Barcoding of small extracellular vesicles with CRISPR-gRNA enables comprehensive, subpopulation-specific analysis of their biogenesis and release regulators.

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.

Cyano-Hydrol green derivatives: Expanding the 9-cyanopyronin-based resonance Raman vibrational palette.

9-cyanopyronin is a promising scaffold that exploits resonance Raman enhancement to enable sensitive, highly multiplexed biological imaging. Here, we developed cyano-Hydrol Green (CN-HG) derivatives as resonance Raman scaffolds to expand the color palette of 9-cyanopyronins. CN-HG derivatives exhibit sufficiently long wavelength absorption to produce strong resonance Raman enhancement for near-infrared (NIR) excitation, and their nitrile peaks are shifted to a lower frequency than those of 9-cyanopyronins.

Selective recognition between aromatics and aliphatics by cage-shaped borates supported by a machine learning approach.

Selective recognition between hydrocarbon moieties is a longstanding issue. Although we developed a π-pocket Lewis acid catalyst with high selectivity for aromatic aldehydes over aliphatic ones, a general strategy for catalyst design remains elusive. As an approach that transfers the molecular recognition based on multiple cooperative non-covalent interactions within the π-pocket to a rational catalyst design, herein, we demonstrate Lewis acid catalysts showing improved selectivity through the support of an ensemble algorithm with random forest, Ada Boost, and XG Boost as a machine learning (ML) approach.

Modular Design Platform for Activatable Fluorescence Probes Targeting Carboxypeptidases Based on ProTide Chemistry.

Carboxypeptidases (CPs) are a family of hydrolases that cleave one or more amino acids from the C-terminal of peptides or proteins and play indispensable roles in various physiological and pathological processes. However, only a few highly activatable fluorescence probes for CPs have been reported, and there is a need for a flexibly tunable molecular design platform to afford a range of fluorescence probes for CPs for biological and medical research. Here, we focused on the unique activation mechanism of ProTide-based prodrugs and established a modular design platform for CP-targeting florescence probes based on ProTide chemistry.

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network.

Background: In cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks.

Individual health-disease phase diagrams for disease prevention based on machine learning.

Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in chronic disease development. Machine-learning techniques have enabled precise personal-level disease prediction by capturing individual differences in multivariate data.

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples.

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions.

Super-resolution vibrational imaging based on photoswitchable Raman probe.

Super-resolution vibrational microscopy is promising to increase the degree of multiplexing of nanometer-scale biological imaging because of the narrower spectral linewidth of molecular vibration compared to fluorescence. However, current techniques of super-resolution vibrational microscopy suffer from various limitations including the need for cell fixation, high power loading, or complicated detection schemes. Here, we present reversible saturable optical Raman transitions (RESORT) microscopy, which overcomes these limitations by using photoswitchable stimulated Raman scattering (SRS).

Development of a GCN-based model to predict in vitro phototoxicity from the chemical structure and HOMO-LUMO gap.

The interaction between sunlight and drugs can lead to phototoxicity in patients who have received such drugs. Phototoxicity assessment is a regulatory requirement globally and one of the main toxicity screening steps in the early stages of drug discovery. An in silico-in vitro approach has been utilized mainly for toxicology assessments at these stages.

Activatable Raman Probes Utilizing Enzyme-Induced Aggregate Formation for Selective Imaging.

Detecting multiple enzyme activities simultaneously with high spatial specificity is a promising strategy to investigate complex biological phenomena, and Raman imaging would be an excellent tool for this purpose due to its high multiplexing capabilities. We previously developed activatable Raman probes based on 9CN-pyronins, but specific visualization of cells with target enzyme activities proved difficult due to leakage of the hydrolysis products from the target cells after activation. Here, focusing on rhodol bearing a nitrile group at the position of 9 (9CN-rhodol), we established a novel mechanism for Raman signal activation based on a combination of aggregate formation (to increase local dye concentration) and the resonant Raman effect along with the bathochromic shift of the absorption, and utilized it to develop Raman probes.

Rapid imaging of thymoma and thymic carcinoma with a fluorogenic probe targeting γ-glutamyltranspeptidase.

In recent years, thoracoscopic and robotic surgical procedures have increasingly replaced median sternotomy for thymoma and thymic carcinoma. In cases of partial thymectomy, the prognosis is greatly improved by ensuring a sufficient margin from the tumor, and therefore intraoperative fluorescent imaging of the tumor is especially valuable in thoracoscopic and robotic surgery, where tactile information is not available. γ-Glutamyl hydroxymethyl rhodamine green (gGlu-HMRG) has been applied for fluorescence imaging of some types of tumors in the resected tissues, and here we aimed to examine its validity for the imaging of thymoma and thymic carcinoma.

9-Cyano-10-telluriumpyronin Derivatives as Red-light-activatable Raman Probes.

Photoactivatable fluorescence probes can track the dynamics of specific cells or biomolecules with high spatiotemporal resolution, but their broad absorption and emission peaks limit the number of wavelength windows that can be employed simultaneously. In contrast, the narrower peak width of Raman signals offers more scope for simultaneous discrimination of multiple targets, and therefore a palette of photoactivatable Raman probes would enable more comprehensive investigation of biological phenomena. Herein we report 9-cyano-10-telluriumpyronin (9CN-TeP) derivatives as photoactivatable Raman probes whose stimulated Raman scattering (SRS) intensity is enhanced by photooxidation of the tellurium atom.

