very small volumes (~50 fL). By trapping enzymes in solutions con-taining their fluorogenic substrate in these small volumes and then sealing up these wells, the fluorescent product of the enzyme–substrate reaction is confined to a small vol-ume rather than being free to diffuse into a large volume. Walt et al. (Rissin and Walt, 2006a) showed that by seal-ing enzymes in femtoliter-volume wells, the number of fluorescent molecules (resorufin) produced by a single enzyme molecule in a short time is sufficient to enable the detection of that enzyme using a standard fluorescence microscope. For example, a single molecule of β-galactosidase confined in 50 fL of 100 µM of its sub-strate resorufin-β-D-pyranogalactoside (RPG) produces approximately 5600 fluorescent resorufin molecules on average (or about 200 nM of resorufin) in 30 s, a concen-tration that can easily be detected under a microscope. The conceptual “trick” of confining enzymes and the product of the enzyme–substrate reaction in sealed femto-liter wells essentially defeats diffusion compared to the case where enzymes are in conventional large volume ELISA wells (~100 µL, i.e., 2 billion times greater volume). This latter “ensemble” method of detection requires millions of enzymes for the fluorescent product to rise above the detection limit of conventional detectors (e.g., plate read-ers and fluorimeters) because as the product diffuses into

Measurement of Single Protein Molecules Using Digital ELISADavid M. RissinDavid H. WilsonDavid C. Duffy ([email protected])

C H A P T E R

2.13

224 The Immunoassay Handbook

a large volume, it is greatly diluted (Rissin et al., 2010). As a result, this leap from traditional ensemble, “analog” measurements to the “digital” measurements of single molecule arrays demonstrated the potential for very large increases in sensitivity (from millions of enzymes down to one). Figure 1 shows the conceptual shift that this single molecule array (or SiMoA) approach provides to move analytical measurements from the analog world to the digital.

This approach to detecting single enzyme molecules had a number of features that made an attractive option for developing a single molecule immunoassay:

� Compatibility with conventional ELISA that uses enzyme labels for detection of proteins.

� Very high sensitivity to enzyme label that could, in principle, reduce the number of enzyme-labeled pro-teins that could be detected down to one.

� The ability at low concentrations, where the ratio of enzyme molecules to wells is small, to use the Poisson statistics to quantify concentration via counting of active wells (Rissin, 2006a), making the method insensitive to variability in enzyme activity and, in principle, more precise.

� The use of arrays allows many single molecules to be imaged simultaneously, thereby decreasing read times over serial single molecule methods (Todd et al., 2007; Nalefski et al., 2006) and allowing the use of statistics to drive precision.

� A very high signal-to-background for a single molecule via enzyme amplification compared to single molecule methods that rely on direct detection of fluorophore-labeled molecules (Todd et al., 2007; Nalefski et al., 2006; Tessler et al., 2009), allowing the use of low-cost cameras and excitation sources.

Inspired by the ability of SiMoA to isolate and detect sin-gle enzymes, we set about to develop an approach for detecting low concentrations of proteins in serum based on the isolation of single enzyme-labeled immunocom-plexes (Rissin et al., 2010). We sought to develop a method that made use of the exquisite sensitivity of SiMoA to enzyme label but was also highly kinetically efficient in the

capture and labeling of proteins, and physically separated the capture processes from the critical sealing step.

Our approach to digital ELISA is shown schematically in Fig. 2. The front end of the assay is adapted from other bead-based immunoassay platforms that have been employed in the IVD industry: microscopic, paramagnetic beads (~2.7 µm diameter) that are coated in capture anti-bodies are used to capture proteins in a complex matrix such as serum, and the captured proteins are labeled with an enzyme via sequential incubations with a biotinylated detec-tion antibody and a streptavidin-β-galactosidase (SβG) con-jugate. Typically, 200,000–500,000 beads are used to capture the proteins in a sample: this number ensures high kinetic efficiency (see THEORETICAL CONSIDERATIONS) and also ensures good efficiency of bead loading into wells. The next step, however, is unique to digital ELISA and facili-tates the high sensitivity of the assay: rather than incubating the beads in bulk in the presence of enzyme substrate and deter-mining the ensemble signal from all beads, we physically isolate as many beads as possible and determine which beads are associated with labeled proteins and which are not, i.e., we digitize the ELISA signal. Digitization of the immunocomplexes is achieved by loading the beads into arrays of femtoliter wells designed to hold a single bead in the presence of substrate, then physically sealing the wells off (using either a rubber gasket or oil) and allowing the reaction of single enzyme molecules with their substrate to develop (typically for 30 s to 2.5 min). Fluorescence images are acquired at the wave-length of the product of the enzyme–substrate reaction at the start and end of the reaction to determine the presence of a single enzyme; white light or fluorescence images are then acquired to identify those wells that contain beads. Image analysis is then used to identify those wells that con-tain beads and the fluorescence intensity of those wells at the product wavelength. Cutoffs are used to identify those wells that contain an enzyme (“on” or active wells) and those wells that do not (“off” or inactive wells). The changes in fluorescence intensity of active wells from the beginning to the end of the experiment are also determined. The aver-age number of enzymes per bead (AEB) is then calculated for the array using the fraction of active wells and their intensity (see below) (Rissin et al., 2011).

• Reaction volume = 100×10−6 L • Diffusion of product = dilution = limited sensitivity• Millions of molecules needed to overcome

background and reach detection limit

Traditional, analog ELISA

• Reaction volume = 50×10−15 L • Diffusion of product defeated by sealing = no dilution

= single molecule sensitivity • One molecule needed to overcome background and

reach detection limit

SiMoA-based, digital ELISA

0

number of enzyme labels10

510

610

70

number of enzyme labels10 10

210

3

FIGURE 1 Comparison of signal generation from enzymes in traditional, “analog” ELISA and digital ELISA using single molecule arrays (SiMoA). In traditional ELISA, enzyme signals are generated in relatively large volume wells, and millions of enzymes are needed for the signal to rise above background. In digital ELISA, the signal from enzymes is generated and confined within femtoliter-volume wells, and only a single enzyme is needed for signal to rise above background. (The color version of this figure may be viewed at www.immunoassayhandbook.com).

225CHAPTER 2.13 Measurement of Single Protein Molecules Using Digital ELISA

So how does single molecule resolution come about from this digitization process? The answer lies in Poisson statistics that describe the probability of events occurring when a positive outcome (an “on” well in this case) is rela-tively rare compared to negative outcomes (“off” wells). Poisson statistics come into play in bead-based immunoas-says when assaying samples containing extremely low con-centrations of protein, such that the number of captured and labeled proteins is smaller than the total number of beads used for capture. This situation occurs around fem-tomolar concentrations for the conditions of our assays: 100 µL of a 1 fM solution contains about 60,200 protein molecules that are distributed across greater numbers of beads (200,000–500,000). At these low protein concentra-tions, the ratio of protein molecules (and the resulting enzyme-labeled complex) to beads is small (<1:1), and we can use the Poisson distribution equation (Eqn (1)) to describe the probability of beads being associated with zero proteins, one protein, two proteins, etc. The Poisson distribution describes the likelihood of a number of possi-ble events occurring if the average number of events is known. If the expected average number of occurrences is µ, then the probability that there are exactly ν occurrences (ν being a nonnegative integer, ν = 0, 1, 2, 3, etc.) is given by

( )

(1)

In digital ELISA, the key variable in Eqn (1)—µ—is equal to the ratio of captured and labeled protein molecules to beads, and ν is the number of bound labeled protein mol-ecules carried by each subpopulation of the entire bead population. µ is, in fact, the fundamental unit of digital ELISA, and we have given it a more descriptive name: the average number of enzyme-labeled protein molecules per bead or AEB for short (Rissin et al., 2011). The single molecule nature of digital ELISA can be illustrated by entering values of µ for low concentrations and determin-ing the fraction of beads that are associated with zero, one,

and two labeled proteins. At 1 fM, assuming that every protein was captured and labeled on 500,000 beads, then µ = AEB ≈ 0.12, and Eqn (1) tells us that the fraction of beads associated with zero proteins (Pµ(0)) is 88.7%, with a single enzyme (Pµ(1)) is 10.7%, and with two enzymes (Pµ(2)) is 0.6%. At 100 aM, the corresponding values are 98.8%, 1.2%, and 0.01% for zero, one, and two proteins, respectively. It is clear from these calculations that at low concentrations, the vast majority of beads that have associ-ated enzyme activity are single enzymes. It is from these considerations of Poisson statistics that the single mole-cule resolution of digital ELISA is derived.

The goal of a digital ELISA experiment is to determine µ (=AEB) and use it as a quantitative parameter to deter-mine protein concentration. So, how is AEB determined from images of arrays where the beads have been deter-mined to be either “on” or “off”? Fortunately, it is not necessary to know whether specific beads are associated with 1, 2, 3, etc. labeled proteins for this analysis. In fact, this approach would not be feasible as a bead associated with a single enzyme label is difficult to distinguish from a bead with two enzyme labels because of the static hetero-geneity associated with enzyme molecule activities, where populations of individual enzyme molecules have been shown to have up to a sevenfold distribution in enzyme activity (Gorris et al., 2007; Xue and Yeung, 1995; Craig et al., 1996): effectively, the beads that are associated with 1, 2, 3 enzymes are indistinguishable from each other. Due to this broad distribution in enzyme activity, only occurrences of ν = 0 can be determined definitively and is equal to the fraction of “off” beads (Pµ(0)). Pµ(0) is straightfor-ward to determine experimentally, and from Eqn (1), it is possible to relate µ to Pµ(0) or the fraction of “off” beads (Eqn (2)).

(2)

Since the fraction of “off” beads is equal to one minus the fraction of “on” beads, it is possible to determine µ or the

capturebeads

(capAb)

detectionantibody

(detAb)

enzymeconjugate

(S G)

enzymesubstrate

SiMoAdetection

Step 1 Step 2 Step 4

sample(protein)

femtoliter-well arrays

seal

Key

Paramagnetic bead coated in capture antibody (capAb)

Target protein molecule (protein)

Enzyme conjugate(e.g., S G)

Biotinylated detection antibody (detAb)

Step 3

β

β

FIGURE 2 Schematic of digital ELISA.(The color version of this figure may be viewed at www.immunoassayhandbook.com). Adapted from Journal of Immunological Methods, Vol. 378, Nos.1–2, Chang et al., “Single Molecule Enzyme-Linked Immunosorbent Assays: Theoretical Considerations,” Pages 102–115, Copyright (2012), with permission from Elsevier.


digitally determined AEB (or AEBdigital), from fon (the frac-tion of “on” beads) using Eqn (3):

(3)

Equation (3) shows that, in digital ELISA, it is the “off” beads that are as important as “on” beads, i.e., without infor-mation of the negative events, single molecule resolution could not be guaranteed. The quantification power of the technique is derived from the ability to determine the frac-tion of beads that are active in a population of predomi-nantly inactive beads. This analysis also demonstrates the digital nature of the method: it is only necessary to count the number of “on” beads to determine the average num-ber of proteins captured and labeled per bead. Digitization makes the method insensitive to variation in the enzyme activity within an experiment and day-to-day. As AEB in the digital regime is only dependent on the presence or absence of enzymes, it does not depend on absolute inten-sities and, therefore, results in greater precision. Further-more, the method is insensitive to the increasing presence of beads associated with two, three, four enzymes as con-centrations increase, i.e., when the method is no longer effectively “single molecule”: Eqn (3) enables AEB to be determined without knowledge of how many labeled pro-teins are associated with specific beads up to a high fon.

