We measure the organization and substructure of HT29 epithelial cells in a monolayer using angle-resolved low-coherence interferometry. This new technique probes cellular structure by measuring scattered light, as in flow cytometry, but offers an advantage in that the structure can be examined in situ, avoiding the need to disrupt the cell monolayer. We determine the size distribution of the cell nuclei by fitting measured light-scattering spectra to the predictions of Mie theory. In addition, we obtain information about the cellular organization and substructure by examining the spatial correlations within the monolayer. A remarkable finding is that the spatial correlations over small length scales take the form of an inverse power law, indicating the fractal nature of the packing of the subcellular structures. We also identify spatial correlations on a scale large compared with the size of a cell, indicating an overlying order within the monolayer.

Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis.

The refractive index (RI) of biological materials is a fundamental parameter for the optical characterization of living systems. Numerous light scattering technologies are grounded in a quantitative knowledge of the refractive index at cellular and subcellular scales. Recent work in quantitative phase microscopy (QPM) has called into question the widely held assumption that the index of the cell nucleus is greater than that of the cytoplasm, a result which disagrees with much of the current literature. In this work, we critically examine the measurement of the nuclear and whole-cell refractive index using QPM, validating that nuclear refractive index is lower than that of cytoplasm in four diverse cell lines and their corresponding isolated nuclei. We further examine Mie scattering and phase-wrapping as potential sources of error in these measurements, finding they have minimal impact. Finally, we use simulation to examine the effects of incorrect RI assumptions on nuclear morphology measurements using angle-resolved scattering information. Despite an erroneous assumption of the nuclear refractive index, accurate measurement of nuclear morphology was maintained, suggesting that light scattering modalities remain effective.

There have been sustained efforts on the part of cell biologists to understand the mechanisms by which cells respond to mechanical stimuli. To this end, many rheological tools have been developed to characterize cellular stiffness. However, measurement of cellular viscoelastic properties has been limited in scope by the nature of most microrheological methods, which require direct mechanical contact, applied at the single-cell level. In this article, we describe, to our knowledge, a new analysis approach for quantitative phase imaging that relates refractive index variance to disorder strength, a parameter that is linked to cell stiffness. Significantly, both disorder strength and cell stiffness are measured with the same phase imaging system, presenting a unique alternative for label-free, noncontact, single-shot imaging of cellular rheologic properties. To demonstrate the potential applicability of the technique, we measure phase disorder strength and shear stiffness across five cellular populations with varying mechanical properties and demonstrate an inverse relationship between these two parameters. The existence of this relationship suggests that predictions of cell mechanical properties can be obtained from examining the disorder strength of cell structure using this, to our knowledge, novel, noncontact technique.

Optical coherence tomography (OCT) is a widely used biomedical imaging tool, primarily in ophthalmology to diagnose and stage retinal diseases. In order to increase access for a wider range of applications and in low resource settings, we developed a portable, low-cost OCT system that has comparable imaging performance to commercially available systems. Here, we present the system design and characterization and compare the system performance to other commercially available OCT systems. In addition, future cost reductions and potential additional applications of the low-cost OCT system are discussed.