Characterizing eye-gaze positions of people with severe motor dysfunction: Novel scoring metrics using eye-tracking and video analysis.

Nonverbal communication with people who have physical disabilities is difficult. Eye-tracking technologies have recently been developed and applied to help people with physical disabilities in their communication. However, the eye-gaze patterns of people with severe motor dysfunction (SMD) have not been analyzed in detail.

Measuring the pH of Acidic Vesicles in Live Cells with an Optimized Fluorescence Lifetime Imaging Probe.

Acidification of intracellular vesicles, such as endosomes and lysosomes, is a key pathway for regulating the function of internal proteins. Most conventional methods of measuring pH are not satisfactory for quantifying the pH inside these vesicles. Here, we investigated the molecular requirements for a fluorescence probe to measure the intravesicular acidic pH in living cells by means of fluorescence lifetime imaging microscopy (FLIM).

Droplet Array-Based Platform for Parallel Optical Analysis of Dynamic Extracellular Vesicle Secretion from Single Cells.

Extracellular vesicles (EVs) are essential intercellular communication tools, but the regulatory mechanisms governing heterogeneous EV secretion are still unclear due to the lack of methods for precise analysis. Monitoring the dynamics of secretion from individually isolated cells is crucial because in bulk analysis, secretion activity can be perturbed by cell-cell interactions, and a cell population rarely performs secretion in a magnitude- or duration-synchronized manner. Although various microfluidic techniques have been adopted to evaluate the abundance of single-cell-derived EVs, none can track their secretion dynamics continually for extended periods.

Development of a fluorescent probe library enabling efficient screening of tumour-imaging probes based on discovery of biomarker enzymatic activities.

Fluorescent probes that can selectively detect tumour lesions have great potential for fluorescence imaging-guided surgery. Here, we established a library-based approach for efficient screening of probes for tumour-selective imaging based on discovery of biomarker enzymes. We constructed a combinatorial fluorescent probe library for aminopeptidases and proteases, which is composed of 380 probes with various substrate moieties.

Rapid imaging of lung cancer using a red fluorescent probe to detect dipeptidyl peptidase 4 and puromycin-sensitive aminopeptidase activities.

Rapid identification of lung-cancer micro-lesions is becoming increasingly important to improve the outcome of surgery by accurately defining the tumor/normal tissue margins and detecting tiny tumors, especially for patients with low lung function and early-stage cancer. The purpose of this study is to select and validate the best red fluorescent probe for rapid diagnosis of lung cancer by screening a library of 400 red fluorescent probes based on 2-methyl silicon rhodamine (2MeSiR) as the fluorescent scaffold, as well as to identify the target enzymes that activate the selected probe, and to confirm their expression in cancer cells. The selected probe, glutamine-alanine-2-methyl silicon rhodamine (QA-2MeSiR), showed 96.

Predictive Diagnostic Approach to Dementia and Dementia Subtypes Using Wireless and Mobile Electroencephalography: A Pilot Study.

Background: Developing a screening method for mild cognitive impairment in the aging population and intervening early in the progression of dementia based on such a method, remains challenging. Electroencephalography (EEG) is a noninvasive and sensitive tool to assess the functional activity of the brain, and wireless and mobile EEG (wmEEG) could serve as an alternative screening technique that is widely tolerable in patients with dementia from the preclinical to severe stage.

Materials And Methods: Using wmEEG, we recorded bioelectrical activity (BA) from the forehead in 101 individuals with dementia and nondementia controls (NCs) during 4 tasks and investigated which task could differentiate dementia from NC.

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge.

Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem.

Evaluation of Kidney Histological Images Using Unsupervised Deep Learning.

Introduction: Evaluating histopathology via machine learning has gained research and clinical interest, and the performance of supervised learning tasks has been described in various areas of medicine. Unsupervised learning of histological images has the advantage of reproducibility for labeling; however, the relationship between unsupervised evaluation and clinical information remains unclear in nephrology.

Methods: We propose an unsupervised approach combining convolutional neural networks (CNNs) and a visualization algorithm to cluster the histological images and calculate the score for patients.

Pericentromeric noncoding RNA changes DNA binding of CTCF and inflammatory gene expression in senescence and cancer.

Cellular senescence causes a dramatic alteration of chromatin organization and changes the gene expression profile of proinflammatory factors, thereby contributing to various age-related pathologies through the senescence-associated secretory phenotype (SASP). Chromatin organization and global gene expression are maintained by the CCCTC-binding factor (CTCF); however, the molecular mechanism underlying CTCF regulation and its association with SASP gene expression remains unclear. We discovered that noncoding RNA (ncRNA) derived from normally silenced pericentromeric repetitive sequences directly impairs the DNA binding of CTCF.

Intracellular Unnatural Catalysis Enabled by an Artificial Metalloenzyme.

Artificial metalloenzymes, constructed by incorporating a synthetic catalyst into the internal spaces of a protein scaffold, can perform noncanonical chemical transformations that are not possible using natural enzymes. The addition of cell-permeable modules to artificial metalloenzymes allows for noncanonical catalysis to be implemented as a function of mammalian cells. In this chapter, we describe a protocol for controlling cellular function through a cascade consisting of an artificial metalloenzyme and a gene-circuit engineered via synthetic biology.