Research ArticleBIOCHEMISTRY

Multiplexed single-molecule enzyme activity analysis for counting disease-related proteins in biological samples

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Science Advances  11 Mar 2020:
Vol. 6, no. 11, eaay0888
DOI: 10.1126/sciadv.aay0888


We established an ultrasensitive method for identifying multiple enzymes in biological samples by using a multiplexed microdevice-based single-molecule enzymatic assay. We used a paradigm in which we “count” the number of enzyme molecules by profiling their single enzyme activity characteristics toward multiple substrates. In this proof-of-concept study of the single enzyme activity–based protein profiling (SEAP), we were able to detect the activities of various phosphoric ester–hydrolyzing enzymes such as alkaline phosphatases, tyrosine phosphatases, and ectonucleotide pyrophosphatases in blood samples at the single-molecule level and in a subtype-discriminating manner, demonstrating its potential usefulness for the diagnosis of diseases based on ultrasensitive detection of enzymes.


Cellular functions are mediated by the activities of diverse enzymes, and hence, determining the functional changes that occur in these enzymes during pathogenesis is crucial for understanding and detecting diseases (1). However, the detection sensitivity of conventional assays for discovering and using enzyme biomarkers for diagnosis needs to be improved. In case of DNA and RNA analysis, enhancing the sensitivity of detection to the single-molecule level has revolutionized biomarker discovery and usage (2). However, the detection methods for proteins, which are thought to contain more functionality-oriented information that can be directly linked to the phenotypes, are yet to attain such a high degree of sensitivity (3).

In this study, we developed a novel assay platform for comprehensively detecting multiple enzymes in biological samples at single protein level for ultrasensitive and quantitative profiling of the disease-related enzymatic activities. This method is based on single-molecule enzyme analysis performed in a microfabricated chamber device, in which single-molecule enzymes in a diluted biological sample are separately loaded into individual microchambers for measuring and detecting its activity (4, 5). Although conventional single-molecule analysis is commonly used to study the biochemical properties of specific enzymes, their application for analyzing biological samples containing complex mixtures of characterized and uncharacterized proteins remains challenging, as it is difficult to predict which enzyme is loaded into each chamber due to random distribution. In this study, we aimed to overcome this limitation by establishing a multisubstrate-based activity profiling strategy by which a single-molecule enzyme in the chamber is identified in terms of its activity parameters toward multiple fluorogenic substrates (Fig. 1). The development of multiple, differentially colored, and reactivity-wide substrates enabled this assay system to discriminately “count” the enzymes in a comprehensive manner.

Fig. 1 Schematic view of SEAP.

In this assay, a mixture of enzymes from biosamples were diluted and separately loaded into microwells of the device, and the numbers of targeted enzyme species were counted using multiplexed single-molecule enzymatic assays, in which the reactivities toward differentially colored and structure-wide substrates are used to characterize the enzyme species in each well.

We performed a proof-of-concept study of single enzyme activity–based protein profiling (SEAP); we developed sets of fluorogenic substrates to detect various phosphoric ester–hydrolyzing enzymatic activities in blood samples at the single-molecule level. We confirmed that the assay was able to discriminately detect enzymes in the blood samples of patients at different pathological stages, as inferred from their altered enzyme activity profiles. This demonstrated the potential usefulness of this method for finding and using enzyme biomarkers in a highly sensitive and selective manner.


Preparation of substrates for the detection of various phosphatases in a microfabricated chamber device

We first established a system to discriminately detect serum phosphatase activities using a microfabricated chamber device. Detection of alkaline phosphatase (ALP) activity in blood samples is commonly used for the diagnosis of several diseases. For instance, ALP levels are elevated during pregnancy and osteogenesis, as well as various diseases such as liver dysfunction and cancer (6, 7). There are four isozymes of ALP, including tissue nonspecific (TNAP), intestinal (ALPI), placental (ALPP), and embryonic (ALPPL2) types, which are present along with other phosphatases, such as acid phosphatases (8) and protein phosphatases (9, 10). As these proteins have different origins, it is required to discriminately detect them in the blood samples to facilitate precise diagnosis. However, it is challenging to do this using standard substrate-based assays (11, 12), as some of these proteins are structurally similar to each other and the populations of some enzymes are too low to be detected under bulk conditions.

We developed a set of fluorogenic substrates for the SEAP of phosphatases based on the following criteria: (i) The probes should be sufficiently suitable for the microdevice-based assay, (ii) the probes should be composed of a set of multiple colors to enable simultaneous activity measurement, and (iii) the reactivity of the probes should be distinctive toward different enzymes and their subtypes.

