Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health
Abstract
:1. Introduction
1.1. Mobile Technologies for POCT
1.2. Optical Detection and Analysis
1.3. Applications for Mobile Optical Detectors
1.4. The Limitations of Low-Cost Mobile POCT Optical Detectors
2. Mobile Imaging Fluorescence Detectors
2.1. Basic Configuration of Mobile Imaging Fluorescence Detectors
2.2. Optical Detectors
2.3. LED Illumination Module
2.4. Assay Plate
2.5. Fluorescence Detection of Stx2 Activity
2.6. CCD-Based Detectors as Versatile Low-Cost Detectors for Food-Borne Toxins
3. Improving the Sensitivity of Fluorescence Optical Detectors
3.1. Computational Enhancement of the Sensitivity of Webcam-Based Detectors
- Image stacking image analysis: A schematic of the image stacking process is shown in Figure 3A. In video mode, the webcam captures n individual frames. Each frame captures pixels with a signal (marked with white circle) and pixels with random noise (marked with arrows). In high noise or low signal individual frames the signal and noise are indistinguishable, reducing the detection level. In the stacked image the random noise is subtracted, lowering random noise in the image while the signal remains constant, resulting in the increased SNR. A comparison between single frame and image stacking is described below.
- Single frame Fluorescein detection: A generic CMOS-based webcam used as a plate reader (Figure 1) equipped with the original 5 mm f3.8 lens was used for detecting Fluorescein (a common florescence dye used in many biological assays). In this experiment, samples in the range of 0–1 mg/mL were analyzed. In Figure 3B, an emission from a single frame of a 36-well plate (rows 1–6 in Figure 3A) with six replicas (columns a–f), where each row is loaded with six different concentrations of Fluorescein (0 mM (water) to 500 mM), is imaged using the webcam and analyzed with ImageJ software (NIH, Bethesda, MD, USA). Thus, it was possible to quantify the intensity of user-specified areas of the image. As shown in the still single frame of Figure 3B, the only signal detected in row 1 is the concentration of 500 μM, and there is no visible signal in the control (water, row #6) except in row 6, column d (marked with a circle), a reference point used to orient the plate. In the ImageJ 3D analysis (Figure 3C), the signal level for each well suggests that there is no strong signal except for the 500 μM (row 1) with an LOD (calculated based on the control (water in row 6) of 1000 μM.
- Image stacking Fluorescein detection: In video mode (30 frames per second), a stream of frames is captured for 10–15 s and saved as a compressed AVI file; this amounts to 300–450 frames. This file is then split into its constituent frames and averaged together through image stacking via ImageJ software [150]. Averaging serves to reduce the effects of random variation in the signal due to noise. Image stacking was used to improve CMOS sensitivity; the plate was detected by the CMOS webcam operating in a video mode enhanced by image stacking (Figure 3D) with the corresponding ImageJ image (Figure 3E), showing a very good signal with a LOD of 60 µM, an LOD similar to a conventional plate reader.
3.2. The Use of Low-Cost Lasers to Increase Light Excitation Combined with Streak Imaging to Improve Detection of Webcam-Based Portable Flow Cytometry
- Configuration of webcam-based mobile flow cytometer: The mobile imaging flow cytometer (Figure 4A) is based on detector configuration of a webcam-based fluorescence plate reader (Figure 1). The optical system was adapted to close-up imaging (e.g. the use of extension tubes and focusing stage) and the LED illuminator (Figure 1A-6) was replaced with a laser. The new device (Figure 4) consists of four modules: (1) a webcam utilized as an imaging sensor; (2) a blue 450 nm 1W laser excitation source that enables high excitation energy and the detection of the cells using the low sensitivity detector; (3) a high throughput flow-cell (Figure 5B); and (4) a focusing stage for image focusing and alignment. The sensor includes the CMOS with the internal electronics of the webcam. The optical system includes a 12-mm f/1.2 CCTV lens, extension tube, and two green emission filters (no excitation filter was needed because of the narrow bandwidth for the laser illumination). The webcam was connected to a computer, which was used to power the webcam and to collect and analyze data. The fluid handling system includes a high throughput flow-cell (Figure 4B) and a programmable syringe pump.