What happens at even higher protein concentrations, where every bead is associated with labeled protein? Does the method reach its upper end of dynamic range? Fortu-nately, the answer is no. Once every bead is associated with an enzyme, and simple “counting” of active beads would not be sensitive to increases in concentration, we switch the analysis into an “analog” mode that makes use of the single molecule information from the digital regime. In this mode, we use the average intensity of all the beads in an array in combination with the intensity produced on aver-age by a single enzyme to determine AEB. By combining the digital counting mode with an intensity-based analog mode, we are able to greatly increase the dynamic range of digital ELISA (Rissin et al., 2011); a schematic of these two regimes is shown in Fig. 3. The “analog” signal for each “on” bead (Ibead) is the intensity difference between the sec-ond and first fluorescence images, i.e., (Ibead,F2 − Ibead,F1). AEB in the analog regime (AEBanalog) is determined from the ratio of the average fluorescence intensity of all active beads in an array ( ) and the average fluorescence inten-sity generated from a single enzyme ( ), corrected for the fraction of beads that are “on” (fon) (Eqn (4)):

(4)

To determine the value of that is used in Eqn (4), we equate the digital (Eqn (3)) and analog (Eqn (4)) AEB terms at fractions of active beads where single enzymes dominate (Eqn (5)). We typically chose arrays with frac-tions of “on” beads <0.1 that meet these criteria, so that

is given by Eqn (6):

, .. , . (5)

, . , .

, .

(6)

Calculation of AEBanalog, therefore, requires each experi-ment to contain arrays in which can be determined, i.e., arrays where fon < 0.1. Samples used to generate cali-bration curves of AEB against concentration typically hav-ing two or three concentrations that meet the fon < 0.1 requirement. Using data from multiple arrays to calculate

, the kinetic activities of thousands of individual enzyme molecules are averaged, and the static heterogene-ity associated with single molecule velocities does not add significant variation to the measurement (Rissin et al., 2011).

From a set of images, both the digital and analog values for AEB can be determined using either Eqn (3) or (4) and plotted as a function of concentration and seamlessly meshed into one calibration curve. The decision to analyze an array using AEBdigital (Eqn (3)) or AEBanalog (Eqn (4)) is based on a fixed cutoff in fon, above which Eqn (4) is used to determine AEB and below which Eqn (3) is used. At lower values of fon (~0.1), the analog intensity contribution of beads with multiple enzymes bound is small, does not vary above the measurement noise of , and the analog intensity measurement is an unreliable method, so AEBdigital must be used. As fon approaches and exceeds one, digital counting is impossible since all beads have at least one enzyme label bound. Between the extremes of fon ≈ 0.1 and fon ≈ 1.0, imprecision considerations using digital counting and analog intensity measurements can be modeled, pro-viding a threshold below which AEBdigital is used and above which AEBanalog is used. The imprecision profiles of the two calculations intersect at approximately fon = 0.7, defin-ing the digital-to-analog threshold (Rissin et al., 2011).

Analytical Sensitivity and Dynamic RangeThe preceding section has described the fundamental approach to single molecule sensitivity and wide dynamic range of digital ELISA, but how does it perform in prac-tice? Before embarking on specific immunoassays to answer this question, we wished to test the sensitivity and dynamic range of the method to the label used in digital ELISA, i.e., SβG. The whole premise of digital ELISA is based on the ability to isolate and detect single enzyme labels, so it was important to determine the kinds of sensi-tivity improvements to enzyme that could be achieved using the SiMoA process shown in Fig. 2, before consider-ing the impact of the kinetics of protein capture and label-ing by antibodies. To test sensitivity to enzyme label, we developed a bead-based assay that was very efficient at cap-turing SβG and used SiMoA to detect enzyme activity on these beads (Rissin et al., 2010). In this assay, we incubated very dilute solutions of SβG with beads coated with biotin, loaded those beads into arrays of femtoliter wells, sealed them in the presence of RPG, imaged the arrays for enzyme activity and bead presence as described above, and determined AEB from the resulting images. Figure 4 shows a representative plot of AEB (and fon) as a function of the concentration of SβG in solution (Rissin et al., 2011). The LOD of this assay was 220 zM of SβG in 100 µL or about 10 enzyme molecules (Rissin et al., 2010). This result confirmed that SiMoA is extremely sensitive to enzyme


label because of the high capture efficiency and detection efficiency. In comparison, we showed that the LOD of a standard fluorescence plate reader to the same set of beads was 15 fM (Rissin et al., 2010), i.e., almost 1 million enzyme labels; the digitization of enzyme signals, therefore, pro-vided almost 105-fold improvement in sensitivity to label. As described elsewhere in this book (see SIGNAL GENERA-TION AND DETECTION SYSTEMS), the detection of alkaline phosphatase using chemiluminescent (CL) substrates—which has been the mainstay of immunodiagnostic systems for almost 20 years—has an LOD of 30 aM. SiMoA is, therefore, over 100 times more sensitive to enzymes than the state-of-the-art immunoassay detection systems.

Based on this greater sensitivity to enzyme label, we developed several digital ELISAs. Our first demonstration was a digital ELISA for prostate specific antigen (PSA), a

protein analyte that is widely measured in clinical diagnos-tics. Conventional PSA tests have LODs around 100 pg/mL, and “ultrasensitive” PSA tests are available with LODs around 3–10 pg/mL (Ferguson et al., 1996). During our ini-tial development of digital ELISA, we observed that using conventional concentrations of the labeling reagents (i.e., biotinylated detection antibody and enzyme conjugate), the SiMoA signals were extremely high even from backgrounds in serum that were depleted of PSA. As a result, we greatly reduced the concentrations of those labeling reagents to get the SiMoA background signals down to a reasonable noise floor for the technology, i.e., about 100 active beads, such that Poisson noise =

√ ≤ 10%, where N is the

number of active beads detected. In reducing these concen-trations, we greatly reduced the backgrounds in terms of equivalent concentrations of PSA to about 1 fM. We also

FIGURE 3 Single molecule (left), digital (middle), and analog (right) regimes of digital ELISA as a function of average enzymes per bead (AEB). (The color version of this figure may be viewed at www.immunoassayhandbook.com). Reprinted with permission from Rissin et al., Analytical Chemistry 83, 2279–2285 (2011). Copyright (2011) American Chemical Society.


discovered that most of the background could be accounted for from the interactions of the labeling reagents with the capture beads. As a result, we were able to detect subfemto-molar concentrations of PSA, i.e., about 200 aM or 6 fg/mL (Fig. 5), an assay that is about 1000 times more sensitive than “ultrasensitive” PSA based on CL detection. The key finding in developing digital ELISA was that the extraordi-nary sensitivity to label allowed us to reduce labeling reagent concentrations, thereby reducing backgrounds and increasing sensitivity to the protein. This important con-cept will be described in more detail in the section on THE-ORETICAL CONSIDERATIONS. Since the initial work with PSA, we have demonstrated that digital ELISA is a general approach for improving dramatically the sensitivities of immunoassays for a number of proteins (Table 1); femto-grams per milliter are typically achieved in digital ELISA compared to picograms per milliliter for conventional technologies. The improvement in sensitivity that we have demonstrated over the best commercial immunoassays averages to about 1000-fold.

The assay for SβG that was described above also allowed us to test the linearity and dynamic range that can be achieved using SiMoA. Figure 6 shows the effectiveness of the Poisson distribution equation for correcting for mul-tiple enzymes per bead in the digital range of the SiMoA readout by converting the fraction of “on” beads to AEB using Eqn (3). Figure 6 shows that AEBdigital maintained a linear response up to ~70% active despite the nonlinear response in fon. Figure 5 shows the dynamic range that can be achieved with respect to enzyme label using the com-bined digital and analog approach described above. Using our established LOD for SβG of 220 zM and the highest detected concentration in the linear range of the curve (316 fM), the linear dynamic range using a combined digi-tal and analog readout was 6.2 logs, with an AEBdigital dynamic range of 4.7 logs, and an AEBanalog dynamic range of 1.5 logs. In an immunoassay, some of that dynamic range to enzyme is consumed by the background of the assay; the dynamic range for protein concentrations mea-sured using digital ELISA is typically >4 logs (Rissin et al., 2010; Song et al., 2011; Zetterberg et al., 2011).

0.00001

0.0001

0.001

0.01

0.1

1

10

100

10-19 10-18 10-17 10-16 10-15 10-14 10-13 10-12

[SβG] (M)

AEB

FIGURE 4 AEB as a function of enzyme conjugate (SβG) captured on biotin-presenting beads read using SiMoA. This figure shows that SiMoA can detect from zeptomolar up to picomolar concentrations of enzyme with a dynamic range of >6. Adapted with permission from Rissin et al., Analytical Chemistry 83, 2279–2285 (2011). Copyright (2011) American Chemical Society.

0 0.1 1 10 100 10000.001

0.01

0.1

1

10

100

[PSA] (pg/mL)

AEB

0 3.3 33 330 3300 33,000[PSA] (fM)

FIGURE 5 AEB as a function of concentration of PSA determined using digital ELISA. The data point on the y-axis corresponds to background, i.e., zero PSA. The low backgrounds and high sensitivity to enzyme of digital ELISA enabled an LOD of about 10 fg/mL PSA. Adapted with permission from Rissin et al., Analytical Chemistry 83, 2279–2285 (2011). Copyright (2011) American Chemical Society.

TABLE 1 LODs of Some Representative Digital ELISAs

ProteinSiMoA LOD (fg/mL)

Fold Increase in Sensitivity Over Best Commercial Analog Test

IL-1β 1 57TNF-α 3 35GM-CSF 3 87p24 5 3000IL-6 5 8PSA 6 1667Aβ42 20 2500tau 20 3000IL-1α 24 42GLP-1 33 333Troponin I 50 60p-tau-231 50 NDp-tau-181 100 600

0 2 4 6 8 100

20

40

60

80

100

0.0

0.2

0.4

0.6

0.8

1.0

[SβG] (fM)

fon

(% a

ctiv

e be

ads)

AEBdigital

fon

AEBdigital

FIGURE 6 Poisson correction in the digital range of digital ELISA. As the fraction of “on” beads (fon) rises above 0.1, a significant proportion of the beads are associated with multiple enzymes, so fon deviates from linearity with concentration. The calculation for AEBdigital (Eqn (3)) accounts for multiple enzymes per bead so is linear with concentration. Adapted with permission from Rissin et al., Analytical Chemistry 83, 2279–2285 (2011). Copyright (2011) American Chemical Society.


These dynamic ranges are not intrinsic limits for digital ELISA. Several factors may limit the dynamic range of the assays, and improvements to instrumentation, arrays, and the enzyme–substrate chemistry could yield significant improvements in dynamic range. In particular:

� The number of beads imaged may limit dynamic range. As >100 beads are needed to prevent significant Pois-son noise, bead loading, and imaging efficiencies that allow about 10,000 beads to be imaged mean that the background fon is forced to around 1%. By improving bead loading efficiency and having larger arrays that can be imaged by CCD cameras with wider fields of view, more beads could be imaged. So, for example, if 100,000 beads could be imaged then Poisson noise considerations would set the background at 0.1%, allowing access to another 10-fold in dynamic range in the most sensitive part of the calibration curve.