Regarding point (i), the development of the fluorogenic substrates suitable for the assay is important for the reliable quantification of enzymatic activity. We found few commercially available fluorogenic substrates that meet this criterion; the commercially available 4-methylumbelliferyl phosphate (4MU-Phos), a representative ALP substrate (13), was able to detect the activity of ALP in the microchamber, although the signals were faint, which made the quantification of the activity difficult (Fig. 2A, left). We hypothesized that this might be due to the leakage of the hydrophobic fluorescent product (4MU) from the chambers into the surrounding oil layers. This is a common problem in microdevice-based assays, as most of the instruments are composed of relatively hydrophobic materials such as organic polymers and lipids (14). To overcome this problem, we designed a novel ALP probe by adding carboxylic acid to increase its hydrophilicity. This probe was prepared based on 7-hydroxycoumarin-3-carboxylic acid (HCCA; scheme S1), and using this, we could successfully detect ALP activity in the microfabricated chamber device (Fig. 2A, right; figs. S1 and S2; and discussion S1). The strategy for increasing the hydrophilicity of the fluorophore can be generally applied to the fluorogenic probes for monitoring enzymatic activities using microdevices. We extended this approach to design probe scaffolds for different color channels (green = 500 to 540 nm and orange-red = 610 to 700 nm) other than that of HCCA-Phos (blue = 430 to 470 nm).

Fig. 2 Development of a set of fluorogenic probes to discriminate various phosphatases.

(A) Fluorescence images of a microdevice containing ALP (from P. pastoris, λ = 0.1, where λ is the existence probability of a protein molecule per well) with fluorescence probes (4MU-Phos and HCCA-Phos, 100 μM) in tris-HCl buffer [100 mM (pH 7.4), containing 1 mM MgCl2 and 0.1% (w/w) CHAPS] after 50-min incubation. (B) Top: Fluorescence spectra of sTG-Phos and sTM-Phos (1 μM) after incubation with or without ALP (from P. pastoris) in diethylamine (DEA) buffer [1 M, containing 1 mM MgCl2 (pH 9.3)]. Excitation wavelengths were 490 nm (for sTG) and 590 nm (sTM). Bottom: Fluorescence images of a microdevice containing ALP (from P. pastoris, λ = 0.1, where λ is the existence probability of a protein molecule per well) obtained with fluorogenic probes (sTG-Phos and sTM-Phos, 100 μM) in DEA buffer [1 M (pH 9.3), containing 1 mM MgCl2 and 0.1% (w/w) CHAPS] after 50-min incubation. a.u., arbitrary units. (C) Structures of phosphatase probes developed for the assay. (D) Left: Scatter plot of the activities of the proteins read out by HCCA-mPhos, sTG-Phos, and sTM-mPhos (100 μM) in DEA buffer [1 M (pH 9.3), containing 1 mM MgCl2 and 0.1% (w/w) CHAPS] in the presence of recombinant TNAP, ALPI (λ = 0.01), and their mixtures (1:1). Plots for the activities toward sTG-Phos (green, horizontal axis) and sTM-mPhos (red, vertical axis) are shown. Right: Experiments were performed with mixtures of different ratios of ALPI and TNAP (horizontal axis), and the percentages of spots that appeared in the area of ALPI were counted (vertical axis). Experiments were performed three times. Error bar, SD (n = 3). The whole dataset is shown in fig. S8. (E) Left: Ratiometric (red/green) fluorescence image of a microdevice containing 1:3000 diluted human serum with fluorogenic probes (sTG-mPhos and sTM-Phos, 30 μM) in DEA buffer [1 M (pH 9.3), containing 1 mM MgCl2 and 0.1% (w/w) CHAPS] after 50-min incubation at 25°C. Middle: Scatter plot of the activities of the proteins in the experiment shown in (E) (left). Each point was assigned to the ALPI or TNAP cluster by discrimination analysis (see Materials and Methods). Right: The comparison of the integral of the activities calculated from the spots assigned as ALPI and the ALPI activity monitored by conventional biochemical assay (see discussion S6). The analysis with serum from 10 individuals is shown.

