Image-Based Single Cell Sorting Automation in Droplet Microfluidics

The use of droplet-based microfluidic technologies for accelerated selection of Yarrowia lipolytica and Phaffia rhodozyma yeast mutants

Microorganisms are widely used for the industrial production of various valuable products, such as pharmaceuticals, food and beverages, biofuels, enzymes, amino acids, vaccines, etc. Research is constantly carried out to improve their properties, mainly to increase their productivity and efficiency and reduce the cost of the processes. The selection of microorganisms with improved qualities takes a lot of time and resources (both human and material); therefore, this process itself needs optimization. In the last two decades, microfluidics technology appeared in bioengineering, which allows for manipulating small particles (from tens of microns to nanometre scale) in the flow of liquid in microchannels. The technology is based on small-volume objects (microdroplets from nano to femtolitres), which are manipulated using a microchip. The chip is made of an optically transparent inert to liquid medium material and contains a series of channels of small size (<1 mm) of certain geometry. Based on the physical and chemical properties of microparticles (like size, weight, optical density, dielectric constant, etc.), they are separated using microsensors. The idea of accelerated selection of microorganisms is the application of microfluidic technologies to separate mutants with improved qualities after mutagenesis. This article discusses the possible application and practical implementation of microfluidic separation of mutants, including yeasts like Yarrowia lipolytica and Phaffia rhodozyma after chemical mutagenesis will be discussed.

Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes

Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

Image-activated pico-injection for single-cell analysis

The addition of reagents into preformed droplets is a crucial yet intricate task in droplet-based applications where sequential reactions is required. Pico-injection offers high throughput and robustness in accomplishing this task, but the existing pico-injection techniques work in an indiscriminate manner, making it difficult to target particular groups of droplets. Here we report image-activated pico-injection (imgPico) for label-free, on-demand reagent supplementation into droplets. The imgPico detects the droplets of interest by real-time image analysis and makes decisions for the downstream pico-injection operation. We studied the performance of different algorithms for the image analysis and optimized the experimental settings of the imgPico. In the validation experiment, the imgPico successfully injected fluorescent dyes into droplets encapsulating one, two, and three cells, respectively, as expected. We further demonstrated the utility of imgPico by targeting droplets encapsulating single cells in droplet-based single-cell RNA sequencing (scRNA-seq) using exceedingly high cell density, and the results showed that the imgPico effectively reduced the presence of doublets in the scRNA-seq data. With the merits of being label-free and versatile, the imgPico represents a technical advance with potential applications in single-cell analysis.

Droplet-based microfluidic platform for detecting agonistic peptides that are self-secreted by yeast expressing a G-protein-coupled receptor

BACKGROUND

Single-cell droplet microfluidics is an important platform for high-throughput analyses and screening because it provides an independent and compartmentalized microenvironment for reaction or cultivation by coencapsulating individual cells with various molecules in monodisperse microdroplets. In combination with microbial biosensors, this technology becomes a potent tool for the screening of mutant strains. In this study, we demonstrated that a genetically engineered yeast strain that can fluorescently sense agonist ligands via the heterologous expression of a human G-protein-coupled receptor (GPCR) and concurrently secrete candidate peptides is highly compatible with single-cell droplet microfluidic technology for the high-throughput screening of new agonistically active peptides.

RESULTS

The water-in-oil microdroplets were generated using a flow-focusing microfluidic chip to encapsulate engineered yeast cells coexpressing a human GPCR [i.e., angiotensin II receptor type 1 (AGTR1)] and a secretory agonistic peptide [i.e., angiotensin II (Ang II)]. The single yeast cells cultured in the droplets were then observed under a microscope and analyzed using image processing incorporating machine learning techniques. The AGTR1-mediated signal transduction elicited by the self-secreted Ang II peptide was successfully detected via the expression of a fluorescent reporter in single-cell yeast droplet cultures. The system could also distinguish Ang II analog peptides with different agonistic activities. Notably, we further demonstrated that the microenvironment of the single-cell droplet culture enabled the detection of rarely existing positive (Ang II-secreting) yeast cells in the model mixed cell library, whereas the conventional batch-culture environment using a shake flask failed to do so. Thus, our approach provided compartmentalized microculture environments, which can prevent the diffusion, dilution, and cross-contamination of peptides secreted from individual single yeast cells for the easy identification of GPCR agonists.

CONCLUSIONS

We established a droplet-based microfluidic platform that integrated an engineered yeast biosensor strain that concurrently expressed GPCR and self-secreted the agonistic peptides. This offers individually isolated microenvironments that allow the culture of single yeast cells secreting these peptides and gaging their signaling activities, for the high-throughput screening of agonistic peptides. Our platform base on yeast GPCR biosensors and droplet microfluidics will be widely applicable to metabolic engineering, environmental engineering, and drug discovery.