� The dynamic range of the CCD camera may limit the dynamic range of digital ELISA. A camera must be sen-sitive to single enzymes at the low end but also not saturate when many enzymes are on each bead. As the “bit-size” of scientific cameras improves (currently, we use 12-bit cameras to minimize instrument cost), the upper end of the dynamic range of digital ELISA will also improve.

� The interval in time between sealing of the wells and imaging of the wells may limit dynamic range. As that time increases, more enzyme substrate is consumed before the first image is taken limiting the concentra-tion of substrate left to be consumed before the second image is taken, i.e., reducing the dynamic range of (Ibead,F2–Ibead,F1) that can be accessed. Instrumentation with faster seal-to-image times will expand the “dynamic range” of the enzyme–substrate concentra-tions available for the measurement and allow higher AEB samples to be quantified. Long seal-to-image times were the major limitation of dynamic range in the first generation SiMoA instruments (Rissin et al., 2011).

� The solubility of the enzyme substrate may limit dynamic range. The solubility of RPG currently limits the amount of substrate available in the measurement to about 100 µM. More soluble substrates would allow higher AEB samples to be analyzed before the stores of enzyme substrate in 50 fL are exhausted by increasingly larger numbers of enzymes on the beads.

Theoretical ConsiderationsThe preceding sections have described the fundamentals of digital ELISA, but the method is still an immunoassay and should follow the precepts outlined in the first chap-ters of this book. This section describes the basic kinetic theory of digital ELISA (Chang et al., 2012a).

In contrast to the ambient analyte assay (see AMBIENT ANALYTE IMMUNOASSAY), digital ELISA generally uses a high concentration of capture antibody rather than mini-mizing it. This approach is designed to kinetically drive

the capture of as many protein molecules in solution as possible by using an excess of capture reagent. By having the maximum possible number of proteins captured on the beads and almost limitless sensitivity to those proteins once they are labeled with enzyme, it is possible to reduce the fraction of those proteins that need to be labeled to detect, reducing labeling reagent concentrations, reduc-ing backgrounds, and, ultimately, yielding an extremely sensitive immunoassay. Here, we will describe a simple binding model for the capture and labeling of proteins using digital ELISA based on the equations presented in Chris Davies’ chapter (see PRINCIPLES OF COMPETITIVE AND IMMUNOMETRIC ASSAYS). One of the attractive features of digital ELISA is that the measurement unit is AEB and, therefore, from knowledge of the number of beads, one can determine the number of molecules captured and detected directly. This property—which is derived from the technique’s ability to count molecules—makes it pos-sible to directly test theoretical models that predict the concentration of bound and unbound species. To demon-strate this model, we will consider the efficiency of each step of the assay shown in Fig. 2 and then present the overall efficiency of digital ELISA in terms of number of molecules detected.

Each of the first three steps of the assay, namely, incuba-tion of capture beads with sample, incubation of captured protein with detection antibody, and incubation of detection-labeled protein with enzyme conjugate, can be modeled kinetically using simple bimolecular interactions (A + B � AB). These equations require knowledge of the concentrations of the two molecular species involved in binding ([A] and [B]), the kinetic parameters (on- and off-rates, kon and koff, and dissociation constant, KD) of the interaction between the two species, and the time of incu-bation. At equilibrium (infinite incubation times), the amounts (concentrations) of the bound species ([AB]) can be determined by solving the quadratic Eqn (7) (which is equivalent to Eqn (28) in the chapter PRINCIPLES OF COM-PETITIVE AND IMMUNOMETRIC ASSAYS (INCLUDING ELISA)):

(7)

where Atotal and Btotal are the total concentrations of the species in the solution (e.g., the total amount of capture antibody or the concentration of target protein). These calculations determine the maximum number of molecules that bind given infinite incubation times. As, in practice, immunoassays typically use shorter incubation times, it is important to understand how the concentration of bound species changes with time for given on- and off-rates. The rate of increase in the concentration of the bound species (AB) is given by Eqn (8):

(8)

There is no analytical solution to Eqn (8) to determine [AB] as a function of time, but numerical calculations can be used to generate this information. The following will describe a) the equilibrium condition and b) the kinetic variation of binding events in each of the first three steps of the assay.


Step 1: Capture of proteins on beads (A = capture antibody [capAb] and B = target protein [protein])

The variation of [capAb–protein] at equilibrium as a function of [proteintotal] (from, say, pM down to aM) can be modeled from experimentally derived estimates of the total concentration of capture antibodies on the beads ([capAbtotal]) at different values of KD. We estimate that each bead is modified with 274,000 antibodies based on depletion of antibody from the coupling reaction (Chang et al., 2012a). Digital ELISA typically uses 500,000 beads in 100 µL of sample, such that [capAbtotal] ≈ 2.3 nM (Chang et al., 2012a). Antibodies that are used in immu-noassays such as ELISA typically have KD in the range 10 pM to 10 nM; Fig. 7 shows plots of the number of capAb–protein complexes formed on 500,000 beads as a function of [proteintotal] for KD ranging from 10 pM to 10 nM based on Eqn (7). The inset table shows the cap-ture efficiencies; capture efficiency is effectively indepen-dent of concentrations until a significant fraction of the antibodies is bound to a protein molecule, i.e., at nano-molar concentrations, well above the protein concentra-tions of interest here.

Figure 7 shows that almost all proteins are captured for KD ≈ 10−10 M; at nanomolar dissociation constants, the cap-ture efficiency is still high (~70%). This observation

suggests that, at equilibrium, digital ELISA will work effectively over a broad range of antibody affinities. In terms of the absolute number of molecules that need to be detected over this range, single molecule arrays can detect down to 10 enzyme labels in 100 µL, equivalent to an AEB of approximately 0.00002. This detection limit is well below the average number of captured molecules predicted by Eqn (7) (right-hand y-axis of Fig. 7). In summary, from a kinetic perspective, assuming typical affinities of anti-bodies, there are plenty of proteins captured at equilib-rium on beads from solutions containing attomolar concentrations and lower to be detected using SiMoA.

In terms of kinetic variation in capture of proteins, Fig. 8 shows plots of [capAb–protein] as a function of time determined numerically using Eqn (8), assuming [capAbtotal] = 2.3 nM and [proteintotal] = 1 fM. These plots were generated using kon and koff values ranging from 104 to 106/M/s and 10−3 to 10−6/s, respectively, corresponding to dissociation constants from 1 nM to 10 pM. These kinetic parameters are typical for the antibodies that we have used to develop digital ELISAs and others (Karlsson et al., 1991) have measured. For example, the PSA capture antibody used in the experiments presented here had kinetic parameters kon = 2.7 × 105/M/s, koff = 3 × 10−6/s, and KD = 12 pM (Chang et al., 2012a).

10-16

10-15

10-14

10-13

10-12

10-11

103

104

105

106

107

108

109

[proteintotal] (M)

no

. o

f c

ap

Ab

-p

ro

te

in

co

mp

le

xe

s (m

ole

cu

le

s)

2000

200

20

2

0.2

0.02

0.002

av

era

ge

n

o. o

f c

ap

Ab

-p

ro

te

in

co

mp

le

xe

s p

er b

ea

d

KD

(M) Capture

Efficiency (%)

10−11 99.56

10−10 95.78

10−9 69.44

10−8 18.52

FIGURE 7 Efficiency of protein capture at equilibrium predicted by Eqn (7). Adapted from Journal of Immunological Methods, Vol. 378, Nos.1–2, Chang et al., “Single Molecule Enzyme-Linked Immunosorbent Assays: Theoretical Considerations,” Pages 102–115. Copyright (2012), with permission from Elsevier.

A (kon = 106 M−1 s−1; koff = 10−3 s−1; KD= 1 nM)

B (kon = 105 M−1 s−1; koff = 10−4 s−1; KD= 1 nM)

C (kon = 104 M−1 s−1; koff = 10−5 s−1; KD= 1 nM)

D (kon = 106 M−1 s−1; koff = 10−4 s−1; KD= 0.1 nM)

E (kon = 105 M−1 s−1; koff = 10−5 s−1; KD= 0.1 nM)

F (kon = 104 M−1 s−1; koff = 10−6 s−1; KD= 0.1 nM)

G (kon = 106 M−1 s−1; koff = 10−5 s−1; KD= 0.01 nM)

H (kon = 105 M−1 s−1; koff = 10−6 s−1; KD= 0.01 nM) [capAbtotal] = 2.3 nM; [proteintotal] = 1 fM

A

G D

F

C

H

B

E

0 200 400 600 800 1000

10-18

10-17

10-16

10-15

time (s)

[c

ap

Ab

-p

ro

te

in

] (M

)

FIGURE 8 Kinetic profile of protein capture predicted by Eqn (8). (The color version of this figure may be viewed at www.immunoassayhandbook.com). Adapted from Journal of Immunological Methods, Vol. 378, Nos.1–2, Chang et al., “Single Molecule Enzyme-Linked Immunosorbent Assays: Theoretical Considerations,” Pages 102–115. Copyright (2012), with permission from Elsevier.


Figure 8 shows that the rate of protein capture is deter-mined largely by the on-rate of the binding reaction for antibodies that are encountered when developing ELISA. After a 1000 s (17 min) incubation, for kon = 106/M/s, the capture efficiency was high, ranging from 67% to 89% for dissociation constants from 1 nM down to 10 pM, respec-tively. For kon = 105/M/s and 104/M/s, the capture efficien-cies are 20% and 2.3%, respectively, independent of KD. The optimal capture antibody, therefore, is one with a high on-rate. That said, as SiMoA is so sensitive to the enzyme used to label captured proteins (see below), for highly effi-cient protein capture, it has been experimentally necessary to label only a fraction of those captured. Therefore, even with antibodies with lower on-rates (kon ~ 104/M/s), it is possible to detect femtomolar concentrations of proteins using digital ELISA.

As well as limitations of adsorption kinetics for the cap-ture of proteins, assays may also be limited by diffusion of the reactants. The use of capture beads, however, essen-tially eliminates this limitation making adsorption kinetics (i.e., the on- and off-rates of the antibodies) the main limi-tation in how many molecules are captured as a function of time. Simple calculations show that the average distance between beads is about 60 µm, and proteins around 30 kDa diffuse that distance in about 30 s. Based on this fact, and a simple model for collision frequencies (Chang et al., 2012a), proteins collide with beads and have the opportunity to be captured by the antibodies many times in an incubation time of 10 min.

Step 2: Labeling of captured proteins with biotinyl-ated detection antibodies (A = complex of capture anti-body and protein [capAb–protein] and B = detection antibody [detAb])

Figure 9 shows plots of the labeling efficiency at equilib-rium of different concentrations of capAb–protein com-plex by detection antibody as a function of the concentration of detection antibody used (at fixed KD) and as a function of KD (at a fixed detection antibody concentration). The plot in Fig. 9A assumed KD = 1 nM for the detection anti-body and a high-efficiency capture antibody (i.e., [capAb–protein] ≈ [proteintotal]), as is reasonable for the capture and detection antibody pair for the PSA digital ELISA. As can be seen from Fig. 9, the labeling efficiency is insensi-tive to the concentration of captured proteins across the analytical region of interest. In exemplary assays, we typi-cally use detection antibody concentrations of 0.1 µg/mL, i.e., around 1 nM, to ensure sufficient labeling and also to minimize the primary source of nonspecific binding (NSB). Figure 9A shows that this concentration should result in a capture efficiency of ~50% for KD = 1 nM. Simi-lar trends can be seen as a function of the affinity (KD) of the detection antibody (Fig. 9B). Lower affinity detection antibodies would need greater concentrations to achieve higher labeling efficiencies, but this approach would also lead to higher NSB.

