Article Cite This: ACS Sens. XXXX, XXX, XXX−XXX
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A Passive Mixing Microfluidic Urinary Albumin Chip for Chronic Kidney Disease Assessment Jiandong Wu,† Dumitru Tomsa,† Michael Zhang,‡ Paul Komenda,‡ Navdeep Tangri,‡ Claudio Rigatto,‡ and Francis Lin*,† †
Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada Seven Oaks General Hospital, Winnipeg, Manitoba R2V 3M3, Canada
‡
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ABSTRACT: Urinary albumin level is an important indicator of kidney damage in chronic kidney disease (CKD) but effective routine albumin detection tools are lacking. In this paper, we developed a low-cost and high accuracy microfluidic urinary albumin chip (UAL-Chip) to rapidly measure albumin in urine. The UAL-Chip offers three major features: (1) we incorporated a fluorescent reaction assay into the chip to improve the detection accuracy; (2) we constructed a passive and continuous mixing module in the chip that provides user-friendly operation and greater signal stability; (3) we applied a pressure-balancing strategy based on the immiscible oil coverage that achieves precise control of the sample-dye mixing ratio. We validated the UAL-Chip using both albumin standards and urine samples from 12 CKD patients and achieved an estimated limit of detection (LOD) of 5.2 μg/mL. The albumin levels in CKD patients’ urine samples measured by UAL-Chip is consistent with the traditional well-plate measurements and clinical results. We foresee the potential of extending this passive and precise mixing platform to assess various disease biomarkers. KEYWORDS: microfluidic device, chronic kidney disease, albuminuria, albumin, point-of-care
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collection is noninvasive, urine is an ideal sample for point-ofcare (POC) detection. There are a variety of methods for assessing urinary albumin excretion, ranging from the simple and convenient colorimetric dipstick methods to more complex immunoassay and high-performance liquid chromatography (HPLC) based methods.6 The traditional dipstick tests involve wetting a colorimetric dye-impregnated test strip with a sample of urine. The albumin concentration is determined by either visually comparing the reaction colors with the color scales on the label or automatically reading from a chemistry analyzer. Readings are only reported in terms of negative, trace, 1+, 2+, 3+, and 4+ or the semiquantitative values of 30, 100, 300, or 2000 mg/dL corresponding to each color change. The limit of detection (LOD) and accuracy of the dipstick tests is usually not good.7,8 For example, the Chemstrip can produce a color change
round 8−16% of people worldwide suffer from chronic kidney disease (CKD), a number that continues growing rapidly, particularly in poor, remote, and underserviced regions of the world.1 The main causes of CKD are diabetes, high blood pressure, and cardiovascular disease.2 There are few signs or symptoms in the early stages of CKD, which makes the early diagnosis dependent on laboratory measures of kidney function. Unless detected and treated early, CKD can progress to endstage kidney failure or death.2 Optimal detection and risk assessment of CKD requires simultaneous estimation of both kidney function (e.g., glomerular filtration rate (GFR)) and kidney damage (e.g., presence of albumin in the urine (albuminuria)).2−4 Albumin is normally absent from the urine; the appearance of 20 μg/mL or higher of albumin in urine is an accepted indicator of albuminuria.5 As the degree of albuminuria is closely associated with risk of progression to kidney failure and death from cardiovascular disease,5 the measurement of urine albumin is a cornerstone for early diagnosis and monitoring of kidney disease. Because urine © XXXX American Chemical Society
Received: September 20, 2018 Accepted: October 5, 2018
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DOI: 10.1021/acssensors.8b01072 ACS Sens. XXXX, XXX, XXX−XXX
Article
ACS Sensors
Figure 1. Illustration of working principle of the UAL-Chip. (a) A complete UAL-Chip with 16 mixing units. (b,c) The operation procedure for a single mixing unit. (d) The fluidic network in a single mixing unit. P1 and P2 are the hydraulic pressures in the two inlets. L1 and L2 are the lengths of the two branch channels. Q1 and Q2 are the flow rates of the two branch channels. (e,f) Explanation of the pressure-balancing strategy. Δh indicates the liquid height difference between the two inlets, which causes the pressure difference. Adding oil to connect these two inlets balances this pressure difference, making the mixing ratio only dependent on the ratio of lengths of the two branch channels, which is identical for all the 16 units.
