High-throughput adjustable deformability cytometry utilizing elasto-inertial focusing and virtual fluidic channel

A Miniature Modular Fluorescence Flow Cytometry System

Fluorescence flow cytometry is a powerful instrument to distinguish cells or particles labelled with high-specificity fluorophores. However, traditional flow cytometry is complex, bulky, and inconvenient for users to adjust fluorescence channels. In this paper, we present a modular fluorescence flow cytometry (M-FCM) system in which fluorescence channels can be flexibly arranged. Modules for particle focusing and fluorescence detection were developed. After hydrodynamical focusing, the cells were measured in the detection modules, which were integrated with in situ illumination and fluorescence detection. The signal-to-noise ratio of the detection reached to 33.2 dB. The crosstalk among the fluorescence channels was eliminated. The M-FCM system was applied to evaluate cell viability in drug screening, agreeing well with the commercial cytometry. The modular cytometry presents several outstanding features: flexibility in setting fluorescence channels, cost efficiency, compact construction, ease of operation, and the potential to upgrade for multifunctional measurements. The modular cytometry provides a multifunctional platform for various biophysical measurements, e.g., electrical impedance and refractive-index detection. The proposed work paves an innovative avenue for the multivariate analysis of cellular characteristics.

Liquid Biopsy Instrument for Ultra-Fast and Label-Free Detection of Circulating Tumor Cells

Rapid diagnosis and real-time monitoring are of great important in the fight against cancer. However, most available diagnostic technologies are time-consuming and labor-intensive and are commonly invasive. Here, we describe CytoExam, an automatic liquid biopsy instrument designed based on inertial microfluidics and impedance cytometry, which uses a deep learning algorithm for the analysis of circulating tumor cells (CTCs). In silico and in vitro experiments demonstrated that CytoExam could achieve label-free detection of CTCs in the peripheral blood of cancer patients within 15 min. The clinical applicability of CytoExam was also verified using peripheral blood samples from 10 healthy donors and >50 patients with breast, colorectal, or lung cancer. Significant differences in the number of collected cells and predicted CTCs were observed between the 2 groups, with variations in the dielectric properties of the collected cells from cancer patients also being observed. The ultra-fast and minimally invasive features of CytoExam may pave the way for new paths for cancer diagnosis and scientific research.

Multiparameter Mechanical Phenotyping for Accurate Cell Identification Using High-Throughput Microfluidic Deformability Cytometry

Mechanical phenotyping has been widely employed for single-cell analysis over recent years. However, most previous works on characterizing the cellular mechanical properties measured only a single parameter from one image. In this paper, the quasi-real-time multiparameter analysis of cell mechanical properties was realized using high-throughput adjustable deformability cytometry. We first extracted 12 deformability parameters from the cell contours. Then, the machine learning for cell identification was performed to preliminarily verify the rationality of multiparameter mechanical phenotyping. The experiments on characterizing cells after cytoskeletal modification verified that multiple parameters extracted from the cell contours contributed to an identification accuracy of over 80%. Through continuous frame analysis of the cell deformation process, we found that temporal variation and an average level of parameters were correlated with cell type. To achieve quasi-real-time and high-precision multiplex-type cell detection, we constructed a back propagation (BP) neural network model to complete the fast identification of four cell lines. The multiparameter detection method based on time series achieved cell detection with an accuracy of over 90%. To solve the challenges of cell rarity and data lacking for clinical samples, based on the developed BP neural network model, the transfer learning method was used for the identification of three different clinical samples, and finally, a high identification accuracy of approximately 95% was achieved.

Sheathless inertial particle focusing methods within microfluidic devices: a review

The ability to manipulate and focus particles within microscale fluidic environments is crucial to advancing biological, chemical, and medical research. Precise and high-throughput particle focusing is an essential prerequisite for various applications, including cell counting, biomolecular detection, sample sorting, and enhancement of biosensor functionalities. Active and sheath-assisted focusing techniques offer accuracy but necessitate the introduction of external energy fields or additional sheath flows. In contrast, passive focusing methods exploit the inherent fluid dynamics in achieving high-throughput focusing without external actuation. This review analyzes the latest developments in strategies of sheathless inertial focusing, emphasizing inertial and elasto-inertial microfluidic focusing techniques from the channel structure classifications. These methodologies will serve as pivotal benchmarks for the broader application of microfluidic focusing technologies in biological sample manipulation. Then, prospects for future development are also predicted. This paper will assist in the understanding of the design of microfluidic particle focusing devices.