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.

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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.

Inflammasome-mediated caspase-1 activation is involved in cell death and the secretion of the proinflammatory cytokine interleukin-1β (IL-1β). Although the dynamics of caspase-1 activation, IL-1β 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-1β release demonstrated that dead macrophages containing activated caspase-1 release a local burst of IL-1β in a digital manner, which identified these macrophages as the main source of IL-1β within cell populations. Our results highlight the value of single-cell analysis in enhancing understanding of the inflammasome system and chronic inflammatory diseases.

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-1β secretion from monocytes after external stimulation. The platform also helped in elucidating the chronological relationship between loss of membrane integrity and IL-1β 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.