Jian Zhou

Flow split control promotes earlier heteroclinic orbit engagement, stabilizing inertial migration and improving target cell routing. Validation included CTC scale particles, tumor cell lines, and an IPMN patient sample.

Cancer survival rates increase with earlier diagnosis. However, many cancers are diagnosed only after symptoms develop, in the later stages of cancer progression. We need a diagnostic tool that can detect cancer earlier. Circulating tumour cells (CTCs) are cells that detach from the main tumour and can enter the bloodstream. The capture and analysis of CTCs can provide an early indicator of cancer. However, rarity of CTCs in blood makes their efficient capture challenging. Inertial microfluidics can be utilised for a label-free separation of CTCs from white blood cells (WBCs) by manipulating cells in the microchannel based on cell properties, achieving good separation performance. Inertial microfluidic devices mainly rely on size-based separation. However, residual WBC carryover limits complete separation. Here, we show that cell mechanical heterogeneity plays a large role in the separation process and should be considered in the design of these devices. We simulate the migration dynamics of CTCs and WBCs in a straight microchannel using a 3D lattice-Boltzmann-immersed-boundary-finite-element solver. Our results demonstrate that WBC migration behaviour changes depending on the deformability of a CTC. The size and deformability of the cells have been shown to determine the cell-specific heteroclinic orbits leading to different interaction types, these interactions alter WBC migration resulting in more/less WBC carryover. This work highlights that both cell-specific heteroclinic orbits and single cell migration rates should be considered in the design of inertial microfluidics for separation.

Recent advances in blood microfluidics have enabled rapid, precise, and cost-effective processing of whole blood. This review examines emerging microfluidic platforms categorized by sample type, including lysed blood, diluted blood, and whole blood.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer accounting for a majority of cases. Despite advances in targeted therapies and immunotherapy, challenges such as tumor heterogeneity, resistance mechanisms, and limited preclinical models hinder treatment efficacy. Traditional cancer models, including 2D cell cultures and animal models, often fail to accurately replicate the lung's complex architecture, microenvironment, and biomechanical cues, leading to poor predictive performance in drug development. Microfluidic-based organ-on-a-chip technology offers a promising alternative by integrating human-derived cells with precisely controlled perfusion, mechanical cues, and tumor–stroma interactions in physiologically relevant 3D models. These platforms enable the study of lung cancer biology, drug responses, and patient-specific therapeutic outcomes with improved accuracy. In this review, we discuss recent advancements in microfluidic systems for recapitulating normal lung physiology and 3D lung cancer microenvironment, covering various microfluidic platforms with applications in disease modeling and drug testing. Unlike other review articles, we bring first-hand insights from clinicians about the current treatment practice for lung cancer and the clinical utilities of lung cancer-on-a-chip models, which bioengineers have been seeking. We also highlight the translational potential of these systems in personalized oncology and the need for interdisciplinary collaborations, particularly with clinicians, to enhance their clinical impact.

Numerical modeling has played a pivotal role in advancing inertial microfluidics, tracing its development from inception and offering deeper insights into the microscale phenomena governing inertial focusing. These computational approaches have simultaneously supported the proliferation of on-chip technologies. Initially adopted across diverse industries for passive and high-throughput operations such as trapping, separation, and sorting of particles, the greatest potential of inertial microfluidics lies in biomedical applications, where it serves as a cornerstone for processing cells in clinical and research settings. As the range of applications continues to expand, microfluidic devices are evolving into increasingly complex systems capable of handling diverse cell types and particles within miniature chip architectures. This growing complexity necessitates the enhancement of conventional numerical techniques and the integration of innovative computational approaches to address these emerging challenges. This review aims to provide an overview of the available numerical techniques, highlighting their advantages and limitations. We explore recent strides in computational inertial microfluidics, emphasizing advancements within the last four years and the emergence of innovative methodologies such as smoothed particle hydrodynamics. Furthermore, we describe the nascent role of machine learning in inertial microfluidics, noting its limited adoption compared to conventional microfluidics and highlighting the potential to transform the field, as well as challenges that need to be overcome.

An inertial microfluidic platform enables label-free enrichment of rare circulating cells in pancreatic cancer, outperforming immunomagnetic separation and highlighting its translational potential for early detection and disease monitoring.

