Experimental measurement and numerical modeling of deformation behavior of breast cancer cells passing through constricted microfluidic channels.

During the multistep process of metastasis, cancer cells encounter various mechanical forces which make them deform drastically. Developing accurate in-silico models, capable of simulating the interactions between the mechanical forces and highly deformable cancer cells, can pave the way for the development of novel diagnostic and predictive methods for metastatic progression. Spring-network models of cancer cell, empowered by our recently proposed identification approach, promises a versatile numerical tool for developing experimentally validated models that can simulate complex interactions at cellular scale. Using this numerical tool, we presented spring-network models of breast cancer cells that can accurately replicate the experimental data of deformation behavior of the cells flowing in a fluidic domain and passing narrow constrictions comparable to microcapillary. First, using high-speed imaging, we experimentally studied the deformability of breast cancer cell lines with varying metastatic potential (MCF-7 (less invasive), SKBR-3 (medium-high invasive), and MDA-MB-231 (highly invasive)) in terms of their entry time to a constricted microfluidic channel. We observed that MDA-MB-231, that has the highest metastatic potential, is the most deformable cell among the three. Then, by focusing on this cell line, experimental measurements were expanded to two more constricted microchannel dimensions. The experimental deformability data in three constricted microchannel sizes for various cell sizes, enabled accurate identification of the unknown parameters of the spring-network model of the breast cancer cell line (MDA-MB-231). Our results show that the identified parameters depend on the cell size, suggesting the need for a systematic procedure for identifying the size-dependent parameters of spring-network models of cells. As the numerical results show, the presented cell models can simulate the entry process of the cell into constricted channels with very good agreements with the measured experimental data.

Sub-terahertz silicon-based on-chip absorption spectroscopy using thin-film model for biological applications.

Spectroscopy in the sub-terahertz (sub-THz) range of frequencies has been utilized to study the picosecond dynamics and interaction of biomolecules. However, widely used free-space THz spectrometers are typically limited in their functionality due to low signal-to-noise ratio and complex setup. On-chip spectrometers can revolutionize THz spectroscopy allowing integration, compactness, and low-cost fabrication. In this paper, a low-loss silicon-based platform is proposed for on-chip sub-THz spectroscopy. Through functionalization of silicon chip and immobilization of bio-particles, we demonstrate the ability to characterize low-loss nano-scale biomolecules across the G-band (0.14-0.22 THz). We also introduce an electromagnetic thin-film model to account for the loading effect of the immobilized biomolecules, i.e. dehydrated streptavidin and immunoglobulin antibody, as two key molecules in the biosensing discipline. The proposed platform was fabricated using a single mask micro-fabrication process, and then measured by a vector network analyzer (VNA), which offers high dynamic range and high spectral resolution measurements. The proposed planar platform is general and paves the way towards low-loss, cost-effective and integrated sub-THz biosensors for the detection and characterization of biomolecules.

Unraveling Cancer Metastatic Cascade Using Microfluidics-based Technologies.

Cancer has long been a leading cause of death. The primary tumor, however, is not the main cause of death in more than 90% of cases. It is the complex process of metastasis that makes cancer deadly. The invasion metastasis cascade is the multi-step biological process of cancer cell dissemination to distant organ sites and adaptation to the new microenvironment site. Unraveling the metastasis process can provide great insight into cancer death prevention or even treatment. Microfluidics is a promising platform, that provides a wide range of applications in metastasis-related investigations. Cell culture microfluidic technologies for in vitro modeling of cancer tissues with fluid flow and the presence of mechanical factors have led to the organ-on-a-chip platforms. Moreover, microfluidic systems have also been exploited for capturing and characterization of circulating tumor cells (CTCs) that provide crucial information on the metastatic behavior of a tumor. We present a comprehensive review of the recent developments in the application of microfluidics-based systems for analysis and understanding of the metastasis cascade from a wider perspective.

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers.

During cancer progression, tumors shed different biomarkers into the bloodstream, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). The analysis of these biomarkers in the blood, known as 'liquid biopsy' (LB), is a promising approach for early cancer detection and treatment monitoring, and more recently, as a means for cancer therapy. Previous reviews have discussed the role of CTCs and ctDNA in cancer progression; however, ctDNA and EVs are rapidly evolving with technological advancements and computational analysis and are the subject of enormous recent studies in cancer biomarkers. In this review, first, we introduce these cell-released cancer biomarkers and briefly discuss their clinical significance in cancer diagnosis and treatment monitoring. Second, we present conventional and novel approaches for the isolation, profiling, and characterization of these markers. We then investigate the mathematical and in silico models that are developed to investigate the function of ctDNA and EVs in cancer progression. We convey our views on what is needed to pave the way to translate the emerging technologies and models into the clinic and make the case that optimized next-generation techniques and models are needed to precisely evaluate the clinical relevance of these LB markers.