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. 2022 May 26;13(6):833.
doi: 10.3390/mi13060833.

Automated Open-Hardware Multiwell Imaging Station for Microorganisms Observation

Alain Gervasi  1 , Pierre Cardol  1 , Patrick E Meyer  2
Affiliations

Automated Open-Hardware Multiwell Imaging Station for Microorganisms Observation

Alain Gervasi et al. Micromachines (Basel). .

Abstract

Bright field microscopes are particularly useful tools for biologists for cell and tissue observation, phenotyping, cell counting, and so on. Direct cell observation provides a wealth of information on cells' nature and physiological condition. Microscopic analyses are, however, time-consuming and usually not easy to parallelize. We describe the fabrication of a stand-alone microscope able to automatically collect samples with 3D printed pumps, and capture images at up to 50× optical magnification with a digital camera at a good throughput (up to 24 different samples can be collected and scanned in less than 10 min). Furthermore, the proposed device can store and analyze pictures using computer vision algorithms running on a low power integrated single board computer. Our device can perform a large set of tasks, with minimal human intervention, that no single commercially available machine can perform. The proposed open-hardware device has a modular design and can be freely reproduced at a very competitive price with the use of widely documented and user-friendly components such as Arduino, Raspberry pi, and 3D printers.

Keywords: IoT; bio-automation; edge computing; microbiology; microscopy; open-hardware.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
General design of the device. Our machine has been made from the chassis of an AnetA8 3D printer (A) whose plastic extruder has been removed and replaced by a magnetic support (B) to install a digital camera (C) and a lens (E) to obtain an optical magnification of 50×. A support for a micropipette tip has been added (D) to allow deposition of samples in two multi-well plates of twelve wells with transparent bottom (F) fixed on the bed of the 3D printer above 24 RGBW addressable LEDs (SK6812). The motherboard of the printer (G) has been modified to be controlled by a single board computer (Jetson Nano) (H) which captures the image of the camera and displays it in real time on the LCD screen (I). A second security camera (J) has also been added to monitor the remote operations.
Figure 2
Figure 2
Modification of the Anet A8 motherboard. This modification allows driving the 3D printer in MQTT via its serial port. The motherboard has been set in “Serial” mode by bridging two pads (A) and then an ESP8266 (B) has been connected to the VCC, GND, TX, and RX of the motherboard. It is worth noting that the serial connection with the microcontroller must be “crossed”, meaning the TX of the ESP8266 connects to the RX of the motherboard and the RX connects to the TX.
Figure 3
Figure 3
Digital microscope and its magnetic support. The microscope is composed of a photo sensor (blue) fixed on the green support with four screws, as well as the lens support (red) placed in front of the sensor and holding an inverted camera lens (black) that allows a 50× optical magnification. The camera is placed on the white magnetic support fixed on the X axis of the 3D printer. The camera support has been 3D printed in translucent ABS using a Prusa I3 MK3 (nozzle: 250 °C, Bed: 100 °C, nozzle: 0.4 mm, layer height: 0.2 mm, filling: 100%). The lens must be perpendicular to the sensor to avoid optical aberrations on the edge of the images.
Figure 4
Figure 4
Automatic sampler. The sample preparation system is composed of a 3D-printed peristaltic pump [30] (orange; A) connected to a micropipette tip held by a magnetic support (black; B) placed next to the camera (white; B).
Figure 5
Figure 5
Sample holder and lighting systems. RGBW LEDs were mounted on the sample holder to illuminate individual wells of two 12-well clear-bottom plates (A); 24- or 96-well plates can also be used (B).
Figure 6
Figure 6
