This dissertation demonstrates the development of a portable and low cost lab-on-a-chip flow cytometer, in which various methods are applied to achieve label-free detections for rapid, accurate and high performance point- of-care applications. This work is based on the measurement of scattering light from cells or beads in the microfluidic channel to either collect scattering signals or image scattering light. An optical-coding technique is proposed to enhance the cell classification and minimize the cost with the compact form. The encoded scattering signals are collected by off-the-shelf PIN photoreceivers. The spatial distribution of cells or beads in the microfluidic channel can be readily extracted with the specific waveform. Although the theory of the inertial effect can explain the spatial distribution of objects with different sizes, the stiffness of cells is found to play a role on the distribution. The stiffness of cells can be used as an effective biomarker for cell classification. Moreover, with the aid of machine learning algorithms, the optical-coding technique can be expanded to proceed a multi-dimensional analysis, which further enhances the performance of the microfluidic flow cytometer. Recently, the study of sub-cellular structure has drawn more attention because the size and shape of cellular nucleus carry important information about biological activities such as apoptosis, cell cycle, cancerous metastasis, etc. However, current technologies have only used labeling technique to image cell nuclei for further observation. Another part of this work is to build up a label-free technique to unambiguously retrieve the information of cell nucleus, more importantly, by using widely available CMOS imagers. The scattering-imaging- based cytometer is demonstrated to record the scattering images from cancer or human white blood cells. The signature of cells can be graphically established to represent the size, shape and orientation of cell nucleus. Such a low-cost, compact, portable lab-on-a-chip cytometer platform can be easily afforded by individual clinics and research labs, satisfying point-of-care diagnosis and telemedicine applications. The development executed in this dissertation is aiming to improve the global health and lead a better life for human beings

Rapid and low-cost pathogen diagnostic approaches are critical for clinical decision-making procedures. Cultivating bacteria often takes days to identify pathogens and provide antimicrobial susceptibilities. The delay in diagnosis may result in compromised treatment and inappropriate antibiotic use. Over the past decades, molecular-based techniques have significantly shortened pathogen identification turnaround time with high accuracy. However, these assays often use complex fluorescent labeling and nucleic acid amplification processes, which limit their use in resource-limited settings. In this work, we demonstrate a wash-free molecular agglutination assay with a straightforward mixing and incubation step that significantly simplifies procedures of molecular testing. By targeting the 16S rRNA gene of pathogens, we perform a rapid pathogen identification within 30 min on a dark-field imaging microfluidic cytometry platform. The dark-field images with low background noise can be obtained using a narrow beam scanning technique with off-the-shelf complementary metal oxide semiconductor (CMOS) imagers such as smartphone cameras. We utilize a machine learning algorithm to deconvolute topological features of agglutinated clusters and thus quantify the abundance of bacteria. Consequently, we unambiguously distinguish Escherichia coli positive from other E. coli negative among 50 clinical urinary tract infection samples with 96% sensitivity and 100% specificity. Furthermore, we also apply this quantitative detection approach to achieve rapid antimicrobial susceptibility testing within 3 h. This work exhibits easy-to-use protocols, high sensitivity, and short turnaround time for point-of-care testing uses.