Inertial focusing has been utilized to advance assisted reproductive technologies (ART) for animal breeding and in vitro fertilization (IVF) by separating sperm cells from biofluids with complex cell backgrounds. While existing studies have aimed to design and optimize sperm separation devices, the fundamental mechanism behind the unique focusing behavior of sperm in spiral channels remains largely unknown: sperm cells focus near the outer wall, whereas most other cells focus near the inner wall. This is primarily due to the lack of a direct modelling scheme for capturing the detailed inertial migration of sperm cells in the spiral channels. In this work, we developed a 3D DNS-PT modeling approach that can predict the inertial focusing of sperm cells with long tails. Unlike previous studies that considered rotating spheres, the novelty of our approach is in extracting the inertial lift force for a triaxial ellipsoid (which represents the asymmetric oval-shaped sperm head) and accounting for the tail effect through appropriate boundary conditions, thus capturing their cumulative impact on sperm focusing behavior. Furthermore, we conducted inertial microfluidics experiments with fluorescent images of spermatozoa to validate the modelling results. We discovered that the effect of the tail, rather than the sperm head shape or orientation, is the primary determinant of the unique inertial focusing position of sperm cells in microchannels. The modelling results provided significant insights into the evolution of particle distribution in the channel cross-section along the flow direction, which was previously unknown due to the limitations of imaging techniques. The predicted particle trajectories enabled detailed analysis and explanation of the distinct migration paths of sperm cells and spherical particles. This work bridges the gap in our understanding of the inertial migration of sperm and other flagellated cells, facilitating the better design and optimization of sorting and separation devices.

We conducted a systematic numerical investigation of spherical, prolate and oblate particles in an inertial shear flow between two parallel walls, using smoothed particle hydrodynamics (SPH). It was previously shown that above a critical Reynolds number, spherical particles experience a supercritical pitchfork bifurcation of the equilibrium position in shear flow between two parallel walls, namely that the central equilibrium position becomes unstable, leading to the emergence of two new off-centre stable positions (Fox et al., J. Fluid Mech., vol. 915, 2021). This phenomenon was unexpected given the symmetry of the system. In addition to confirming this finding, we found, surprisingly, that ellipsoidal particles can also return to the centre position from the off-centre positions when the particle Reynolds number is further increased, while spherical particles become unstable under this increased Reynolds number. By utilizing both SPH and the finite element method for flow visualization, we explained the underlining mechanism of this reverse of bifurcation by altered streamwise vorticity and symmetry breaking of pressure. Furthermore, we expanded our investigation to include asymmetric particles, a novel aspect that had not been previously modelled, and we observed similar trends in particle dynamics for both symmetric and asymmetric ellipsoidal particles. While further validation through laboratory experiments is necessary, our research paves the road for development of new focusing and separation methods for shaped particles.

Patient-derived organoids (PDOs) serve as invaluable 3D tumor models, retaining the histological complexity and genetic heterogeneity found in primary tumors. However, the limitation of small sample volumes and the lack of tailored platforms have hindered the research using PDOs. Within the tumor microenvironment, cancer-associated fibroblasts play a pivotal role in influencing drug sensitivity. In this study, we introduce an agarose microwell platform designed for PDO-based tumor and tumor microenvironment models, enabling rapid drug screening and resistance studies with small sample volumes. These microwells, constructed using 3D printing molds, feature a U-shaped bottom and 200 μm diameter. We successfully generated co-culture spheroids of non-small cell lung carcinoma (NSCLC) cells, including NCI-H358 or A549, and NSCLC PDOs F231 or F671 with fibroblast cell line, WI-38. Our results demonstrate the production of uniformly-sized spheroids (coefficient of variation 80% after 1 week), and fibroblast-induced drug resistance. The PDOs maintained their viability (>81% after 2 weeks) and continued to proliferate. Notably, when exposed to adagrasib, a KRAS^G12C inhibitor, we observed reduced cytotoxicity in KRAS^G12C-mutant spheroids when co-cultured with fibroblasts or their supernatant. The fibroblast supernatant sustained proliferative signals in tumor models. Taking into account the physical features, viability, and drug resistance acquired through supernatants from the fibroblasts, our platform emerges as a suitable platform for in vitro tumor modeling and the evaluation of drug efficacy using patient-derived tissues.

Microfluidic methods have proven to be effective in separation and isolation of cells for a wide range of biomedical applications. Among these methods, physical trapping is a label-free isolation approach that relies on cell size as the selective phenotype to retain target cells on-chip for follow-up analysis and imaging. In silico models have been used to optimize the design of such hydrodynamic traps and to investigate cancer cell transmigration through narrow constrictions. While most studies focus on computational fluid dynamics (CFD) analysis of flow over cells and/or pillar traps, a quantitative analysis of mechanical interaction between cells and trapping units is missing. The existing literature centers on longitudinally extended geometries (e.g., micro-vessels) to understand the biological phenomenon rather than designing an effective cell trap. In this work, we aim to make an experimentally informed prediction of the critical pressure for a cell to pass through a trapping unit as a function of cell morphology and trapping unit geometry. Our findings show that a hyperelastic material model accurately captures the stress-related softening behavior observed in cancer cells passing through micro-constrictions. These findings are used to develop a model capable of predicting and extrapolating critical pressure values. The validity of the model is assessed with experimental data. Regression analysis is used to derive a mathematical framework for critical pressure. Coupled with CFD analysis, one can use this formulation to design efficient microfluidic devices for cell trapping and potentially perform downstream analysis of trapped cells.