In case of point (ii), we developed probe scaffolds using TokyoGreen (TG) (15) and TokyoMagenta (TM) (16), which are green and red fluorophores bearing phenol groups that can be protected by a phosphate group, and designed a set of multicolored phosphatase probes. Fluorescence activation of TG-based probes in the targeted reaction was designed on the basis of the optimized process of photo-induced electron transfer (15). Specifically, the excited state of the fluorophore in the intact (protected) probe was designed to accept a single electron from the 8-aryl group of xanthene so that the fluorophore can revert back to its ground state without emitting fluorescence, whereas this did not occur in the reaction product (deprotected phenolate fluorophore). We optimized the scaffold by tuning the electron density of the 8-aryl group of TG (fig. S3 and discussion S2) and synthesized a 2,4-dimethoxybenzenesulfonic acid derivative, which was expected to exhibit marked increase in fluorescence upon hydrolysis of the protected form to the phenolate while showing sufficient hydrophilicity due to the presence of a sulfonic acid moiety (schemes S2 and S3, fig. S4, and table S1). We synthesized the ALP probe based on a novel scaffold, called sTG, and confirmed that the probe, sTG-Phos, exhibited 256-fold fluorescence increase when it reacted with ALP (Fig. 2B, left; fig. S5; and table S2). With sufficient hydrophilicity, this probe was considered suitable for the microchamber device–based assay (figs. S6 and S7), and using this, the single-molecule activities of ALP could be analyzed in the device (figs. S8 and S9, table S3, and discussion S3). Fluorescein diphosphate (FDP) is typically applied in microchamber-based assays in the green color channel (17), but it is not ideal for precisely quantifying the activity as two phosphoric esters must be cleaved for complete fluorescence activation (15). We confirmed that the fluorescence activation of sTG-Phos was eightfold faster than FDP (fig. S10). In the same manner, we prepared a red fluorescent scaffold using sulfonic acid–modified TM (named as sTM; scheme S4) and confirmed that the phosphorylated probe sTM-Phos exhibited 196-fold fluorescence increase upon reaction with ALP (Fig. 2B, right; figs. S4 and S5; and tables S1 and S2).

With regard to point (iii), to discriminate the different phosphatases in the microchamber device, we prepared probes with diverse phosphoric ester–based reaction sites (schemes S5 and S6 and Fig. 2C). In addition to direct protection of the fluorophores with a phosphate group, we applied two types of reactions, which involved cleavage of phosphoric ester leading to liberation of the fluorophore. We introduced a methylene-oxy linker between the phosphate and the fluorophore moieties to modify the reactivity of the probe toward different classes of phosphatases (18). On the other hand, another probe was prepared with a p-hydroxybenzyl alcohol linker, in which exposure of the phenol group was expected to trigger 1,6-elimination to release free fluorophore (19) (discussion S4).

Michaelis-Menten analysis of the ALP subtypes showed that each reaction site exhibited different kinetic parameters toward different ALP classes (fig. S11 and table S4). Then, we tested combinations of prepared probes with different colors and reactive moieties to discriminately detect different ALPs in the chamber. We confirmed that a set of probes, consisting of HCCA-mPhos (blue), sTG-Phos (green), and sTM-mPhos (red), used in diethylamine hydrochloride (DEA-HCl) buffer (pH 9.3) was able to discriminately detect and count two different recombinant ALPs, TNAP and ALPI, in the microchamber device, while these two enzymes have 50 to 60% sequence homology (Fig. 2D, fig. S12, and discussion S5) (6). The detection limit (signal +3 × SD of the background) of TNAP and ALPI was 8.8 and 8.3 fM, respectively, and the detectability was not substantially affected by presence of both the enzymes (figs. S13, S14, and S15).

Detection of single-molecule phosphatase activities in serum

We applied this assay platform for single-molecule analysis of ALPs in the human serum. The conditions mentioned above that were used to discriminate between TNAP and ALPI were also applied to test the 1:3000 diluted serum sample (possibility of a targeted enzyme being present per single chamber, λ = 0.01). As a result, we detected the activity clusters at the same positions as observed with in case of recombinant TNAP and ALPI (Fig. 2E, middle), and the integral of the activities calculated from the spots assigned to ALPI reflected the ALPI activity as evaluated by conventional biochemical assays (Fig. 2E, right; table S5; and discussion S6). To our knowledge, this is the first study that detected the activities of ALPs in blood samples at the single-molecule level, and we found that there were two activity states for both TNAP and ALPI (fig. S17, indicated by arrows and arrowheads). The two populations did not follow single Poisson distribution, and we speculate that it might be due to the presence of different oligomeric states of the enzymes in the solution (20, 21). The biphasic patterns of activities differed between the enzymes in serum and that from recombinant sources (Fig. 2, D and E, and fig. S17), suggesting that the ratios of the oligomeric states of TNAP and ALPI were not the same in the serum and in recombinant preparations.

