3-Step flow focusing enables multidirectional imaging of bioparticles for imaging flow cytometry

Neuromorphic-enabled video-activated cell sorting

Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition. However, existing IACS frameworks suffer from the challenges of 3D information loss and processing latency dilemma in real-time sorting operation. Herein, we establish a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, designed to achieve high-dimensional spatiotemporal characterization content alongside high-throughput sorting of particles in wide field of view. NEVACS adopts event camera, CPU, spiking neural networks deployed on a neuromorphic chip, and achieves sorting throughput of 1000 cells/s with relatively economic hybrid hardware solution (~$10 K for control) and simple-to-make-and-use microfluidic infrastructures. Particularly, the application of NEVACS in classifying regular red blood cells and blood-disease-relevant spherocytes highlights the accuracy of using video over a single frame (i.e., average error of 0.99% vs 19.93%), indicating NEVACS' potential in cell morphology screening and disease diagnosis.

Recent Technologies on 2D and 3D Imaging Flow Cytometry

Imaging flow cytometry is a technology that performs microscopy image analysis of cells within flow cytometry and allows high-throughput, high-content cell analysis based on their intracellular molecular distribution and/or cellular morphology. While the technology has been available for a couple of decades, it has recently gained significant attention as technical limitations for higher throughput, sorting capability, and additional imaging dimensions have been overcome with various approaches. These evolutions have enabled imaging flow cytometry to offer a variety of solutions for life science and medicine that are not possible with conventional flow cytometry or microscopy-based screening. It is anticipated that the extent of applications will expand in the upcoming years as the technology becomes more accessible through dissemination. In this review, we will cover the technical advances that have led to this new generation of imaging flow cytometry, focusing on the advantages and limitations of each technique.

On-chip light sheet illumination for nanoparticle tracking in microfluidic channels

A simple and inexpensive method is presented to efficiently integrate light sheet illumination in a microfluidic chip for dark-field microscopic tracking and sizing of nanoparticles. The basic idea is to insert an optical fiber inside a polydimethylsiloxane (PDMS) elastomer microfluidic chip and use it as a cylindrical lens. The optical fiber is in this case no longer seen as only an optical waveguide but as a ready-made micro-optical component that is inexpensive and easy to source. Upon insertion, the optical fiber stretches the PDMS microchannel walls, which has two effects. The first effect is to tone down the intrinsic ripples in the PDMS that would otherwise create inhomogeneities in the light sheet illumination. The second effect is to remove any obliqueness of the channel wall and constrain it to be strictly perpendicular to the propagation of the illumination, avoiding the formation of a prismatic diopter. Through calculations, numerical simulations and measurements, we show that the optimal configuration consists in creating a slowly converging light sheet so that its axial thickness is almost uniform along the tracked area. The corresponding thickness was estimated at 12 μm, or 10 times the depth of field of the optical system. This leads to an at least six-fold increase in the signal-to-noise ratio compared to the case without the cylindrical lens. This original light-sheet configuration is used to track and size spherical gold nanoparticles with diameters of 80 nm and 50 nm.

Three-dimensional rotation of deformable cells at a bipolar electrode array using a rotating electric field

Three-dimensional rotation of cells is imperative in a variety of applications such as biology, medicine, and chemistry. We report for the first time a versatile approach for executing controllable 3D rotation of cells or particles at a bipolar electrode (BPE) array using a rotating electric field. The versatility of this method is demonstrated by 3D rotating various cells including yeast cells and K562 cells and the cells can be rotated to a desired orientation and immobilized for further operations. Our results demonstrate how electrorotation torque, induced charge electroosmosis (ICEO) flow and dielectrophoresis can be exerted on certain cells for modulating the rotation axis, speed, and direction. ICEO-based out-of-plane rotation is capable of rotating various cells in a vertical plane regardless of their shape and size. It can realize cell orientation by rotating cells toward a specific angle and enable cell rotation by steadily rotating multiple cells at a controllable speed. The rotation spectrum for in-plane rotation is further used to extract the cellular dielectric properties. This work offers a flexible method for controllable, contactless and precise rotation of different cells or particles, offering a rapid, high-throughput, and nondestructive rotation method for cell analysis and drug discovery.

A review on intelligent impedance cytometry systems: Development, applications and advances

Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.

A Drop-in, Focus-Extending Phase Mask Simplifies Microscopic and Microfluidic Imaging Systems for Cost-Effective Point-of-Care Diagnostics

Microscopic imaging and imaging flow cytometry have wide potential in point-of-care assays; however, their narrow depth of focus necessitates precise mechanical or fluidic focus control of a sample in order to acquire high-quality images that can be used for downstream analysis, increasing the cost and complexity of the imaging system. This complexity represents a barrier to miniaturization and translation of point-of-care assays based on microscopic imaging or imaging flow cytometry. To address this challenge, we present a simple drop-in phase mask with a physics-informed, circularly symmetric asphere phase profile that extends the depth of focus by >5-fold while largely preserving the image quality compared to other depth extending methods. We show that such a focus-extended system overcomes manufacturing tolerances in low-cost sample chambers, enlarges the useable field-of-view of low-cost objectives, and permits increased throughput and precision in flow imaging systems without the need for complex flow-focusing. As the image quality is preserved without the need for postacquisition image restoration, our solution is also highly appropriate for on-line applications such as cell sorting.

