A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells

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.

Photosensitive Hydrogel with Temperature-Controlled Reversible Nano-Apertures for Single-Cell Protein Analysis

Single cell western blot (scWB) is one of the most important methods for cellular heterogeneity profiling. However, current scWB based on conventional photoactive polyacrylamide hydrogel material suffers from the tradeoff between in-gel probing and separation resolution. Here, a highly sensitive temperature-controlled single-cell western blotting (tc-scWB) method is introduced, which is based on a thermo/photo-dualistic-sensitive polyacrylamide hydrogel, namely acrylic acid-functionalized graphene oxide (AFGO) assisted, N-isopropylacrylamide modified polyacrylamide (ANP) hydrogel. The ANP hydrogel is contracted at high-temperature to constrain protein band diffusion during microchip electrophoretic separation, while the gel aperture is expanded under low-temperature for better antibody penetration into the hydrogel. The tc-scWB method enables the separation and profiling of small-molecule-weight proteins with highly crosslinked gel (12% T) in SDS-PAGE. The tc-scWB is demonstrated on three metabolic and ER stress-specific proteins (CHOP, MDH2 and FH) in four pancreatic cell subtypes, revealing the expression of key enzymes in the Krebs cycle is upregulated with enhanced ER stress. It is found that ER stress can regulate crucial enzyme (MDH2 and FH) activities of metabolic cascade in cancer cells, boosting aerobic respiration to attenuate the Warburg effect and promote cell apoptosis. The tc-scWB is a general toolbox for the analysis of low-abundance small-molecular functional proteins at the single-cell level.

Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors

Chimeric antigen receptor (CAR) T cells express antigen-specific synthetic receptors, which upon binding to cancer cells, elicit T cell anti-tumor responses. CAR T cell therapy has enjoyed success in the clinic for hematological cancer indications, giving rise to decade-long remissions in some cases. However, CAR T therapy for patients with solid tumors has not seen similar success. Solid tumors constitute 90% of adult human cancers, representing an enormous unmet clinical need. Current approaches do not solve the central problem of limited ability of therapeutic cells to migrate through the stromal matrix. We discover that T cells at low and high density display low- and high-migration phenotypes, respectively. The highly migratory phenotype is mediated by a paracrine pathway from a group of self-produced cytokines that include IL5, TNFα, IFNγ, and IL8. We exploit this finding to "lock-in" a highly migratory phenotype by developing and expressing receptors, which we call velocity receptors (VRs). VRs target these cytokines and signal through these cytokines' cognate receptors to increase T cell motility and infiltrate lung, ovarian, and pancreatic tumors in large numbers and at doses for which control CAR T cells remain confined to the tumor periphery. In contrast to CAR therapy alone, VR-CAR T cells significantly attenuate tumor growth and extend overall survival. This work suggests that approaches to the design of immune cell receptors that focus on migration signaling will help current and future CAR cellular therapies to infiltrate deep into solid tumors.

Increased vesicular dynamics and nanoscale clustering of IL-2 after T cell activation

T cells coordinate intercellular communication through the meticulous regulation of cytokine secretion. Direct visualization of vesicular transport and intracellular distribution of cytokines provides valuable insights into the temporal and spatial mechanisms involved in regulation. Employing Jurkat E6-1 T cells and interleukin-2 (IL-2) as a model system, we investigated vesicular dynamics using single-particle tracking and the nanoscale distribution of intracellular IL-2 in fixed T cells using superresolution microscopy. Live-cell imaging revealed that in vitro activation resulted in increased vesicular dynamics. Direct stochastic optical reconstruction microscopy and 3D structured illumination microscopy revealed nanoscale clustering of IL-2. In vitro activation correlated with spatial accumulation of IL-2 nanoclusters into more pronounced and elongated clusters. These observations provide visual evidence that accelerated vesicular transport and spatial concatenation of IL-2 clusters at the nanoscale may constitute a potential mechanism for modulating cytokine release by Jurkat T cells.

Microfluidic platform for omics analysis on single cells with diverse morphology and size: A review

BACKGROUND

Microfluidic techniques have emerged as powerful tools in single-cell research, facilitating the exploration of omics information from individual cells. Cell morphology is crucial for gene expression and physiological processes. However, there is currently a lack of integrated analysis of morphology and single-cell omics information. A critical challenge remains: what platform technologies are the best option to decode omics data of cells that are complex in morphology and size?

RESULTS

This review highlights achievements in microfluidic-based single-cell omics and isolation of cells based on morphology, along with other cell sorting methods based on physical characteristics. Various microfluidic platforms for single-cell isolation are systematically presented, showcasing their diversity and adaptability. The discussion focuses on microfluidic devices tailored to the distinct single-cell isolation requirements in plants and animals, emphasizing the significance of considering cell morphology and cell size in optimizing single-cell omics strategies. Simultaneously, it explores the application of microfluidic single-cell sorting technologies to single-cell sequencing, aiming to effectively integrate information about cell shape and size.

SIGNIFICANCE AND NOVELTY

The novelty lies in presenting a comprehensive overview of recent accomplishments in microfluidic-based single-cell omics, emphasizing the integration of different microfluidic platforms and their implications for cell morphology-based isolation. By underscoring the pivotal role of the specialized morphology of different cells in single-cell research, this review provides robust support for delving deeper into the exploration of single-cell omics data.