Dynamic video recognition for cell-encapsulating microfluidic droplets

Droplet microfluidics is a highly sensitive and high-throughput technology extensively utilized in biomedical applications, such as single-cell sequencing and cell screening. However, its performance is highly influenced by the droplet size and single-cell encapsulation rate (following random distribution), thereby creating an urgent need for quality control. Machine learning has the potential to revolutionize droplet microfluidics, but it requires tedious pixel-level annotation for network training. This paper investigates the application software of the weakly supervised cell-counting network (WSCApp) for video recognition of microdroplets. We demonstrated its real-time performance in video processing of microfluidic droplets and further identified the locations of droplets and encapsulated cells. We verified our methods on droplets encapsulating six types of cells/beads, which were collected from various microfluidic structures. Quantitative experimental results showed that our approach can not only accurately distinguish droplet encapsulations (micro-F1 score > 0.94), but also locate each cell without any supervised location information. Furthermore, fine-tuning transfer learning on the pre-trained model also significantly reduced (>80%) annotation. This software provides a user-friendly and assistive annotation platform for the quantitative assessment of cell-encapsulating microfluidic droplets.

Image-Based Feedback of Multi-Component Microdroplets for Ultra-Monodispersed Library Preparation

Using devices with microfluidic channels can allow for precise control over liquids flowing through them. Merging flows of immiscible liquids can create emulsions with highly monodispersed microdroplets within a carrier liquid, which are ideal for miniaturised reaction vessels which can be generated with a high throughput of tens of thousands of droplets per second. Control of the size and composition of these droplets is generally performed by controlling the pumping system pushing the liquids into the device; however, this is an indirect manipulation and inadequate if absolute precision is required in the size or composition of the droplets. In this work, we extend the previous development of image-based closed-loop feedback control over microdroplet generation to allow for the control of not only the size of droplets but also the composition by merging two aqueous flows. The feedback allows direct control over the desired parameters of volume and ratio of the two components over a wide range of ratios and outperforms current techniques in terms of monodispersity in volume and composition. This technique is ideal for situations where precise control over droplets is critical, or where a library of droplets of different concentrations but the same volume is required.

Recent advances in droplet sequential monitoring methods for droplet sorting

Droplet microfluidics is an attractive technology to run parallel experiments with high throughput and scalability while maintaining the heterogeneous features of individual samples or reactions. Droplet sorting is utilized to collect the desired droplets based on droplet characterization and in-droplet content evaluation. A proper monitoring method is critical in this process, which governs the accuracy and maximum frequency of droplet handling. Until now, numerous monitoring methods have been integrated in the microfluidic devices for identifying droplets, such as optical spectroscopy, mass spectroscopy, electrochemical monitoring, and nuclear magnetic resonance spectroscopy. In this review, we summarize the features of various monitoring methods integrated into droplet sorting workflow and discuss their suitable condition and potential obstacles in use. We aim to provide a systematic introduction and an application guide for choosing and building a droplet monitoring platform.

Towards single cell encapsulation for precision biology and medicine

The primary impetus of therapeutic cell encapsulation in the past several decades has been to broaden the options for donor cell sources by countering against immune-mediated rejection. However, another significant advantage of encapsulation is to provide donor cells with physiologically relevant cues that become compromised in disease. The advances in biomaterial design have led to the fundamental insight that cells sense and respond to various signals encoded in materials, ranging from biochemical to mechanical cues. The biomaterial design for cell encapsulation is becoming more sophisticated in controlling specific aspects of cellular phenotypes and more precise down to the single cell level. This recent progress offers a paradigm shift by designing single cell-encapsulating materials with predefined cues to precisely control donor cells after transplantation.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

Enhancing single-cell biology through advanced AI-powered microfluidics

Microfluidic technology has largely benefited both fundamental biological research and translational clinical diagnosis with its advantages in high-throughput, single-cell resolution, high integrity, and wide-accessibility. Despite the merits we obtained from microfluidics in the last two decades, the current requirement of intelligence in biomedicine urges the microfluidic technology to process biological big data more efficiently and intelligently. Thus, the current readout technology based on the direct detection of the signals in either optics or electrics was not able to meet the requirement. The implementation of artificial intelligence (AI) in microfluidic technology matches up with the large-scale data usually obtained in the high-throughput assays of microfluidics. At the same time, AI is able to process the multimodal datasets obtained from versatile microfluidic devices, including images, videos, electric signals, and sequences. Moreover, AI provides the microfluidic technology with the capability to understand and decipher the obtained datasets rather than simply obtaining, which eventually facilitates fundamental and translational research in many areas, including cell type discovery, cell signaling, single-cell genetics, and diagnosis. In this Perspective, we will highlight the recent advances in employing AI for single-cell biology and present an outlook on the future direction with more advanced AI algorithms.

Deep learning with microfluidics for on-chip droplet generation, control, and analysis

Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.

