FluoroCellTrack: An algorithm for automated analysis of high-throughput droplet microfluidic data

Automatic detection of fluorescent droplets for droplet digital PCR: a device capable of processing multiple microscope images

Droplet digital PCR (ddPCR) is recognized as a high-precision method for nucleic acid quantification, extensively utilized in biomedical research and clinical diagnostics. This technique employs microfluidic technology to partition the nucleic acid-containing reaction mixture into discrete droplets for amplification, achieving absolute quantification by identifying and enumerating the number of fluorescent droplets. The accuracy of droplet quantification is pivotal to the success of the assay. However, current image-processing tools are operationally complex, and commercial instruments are costly. Moreover, the designed algorithms exhibit a need for enhanced accuracy and are often restricted to use by trained personnel with specific microscopy equipment. In response to these challenges, we introduce an automated device (A-MMD), capable of detecting fluorescent droplets in ddPCR images captured by multiple microscopes. The device integrates three distinct algorithms tailored for the image processing of Laser Scanning Confocal Microscopy (LSCM), inverted microscopy, and self-assembled microscopy. Experimental validation using λ DNA demonstrated a 100.00% identification rate for positive droplets across all three image types, and the average identification rates for total droplets being 99.27% for LSCM, 98.96% for inverted microscopy, and 99.08% for self-assembled microscopy. Furthermore, the A-MMD is equipped with a user-friendly interface (UI) that streamlines the operational process, enabling non-specialists to efficiently perform droplet detection tasks. Our device not only has good environmental adaptability and identification accuracy, but also significantly reduces costs and operational complexity. It offers an economical, efficient, and user-friendly solution for ddPCR image analysis, thereby further propelling the advancement and application of nucleic acid detection technology.

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.

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).

WSCNet: Biomedical Image Recognition for Cell Encapsulated Microfluidic Droplets

Microfluidic droplets accommodating a single cell as independent microreactors are frequently demanded for single-cell analysis of phenotype and genotype. However, challenges exist in identifying and reducing the covalence probability (following Poisson's distribution) of more than two cells encapsulated in one droplet. It is of great significance to monitor and control the quantity of encapsulated content inside each droplet. We demonstrated a microfluidic system embedded with a weakly supervised cell counting network (WSCNet) to generate microfluidic droplets, evaluate their quality, and further recognize the locations of encapsulated cells. Here, we systematically verified our approach using encapsulated droplets from three different microfluidic structures. Quantitative experimental results showed that our approach can not only distinguish droplet encapsulations (F1 score > 0.88) but also locate each cell without any supervised location information (accuracy > 89%). The probability of a "single cell in one droplet" encapsulation is systematically verified under different parameters, which shows good agreement with the distribution of the passive method (Residual Sum of Squares, RSS < 0.5). This study offers a comprehensive platform for the quantitative assessment of encapsulated microfluidic droplets.

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.

Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images

The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is- rather than improving the segmentation quality compared to the baseline method- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitatively on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).

An image-to-answer algorithm for fully automated digital PCR image processing

The digital polymerase chain reaction (dPCR) is an irreplaceable variant of PCR techniques due to its capacity for absolute quantification and detection of rare deoxyribonucleic acid (DNA) sequences in clinical samples. Image processing methods, including micro-chamber positioning and fluorescence analysis, determine the reliability of the dPCR results. However, typical methods demand high requirements for the chip structure, chip filling, and light intensity uniformity. This research developed an image-to-answer algorithm with single fluorescence image capture and known image-related error removal. We applied the Hough transform to identify partitions in the images of dPCR chips, the 2D Fourier transform to rotate the image, and the 3D projection transformation to locate and correct the positions of all partitions. We then calculated each partition's average fluorescence amplitudes and generated a 3D fluorescence intensity distribution map of the image. We subsequently corrected the fluorescence non-uniformity between partitions based on the map and achieved statistical results of partition fluorescence intensities. We validated the proposed algorithms using different contents of the target DNA. The proposed algorithm is independent of the dPCR chip structure damage and light intensity non-uniformity. It also provides a reliable alternative to analyze the results of chip-based dPCR systems.

