Seeing ghosts: In fluorescence-activated cell sorting, characteristic target features are labeled with a specific fluorophore, and cells displaying different fluorophores are sorted. Ota et al. describe a technique called ghost cytometry that allows cell sorting based on the morphology of the cytoplasm, labeled with a single-color fluorophore. The motion of cells relative to a patterned optical structure provides spatial information that is compressed into temporal signals, which are sequentially measured by a single-pixel detector. Images can be reconstructed from this spatial and temporal information, but this is computationally costly. Instead, using machine learning, cells are classified directly from the compressed signals, without reconstructing an image. The method was able to separate morphologically similar cell types in an ultrahigh-speed fluorescence imaging–activated cell sorter. Science , this issue p. 1246

Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive ‘imaging’ data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.

Small extracellular vesicles (sEVs) are important intercellular information transmitters in various biological contexts, but their release processes remain poorly understood. Herein, we describe a high-throughput assay platform, CRISPR-assisted individually barcoded sEV-based release regulator (CIBER) screening, for identifying key players in sEV release. CIBER screening employs sEVs barcoded with CRISPR-gRNA through the interaction of gRNA and dead Cas9 fused with an sEV marker. Barcode quantification enables the estimation of the sEV amount released from each cell in a massively parallel manner. Barcoding sEVs with different sEV markers in a CRISPR pooled-screening format allows genome-wide exploration of sEV release regulators in a subpopulation-specific manner, successfully identifying previously unknown sEV release regulators and uncovering the exosomal/ectosomal nature of CD63⁺/CD9⁺ sEVs, respectively, as well as the synchronization of CD9⁺ sEV release with the cell cycle. CIBER should be a valuable tool for detailed studies on the biogenesis, release, and heterogeneity of sEVs.

3D light‐sheet microscopy is a powerful tool to obtain content‐rich information of cell populations. However, the limited frame rate of high‐quantum‐efficiency cameras hinders its application in high‐throughput flow cytometry. Herein, a high‐throughput 3D‐imaging flow cytometry technique based on light‐sheet microscopy that can screen thousands of cells per second is introduced. This method enables fast, parallel optofluidic scanning of cells by performing 1D acoustofluidic focusing on multiple cells under wide‐field light‐sheet microscopy with a single objective lens. Multicolor 3D‐imaging flow cytometry with cell‐line samples at a record detection throughput of over 2000 cells s⁻¹ is demonstrated. Furthermore, an unprecedentedly large‐scale 3D‐morphology‐based flow cytometric analysis at an order of 10⁵ cells is demonstrated. Results show that the system is able to capture subcellular structures even at high throughputs and obtain cellular information that is overlooked under 2D‐imaging cytometry.