Identification of druggable regulators of cell secretion via a kinome-wide screen and high-throughput immunomagnetic cell sorting

The identification of genetic regulators of cell secretions is challenging because it requires the sorting of a large number of cells according to their secretion patterns. Here we report the development and applicability of a high-throughput microfluidic method for the analysis of the secretion levels of large populations of immune cells. The method is linked with a kinome-wide loss-of-function CRISPR screen, immunomagnetically sorting the cells according to their secretion levels, and the sequencing of their genomes to identify key genetic modifiers of cell secretion. We used the method, which we validated against flow cytometry for cytokines secreted from primary mouse CD4+ (cluster of differentiation 4-positive) T cells, to discover a subgroup of highly co-expressed kinase-coding genes that regulate interferon-gamma secretion by these cells. We validated the function of the kinases identified using RNA interference, CRISPR knockouts and kinase inhibitors and confirmed the druggability of selected kinases via the administration of a kinase inhibitor in an animal model of colitis. The technique may facilitate the discovery of regulatory mechanisms for immune-cell activation and of therapeutic targets for autoimmune diseases.

Applications of single-cell omics for chimeric antigen receptor T cell therapy

Chimeric antigen receptor (CAR) T cell therapy is a promising cancer treatment modality. The breakthroughs in CAR T cell therapy were, in part, possible with the help of cell analysis methods, such as single-cell analysis. Bulk analyses have provided invaluable information regarding the complex molecular dynamics of CAR T cells, but their results are an average of thousands of signals in CAR T or tumour cells. Since cancer is a heterogeneous disease where each minute detail of a subclone could change the outcome of the treatment, single-cell analysis could prove to be a powerful instrument in deciphering the secrets of tumour microenvironment for cancer immunotherapy. With the recent studies in all aspects of adoptive cell therapy making use of single-cell analysis, a comprehensive review of the recent preclinical and clinical findings in CAR T cell therapy was needed. Here, we categorized and summarized the key points of the studies in which single-cell analysis provided insights into the genomics, epigenomics, transcriptomics and proteomics as well as their respective multi-omics of CAR T cell therapy.

Recent advances of nanopore technique in single cell analysis

Single cells and their dynamic behavior are closely related to biological research. Monitoring their dynamic behavior is of great significance for disease prevention. How to achieve rapid and non-destructive monitoring of single cells is a major issue that needs to be solved urgently. As an emerging technology, nanopores have been proven to enable non-destructive and label-free detection of single cells. The structural properties of nanopores enable a high degree of sensitivity and accuracy during analysis. In this article, we summarize and classify the different types of solid-state nanopores that can be used for single-cell detection and illustrate their specific applications depending on the size of the analyte. In addition, their research progress in material transport and microenvironment monitoring is also highlighted. Finally, a brief summary of existing research challenges and future trends in nanopore single-cell analysis is tentatively provided.

The progressive trend of modeling and drug screening systems of breast cancer bone metastasis

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites.The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed.

Deciphering single-cell protein secretion and gene expressions by constructing cell-antibody conjugates

Secreted proteins play critical roles in regulating immune responses, exerting cytotoxic effects on tumor cells, promoting inflammatory processes, and influencing cellular metabolism. Deciphering the intricate relationship between the heterogeneity of secreted proteins and their transcriptional states is pivotal in the study of cellular heterogeneity. Here we proposed a cell-antibody conjugate-based sequencing methodology (Cellab-seq) for joint characterization of secreted proteins and transcriptome. Cellab-seq utilizes a chemoenzymatic strategy to construct cell-antibody conjugates, which enables the capture of secreted proteins and their signal transduction with the incorporation of barcode detection antibodies. We applied Cellab-seq to investigate how gene expression influences the activity of secreted proteins in NK cells. Altogether, this strategy facilitates a nuanced understanding of cellular dynamics under diverse physiological conditions, ultimately contributing to the prevention, diagnosis and treatment of diseases.

Microfluidics Coupled Mass Spectrometry for Single Cell Multi-Omics

Population-level analysis masks significant heterogeneity between individual cells, making it difficult to accurately reflect the true intricacies of life activities. Microfluidics is a technique that can manipulate individual cells effectively and is commonly coupled with a variety of analytical methods for single-cell analysis. Single-cell omics provides abundant molecular information at the single-cell level, fundamentally revealing differences in cell types and biological states among cell individuals, leading to a deeper understanding of cellular phenotypes and life activities. Herein, this work summarizes the microfluidic chips designed for single-cell isolation, manipulation, trapping, screening, and sorting, including droplet microfluidic chips, microwell arrays, hydrodynamic microfluidic chips, and microchips with microvalves. This work further reviews the studies on single-cell proteomics, metabolomics, lipidomics, and multi-omics based on microfluidics and mass spectrometry. Finally, the challenges and future application of single-cell multi-omics are discussed.

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

Texture-structure-based liquid metal filling for blind-end microchannels and its application on multi-layer chips

This research work reports a novel method to achieve fast liquid metal (LM) injection in blind-end microchannels which is especially suitable for multi-layer microfluidic chips. This method is based on a texture-like surface bonding technology. The texture-like surface is fabricated on a polydimethylsiloxane (PDMS) slab with standard soft-lithography technology and bonded with another PDMS slab with microelectrode patterns on it. When injected with LM, the texture-like structure can prevent the LM from entering but allows the air inside to be released during the injection to achieve perfect blind-end complex LM electrodes. The experimental results show that it can achieve fast and perfect LM injection in the blind-end pattern and can also prevent the large area of the flat chamber from collapsing during bonding. We also parametrically studied the texture structure's size for bonding strength between the texture structure and the blank PDMS surface. In addition, we integrate three layers of blind-end complex liquid metal patterns into one multi-layer chip using this technology and later use this structure to realize series connection of two LM-based electroosmotic micropumps (EOP). Compared with the conventional LM-based EOP, the structure of the EOP chip was greatly simplified and resulted in a higher level of integration.