Next, in an effort to apply this platform for the comprehensive phosphatase activity profiling to characterize altered enzymatic activities between different biological samples, we searched for the reaction conditions that can simultaneously detect more diverse phosphatase species than the ALPs, such as acid phosphatases and protein phosphatases. Among various conditions tested (table S6), pH was the most decisive factor to determine the activity patterns; in basic media, clusters of TNAP and ALPI were dominantly detected, but when the pH was adjusted to neutral, it led to increased numbers of activity clusters (fig. S18 and discussion S7). As an optimized assay condition, we set the measurement pH to 7.4 and used a set of three probes, HCCA-mPhos (blue, methylene-oxy linker), sTG-qmPhos (green, p-hydroxybenzyl alcohol linker), and sTM-Phos (red), to detect various phosphatase clusters in the blood samples (Fig. 3, A and B, and figs. S19 and S20). Under this condition, we detected fractions of enzymes in serum that responded only to sTG-qmPhos (Fig. 3B and fig. S21). This kind of substrate specificity, i.e., preference to react with a structurally flexible phenolic phosphoric ester rather than alcoholic or structurally bulky phenolic phosphoric ester, was assigned to protein tyrosine phosphatases (PTPs; fig. S22). There have been no previous reports on PTP activities in serum samples. Here, we confirmed their presence by using PTP inhibitor bisperoxo(1,10-phenanthroline)oxovanadate [bpV(phen)] (22), which led to decrease in the cluster size (fig. S21). Then, the overall activity profiles under this condition were evaluated in the serum samples from five patients with diabetes and compared with those of five healthy individuals (Fig 3B; fig. S23, A and B; and discussion S5). One notable difference was that the serum from patients with diabetes tended to show enlarged size of the cluster assigned as ALPI in the high activity states (Fig. 3B, red squares, and fig. S23C). The elevation of serum ALPI activity in patients with diabetes is consistent with the results of a previous study performed with relatively large numbers of patients (23). This was also confirmed in our tested samples (table S5 and fig. S23C), while the change was not significant, which can be partly attributed the fact that the ALP activities also reflected personal differences and pathogenic differences.

Fig. 3 Applications of SEAP methodology.

(A) Representative fluorescence overlay image of a microdevice containing 1:3000 diluted serum with fluorogenic probes [30 μM; HCCA-mPhos (blue), sTG-qmPhos (green), and sTM-Phos (red)] in tris-HCl buffer [100 mM (pH 7.4), containing 1 mM MgCl2, 100 mM dithiothreitol, and 0.1% (w/w) CHAPS] after 2-hour incubation. The larger device image is shown in fig. S11. (B) Scatter plots of activity acquired for five samples from healthy individuals and five from patients with diabetes [exemplified by (A)]. Data were subjected to cluster analysis as described in discussion S5 and Materials and Methods. The points assigned as ALPI are surrounded by red rectangles. The whole datasets are shown in fig. S12. (C) Left: Overlay fluorescence image of a microdevice containing 1:500 diluted serum with fluorogenic probes [sTG-mdTMP (green) and sTM-dCMP (red), 100 μM] in tris-HCl buffer [100 mM (pH 9.3), containing 1 mM MgCl2 and 0.5% (w/w) CHAPS] after 40-min incubation. Right: The chemical structures of sTG-mdTMP and sTM-dCMP. (D) Scatter plots of activity data for plasma samples derived from 14 healthy individuals and 31 patients with cancer [exemplified by (C)]. Points were assigned to three clusters by cluster analysis (see discussion S8 and Materials and Methods). (E) The dot plot shows the number of plots assigned to cluster 1 in (D). P value was analyzed using Mann-Whitney U test.

Extension of the method to ectonucleotide pyrophosphatases and discovery of a candidate pancreatic cancer biomarker

As an extension of this study, we applied the assay principle to investigate broader enzyme species by preparing another set of fluorescent probes. We focused on another class of phosphate ester–cleaving enzymes, ectonucleotide pyrophosphatases (ENPPs). Seven classes of enzymes have been characterized in this class, which are considered to contribute to the metabolism of extracellular nucleotides and lipid mediators (24). One of the best-characterized members, ENPP2 or autotaxin, has been detected in the serum and examined as a candidate biomarker of tumors (25, 26). However, the presence of other ENPP species in blood samples has not been reported, presumably because of their low abundance. Although these extracellular enzymes are not secretory such as autotaxin, we considered that they might be released into the circulation during certain pathological conditions. In a conventional multiwell plate–based fluorometric assay, ENPP activities were not detectable in the human serum (fig. S25), but we tried to detect them using the highly sensitive microdevice-based assay.

