Machine learning-based cytokine microarray digital immunoassay analysis

Beyond CAR-T: The rise of CAR-NK cell therapy in asthma immunotherapy

Asthma poses a major public health burden. While existing asthma drugs manage symptoms for many, some patients remain resistant. The lack of a cure, especially for severe asthma, compels exploration of novel therapies. Cancer immunotherapy successes with CAR-T cells suggest its potential for asthma treatment. Researchers are exploring various approaches for allergic diseases including membrane-bound IgE, IL-5, PD-L2, and CTLA-4 for asthma, and Dectin-1 for fungal asthma. NK cells offer several advantages over T cells for CAR-based immunotherapy. They offer key benefits: (1) HLA compatibility, meaning they can be used in a wider range of patients without the need for matching tissue types. (2) Minimal side effects (CRS and GVHD) due to their limited persistence and cytokine profile. (3) Scalability for "off-the-shelf" production from various sources. Several strategies have been introduced that highlight the superiority and challenges of CAR-NK cell therapy for asthma treatment including IL-10, IFN-γ, ADCC, perforin-granzyme, FASL, KIR, NCRs (NKP46), DAP, DNAM-1, TGF-β, TNF-α, CCL, NKG2A, TF, and EGFR. Furthermore, we advocate for incorporating AI for CAR design optimization and CRISPR-Cas9 gene editing technology for precise gene manipulation to generate highly effective CAR constructs. This review will delve into the evolution and production of CAR designs, explore pre-clinical and clinical studies of CAR-based therapies in asthma, analyze strategies to optimize CAR-NK cell function, conduct a comparative analysis of CAR-T and CAR-NK cell therapy with their respective challenges, and finally present established novel CAR designs with promising potential for asthma treatment.

CAR-NKT Cells in Asthma: Use of NKT as a Promising Cell for CAR Therapy

NKT cells, unique lymphocytes bridging innate and adaptive immunity, offer significant potential for managing inflammatory disorders like asthma. Activating iNKT induces increasing IFN-γ, TGF-β, IL-2, and IL-10 potentially suppressing allergic asthma. However, their immunomodulatory effects, including granzyme-perforin-mediated cytotoxicity, and expression of TIM-3 and TRAIL warrant careful consideration and targeted approaches. Although CAR-T cell therapy has achieved remarkable success in treating certain cancers, its limitations necessitate exploring alternative approaches. In this context, CAR-NKT cells emerge as a promising approach for overcoming these challenges, potentially achieving safer and more effective immunotherapies. Strategies involve targeting distinct IgE-receptors and their interactions with CAR-NKT cells, potentially disrupting allergen-mast cell/basophil interactions and preventing inflammatory cytokine release. Additionally, targeting immune checkpoints like PDL-2, inducible ICOS, FASL, CTLA-4, and CD137 or dectin-1 for fungal asthma could further modulate immune responses. Furthermore, artificial intelligence and machine learning hold immense promise for revolutionizing NKT cell-based asthma therapy. AI can optimize CAR-NKT cell functionalities, design personalized treatment strategies, and unlock a future of precise and effective care. This review discusses various approaches to enhancing CAR-NKT cell efficacy and longevity, along with the challenges and opportunities they present in the treatment of allergic asthma.

Microfluidic Digital Immunoassay for Point-of-Care Detection of NT-proBNP from Whole Blood

The high prevalence and economic burden of heart failure remain a challenge to global health. This lifelong disease leads to a buildup of permanent heart damage, making early detection and frequent monitoring crucial for effective treatment. N-terminal proBNP (NT-proBNP) is an important biomarker for monitoring the disease state, but current commercial and research NT-proBNP assays require phlebotomy and bulky equipment or do not satisfy clinical requirements such as sensitivity and detection thresholds. Here, we report a point-of-care (POC) compatible microfluidic digital immunoassay that can quantify the NT-proBNP concentration in a small volume of whole blood. Our automated microfluidic device takes whole blood samples mixed with biotinylated detection antibodies and passes through a plasma filter to react with a capture antibody-functionalized sensor surface. Streptavidin-coated gold nanoparticles (GNPs) are then released to mark the surface-bound single NT-proBNP immunocomplexes and recorded with bright-field microscopy. NT-proBNP concentrations in the sample are quantified via a hybrid digital/analog calibration curve. Digital counts of bound GNPs are used as readout signal at low concentrations for high sensitivity detection, and GNP pixel occupancies are used at high concentrations for extended dynamic range. With this approach, we detected NT-proBNP in the range of 8.24-10 000 pg/mL from 7 μL of whole blood in 10 min, with a limit of detection of 0.94 pg/mL. Finally, the method was validated with 15 clinical serum samples, showing excellent linear correlation (r = 0.998) with Roche's Elecsys proBNP II assay. This evidence indicates that this method holds promise for decentralized monitoring of heart failure.