The adsorption kinetics of labeling capAb–protein com-plexes with detection antibodies (to form capAb–protein–detAb complexes) are shown in Fig. 10. This figure shows plots of [capAb–protein–detAb] as a function of time determined numerically, assuming [capAbtotal] = 2.3 nM, [proteintotal] = 1 fM, and [detAb] = 1 nM. These plots were

generated using kon and koff values ranging from 104 to 106/M/s and 10−3 to 10−6/s, respectively. As for capture of proteins, it is clear from Fig. 10 that the rate of labeling of captured protein by detection antibodies is determined largely by the on-rate of the binding reaction. The optimal detection antibody, therefore, is one with a high on-rate. That said, for antibodies with slower on-rates, signals can be increased by increasing the labeling efficiency of the enzyme step. As an example of typical labeling efficiencies as a func-tion of time, 1 nM of detection antibody used in the PSA digital ELISA is predicted to label approximately 22% of the captured proteins within 1000 s. As for capture of proteins, the labeling of proteins is generally not diffusion limited.

Step 3: Labeling of biotinylated detection antibod-ies with enzyme conjugate (A = complexes of capture antibody, protein, and detection antibody [capAb- protein-detAb] and B = enzyme conjugate [SβG])

Equation (7) predicts—at concentrations of SβG typi-cally used in digital ELISA (1–150 pM)—that the capAb–protein–detAb complexes will essentially be 100% labeled by enzyme conjugate (SβG) at equilibrium, assuming KD = 10−15 M for the interaction between SβG and the biotinylated detection antibody. Figure 11 shows plots of [capAb–protein–detAb–SβG] as a function of time deter-mined numerically using Eqn (8), assuming [capAbtotal] = 2.3 nM, [proteintotal] = 1 fM, and [detAb] = 1 nM, at several different concentrations of SβG. These plots were gener-ated assuming kon = 5.1 × 106/M/s, koff = 5.1 × 10−9/s for the interaction between streptavidin and biotin and that each

10-16

10-15

10-14

10-13

10-12

10-11

10-16

10-15

10-14

10-13

10-12

10-11

0.01

0.1

1

10

100

100 nM10 nM1 nM100 pM10 pM1 pM

[capAb-protein] (M)

[capAb-protein] (M)

fra

ctio

n o

f c

ap

Ab

-p

ro

te

in

co

mp

lex

es

la

be

led

wit

h d

etA

b (%

)

fra

ctio

n o

f c

ap

Ab

-p

ro

te

in

co

mp

lex

es

la

be

led

wit

h d

etA

b (%

)

[detAb]

(b)

(a)

0

20

40

60

80

100

10 nM1 nM100 pM10 pM

KD

of

detAb

FIGURE 9 Efficiency of labeling of captured proteins by detection antibodies at equilibrium predicted by Eqn (7). Adapted from Journal of Immunological Methods, Vol. 378, Nos.1–2, Chang et al., “Single Molecule Enzyme-Linked Immunosorbent Assays: Theoretical Considerations,” Pages 102–115. Copyright (2012), with permission from Elsevier.


detection antibody is able to bind to a maximum of one enzyme conjugate. Figure 11 demonstrates that, at concen-trations of SβG from 15 to 50 pM, only a fraction of the capAb–protein–detAb complex are being labeled with an enzyme. For example, after a 30 min incubation with [SβG] = 15 pM (as used for the PSA digital ELISA and oth-ers), only 13% of the complexes are labeled with an enzyme. This under-labeling arises from the ability to use the high sensitivity of SiMoA to minimize the concentration of enzyme conjugate used in the assay that gives rise to back-ground signals that limit assay sensitivity. Complete label-ing of these complexes is readily achieved kinetically, but this process would increase NSB and increase the average enzymes per bead such that the full dynamic range and sen-sitivity of SiMoA could not be exploited. As for the other steps of the assay, the labeling of detection antibodies with enzyme conjugate is generally not diffusion limited.

OVERALL EFFICIENCY OF CAPTURE AND LABELING STEPSBy determining the product of the kinetic efficiencies of each individual step in digital ELISA, it is possible to determine the overall efficiency of the assay in terms of

molecules captured and labeled. Knowing the number of beads used then allows a theoretical AEB value to be deter-mined for each hypothetic experiment and for these theo-retical values to be compared to experimental data. Table 2 shows theoretical and corresponding experimental values of AEB. The theoretical values were determined as described above using Eqns (7) and (8) and different values for the incubation time and reagent concentrations. It is clear from Table 2 that, at equilibrium, digital ELISA is effi-cient overall and that the design of digital ELISA does, indeed, make it possible to detect most of the molecules in the system. A comparison of equilibrium conditions and those used in a typical assay indicate how the signals are kinetically controlled with the beneficial side effect of reducing backgrounds as described above. Typically, we observe background signals that are equivalent to adding 1 fM of analyte to the sample as described previously (AEB < 0.005). We note that if the system was allowed to reach equilibrium, such a 1 fM background signal would correspond to AEB ≈ 0.03 or about 750 active beads (assuming that about 25,000 beads are detected), much greater than a preferred noise floor of about 100 active beads. As a result, we must kinetically control the signals in digital ELISA to make use of the full dynamic range and sensitivity of the method. In the “120–60–30 min” assay that we have published previously, Table 2 shows that by using a low concentration of SβG, we reduced the labeling efficiency from close to 100% at equilibrium to about 13%, bringing the background down to the noise floor, so while the overall efficiency is relatively low (2.9%), the full dynamic range of the arrays is utilized. Another approach to kinetically controlling the signals is to use shorter incubation times with higher concentra-tions of labeling reagents. Table 2 shows an example of a “10–10–10 min” assay, where higher concentrations of both detection antibody and enzyme conjugate can be used to achieve the same AEB values in shorter times. This process of optimizing the concentrations of detec-tion antibody and SβG for fixed incubation times to bring backgrounds down to the Poisson noise floor is carried out every time a new assay is developed.

Step 4: Detection of enzymes by SiMoAThe final step of the digital ELISA process is to detect the enzymes that are associated with immunocomplexes on

A (kon = 106 M−1 s−1; koff = 10−3 s−1; KD = 1 nM)

B (kon = 105 M−1 s−1; koff = 10−4 s−1; KD = 1 nM)

C (kon = 104 M−1 s−1; koff = 10−5 s−1; KD = 1 nM)

D (kon = 106 M−1 s−1; koff = 10−4 s−1; KD = 0.1 nM)

E (kon = 105 M−1 s−1; koff = 10−5 s−1; KD = 0.1 nM)

F (kon = 104 M−1 s−1; koff = 10−6 s−1; KD = 0.1 nM)

G (kon = 106 M−1 s−1; koff = 10−5 s−1; KD = 0.01 nM)

H (kon = 105 M−1 s−1; koff = 10−6 s−1; KD = 0.01 nM) [capAbtotal] = 2.3 nM; [proteintotal] = 1 fM[detAb] = 1 nM

A

G D

F

C

HB

E

0 200 400 600 800 1000

10-18

10-17

10-16

10-15

time (s)

[c

ap

Ab

-p

ro

te

in

-d

etA

b] (M

)

PSA (kon = 3.9 x 105 M−1 s−1; koff = 8.6 x 10−4 s−1; KD= 2 nM)

PSA

FIGURE 10 Kinetic profile of labeling of captured protein by detection antibodies predicted by Eqn (8). (The color version of this figure may be viewed at www.immunoassayhandbook.com). Adapted from Journal of Immunological Methods, Vol. 378, Nos.1–2, Chang et al., “Single Molecule Enzyme-Linked Immunosorbent Assays: Theoretical Considerations,” Pages 102–115. Copyright (2012), with permission from Elsevier.

[c

ap

Ab

-p

ro

te

in

-d

etA

b-S

βG] (M

)

[SβG]

[capAbtotal] = 2.3 nM; [proteintotal] = 1 fM; [detAb] = 1 nM

0 200 400 600 800 1000

10-18

10-17

10-16

10-15

1.5 pM15 pM50 pM150 pM

time (s)

FIGURE 11 Kinetic profile of labeling of captured protein–detection antibody complexes by enzyme conjugate predicted by Eqn (8). The horizontal dotted line corresponds to fully labeled complexes for the conditions shown. Adapted from Journal of Immunological Methods, Vol. 378, Nos.1–2, Chang et al., “Single Molecule Enzyme-Linked Immunosorbent Assays: Theoretical Considerations,” Pages 102–115. Copyright (2012), with permission from Elsevier.


beads. We demonstrated earlier in this chapter that the conceptual trick of SiMoA of confining an enzyme and its substrate in a femtoliter well is capable of detecting large numbers of single enzymes and that the efficiency of this process is very high. The data shown in Fig. 3 indicated an efficiency of capturing and detecting enzyme label >70% and close to 100% of those enzymes that are captured and loaded into wells are detected (Rissin et al., 2010).

Given the high efficiency of detecting single enzymes, the limit to sensitivity and achieving the goal of “detecting every molecule in the system” is bead loading efficiency. While digital ELISA is a ratiometric method, the sensitiv-ity at extremely low AEB may be limited by the number of beads detected. Using arrays made from glass that con-tained 50,000 wells and loading beads via centrifugation, we were able to detect approximately 25,000–30,000 beads from 200,000 beads used in a digital ELISA, an overall effi-ciency of <15% (Rissin et al., 2010). To improve this effi-ciency and detect the remaining 85% of enzymes, three improvements in the SiMoA process may be utilized. First, the assay may make use of arrays with more wells. We have developed arrays based on microreplication in plastics with 216,000 wells (Kan et al., 2012). These manufacturing pro-cesses, based on the same methods used to create DVDs (Kan et al., 2012), make it possible to generated arrays with millions of wells. Second, CCD cameras with sufficient resolution and chip size to image large arrays can be uti-lized. Using current scientific cameras with resolutions >8 megapixels, we are able to image approximately 200,000 wells. Finally, the efficiency of delivery of magnetic beads to the wells may be improved. Dead volume and the limited area that the wells occupy on the array surface mean that bead delivery is an inefficient process. Typically hundreds of thousands of beads are required to deliver tens of thou-sands of beads to wells. While this limitation is generally unimportant for a ratiometric method, the low-end sensi-tivity and consequently the overall dynamic range of the method may be improved by getting more beads into wells.