only when the albumin concentration is higher than 60 μg/mL, which is 3 times higher than the recommended 20 μg/mL threshold to determine microalbuminuria. Furthermore, the emersion time of the strip in the urine and the standby time after taking it out before reading the signal are critical, raising the potential for operator errors, (e.g., color changes that occur after 2 min are usually of no diagnostic value). To improve the detection limit, the Micral-Test strip is developed based on an immunological process. In this test, urine first passes through a conjugate fleece where albumin binds to specific, gold-labeled antibodies and then flows to a detection pad. A chemical reaction in the detection pad produces a color that is compared visually to color blocks, with colors representing albumin concentrations of 0, 20, 50, and 100 μg/mL. Although MicralTest strip has better accuracy, the ∼$10 cost per chip (vs 3 times lower than the normal range (clinical cutoff level: ∼20 μg/mL). Compared with traditional well-plate method, UAL-Chip also showed an advantage in signal stability due to its continuous mixing property. In the well-plate method, the sample and dye signal are premixed and loaded into a well. The fluorescent signal decays gradually due to concentration change caused by the solvent evaporation. In the UAL-Chip, fresh reagents flow into the detection zone continuously; the sealed channel and the oil that covers the reagent wells prevent evaporation as well. As illustrated in Figure 3c, the signal of the UAL-Chip maintained stable in the 1 h stability test while the signal using well-plate decayed significantly. The standard deviation of the signal intensity in 1 h is 0.36 for the UAL-Chip and 2.73 for the wellplate method. The signal decays about 44% after 1 h in wellplate. Validation of the UAL-Chip Using CKD Samples. The developed method was further validated using real clinical urine samples from patients who have been diagnosed with CKD. We first measured the albumin concentration in the urine samples from 12 CKD patients using the UAL-Chip and well-plate method. Because the dynamic range of the albumin concentration is large, a series of dilution of the urine samples were prepared and measured. The concentrations were calculated based on the calibration curve. The albumin test results using the UAL-Chip agreed with the traditional well-plate method (Figure 3d). The Pearson correlation coefficient of these two measurements is 0.99. We further compared the albumin results measured by the UAL-Chip with the clinical results (measured by Roche Cobas c501 using an immunoturbidimetric assay). Although clear positive correlation was observed between these two measurements (the Pearson correlation coefficient is 0.73; Figure 3e), they are not in perfect agreement, especially for the samples that have high albumin level. There are several reasons for these differences. First, the tests were performed on different samples collected on different dates. Urinary albumin excretion rates in CKD vary significantly from day to day, diminishing the correlation between samples collected on different days. Moreover, this variability is higher at higher levels of albumin excretion. In addition, the multiple dilutions required to fit the measurement into the linear range of the calibration curve will have further weakened correlation at high albumin levels.