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

Multiplex Assay of Cytokines with Tunable Detection Ranges for the Precise Diagnosis of Breast Cancer

Precise profiling of the cytokine panel consisting of different levels of cytokines can provide personalized information about several diseases at certain stages. In this study, we have designed and fabricated an "all-in-one" diagnostic tool kit to bioassay multiple inflammatory cytokines ranging from picograms per milliliter to μg/mL in a small cytokine panel. Taking advantage of the kit fabricated by the DNA-encoded assembly of nanocatalysts in dynamic regulation and signal amplification, we have demonstrated the multiplex, visual, and quantitative detection of C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) with limits of detection of 1.6 ng/mL (61.54 pM), 20 pg/mL (1.57 pM), and 4 pg/mL (0.19 pM), respectively. This diagnostic tool kit can work well with commercial kits for detecting serum cytokines from breast cancer patients treated with immunotherapies. Furthermore, a small cytokine panel composed of CRP, PCT, and IL-6 is revealed to be significantly heterogeneous in each patient and highly dynamic for different treatment courses, showing promise as a panel of quantitative biomarker candidates for individual treatments. So, our work may provide a versatile diagnostic tool kit for the visual detection of clinical biomarkers with an adjustable broad detection range.

Unrevealing the connection between real sample analysis and analytical method. The case of cytokines

Cytokines are compounds that belong to a special class of signaling biomolecules that are responsible for several functions in the human body, being involved in cell growth, inflammatory, and neoplastic processes. Thus, they represent valuable biomarkers for diagnosing and drug therapy monitoring certain medical conditions. Because cytokines are secreted in the human body, they can be detected in both conventional samples, such as blood or urine, but also in samples less used in medical practice such as sweat or saliva. As the importance of cytokines was identified, various analytical methods for their determination in biological fluids were reported. The gold standard in cytokine detection is considered the enzyme-linked immunosorbent assay method and the most recent ones have been considered and compared in this study. It is known that the conventional methods are accompanied by a few disadvantages that new methods of analysis, especially electrochemical sensors, are trying to overcome. Electrochemical sensors proved to be suited for the elaboration of integrated, portable, and wearable sensing devices, which could also facilitate cytokines determination in medical practice.

Recent advances in point-of-care testing of COVID-19

Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.

Miniaturized microarray-format digital ELISA enabled by lithographic protein patterning

The search for reliable protein biomarker candidates is critical for early disease detection and treatment. However, current immunoassay technologies are failing to meet increasing demands for sensitivity and multiplexing. Here, the authors have created a highly sensitive protein microarray using the principle of single-molecule counting for signal amplification, capable of simultaneously detecting a panel of cancer biomarkers at sub-pg/mL levels. To enable this amplification strategy, the authors introduce a novel method of protein patterning using photolithography to subdivide addressable arrays of capture antibody spots into hundreds of thousands of individual microwells. This allows for the total sensor area to be miniaturized, increasing the total possible multiplex capacity. With the immunoassay realized on a standard 75x25 mm form factor glass substrate, sample volume consumption is minimized to <10 μL, making the technology highly efficient and cost-effective. Additionally, the authors demonstrate the power of their technology by measuring six secretory factors related to glioma tumor progression in a cohort of mice. This highly sensitive, sample-sparing multiplex immunoassay paves the way for researchers to track changes in protein profiles over time, leading to earlier disease detection and discovery of more effective treatment using animal models.

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.

