Smartphone-based cytometric biosensors for point-of-care cellular diagnostics:

Brightfield microscopy is a simple method that has been frequently used in traditional cytometry, such as cell counting with a hemocytometer on an optical microscope. On the smartphone, brightfield imaging can be achieved by adding an external lens in front of the smartphone camera and using a white light-emitting diode (LED) as the illumination source. The optical magnification (M) can be calculated as: M = f₁/f₂, where f₁ is the focal length of the built-in lens of the smartphone camera, and f₂ is the focal length of the external lens. As such, using an external lens with a shorter focal length can bring higher magnification and better spatial resolution. Currently, smartphone brightfield microscopy has achieved submicron resolution,²⁷27. Bogoch II, Koydemir HC, Tseng D, Ephraim RKD, Duah E, Tee J, Andrews JR, Ozcan A. Evaluation of a mobile phone-based microscope for screening of Schistosoma haematobium infection in rural Ghana. Am. J. Trop. Med. Hyg. 2017;96:1468–1471. https://doi.org/10.4269/ajtmh.16-0912 which enables users to visualize single cells with great subcellular details on the smartphone. By analyzing images from the smartphone microscope, both quantity and morphology information of different cells can be obtained.

Smartphone brightfield imaging has been applied for cytometric counting of various targets such as blood cells and pathogens. In 2009, Breslauer et al. reported a mobile phone microscope for blood cell imaging under brightfield conditions. By using a set of benchtop microscope objective and eyepiece, they imaged the malaria-infected blood sample and sickle cell anemia sample.²⁹29. Breslauer DN, Maamari RN, Switz NA, Lam WA, Fletcher DA. Mobile phone based clinical microscopy for global health applications. PLoS One 2009;4:e6320. https://doi.org/10.1371/journal.pone.0006320 Zhu et al. reported a smartphone imaging cytometer which is capable of counting RBCs through the brightfield mode. An f = 4 mm external lens was placed in front of the smartphone camera. Captured images were analyzed by a smartphone app, and a correlation between the smartphone counter and traditional flow cytometer was established.⁴⁷47. Zhu H, Sencan I, Wong J, et al. Cost-effective and rapid blood analysis on a cell-phone. Lab Chip 2013;13:1282–1288. https://doi.org/10.1039/c3lc41408f Rapid CD4 cell testing has also been demonstrated on a smartphone-based brightfield microscope by Kanakasabapathy et al. (Fig. 4a). CD4 cells flowed through the imaging chamber and were captured on a microfluidic chip that was functionalized with anti-CD4 antibodies. After washing with the buffer solution, the chip was imaged by the smartphone scope, and the numbers of CD4 cells were quantified and converted into the cell concentration in the sample. Both CD4-spiked samples and whole blood samples were tested on the smartphone cytometer, and the results were compared with those obtained by FACS, a gold-standard method in CD4 cell quantification.⁴⁰40. Kanakasabapathy MK, Pandya HJ, Draz MS, et al. Rapid, label-free CD4 testing using a smartphone compatible device. Lab Chip 2017;17:2910–2919. https://doi.org/10.1039/c7lc00273d Somatic cell count (SCC) is an important indicator of dairy quality control. Zeng et al. designed an imaging platform to perform the SCC test on a smartphone. They used a single-ball lens for magnification and attached it onto the smartphone. The diluted milk sample was injected into a microfluidic chamber for SCC imaging. A custom algorithm and smartphone app were developed to identify and count single somatic cells rapidly.⁴⁸48. Zeng Y, Jin K, Li J, et al. A low cost and portable smartphone microscopic device for cell counting. Sens. Actuators, A 2018;274:57–63. https://doi.org/10.1016/j.sna.2018.03.009

Smartphone-based video microscopy has also been used for cell counting. Moravapalle et al. designed a smartphone blood cytometer with an XYZ translational stage for large-area scanning and imaging. Video clips that scanned the whole 3 mm by 3 mm imaging area were recorded. Each frame in the video was then extracted and processed with an algorithm that is pre-trained to recognize and generate cell counts. This algorithm is also capable of preventing duplicated counts by identifying the built-in square patterns on the hemocytometer and only counts once for the cells in each square. A relatively low counting error of 7% was achieved for RBC counting on this platform.⁴⁹49. Moravapalle UP, Deshpande A, Kapoor A, Ramjee R, Ravi P. Blood count on a smartphone microscope: Challenges. Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications 2017:37–42. D’Ambrosio and colleagues quantified blood-borne filarial parasites by recording video clips of whole blood samples with a smartphone imaging cytometer that provides a large FOV of 4 × 3.2 mm² (Fig. 4b). In this integrated platform, a microcontroller was used to control the liquid flow and optical illumination on the smartphone microscope. Short videos were acquired when the blood samples flowed through the sample chamber. In order to identify the presence of parasites, each frame in the video was subtracted with the averaged image of all the frames. The peaks on the differential image suggested the presence of the parasite. By imaging blood samples from a total area of 5 FOVs, which corresponded to a total sample volume of ⁵⁰50. D’ambrosio MV, Bakalar M, Bennuru S, Reber C, Skandarajah A, Nilsson L, Switz N, Kamgno J, Pion S, Boussinesq M. <span…