SPECIFICITY OF DIGITAL ELISAOnce analytical sensitivity to very low numbers of mole-cules has been addressed—and the high efficiency and

single molecule nature of SiMoA certainly create this situation—then the overall sensitivity of an immunoassay may depend on the background of the assay. Backgrounds in assays arise from the binding of nontarget proteins to the beads and their subsequent detection in arrays, i.e., it is dependent on the specificity of the assay process. It is more challenging to model specificity quantitatively com-pared to sensitivity, but qualitative arguments about spec-ificity are useful. Consider a capture antibody with a nanomolar KD. The cross-reactivities of ELISA antibod-ies are often determined for closely related proteins. Assuming that the KD of the antibody with the protein of the highest cross-reactivity is 1 µM (1000-fold specificity), the specificity of the antibody for the protein in question is about 1000-fold in concentration, not useful when try-ing to discriminate 1012-fold differences in concentration. The use of a second labeling antibody greatly improves the specificity of the assay. Assuming that the second anti-body also has nanomolar affinity for the target protein but has lower cross-reactivity than the detection antibody, say, KD = 1 mM, then the selectivity would be about 109-fold. This qualitative specificity starts to approach the dis-crimination we have demonstrated using digital ELISA (detecting femtomolar concentrations of a specific pro-tein against millimolar concentrations of nontarget pro-teins in serum and plasma). The greater specificity observed has roots in surface effects that reduce off-rates of proteins on surfaces. Lower off-rates of proteins bound to immobilized antibodies compared to antibodies in solution have been observed and are likely due to rebind-ing effects on surfaces and possible multivalent inter-actions as described in the chapter PRINCIPLES OF COMPETITIVE AND IMMUNOMETRIC ASSAYS. The very low off-rates of proteins captured on beads means that we are able to wash the beads vigorously after sample incubation without any noticeable loss of signal. This vigorous wash-ing presumably washes away many proteins of much higher concentration but much lower affinity. These sur-face effects could contribute another 102- to 106-fold dis-crimination and make the overall specificity of the assay >1011-fold.

Despite the lack of quantitative models for specificity, the high specificity achieved by digital ELISA (detection

TABLE 2 Predicted Values of AEB against Experimental Values for Different Incubation Times and Reagent Concentrations

Condition[PSA] (pg/mL)

[PSA] (fM)

Capture Efficiency

Detection Label Efficiency

Enzyme Label Efficiency

Overall Efficiency

Number of Molecules Labeled

Predicted AEB

Experimental AEB − AEB[PSA] = 0

Equilibrium 0.1 3.33 99.5% 23.4% 100% 23.24% 46,644 0.0933 NA[DetAb] = 0.67 nM 1 33.3 99.5% 23.4% 100% 23.24% 466,440 0.9329 NA[SβG] = 15 pM 10 333 99.5% 23.4% 100% 23.24% 4,664,402 9.329 NA

120–60–30 min 0.1 3.33 98.3% 22.9% 12.9% 2.91% 5849 0.0117 0.0146[DetAb] = 0.67 nM 1 33.3 98.3% 22.9% 12.9% 2.91% 58,489 0.1169 0.1444[SβG] = 15 pM 10 333 98.3% 22.9% 12.9% 2.91% 584,669 1.169 1.557

10–10–10 min 0.1 3.33 30.5% 56.8% 20.6% 3.57% 7163 0.0143 0.0042[DetAb] = 5 nM 1 33.3 30.5% 56.8% 20.6% 3.57% 71,634 0.1433 0.1021[SβG] = 75 pM 10 333 30.5% 56.8% 20.6% 3.57% 716,266 1.433 1.172

Adapted from Journal of Immunological Methods, Vol. 378, Nos.1–2, Chang et al., “Single Molecule Enzyme-Linked Immunosorbent Assays: Theoretical Considerations,” Pages 102−115, Copyright

(2012), with permission from Elsevier.


of 5 × 10−17 M of a target protein in 10−4 M of nontarget proteins in plasma) is unquestionable. Most importantly, we have not observed significant background signals from matrix proteins; all of the background signals seem to arise from the interaction of the labeling reagents with the cap-ture antibodies, a phenomenon that the high sensitivity of SiMoA is ideally suited to reducing.

Assay DevelopmentThis section deals with the practical aspects of developing digital ELISAs. Many of the methodologies described here were refined and developed from technologies employed in other immunoassays. There are some details, however, that are unique to digital ELISA, and stem from the requirements to detect single molecules on singulated beads.

REAGENTSAn important reagent in digital ELISA is the paramagnetic beads coated in capture antibodies. These beads are avail-able from several reliable commercial sources (e.g., Agi-lent’s LodeStar™ beads; Life Technology’s Dynabeads®; and Thermo Fisher’s Sera-Mag® beads). These beads are selected based on being appropriately sized (~3 µm) to enable trapping of the beads in wells, having excellent size homogeneity, and being available in a variety of surface chemistries. Paramagnetic bead-based immunoassays have been used previously in automated immunodiagnostic analyzers (Kourilov and Steinitz, 2002). These beads are manufactured using several different processes and com-positions, but all incorporate iron oxide into the bead. The iron oxide causes them to be attracted to magnets, such as commercially available 96-well plate magnets and auto-mated immunoassay magnetic bead washing stations. An important characteristic of the beads is that they respond to magnets but are not magnets themselves; they retain no residual magnetism following removal of the magnet and re-disperse readily in solution. The major advantage of paramagnetic beads for use in immunoassays is ease of separation and washing.

To attach capture antibodies to these beads for digital ELISA, it is most convenient to use beads that present high densities of carboxylic acid groups on the surface of the beads. Functionalization of the bead surface with anti-bodies for sandwich immunoassays involves straightfor-ward carbodiimide chemistry (Hermanson, 2008). The most common cross-linking agent is 1-ethyl-3-(3-dimeth-ylaminopropyl) carbodiimide, also known as EDC or EDAC. This compound is a water-soluble carbodiimide usually obtained as the hydrochloride. It is typically employed in the 4.0–6.0 pH range and is used as a car-boxyl-activating agent for the coupling of primary amines on the antibody to yield amide bonds. EDC can be used in combination with N-hydroxysuccinimide (NHS) or sulfo-NHS to increase coupling efficiency or create a stable amine-reactive product. In routine antibody conjugations to carboxylated beads, EDC coupling without NHS is usually sufficient to yield stable coated beads for digital ELISA.

For preparation of antibody-coated beads for use in digital ELISA, it is advantageous that the majority of the beads remain monomeric so that the beads can fit into microwells that are sized for single beads. Conventional ensemble bead-based immunoassays may not be as sensi-tive to aggregation state, but particular care needs to be taken for digital ELISA. Depending on coating condi-tions and the solubility properties of the coating antibody, beads may exhibit varying amounts of aggregation during antibody coupling. The aggregation state of the beads can be monitored using either a Coulter counter (e.g., the Multisizer from Beckman Coulter) or fluorescence microscopy; the reaction conditions are optimized on the basis of maximizing antibody coupling efficiency while maintaining the bead in a monomeric state. In general, excessive EDC and high antibody concentrations favor cross-linking and bead aggregation. If aggregation is observed, the concentrations of these two reactants may be reduced to produce a largely monomeric bead reagent with high antibody loading (determined using optical density measurements of the antibody solution and typi-cally 50–100 µg/billion beads), resulting in an optimized bead for digital ELISA.

Biotinylation of detector antibodies can be performed readily using commercially available kits that are based on modification of primary amines on the antibodies by NHS-esters of biotin. We usually use kits (e.g., Chroma-Link™ from Solulink) that enable the precise quantifica-tion of the number of biotin groups incorporated on average into the antibodies. The signals and backgrounds in digital ELISA are dependent on the number of biotins incorporated; this number can vary widely with different detection antibodies. By measuring the number of biotin groups, we ensure greater lot-to-lot reproducibility. That said, it is possible to use biotinylation kits that do not quantify the number of biotins (e.g., EZ-Link from Thermo Fisher). In general, we attempt to incorporate as many biotin groups as possible into the antibody without causing its precipitation. Typical biotin incorporation generally ranges from 5 to 9 biotins per antibody, although much lower numbers have been observed to work in assays.

The preparation of SβG conjugate for digital ELISA requires care. Commercial sources of SβG are often aggregated, a state that is not observed in ensemble assays but has a dramatic impact on single molecule assays. Instead of high numbers of wells containing single enzymes as can be achieved using monomeric SβG, an aggregated conjugate will give rise to array images con-taining fewer, brighter wells, and can severely impact the detection efficiency of the assay. We have developed a method for conjugating streptavidin to β-galactosidase that ensures that the majority of conjugates contain only one enzyme molecule (Rissin et al., 2011). Streptavidin and β-galactosidase are conjugated using EDC cross-linking, and HPLC characterization of the conjugate indicated that >80% of the conjugate molecules contained one β-galactosidase molecule, with an average of 1.2 enzymes/conjugate. Comparison to molecular weight standards indicated that the average number of streptavi-din molecules conjugated to each enzyme molecule was 2.7 (Rissin et al., 2011).


ASSAY OPTIMIZATION AND PROTOCOLSOptimization of digital ELISA generally follows standard approaches for maximizing assay dose response while min-imizing background and interferences. In general, a start-ing point of 500,000 beads per reaction is used, and detector and SβG are titrated for signal-to-background of samples in which the target protein is spiked into a suitable blank sample (e.g., bovine serum for human protein tar-gets). Optimal detection antibody and SβG concentrations depend mainly on the on-rate of the detector antibody and incubation times chosen for the binding steps and can be predicted using the equations presented above. Back-ground signals are less predictable, but the lowest possible background is typically achieved using concentrations of the labels that are as low as possible, an approach that may benefit from longer incubation times. A desirable configuration combines analyte capture conditions yield-ing high-binding efficiency (>30%) with labeling efficien-cies minimized to bring the background signals close to the noise floor of the technology, i.e., about 100 “on” beads. For an exemplary full-length research assay (2–3 h), detec-tion antibody and SβG concentrations are 0.05–0.5 µg/mL and 10–50 pM, respectively, resulting in labeling of approx-imately 3% of the captured analyte (Chang et al., 2012a).

Typical digital ELISAs are performed with formation of the capture antibody–protein–SβG complex in 1-, 2-, or 3-step formats. Three-step assays—in which the sample, detection antibody, and enzyme conjugate incubations are done separately with washes in between—are most com-mon and typically provide the greatest sensitivity for anti-bodies with high on-rates. During each wash cycle, a magnet is used to gather beads, the supernatant is aspi-rated by an automated liquid handling robot, the magnet is removed, and then a wash buffer is added to disperse the beads. This cycle is repeated several times for each assay wash step. Following the final wash after enzyme conju-gate, the beads are resuspended in 15–25 µL of buffer con-taining RGP substrate, and the beads are loaded into the femtoliter well arrays. If during assay optimization, a highly sensitive digital ELISA has not been developed suc-cessfully, there are generally two culprits: either poor anti-body kinetics or high background interactions, the latter of which is described in the next section. Where poor on- or off-rates are suspected (or have been measured), higher sensitivity assays can often be achieved using one- or two-step assays. In a two-step format, sample is incubated with both beads and detection antibody (which may follow an initial incubation of sample with beads), the beads are washed, and then incubated with SβG. In a one-step assay, sample, beads, detection antibody, and enzyme conjugate are all combined together. The one- and two-step assays generally result in increased backgrounds but can reduce assay processing times, such that poor antibody kinetics can be overcome.

BACKGROUNDS ARISING FROM REAGENT, MATRIX, AND INTERFERENCE EFFECTSWhile single molecule sensitivity is transformative in terms of detecting fewer and fewer labeled protein mole-cules, if that sensitivity is accompanied by poor specificity,

specific immunoassays cannot be achieved. The molecular interactions that can cause these changes in backgrounds typically arise from three main sources:

� Nonspecific interactions of labeling reagents with the beads in the absence of target protein.

� Interactions of molecules in the matrix (e.g., serum or plasma) that are endogenous and bind to beads, get labeled with enzymes, and give rise to false-positive signals.

� Interactions of molecules in the matrix (e.g., serum or plasma) that are immunologically derived, interact spe-cifically with the capture or detection antibodies and give rise to false-positive or suppressed signals, commonly known as heterophilic antibodies.