Multispectral Imaging Flow Cytometry with Spatially and Spectrally Resolving Snapshot-Mosaic Cameras for the Characterization and Classification of Bioparticles

In the development and optimization of biotechnological cultivation processes the continuous monitoring through the acquisition and interpretation of spectral and morphological properties of bioparticles are challenging. There is therefore a need for the parallel acquisition and interpretation of spatially and spectrally resolved measurements with which particles can be characterized and classified in-flow with high throughput. Therefore, in this paper we investigated the scientific and technological connectivity of standard imaging flow cytometry (IFC) with filter-on-chip based spatially and spectrally resolving snapshot-mosaic cameras for photonic sensing and control in a smart and innovative microfluidic device. For the investigations presented here we used the microalgae Haematococcus pluvialis (HP). These microalgae are used commercially to produce the antioxidant keto-carotenoid astaxanthin. Therefore, HP is relevant to practically demonstrate the usability of the developed system for Multispectral Imaging Flow Cytometry (MIFC) platform. The extension of standard IFC with snapshot-mosaic cameras and multivariate data processing is an innovative approach for the in-flow characterization and derived classification of bioparticles. Finally, the multispectral data acquisition and the therefore developed methodology is generalizable and enables further applications far beyond the here characterized population of HP cells.

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.

Electro-optical classification of pollen grains via microfluidics and machine learning

In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. Methods: We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. Results: The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7 % for the electrical classifier, 76.7 % for the optical classifier and 84.2 % for the multimodal classifier. The accuracy is 82.8 % for the electrical classifier, 84.1 % for the optical classifier and 88.3 % for the multimodal classifier. Conclusion: The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. Significance: The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.

Three step flow focusing enables image-based discrimination and sorting of late stage 1 Haematococcus pluvialis cells

Label-free and gentle separation of cell stages with desired target properties from mixed stage populations are a major research task in modern biotechnological cultivation process and optimization of micro algae. The reported microfluidic sorter system (MSS) allows the subsequent investigation of separated subpopulations. The implementation of a viability preserving MSS is shown for separation of late stage 1 Haematococcus pluvialis (HP) cells form a mixed stage population. The MSS combines a three-step flow focusing unit for aligning the cells in single file transportation mode at the center of the microfluidic channel with a pure hydrodynamic sorter structure for cell sorting. Lateral displacement of the cells into one of the two outlet channels is generated by piezo-actuated pump chambers. In-line decision making for sorting is based on a user-definable set of image features and properties. The reported MSS significantly increased the purity of target cells in the sorted population (94%) in comparison to the initial mixed stage population (19%).

Flow cytometric analysis of pollen and spores: An overview of applications and methodology

Pollen grains are the male gametophytes in a seed-plant life cycle. Their small, particulate nature and crucial role in plant reproduction have made them an attractive object of study using flow cytometry (FCM), with a wide range of applications existing in the literature. While methodological considerations for many of these overlap with those for other tissue types (e.g., general considerations for the measurement of nuclear DNA content), the relative complexity of pollen compared to single cells presents some unique challenges. We consider these here in the context of both the identification and isolation of pollen and its subunits, and the types of research applications. While the discussion here mostly concerns pollen, the general principles described here can be extended to apply to spores in ferns, lycophytes, and bryophytes. In addition to recommendations provided in more general studies, some recurring and notable issues related specifically to pollen and spores are highlighted.

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

The biophysical analysis of single-cells by microfluidic impedance cytometry is emerging as a label-free and high-throughput means to stratify the heterogeneity of cellular systems based on their electrophysiology. Emerging applications range from fundamental life-science and drug assessment research to point-of-care diagnostics and precision medicine. Recently, novel chip designs and data analytic strategies are laying the foundation for multiparametric cell characterization and subpopulation distinction, which are essential to understand biological function, follow disease progression and monitor cell behaviour in microsystems. In this tutorial review, we present a comparative survey of the approaches to elucidate cellular and subcellular features from impedance cytometry data, covering the related subjects of device design, data analytics (i.e., signal processing, dielectric modelling, population clustering), and phenotyping applications. We give special emphasis to the exciting recent developments of the technique (timeframe 2017-2020) and provide our perspective on future challenges and directions. Its synergistic application with microfluidic separation, sensor science and machine learning can form an essential toolkit for label-free quantification and isolation of subpopulations to stratify heterogeneous biosystems.