Machine learning-aided quantification of antibody-based cancer immunotherapy by natural killer cells in microfluidic droplets

Imaging-Based Efficacy Evaluation of Cancer Immunotherapy in Engineered Tumor Platforms and Tumor Organoids

Cancer immunotherapy is used to treat tumors by modulating the immune system. Although the anticancer efficacy of cancer immunotherapy has been evaluated prior to clinical trials, conventional in vivo animal and endpoint models inadequately replicate the intricate process of tumor elimination and reflect human-specific immune systems. Therefore, more sophisticated models that mimic the complex tumor-immune microenvironment must be employed to assess the effectiveness of immunotherapy. Additionally, using real-time imaging technology, a step-by-step evaluation can be applied, allowing for a more precise assessment of treatment efficacy. Here, an overview of the various imaging-based evaluation platforms recently developed for cancer immunotherapeutic applications is presented. Specifically, a fundamental technique is discussed for stably observing immune cell-based tumor cell killing using direct imaging, a microwell that reproduces a confined space for spatial observation, a droplet assay that facilitates cell-cell interactions, and a 3D microphysiological system that reconstructs the vascular environment. Furthermore, it is suggested that future evaluation platforms pursue more human-like immune systems.

Expanding CAR-T cell immunotherapy horizons through microfluidics

Chimeric antigen receptor (CAR)-T cell therapies have revolutionized cancer treatment, particularly in hematological malignancies. However, their application to solid tumors is limited, and they face challenges in safety, scalability, and cost. To enhance current CAR-T cell therapies, the integration of microfluidic technologies, harnessing their inherent advantages, such as reduced sample consumption, simplicity in operation, cost-effectiveness, automation, and high scalability, has emerged as a powerful solution. This review provides a comprehensive overview of the step-by-step manufacturing process of CAR-T cells, identifies existing difficulties at each production stage, and discusses the successful implementation of microfluidics and related technologies in addressing these challenges. Furthermore, this review investigates the potential of microfluidics-based methodologies in advancing cell-based therapy across various applications, including solid tumors, next-generation CAR constructs, T-cell receptors, and the development of allogeneic "off-the-shelf" CAR products.

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

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.

High-throughput microfluidic droplets in biomolecular analytical system: A review

Droplet microfluidic technology has revolutionized biomolecular analytical research, as it has the capability to reserve the genotype-to-phenotype linkage and assist for revealing the heterogeneity. Massive and uniform picolitre droplets feature dividing solution to the level that single cell and single molecule in each droplet can be visualized, barcoded, and analyzed. Then, the droplet assays can unfold intensive genomic data, offer high sensitivity, and screen and sort from a large number of combinations or phenotypes. Based on these unique advantages, this review focuses on up-to-date research concerning diverse screening applications utilizing droplet microfluidic technology. The emerging progress of droplet microfluidic technology is first introduced, including efficient and scaling-up in droplets encapsulation, and prevalent batch operations. Then the new implementations of droplet-based digital detection assays and single-cell muti-omics sequencing are briefly examined, along with related applications such as drug susceptibility testing, multiplexing for cancer subtype identification, interactions of virus-to-host, and multimodal and spatiotemporal analysis. Meanwhile, we specialize in droplet-based large-scale combinational screening regarding desired phenotypes, with an emphasis on sorting for immune cells, antibodies, enzymatic properties, and proteins produced by directed evolution methods. Finally, some challenges, deployment and future perspective of droplet microfluidics technology in practice are also discussed.

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 advances in microfluidic-based cancer immunotherapy-on-a-chip strategies

Despite several extraordinary improvements in cancer immunotherapy, its therapeutic effectiveness against many distinct cancer types remains mostly limited and requires further study. Different microfluidic-based cancer immunotherapy-on-a-chip (ITOC) systems have been developed to help researchers replicate the tumor microenvironment and immune system. Numerous microfluidic platforms can potentially be used to perform various on-chip activities related to early clinical cancer immunotherapy processes, such as improving immune checkpoint blockade therapy, studying immune cell dynamics, evaluating cytotoxicity, and creating vaccines or organoid models from patient samples. In this review, we summarize the most recent advancements in the development of various microfluidic-based ITOC devices for cancer treatment niches and present future perspectives on microfluidic devices for immunotherapy research.

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.

Gold-seaurchin based immunomodulator enabling photothermal intervention and αCD16 transfection to boost NK cell adoptive immunotherapy