Figure 12 shows schematically some of the interactions that can falsely elevate or suppress signals in digital ELISA (or any immunoassay) over specific signals (Fig. 12a). NSB effects (Fig. 12b–e) represent a key challenge limiting sen-sitivity for all immunoassays. Digital ELISA experiments have shown that under most circumstances, the majority of background signals come from the nonspecific interaction of the detection antibody (Fig. 12b–d) and enzyme conju-gate (Fig. 12e) interaction with the bead. In fact, approxi-mately 2/3 of background in digital ELISA is often associated with the detector antibody binding to the “lawn” of capture antibodies coating the bead (Fig. 12d), while approximately 1/3 is associated with SβG binding to the capture bead surface (Fig. 12e). These sources of back-ground can be suppressed through blocking of beads after antibody coupling using suitable proteins (e.g., bovine serum albumin, casein, etc.), high-efficiency washing of beads (number of washes, detergent type, and concentra-tion), generic additives used for reagent incubations (e.g., newborn calf serum [NCS]), and reagent additives designed to block the specific interactions that give rise to back-grounds. In the latter case, backgrounds can be reduced, for example, for a goat antihuman detection antibody by add-ing goat serum or a high concentration of goat IgG to spe-cifically block the interaction sites of the detection antibody on the capture surface.

Beyond these ubiquitous reagent labeling backgrounds, “matrix effects,” i.e., the milieu of potential nonspecific endogenous-binding interactions that can affect the accu-rate quantification of target analyte (Wood, 1991), are also commonly encountered. Included in this milieu are indeterminate nonspecific background protein binding (e.g., albumins, IgG), complement factors, endogenous compounds such as lipids, bilirubin, and hemoglobin, anti-animal antibodies (interacting with animal-derived immu-noassay reagents), rheumatoid factors, fibrin, etc. (Selby, 1999). Reducing the population of this cast of unwanted participants from the outset can result in a significant improvement in analyte quantification accuracy. For example, an analyte that exhibits affinity for albumin may exhibit under-recovery when assayed undiluted, but pre-dilution results in higher recovery (as would be uncovered in a dilution linearity study). We have found that a simple sample pre-dilution of 1/4 in PBS buffer containing 0.1% Tween-20 prior to the addition of sample to beads can greatly improve dilution recovery. If the specific


interactions that give rise to increased or suppressed sig-nals are identified then it is generally possible to eliminate or reduce these interactions. For example, we discovered that complement activation in serum suppressed the sig-nals in a digital ELISA for TNF-α (Song et al., 2011). To reduce suppression, we switched to testing this analyte in plasma.

Potential sample-associated bridging mechanisms include heterophilic antibodies (Kricka, 1999) (Fig. 12f) and other potential nonspecific, cross-reactive substances (Fig. 12g) (Bartels et al., 2011). In addition to bridging conjugates for false elevation of signals, heterophilic and nonspecific or cross-reacting substances can block conju-gate capture by the beads by various mechanisms, leading to falsely suppressed signals (Fig. 12h–l). Heterophilic antibody interactions (Fig. 12h–i) are managed by inclu-sion of commercial heterophilic blockers in the sample and/or bead diluent. We have also used a pre-incubation of sample with a protein G column to remove all antibod-ies from the sample and eliminate a high background derived from heterophilic antibodies (Song et al., 2011). Effects from nonspecific substances (Fig. 12j–l) are man-aged through sample pre-dilution, diluent additives, such as newborn calf serum, and high-efficiency bead washing.

In general, we use simple control experiments to iden-tify the molecular interaction or interactions that cause increases in background or inexplicable increases in signal. Once these interactions are identified, it is usually possible to mitigate their effects and develop a highly sensitive digi-tal ELISA. We also note that the high sensitivity of digital ELISA can, in fact, be a strategy for minimizing matrix and heterophilic interferences. Dilution often rapidly reduces these unwanted interactions in immunoassays, and digital ELISA often has an excess of sensitivity to allow significant

dilution while maintaining the sensitivity and dynamic range to the target protein of interest.

DOSE RESPONSE AND LINEARITYDigital ELISAs typically exhibit linear dose responses down to background. Figure 5 shows a representative dose response for an investigational digital immunoassay for PSA across three and a half logs of range. The signal-to-background was 4.33 (SD 0.76) at 0.1 pg/mL in a study of 20 such calibration curves over 10 days (Wilson et al., 2011). Linearity, conducted with guidance from CLSI protocol EP6-A (Clinical Laboratory Standards Institute, 2003), was evaluated with admixtures of female serum exhibiting relatively high and very low PSA levels. Linear and third order polynomial fit goodness was virtually iden-tical (R2 = 0.988 and 0.990, respectively). The deviation from linearity between the two models was within 5% across the range.

SENSITIVITYFigure 13 depicts the profile of coefficients of variation (CVs) obtained for the investigational digital PSA assay (Wilson et al., 2011). CV profiles represent conversion of signal standard deviation into concentration standard deviation and can be generated using the equation repre-senting the best fit of the calibration curve. With conven-tional immunoassays, as dose–response becomes lost in background at the low end of the dynamic range and can taper off at the high end of the dynamic range, measure-ment variation can convert into rapidly increasing concen-tration CVs at both ends of the range. Generally, the task of the assay developer is to optimize the assay response to

A

Y

Streptavidin-β-galactosidase

Biotinylated detection antibody

Capture antibody

Heterophilic antibody

Blocking protein

Interfering or cross reactivesubstance

YY

Analyte

Background &

false elevation

False

suppression

Key

FIGURE 12 Sources of nonspecific-binding interactions that can lead to increases or suppression of signals in digital ELISA and other immunoassays. (The color version of this figure may be viewed at www.immunoassayhandbook.com).


maximize quantification precision in the most clinically important areas of the dynamic range; often conditions that favor good dose response in one portion of the range come at the expense of another.

Due to the linear dose–response and robust signal-to-background ratio, the CV profile for the digital PSA assay remains flat across the range, finally turning up in the fem-tograms per milliter range. The data reflect replicate CVs (n = 3) obtained over a 6 week period of testing calibrators, controls, and female serum samples. A nonlinear power fit was applied to the data to estimate the point at which con-centration variation reached 20%, typically taken as an assays limit of quantification (LOQ). From the equation of the power fit, the LOQ was calculated as 0.0352 pg/mL (standard error = 0.0340–0.0387 pg/mL). This LOQ is 100-fold more sensitive than conventional 3rd generation ultrasensitive immunoassays for PSA.

REPRODUCIBILITYBecause digital ELISA counts molecules and is insensitive to the variation in signal output from individual enzyme molecules in the digital range, good reproducibility per-formance is expected. A demonstration of reproducibility is depicted in Fig. 14 with guidance from CLSI EP5-A2 (Clinical Laboratory Standards Institute, 2004). Four sam-ples, consisting of PSA spiked into 25% NCS, were assayed in triplicate in each of two runs per day for 10 days (n = 60 for each sample). The lowest sample was prepared near the LOQ (0.035 pg/mL). Because each reportable result is based on triplicate measurements, this protocol gave two results per day for each sample. The plate map was config-ured such that each PSA result spanned multiple columns, such that replicates included variation from different

groups of arrays. PSA results were calculated from within-plate calibration curves. Thus, the overall study compre-hended array processing variation, calibration variation, and within-run, between-run, and day-to-day variation. Total CVs across all variation sources were less than 10% from 1 to 52 pg/mL PSA. The total CV for the 0.04 pg/mL sample was 18.3%, consistent with the LOQ estimate (20% CV at 0.035 pg/mL).

ACCURACYDetermining the accuracy of digital ELISA can be chal-lenging because no other gold standard method exists at subfemtomolar concentrations to which the technology can be compared. Comparison to conventional immunoas-says, therefore, necessitates sample pre-dilution using higher abundance analytes. PSA again provided a conve-nient test of accuracy as conventional immunoassays for PSA measure concentrations in the nanograms per millili-ter range, six logs higher than the LOQ for a PSA digital ELISA (Wilson et al., 2011). Standardization of the digital PSA assay with WHO standards permitted a direct com-parison to a commercially available assay for PSA stan-dardized to the same set of WHO standards. As a demonstration of the commutability of PSA digital ELISA results to results from a conventional immunoassay, 40 serum samples from normal males and eight serum sam-ples from patients who had undergone radical prostatec-tomy (but with PSA levels high enough for measurement in the conventional method) were assayed with both meth-ods (Fig. 15). All samples were diluted 100-fold prior to testing in the PSA digital ELISA. The assays exhibited no discernable differences throughout the range of results (0.17 to >13 ng/mL, mean bias 0.024 ng/mL).

FIGURE 13 Within-run imprecision of digital ELISA for PSA based on sample replicate CVs determined over 6 weeks. (The color version of this figure may be viewed at www.immunoassayhandbook.com). Figure adapted with permission from Ref. Wilson et al. (2011) (Copyright of the American Association for Clinical Chemistry, 2011).


CALIBRATIONThe number and positioning of calibrators for digital ELISA depends on the desired use of the assay. As with any assay, accuracy is generally related to an accurate rep-resentation of the dose response characteristics with well-placed calibrators, and a close approximation of the dose response through appropriate curve fitting. If high accu-racy is needed at extremely low analyte concentration, additional calibrators may be placed in this area to

minimize rapidly magnifying calibration read-back error on a percentage basis. An excellent discussion of calibra-tion curve fitting can be found elsewhere in this book (see CALIBRATION CURVE FITTING).

ASSAY SPEEDAs described in the THEORETICAL CONSIDERATIONS section, the use of beads to capture and label, and the counting of single molecules, means that the high efficiency of digital

FIGURE 14 Day-to-day imprecision of digital ELISA for PSA for twenty 96-well plates run over a period of 10 days. (The color version of this figure may be viewed at www.immunoassayhandbook.com). Figure adapted with permission from Ref. Wilson et al. (2011) (Copyright of the American Association for Clinical Chemistry, 2011).

FIGURE 15 Accuracy of digital ELISA for PSA compared to a standard immunoassay. (The color version of this figure may be viewed at www.immunoassayhandbook.com). Figure adapted with permission from ref. Wilson et al. (2011) (Copyright of the American Association for Clinical Chemistry, 2011).


ELISA can be leveraged to increase the speed of assays. As opposed to analog approaches to immunoassays, where typically the binding events have to be driven to equilib-rium in order to create enough labeled immunocomplexes to reach the detection limit of ensemble measurements of enzyme, digital ELISA is kinetically controlled to stay within its dynamic range. This kinetic control can be achieved by either using low concentrations of labeling reagents and relatively long incubation times to minimize backgrounds (Rissin et al., 2010) or using higher concen-trations and short incubations (see Table 2) (Chang et al., 2012a). The unique kinetic properties of digital ELISA provide the intriguing possibility, therefore, to leverage high sensitivity to perform very rapid (seconds to minutes) assays for analytes that are present at relatively high con-centrations. Optimizing for speed, however, requires attention to the potential for increasing background from higher label concentrations.

InstrumentationThe instrumentation needed to perform digital ELISA is conceptually straightforward, and many aspects of the instruments build from well-established technologies. The instrumentation required for performing digital ELISA is as follows:

� A liquid handling robot capable of performing the incu-bation and wash steps involving paramagnetic beads.

