Quantitative live-cell imaging of secretion activity reveals dynamic immune responses.

Quantification of cytokine secretion has facilitated advances in the field of immunology, yet the dynamic and varied secretion profiles of individual cells, particularly those obtained from limited human samples, remain obscure. Herein, we introduce a technology for quantitative live-cell imaging of secretion activity (qLCI-S) that enables high-throughput and dual-color monitoring of secretion activity at the single-cell level over several days, followed by transcriptome analysis of individual cells based on their phenotype. The efficacy of qLCI-S was demonstrated by visualizing the characteristic temporal pattern of cytokine secretion of group 2 innate lymphoid cells, which constitute less than 0.01% of human peripheral blood mononuclear cells, and by revealing minor subpopulations with enhanced cytokine production. The underlying mechanism of this feature was linked to the gene expression of stimuli receptors. This technology paves the way for exploring gene expression signatures linked to the spatiotemporal dynamic nature of various secretory functions.

Time-dependent cell-state selection identifies transiently expressed genes regulating ILC2 activation.

The decision of whether cells are activated or not is controlled through dynamic intracellular molecular networks. However, the low population of cells during the transition state of activation renders the analysis of the transcriptome of this state technically challenging. To address this issue, we have developed the Time-Dependent Cell-State Selection (TDCSS) technique, which employs live-cell imaging of secretion activity to detect an index of the transition state, followed by the simultaneous recovery of indexed cells for subsequent transcriptome analysis. In this study, we used the TDCSS technique to investigate the transition state of group 2 innate lymphoid cells (ILC2s) activation, which is indexed by the onset of interleukin (IL)-13 secretion. The TDCSS approach allowed us to identify time-dependent genes, including transiently induced genes (TIGs). Our findings of IL4 and MIR155HG as TIGs have shown a regulatory function in ILC2s activation.

Annexin A1 is a cell-intrinsic metalloregulator of zinc in human ILC2s.

Group 2 innate lymphoid cells (ILC2s) produce large amounts of type 2 cytokines including interleukin-5 (IL-5) and IL-13 in response to various stimuli, causing allergic and eosinophilic diseases. However, the cell-intrinsic regulatory mechanisms of human ILC2s remain unclear. Here, we analyze human ILC2s derived from different tissues and pathological conditions and identify ANXA1, encoding annexin A1, as a commonly highly expressed gene in non-activated ILC2s. The expression of ANXA1 decreases when ILC2s activate, but it increases autonomously as the activation subsides. Lentiviral vector-based gene transfer experiments show that ANXA1 suppresses the activation of human ILC2s. Mechanistically, ANXA1 regulates the expression of the metallothionein family genes, including MT2A, which modulate intracellular zinc homeostasis. Furthermore, increased intracellular zinc levels play an essential role in the activation of human ILC2s by promoting the mitogen-activated protein kinase (MAPK) and nuclear factor κB (NF-κB) pathways and GATA3 expression. Thus, the ANXA1/MT2A/zinc pathway is identified as a cell-intrinsic metalloregulatory mechanism for human ILC2s.

Whole-embryonic identification of maternal microchimeric cell types in mouse using single-cell RNA sequencing.

Even though the mother and the fetus of placental mammals are immunologically non-self with respect to one other, mutual exchange of small numbers of cells between them is known to occur. Maternal cells entering the fetus, called maternal microchimeric cells (MMc cells), are thought to be involved in different physiological phenomena, such as establishing immune tolerance, tissue repair, and the pathogenesis or deterioration of some inflammatory diseases and congenital malformations. While specific MMc cell types have been reported as associated with these phenomena, the contribution of MMc cells to these different outcomes remains unknown. As one possibility, we hypothesized that different embryos have differing repertoires of MMc cell types, leading to or biasing embryos toward different fates. To date, no studies have succeeded in identifying the MMc cell type repertoire of a single embryo. Accordingly, here, we isolated MMc cells from whole mouse embryos, determined their types, and analyzed their MMc cell type variability. By combining our previously established, whole-embryonic MMc isolation method with single-cell RNA sequencing, we successfully estimated the cell type repertoires of MMc cells isolated from 26 mouse embryos. The majority of MMc cells were immune-related cells, such as myeloid cells and granulocytes. We also detected stem cell-like MMc cells expressing proliferation marker genes and terminally differentiated cells. As hypothesized, we noted statistically significant inter-individual variation in the proportion of immune-related cells in the different embryos. We here successfully estimated MMc cell types in individual whole mouse embryos. The proportion of immune-related cells significantly differed among the individual embryos, suggesting that the variations are one of the potential mechanisms underlying the differing MMc-related physiological phenomena in offspring. These findings provide insight into cell-level epigenetics by maternal cells.