Despite huge potentials of NK cells in adoptive cell therapy (ACT), formidable physical barriers of the tumor tissue and deficiency of recognizing signals on tumor cells severely prevent NK cell infiltrating, activating and killing performances. Herein, a nano-immunomodulator AuNSP@αCD16 (CD16 antibody encoding plasmid) is explored to remodel the tumor microenvironment (TME) for improving the antitumor effects of adoptive NK cells. The as-prepared AuNSP, with a seaurchin-like gold core and a cationic polymer shell, exhibited a high gene transfection efficiency and a stable NIR-II photothermal capacity. The AuNSP could trigger mild photothermal intervention to partly destroy tumors and collapse the dense physical barriers, making a permeable TME for NK cell infiltration. What's more, the AuNSP could achieve αCD16 gene transfection to modify tumor surface with CD16 antibody, marking a unique structure on tumor cells for NK cell recognition and then lead to strong NK cell activation by CD16-mediated antibody-dependent cellular cytotoxicity (ADCC). As expected, the designed AuNSP@αCD16 induced an immune-favorable TME for NK cell performing killing functions against solid tumors, increasing the release of cytolytic granules and proinflammatory cytokines, which ultimately achieved a robustly boosted NK cell-based immunotherapy. Hence, the AuNSP@αCD16-mediated TME reconstituting strategy provides a substantial perspective for NK-based ACT on solid tumors. STATEMENT OF SIGNIFICANCE: In adoptive cell therapy (ACT), natural killer (NK) cells exhibit greater off-the-shelf utility and improved safety comparing with T cells, but the efficacy of NK cell therapy is severely compromised by formidable physical barriers of the tumor tissue and deficiency of NK cell recognizing signals on tumor cells. Herein, a nano-immunomodulator AuNSP@αCD16, with the abilities of inducing mild photothermal intervention and modifying the tumor cell surface with αCD16, is explored to reconstruct an infiltration-favorable and activation-facilitating tumor microenvironment for NK cells to perform killing functions. Such a simple and safe strategy is believed as a very promising candidate for future NK-based ACT.

Hydrogels for Single-Cell Microgel Production: Recent Advances and Applications

Single-cell techniques have become more and more incorporated in cell biological research over the past decades. Various approaches have been proposed to isolate, culture, sort, and analyze individual cells to understand cellular heterogeneity, which is at the foundation of every systematic cellular response in the human body. Microfluidics is undoubtedly the most suitable method of manipulating cells, due to its small scale, high degree of control, and gentle nature toward vulnerable cells. More specifically, the technique of microfluidic droplet production has proven to provide reproducible single-cell encapsulation with high throughput. Various in-droplet applications have been explored, ranging from immunoassays, cytotoxicity assays, and single-cell sequencing. All rely on the theoretically unlimited throughput that can be achieved and the monodispersity of each individual droplet. To make these platforms more suitable for adherent cells or to maintain spatial control after de-emulsification, hydrogels can be included during droplet production to obtain “microgels.” Over the past years, a multitude of research has focused on the possibilities these can provide. Also, as the technique matures, it is becoming clear that it will result in advantages over conventional droplet approaches. In this review, we provide a comprehensive overview on how various types of hydrogels can be incorporated into different droplet-based approaches and provide novel and more robust analytic and screening applications. We will further focus on a wide range of recently published applications for microgels and how these can be applied in cell biological research at the single- to multicell scale.

Intelligent control of nanoparticle synthesis through machine learning

The synthesis of nanoparticles is affected by many reaction conditions, and their properties are usually determined by factors such as their size, shape and surface chemistry. In order for the synthesized nanoparticles to have functions suitable for different fields (for example, optics, electronics, sensor applications and so on), precise control of their properties is essential. However, with the current technology of preparing nanoparticles on a microreactor, it is time-consuming and laborious to achieve precise synthesis. In order to improve the efficiency of synthesizing nanoparticles with the expected functionality, the application of machine learning-assisted synthesis is an intelligent choice. In this article, we mainly introduce the typical methods of preparing nanoparticles on microreactors, and explain the principles and procedures of machine learning, as well as the main ways of obtaining data sets. We have studied three types of representative nanoparticle preparation methods assisted by machine learning. Finally, the current problems in machine learning-assisted nanoparticle synthesis and future development prospects are discussed.

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

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

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.

Microfluidics for Peptidomics, Proteomics, and Cell Analysis

Microfluidics is the advanced microtechnology of fluid manipulation in channels with at least one dimension in the range of 1-100 microns. Microfluidic technology offers a growing number of tools for manipulating small volumes of fluid to control chemical, biological, and physical processes relevant to separation, analysis, and detection. Currently, microfluidic devices play an important role in many biological, chemical, physical, biotechnological and engineering applications. There are numerous ways to fabricate the necessary microchannels and integrate them into microfluidic platforms. In peptidomics and proteomics, microfluidics is often used in combination with mass spectrometric (MS) analysis. This review provides an overview of using microfluidic systems for peptidomics, proteomics and cell analysis. The application of microfluidics in combination with MS detection and other novel techniques to answer clinical questions is also discussed in the context of disease diagnosis and therapy. Recent developments and applications of capillary and microchip (electro)separation methods in proteomic and peptidomic analysis are summarized. The state of the art of microchip platforms for cell sorting and single-cell analysis is also discussed. Advances in detection methods are reported, and new applications in proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices and determination of their physicochemical parameters are highlighted.

Is microfluidics the "assembly line" for CRISPR-Cas9 gene-editing?

Acclaimed as one of the biggest scientific breakthroughs, the technology of CRISPR has brought significant improvement in the biotechnological spectrum-from editing genetic defects in diseases for gene therapy to modifying organisms for the production of biofuels. Since its inception, the CRISPR-Cas9 system has become easier and more versatile to use. Many variants have been found, giving the CRISPR toolkit a great range that includes the activation and repression of genes aside from the previously known knockout and knockin of genes. Here, in this Perspective, we describe efforts on automating the gene-editing workflow, with particular emphasis given on the use of microfluidic technology. We discuss how automation can address the limitations of gene-editing and how the marriage between microfluidics and gene-editing will expand the application space of CRISPR.

Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning

Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.

Step emulsification in microfluidic droplet generation: mechanisms and structures

Step emulsification for micro- and nano-droplet generation is reviewed in brief, including the emulsion mechanisms and microfluidic devices.