ENPPs favor the phosphate diesters as substrates (27, 28), and we synthesized green and red fluorescent probes based on sTG and sTM with diverse nucleotides as reactive sites (scheme S7 and fig. S26). Then, they were tested using the microdevice-based assay, and the activities could be detected in human plasma samples. Particularly, using certain probe pairs such as sTG-methylene-oxy deoxythymidine monophosphate (mdTMP) and sTM-deoxycytidine monophosphate (dCMP), more than a single cluster with distinctive reactivity profiles were detected (Fig. 3C and fig. S27), indicating the presence of two or more ENPP species. We speculate that the major cluster predominantly responded to sTG-mdTMP corresponded to autotaxin (ENPP2), while other clusters present at relatively smaller populations were considered to come from ENPP1 and ENPP3. Then, we applied this condition to profile the activities of ENPPs in the plasma from pancreatic cancer specimens to identify novel biomarker candidates. The size of an activity cluster was significantly elevated in the plasma from patients with cancer compared with ones from healthy individuals (Fig. 3, D and E), which was assigned to be the cluster of ENPP3 (fig. S28 and discussion S8). The enzyme (also known as CD203c) is known to be up-regulated on the surface of activated basophils (29), and we predicted that the level of the enzyme in the blood of the patients with pancreatic cancer increased due to the increase in the number of activated basophils, which occurs along with cancer progression (30). To our knowledge, this is the first report showing the detection of ENPP3 in the blood samples and characterizing its differences in pathological conditions. This might be attributed to the fact that the presence of the enzyme was not so high in the blood and could be hardly detected using conventional activity assays (fig. S25) and antibody-based assays (fig. S30). However, it was possible to detect the activity using the SEAP platform, which has a much lower detection sensitivity (the limit of detection of ENPP3 was 1.8 fM; fig. S29). For the present discovery to be used for the diagnosis of pancreatic cancers, especially at early stages, increase in the area under the curve (AUC) values might be required (fig. S31), and we consider that the use of additional biomarker enzymes in combination will make it possible to enhance the sensitivity and selectivity of diagnosis. To this end, the current results are encouraging as they demonstrate the suitability of the SEAP platform for characterizing biomarker candidates, which are significantly altered in pathological states but are difficult to evaluate using conventional assays.


Single-molecule enzyme assays have been developed as powerful analytical tools for understanding the biochemical characteristics of targeted enzymes such as their structural variations and activity fluctuations (31). However, a limitation of the conventional assays for use with complex biological samples is their inherent inability to discriminately detect multiple types of enzymes. We overcame this limitation by using a strategy of profiling each enzyme based on its activity at the single protein level and succeeded in the first instance to apply single enzyme activity assays to discriminately detect multiple enzymes in clinically meaningful biological samples. For the reliable identification and application of biomarkers, the sensitivity and comprehensiveness of the strategy are critical (32), and the marked improvement of sensitivity has been realized in DNA and RNA analysis by using the single-molecule analysis platforms (2). In a similar way, methods for determining the sequences of proteins at the single-molecule level have been recently extensively developed (3335). However, the function of a protein is not merely controlled by the primary structure, and it can be dynamically modified by changes in the secondary, tertiary, and quaternary structures (1). Therefore, we consider that our system of directly detecting the functions of enzymes, such as enzymatic activities, at single protein level in biological samples serves as a functionally informative assay platform to precisely understand the relationship between altered protein function and pathologies. While this proof-of-concept study focused on phosphoric ester–hydrolyzing enzymes, our approach can be applied to a wide range of enzymes whose activities can be monitored by fluorogenic substrate–based assays. Given the accelerating development of novel sensor molecules and design strategies (36, 37), this assay principle has enormous potential for sensitive, accurate, and informative enzymomics studies at the single-molecule level.


General materials

ALP from Pichia pastoris was purchased from Roche. Recombinant protein tyrosine phosphatase 1B (PTP1B) was purchased from Enzo. Other recombinant proteins were purchased from OriGene. ENPPs were prepared according to the literature (27). General chemicals were of the best grade available—supplied by AdipoGen, Tokyo Chemical Industry, Wako Pure Chemical Industries, and Sigma-Aldrich—and were used without further purification.

Serum samples from patients with diabetes

Serum samples from patients with diabetes and healthy individuals were purchased from in.vent Diagnostica GmbH. The experiments are covered under the company’s ethical guidance and the approval of patients.

Plasma samples from healthy controls and patients with pancreatic cancer

Plasma samples (n = 45) were obtained from patients with untreated pancreatic ductal adenocarcinoma (n = 31) and healthy controls (n = 14) at the National Cancer Center Hospital and Osaka Medical College between 2006 and 2008. Written informed consent was obtained from all of the participants. Blood samples were collected in EDTA glass tubes. The supernatant was separated by centrifugation and cryopreserved at −80°C until analysis. All samples were processed in the same manner. The study was reviewed and approved by the ethics committees of the National Cancer Center (authorization no. 2014-246) and Tokyo University (authorization no. 30-9). Patients were classified as having clinical disease stage I, II, III, or IV according to the International Union Against Cancer (38, 39).