Harnessing Nanomaterials for Cancer Sonodynamic Immunotherapy

Immunotherapy has made remarkable strides in cancer therapy over the past decade. However, such emerging therapy still suffers from the low response rates and immune-related adverse events. Various strategies have been developed to overcome these serious challenges. Therein, sonodynamic therapy (SDT), as a non-invasive treatment, has received ever-increasing attention especially in the treatment of deep-seated tumors. Significantly, SDT can effectively induce immunogenic cell death to trigger systemic anti-tumor immune response, termed sonodynamic immunotherapy. The rapid development of nanotechnology has revolutionized SDT effects with robust immune response induction. As a result, more and more innovative nanosonosensitizers and synergistic treatment modalities are established with superior efficacy and safe profile. In this review, the recent advances in cancer sonodynamic immunotherapy are summarized with a particular emphasis on how nanotechnology can be explored to harness SDT for amplifying anti-tumor immune response. Moreover, the current challenges in this field and the prospects for its clinical translation are also presented. It is anticipated that this review can provide rational guidance and facilitate the development of nanomaterials-assisted sonodynamic immunotherapy, helping to pave the way for next-generation cancer therapy and eventually achieve a durable response in patients.

Omics and systems view of innate immune pathways

Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.

Mass spectrometry-based phosphoproteomics in clinical applications

Protein phosphorylation is an essential post-translational modification that regulates many aspects of cellular physiology, and dysregulation of pivotal phosphorylation events is often responsible for disease onset and progression. Clinical analysis on disease-relevant phosphoproteins, while quite challenging, provides unique information for precision medicine and targeted therapy. Among various approaches, mass spectrometry (MS)-centered characterization features discovery-driven, high-throughput and in-depth identification of phosphorylation events. This review highlights advances in sample preparation and instrument in MS-based phosphoproteomics and recent clinical applications. We emphasize the preeminent data-independent acquisition method in MS as one of the most promising future directions and biofluid-derived extracellular vesicles as an intriguing source of the phosphoproteome for liquid biopsy.

Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning

The cascade of immune responses involves activation of diverse immune cells and release of a large amount of cytokines, which leads to either normal, balanced inflammation or hyperinflammatory responses and even organ damage by sepsis. Conventional diagnosis of immunological disorders based on multiple cytokines in the blood serum has varied accuracy, and it is difficult to distinguish normal inflammation from sepsis. Herein, we present an approach to detect immunological disorders through rapid, ultrahigh-multiplex analysis of T cells using single-cell multiplex in situ tagging (scMIST) technology. scMIST permits simultaneous detection of 46 markers and cytokines from single cells without the assistance of special instruments. A cecal ligation and puncture sepsis model was built to supply T cells from two groups of mice that survived the surgery or died after 1 day. The scMIST assays have captured the T cell features and the dynamics over the course of recovery. Compared with cytokines in the peripheral blood, T cell markers show different dynamics and cytokine levels. We have applied a random forest machine learning model to single T cells from two groups of mice. Through training, the model has been able to predict the group of mice through T cell classification and majority rule with 94% accuracy. Our approach pioneers the direction of single-cell omics and could be widely applicable to human diseases.

Fluorescence-Enhanced Microfluidic Biosensor Platform Based on Magnetic Beads with Highly Stable ZnO Nanorods for Biomarker Detection

Existing affinity-based fluorescence biosensing systems for monitoring of biomarkers often utilize a fixed solid substrate immobilized with capture probes limiting their use in continuous or intermittent biomarker detection. Furthermore, there have been challenges of integrating fluorescence biosensors with a microfluidic chip and low-cost fluorescence detector. Herein, we demonstrated a highly efficient and movable fluorescence-enhanced affinity-based fluorescence biosensing platform that can overcome the current limitations by combining fluorescence enhancement and digital imaging. Fluorescence-enhanced movable magnetic beads (MBs) decorated with zinc oxide nanorods (MB-ZnO NRs) were used for digital fluorescence-imaging-based aptasensing of biomolecules with improved signal-to-noise ratio. High stability and homogeneous dispersion of photostable MB-ZnO NRs were obtained by grafting bilayered silanes onto the ZnO NRs. The ZnO NRs formed on MB significantly improved the fluorescence signal up to 2.35 times compared to the MB without ZnO NRs. Moreover, the integration of a microfluidic device for flow-based biosensing enabled continuous measurements of biomarkers in an electrolytic environment. The results showed that highly stable fluorescence-enhanced MB-ZnO NRs integrated with a microfluidic platform have significant potential for diagnostics, biological assays, and continuous or intermittent biomonitoring.

Proteomics mining of cancer hallmarks on a single-cell resolution

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

High-throughput spatiotemporal monitoring of single-cell secretions via plasmonic microwell arrays

Methods for the analysis of cell secretions at the single-cell level only provide semiquantitative endpoint readouts. Here we describe a microwell array for the real-time spatiotemporal monitoring of extracellular secretions from hundreds of single cells in parallel. The microwell array incorporates a gold substrate with arrays of nanometric holes functionalized with receptors for a specific analyte, and is illuminated with light spectrally overlapping with the device's spectrum of extraordinary optical transmission. Spectral shifts in surface plasmon resonance resulting from analyte-receptor bindings around a secreting cell are recorded by a camera as variations in the intensity of the transmitted light while machine-learning-assisted cell tracking eliminates the influence of cell movements. We used the microwell array to characterize the antibody-secretion profiles of hybridoma cells and of a rare subset of antibody-secreting cells sorted from human donor peripheral blood mononuclear cells. High-throughput measurements of spatiotemporal secretory profiles at the single-cell level will aid the study of the physiological mechanisms governing protein secretion.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