Nanaomycin E inhibits NLRP3 inflammasome activation by preventing mitochondrial dysfunction.

Nod-like receptor family pyrin domain-containing 3 (NLRP3) is a cytosolic innate immune receptor that senses organelle dysfunction induced by various stimuli, such as infectious, environmental, metabolic and drug stresses. Upon activation, NLRP3 forms an inflammasome with its adaptor protein apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) and caspase-1, to trigger the release of inflammatory cytokines. The development of effective anti-inflammatory drugs targeting the NLRP3 inflammasome is in high demand as its aberrant activation often causes inflammatory diseases. Here, we found that nanaomycin A (NNM-A), a quinone-based antibiotic isolated from Streptomyces, effectively inhibited NLRP3 inflammasome-mediated inflammatory responses induced by imidazoquinolines, including imiquimod. Interestingly, its epoxy derivative nanaomycin E (NNM-E) showed a comparable inhibitory effect against the NLRP3 inflammasome-induced release of interleukin (IL)-1ß and IL-18 from macrophages, with a much lower toxicity than NNM-A. NNM-E inhibited ASC oligomerization and caspase-1 cleavage, both of which are hallmarks of NLRP3 inflammasome activation. NNM-E reduced mitochondrial damage and the production of reactive oxygen species, thereby preventing the activation of the NLRP3 inflammasome. NNM-E treatment markedly alleviated psoriasis-like skin inflammation induced by imiquimod. Collectively, NNM-E inhibits NLRP3 inflammasome activation by preventing mitochondrial dysfunction with little toxicity and showed an anti-inflammatory effect in vivo. Thus, NNM-E could be a potential lead compound for developing effective and safe anti-inflammatory agents for the treatment of NLRP3 inflammasome-mediated inflammatory diseases.

Upregulation of IL-4 receptor signaling pathway in circulating ILC2s from asthma patients.

Background: Group 2 innate lymphoid cells (ILC2s) produce type 2 cytokines by stimulation with epithelial cell-derived cytokines and are implicated in the pathogenesis of various allergic diseases, including asthma. However, differences in the molecular characteristics of ILC2s between patients with asthma and healthy subjects remain unclear. Objective: We sought to evaluate differences in cytokine production capacity and gene expression profile of ILC2s in the peripheral blood of patients with asthma and healthy subjects. Methods: We evaluated ILC2s derived from 15 patients with asthma and 7 healthy subjects using flow cytometry, live-cell imaging of secretion activity analysis, and RNA-sequencing. Results: ILC2s were sorted as CD45+Lineage-CRTH2+CD127+CD161+ cells from the peripheral blood of patients with asthma and healthy subjects, and the number of ILC2s was decreased in patients with asthma (851 ± 1134 vs 2679 ± 3009 cells/20 mL blood; P = .0066). However, patient-derived ILC2s were activated to produce more IL-5 and IL-13 in response to stimulation with IL-2, IL-33, and thymic stromal lymphopoietin compared with healthy subject-derived ILC2s (P = .0032 and P = .0085, respectively). Furthermore, RNA-sequencing analysis revealed that patient-derived ILC2s had different gene expression profiles, such as increased expression in cell growth-related genes (CDKN1b, CCNG2, CCND2, CCN1), prostaglandin E receptor (PTGER2), and IL-4 receptor. In addition, a gene set of the IL-4 receptor signaling pathway was significantly upregulated in ILC2s in patients with asthma (P = .042). Conclusions: Our results suggest that circulating ILC2s in patients with asthma are preactivated via the IL-4 receptor signaling pathway and produce IL-5 and IL-13 vigorously by stimulation.

Live-Cell Imaging Technique to Visualize DAMPs Release During Regulated Cell Death.

The present protocol introduces a live-cell imaging of secretion activity (LCI-S) that is useful to visualize the real-time release of molecules from individual cells using an immunoassay coupled with total internal reflection fluorescence (FL) microscopy. This novel "live"-cell imaging technique has helped uncover the dynamics of regulated cell "death" by using this new approach. This protocol can observe the final stages of the regulated cell death process via single-cell imaging by targeting the extracellular release of damage-associated molecular patterns (DAMPs) from the cells expressing fluorescence resonance energy transfer (FRET) biosensors, such as a sensor for MLKL activation by RIPK3 based on FRET (SMART) and a sensor for caspase-1 activation based on FRET (SCAT1), which specifically identify the occurrence of regulated cell death processes.