Node-RED dashboard. The graphical user interface is composed of four panels (debug cam, light, manual control, and cam). The first panel (A) allows access to the security camera (Cam) to remotely view the machine to ensure its proper working or to access the microscope image (Microscope). The second panel (B) allows controlling the light. The control panel (C) allows manually moving the camera on its three axes and returns the current position of the camera. The plate menu (D) allows moving the camera to various pre-programmed positions, and turning on the light in individual wells. The “SP” (safe position) button sends automatically the camera to the upper right corner of the robot and advances the plate towards the user, thus facilitating the installation of multi-well plates, while the “LP” (last position) button returns the camera to its previous position (in case the user accidentally moves the camera). The panel (E) allows capturing and naming of images. Finally, panel (F) performs the autofocus.
Figure 7
Figure 7
Autofocus. The autofocus procedure consists in measuring the sharpness of 20 images captured at different heights to determine the optimal camera positioning. The arrows schematize the vertical movement of the camera (in mm) while the plot represents the “relative sharpness” of the capture returned by the OpenCV Laplacian function.
Figure 8
Figure 8
Image analysis process of an E. gracilis sample. An image is captured after performing the autofocus (A) and is converted to a binary thresholded image (B) used for the edge detection (C). The areas lower than a predefined threshold are removed and the annotation (number of cells and dimensions) are added to the image (D).
Figure 9
Figure 9
Microscope calibration of two lenses with a 1920 × 1080p camera. The 2.8 mm lens (A) was calibrated with a 10 µm/division slide and the 16 mm lens (B) has been calibrated with a 100 µm/division reference slide.
Figure 10
Figure 10
Examples of micrographs. Euglena gracilis (A) and Chlamydomonas reinhardtii (B) micrographs captured by our DIY microscope using a 2.8 mm lens that produced a 50× magnification.
Figure 11
Figure 11
Evaluation of the repeatability of the camera positioning. Ten images have been captured and superimposed after homing and successive repositioning of the camera. The blue lines represent the deviation from the initial position and the red lines represent the deviation from the previous position.
Figure 12
Figure 12
Observation of the evolution of morphology of algal cells exposed to chemicals. Briefly, 2 mL cultures of E. gracilis (blue) and C. reinhardtii (red) with an initial concentration of 105 cells/mL were exposed during 10 days to different concentrations of cycloheximide (0.5 µg/mL, A1; 1 µg/mL, A2; 1.5 µg/mL, A3; 2 µg/mL, A4), DMSO (1%, B1; 2%, B2; 4%, B3; 6%, B4), ethanol (0.5%, C1; 1%, C2; 1.5%, C3; 2%, C4), and streptomycin (150 µg/mL, D1; 350 µg/mL, D2; 500 µg/mL, D3; 750 µg/mL, D4). Top pictures: control cultures. In brief, most chemical concentrations slow growth of both algae; DMSO modifies the length of E. gracilis cells; presence of ethanol causes bleaching of E. gracilis cells and streptomycin causes the appearance of colorless spheroids in E. gracilis.
Figure 13
Figure 13
Effect of light color on microalgae motility. Aggregation of C. reinhardtii cells (7 × 106 cells mL−1) exposed to white light (A–F) or blue light (G–I) after a 5 min treatment with red light. The concentration of the culture does not change between the beginning and the end of the experiment, the lower number of cells seen at the beginning of the experiment comes from the fact that the algae are able to swim freely in three dimensions and can, therefore, move away from the camera’s focus.
Figure 14
Figure 14
Microalgae tracking. (A) Tracking a E. gracilis cell to determine its trajectory. Cell concentration in the well was 2 × 105 cells mL−1. The tracking is performed with a OpenCV script allowing the user to click on the cell to track. The cell’s path will then be drawn in real time on the live video. (B) Analysis of the direction of movement of all the cells in a culture. Green lines allow determining the direction and speed of the algae (the length of the lines is relative to the speed). Both scripts allow obtaining the speed in µm/s.
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Grants and funding

This research was funded by the Uliège welcome grant of P.E.M and the Belgian Fonds de la Recherche Scientifique F.R.S.-FNRS (PDR T.0032), BELSPO BRAIN (project B2/212/PI/PORTAL) and European Research Council (ERC, H2020-EU BEAL project 682580). P.C. is Senior Research Associate from F.R.S.-FNRS.