Nuclear magnetic resonance (NMR) spectra were recorded on a JEOL JNM-LA400 at 400 MHz for 1H NMR and at 100 MHz for 13C NMR. Mass spectra (MS) were measured on a JEOL JMS-T100LP AccuTOF LC-plus 4G (electrospray ionization). Preparative high-performance liquid chromatography (HPLC) was performed on an Inertsil ODS-3 (10.0 × 250 mm) column (GL Sciences Inc.) using an HPLC system composed of a pump (PU-2080, JASCO) and a detector (MD-2015). Preparative medium pressure liquid chromatography (MPLC) was performed on an Isolera One purification system (Biotage) equipped with a Biotage SNAP Ultra C18 column (for reverse phase separation) or on an MPLC system composed of a pump and a detector (EPCLC-AI-580S, Yamazen) and equipped with a silica gel column (silica gel 40 μm or Amino 40 μm, Yamazen) (for normal phase separation). Liquid chromatography–mass spectrometry analysis was performed on an Acquity UPLC H-Class System (Waters) equipped with an Acquity UPLC BEH C18 1.7 μm (2.1 × 50 mm) column (Waters) and an MS detector (QDa, Waters).

UV-visible absorption and fluorescence spectroscopy

Ultraviolet (UV)–visible spectra were obtained on a Shimadzu UV-1850. Fluorescence spectroscopic studies were performed on a Hitachi F-7100. The slit width was 5.0 nm for both excitation and emission. The photomultiplier voltage was 400 V.

Enzyme activity measurement with a plate reader

For figs. S2, S11, S21, S22, and S25, the fluorescence in 384-well black plates (Greiner Bio-One) was measured with a microplate reader (2103 EnVision, PerkinElmer Inc.). To determine the enzymatic activities, the time course of the fluorescence intensity (excitation and emission: 335 to 375 nm/448 to 473 nm for umbelliferone derivatives and 478 to 492 nm/523 to 548 nm for TG derivatives) was measured at 1-min intervals at 25°C, and the initial velocity of fluorescence increase was converted to enzymatic activity using the corresponding fluorescent products (1 μM) as standards. For fig. S26, the fluorescence was measured in 96-well half area plates (Corning) with a microplate reader (ARVO X5, PerkinElmer Inc.). To determine the enzymatic activities, the time course of the fluorescence intensity (excitation and emission: 478 to 492 nm/523 to 548 nm for TG derivatives and 519 to 544 nm/565 to 625 nm for TM derivatives) was measured at 5-min intervals at 25°C. For the measurement of ALPI activities in serum by conventional biochemical assay, the absorbance was measured in 384-well clear plates (NuncTM) with a microplate reader (SH-8000 Lab, Corona Electric Co. Ltd.). To determine enzymatic activities, the time course of the absorbance (at 405 nm) was measured at 1-min intervals at 25°C, and the initial velocity of absorbance increase was converted to enzymatic activity on the basis of measurements of 10, 30, and 100 μM standards.

Preparation of microdevices for assay

Chamber array devices were prepared on the basis of a previous report (40). A glass coverslip (24 mm by 32 mm) was sonicated in acetone and isopropanol and deionized in water for 10 min. Then, the coverslip was immersed in 10 M KOH for several hours and rinsed with deionized water. It was spin-coated with an amorphous fluorocarbon polymer (CYTOP 816AP, Asahi Glass, Japan) at 3400 rpm for 30 s and baked for 1 hour on a hot plate at 180°C. The thickness of the CYTOP layer was 3 μm. The CYTOP-coated coverslip was spin-coated with a positive photoresist (AZ-4903, AZ Electronic Materials, USA) at 7500 rpm for 60 s and baked at 55°C for 3 min and then at 110°C for 5 min. Subsequently, photolithography was carried out using a photomask with 3-μm holes, separated by 3 μm. The resist-patterned coverslip was dry-etched with O2 plasma in a reactive ion etching system (RIE-10NR, Samco) to remove the exposed CYTOP. The substrate was cleaned and rinsed with acetone and isopropanol to remove the remaining photoresist. The resulting CYTOP-on-coverslip substrate had an array of exposed SiO2 patterns with a diameter of 4.3 μm, each held a water droplet in the digital enzyme assay.

Digital enzyme assay in the microdevice

The enzyme stock solution was diluted with the assay buffer containing fluorescence probes. Next, 15 μl of reaction mixture was introduced into the microdevice by manual pipetting. Then, 40 μl of AE-3000 (AGC Chemicals) and 40 μl of Fomblin perfluoropolyether (PFPE) (Solvay) were introduced into the microdevice to flush out excess reaction mixture and to form water/oil droplets. The fluorescence imaging was performed using a Leica Application Suite Advanced Fluorescence instrument with a TCS SP8 and 20× dry objective lens (20× HC PL APO CS2 0.75 dry). The light sources were diode, argon, and helium-neon lasers. The excitation wavelength and the emission wavelength were 405 nm/432 to 500 nm for umbelliferone derivatives, 514 nm/530 to 580 nm for TG derivatives, and 594 nm/610 to 700 nm for TM derivatives. All measurements were performed at 25°C. Optimized conditions for enzymatic assays were as follows. For ALPs, biological samples were mixed with HCCA-mPhos (30 μM), sTG-mPhos (30 μM), and sTM-Phos (30 μM) in DEA-HCl buffer (1 M, pH 9.3) at 25°C, and the fluorescence increase was monitored for 40 min (for Fig. 2, D and E, and figs. S12 to S15, S17, and S24). For general phosphatases, biological samples were mixed with HCCA-mPhos (30 μM), sTG-qmPhos (30 μM), and sTM-Phos (30 μM) in tris-HCl buffer (100 mM, pH 7.4) at 25°C, and the fluorescence increase was monitored for 2 hours (for Fig. 3, A and B, and figs. S19 to S21, and S23). For ENPPs, biological samples were mixed with sTG-mdTMP (100 μM) and sTM-dCMP (100 μM) in tris-HCl buffer (100 mM, pH 9.3) at 25°C, and the fluorescence increase was monitored for 40 min (for Fig. 3, C and D, and figs. S28 and S29).