Active learning applied to automated physical systems increases the rate of discovery

Active machine learning is widely used in computational studies where repeated numerical simulations can be conducted on high performance computers without human intervention. But translation of these active learning methods to physical systems has proven more difficult and the accelerated pace of discoveries aided by these methods remains as yet unrealized. Through the presentation of a general active learning framework and its application to large-scale boundary layer wind tunnel experiments, we demonstrate that the active learning framework used so successfully in computational studies is directly applicable to the investigation of physical experimental systems and the corresponding improvements in the rate of discovery can be transformative. We specifically show that, for our wind tunnel experiments, we are able to achieve in approximately 300 experiments a learning objective that would be impossible using traditional methods.

iSort enables automated complex microfluidic droplet sorting in an effort to democratize technology

Fluorescence-activated droplet sorting (FADS) is a widely used microfluidic technique for high-throughput screening. However, it requires highly trained specialists to determine optimal sorting parameters, and this results in a large combinatorial space that is challenging to optimize systematically. Additionally, it is currently challenging to track every single droplet within a screen, leading to compromised sorting and "hidden" false-positive events. To overcome these limitations, we have developed a setup in which the droplet frequency, spacing, and trajectory at the sorting junction are monitored in real time using impedance analysis. The resulting data are used to continuously optimize all parameters automatically and to counteract perturbations, resulting in higher throughput, higher reproducibility, increased robustness, and a beginner-friendly character. We believe this provides a missing piece for the spreading of phenotypic single-cell analysis methods, similar to what we have seen for single-cell genomics platforms.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Recent progress in high-throughput droplet screening and sorting for bioanalysis

Owing to its ability to isolate single cells and perform high-throughput sorting, droplet sorting has been widely applied in several research fields. Compared with flow cytometry, droplet allows the encapsulation of single cells for cell secretion or lysate analysis. With the rapid development of this technology in the past decade, various droplet sorting devices with high throughput and accuracy have been developed. A droplet sorter with the highest sorting throughput of 30,000 droplets per second was developed in 2015. Since then, increased attention has been paid to expanding the possibilities of droplet sorting technology and strengthening its advantages over flow cytometry. This review aimed to summarize the recent progress in droplet sorting technology from the perspectives of device design, detection signal, actuating force, and applications. Technical details for improving droplet sorting through various approaches are introduced and discussed. Finally, we discuss the current limitations of droplet sorting for single-cell studies along with the existing gap between the laboratory and industry and provide our insights for future development of droplet sorters.

Selectable encapsulated cell quantity in droplets via label-free electrical screening and impedance-activated sorting

Single-cell encapsulation in droplets has become a powerful tool in immunotherapy, medicine discovery, and single-cell analysis, thanks to its capability for cell confinement in picoliter volumes. However, the purity and throughput of single-cell droplets are limited by random encapsulation process, which resuts in a majority of empty and multi-cells droplets. Herein we introduce the first label-free selectable cell quantity encapsulation in droplets sorting system to overcome this problem. The system utilizes a simple and reliable electrical impedance based screening (98.9% of accuracy) integrated with biocompatible acoustic sorting to select single-cell droplets, achieving 90.3% of efficiency and up to 200 ​Hz of throughput, by removing multi-cells (∼60% of rejection) and empty droplets (∼90% of rejection). We demonstrate the use of the droplet sorting to improve the throughput of single-cell encapsulation by ∼9-fold compared to the conventional random encapsulation process.

Advances in Microfluidics for Single Red Blood Cell Analysis

The utilizations of microfluidic chips for single RBC (red blood cell) studies have attracted great interests in recent years to filter, trap, analyze, and release single erythrocytes for various applications. Researchers in this field have highlighted the vast potential in developing micro devices for industrial and academia usages, including lab-on-a-chip and organ-on-a-chip systems. This article critically reviews the current state-of-the-art and recent advances of microfluidics for single RBC analyses, including integrated sensors and microfluidic platforms for microscopic/tomographic/spectroscopic single RBC analyses, trapping arrays (including bifurcating channels), dielectrophoretic and agglutination/aggregation studies, as well as clinical implications covering cancer, sepsis, prenatal, and Sickle Cell diseases. Microfluidics based RBC microarrays, sorting/counting and trapping techniques (including acoustic, dielectrophoretic, hydrodynamic, magnetic, and optical techniques) are also reviewed. Lastly, organs on chips, multi-organ chips, and drug discovery involving single RBC are described. The limitations and drawbacks of each technology are addressed and future prospects are discussed.

Droplets microfluidics platform-A tool for single cell research

Cells are the most basic structural and functional units of living organisms. Studies of cell growth, differentiation, apoptosis, and cell-cell interactions can help scientists understand the mysteries of living systems. However, there is considerable heterogeneity among cells. Great differences between individuals can be found even within the same cell cluster. Cell heterogeneity can only be clearly expressed and distinguished at the level of single cells. The development of droplet microfluidics technology opens up a new chapter for single-cell analysis. Microfluidic chips can produce many nanoscale monodisperse droplets, which can be used as small isolated micro-laboratories for various high-throughput, precise single-cell analyses. Moreover, gel droplets with good biocompatibility can be used in single-cell cultures and coupled with biomolecules for various downstream analyses of cellular metabolites. The droplets are also maneuverable; through physical and chemical forces, droplets can be divided, fused, and sorted to realize single-cell screening and other related studies. This review describes the channel design, droplet generation, and control technology of droplet microfluidics and gives a detailed overview of the application of droplet microfluidics in single-cell culture, single-cell screening, single-cell detection, and other aspects. Moreover, we provide a recent review of the application of droplet microfluidics in tumor single-cell immunoassays, describe in detail the advantages of microfluidics in tumor research, and predict the development of droplet microfluidics at the single-cell level.

