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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.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

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.

Understanding metabolism in live microalgae is crucial for efficient biomaterial engineering, but conventional methods fail to evaluate heterogeneous populations of motile microalgae due to the labelling requirements and limited imaging speeds. Here, we demonstrate label-free video-rate metabolite imaging of live Euglena gracilis and statistical analysis of intracellular metabolite distributions under different culture conditions. Our approach provides further insights into understanding microalgal heterogeneity, optimizing culture methods and screening mutant microalgae.

Centromeres and large-scale structural variants evolve and contribute to genome diversity during vertebrate speciation. Here, we perform de novo long-read genome assembly of three inbred medaka strains that are derived from geographically isolated subpopulations and undergo speciation. Using single-molecule real-time (SMRT) sequencing, we obtain three chromosome-mapped genomes of length ~734, ~678, and ~744Mbp with a resource of twenty-two centromeric regions of length 20–345kbp. Centromeres are positionally conserved among the three strains and even between four pairs of chromosomes that were duplicated by the teleost-specific whole-genome duplication 320–350 million years ago. The centromeres do not all evolve at a similar pace; rather, centromeric monomers in non-acrocentric chromosomes evolve significantly faster than those in acrocentric chromosomes. Using methylation sensitive SMRT reads, we uncover centromeres are mostly hypermethylated but have hypomethylated sub-regions that acquire unique sequence compositions independently. These findings reveal the potential of non-acrocentric centromere evolution to contribute to speciation.

Phase distortions due to scattering in random media restrict optical focusing beyond one transport mean free path. However, scattering can be compensated for by applying a correction to the illumination wavefront using spatial light modulators. One method of obtaining the wavefront correction is by iterative determination using an optimization algorithm. In the past, obtaining a feedback signal required either direct optical access to the target region, or invasive embedding of molecular probes within the random media. Here, we propose using ultrasonically encoded light as feedback to guide the optimization dynamically and non-invasively. In our proof-of-principle demonstration, diffuse light was refocused to the ultrasound focal zone, with a focus-to-background ratio of more than one order of magnitude after 600 iterations. With further improvements, especially in optimization speed, the proposed method should find broad applications in deep tissue optical imaging and therapy.

We present a new method for high speed tracking of fluorescence time series from single proteins. The method uses a fast sample flow and a modified confocal microscopy, line confocal microscopy, and achieves the time resolution of less than 20 μs. The obtained time series from the B domain of protein A labeled with donor and acceptor fluorophores suggest conformational heterogeneity and dynamic fluctuations in the unfolded state.