Image processing

The fluorescence increase was calculated by linear fitting of four time points of fluorescence intensity inside regions of interest (ROIs) corresponding to each well. ROIs were set by the following procedure. First, a suitable threshold for almost all wells in the microdevice was set, and the areas meeting appropriate conditions (diameter, 10 to 50 pixels; circularity, 0.7 to 1.0) were selected as ROIs. Second, each ROI was reset to a circular ROI whose center was the same as that of the original ROI and whose diameter was 8 pixels. All processing was performed with ImageJ (

Discrimination analysis

In Fig. 2 (D and E), and figs. S12, S15, S17, and S24, each well was assigned to the ALPI cluster or the TNAP cluster using discrimination analysis. The training data for ALPI and TNAP were decided by measuring the purified recombinant protein (Fig. 2D, left and middle left). The Mahalanobis’ distance from the training data of each well was measured, and the well was assigned to the cluster that has the smallest Mahalanobis’ distance.

Cluster analysis

The assay was performed under conditions where λ (probability of existence of a protein molecule in a well) was around 0.01. We treated each spot as a representative of a single enzyme molecule. Cluster analysis was performed using a variational Bayesian-Gaussian mixture model (implemented in Python with scikit-learn;; the model assigns all data points to a mixture of unidentified numbers of Gaussian distributions by means of the variational Bayesian method. The initial cluster number was set to 9 (in phosphatase assay, Fig. 3B and figs. S20 and S23) or 3 (in ENPP assay, Fig. 3D and fig. S28). We omitted the data from the empty wells, defined as wells whose Mahalanobis’ distance was less than or equal to the threshold calculated from the assay with blank buffer. The wells with apparent increase in fluorescence below zero for all wavelengths (in phosphatase assay) or for the green wavelength (in ENPPs assay) were also omitted from the analysis.

Fluorescence image of microdevice

Whole image of microdevice (fig. S19) was acquired with fluorescence microscope BZ-X700 (Keyence) using the tile-scanning mode. Channels for 4′6-diamidino-2-phenylindole (blue), fluorescein isothiocyanate (green), and tetramethylrhodamine (red) were used to detect fluorescence signals of probes in corresponding color ranges.

Western blotting

Western blotting was performed with serum samples treated with the Pierce Albumin Depletion Kit (Thermo Fisher Scientific) for the removal of serum albumin. All the electrophoresis samples were diluted to 1:10 of the intact serum for the loading. The proteins were separated and detected using an automated capillary electrophoresis system (Simple Western system and Compass software; ProteinSimple) using Wes Separation Capillary Cartridges for 12 to 230 kDa. Antibodies against ENPP3 (Abcam, ab233777) were used at a dilution of 1:50. The signals were detected using a horseradish peroxidase–conjugated secondary anti-mouse antibody and were visualized using ProteinSimple software.


Supplementary material for this article is available at

Discussion S1. Increased hydrophilicity of HCCA-Phos and its contribution to the increased reliability of microdevice-based assay.

Discussion S2. Optimization of photo-induced electron transfer in sTG.

Discussion S3. Assignment of the activity states of ALPs from P. pastoris.

Discussion S4. Preparation of probes with distinctive reactivities to monitor phosphatase activities.

Discussion S5. Assignment of ALPI clusters in SEAP of phosphatases.

Discussion S6. Evaluation of ALPI activity in conventional biochemical assay.

Discussion S7. Optimization of the assay conditions.

Discussion S8. Assignment of ENPP3 in SEAP of ENPPs.

Scheme S1. Preparation of HCCA-Phos.

Scheme S2. Preparation of 2,4-dimethoxybenzenesulfonic acid– or 2,5-dimethoxybenzenesulfonic acid–substituted TGs.

Scheme S3. Preparation of sTG-Phos.

Scheme S4. Preparation of sTM and sTM-Phos.

Scheme S5. Preparation of HCCA-mPhos, sTG-mPhos, and sTM-mPhos.