Artificial Intelligence-Controlled Microfluidic Device for Fluid Automation and Bubble Removal of Immunoassay Operated by a Smartphone

There have been tremendous innovations in microfluidic clinical diagnostics to facilitate novel point-of-care testing (POCT) over the past decades. However, the automatic operation of microfluidic devices that minimize user intervention still lacks reliability and repeatability because microfluidic errors such as bubbles and incomplete filling pose a major bottleneck in commercializing the microfluidic devices for clinical testing. In this work, for the first time, various states of microfluid were recognized to control immunodiagnostics by artificial intelligence (AI) technology. The developed AI-controlled microfluidic platform was operated via an Android smartphone, along with a low-cost polymer device to effectuate enzyme-linked immunosorbent assay (ELISA). To overcome the limited machine-learning capability of smartphones, the region-of-interest (ROI) cascading and conditional activation algorithms were utilized herein. The developed microfluidic chip was incorporated with a bubble trap to remove any bubbles detected by AI, which helps in preventing false signals during immunoassay, as well as controlling the reagents' movement with an on-chip micropump and valve. Subsequently, the developed immunosensing platform was tested for conducting real ELISA using a single microplate from the 96-well to detect the Human Cardiac Troponin I (cTnI) biomarker, with a detection limit as low as 0.98 pg/mL. As a result, the developed platform can be envisaged as an AI-based revolution in microfluidics for point-of-care clinical diagnosis.

Three-Dimensional Avian Hematopoietic Stem Cell Cultures as a Model for Studying Disease Pathogenesis

Three-dimensional (3D) cell culture is attracting increasing attention today because it can mimic tissue environments and provide more realistic results than do conventional cell cultures. On the other hand, very little attention has been given to using 3D cell cultures in the field of avian cell biology. Although mimicking the bone marrow niche is a classic challenge of mammalian stem cell research, experiments have never been conducted in poultry on preparing in vitro the bone marrow niche. It is well known, however, that all diseases cause immunosuppression and target immune cells and their development. Hematopoietic stem cells (HSC) reside in the bone marrow and constitute a source for immune cells of lymphoid and myeloid origins. Disease prevention and control in poultry are facing new challenges, such as greater use of alternative breeding systems and expanding production of eggs and chicken meat in developing countries. Moreover, the COVID-19 pandemic will draw greater attention to the importance of disease management in poultry because poultry constitutes a rich source of zoonotic diseases. For these reasons, and because they will lead to a better understanding of disease pathogenesis, in vivo HSC niches for studying disease pathogenesis can be valuable tools for developing more effective disease prevention, diagnosis, and control. The main goal of this review is to summarize knowledge about avian hematopoietic cells, HSC niches, avian immunosuppressive diseases, and isolation of HSC, and the main part of the review is dedicated to using 3D cell cultures and their possible use for studying disease pathogenesis with practical examples. Therefore, this review can serve as a practical guide to support further preparation of 3D avian HSC niches to study the pathogenesis of avian diseases.

Co-culture of Two Different Cell Lines in a Two-Layer Microfluidic Device

Microfluidic devices have become a promising alternative approach for cellular co-culture. Many approaches incorporate a semipermeable barrier to physically separate, yet chemically connect, two cell types; however, the majority of these approaches utilize batch culture conditions which can result in nutrient depletion and waste accumulation. This chapter describes an alternative approach that allows for the continuous infusion of media, relieving the constraints of batch culture. The microfluidic device consists of two separate layers: a bottom layer of 3% (w/v) agarose to facilitate chemical diffusion and a top polydimethylsiloxane (PDMS) layer into which four parallel fluidic channels were imprinted. The microfluidic approach allows for facile visualization of cells with light microscopy and the ability to add (or subtract) drugs or biomolecules to interrogate the system or modulate the cellular response. Finally, the approach allows for terminal immunostaining of either (or both) cell types.

Fluorescent visualization of oil displacement in a microfluidic device for enhanced oil recovery applications

A microfluidic device was developed to mimic the reservoir pore-scale and track the oil/water phases during air flooding. The chip was generated by combining soft-lithography and NOA81 replication. A unique feature of this approach is the inclusion of fluorescent dyes into the oil/water phases, allowing for real-time visualization of oil recovery without altering the phases' surface properties. As a proof of concept, the air was injected into the water/oil-flooded device for enhanced oil recovery applications.

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

Droplet microfluidics has revealed innovative strategies in biology and chemistry. This advancement has delivered novel quantification methods, such as droplet digital polymerase chain reaction (ddPCR) and an antibiotic heteroresistance analysis tool. For droplet analysis, researchers often use image-based detection techniques. Unfortunately, the analysis of images may require specific tools or programming skills to produce the expected results. In order to address the issue, we explore the potential use of standalone freely available software to perform image-based droplet detection. We select the four most popular software and classify them into rule-based and machine learning-based types after assessing the software's modules. We test and evaluate the software's (i) ability to detect droplets, (ii) accuracy and precision, and (iii) overall components and supporting material. In our experimental setting, we find that the rule-based type of software is better suited for image-based droplet detection. The rule-based type of software also has a simpler workflow or pipeline, especially aimed for non-experienced users. In our case, CellProfiler (CP) offers the most user-friendly experience for both single image and batch processing analyses.

