Real-time impedimetric droplet measurement (iDM)

User-friendly analysis of droplet array images

Water-in-oil droplets allow performing massive experimental parallelization and high-throughput studies, such as single-cell experiments. However, analyzing such vast arrays of droplets usually requires advanced expertise and sophisticated workflow tools, which limits accessibility for a wider user base in the fields of chemistry and biology. Thus, there is a need for more user-friendly tools for droplet analysis. In this article, we deliver a set of analytical pipelines for user-friendly analysis of typical scenarios in droplet experiments. We built pipelines that combine various open-source image-analysis software with a custom-developed data processing tool called "EasyFlow". Our pipelines are applicable to the typical experimental scenarios that users encounter when working with droplets: i) mono- and polydisperse droplets, ii) brightfield and fluorescent images, iii) droplet and object detection, iv) signal profile of droplets and objects (e.g., fluorescence).

Fluid flow to mimic organ function in 3D in vitro models

Many different strategies can be found in the literature to model organ physiology, tissue functionality, and disease in vitro; however, most of these models lack the physiological fluid dynamics present in vivo. Here, we highlight the importance of fluid flow for tissue homeostasis, specifically in vessels, other lumen structures, and interstitium, to point out the need of perfusion in current 3D in vitro models. Importantly, the advantages and limitations of the different current experimental fluid-flow setups are discussed. Finally, we shed light on current challenges and future focus of fluid flow models applied to the newest bioengineering state-of-the-art platforms, such as organoids and organ-on-a-chip, as the most sophisticated and physiological preclinical platforms.

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.

dDrop-Chip: disposable film-chip microfluidic device for real-time droplet feedback control

A cost-effective, simple to use, and automated technique that can provide real-time feedback control for droplet generation is required to obtain droplets with high-throughput, stability, and uniformity. This study introduces a disposable droplet generation microfluidic device (dDrop-Chip) that can simultaneously control both droplet size and production rate in real time. The dDrop-Chip consists of a reusable sensing substrate and a disposable microchannel that can be assembled using vacuum pressure. It also integrates a droplet detector and a flow sensor on-chip, enabling real-time measurement and feedback control of droplet size and sample flow rate. The dDrop-Chip has the additional advantage of being disposable, which can prevent chemical and biological contamination, due to low manufacturing cost by the film-chip technique. We demonstrate benefits of the dDrop-Chip by controlling droplet size at a fixed sample flow rate and the production rate at a fixed droplet size using real-time feedback control. The experimental results show that the dDrop-Chip consistently generates monodisperse droplets with a length of 219.36 ± 0.08 μm (CV 0.036%) at a production rate of 32.38 ± 0.48 Hz using the feedback control, while without feedback control, there is a significant deviation in droplet length (224.18 ± 6.69 μm, CV 2.98%) and production rate (33.94 ± 1.72 Hz) despite the use of identical devices. Therefore, the dDrop-Chip is a reliable, cost-effective, and automated technique for generating droplets of controlled size and production rate in real time, making it suitable for various droplet-based applications.

Real-time precise microfluidic droplets label-sequencing combined in a velocity detection sensor

Droplets microfluidics is broadening the range of Lab on a Chip solutions that, however, still suffer from the lack of an adequate level of integration of optical detection and sensors. In fact, droplets are currently monitored by imaging techniques, mostly limited by a time-consuming data post-processing and big data storage. This work aims to overcome this weakness, presenting a fully integrated opto-microfluidic platform able to detect, label and characterize droplets without the need for imaging techniques. It consists of optical waveguides arranged in a Mach Zehnder's configuration and a microfluidic circuit both coupled in the same substrate. As a proof of concept, the work demonstrates the performances of this opto-microfluidic platform in performing a complete and simultaneous sequence labelling and identification of each single droplet, in terms of its optical properties, as well as velocity and lengths. Since the sensor is realized in lithium niobate crystals, which is also highly resistant to chemical attack and biocompatible, the future addition of multifunctional stages into the same substrate can be easily envisioned, extending the range of applicability of the final device.

Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry

High-throughput microflow cytometry has become a focal point of research in recent years. In particular, droplet microflow cytometry (DMFC) enables the analysis of cells reacting to different stimuli in chemical isolation due to each droplet acting as an isolated microreactor. Furthermore, at high flow rates, the droplets allow massive parallelization, further increasing the throughput of droplets. However, this novel methodology poses unique challenges related to commonly used fluorometry and fluorescent microscopy techniques. We review the optical sensor technology and light sources applicable to DMFC, as well as analyze the challenges and advantages of each option, primarily focusing on electronics. An analysis of low-cost and/or sufficiently compact systems that can be incorporated into portable devices is also presented.

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.

A Bayesian Approach for Coincidence Resolution in Microfluidic Impedance Cytometry

OBJECTIVE

Cell counting and characterization is fundamental for medicine, science and technology. Coulter-type microfluidic devices are effective and automated systems for cell/particle analysis, based on the electrical sensing zone principle. However, their throughput and accuracy are limited by coincidences (i.e., two or more particles passing through the sensing zone nearly simultaneously), which reduce the observed number of particles and may lead to errors in the measured particle properties. In this work, a novel approach for coincidence resolution in microfluidic impedance cytometry is proposed.

METHODS

The approach relies on: (i) a microchannel comprising two electrical sensing zones and (ii) a model of the signals generated by coinciding particles. Maximum a posteriori probability (MAP) estimation is used to identify the model parameters and therefore characterize individual particle properties.

RESULTS

Quantitative performance assessment on synthetic data streams shows a counting sensitivity of 97% and a positive predictive value of 99% at concentrations of 2×10⁶ particles/ml. An application to red blood cell analysis shows accurate particle characterization up to a throughput of about 2500 particles/s. An original formula providing the expected number of coinciding particles is derived, and good agreement is found between experimental results and theoretical predictions.

CONCLUSION

The proposed cytometer enables the decomposition of signals generated by coinciding particles into individual particle contributions, by using a Bayesian approach.

SIGNIFICANCE

This system can be profitably used in applications where accurate counting and characterization of cell/particle suspensions over a broad range of concentrations is required.

Alternative screening method for analyzing the water samples through an electrical microfluidics chip with classical microbiological assay comparison of P. aeruginosa

Pseudomonas aeruginosa is a pathogenic bacterium in fresh water supplies that creates a risk for public health. Microbiological analysis of drinking water samples is time consuming and requires qualified personnel. Here we offer a screening system for rapid analysis of spring water that has the potential to be turned into a point-of-need system by means of simple mechanism. The test, which takes 1 h to complete, electrically interrogates the particles through a microfluidic chip suspended in the water sample. We tested the platform using water samples with micro beads and water samples spiked with P. aeruginosa at various concentrations. The mono disperse micro beads were used to evaluate the performance of the system. The results were verified by the gold standard membrane filtration method, which yielded a positive test result only for the P. aeruginosa spiked samples. Detection of 0-11 k bacteria in 30 μL samples was successfully completed in 1 h and compared with a conventional microbiological method. The presented method is a good candidate for a rapid, on-site, screening test that can result in a significant reduction in cost and analysis time compared to microbiological analyses routinely used in practice.

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting. Graphical Abstract.