Time-Lapse Imaging of Necroptosis and DAMP Release at Single-Cell Resolution.

Necroptosis is a regulated form of necrosis that depends on receptor-interacting protein kinase (RIPK)3 and mixed lineage kinase domain-like protein (MLKL). Necroptotic cells release a variety of cellular and nuclear factors, referred to as danger-associated molecular patterns (DAMPs). We recently developed a förster resonance energy transfer (FRET) biosensor, termed SMART (a sensor for MLKL activation based on FRET). SMART comprises a fragment of MLKL, and it monitors necroptosis, but not apoptosis or necrosis. We performed live-cell imaging for secretion activity (LCI-S) to observe the release of high-mobility group box 1 (HMGB1) from necroptotic cells at single-cell resolution. Moreover, we combined SMART and LCI-S imaging techniques and found two different modes of HMGB1 release from necroptotic cells. Thus, SMART and LCI-S are valuable tools for investigating intimate cross talk between necroptosis and DAMP release at single-cell resolution.

Raman image-activated cell sorting.

The advent of image-activated cell sorting and imaging-based cell picking has advanced our knowledge and exploitation of biological systems in the last decade. Unfortunately, they generally rely on fluorescent labeling for cellular phenotyping, an indirect measure of the molecular landscape in the cell, which has critical limitations. Here we demonstrate Raman image-activated cell sorting by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping. Specifically, the technology enables real-time SRS-image-based sorting of single live cells with a throughput of up to ~100 events per second without the need for fluorescent labeling. To show the broad utility of the technology, we show its applicability to diverse cell types and sizes. The technology is highly versatile and holds promise for numerous applications that are previously difficult or undesirable with fluorescence-based technologies.

Intelligent image-activated cell sorting 2.0.

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of ∼2000 events per second and a high sensitivity of ∼50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology.

Microfluidic Immunoassays for Time-Resolved Measurement of Protein Secretion from Single Cells.

Measurement of humoral factors secreted from cells has served as an indispensable method to monitor the states of a cell ensemble because humoral factors play crucial roles in cell-cell interaction and aptly reflect the states of individual cells. Although a cell ensemble consisting of a large number of cells has conventionally been the object of such measurements, recent advances in microfluidic technology together with highly sensitive immunoassays have enabled us to quantify secreted humoral factors even from individual cells in either a population or a temporal context. Many groups have reported various miniaturized platforms for immunoassays of proteins secreted from single cells. This review focuses on the current status of time-resolved assay platforms for protein secretion with single-cell resolution. We also discuss future perspectives of time-resolved immunoassays from the viewpoint of systems biology.

Author Correction: A practical guide to intelligent image-activated cell sorting.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A practical guide to intelligent image-activated cell sorting.

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.

Development of novel methods that monitor necroptosis and the release of DAMPs at the single cell resolution.

Necroptosis is a regulated form of necrosis that depends on receptor-interacting protein kinase (RIPK)3 and mixed lineage kinase domain-like protein (MLKL). While danger-associated molecular pattern (DAMP)s are released from dead cells and involved in various pathological conditions, the mechanisms underlying regulation of the release of DAMPs are not fully understood. Apoptosis and pyroptosis can be detected by several types of sensors such as Forster resonance energy transfer (FRET) biosensors, termed SCAT1 (a sensor for caspase 1 activation based on FRET) and SCAT3, respectively. These sensors have provided better understanding of pyroptosis and apoptosis in vitro and in vivo. However, there have been no biosensors to monitor necroptosis. Development of a FRET biosensor that monitors necroptosis and generation of transgenic mice expressing such FRET biosensor might be useful to understand the mechanisms underlying the execution of necroptosis and also the consequences of necroptosis in vivo. In our recent study (Nat Commun, 9(1):4457), we developed a FRET biosensor for necroptosis, termed SMART (a sensor for MLKL activation by RIPK3 based on FRET). SMART is composed of a fragment of MLKL and monitors necroptosis, but not apoptosis or necrosis. Moreover, we recently developed a platform called Live-Cell Imaging for Secretion activity (LCI-S) to monitor protein secretion at the single cell level. This platform has enabled us to monitor the release of HMGB1 (High Mobility Group Box 1), one of the DAMPs, at the single cell level and reveals two different modes of the release of HMGB1 from necroptotic cells.