Droplet Detection and Sorting System in Microfluidics: A Review

Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.

Versatile, facile and low-cost single-cell isolation, culture and sequencing by optical tweezer-assisted pool-screening

Real-time image-based sorting of target cells in a precisely indexed manner is desirable for sequencing or cultivating individual human or microbial cells directly from clinical or environmental samples; however, the versatility of existing methods is limited as they are usually not broadly applicable to all cell sizes. Here, an optical tweezer-assisted pool-screening and single-cell isolation (OPSI) system is established for precise, indexed isolation of individual bacterial, yeast or human-cancer cells. A controllable static flow field that acts as a cell pool is achieved in a microfluidics chip, to enable precise and ready screening of cells of 1 to 40 μm in size by bright-field, fluorescence, or Raman imaging. The target cell is then captured by a 1064 nm optical tweezer and deposited as one-cell-harboring nanoliter microdroplets in a one-cell-one-tube manner. For bacterial, yeast and human cells, OPSI achieves a >99.7% target-cell sorting purity and a 10-fold elevated speed of 10-20 cells per min. Moreover, OPSI-based one-cell RNA-seq of human cancer cells yields high quality and reproducible single-cell transcriptome profiles. The versatility, facileness, flexibility, modularized design, and low cost of OPSI suggest its broad applications for image-based sorting of target cells.

High precision, high throughput generation of droplets containing single cells

The Poisson limit is a major problem for the isolation of single cells in different single-cell technologies and applications. In droplet-based single-cell assays, a scheme that is increasingly popular, the intrinsic randomness during single-cell encapsulation in droplets requires most of the created droplets to be empty, which has a profound impact on the efficiency and throughput of such techniques, and on the predictability of the combinatory droplet assays. Here we present a simple passive microfluidic system overcoming this limitation with unprecedented efficacy, allowing the generation of single-cell droplets for a wide range of operating conditions, with extremely high throughput (more than 22 000 single-cell loaded droplets per minute) and with an extremely low fault ratio (doublets or empty droplets), applicable to any cells and deformable particles. This versatile technique will shift the paradigm of single-cell encapsulation and will impact single-cell sequencing, rare cell isolation, multicellular/bead studies in immunology or cancer biology, etc.

ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories.

Multiplexed microfluidic chip for cell co-culture

Paracrine signaling is challenging to study in vitro, as conventional culture tools dilute soluble factors and offer little to no spatiotemporal control over signaling. Microfluidic chips offer potential to address both of these issues. However, few solutions offer both control over onset and duration of cell-cell communication, and high throughput. We have developed a microfluidic chip designed to culture cells in adjacent chambers, separated by valves to selectively allow or prevent exchange of paracrine signals. The chip features 16 fluidic inputs and 128 individually-addressable chambers arranged in 32 sets of 4 chambers. Media can be continuously perfused or delivered by diffusion, which we model under different culture conditions to ensure normal cell viability. Immunocytochemistry assays can be performed in the chip, which we modeled and fine-tuned to reduce total assay time to 1 h. Finally, we validate the use of the chip for co-culture studies by showing that HEK293Ta cells respond to signals secreted by RAW 264.7 immune cells in adjacent chambers, only when the valve between the chambers is opened.

Novel Pumping Methods for Microfluidic Devices: A Comprehensive Review

This review is an account of methods that use various strategies to control microfluidic flow control with high accuracy. The reviewed systems are divided into two large groups based on the way they create flow: passive systems (non-mechanical systems) and active (mechanical) systems. Each group is presented by a number of device fabrications. We try to explain the main principles of operation, and we list advantages and disadvantages of the presented systems. Mechanical systems are considered in more detail, as they are currently an area of increased interest due to their unique precision flow control and "multitasking". These systems are often applied as mini-laboratories, working autonomously without any additional operations, provided by humans, which is very important under complicated conditions. We also reviewed the integration of autonomous microfluidic systems with a smartphone or single-board computer when all data are retrieved and processed without using a personal computer. In addition, we discuss future trends and possible solutions for further development of this area of technology.

