Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow

Developing a Reliable Holographic Flow Cyto-Tomography Apparatus by Optimizing the Experimental Layout and Computational Processing

Digital Holographic Tomography (DHT) has recently been established as a means of retrieving the 3D refractive index mapping of single cells. To make DHT a viable system, it is necessary to develop a reliable and robust holographic apparatus in order that such technology can be utilized outside of specialized optics laboratories and operated in the in-flow modality. In this paper, we propose a quasi-common-path lateral-shearing holographic optical set-up to be used, for the first time, for DHT in a flow-cytometer modality. The proposed solution is able to withstand environmental vibrations that can severely affect the interference process. Furthermore, we have scaled down the system while ensuring that a full 360° rotation of the cells occurs in the field-of-view, in order to retrieve 3D phase-contrast tomograms of single cells flowing along a microfluidic channel. This was achieved by setting the camera sensor at 45° with respect to the microfluidic direction. Additional optimizations were made to the computational elements to ensure the reliable retrieval of 3D refractive index distributions by demonstrating an effective method of tomographic reconstruction, based on high-order total variation. The results were first demonstrated using realistic 3D numerical phantom cells to assess the performance of the proposed high-order total variation method in comparison with the gold-standard algorithm for tomographic reconstructions: namely, filtered back projection. Then, the proposed DHT system and the processing pipeline were experimentally validated for monocytes and mouse embryonic fibroblast NIH-3T3 cells lines. Moreover, the repeatability of these tomographic measurements was also investigated by recording the same cell multiple times and quantifying the ability to provide reliable and comparable tomographic reconstructions, as confirmed by a correlation coefficient greater than 95%. The reported results represent various steps forward in several key aspects of in-flow DHT, thus paving the way for its use in real-world applications.

Machine Learning-Driven Multiobjective Optimization: An Opportunity of Microfluidic Platforms Applied in Cancer Research

Cancer metastasis is one of the primary reasons for cancer-related fatalities. Despite the achievements of cancer research with microfluidic platforms, understanding the interplay of multiple factors when it comes to cancer cells is still a great challenge. Crosstalk and causality of different factors in pathogenesis are two important areas in need of further research. With the assistance of machine learning, microfluidic platforms can reach a higher level of detection and classification of cancer metastasis. This article reviews the development history of microfluidics used for cancer research and summarizes how the utilization of machine learning benefits cancer studies, particularly in biomarker detection, wherein causality analysis is useful. To optimize microfluidic platforms, researchers are encouraged to use causality analysis when detecting biomarkers, analyzing tumor microenvironments, choosing materials, and designing structures.

Review: tomographic imaging flow cytometry

Within the last decades, conventional flow cytometry (FC) has evolved as a powerful measurement method in clinical diagnostics, biology, life sciences and healthcare. Imaging flow cytometry (IFC) extends the power of traditional FC by adding high resolution optical and spectroscopic information. However, the conventional IFC only provides a 2D projection of a 3D object. To overcome this limitation, tomographic imaging flow cytometry (tIFC) was developed to access 3D information about the target particles. The goal of tIFC is to visualize surfaces and internal structures in a holistic way. This review article gives an overview of the past and current developments in tIFC.

Fused Filament Fabrication (FFF) for Manufacturing of Microfluidic Micromixers: An Experimental Study on the Effect of Process Variables in Printed Microfluidic Micromixers

The need for accessible and inexpensive microfluidic devices requires new manufacturing methods and materials as a replacement for traditional soft lithography and polydimethylsiloxane (PDMS). Recently, with the advent of modern additive manufacturing (AM) techniques, 3D printing has attracted attention for its use in the fabrication of microfluidic devices and due to its automated, assembly-free 3D fabrication, rapidly decreasing cost, and fast-improving resolution and throughput. Here, fused filament fabrication (FFF) 3D printing was used to create microfluidic micromixers and enhance the mixing process, which has been identified as a challenge in microfluidic devices. A design of experiment (DoE) was performed on the effects of studied parameters in devices that were printed by FFF. The results of the colorimetric approach showed the effects of different parameters on the mixing process and on the enhancement of the mixing performance in printed devices. The presence of the geometrical features on the microchannels can act as ridges due to the nature of the FFF process. In comparison to passive and active methods, no complexity was added in the fabrication process, and the ridges are an inherent property of the FFF process.

A Design of Microfluidic Chip with Quasi-Bessel Beam Waveguide for Scattering Detection of Label-Free Cancer Cells

Light scattering detection in microfluidic chips provides an important tool to identify cancer cells without any label processes. However, forward small-angle scattering signals of cells, which are related to their sizes and morphologies, are hard to be detected accurately when scattering angle is less than 11° in microfluidic chips by traditional lighting design due to the influence of incident beam. Therefore, cell's size and morphology being the golden standard for clinical detection may lose their efficacy in recognizing cancer cells from healthy ones. In this article, a novel lighting design in microfluidic chips is put forward in which traditional incident Gaussian beam can be modulated into quasi-Bessel beam by a microprism and waveguide. The quasi-Bessel beam's advantages of nondiffraction theoretically make forward scattering (FS) detection less than 11° possibly. Our experimental results for peripheral blood lymphocytes of human beings and cultured HeLa cells show that the detection rates increase by 47.87% and 46.79%, respectively, by the novel designed microfluidic chip compared to traditional Gaussian lighting method in microfluidic chips. © 2019 International Society for Advancement of Cytometry.

Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification

Cell classification based on phenotypical, spatial, and genetic information greatly advances our understanding of the physiology and pathology of biological systems. Technologies derived from next generation sequencing and fluorescent activated cell sorting are cornerstones for cell- and genomic-based assays supporting cell classification and mapping. However, there exists a deficiency in technology space to rapidly isolate cells based on high content image information. Fluorescence-activated cell sorting can only resolve cell-to-cell variation in fluorescence and optical scattering. Utilizing microfluidics, photonics, computation microscopy, real-time image processing and machine learning, we demonstrate an image-guided cell sorting and classification system possessing the high throughput of flow cytometer and high information content of microscopy. We demonstrate the utility of this technology in cell sorting based on (1) nuclear localization of glucocorticoid receptors, (2) particle binding to the cell membrane, and (3) DNA damage induced γ-H2AX foci. © 2019 International Society for Advancement of Cytometry.

Review: imaging technologies for flow cytometry

High-throughput single cell imaging is a critical enabling and driving technology in molecular and cellular biology, biotechnology, medicine and related areas. Imaging flow cytometry combines the single-cell imaging capabilities of microscopy with the high-throughput capabilities of conventional flow cytometry. Recent advances in imaging flow cytometry are remarkably revolutionizing single-cell analysis. This article describes recent imaging flow cytometry technologies and their challenges.