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.

Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering.

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.

Wash-free and selective imaging of epithelial cell adhesion molecule (EpCAM) expressing cells with fluorogenic peptide ligands.

Detection of the cells expressing an epithelial cell adhesion molecule (EpCAM) is a crucial step to identify circulating tumor cells (CTCs) from blood. To detect the EpCAM, we here designed and synthesized a series of fluorogenic peptides. Specifically, we functionalized an EpCAM-binding peptide, Ep114, by replacing its amino acids to an aminophenylalanine that was modified with environmentally sensitive 7-nitro-2,1,3-benzoxadiazole (NBD-amPhe). Among six synthesized peptides, we have found that two peptides, Q4X and V6X (X represents NBD-amPhe), retain the Ep114's binding ability and specifically mark EpCAM-expressing cells by just adding these peptides to the cultivation medium. Our wash-free, fluorogenic peptide ligands would boost the development of next generation devices for CTC diagnoses.

A Novel System to Detect Circulating Tumor Cells Using Two Different Size-selective Microfilters.

BACKGROUND/AIM: Clusters of circulating tumor cells (CTCs) increase metastatic potential compared to single CTC. However, conventional technologies have been unable to generate an accurate analysis of single and cluster CTCs in the peripheral blood. We propose an effective strategy to detect and isolate both single and cluster CTCs using two size-selective microfilters. MATERIALS AND METHODS: Five ml of whole blood were collected from 10 patients with epidermal growth factor receptor mutation-positive non-small cell lung cancer. Single and cluster CTCs were identified using precision microfiltration membranes with two distinct pore sizes together with anti-EpCAM antibody labeling. RESULTS: Single and cluster CTCs were detected by simultaneously using two size-selective microfilters. The EGFR-L858R mutation was detected in the DNA from cells captured using both microfilters. CONCLUSION: Our method can be used to detect and isolate single and cluster CTCs in the whole blood and may facilitate the development of a liquid biopsy strategy.

Subtypes of tumour cell-derived small extracellular vesicles having differently externalized phosphatidylserine.

Phosphatidylserine (PS) has skewed distributions in the plasma membrane and is preferentially located in the inner leaflet of normal cells. Tumour cells, however, expose PS at the outer leaflet of cell surfaces, thereby potentially modulating the bio-signalling of cells. Interestingly, exosomes - or, more properly, small extracellular vesicles (sEVs) - which are secreted from tumour cells, are enriched with externalized PS, have been proposed as being involved in the progression of cancers, and could be used as a marker for tumour diagnostics. However, the sEV fractions prepared from various methods are composed of different subtypes of vesicles, and knowledge about the subtypes enriched with exposed PS is still limited. Here, we differentiated sEVs from cancer cell lines by density gradient centrifugation and characterized the separated fractions by using gold-labelling of PS in atomic force microscopy, thrombin generation assay, size and zeta potential measurements, and western blot analysis. These analyses revealed a previously unreported PS+-enriched sEV subtype, which is characterized by a lower density than that of canonical exosomes (1.06 g/ml vs. 1.08 g/ml), larger size (122 nm vs. 105 nm), more negative zeta potential (-28 mV vs. -21 mV), and lower abundance of canonical exosomal markers. The identification of the PS-exposed subtype of sEVs will provide deeper insight into the role of EVs in tumour biology and enhance the development of EV-based tumour diagnosis and therapy.

Host Cell Prediction of Exosomes Using Morphological Features on Solid Surfaces Analyzed by Machine Learning.

Exosomes are extracellular nanovesicles released from any cells and found in any body fluid. Because exosomes exhibit information of their host cells (secreting cells), their analysis is expected to be a powerful tool for early diagnosis of cancers. To predict the host cells, we extracted multidimensional feature data about size, shape, and deformation of exosomes immobilized on solid surfaces by atomic force microscopy (AFM). The key idea is combination of support vector machine (SVM) learning for individual exosome particles and their interpretation by principal component analysis (PCA). We observed exosomes derived from three different cancer cells on SiO2/Si, 3-aminopropyltriethoxysilane-modified-SiO2/Si, and TiO2 substrates by AFM. Then, 14-dimensional feature vectors were extracted from AFM particle data, and classifiers were trained in 14-dimensional space. The prediction accuracy for host cells of test AFM particles was examined by the cross-validation test. As a result, we obtained prediction of exosome host cells with the best accuracy of 85.2% for two-class SVM learning and 82.6% for three-class one. By PCA of the particle classifiers, we concluded that the main factors for prediction accuracy and its strong dependence on substrates are incremental decrease in the PCA-defined aspect ratio of the particles with their volume.

Isolation of human salivary extracellular vesicles by iodixanol density gradient ultracentrifugation and their characterizations.

Diagnostic methods that focus on the extracellular vesicles (EVs) present in saliva have been attracting great attention because of their non-invasiveness. EVs contain biomolecules such as proteins, messenger RNA (mRNA) and microRNA (miRNA), which originate from cells that release EVs, making them an ideal source for liquid biopsy. Although there have been many reports on density-based fractionation of EVs from blood and urine, the number of reports on EVs from saliva has been limited, most probably because of the difficulties in separating EVs from viscous saliva using density gradient centrifugation. This article establishes a protocol for the isolation of EVs from human saliva using density gradient centrifugation. The fractionated salivary EVs were characterized by atomic force microscopy, western blot and reverse transcription polymerase chain reaction. The results indicate that salivary EVs have a smaller diameter (47.8±12.3 nm) and higher density (1.11 g/ml) than EVs isolated from conditioned cell media (74.0±23.5 nm and 1.06 g/ml, respectively). Additionally, to improve the throughput of density-based fractionation of EVs, the original protocol was further modified by using a fixed angle rotor instead of a swinging rotor. It was also confirmed that several miRNAs were expressed strongly in the EV-marker-expressing fractions.