(8)
After adding the oil on the top and connecting the two inlets, the pressure difference becomes ΔP = P1 − P2 = (ρreagent − ρoil ) × g × Δh
(9)
If the density of the oil is very close to the density of the reagent, the ρreagent − ρoil becomes very small and thus the pressure difference ΔP becomes negligible. To give an example, we assume the height difference between the two loading ports is 1 mm; the density of the reagent and oil is 1 g/cm3 and 0.963 g/ cm3, respectively. The pressure difference ΔP between the two ports will be 10 Pa before balancing and 0.37 Pa after balancing. The difference could be further decreased if we use the oil that has the density closer to the reagent. To validate this method, we fabricated a device with 16 parallel mixing units and we used 4 of them to test the flowbalancing strategy (Figure 2a). We added the FITC-Dextran dye and water to the two inlets, respectively. Then we measured the fluorescent profile in the area just after the two streams converge (Figure 2b). The duty ratio in the profile can be used to indirectly represent the mixing ratio of the dye and water. In this experiment, we purposely loaded different volumes in the four
Figure 2. Validation of the pressure-balancing strategy. (a) Four identical mixing units were used to perform this test. (b) The fluorescent image just after the converging area. The white dash box indicates the region to plot the intensity profile. (c) The intensity profile in the four channels before pressure-balancing. (d) The intensity profile in the four channels after pressure-balancing. D
DOI: 10.1021/acssensors.8b01072 ACS Sens. XXXX, XXX, XXX−XXX
Article
ACS Sensors
Figure 3. Validation of the UAL-Chip using albumin standards and CKD urine samples. (a) The fluorescent images for different albumin concentrations. (b) The calibration curve of the fluorescent intensity against the albumin standard concentration. The solid line is the linear fit of the data (R2 = 0.99). The error bar in (b) and (c) is the standard error of the mean (SEM). (c) Photostability test between the UAL-Chip and well-plate. (d) Comparison between the UAL-Chip measurements and well-plate measurements (Pearson correlation coefficient = 0.99; slope = 0.95). (e) Correlation between albumin levels from UAL-Chip results and clinical results (Pearson correlation coefficient = 0.73).
sensitivity, specificity, and positive and negative predictive values of using the albumin levels by the UAL-Chip results to determine albuminuria, we used cross-tabulation tables for pairs of albumin positivity level ≥20 μg/mL (or ≥200 μg/mL) and the reference standard of an UACR ≥3 mg/mmol (or ≥30 mg/ mmol) (Tables S2 and S3). The accuracy values are as shown in Table 2. The sensitivity, specificity, PPV, NPV, and accuracy of
We then used the recommended cutoffs of the urine albumin levels for albuminuria (normal: 200 μg/mL)5 to classify the patients into different groups and compared the classification accuracy between the UAL-Chip results and clinical albumin results (Table 1, detail measurement results Table 1. Classification Accuracy According to the Albumin Levels from the UAL-Chip Results and the Clinical Results (Cobas c501, Immunoturbidimetry)a Albumin level (μg/mL)
UAL-Chip
Table 2. Diagnostic Accuracy of the UAL-Chip Results for Detections of UACR ≥ 3 mg/mmol and UACR ≥ 3 mg/mmol (from Total 12 Patients)
Clinical test
Normal (200) 1, 3, 5, 6, 9, 10, 11, 12 1, 3, 5, 6, 9, 10, 11, 12
Albumin ≥20 μg/mL (UAL-Chip) for detection of UACR ≥ 3 mg/mmol Albumin ≥200 μg/mL (UAL-Chip) for detection of UACR ≥ 30 mg/mmol
a
The numbers in the table indicate different patients (totally 12 patients).
were shown in the Table S1). Only two patients were misclassified in the normal group and the microalbuminuria group. The classification accuracy was 10/12 = 83%. In clinical practice, UACR instead of albumin alone is considered a better indicator of kidney damage as it can control for variations in urine concentration.4 Three albuminuria categories (A1: UACR 30 mg/mmol, severely increased) are usually employed to indicate the kidney damage levels.4 To evaluate the
Sensitivity
Specificity
PPV
NPV
Accuracy
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
the UAL-Chip results are 100% for both the detection of UACR ≥3 mg/mmol and UACR ≥30 mg/mmol from the test results of 12 patients. Although the sample size of 12 patients is relatively small, these results at least in part suggest that UAL-Chip is reliable in testing clinical urine samples and its measurement is comparable with traditional methods for measurement in a clinical setting. Comparison of the UAL-Chip with the Dipstick Strips and Other Laboratory Methods. Table 3 shows the E
DOI: 10.1021/acssensors.8b01072 ACS Sens. XXXX, XXX, XXX−XXX
Article
ACS Sensors Table 3. Comparison of the UAL-Chip with the Dipstick Strips and Other Laboratory Methodsa Assays Dipstick strips
Immunologically based laboratory methods
HPLC laboratory method UAL-Chip
LOD for albumin
Detection speed
Signal stability
Cost
Multistix 10 SG
150 mg/L
Fast (