� A “load-seal-image” (LSI) module to load the beads into arrays of femtoliter wells, to seal the arrays to iso-late single molecules, and to image the arrays to deter-mine the location of beads and enzymes.

� Arrays of femtoliter-sized wells, i.e., the SiMoA consumable.

Liquid handling robots for the precise handling of para-magnetic beads in individual, serially processed cuvettes are ubiquitous in the immunodiagnostics industry. A fully automated digital ELISA system, therefore, can be devel-oped and refined from the “front end” of these systems, and, in part, by replacing the detection module of these systems, e.g., a luminometer, with an LSI module. It is also possible to adapt commercial, research-based liquid han-dling robots (e.g., the EVO series from Tecan) to perform the liquid handling steps using magnets placed on the deck of the robot. Our first generation assay system made use of a Tecan system and processed samples in a 96-well format. One challenge to this approach is dissociation of detection antibody that can reduce the signal from the start of the plate to the end resulting in high CVs for across-plate rep-licates (Chang et al., 2012). This problem can be addressed by drying beads in arrays of wells in the presence of sucrose, essentially “freezing” the immunocomplex in place, and allow variations in time from removal of detection anti-body and SiMoA readout. In our second generation, a fully automated system made use of cuvette-based processing in which each sample is treated identically in terms of incuba-tion time, temperature, etc., negating the deleterious effect of antibody dissociation and so-called “plate effects.” The ability to use existing liquid handling robots for digital ELISA also means that the technology is compatible with

the current immunodiagnostic workflows in hospital and reference laboratories, e.g., highly automated serum tube track processors.

The LSI module is unique to digital ELISA but again builds on existing technologies. One of the beauties of the technique is that it achieves single molecule sensitiv-ity by making the enzymes do the hard work, shifting the sensitivity burden away from the detection hardware. Rather than trying to detect 1–6 fluorophores on a single molecule (as has been tried commonly in the past) (Todd et al., 2007; Nalefski et al., 2006; Tessler et al., 2009), in SiMoA each enzyme label generates thousands of fluoro-phores that are confined in a very small volume meaning that signal from a single molecule can be detected with a standard microscope, i.e., one equipped with a white light source or LEDs and an inexpensive CCD camera. Lasers and expensive, cooled detectors are not needed to achieve single molecule sensitivity. This fact reduces the com-plexity and cost of the instrumentation. SiMoA also addresses the challenges of signal-to-background that are common in single molecule detection. Trying to detect a few fluorophores on a single molecule is a challenge because any background fluorescence can swamp the sin-gle molecule signal. This problem has lead to significant efforts to develop low fluorescence materials and mini-mize the impact of environmental factors, e.g., dust, on background fluorescence. SiMoA does not suffer from such problems as the signal from a single enzyme in 50 fL is much greater than any endogenous fluorescence. As a result, SiMoA can be performed using several common substrates (e.g., glass and some plastics) and does not require a clean environment. Our first generation system, in fact, was based on off-the-shelf microscope objectives and a low-cost CCD camera and operated in the open laboratory.

The arrays of femtoliter wells that form the SiMoA con-sumable can be manufactured using a variety of methods common in the microfabrication industry. In our first gen-eration system, the arrays were fabricated by etching fiber optical bundles, where the core glass etched faster than the cladding glass to create microwell depressions (Pantano and Walt, 1996); these arrays had been used previously in the genomics industry for gene expression profiling of ran-dom bead arrays (Kuhn et al., 2004). The arrays were arranged in rows of 8 on the same pitch as a 96-well microtiter plate to facilitate simple liquid handling; these arrays could be reused via polishing and re-etching. Glass arrays are, however, relatively expensive, prone to break-age, and the sealing method—active force applied between the arrays and a sheet of silicone gasket—is not readily amenable to automation.

In our second generation, fully automated system, we have developed an array consumable that addresses the limitations of the glass arrays (Kan et al., 2012). This con-sumable is composed of a disk made of two pieces of cyclic olefin polymer molded using a DVD manufactur-ing method developed by Sony DADC (Fig. 16A). One half of the disk has 24 arrays of femtoliter wells molded into it, and each array contains 216,000 40 fL volume microwells. The other half of the disk has fluidic chan-nels and through-holes molded into it to allow the deliv-ery of beads to the arrays by an automated pipetting system. The two halves of the disks are laser bonded


together to make a single contiguous part that can be convenient stacked for packaging and insertion into the instrument. The arrays are low cost (cost per array in the order of 10 s of cents can be achieved at high volume manufacture), robust, and are well suited to handling by automated systems.

An additional benefit to the disk is that it enables seal-ing of the wells in a method that is more readily compat-ible with automated systems. In this process, a suspension of beads in RPG is delivered into the fluidic channel through an entry hole and flow across the array by nega-tive pressure applied at an outlet vent at the end of the channel. The beads fall into the wells by gravity (they have a high specific gravity because of the iron content). Fluorinated oil is then injected into the fluidic channel again using an automated pipettor. The oil serves two purposes. First, it pushes the beads on the surface of the array that did not fall into wells away in the aqueous phase, thereby avoiding any interference from bulk signal of an ensemble of beads. Secondly, it confines the RPG solution and bead in the femtoliter wells, sealing them off and stopping diffusion of the product of the enzyme–substrate reaction out of the well. The aqueous solution in the well is extremely well trapped by the oil and no leaking of signal is observed. Furthermore, optical cross-talk between adjacent wells—measured using

fluorescent beads—is very low; each well is essentially an isolated femtoliter reactor. All of these processes can be achieved using automated, liquid handling steps, enabling a fully-automated system to be developed.

The simplicity and design of the SiMoA method has enabled us to develop (in collaboration with Stratec Bio-medical AG) a fully automated instrument that fits in with the current workflows of the immunodiagnostics industry; a schematic of the instrument is shown in Fig. 16B. The system allows the user to load samples, reagents, and con-sumables onto the instrument, and then automatically pro-cesses each sample individually in a cuvette using a liquid handling robot. The system schedules the liquid handling processes to occur such that each sample experiences the same incubation and detection times. The robot then delivers beads into the SiMoA disk and seals the wells up with oil. Customized optics have been developed to image 200,000 wells of the array, allowing greater dynamic range to be accessed. Software also enables analysis of the images and conversion of AEB to concentration via calibration samples and curve fitting.

ApplicationsThe high sensitivity of digital ELISA opens numerous diagnostic and research applications, as well as other improvements in how immunoassays are performed. It is important to point out, however, that in most instances digital ELISA is a validation and diagnostic technology and not a discovery technology: antibodies are needed to perform the assay, so the protein has to have been discov-ered first. The current bottleneck in clinical proteomics research is, however, not discovery. Mass spectrometry, gene expression profiling, and next-generation sequencing has resulted in the discovery of many putative protein (or gene product) markers, mostly by identifying over-expressed molecules in tissue samples. When antibodies are generated in order to develop immunoassays to mea-sure these markers in more accessible bodily fluids (e.g., blood), it is often the case that the assays are not sensitive enough, and promising markers are left to flounder. Digi-tal ELISA could bridge this “translational gap” by enabling the measurement of these new markers in blood and result in the development of more clinical assays for proteins and, ultimately, approval of more immunodiagnostic tests.

Beyond validating new protein biomarkers, the greater sensitivity of digital ELISA should be a powerful method for making the measurement of current, well-established proteins more clinically useful. For examples, much greater sensitivity to proteins using digital ELISA can be used to:

� Develop “blood tests for the brain.” Simple blood tests for neurological disorders do not exist today because the tight blood–brain barrier (BBB) does not allow suffi-cient concentrations of proteins to cross from the cen-tral nervous system into systemic circulation. To probe the biochemistry of the brain, magnetic resonance imaging or spinal taps to obtain cerebral spinal fluid (CSF) are needed. Digital ELISA opens the possibility of detecting in systemic blood the small amounts of proteins that cross the BBB.

216,000-wellarray

fluidic inletport

vent port

500 µm-deepchannel

(a)

(b)

FIGURE 16 (a) SiMoA disk. (The color version of this figure may be viewed at www.immunoassayhandbook.com). (Figure adapted with permission from ref. Kan et al. (2012) [Reproduced by permission of The Royal Society of Chemistry, 2012; http://dx.doi.org/10.1039/C2LC20744C].) (b) Schematic model of the fully automated SiMoA instrument (Image provided courtesy of Continuum [Boston, MA]).


� We have measured the concentrations of proteins that are the hallmark of Alzheimer’s disease (Aβ1–42) (Zetterberg et al., 2011) and neurological damage (tau) (Randall et al., 2012) in the plasma of patients who have undergone hypoxia after a heart attack. The concentrations of these proteins were in the low to sub-picogram per milliliter levels and were correlated to long-term cognitive outcome that was not detected by clinical observations in the emer-gency room.

� Enable the earliest detection of the proteins from viruses and bacteria that are the pathogenic agents that cause the symptoms of infectious diseases. Today, only amplifi-cation-based nucleic acid testing (NAT, using, for example, the polymerase chain reaction)—which is complex and expensive—is sensitive enough to detect viruses and bacteria at the earliest stages of infection. Nature provides a natural “amplification,” however, by producing thousands of copies of proteins for each gene copy in a virus or bacterium. Combining this amplification with 1000-fold improvements in IA sen-sitivity of digital ELISA makes it possible to detect viruses and bacteria at the levels (<100 viruses per mil-liliter) that was only possible using NAT. Furthermore, an IA provides information on the pathogenicity of the virus or bacteria that is not possible using gene identification.

� We have measured the p24 capsid protein of HIV down to 5 fg/mL, equivalent to about 50 copies of virus per mL (Chang et al., 2012b). Measurement of the protein in the serum of individuals with early HIV infection showed that digital ELISA could detect infection as early as NAT.

� The reliable and early detection of cancer recurrence. A com-mon treatment for cancer is to surgically remove the source of cancer. After these surgeries, the concentra-tions of protein biomarkers that were expressed by the tumor drop below the detection limits of traditional IAs. With digital ELISA, it is possible to detect the residual amounts of these proteins post-surgery and to monitor increases in their concentration that would indicate that the cancer had not been completely removed or had returned. The method could provide prognostic predictions of outcomes, as well as early detection of recurrence.

� We have shown that digital ELISA can be used to predict the prognostic outcome for patients with prostate cancer after radical prostatectomy based on the measurement of PSA months after surgery rather than years as is possible with current “ultra-sensitive” PSA tests (Wilson et al., 2011; Lepor et al., 2011).

� Enable the monitoring of the efficacy of the next generation of antibody therapeutics. Antibodies are proving to be highly efficacious drugs. It is the case, however, that current technologies are not capable of measuring the concentrations in blood of the proteins that these anti-bodies bind to and clear from the body. Clinicians, therefore, do not have convenient biochemical markers with which to determine the efficacy of the treatment or to monitor patient compliance. Digital ELISA allows the detection of these proteins (with appropriate

treatments to avoid assay interference from the drug itself or antibodies that are produced by the body in response to these treatments), so that changes in their concentrations can be monitored during a course of treatment or during initial clinical trials.

� We have shown that digital ELISA could measure small changes in the concentration of TNF-α in Crohn’s disease patients during a course of anti-TNF-α drug (Song et al., 2011).

� Enable the early detection of disease. The ability to mea-sure low concentrations of proteins should make it pos-sible to detect the onset of diseases earlier. For examples, the measurement of low concentrations of specific protein markers of cancer (e.g., CA-125) and cardiovascular disease (e.g., troponin) in healthy indi-viduals could presage the development of chronic diseases.

