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.

Deep imaging flow cytometry.

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.

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.

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.

Virtual-freezing fluorescence imaging flow cytometry.

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s-1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

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.

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.

Single-cell imaging of caspase-1 dynamics reveals an all-or-none inflammasome signaling response.

Inflammasome-mediated caspase-1 activation is involved in cell death and the secretion of the proinflammatory cytokine interleukin-1ß (IL-1ß). Although the dynamics of caspase-1 activation, IL-1ß secretion, and cell death have been examined with bulk assays in population-level studies, they remain poorly understood at the single-cell level. In this study, we conducted single-cell imaging using a genetic fluorescence resonance energy transfer sensor that detects caspase-1 activation. We determined that caspase-1 exhibits all-or-none (digital) activation at the single-cell level, with similar activation kinetics irrespective of the type of inflammasome or the intensity of the stimulus. Real-time concurrent detection of caspase-1 activation and IL-1ß release demonstrated that dead macrophages containing activated caspase-1 release a local burst of IL-1ß in a digital manner, which identified these macrophages as the main source of IL-1ß within cell populations. Our results highlight the value of single-cell analysis in enhancing understanding of the inflammasome system and chronic inflammatory diseases.

Real-time single-cell imaging of protein secretion.

Protein secretion, a key intercellular event for transducing cellular signals, is thought to be strictly regulated. However, secretion dynamics at the single-cell level have not yet been clarified because intercellular heterogeneity results in an averaging response from the bulk cell population. To address this issue, we developed a novel assay platform for real-time imaging of protein secretion at single-cell resolution by a sandwich immunoassay monitored by total internal reflection microscopy in sub-nanolitre-sized microwell arrays. Real-time secretion imaging on the platform at 1-min time intervals allowed successful detection of the heterogeneous onset time of nonclassical IL-1ß secretion from monocytes after external stimulation. The platform also helped in elucidating the chronological relationship between loss of membrane integrity and IL-1ß secretion. The study results indicate that this unique monitoring platform will serve as a new and powerful tool for analysing protein secretion dynamics with simultaneous monitoring of intracellular events by live-cell imaging.

Toward an understanding of immune cell sociology: real-time monitoring of cytokine secretion at the single-cell level.

The immune system is a very complex and dynamic cellular system, and its intricacies are considered akin to those of human society. Disturbance of homeostasis of the immune system results in various types of diseases; therefore, the homeostatic mechanism of the immune system has long been a subject of great interest in biology, and a lot of information has been accumulated at the cellular and the molecular levels. However, the sociological aspects of the immune system remain too abstract to address because of its high complexity, which mainly originates from a large number and variety of cell-cell interactions. As long-range interactions mediated by cytokines play a key role in the homeostasis of the immune system, cytokine secretion analyses, ranging from analyses of the micro level of individual cells to the macro level of a bulk of cell ensembles, provide us with a solid basis of a sociological viewpoint of the immune system. In this review, as the first step toward a comprehensive understanding of immune cell sociology, cytokine secretion of immune cells is surveyed with a special emphasis on the single-cell level, which has been overlooked but should serve as a basis of immune cell sociology. Now that it has become evident that large cell-to-cell variations in cytokine secretion exist at the single-cell level, we face a tricky yet interesting question: How is homeostasis maintained when the system is composed of intrinsically noisy agents? In this context, we discuss how the heterogeneity of cytokine secretion at the single-cell level affects our view of immune cell sociology. While the apparent inconsistency between homeostasis and cell-to-cell heterogeneity is difficult to address by a conventional reductive approach, comparison and integration of single-cell data with macroscopic data will offer us a new direction for the comprehensive understanding of immune cell sociology.

Single-molecule tracking of mRNA in living cells.

Some mRNAs localize to specific regions within eukaryotic cells to express their functions. The movement and localization of mRNA molecules provides valuable information about how they concentrate to particular regions. Recent technical advances in optical microscopy and image analysis algorithms enable real-time tracking of single mRNA molecules in living cells. This chapter presents the methods to visualize and track single ß-actin mRNA molecules that localize at the leading edge of chicken embryo fibroblasts. Furthermore, this chapter presents an analysis approach for single-molecule tracking data to extract quantitative information about the microenvironments of the mRNA molecules.

Size-dependent accumulation of mRNA at the leading edge of chicken embryo fibroblasts.

beta-actin mRNA localizes to the leading edge of a living chicken embryo fibroblast. Recently we proposed that the mRNA maintains its localization at the leading edge by utilizing the heterogeneity of cytoplasmic microstructure (Yamagishi et al., 2009 [10]). In this study, we observed the intracellular distribution of beta-actin mRNA variants to elucidate the mechanism of mRNA localization at the leading edge. We found that the degree of localization correlated positively with the molecular mass of the mRNA variants. We further demonstrated that the molecular mass-dependent localization was found even with dextrans, which have no biological function. The dependency of localization on molecular mass suggested that the barrier effect caused by the physical obstruction of the cytoplasmic microstructure is one of the major factors controlling mRNA localization in motile fibroblasts.

Single-molecule imaging of beta-actin mRNAs in the cytoplasm of a living cell.

Beta-actin mRNA labeled with an MS2-EGFP fusion protein was expressed in chicken embryo fibroblasts and its localization and movement were analyzed by single-molecule imaging. Most beta-Actin mRNAs localized to the leading edge, while some others were observed in the perinuclear region. Singe-molecule tracking of individual mRNAs revealed that the majority of mRNAs were in unrestricted Brownian motion at the leading edge and in restricted Brownian motion in the perinuclear region. The macroscopic diffusion coefficient of mRNA (D(MACRO)) at the leading edge was 0.3 microm(2)/s. On the other hand, D(MACRO) in the perinuclear region was 0.02 microm(2)/s. The destruction of microfilaments with cytochalasin D, which is known to delocalize beta-actin mRNAs, led to an increase in D(MACRO) to 0.2 microm(2)/s in the perinuclear region. These results suggest that the microstructure, composed of microfilaments, serves as a barrier for the movement of beta-actin mRNA.