Integrated technologies for continuous monitoring of organs-on-chips: Current challenges and potential solutions

Organs-on-chips (OoCs) are biomimetic in vitro systems based on microfluidic cell cultures that recapitulate the in vivo physicochemical microenvironments and the physiologies and key functional units of specific human organs. These systems are versatile and can be customized to investigate organ-specific physiology, pathology, or pharmacology. They are more physiologically relevant than traditional two-dimensional cultures, can potentially replace the animal models or reduce the use of these models, and represent a unique opportunity for the development of personalized medicine when combined with human induced pluripotent stem cells. Continuous monitoring of important quality parameters of OoCs via a label-free, non-destructive, reliable, high-throughput, and multiplex method is critical for assessing the conditions of these systems and generating relevant analytical data; moreover, elaboration of quality predictive models is required for clinical trials of OoCs. Presently, these analytical data are obtained by manual or automatic sampling and analyzed using single-point, off-chip traditional methods. In this review, we describe recent efforts to integrate biosensing technologies into OoCs for monitoring the physiologies, functions, and physicochemical microenvironments of OoCs. Furthermore, we present potential alternative solutions to current challenges and future directions for the application of artificial intelligence in the development of OoCs and cyber-physical systems. These "smart" OoCs can learn and make autonomous decisions for process optimization, self-regulation, and data analysis.

On-Chip Analysis of Protein Secretion from Single Cells Using Microbead Biosensors

The profiling of the effector functions of single immune cells─including cytokine secretion─can lead to a deeper understanding of how the immune system operates and to potential diagnostics and therapeutical applications. Here, we report a microfluidic device that pairs single cells and antibody-functionalized microbeads in hydrodynamic traps to quantitate cytokine secretion. The device contains 1008 microchambers, each with a volume of ∼500 pL, divided into six different sections individually addressed to deliver an equal number of chemical stimuli. Integrating microvalves allowed us to isolate cell/bead pairs, preventing cross-contamination with factors secreted by adjacent cells. We implemented a fluorescence sandwich immunoassay on the biosensing microbeads with a limit of detection of 9 pg/mL and were able to detect interleukin-8 (IL-8) secreted by single blood-derived human monocytes in response to different concentrations of LPS. Finally, our platform allowed us to observe a significant decrease in the number of IL-8-secreting monocytes when paracrine signaling becomes disrupted. Overall, our platform could have a variety of applications for which the analysis of cellular function heterogeneity is necessary, such as cancer research, antibody discovery, or rare cell screening.

Defining T cell receptor repertoires using nanovial-based affinity and functional screening

The ability to selectively bind to antigenic peptides and secrete cytokines can define populations of cells with therapeutic potential in emerging T cell receptor (TCR) immunotherapies. We leverage cavity-containing hydrogel microparticles, called nanovials, each coated with millions of peptide-major histocompatibility complex (pMHC) monomers to isolate antigen-reactive T cells. T cells are captured and activated by pMHCs and secrete cytokines on nanovials, allowing sorting based on both affinity and function. The TCRs of sorted cells on nanovials are sequenced, recovering paired αβ-chains using microfluidic emulsion-based single-cell sequencing. By labeling nanovials having different pMHCs with unique oligonucleotide-barcodes we could link TCR sequence to targets with 100% accuracy. We identified with high specificity an expanded repertoire of functional TCRs targeting viral antigens compared to standard techniques.

Fast trimer statistics facilitate accurate decoding of large random DNA barcode sets even at large sequencing error rates

Predefined sets of short DNA sequences are commonly used as barcodes to identify individual biomolecules in pooled populations. Such use requires either sufficiently small DNA error rates, or else an error-correction methodology. Most existing DNA error-correcting codes (ECCs) correct only one or two errors per barcode in sets of typically ≲10⁴ barcodes. We here consider the use of random barcodes of sufficient length that they remain accurately decodable even with ≳6 errors and even at [Formula: see text] or 20% nucleotide error rates. We show that length ∼34 nt is sufficient even with ≳10⁶ barcodes. The obvious objection to this scheme is that it requires comparing every read to every possible barcode by a slow Levenshtein or Needleman-Wunsch comparison. We show that several orders of magnitude speedup can be achieved by (i) a fast triage method that compares only trimer (three consecutive nucleotide) occurence statistics, precomputed in linear time for both reads and barcodes, and (ii) the massive parallelism available on today's even commodity-grade Graphics Processing Units (GPUs). With 10⁶ barcodes of length 34 and 10% DNA errors (substitutions and indels), we achieve in simulation 99.9% precision (decode accuracy) with 98.8% recall (read acceptance rate). Similarly high precision with somewhat smaller recall is achievable even with 20% DNA errors. The amortized computation cost on a commodity workstation with two GPUs (2022 capability and price) is estimated as between US$ 0.15 and US$ 0.60 per million decoded reads.

Three-Dimensional in Vitro Models: A Promising Tool To Scale-Up Breast Cancer Research

Common models used in breast cancer studies, including two-dimensional (2D) cultures and animal models, do not precisely model all aspects of breast tumors. These models do not well simulate the cell-cell and cell-stromal interactions required for normal tumor growth in the body and lake tumor like microenvironment. Three-dimensional (3D) cell culture models are novel approaches to studying breast cancer. They do not have the restrictions of these conventional models and are able to recapitulate the structural architecture, complexity, and specific function of breast tumors and provide similar in vivo responses to therapeutic regimens. These models can be a link between former traditional 2D culture and in vivo models and are necessary for further studies in cancer. This review attempts to summarize the most common 3D in vitro models used in breast cancer studies, including scaffold-free (spheroid and organoid), scaffold-based, and chip-based models, particularly focused on the basic and translational application of these 3D models in drug screening and the tumor microenvironment in breast cancer.