A20 prevents inflammasome-dependent arthritis by inhibiting macrophage necroptosis through its ZnF7 ubiquitin-binding domain.

Deficiency in the deubiquitinating enzyme A20 causes severe inflammation in mice, and impaired A20 function is associated with human inflammatory diseases. A20 has been implicated in negatively regulating NF-κB signalling, cell death and inflammasome activation; however, the mechanisms by which A20 inhibits inflammation in vivo remain poorly understood. Genetic studies in mice revealed that its deubiquitinase activity is not essential for A20 anti-inflammatory function. Here we show that A20 prevents inflammasome-dependent arthritis by inhibiting macrophage necroptosis and that this function depends on its zinc finger 7 (ZnF7). We provide genetic evidence that RIPK1 kinase-dependent, RIPK3-MLKL-mediated necroptosis drives inflammasome activation in A20-deficient macrophages and causes inflammatory arthritis in mice. Single-cell imaging revealed that RIPK3-dependent death caused inflammasome-dependent IL-1ß release from lipopolysaccharide-stimulated A20-deficient macrophages. Importantly, mutation of the A20 ZnF7 ubiquitin binding domain caused arthritis in mice, arguing that ZnF7-dependent inhibition of necroptosis is critical for A20 anti-inflammatory function in vivo.

Addendum: A FRET biosensor for necroptosis uncovers two different modes of the release of DAMPs.

The cDNA sequence of human SMART described in this Article was misreported, as described in the accompanying Addendum. This error does not affect the results or any conclusion of the Article.

A FRET biosensor for necroptosis uncovers two different modes of the release of DAMPs.

Necroptosis is a regulated form of necrosis that depends on receptor-interacting protein kinase (RIPK)3 and mixed lineage kinase domain-like (MLKL). While danger-associated molecular pattern (DAMP)s are involved in various pathological conditions and released from dead cells, the underlying mechanisms are not fully understood. Here we develop a fluorescence resonance energy transfer (FRET) biosensor, termed SMART (a sensor for MLKL activation by RIPK3 based on FRET). SMART is composed of a fragment of MLKL and monitors necroptosis, but not apoptosis or necrosis. Mechanistically, SMART monitors plasma membrane translocation of oligomerized MLKL, which is induced by RIPK3 or mutational activation. SMART in combination with imaging of the release of nuclear DAMPs and Live-Cell Imaging for Secretion activity (LCI-S) reveals two different modes of the release of High Mobility Group Box 1 from necroptotic cells. Thus, SMART and LCI-S uncover novel regulation of the release of DAMPs during necroptosis.

Intelligent Image-Activated Cell Sorting.

A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.

Challenges in Developing Protein Secretion Assays at a Single-Cell Level.

In addition to direct physical interactions between/among cells, the secretion of humoral factors from living cells is a critical process for cell-cell communications. A well-known extracellular signaling event is mediated by immune cell cytokines/chemokines. Because cell-cell communication is crucial in immune cell sociology, protein secretion assays first attracted a broad range of immunology interests. Now that we have entered an era of systems biology, cell-cell interactions mediated by secreted molecules should be revisited to understand the dynamics and homeostasis of the cell society as a whole. Of more importance, recent advances in detection and microfluidics technologies enable us to monitor protein secretion in real time rather than as a snapshot from the past, which gives us an opportunity to more deeply understand the logic of mammalian cell sociology. This chapter reviews the recent progress in and future direction of protein secretion assays, particularly from a mammalian cell sociology viewpoint.

Construction of a system using a deep learning algorithm to count cell numbers in nanoliter wells for viable single-cell experiments.

For single-cell experiments, it is important to accurately count the number of viable cells in a nanoliter well. We used a deep learning-based convolutional neural network (CNN) on a large amount of digital data obtained as microscopic images. The training set consisted of 103 019 samples, each representing a microscopic grayscale image. After extensive training, the CNN was able to classify the samples into four categories, i.e., 0, 1, 2, and more than 2 cells per well, with an accuracy of 98.3% when compared to determination by two trained technicians. By analyzing the samples for which judgments were discordant, we found that the judgment by technicians was relatively correct although cell counting was often difficult by the images of discordant samples. Based on the results, the system was further enhanced by introducing a new algorithm in which the highest outputs from CNN were used, increasing the accuracy to higher than 99%. Our system was able to classify the data even from wells with a different shape. No other tested machine learning algorithm showed a performance higher than that of our system. The presented CNN system is expected to be useful for various single-cell experiments, and for high-throughput and high-content screening.