Comprehensive single-shot biophysical cytometry using simultaneous quantitative phase imaging and Brillouin spectroscopy

Single-cell analysis, or cytometry, is a ubiquitous tool in the biomedical sciences. Whereas most cytometers use fluorescent probes to ascertain the presence or absence of targeted molecules, biophysical parameters such as the cell density, refractive index, and viscosity are difficult to obtain. In this work, we combine two complementary techniques-quantitative phase imaging and Brillouin spectroscopy-into a label-free image cytometry platform capable of measuring more than a dozen biophysical properties of individual cells simultaneously. Using a geometric simplification linked to freshly plated cells, we can acquire the cellular diameter, volume, refractive index, mass density, non-aqueous mass, fluid volume, dry volume, the fractional water content of cells, both by mass and by volume, the Brillouin shift, Brillouin linewidth, longitudinal modulus, longitudinal viscosity, the loss modulus, and the loss tangent, all from a single acquisition, and with no assumptions of underlying parameters. Our methods are validated across three cell populations, including a control population of CHO-K1 cells, cells exposed to tubulin-disrupting nocodazole, and cells under hypoosmotic shock. Our system will unlock new avenues of research in biophysics, cell biology, and medicine.

Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets

Encapsulation of cells inside microfluidic droplets is central to several applications involving cellular analysis. Although, theoretically the encapsulation statistics are expected to follow a Poisson distribution, experimentally this may not be achieved due to lack of full control of the experimental variables and conditions. Therefore, there is a need for automatic detection of droplets and cell count enumeration within droplets so a process control feedback to adjust experimental conditions can be implemented. In this study, we use a deep learning object detector called You Only Look Once (YOLO), an influential class of object detectors with several benefits over traditional methods. This paper investigates the application of both YOLOv3 and YOLOv5 object detectors in the development of an automated droplet and cell detector. Experimental data was obtained from a microfluidic flow focusing device with a dispersed phase of cancer cells. The microfluidic device contained an expansion chamber downstream of the droplet generator, allowing for visualization and recording of cell-encapsulated droplet images. In the procedure, a droplet bounding box is predicted, then cropped from the original image for the individual cells to be detected through a separate model for further examination. The system includes a production set for additional performance analysis with Poisson statistics while providing an experimental workflow with both droplet and cell models. The training set is collected and preprocessed before labeling and applying image augmentations, allowing for a generalizable object detector. Precision and recall were utilized as a validation and test set metric, resulting in a high mean average precision (mAP) metric for an accurate droplet detector. To examine model limitations, the predictions were compared to ground truth labels, illustrating that the YOLO predictions closely matched with the droplet and cell labels. Furthermore, it is demonstrated that droplet enumeration from the YOLOv5 model is consistent with hand counted ratios and the Poisson distribution, confirming that the platform can be used in real-time experiments for cell encapsulation optimization.

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy

Single-cell imaging and sorting are critical technologies in biology and clinical applications. The power of these technologies is increased when combined with microfluidics, fluorescence markers, and machine learning. However, this quest faces several challenges. One of these is the effect of the sample flow velocity on the classification performances. Indeed, cell flow speed affects the quality of image acquisition by increasing motion blur and decreasing the number of acquired frames per sample. We investigate how these visual distortions impact the final classification task in a real-world use-case of cancer cell screening, using a microfluidic platform in combination with light sheet fluorescence microscopy. We demonstrate, by analyzing both simulated and experimental data, that it is possible to achieve high flow speed and high accuracy in single-cell classification. We prove that it is possible to overcome the 3D slice variability of the acquired 3D volumes, by relying on their 2D sum z-projection transformation, to reach an efficient real time classification with an accuracy of 99.4% using a convolutional neural network with transfer learning from simulated data. Beyond this specific use-case, we provide a web platform to generate a synthetic dataset and to investigate the effect of flow speed on cell classification for any biological samples and a large variety of fluorescence microscopes (https://www.creatis.insa-lyon.fr/site7/en/MicroVIP).

High-throughput probing macrophage-bacteria interactions at the single cell level with microdroplets

Pathogenic infections may lead to disruption of homeostasis, thus becoming a serious threat to the human health. Understanding the interactions between bacteria and macrophages is critical for therapeutic development against sepsis or inflammatory bowel disease. Here, we report a technique using droplet biosensors for the detection of nitric oxide (NO) secreted by a single macrophage under inflammatory stimuli. We demonstrated that the limit of detection can be promoted more than two orders of magnitude by our approach, in comparison to the conventional microplate format. The experiments of co-encapsulating single macrophages and different numbers of Escherichia coli (E. coli) enabled fluorescence monitoring of NO secretion by single macrophages over the incubation, and investigation of their interactions inside the isolated droplet for their separate fates. Our approach provides a unique platform to study the bacteria-macrophage interactions at the single cell level.

Frontiers in single cell analysis: multimodal technologies and their clinical perspectives

Single cell multimodal analysis is at the frontier of single cell research: it defines the roles and functions of distinct cell types through simultaneous analysis to provide unprecedented insight into cellular processes. Current single cell approaches are rapidly moving toward multimodal characterizations. It replaces one-dimensional single cell analysis, for example by allowing for simultaneous measurement of transcription and post-transcriptional regulation, epigenetic modifications and/or surface protein expression. By providing deeper insights into single cell processes, multimodal single cell analyses paves the way to new understandings in various cellular processes such as cell fate decisions, physiological heterogeneity or genotype-phenotype linkages. At the forefront of this, microfluidics is key for high-throughput single cell analysis. Here, we present an overview of the recent multimodal microfluidic platforms having a potential in biomedical research, with a specific focus on their potential clinical applications.