Future of Digital Assays to Resolve Clinical Heterogeneity of Single Extracellular Vesicles

Extracellular vesicles (EVs) are complex lipid membrane vehicles with variable expressions of molecular cargo, composed of diverse subpopulations that participate in the intercellular signaling of biological responses in disease. EV-based liquid biopsies demonstrate invaluable clinical potential for overhauling current practices of disease management. Yet, EV heterogeneity is a major needle-in-a-haystack challenge to translate their use into clinical practice. In this review, existing digital assays will be discussed to analyze EVs at a single vesicle resolution, and future opportunities to optimize the throughput, multiplexing, and sensitivity of current digital EV assays will be highlighted. Furthermore, this review will outline the challenges and opportunities that impact the clinical translation of single EV technologies for disease diagnostics and treatment monitoring.

Counting Protein Number in a Single Cell by a Picoliter Liquid Operating Technology

Ultra-low-copy number proteins play a crucial role in exploring cellular heterogeneity and the insight of protein biomarkers in a single cell. However, counting ultra-low-copy number target proteins in a single cell remains a grand challenge. Herein, we developed a so-called single-cell picoliter liquid operating technology for counting target proteins in a single cell. An ingenious volume-controllable sampling technique was employed to capture a single cell for subsequent analysis. Remarkably, 50 pL of sample volume was employed for sample preparation, single-cell capture, in-droplet lysis, and target protein immobilization on a functionalized coverslip in a monolayer. Then, target protein antibodies coupled with quantum dots were added and incubated to label those immobilized proteins. After clean-up, a single-view image under 100× objective was taken, and the 80 × 80 μm² view image was then applied to count the precise copy number of the target proteins in the single cell. Furthermore, good linearity and repeatability were achieved for ultra-low-copy number proteins, ranging from 1 to 1500. Finally, the expression level of human epidermal growth factor receptor 2 in single cells from both MCF-7 and MDA-MB-231 cell lines was also analyzed. In a word, this work stimulated the development of capillary-based single-cell analysis and updated the connotation of counting ultra-low-copy number proteins.

Multiplexed Single-Cell in Situ Protein Profiling

The ability to profile a large number of different proteins in individual cells in their native cellular locations is critical to accelerate our understanding of normal cell physiology and disease pathogenesis. Bulk cell protein quantification masks the cell heterogeneity in complex biological systems, while conventional immunofluorescence or immunohistochemistry are limited by their low multiplexing capacity. Recent technological advances in multiplexed protein imaging approaches allow many distinct proteins to be analyzed in single cells in situ. These methods will bring new insights into various biological and biomedical fields, such as cell type and subtype classification, signaling network regulation, tissue architecture, and disease diagnosis and prognosis, along with treatment monitoring. In this Review, we will describe the recent advances of multiplexed single-cell in situ protein profiling technologies, discuss their unique advantages and limitations, highlight their applications in biology and medicine, present the current challenges, and propose potential solutions.

Proteomics approaches to characterize the immune responses in cancer

Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.

Microfluidic T Cell Selection by Cellular Avidity

No T cell receptor (TCR) T cell therapies have obtained clinical approval. The lack of strategies capable of selecting and recovering potent T cell candidates may be a contributor to this. Existing protocols for selecting TCR T cell clones for cell therapies such as peptide multimer methods have provided effective measurements on TCR affinities. However, these methods lack the ability to measure the collective strength of intercellular interactions (i.e., cellular avidity) and markers of T cell activation such as immunological synapse formation. This study describes a novel microfluidic fluid shear stress-based approach to identify and recover highly potent T cell clones based on the cellular avidity between living T cells and tumor cells. This approach is capable of probing approximately up to 10 000 T cell-tumor cell interactions per run and can recover potent T cells with up to 100% purity from mixed populations of T cells within 30 min. Markers of cytotoxicity, activation, and avidity persist when recovered high cellular avidity T cells are subsequently exposed to fresh tumor cells. These results demonstrate how microfluidic probing of cellular avidity may fast track the therapeutic T cell selection process and move the authors closer to precision cancer immunotherapy.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

Emerging Technologies for Understanding Platelet Diversity

This review discusses our understanding of platelet diversity with implications for the roles of platelets in hemostasis and thrombosis and identifies advanced technologies set to provide new insights. We use the term diversity to capture intrasubject platelet variability that can be intrinsic or governed by the environment and lead to a heterogeneous response pattern of aggregation, clot promotion, and external communication. Using choice examples, we discuss how the use of advanced technologies can provide new insights into the underlying causes of platelet molecular, structural, and functional diversity. As sources of diversity, we discuss the proliferating megakaryocytes with different allele-specific expression patterns, the asymmetrical formation of proplatelets, changes in platelets induced by aging and priming, interplatelet heterogeneity in thrombus organization and stability, and platelet-dependent communications. We provide indications how current knowledge gaps can be addressed using promising technologies, such as next-generation sequencing, proteomic approaches, advanced imaging techniques, multicolor flow and mass cytometry, multifunctional microfluidics assays, and organ-on-a-chip platforms. We then argue how this technology base can aid in characterizing platelet populations and in identifying platelet biomarkers relevant for the treatment of cardiovascular disease.

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment

Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.