A tissue chip with integrated digital immunosensors: In situ brain endothelial barrier cytokine secretion monitoring

Organ-on-a-chip platforms have potential to offer more cost-effective, ethical, and human-resembling models than animal models for disease study and drug discovery. Particularly, the Blood-Brain-Barrier-on-a-chip (BBB-oC) has emerged as a promising tool to investigate several neurological disorders since it promises to provide a model of the multifunctional tissue working as an important node to control pathogen entry, drug delivery and neuroinflammation. A comprehensive understanding of the multiple physiological functions of the tissue model requires biosensors detecting several tissue-secreted substances in a BBB-oC system. However, current sensor-integrated BBB-oC platforms are only available for tissue membrane integrity characterization based on permeability measurement. Protein secretory pathways are closely associated with the tissue's various diseased conditions. At present, no biosensor-integrated BBB-oC platform exists that permits in situ tissue protein secretion analysis over time, which prohibits researchers from fully understanding the time-evolving pathology of a tissue barrier. Herein, the authors present a platform named "Digital Tissue-BArrier-CytoKine-counting-on-a-chip (DigiTACK)," which integrates digital immunosensors into a tissue chip system and demonstrates on-chip multiplexed, ultrasensitive, longitudinal cytokine secretion profiling of cultured brain endothelial barrier tissues. The integrated digital sensors utilize a novel beadless microwell format to perform an ultrafast "digital fingerprinting" of the analytes while achieving a low limit of detection (LoD) around 100-500 fg/mL for mouse MCP1 (CCL2), IL-6 and KC (CXCL1). The DigiTACK platform is extensively applicable to profile temporal cytokine secretion of other barrier-related organ-on-a-chip systems and can provide new insight into the secretory dynamics of the BBB by sequentially controlled experiments.

Optofluidic imaging meets deep learning: from merging to emerging

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Digital detection of proteins

This paper reviews methods for detecting proteins based on molecular digitization, i.e., the isolation and detection of single protein molecules or singulated ensembles of protein molecules. The single molecule resolution of these methods has resulted in significant improvements in the sensitivity of immunoassays beyond what was possible using traditional "analog" methods: the sensitivity of some digital immunoassays approach those of methods for measuring nucleic acids, such as the polymerase chain reaction (PCR). The greater sensitivity of digital protein detection has resulted in immuno-diagnostics with high potential societal impact, e.g., the early diagnosis and therapeutic intervention of Alzheimer's Disease. In this review, we will first provide the motivation for developing digital protein detection methods given the limitations in the sensitivity of analog methods. We will describe the paradigm shift catalyzed by single molecule detection, and will describe in detail one digital approach - which we call digital bead assays (DBA) - based on the capture and labeling of proteins on beads, identifying "on" and "off" beads, and quantification using Poisson statistics. DBA based on the single molecule array (Simoa) technology have sensitivities down to attomolar concentrations, equating to ∼10 proteins in a 200 μL sample. We will describe the concept behind DBA, the different single molecule labels used, the ways of analyzing beads (imaging of arrays and flow), the binding reagents and substrates used, and integration of these technologies into fully automated and miniaturized systems. We provide an overview of emerging approaches to digital protein detection, including those based on digital detection of nucleic acids labels, single nanoparticle detection, measurements using nanopores, and methods that exploit the kinetics of single molecule binding. We outline the initial impact of digital protein detection on clinical measurements, highlighting the importance of customized assay development and translational clinical research. We highlight the use of DBA in the measurement of neurological protein biomarkers in blood, and how these higher sensitivity methods are changing the diagnosis and treatment of neurological diseases. We conclude by summarizing the status of digital protein detection and suggest how the lab-on-a-chip community might drive future innovations in this field.

Artificial Intelligence-Assisted Digital Immunoassay Based on a Programmable-Particle-Decoding Technique for Multitarget Ultrasensitive Detection

The development of a multitarget ultrasensitive immunoassay is significant to fields such as medical research, clinical diagnosis, and food safety inspection. In this study, an artificial intelligence (AI)-assisted programmable-particle-decoding technique (APT)-based digital immunoassay system was developed to perform multitarget ultrasensitive detection. Multitarget was encoded by programmable polystyrene (PS) microspheres with different characteristics (particle size and number), and subsequent visible signals were recorded under an optical microscope after the immune reaction. The resultant images were further analyzed using a customized, AI-based computer vision technique to decode the intrinsic properties of polystyrene microspheres and to reveal the types and concentrations of targets. Our strategy has successfully detected multiple inflammatory markers in clinical serum and antibiotics with a broad detection range from pg/mL to μg/mL without extra signal amplification and conversion. An AI-based digital immunoassay system exhibits great potential to be used for the next generation of multitarget detection in disease screening for candidate patients.