Scheme S6. Preparation of sTG-qmPhos.

Scheme S7. Preparation of ENPP probes.

Fig. S1. Suitability of coumarin derivatives for microchamber.

Fig. S2. Comparison of the reactivity of HCCA-Phos and 4MU-Phos against ALP.

Fig. S3. Optical properties of developed TG derivatives.

Fig. S4. Optical properties of developed fluorescence dyes.

Fig. S5. Optical properties of developed fluorescence probes.

Fig. S6. Stability of fluorescent signals of sTG and sTM in microchamber.

Fig. S7. Linearity of fluorescent dye concentration and fluorescence intensity of wells.

Fig. S8. Suitability of sTG-Phos for single-molecule microdevice analysis of ALP activity.

Fig. S9. Linearity of enzyme kinetics analyzed using the microdevice-based activity detection of ALP.

Fig. S10. Comparison of the reactivity of sTG-Phos and FDP in microdevice-based activity detection of ALP.

Fig. S11. Michaelis-Menten analyses of the reactivities of developed fluorescent probes toward different ALP isozymes.

Fig. S12. Scatter plots of the activities of the proteins in the experiment shown in Fig. 2D (right).

Fig. S13. Detection limit of ALPI in microdevice-based assay.

Fig. S14. Detection limit of TNAP in the microdevice-based assay.

Fig. S15. Limit of detection of ALPI in mixture.

Fig. S16. Distributions of single-molecule enzyme activities measured by microdevice-based assay.

Fig. S17. Scatter plots of the activities of the proteins in the experiment shown in Fig. 2 (D and E).

Fig. S18. pH-dependent changes of clusters of phosphatase activities in human serum.

Fig. S19. Fluorescence overlay image of microdevice prepared in the same condition as in Fig. 3A.

Fig. S20. All serum measurement data of 10 individuals.

Fig. S21. Detection of fractions of phosphatases with unique reactivity against sTG-qmPhos.

Fig. S22. Comparison of the reactivity of HCCA-mPhos, sTG-qmPhos, and sTM-Phos against TNAP and PTP1B.

Fig. S23. Assignment of spots for ALPI at high activity states in pH 7.4 condition.

Fig. S24. Detection of TNAP and ALPI activities in serum samples.

Fig. S25. Reactivity of ENPPs in human serum compared to recombinant ENPP2.

Fig. S26. Reactivity of ENPPs toward probes based on sTG and sTM with diverse nucleotide reactive sites.

Fig. S27. Choice of sets of fluorescent probes to monitor ENPP activities in plasma.

Fig. S28. Assignment of spots for ENPP3.

Fig. S29. Limit of detection of ENPP3 in microdevice-based assay.

Fig. S30. The result of Western blotting analysis of ENPP3 detection in serum.

Fig. S31. Analysis of ENPP3 activities based on cancer stages and receiver operating characteristic curves and AUC values for diagnosis of patients with pancreatic cancer.

Table S1. Fluorescence quantum yields of developed dyes.

Table S2. Photophysical properties of developed probes.

Table S3. The number of chambers in each peak in the experiment shown in fig. S8.

Table S4. kcat and Km values of developed probes with different ALP isozymes.

Table S5. Evaluation of ALPI activity in serum samples of patients with diabetes.

Table S6. Optimization of assay conditions for SEAP of phosphatases.

References (41, 42)

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Acknowledgments: Funding: This work was financially supported by MEXT (24655147, 15H05371, 15 K14937, 17 K19477, 18H04538, and 19H02846 to T.K; 15 K18899 to M.K.; and 16H05103 to H.Na), JST (PRESTO and PRESTO Network to T.K. and R.W.), the Council for Science, Technology, and Innovation (Cabinet office, the Government of Japan, to H.No. and Y.U.), and AMED [RRIME to R.W., P-CREATE (18cm0106403h0003) to K.H., and CREST to Y.U.]. S.S. is the recipient of a JSPS scholarship. T.K. received support from the Naito Foundation, the Mochida Memorial Foundation for Medical and Pharmaceutical Research, and the Tokyo Biochemical Research Foundation. Y.Z. was supported by a JSPS fellowship program. Author contributions: T.K. and R.W. conceived the experimental design. S.S. synthesized and characterized most of the compounds and performed the fluorometric assays using microchambers. M.K. and H.Na. synthesized and characterized the fluorogenic probes for ENPPs. R.W., Y.Z., and H.No. prepared microchambers and supported the fluorometric assays. Y.Z. established the microfabrication protocol and digital enzyme assay system. T.I. supported the mathematical analyses. T.U., T.O., and K.H. prepared for the plasma samples from patients with pancreatic cancer. The experimental data were analyzed under the supervision of T.K., R.W., H.No., and Y.U. The manuscript was written by S.S., T.K., and R.W. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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