Nanotechnology Tools Enabling Biological Discovery

Over the years, the engineering aspect of nanotechnology has been significantly exploited. Medical intervention strategies have been developed by leveraging existing molecular biology knowledge and combining it with nanotechnology tools to improve outcomes. However, little attention has been paid to harnessing the strengths of nanotechnology as a biological discovery tool. Fundamental understanding of controlling dynamic biological processes at the subcellular level is key to developing personalized therapeutic and diagnostic interventions. Single-cell analyses using intravital microscopy, expansion microscopy, and microfluidic-based platforms have been helping to better understand cell heterogeneity in healthy and diseased cells, a major challenge in oncology. Also, single-cell analysis has revealed critical signaling pathways and biological intracellular components with key biological functions. The physical manipulation enabled by nanotools can allow real-time monitoring of biological changes at a single-cell level by sampling intracellular fluid from the same cell. The formation of intercellular highways by nanotube-like structures has important clinical implications such as metastasis development. The integration of nanomaterials into optical and molecular imaging techniques has rendered valuable morphological, structural, and biological information. Nanoscale imaging unravels mechanisms of temporality by enabling the visualization of nanoscale dynamics never observed or measured between individual cells with standard biological techniques. The exceptional sensitivity of nanozymes, artificial enzymes, make them perfect components of the next-generation mobile diagnostics devices. Here, we highlight these impactful cancer-related biological discoveries enabled by nanotechnology and producing a paradigm shift in cancer research and oncology.

IL-2 secretion-based sorting of single T cells using high-throughput microfluidic on-cell cytokine capture

Secreted proteins are critical for the coordination of potent immune defenses, such as in engineered T cell therapies, however, there are few widely accessible approaches to accurately analyze and sort large numbers of cells based on their secretory functions. We report a workflow for the rapid screening and sorting of single individual T cells based on IL-2 secretion accumulated at high concentrations in nanoliter droplets and encoded back onto the secreting cell's surface. In our method, droplets are used solely to partition cells, enabling rapid accumulation of signals onto cell surfaces, and eliminating diffusive crosstalk between neighbors. All downstream sorting leverages conventional high-throughput and readily accessible flow cytometry after the emulsion is disrupted. We achieve monodisperse droplet generation (CV < 10%) at flow rates up to 200 μL min-1 using step emulsification, enabling processing of entire libraries of cells within tens of minutes without significant secretion crosstalk. In comparison to our approach, strong mitogenic activation overwhelmed the conventional bulk on-cell cytokine assay, rendering labeled, non-activated cells indistinguishable from actively secreting neighbors within one hour. Processing of identical cell mixtures following droplet encapsulation yielded no apparent crosstalk even after three hours. Instead, IL-2 production spanning several orders of magnitude was observed from roughly 20% of analyzed activated lymphocytes, representing an at least 10-fold increase in dynamic range compared to unencapsulated cells. Secreting cells could also be sorted using fluorescence activated cell sorting (FACS). The approach can ultimately enable sorting of cells based on functional properties with higher accuracy in a more accessible format to life science researchers.

MIMAS: microfluidic platform in tandem with MALDI mass spectrometry for protein quantification from small cell ensembles

Understanding cell-to-cell variation at the molecular level provides relevant information about biological phenomena and is critical for clinical and biological research. Proteins carry important information not available from single-cell genomics and transcriptomics studies; however, due to the minute amount of proteins in single cells and the complexity of the proteome, quantitative protein analysis at the single-cell level remains challenging. Here, we report an integrated microfluidic platform in tandem with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the detection and quantification of targeted proteins from small cell ensembles (> 10 cells). All necessary steps for the assay are integrated on-chip including cell lysis, protein immunocapture, tryptic digestion, and co-crystallization with the matrix solution for MALDI-MS analysis. We demonstrate that our approach is suitable for protein quantification by assessing the apoptotic protein Bcl-2 released from MCF-7 breast cancer cells, ranging from 26 to 223 cells lysed on-chip (8.75 nL wells). A limit of detection (LOD) of 11.22 nM was determined, equivalent to 5.91 × 10⁷ protein molecules per well. Additionally, the microfluidic platform design was further improved, establishing the successful quantification of Bcl-2 protein from MCF-7 cell ensembles ranging from 8 to 19 cells in 4 nL wells. The LOD in the smaller well designs for Bcl-2 resulted in 14.85 nM, equivalent to 3.57 × 10⁷ protein molecules per well. This work shows the capability of our approach to quantitatively assess proteins from cell lysate on the MIMAS platform for the first time. These results demonstrate our approach constitutes a promising tool for quantitative targeted protein analysis from small cell ensembles down to single cells, with the capability for multiplexing through parallelization and automation.

High-Throughput, Living Single-Cell, Multiple Secreted Biomarker Profiling Using Microfluidic Chip and Machine Learning for Tumor Cell Classification

Secreted proteins provide abundant functional information on living cells and can be used as important tumor diagnostic markers, of which profiling at the single-cell level is helpful for accurate tumor cell classification. Currently, achieving living single-cell multi-index, high-sensitivity, and quantitative secretion biomarker profiling remains a great challenge. Here, a high-throughput living single-cell multi-index secreted biomarker profiling platform is proposed, combined with machine learning, to achieve accurate tumor cell classification. A single-cell culture microfluidic chip with self-assembled graphene oxide quantum dots (GOQDs) enables high-activity single-cell culture, ensuring normal secretion of biomarkers and high-throughput single-cell separation, providing sufficient statistical data for machine learning. At the same time, the antibody barcode chip with self-assembled GOQDs performs multi-index, highly sensitive, and quantitative detection of secreted biomarkers, in which each cell culture chamber covers a whole barcode array. Importantly, by combining the K-means strategy with machine learning, thousands of single tumor cell secretion data are analyzed, enabling tumor cell classification with a recognition accuracy of 95.0%. In addition, further profiling of the grouping results reveals the unique secretion characteristics of subgroups. This work provides an intelligent platform for high-throughput living single-cell multiple secretion biomarker profiling, which has broad implications for cancer investigation and biomedical research.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