Recent advances in microfluidic devices for single-cell cultivation: methods and applications

Single-cell analysis is essential to improve our understanding of cell functionality from cellular and subcellular aspects for diagnosis and therapy. Single-cell cultivation is one of the most important processes in single-cell analysis, which allows the monitoring of actual information of individual cells and provides sufficient single-cell clones and cell-derived products for further analysis. The microfluidic device is a fast-rising system that offers efficient, effective, and sensitive single-cell cultivation and real-time single-cell analysis conducted either on-chip or off-chip. Here, we introduce the importance of single-cell cultivation from the aspects of cellular and subcellular studies. We highlight the materials and structures utilized in microfluidic devices for single-cell cultivation. We further discuss biological applications utilizing single-cell cultivation-based microfluidics, such as cellular phenotyping, cell-cell interactions, and omics profiling. Finally, present limitations and future prospects of microfluidics for single-cell cultivation are also discussed.

FPGA Integrated Optofluidic Biosensor for Real-Time Single Biomarker Analysis

Integrated optofluidic biosensors can fill the need for sensitive, amplification-free, multiplex single molecule detection which is relevant for containing the spread of infectious diseases such as COVID-19. Here, we demonstrate a rapid sample-to-answer scheme that uses a field programmable gate array (FPGA) to enable live monitoring of single particle fluorescence analysis on an optofluidic chip. Fluorescent nanobeads flowing through a micro channel are detected with 99% accuracy and particle concentrations in clinically relevant ranges from 3.4×104 to 3.4 × 106/ml are determined within seconds to a few minutes without the need for post-experiment data extraction and analysis. In addition, other extract salient experimental parameters such as dynamic flow rate changes can be monitored in real time. The sensor is validated with real-time fluorescence detection of single bacterial plasmid DNA at attomolar concentrations, showing excellent promise for implementation as a point of care (POC) diagnostic tool.

High-Throughput Single-Cell TCR-pMHC Dissociation Rate Measurements Performed by an Autonomous Microfluidic Cellular Processing Unit

We developed an integrated microfluidic cellular processing unit (mCPU) capable of autonomously isolating single cells and performing measurements and on-the-fly analysis of cell-surface dissociation rates, followed by recovery of selected cells. We performed proof-of-concept, high-throughput single-cell experiments characterizing pMHC-TCR interactions on live CD8+ T cells. The mCPU platform analyzed TCR-pMHC dissociation rates with a throughput of 50 cells per hour and hundreds of cells per run, and we demonstrate that cells can be selected, enriched, and easily recovered from the device.

Exploiting machine learning for bestowing intelligence to microfluidics

Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.

Acoustofluidic Droplet Sorter Based on Single Phase Focused Transducers

Droplet microfluidics has revolutionized the biomedical and drug development fields by allowing for independent microenvironments to conduct drug screening at the single cell level. However, current microfluidic sorting devices suffer from drawbacks such as high voltage requirements (e.g., >200 Vpp), low biocompatibility, and/or low throughput. In this article, a single-phase focused transducer (SPFT)-based acoustofluidic chip is introduced, which outperforms many microfluidic droplet sorting devices through high energy transmission efficiency, high accuracy, and high biocompatibility. The SPFT-based sorter can be driven with an input power lower than 20 Vpp and maintain a postsorting cell viability of 93.5%. The SPFT sorter can achieve a throughput over 1000 events per second and a sorting purity up to 99.2%. The SPFT sorter is utilized here for the screening of doxorubicin cytotoxicity on cancer and noncancer cells, proving its drug screening capability. Overall, the SPFT droplet sorting device shows great potential for fast, precise, and biocompatible drug screening.

Sorting droplets into many outlets

Droplet microfluidics is a commercially successful technology, widely used in single cell sequencing and droplet PCR. Combining droplet making with droplet sorting has also been demonstrated, but so far found limited use, partly due to difficulties in scaling manufacture with injection molded plastics. We introduce a droplet sorting system with several new elements, including: 1) an electrode design combining metallic and ionic liquid parts, 2) a modular, multi-sorting fluidic design with features for keeping inter-droplet distances constant, 3) using timing parameters calculated from fluorescence or scatter signal triggers to precisely actuate dozens of sorting electrodes, 4) droplet collection techniques, including ability to collect a single droplet, and 5) a new emulsion breaking method to collect aqueous samples for downstream analysis. We use these technologies to build a fluorescence based cell sorter that can sort with high (>90%) purity. We also show that these microfluidic designs can be translated into injection molded thermoplastic, suitable for industrial production. Finally, we tally the advantages and limitations of these devices.