A deep learning based method for automatic analysis of high-throughput droplet digital PCR images

Droplet digital PCR (ddPCR) is a technique for absolute quantification of nucleic acid molecules and is widely used in biomedical research and clinical diagnosis. ddPCR partitions the reaction solution containing target molecules into a large number of independent microdroplets for amplification and performs quantitative analysis of target molecules by calculating the proportion of positive droplets by the principle of Poisson distribution. Accurate recognition of positive droplets in ddPCR images is of great importance to guarantee the accuracy of target nucleic acid quantitative analysis. However, hand-designed operators are sensitive to interference and have disadvantages such as low contrast, uneven illumination, low sample copy number, and noise, and their accuracy and robustness still need to be improved. Herein, we developed a deep learning-based high-throughput ddPCR droplet detection framework for robust and accurate ddPCR image analysis, and the experimental results show that our method achieves excellent performance in the recognition of positive droplets (99.71%) within a limited time. By combining the Hough transform and a convolutional neural network (CNN), our novel method can automatically filter out invalid droplets that are difficult to be identified by local or global encoding methods and realize high-precision localization and classification of droplets in ddPCR images under variable exposure, contrast, and uneven illumination conditions without the need for image pre-processing and normalization processes.

Merging microfluidics with luminescence immunoassays for urgent point-of-care diagnostics of COVID-19

The Coronavirus disease 2019 (COVID-19) outbreak has urged the establishment of a global-wide rapid diagnostic system. Current widely-used tests for COVID-19 include nucleic acid assays, immunoassays, and radiological imaging. Immunoassays play an irreplaceable role in rapidly diagnosing COVID-19 and monitoring the patients for the assessment of their severity, risks of the immune storm, and prediction of treatment outcomes. Despite of the enormous needs for immunoassays, the widespread use of traditional immunoassay platforms is still limited by high cost and low automation, which are currently not suitable for point-of-care tests (POCTs). Microfluidic chips with the features of low consumption, high throughput, and integration, provide the potential to enable immunoassays for POCTs, especially in remote areas. Meanwhile, luminescence detection can be merged with immunoassays on microfluidic platforms for their good performance in quantification, sensitivity, and specificity. This review introduces both homogenous and heterogenous luminescence immunoassays with various microfluidic platforms. We also summarize the strengths and weaknesses of the categorized methods, highlighting their recent typical progress. Additionally, different microfluidic platforms are described for comparison. The latest advances in combining luminescence immunoassays with microfluidic platforms for POCTs of COVID-19 are further explained with antigens, antibodies, and related cytokines. Finally, challenges and future perspectives were discussed.

Multiplex Analysis of Serum Cytokine Profiles in Systemic Lupus Erythematosus and Multiple Sclerosis

Changes in cytokine profiles and cytokine networks are known to be a hallmark of autoimmune diseases, including systemic lupus erythematosus (SLE) and multiple sclerosis (MS). However, cytokine profiles research studies are usually based on the analysis of a small number of cytokines and give conflicting results. In this work, we analyzed cytokine profiles of 41 analytes in patients with SLE and MS compared with healthy donors using multiplex immunoassay. The SLE group included treated patients, while the MS patients were drug-free. Levels of 11 cytokines, IL-1b, IL-1RA, IL-6, IL-9, IL-10, IL-15, MCP-1/CCL2, Fractalkine/CX3CL1, MIP-1a/CCL3, MIP-1b/CCL4, and TNFa, were increased, but sCD40L, PDGF-AA, and MDC/CCL22 levels were decreased in SLE patients. Thus, changes in the cytokine profile in SLE have been associated with the dysregulation of interleukins, TNF superfamily members, and chemokines. In the case of MS, levels of 10 cytokines, sCD40L, CCL2, CCL3, CCL22, PDGF-AA, PDGF-AB/BB, EGF, IL-8, TGF-a, and VEGF, decreased significantly compared to the control group. Therefore, cytokine network dysregulation in MS is characterized by abnormal levels of growth factors and chemokines. Cross-disorder analysis of cytokine levels in MS and SLE showed significant differences between 22 cytokines. Protein interaction network analysis showed that all significantly altered cytokines in both SLE and MS are functionally interconnected. Thus, MS and SLE may be associated with impaired functional relationships in the cytokine network. A cytokine correlation networks analysis revealed changes in correlation clusters in SLE and MS. These data expand the understanding of abnormal regulatory interactions in cytokine profiles associated with autoimmune diseases.