Preclinical Evaluation of CAR T Cell Function: In Vitro and In Vivo Models

Immunotherapy using chimeric antigen receptor (CAR) T cells is a rapidly emerging modality that engineers T cells to redirect tumor-specific cytotoxicity. CAR T cells have been well characterized for their efficacy against B cell malignancies, and rigorously studied in other types of tumors. Preclinical evaluation of CAR T cell function, including direct tumor killing, cytokine production, and memory responses, is crucial to the development and optimization of CAR T cell therapies. Such comprehensive examinations are usually performed in different types of models. Model establishment should focus on key challenges in the clinical setting and the capability to generate reliable data to indicate CAR T cell therapeutic potency in the clinic. Further, modeling the interaction between CAR T cells and tumor microenvironment provides additional insight for the future endeavors to enhance efficacy, especially against solid tumors. This review will summarize both in vitro and in vivo models for CAR T cell functional evaluation, including how they have evolved with the needs of CAR T cell research, the information they can provide for preclinical assessment of CAR T cell products, and recent technology advances to test CAR T cells in more clinically relevant models.

Rise of the SARS-CoV-2 Variants: can proteomics be the silver bullet?

INTRODUCTION

The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role.

AREAS COVERED

In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, and in silico docking studies that have helped augment the fight against existing diseases were useful in preparing us to tackle SARS-CoV-2 variants. We also propose an action plan for a pipeline to combat emerging pandemics using proteomics technology by adopting uniform standard operating procedures and unified data analysis paradigms.

EXPERT OPINION

The knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.

Biosensors to Monitor Cell Activity in 3D Hydrogel-Based Tissue Models

Three-dimensional (3D) culture models have gained relevant interest in tissue engineering and drug discovery owing to their suitability to reproduce in vitro some key aspects of human tissues and to provide predictive information for in vivo tests. In this context, the use of hydrogels as artificial extracellular matrices is of paramount relevance, since they allow closer recapitulation of (patho)physiological features of human tissues. However, most of the analyses aimed at characterizing these models are based on time-consuming and endpoint assays, which can provide only static and limited data on cellular behavior. On the other hand, biosensing systems could be adopted to measure on-line cellular activity, as currently performed in bi-dimensional, i.e., monolayer, cell culture systems; however, their translation and integration within 3D hydrogel-based systems is not straight forward, due to the geometry and materials properties of these advanced cell culturing approaches. Therefore, researchers have adopted different strategies, through the development of biochemical, electrochemical and optical sensors, but challenges still remain in employing these devices. In this review, after examining recent advances in adapting existing biosensors from traditional cell monolayers to polymeric 3D cells cultures, we will focus on novel designs and outcomes of a range of biosensors specifically developed to provide real-time analysis of hydrogel-based cultures.

Fluorescence based miniaturized microfluidic and nanofluidic systems for biomedical applications

Over the last two decades miniaturized microfluidic and nanofluidic systems with fluorescence setup emerged as a powerful technological platform for diverse biomedical applications. Bio-macromolecules such as nucleic acids and proteins are the core cellular components, their single molecule analysis allow us to understand biological processes, disease creation and progression, and development of novel treatment policies. Design and development of foolproof treatment methods requires rigorously analysis of nucleic acids and proteins such as length quantifications, sequence profiling, sequence mapping, analysis of conformational changes, analysis and recognition of epigenetic changes, and their interactions with other biomolecules. Miniaturized microfluidic and nanofluidic systems with fluorescence spectroscopy enable worldwide researchers to perform nucleic acids and proteins extractions and single molecule analysis from the trace amount of biological samples. In the present chapter we mostly highlighted over one decade applications of microfluidic and nanofluidic systems for single cell micro ribonucleic acid (miRNA) isolation and detection, deoxyribonucleic acid (DNA) mapping, DNA barcoding, identification of epigenetic mark on single DNA molecule, DNA-protein interactions study, protein sensing, protein sequencing, protein binding kinetics and many other applications. We also presented the recently reported microfluidic platform for the preparation of reproducible unisize aggregation induced emission (AIE) active nanomaterials and their biological applications.

Single-cell sorting based on secreted products for functionally defined cell therapies

Cell therapies have emerged as a promising new class of "living" therapeutics over the last decade and have been particularly successful for treating hematological malignancies. Increasingly, cellular therapeutics are being developed with the aim of treating almost any disease, from solid tumors and autoimmune disorders to fibrosis, neurodegenerative disorders and even aging itself. However, their therapeutic potential has remained limited due to the fundamental differences in how molecular and cellular therapies function. While the structure of a molecular therapeutic is directly linked to biological function, cells with the same genetic blueprint can have vastly different functional properties (e.g., secretion, proliferation, cell killing, migration). Although there exists a vast array of analytical and preparative separation approaches for molecules, the functional differences among cells are exacerbated by a lack of functional potency-based sorting approaches. In this context, we describe the need for next-generation single-cell profiling microtechnologies that allow the direct evaluation and sorting of single cells based on functional properties, with a focus on secreted molecules, which are critical for the in vivo efficacy of current cell therapies. We first define three critical processes for single-cell secretion-based profiling technology: (1) partitioning individual cells into uniform compartments; (2) accumulating secretions and labeling via reporter molecules; and (3) measuring the signal associated with the reporter and, if sorting, triggering a sorting event based on these reporter signals. We summarize recent academic and commercial technologies for functional single-cell analysis in addition to sorting and industrial applications of these technologies. These approaches fall into three categories: microchamber, microfluidic droplet, and lab-on-a-particle technologies. Finally, we outline a number of unmet needs in terms of the discovery, design and manufacturing of cellular therapeutics and how the next generation of single-cell functional screening technologies could allow the realization of robust cellular therapeutics for all patients.