A cell-cell atlas approach for understanding symbiotic interactions between microbes

Natural environments are composed of a huge diversity of microorganisms interacting with each other to form complex functional networks. Our understanding of the operative nature of host-symbiont associations is limited because propagating such associations in a laboratory is challenging. The advent of single-cell technologies applied to, for example, animal cells and apicomplexan parasites has revolutionized our understanding of development and disease. Such cell atlas approaches generate maps of cell-specific processes and variations within cellular populations. These methods can now be combined with cellular-imaging so that interaction stage versus transcriptome state can be quantized for microbe-microbe interactions. We predict that the combination of these methods applied to the study of symbioses will transform our understanding of many ecological interactions, including those sampled directly from natural environments.

UV-Vis Spectra-Activated Droplet Sorting for Label-Free Chemical Identification and Collection of Droplets

We introduce the UV-vis spectra-activated droplet sorter (UVADS) for high-throughput label-free chemical identification and enzyme screening. In contrast to previous absorbance-based droplet sorters that relied on single-wavelength absorbance in the visible range, our platform collects full UV-vis spectra from 200 to 1050 nm at up to 2100 spectra per second. Our custom-built open-source software application, "SpectraSorter," enables real-time data processing, analysis, visualization, and selection of droplets for sorting with any set of UV-vis spectral features. An optimized UV-vis detection region extended the absorbance path length for droplets and allowed for the direct protein quantification down to 10 μM of bovine serum albumin at 280 nm. UV-vis spectral data can distinguish a variety of different chemicals or spurious events (such as air bubbles) that are inaccessible at a single wavelength. The platform is used to measure ergothionase enzyme activity from monoclonal microcolonies isolated in droplets. In a label-free manner, we directly measure the ergothioneine substrate to thiourocanic acid product conversion while tracking the microcolony formation. UVADS represents an important new tool for high-throughput label-free in-droplet chemical analysis.

Micro-Droplet Platform for Exploring the Mechanism of Mixed Field Agglutination in B3 Subtype

B3 is the most common subtype of blood group B in the Taiwanese population, and most of the B3 individuals in the Taiwanese population have the IVS3 + 5 G > A (rs55852701) gene variation. Additionally, a typical mixed field agglutination is observed when the B3 subtype is tested with anti-B antibody or anti-AB antibody. The molecular biology of the gene variation in the B3 subtype has been identified, however, the mechanism of the mixed field agglutination caused by the type B3 blood samples is still unclear. Therefore, the purpose of this study was to understand the reason for the mixed field agglutination caused by B3. A micro-droplet platform was used to observe the agglutination of type B and type B3 blood samples in different blood sample concentrations, antibody concentrations, and at reaction times. We found that the agglutination reaction in every droplet slowed down with an increase in the dilution ratio of blood sample and antibody, whether type B blood or type B3 blood was used. However, as the reaction time increased, the complete agglutination in the droplet was seen in type B blood, while the mixed field agglutination still occurred in B3 within 1 min. In addition, the degree of agglutination was similar in each droplet, which showed high reproducibility. As a result, we inferred that there are two types of cells in the B3 subtype that simultaneously create a mixed field agglutination, rather than each red blood cell carrying a small amount of antigen, resulting in less agglutination.

Label-free image-encoded microfluidic cell sorter with a scanning Bessel beam

The microfluidic-based, label-free image-guided cell sorter offers a low-cost, high information content, and disposable solution that overcomes many limitations in conventional cell sorters. However, flow confinement for most microfluidic devices is generally only one-dimensional using sheath flow. As a result, the equilibrium distribution of cells spreads beyond the focal plane of commonly used Gaussian laser excitation beams, resulting in a large number of blurred images that hinder subsequent cell sorting based on cell image features. To address this issue, we present a Bessel-Gaussian beam image-guided cell sorter with an ultra-long depth of focus, enabling focused images of >85% of passing cells. This system features label-free sorting capabilities based on features extracted from the output temporal waveform of a photomultiplier tube (PMT) detector. For the sorting of polystyrene beads, SKNO1 leukemia cells, and Scenedesmus green algae, our results indicate a sorting purity of 97%, 97%, and 98%, respectively, showing that the temporal waveforms from the PMT outputs have strong correlations with cell image features. These correlations are also confirmed by off-line reconstructed cell images from a temporal-spatial transformation algorithm tailored to the scanning Bessel-Gaussian beam.

Droplet flow cytometry for single-cell analysis

The interrogation of single cells has revolutionised biology and medicine by providing crucial unparalleled insights into cell-to-cell heterogeneity. Flow cytometry (including fluorescence-activated cell sorting) is one of the most versatile and high-throughput approaches for single-cell analysis by detecting multiple fluorescence parameters of individual cells in aqueous suspension as they flow past through a focus of excitation lasers. However, this approach relies on the expression of cell surface and intracellular biomarkers, which inevitably lacks spatial and temporal phenotypes and activities of cells, such as secreted proteins, extracellular metabolite production, and proliferation. Droplet microfluidics has recently emerged as a powerful tool for the encapsulation and manipulation of thousands to millions of individual cells within pico-litre microdroplets. Integrating flow cytometry with microdroplet architectures surrounded by aqueous solutions (e.g., water-in-oil-in-water (W/O/W) double emulsion and hydrogel droplets) opens avenues for new cellular assays linking cell phenotypes to genotypes at the single-cell level. In this review, we discuss the capabilities and applications of droplet flow cytometry (DFC). This unique technique uses standard commercially available flow cytometry instruments to characterise or select individual microdroplets containing single cells of interest. We explore current challenges associated with DFC and present our visions for future development.