Microfluidics Technology in SARS-CoV-2 Diagnosis and Beyond: A Systematic Review

With the progression of the COVID-19 pandemic, new technologies are being implemented for more rapid, scalable, and sensitive diagnostics. The implementation of microfluidic techniques and their amalgamation with different detection techniques has led to innovative diagnostics kits to detect SARS-CoV-2 antibodies, antigens, and nucleic acids. In this review, we explore the different microfluidic-based diagnostics kits and how their amalgamation with the various detection techniques has spearheaded their availability throughout the world. Three other online databases, PubMed, ScienceDirect, and Google Scholar, were referred for articles. One thousand one hundred sixty-four articles were determined with the search algorithm of microfluidics followed by diagnostics and SARS-CoV-2. We found that most of the materials used to produce microfluidics devices were the polymer materials such as PDMS, PMMA, and others. Centrifugal force is the most commonly used fluid manipulation technique, followed by electrochemical pumping, capillary action, and isotachophoresis. The implementation of the detection technique varied. In the case of antibody detection, spectrometer-based detection was most common, followed by fluorescence-based as well as colorimetry-based. In contrast, antigen detection implemented electrochemical-based detection followed by fluorescence-based detection, and spectrometer-based detection were most common. Finally, nucleic acid detection exclusively implements fluorescence-based detection with a few colorimetry-based detections. It has been further observed that the sensitivity and specificity of most devices varied with implementing the detection-based technique alongside the fluid manipulation technique. Most microfluidics devices are simple and incorporate the detection-based system within the device. This simplifies the deployment of such devices in a wide range of environments. They can play a significant role in increasing the rate of infection detection and facilitating better health services.

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.

Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients

Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features: (i) a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; (ii) an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; (iii) a rapid and accurate machine-learning-based image processing method for digital signal analysis. The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1-10,000 pg mL-1 and ultralow detection limits down to 0.46-1.36 pg mL-1 using a minimum of 3 μL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring.

Ultrasensitive Multiparameter Phenotyping of Rare Cells Using an Integrated Digital-Molecular-Counting Microfluidic Well Plate

Integrated microfluidic cellular phenotyping platforms provide a promising means of studying a variety of inflammatory diseases mediated by cell-secreted cytokines. However, immunosensors integrated in previous microfluidic platforms lack the sensitivity to detect small signals in the cellular secretion of proinflammatory cytokines with high precision. This limitation prohibits researchers from studying cells secreting cytokines at low abundance or existing at a small population. Herein, the authors present an integrated platform named the "digital Phenoplate (dPP)," which integrates digital immunosensors into a microfluidic chip with on-chip cell assay chambers, and demonstrates ultrasensitive cellular cytokine secretory profile measurement. The integrated sensors yield a limit of detection as small as 0.25 pg mL-1 for mouse tumor necrosis factor alpha (TNF-α). Each on-chip cell assay chamber confines cells whose population ranges from ≈20 to 600 in arrayed single-cell trapping microwells. Together, these microfluidic features of the dPP simultaneously permit precise counting and image-based cytometry of individual cells while performing parallel measurements of TNF-α released from rare cells under multiple stimulant conditions for multiple samples. The dPP platform is broadly applicable to the characterization of cellular phenotypes demanding high precision and high throughput.

Next-Generation Implementation of Chimeric Antigen Receptor T-Cell Therapy Using Digital Health

Chimeric antigen receptor T-cell (CAR-T) therapy is a paradigm-shifting immunotherapy modality in oncology; however, unique toxicities such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome limit its ability to be implemented more widely in the outpatient setting or at smaller-volume centers. Three operational challenges with CAR-T therapy include the following: (1) the logistics of toxicity monitoring, ie, with frequent vital sign checks and neurologic assessments; (2) the specialized knowledge required for toxicity management, particularly with regard to CRS and immune effector cell-associated neurotoxicity syndrome; and (3) the need for high-quality symptomatic and supportive care during this intensive period. In this review, we explore potential niches for digital innovations that can improve the implementation of CAR-T therapy in each of these domains. These tools include patient-facing technologies and provider-facing platforms: for example, wearable devices and mobile health apps to screen for fevers and encephalopathy, electronic patient-reported outcome assessments-based workflows to assist with symptom management, machine learning algorithms to predict emerging CRS in real time, clinical decision support systems to assist with toxicity management, and digital coaching to help maintain wellness. Televisits, which have grown in prominence since the novel coronavirus pandemic, will continue to play a key role in the monitoring and management of CAR-T-related toxicities as well. Limitations of these strategies include the need to ensure care equity and stakeholder buy-in, both operationally and financially. Nevertheless, once developed and validated, the next-generation implementation of CAR-T therapy using these digital tools may improve both its safety and accessibility.