Ipilimumab alone or in combination with nivolumab in patients with advanced melanoma who have progressed or relapsed on PD-1 blockade: clinical outcomes and translational biomarker analyses

BACKGROUND

There are no validated biomarkers that can aid clinicians in selecting who would best benefit from anticytotoxic T lymphocyte-associated antigen 4 monotherapy versus combination checkpoint blockade in patients with advanced melanoma who have progressive disease after programmed death 1 (PD-1) blockade.

METHODS

We conducted a randomized multicenter phase II trial in patients with advanced melanoma. Patients were randomly assigned to receive either 1 mg/kg of nivolumab plus 3 mg/kg of ipilimumab or 3 mg/kg of ipilimumab every 3 weeks for up to four doses. Patients were stratified by histological subtype and prior response to PD-1 therapy. The primary clinical objective was overall response rate by week 18. Translational biomarker analyses were conducted in patients with blood and tissue samples.

RESULTS

Objective responses were seen in 5 of 9 patients in the ipilimumab arm and 2 of 10 patients in the ipilimumab+nivolumab arm; disease control rates (DCRs) (66.7% vs 60.0%) and rates of grade 3-4 adverse events (56% vs 50%) were comparable between arms. In a pooled analysis, patients with clinical benefit (CB), defined as Response Evaluation Criteria in Solid Tumors response or progression-free for 6 months, showed increased circulating CD4+ T cells with higher polyfunctionality and interferon gamma production following treatment. Tumor profiling revealed enrichment of NRAS mutations and activation of transcriptional programs associated with innate and adaptive immunity in patients with CB.

CONCLUSIONS

In patients with advanced melanoma that previously progressed on PD-1 blockade, objective responses were seen in both arms, with comparable DCRs. Findings from biomarker analyses provided hypothesis-generating signals for validation in future studies of larger patient cohorts.

TRIAL REGISTRATION NUMBER

NCT02731729.

Multiplexed Single-Cell in situ RNA Profiling

The ability to quantify a large number of varied transcripts in single cells in their native spatial context is crucial to accelerate our understanding of health and disease. Bulk cell RNA analysis masks the heterogeneity in the cell population, while the conventional RNA imaging approaches suffer from low multiplexing capacity. Recent advances in multiplexed fluorescence in situ hybridization (FISH) methods enable comprehensive RNA profiling in individual cells in situ. These technologies will have wide applications in many biological and biomedical fields, including cell type classification, signaling network analysis, tissue architecture, disease diagnosis and patient stratification, etc. In this minireview, we will present the recent technological advances of multiplexed single-cell in situ RNA profiling assays, discuss their advantages and limitations, describe their biological applications, highlight the current challenges, and propose potential solutions.

Single-cell polyfunctional proteomics of CD4 cells from patients with AML predicts responses to anti-PD-1-based therapy

Acute myeloid leukemia (AML) remains a difficult disease to treat disease. In a phase 2 clinical trial in patients with relapsed/refractory AML, combining the hypomethylating agent, azacitidine, with the PD-1 checkpoint inhibitor, nivolumab, demonstrated encouraging response rates (33%), median event-free, and overall survival, compared with a historical cohort of contemporary patients treated with azacitidine-based therapies, with an acceptable safety profile. Biomarkers of response are yet to be determined. In this study, we leveraged a multiplexed immune assay to assess the functional states of CD4+ and CD8+ cells at a single-cell level in pretherapy bone marrows in 16 patients with relapsed/refractory AML treated with azacitidine/nivolumab. Effector CD4+ but not CD8+ cells had distinct polyfunctional groups and were associated with responses and better outcomes. Further evaluation of the polyfunctional strength index composition across cell types revealed that interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) were the major drivers of enhanced polyfunctionality index of pretherapy CD4+ subset, whereas Granzyme B, IFN-γ, MIP-1b, and TNF-α drove the nonsignificantly enhanced pretreatment Polyfunctional Strength Index of CD8+ subset in the responders. Single-cell polyfunctional assays were predictive of response in AML and may have a potential role as a biomarker in the wider sphere of immunotherapy.

Single-Cell Multiomics Analysis for Drug Discovery

Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

An overview of multiplexed analyses of CAR T-cell therapies: insights and potential

INTRODUCTION

Cancer immunotherapy is a rapidly growing field with exponential advancement in engineered immune cell-based therapies. For instance, an engineered chimeric antigen receptor (CAR) can be introduced in T-cells or other immune cells and adoptively transferred to target and kill cancer cells in hematologic malignancies or solid tumors. The first CAR-T-cell (CAR-T) therapy has been developed against CD19, a B-cell marker expressed on lymphoma and lymphoblastic leukemia. To allow for personalized treatment, proteomics approaches could provide insights into biomarkers for CAR-T therapy efficacy and toxicity.

AREAS COVERED

We researched the most recent technology methods of biomarker evaluation used in the laboratory and clinical setting. Publications of CAR-T biomarkers were then systematically reviewed to provide a narrative of the most validated biomarkers of CAR-T efficacy and toxicity. Examples of biomarkers include CAR-T functionality and phenotype as well as interleukin-6 and other cytokines.

EXPERT COMMENTARY

Biomarkers of CAR-T efficacy and toxicity have been identified, but still need to be validated and standardized across institutions. Moreover, few are used in the clinical setting due to limitations in real-time technology. Expansion of biomarker research could provide better understanding of patient response and risk of life-threatening side effects with potential for improved precision medicine.