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%).

Antibodies to watch in 2021

In this 12th annual installment of the Antibodies to Watch article series, we discuss key events in antibody therapeutics development that occurred in 2020 and forecast events that might occur in 2021. The coronavirus disease 2019 (COVID-19) pandemic posed an array of challenges and opportunities to the healthcare system in 2020, and it will continue to do so in 2021. Remarkably, by late November 2020, two anti-SARS-CoV antibody products, bamlanivimab and the casirivimab and imdevimab cocktail, were authorized for emergency use by the US Food and Drug Administration (FDA) and the repurposed antibodies levilimab and itolizumab had been registered for emergency use as treatments for COVID-19 in Russia and India, respectively. Despite the pandemic, 10 antibody therapeutics had been granted the first approval in the US or EU in 2020, as of November, and 2 more (tanezumab and margetuximab) may be granted approvals in December 2020.* In addition, prolgolimab and olokizumab had been granted first approvals in Russia and cetuximab saratolacan sodium was first approved in Japan. The number of approvals in 2021 may set a record, as marketing applications for 16 investigational antibody therapeutics are already undergoing regulatory review by either the FDA or the European Medicines Agency. Of these 16 mAbs, 11 are possible treatments for non-cancer indications and 5 are potential treatments for cancer. Based on the information publicly available as of November 2020, 44 antibody therapeutics are in late-stage clinical studies for non-cancer indications, including 6 for COVID-19, and marketing applications for at least 6 (leronlimab, tezepelumab, faricimab, ligelizumab, garetosmab, and fasinumab) are planned in 2021. In addition, 44 antibody therapeutics are in late-stage clinical studies for cancer indications. Of these 44, marketing application submissions for 13 may be submitted by the end of 2021. *Note added in proof on key events announced during December 1-21, 2020: margetuximab-cmkb and ansuvimab-zykl were approved by FDA on December 16 and 21, 2020, respectively; biologics license applications were submitted for ublituximab and amivantamab.

Improved efficiency of urine cell image segmentation using droplet microfluidics technology

Recent advances in the recognition of biological samples using machine vision have made this technology increasingly important in research and detection. Image segmentation is an important step in this process. This study focuses on how to reduce the interference factors such as the overlap between different types (or within the same type) of urine cells according to microfluidics and improve the machine vision segmentation accuracy for cell images. In this study, we demonstrate that the platform can realize this hypothesis using urine cell image segmentation as an example application. We first discuss the reported urine cell droplet microfluidic chip system, which can realize the test conditions in which urine cells are encapsulated in the droplet and isolated from salt crystallization and/or bacteria and other urine-formed elements. Then, based on the analysis conditions set in the aforementioned experiment, the proportions of red blood cells, white blood cells, and squamous epithelial cells covered by various formed elements in the total urine cells in the same urine sample are measured. We simultaneously analyze the percentage of urine cells covered by salt crystallization and the incidence of overlapping between urine cells. Finally, the Otsu algorithm is used to segment the urine cell images encapsulated by the droplet and the urine cell images not encapsulated by the droplet, and the Dice, Jaccard, precision, and recall values are calculated. The results suggest that the method of encapsulating single cells based on droplets can improve the image segmentation effect without optimizing the algorithm.

On-Chip Multiple Particle Velocity and Size Measurement Using Single-Shot Two-Wavelength Differential Image Analysis

Precise and quick measurement of samples' flow velocities is essential for cell sorting timing control and reconstruction of acquired image-analyzed data. We developed a simple technique for the single-shot measurement of flow velocities of particles simultaneously in a microfluidic pathway. The speed was calculated from the difference in the particles' elongation in an acquired image that appeared when two wavelengths of light with different irradiation times were applied. We ran microparticles through an imaging flow cytometer and irradiated two wavelengths of light with different irradiation times simultaneously to those particles. The mixture of the two wavelength transmitted lights was divided into two wavelengths, and the images of the same microparticles for each wavelength were acquired in a single shot. We estimated the velocity from the difference of its elongation divided by the difference of irradiation time by comparing these two images. The distribution of polystyrene beads' velocity was parabolic and highest at the center of the flow channel, consistent with the expected velocity distribution of the laminar flow. Applying the calculated velocity, we also restored the accurate shapes and cross-sectional areas of particles in the images, indicating this simple method for improving of imaging flow cytometry and cell sorter for diagnostic screening of circulating tumor cells.