- Webcam-based flow cytometer wide-field imaging: A high-throughput flow-cell (Figure 4B), which enables wide field rapid analysis and reduces the size of the imaging files used for analysis, was constructed in which (1) a glass or quartz microscope slide was used as a lower layer; (2) a middle layer laser was machined from 1.6 mm 3M 9770 double-sided adhesive transfer tape to define the geometry and depth of the fluid channel; and (3) a top layer comprising a glass or quartz microscope slide with two holes drilled for the inlet and outlet ports was aligned with the ends of the fluid channel layer. A wide flow channel (e.g., 20 mm) enables the sample flow rate to be increased and provides the capability to analyze the large sample volumes needed to detect rare cells. The wide cell enables imaging of cells moving a long distance in the flow cell, which maximizes the residence time of cells in the interrogation window of the field of view and maximizes the number of fluorescent cells imaged. The fluid volume of the interrogation window was maximized by the microscope-slide dimensions. The channel depth (~1.6 mm) kept the flow field within the depth of field of the lens being used (Pentax CCTV 12mm f/1.2, operated at approximately f/2.4 to reduce field curvature and improve depth of field). The lens was placed at a distance of approximately 20 mm from the webcam CMOS (using an extension tube), enabling the lens to focus at very close range on the entire detection field of the flow cell. To provide approximately uniform excitation across the width of the channel, the laser source was injected into the side of the flow cell (Figure 4C) at an angle that formed a linear band of excitation across the center of the field of view. For high sensitivity and high image quality, a Sony PlayStation® Eye webcam was used as the imaging sensor.
- Cell streak imaging cytometry: Imaging of a large volume of moving cells was accomplished by increasing the flow cell volumetric rate up to to 20,000 μL/min. Images of the moving cells were obtained using “streak photography,” which allows imaging of moving objects at a low frame rate to be captured as short streaks in the final image (Figure 5B). Figure 6A shows schematically a fluorescently labeled cell traversing a number of pixels; the movement is captured by a CMOS detector on multiple pixels. The number of pixels corresponds to the cell distance, while the brightness of the pixels corresponds to the accumulation of the light emitted, with a maximum brightness achieved in the pixel at the image center (Figure 5A iii and iv). An actual image of such a cell is shown in Figure 5B. The direction and relative length of these streaks can be used to measure localized fluid motion. To further increase sensitivity, the signal-to-noise ratio of the images was also enhanced by combining three imaging methods: (1) CMOS color channel selection, (2) background subtraction, and (3) pixel binning [141]. Because the emission of the dye used (SYTO-9) is in the green range (498 nm), noise was reduced by using only the green pixels of the CMOS for the analysis and two green emission filters (on both sides of the lens, see Figure 4A). In order to reduce noise, as shown in Figure 5C, each column of pixels is averaged over the streak length n to produce a single averaged row of pixels, labeled avg(n). Figure 5D shows a plot of pixel values before (i) and after (ii) averaging, showing a three-fold improvement in SNR. The plot in (i) is for the row with the brightest pixel value quantitation, shown in Figure 5D.
- Streak imaging signal enhancement: To improve detection, only the green channel video images of samples passing through the flow cell were used to improve cell image visibility and reduce noise from the red and blue channels, which do not have significant green color signal. Figure 7A illustrates a single raw webcam image of human THP-1 monocytes stained with SYTO-9 florescence dye (with an excitation maximum at 483 nm and fluorescence emission maximum at 503 nm), showing a fluorescent cell streak (circled and marked with arrows) with the excitation laser line autofluorescence at the center. The average of all 720 video frames from one sample yields (B) a single frame containing only background autofluorescent signal of the green channel of video. This background (Figure 7B) is subtracted from each frame (Figure 5A) to yield an enhanced image (Figure 7C) with improved cell streak visibility.