� Enable the detection of proteins from the fetus in maternal blood. Molecules from the fetus are known to cross into maternal blood but at much lower concentrations. Dig-ital ELISA could be used to detect these proteins non-invasively and determine the biochemical developmental status of the fetus without the use of amniocentesis.

� Detect proteins in alternative bodily fluids where protein is diluted compared to fluids that are more difficult to obtain. For example, the detection of proteins in urine or saliva, which is easier to obtain than blood, but in which we expect protein concentrations to be reduced.

� Enable the detection of proteins in samples of very low vol-umes. In many applications, the volume of sample that is available for testing is much lower (0.001–0.01 mL) than is available from a blood draw (milliliter). For examples: in the development of pharmaceuticals, sam-ples are often obtained from animal models based on small mammals; testing of neonatal blood that gener-ates small volumes of sample. The sensitivity of digital ELISA enables these low volume samples to be diluted by a factor of 10 or 100 to enable testing but still remain within the LODs of the method.

FutureWith single molecule sensitivity, high throughput, full automation, competitive costs for instruments and con-sumables, and a host of potential applications, digital ELISA should have applications in life and medical science research, biopharmaceuticals, and clinical diagnostics for years to come. The technology, however, is still in its infancy and single molecule capabilities will likely drive innovations in the development of immunoassays for the next 50 years. Once the technology is widely employed amongst clinical researchers, these researchers may dis-cover new applications for digital ELISA that have not been dreamed of yet. The method is amenable to innova-tions to improve the number of proteins that can be detected simultaneously (multiplexing) and for further improvements in sensitivity, throughput, and dynamic range. Most exciting is the potential to leverage continu-ing improvements in miniaturization to develop integrated devices that will allow digital ELISA to be performed on a single microfluidic chip (Kan et al., 2012). Such a device


could enable the development of a holy grail of IA tech-nologies: a sensitive, reliable system that can be used at the point of care by nontechnical personnel.

References and Further ReadingArmani, A.M., Kulkarni, R.P., Fraser, S.E., Flagan, R.C. and Vahala, K.J. Label-

free, single-molecule detection with optical microcavities. Science 317, 783–787 (2007).

Bartels, E.M., Wätjen, I., Andersen, E., Danneskiold-Samsøe, B., Bliddal, H. and Ribel-Madsen, S. Rheumatoid factor and its interference with cytokine mea-surements: problems and solutions. Arthritis 2011, 741071 (2011).

Chang, L., Rissin, D.M., Fournier, D.F., Piech, T., Patel, P.P., Wilson, D.H. and Duffy, D.C. Single molecule enzyme-linked immunosorbent assays: theoretical considerations. J. Immunol. Meth. 378, 102–115 (2012a).

Chang, L., Song, L., Fournier, D.R., Kan, C.W., Patel, P.P., Ferrell, E.P., Pink, B.A., Minnehan, K.A., Hanlon, D.W., Duffy, D.C. and Wilson, D.H., Simple diffusion-constrained immunoassay for p24 protein with the sensitivity of nucleic acid amplification for detecting acute HIV infection. J Virol Methods. 2012 Oct 2. pii: S0166-0934(12)00296-0. doi: 10.1016/j.jviromet.2012.08.017. [Epub ahead of print].

Clinical Laboratory Standards Institute. Evaluation of the Linearity of Quantitative Measurement Procedure: A Statistical Approach; Approved Guideline. 2nd edn. CLSI Document EP6-A, 2003.

Clinical Laboratory Standards Institute. Evaluation of Precision Performance of Quantitative Measurement Methods; Approved Guideline. 2nd edn. CLSI Document EP5–A2, 2004.

Craig, D.B., Arriaga, E.A., Wong, J.C.Y., Lu, H. and Dovichi, N.J. Studies on single alkaline phosphatase molecules: reaction rate and activation energy of a reaction catalyzed by a single molecule and the effect of thermal denaturation—the death of an enzyme. J. Am. Chem. Soc. 118, 5245–5253 (1996).

Davies, C. Introduction to immunoassay principles. In: The Immunoassay Handbook, 3rd edn (ed Wild, D.), 3–37 (Elsevier Ltd., Oxford, 2005).

Ferguson, R.A., Yu, H., Kalyvas, M., Zammit, S. and Diamandis, E.P. Ultrasensitive detection of prostate-specific antigen by a time-resolved immunofluorometric assay and the immulite immunochemiluminescent third-generation assay: potential applications in prostate and breast cancers. Clin. Chem. 42, 675–684 (1996).

Gorris, H.H., Rissin, D.M. and Walt, D.R. Stochastic inhibitor release and binding from single-enzyme molecules. Proc. Nat. Acad. Sci. 104, 17680–17685 (2007).

Hermanson, G.T. Bioconjugate Techniques. 2nd edn, (Academic Press, 2008).Kan, C.W., Rivnak, A.J., Campbell, T.G., Piech, T., Rissin, D.M., Mösl, M.,

Peterça, A., Niederberger, H.P., Minnehan, K.A., Patel, P.P., Ferrell, E.P., Meyer, R.E., Chang, L., Wilson, D.H., Fournier, D.R. and Duffy, D.C. Isolation and detection of single molecules on paramagnetic beads using sequential fluid flows in microfabricated polymer array assemblies. Lab. Chip 12, 987–995 (2012).

Karlsson, R., Michaelsson, A. and Mattsson, L. Kinetic analysis of monoclonal antibody-antigen interactions with a new biosensor based analytical system. J. Immunol. Methods 145, 229–240 (1991).

Kourilov, V. and Steinitz, M. Magnetic-bead enzyme-linked immunosorbent assay verifies adsorption of ligand and epitope accessibility. Anal. Biochem. 311, 166–170 (2002).

Kricka, L.J. Human anti-animal antibody interference in immunological assays. Clin. Chem. 45, 942–956 (1999).

Kuhn, K., Baker, S.C., Chudin, E., Lieu, M.H., Oeser, S., Bennett, H., Rigault, P., Barker, D., McDaniel, T.K. and Chee, M.S. A novel, high-performance ran-dom array platform for quantitative gene expression profiling. Genome Res. 14, 2347–2356 (2004).

Lepor, H., Cheli, C.D., Thiel, R.P., Taneja, S.S., Laze, J., Chan, D.W., Sokoll, L.J., Mangold, L. and Partin, A.W. Clinical evaluation of a novel method for the measurement of prostate-specific antigen, AccuPSA(TM), as a predictor of 5-year biochemical recurrence-free survival after radical prostatectomy: results of a pilot study, BJU Int. (2011). Oct 12, doi: 10.1111/j.1464-410X.2011.10568.x.

Nalefski, E.A., D’Antoni, C.M., Ferrell, E.P., Lloyd, J.A., Qiu, H., Harris, J.L. and Whitney, D.H. Single-molecule detection for femtomolar quantification of proteins in heterogeneous immunoassays. Clin. Chem. 52, 2172–2175 (2006).

Pantano, P. and Walt, D.R. Ordered Nanowell arrays. Chem. Mater. 8, 2832–2835 (1996).

Randall, J., Mörtberg, E., Provuncher, G.K., Fournier, D.R., Duffy, D.C., Rubertsson, S., Blennow, K., Zetterberg, H. and Wilson D.H., Tau proteins in serum predict neurological outcome after hypoxic brain injury from cardiac arrest: Results of a pilot study, Resuscitation, 2012 Aug 9. [Epub ahead of print].

Rissin, D.M. and Walt, D.R. Digital concentration readout of single enzyme mol-ecules using femtoliter arrays and Poisson statistics. Nano Lett. 6, 520–523 (2006a).

Rissin, D.M. and Walt, D.R. Digital readout of target binding with attomole detec-tion limits via enzyme amplification in femtoliter arrays. J. Am. Chem. Soc. 128, 6286–6287 (2006b).

Rissin, D.M., Kan, C.W., Campbell, T.G., Howes, S.C., Fournier, D.R., Song, L., Piech, T., Patel, P.P., Chang, L., Rivnak, A.J., Ferrell, E.P., Randall, J.D., Provuncher, G.K., Walt, D.R. and Duffy, D.C. Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concen-trations. Nat. Biotechnol. 28, 595–599 (2010).

Rissin, D.M., Fournier, D.R., Piech, T., Kan, C.W., Campbell, T.G., Song, L., Chang, L., Rivnak, A.J., Patel, P.P., Provuncher, G.K., Ferrell, E.P., Howes, S.C., Pink, B.A., Minnehan, K.A., Wilson, D.H. and Duffy, D.C. Simultaneous detection of single molecules and singulated ensembles of molecules enables immunoassays with broad dynamic range. Anal. Chem. 83, 2279–2285 (2011).

Rondelez, Y., Tresset, G., Tabata, K.V., Arata, H., Fujita, H., Takeuchi, S. and Noji, H. Microfabricated arrays of femtoliter chambers allow single molecule enzymology. Nat. Biotechnol. 23, 361–365 (2005).

Selby, C. Interference in immunoassay. Ann. Clin. Biochem. 36, 704–721 (1999).Song, L., Hanlon, D.W., Chang, L., Provuncher, G.K., Kan, C.W., Campbell,

T.G., Fournier, D.R., Ferrell, E.P., Rivnak, A.J., Pink, B.A., Minnehan, K.A., Patel, P.P., Wilson, D.H., Till, M.A., Faubion, W.A. and Duffy, D.C. Single molecule measurements of tumor necrosis factor α and interleukin-6 in the plasma of patients with Crohn’s disease. J. Immunol. Methods 372, 177–186 (2011).

Tessler, L.A., Reifenberger, J.G. and Mitra, R.D. Protein quantification in complex mixtures by solid phase single-molecule counting. Anal. Chem. 81, 7141–7148 (2009).

Todd, J., Freese, B., Lu, A., Held, D., Morey, J., Livingston, R. and Goix, P. Ultrasensitive flow-based immunoassays using single-molecule counting. Clin. Chem. 53, 1990–1995 (2007).

Wilson, D.H., Hanlon, D.W., Provuncher, G.K., Chang, L., Song, L., Patel, P.P., Ferrell, E.P., Lepor, H., Partin, A.W., Chan, D.W., Sokoll, L.J., Cheli, C.D., Thiel, R.P., Fournier, D.R. and Duffy, D.C. Fifth-generation digital immuno-assay for prostate-specific antigen by single molecule array technology. Clin. Chem. 57, 1712–1721 (2011).

Wood, W.G. “Matrix effects” in immunoassays. Scand. J. Clin. Lab. Invest. Suppl. 205, 105–112 (1991).

Xue, Q. and Yeung, E.S. Differences in the chemical reactivity of individual mol-ecules of an enzyme. Nature 373, 681–683 (1995).

Zetterberg, H., Mortberg, E., Song, L., Chang, L., Provuncher, G.K., Patel, P.P., Ferrell, E.P., Fournier, D.R., Kan, C.W., Campbell, T.G., Meyer, R., Rivnak, A.J., Pink, B.A., Minnehan, K.A., Piech, T., Rissin, D.M., Duffy, D.C., Rubertsson, S., Wilson, D.H. and Blennow, K. Hypoxia due to cardiac arrest induces a time-dependent increase in serum amyloid β levels in humans. PLoS One 6(12), e28263 (2011).

the immuassay handbook parte30

Health & Medicine