High-Throughput Routes to Biomaterials Discovery

Many existing clinical treatments are limited in their ability to completely restore decreased or lost tissue and organ function, an unenviable situation only further exacerbated by a globally aging population. As a result, the demand for new medical interventions has increased substantially over the past 20 years, with the burgeoning fields of gene therapy, tissue engineering, and regenerative medicine showing promise to offer solutions for full repair or replacement of damaged or aging tissues. Success in these fields, however, inherently relies on biomaterials that are engendered with the ability to provide the necessary biological cues mimicking native extracellular matrixes that support cell fate. Accelerating the development of such "directive" biomaterials requires a shift in current design practices toward those that enable rapid synthesis and characterization of polymeric materials and the coupling of these processes with techniques that enable similarly rapid quantification and optimization of the interactions between these new material systems and target cells and tissues. This manuscript reviews recent advances in combinatorial and high-throughput (HT) technologies applied to polymeric biomaterial synthesis, fabrication, and chemical, physical, and biological screening with targeted end-point applications in the fields of gene therapy, tissue engineering, and regenerative medicine. Limitations of, and future opportunities for, the further application of these research tools and methodologies are also discussed.

Devices and techniques used to obtain and analyze three-dimensional cell cultures

Cell cultures are indispensable for both basic and applied research. Advancements in cell culture and analysis increase their utility for basic research and translational applications. A marked development in this direction is advent of three-dimensional (3D) cultures. The extent of advancement in 3D cell culture methods over the past decade has warranted referring to a single cell type being cultured as an aggregate or spheroid using simple scaffolds as "traditional." In recent years, the development of "next-generation" devices has enabled cultured cells to mimic their natural environments much better than the traditional 3D culture systems. Automated platforms like chip-based devices, magnetic- and acoustics-based assembly devices, di-electrophoresis (DEP), micro pocket cultures (MPoC), and 3D bio-printing provide a dynamic environment compared to the rather static conditions of the traditional simple scaffold-based 3D cultures. Chip-based technologies, which are centered on principles of microfluidics, are revolutionizing the ways in which cell culture and analysis can be compacted into table-top instruments. A parallel evolution in analytical devices enabled efficient assessment of various complex physiological and pathological endpoints. This is augmented by concurrent development of software enabling rapid large-scale automated data acquisition and analysis like image cytometry, elastography, optical coherence tomography, surface-enhanced Raman scattering (SERS), and biosensors. The techniques and devices utilized for the purpose of 3D cell culture and subsequent analysis depend primarily on the requirement of the study. We present here an in-depth account of the devices for obtaining and analyzing 3D cell cultures.

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.

Evaluation of intercellular communication between breast cancer cells and adipose-derived stem cells via passive diffusion in a two-layer microfluidic device

Breast cancer tumorigenesis and response to therapy is regulated by cancer cell interactions with the tumor microenvironment (TME). Breast cancer signaling to the surrounding TME results in a heterogeneous and diverse tumor microenvironment, which includes the production of cancer-associated fibroblasts, macrophages, adipocytes, and stem cells. The secretory profile of these cancer-associated cell types results in elevated chemokines and growth factors that promote cell survival and proliferation within the tumor. Current co-culture approaches mostly rely on transwell chambers to study intercellular signaling between adipose-derived stem cells (ASCs) and cancer cells; however, these methods are limited to endpoint measurements and lack dynamic control. In this study, a 4-channel, "flow-free" microfluidic device was developed to co-culture triple-negative MDA-MB-231 breast cancer cells and ASCs to study intercellular communication between two distinct cell types found in the TME. The device consists of two layers: a top PDMS layer with four imprinted channels coupled with a bottom agarose slab enclosed in a Plexiglas chamber. For dynamic co-culture, the device geometry contained two centered, flow-free channels, which were supplied with media from two outer flow channels via orthogonal diffusion through the agarose. Continuous fresh media was provided to the cell culture channel via passive diffusion without creating any shearing effect on the cells. The device geometry also allowed for the passive diffusion of cytokines and growth factors between the two cell types cultured in parallel channels to initiate cell-to-cell crosstalk. The device was used to show that MDA-MB-231 cells co-cultured with ASCs exhibited enhanced growth, a more aggressive morphology, and polarization toward the ASCs. The MDA-MB-231 cells were found to exhibit a greater degree of resistance to the drug paclitaxel when co-cultured with ASCs when compared to single culture studies. This microfluidic device is an ideal platform to study intercellular communication for many types of cells during co-culture experiments and allows for new investigations into stromal cell-mediated drug resistance in the tumor microenvironment.