- Streak imaging cytometry detection of rare cells: The relationship between volumetric sample flow rate, linear particle velocity, and the length of the streak in the wide field flow cell field of view is shown in Figure 7. In this experiment, CYTO-9 labeled THP-1 monocytes were injected at flow rates between 100 μL/min and 20 mL/min (Figure 7). The cells (marked with arrows) were captured at 20 fps (exposure time 1/20 s). The length of the streak is proportional to the flow rate. It was found that there were distinct linear (Figure 7D) ranges of operation. At higher flow rates (Figure 7E), non-linear cell velocity was measured, with a linear trend line plotted for comparison. Non-linearity in the relationship between flow rate and particle velocity is attributed to viscoelastic creep of the flow cell, resulting in increasing cross-sectional area at higher pressures.
4. Cost Considerations for Global Health
- The webcam used as a detector ranges from ~$5 for a basic generic detector (from various suppliers, such as those found on eBay or Alibaba) to ~$10 for a Sony Playstation Eye webcam (eBay, San Jose, CA, USA).
- Multi-wavelength LED White/Green/Blue/Red 48 LED SMD will cost ~$3 (eBay or Alibaba, Hangzhou, China).
- Chroma filters will be one of the most expensive components at ~$70 (Chroma, Bellows Falls, VT, USA).
- ImageJ Imaging software (NIH) for processing images is obtained as freeware.
- Playstation Eye Webcam ~$10 (eBay)
- Chroma filters ~$70 (Chroma)
- Blue 450 nm 1W laser pointer ~$50 (eBay or Alibaba)
- 12 mm f/1.2 CCTV lens ~$7 (eBay or Alibaba)
- Peristaltic pump ~$6 (eBay or Alibaba) or syringe pump, Razel R-99 ~$160 (eBay)
- ImageJ Imaging software (NIH) is free
5. Factors Contributing to Improving the Sensitivity of Mobile, Low-Cost Optical Devices for Fluorescent Detection
- Webcams: While lenses on smartphones are not interchangeable and require additional lens attachments to change the optics, resulting in degraded image quality, many webcams permit lenses to be easily changed (e.g., a f/1.2 lens can be used to maximize the amount of light transmitted to the sensor).
- LEDs: Increasing the power of the excitation source in fluorescent detection by increasing the intensity of the LED illumination (i.e., the use of more LEDs) increases the fluorescent signal.
- Cameras: Using cooled CCD/CMOS devices reduces thermal noise and improves SNR for more sensitive detection, but they are substantially more expensive than webcams.
- Lasers: The use of low-cost lasers equipped with line generator, or removing the laser lens, may increase light intensity and provide narrow wavelength illumination.
- Exposure time: For single frame imaging, some webcams allow for long exposure times (>1 s). Longer exposure can be used to detect faint optical signals; however, longer exposure times can also increase the thermal noise level in the images, requiring the active cooling found in more expensive cameras to control it.
- Video imaging: The use of video imaging mode combined with the image stacking computational approach results in an improved SNR.
- Streak imaging: The use of streak imaging with video mode enables the path of a cell to be captured over many pixels, which reduces the size of the imaging files needed for analysis. It also reduces the time for analysis, and enhances the imaging capabilities of imaging sensors having high noise levels.
- Filters: The quality of filters is very critical. Using high-quality, narrow band filters at the emission/excitation wavelengths reduces noise and improves detection.
- Assays: Fluorescence-based assays generating strong signals are ideal for low-sensitivity optical devices. For immunoassays, primary antibody immobilization can be enhanced by increasing the surface area for antibody binding through the use of nanoparticles, such as gold nanoparticles [121] or carbon nanotubes [118,119].
6. Conclusions
Acknowledgments
Conflicts of Interest
References
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Rasooly, R.; Bruck, H.A.; Balsam, J.; Prickril, B.; Ossandon, M.; Rasooly, A. Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health. Diagnostics 2016, 6, 19. https://doi.org/10.3390/diagnostics6020019
Rasooly R, Bruck HA, Balsam J, Prickril B, Ossandon M, Rasooly A. Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health. Diagnostics. 2016; 6(2):19. https://doi.org/10.3390/diagnostics6020019
Chicago/Turabian StyleRasooly, Reuven, Hugh Alan Bruck, Joshua Balsam, Ben Prickril, Miguel Ossandon, and Avraham Rasooly. 2016. "Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health" Diagnostics 6, no. 2: 19. https://doi.org/10.3390/diagnostics6020019