A CCD-based reader combined with CdS quantum dot-labeled lateral flow strips for ultrasensitive quantitative detection of CagA
© Gui et al.; licensee Springer. 2014
Received: 12 November 2013
Accepted: 19 January 2014
Published: 4 February 2014
Immunochromatographic assays are widely used to detect many analytes. CagA is proved to be associated closely with initiation of gastric carcinoma. Here, we reported that a charge-coupled device (CCD)-based test strip reader combined with CdS quantum dot-labeled lateral flow strips for quantitative detection of CagA was developed, which used 365-nm ultraviolet LED as the excitation light source, and captured the test strip images through an acquisition module. Then, the captured image was transferred to the computer and was processed by a software system. A revised weighted threshold histogram equalization (WTHE) image processing algorithm was applied to analyze the result. CdS quantum dot-labeled lateral flow strips for detection of CagA were prepared. One hundred sera samples from clinical patients with gastric cancer and healthy people were prepared for detection, which demonstrated that the device could realize rapid, stable, and point-of-care detection, with a sensitivity of 20 pg/mL.
Up to date, lateral flow tests, also called lateral flow immunochromatographic assays, have been widely used in qualitative and semiquantitative detection of biomarkers. This technology utilizes antigen-antibody reaction features to detect numbers of analytes, including antigens, antibodies, and even the products of nucleic acid amplification tests [1, 2]. They have merits of user-friendly format, rapid detection, long-term stability, and relatively low cost [3, 4]. However, most colloidal gold lateral flow tests are analyzed by naked eyes, which is subjective and inaccurate. For these reasons, many groups have engaged in developing novel labeling materials to replace colloidal gold. Quantum dots (QDs), one kind of novel nanomaterial, are composed of periodic groups of II-IV, III-V, or IV-VI semiconductor material. Quantum dots have many advantages compared with colloidal gold, such as high fluorescent intensity, broad absorption spectra, narrow and symmetric emission bands, and excellent stability [5–12]. On the other hand, most lateral flow tests could only implement qualitative detection. In order to realize quantitative detection, some groups [13–17] have dedicated to this issue. Huang et al.  utilized a photomultiplier tube (PMT) as a signal acquisition device for up-conversion of nanoparticle-labeled test strips. Although PMT has high sensitivity, it is with a limited surveyed area. Mei's group  chose a complementary metal oxide semiconductor (CMOS) image sensor to capture test strip images. Besides, our group  employed a charge-coupled device (CCD) with an image acquisition card as an image sensor to capture test strip images. However, the image acquisition limited the application of this instrument and, at the same time, resulted in complexity and high cost. In this article, an improved test strip reader is presented.
Gastric carcinoma is one of the common malignant tumors in the world . Its morbidity and mortality, respectively, rank second and third among all malignant tumors. Nevertheless, only 10% or so patients were diagnosed with early gastric cancer (EGC) in China. Moreover, compared with ones suffering with late gastric cancer, patients with EGC have a higher survival rate , so early diagnosis of gastric carcinoma is of great importance. It is confirmed that Helicobacter pylori with cytotoxin-associated protein (CagA) is closely associated with gastric carcinoma's initiation and development [21–23]. If we could detect CagA as soon as possible, we might decrease or avoid development of gastric carcinoma via reasonable therapy. To realize this goal, we designed and prepared the device for ultrasensitive detection of CagA.
Herein, we reported that an improved CCD-based test strip reader was designed and developed. Besides, a corresponding software system was also developed for human-machine interaction. According to the CCD image sensor, test strip images were captured and then transmitted to the control computer. Afterward, the software system would finish the data analysis and present diagnostic results in the form of reports, which is a convenient diagnostic system for clinical physicians.
Materials and methods
Composition of test strips
Design of the hardware system
A quadrate ultraviolet LED as excitation light source was to make sure that samples accept the same amount of irradiation. It is also critical to select a good optical sensor. Photodiode, photomultiplier tube, linear CCDs, and image sensors are widely used optical sensors. However, photodiode, photomultiplier tube, and linear CCDs have a limited surveyed area. On the contrary, image sensors could provide a more flexible and wider detection area. Moreover, image sensors could realize short time detection . Based on the above benefits, we decided to employ an image sensor. CCD and CMOS are two most popularly used image sensors. Compared with CMOS, CCD has the advantages of low noise and better imaging quality , so a color CCD image sensor was chosen. This digital CCD image sensor with a USB not only solved the problem of employing an image acquisition card but also provided stable and rapid data transmission.
The QD test strip was irradiated by an excitation light source and then produced fluorescence, which could be captured by the CCD image sensor. The captured image was transmitted to the computer and went through further processing to complete calculation of test results. In order to decrease background interference, an ultraviolet filter was added to resist the excitation light source. A lithium battery was adopted as power supply, providing a light source for places without electric supply.
Development of the software system
In test strip images, the useful information was only T-line and C-line. However, there always existed intense background noise that requires to be separated. Therefore, an appropriate algorithm was proposed to reach this goal.
Calculate the average gray value of all pixels named T. Then, scan all the pixels. These pixels’ value smaller than T will decrease a constant C.
Calculate the probability density function (PDF) P(k). P(k) = n k /N, k = 0,1…, K − 1, where n k is the number of pixels with gray level k.
Compute an upper limit P u and a lower limit P l with great importance. P u = v · P max, where P max is the highest probability value and v represents the upper threshold normalized to P max (v belongs between 0 and 1). P l is a fixed value, which filters some very low probability values. Herein, P l was set as 0.1%.
Define the new PDF. . This step will remove very low probability pixels and limit very high probability pixels (background pixels).
Calculate the cumulative distribution function C n(k). .
Obtain the output image. O(N) = n · W out · C n(k), where W out is equal to the biggest value subtracting the smallest value and n represents the number of superposition.
Characterization of CdSe QDs
Image processing results
Diagnosis of CagA samples
In conclusion, a CCD-based reader was designed and fabricated, the quantitative analysis software was compiled, and the resultant CCD-based reader system was used for quantitative analysis of examined CagA antigen on the strips. A fluorescence detection system of lateral flow strip was developed. A revised WTHE algorithm was used to enhance captured QD test strip images. Practical results indicated that the system could quickly and accurately detect the fluorescence signal. QD lateral flow tests were used with different concentrations to detect CagA samples and indicated that the sensitivity of this device was 20 pg/mL. For a future study, test strips with multilines could be detected and some wireless technologies could also be applied in similar instruments. More nanoparticles could be applied for improving sensitivity, which is also a big issue.
DC is a professor of Shanghai Jiao Tong University. His research interests include the synthesis of nanomaterials and their application in the biomedical field. KW is a lecturer of Shanghai Jiao Tong University. Her scientific interests are nanotechnology development of early cancer detection and screening equipment, nonmaterial molecular imaging, and biocompatibility evaluation. CL is a PhD candidate of Shanghai Jiao Tong University. XD and CG are both master students of Shanghai Jiao Tong University.
We are grateful for the financial support by the Chinese 973 Project (2010CB933902 and 2011CB933100), National Natural Science Foundation of China (No.81101169, 81225010, and 81327002), Shanghai Science and Technology Fund (13 nm1401500 and 11 nm0504200), Important National Science and Technology Specific Projects(2009ZX10004-311), and 863 High-Tech Project of China (2012AA0022703).
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