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.

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.

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.

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.

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.

Characterization of multiple alternative forms of heterogeneous nuclear ribonucleoprotein K by phosphate-affinity electrophoresis.

The phosphorylation of heterogeneous nuclear ribonucleoprotein K (hnRNP K) is thought to play an important role in cell regulation and signal transduction. However, the relationship between hnRNP K phosphorylation and cellular events has only been indirectly examined, and the phosphorylated forms of endogenous hnRNP K have not been biochemically characterized in detail. In this study, we extensively examined the phosphorylated forms of endogenous hnRNP K by direct protein-chemical characterization using phosphate-affinity electrophoresis followed by immunoblotting and MS. Phosphate-affinity electrophoresis enabled us to sensitively detect and separate the phosphorylated forms of hnRNP K. When we used 2-DE with phosphate-affinity SDS-PAGE in the second dimension, the nuclear fraction contained more than 20 spots of endogenous hnRNP K on the 2-D map. We determined that the multiple forms of hnRNP K were produced mainly by alternative splicing of the single hnRNP K gene and phosphorylation of Ser116 and/or Ser284. Furthermore, the subcellular localization of these proteins revealed by the 2-D gel correlated with their phosphorylation states and alternative splicing patterns. The results also indicated that the multiple forms of hnRNP K were differentially modulated in response to external stimulation with bacterial lipopolysaccharide or serum.

On-chip identification and interaction analysis of gel-resolved proteins using a diamond-like carbon-coated plate.

We developed a novel protein chip made of a diamond-like, carbon-coated stainless steel plate (DLC plate), the surface of which is chemically modified with N-hydroxysuccinimide ester. To produce a high-density protein chip using the DLC plate, proteins separated by SDS gel electrophoresis or two-dimensional electrophoresis were electroblotted onto the DLC plate and immobilized covalently. A high blotting efficiency (25-70%) for transferring proteins from the gels onto the DLC plates was achieved by improvement of the electrophoresis device and electroblotting techniques. With the use of the DLC plate, we developed novel techniques to identify proteins immobilized on the chip and to detect protein-protein interactions on the chip by mass spectrometric analysis. We also developed a technique to identify post-translationally modified proteins, such as glycoproteins, on the protein chip.