RIKEN Center for Biosystems Dynamics Research

Organ morphogenesis is essential for not only integrated organ functions in the body but also the determination of adult-tissue stem cells and their niche. Only hair follicle organs can cyclically regenerate in the variable bulbous germinative region as a form of programmed organ regeneration in adults. Here, we identified a cell population, hair follicle organ-intrinsic mesenchymal cells, with PDGFRα+/Sca1+/CD34high+ mesenchymal cells, from the boundary zone of the epithelial stem cell niche by using the organ germ method, which plays essential roles in entering and promoting the downgrowth phase of the hair cycle. The bioengineered hair follicle germ reconstituted from bulge region-derived epithelial stem cells, dermal papillae and mesenchymal cells has full functions, including downgrowth, full-size hair follicle regeneration and the hair cycle, both in in vitro artificial skin and after intracutaneous skin transplantation. This study provides significant contributions to the basic and medical science of adult organ-inductive potential stem cells and their niches in organ morphogenesis and the adult hair cycle.

Microfluidics and nanofluidics have contributed much to the fields of chemistry and biochemistry. The small sizes of micro and nanochannels provide short diffusion lengths, resulting in highly efficient reactions with control over channel size, flow and temperature. In this review, progress in chemical and biochemical reactors based on micro and nanochannels is summarized. Various types of reactors such as homogeneous and heterogeneous catalytic reactors based on wall-coated, packed-bed or monolithic column designs are examined. The ultra-small spaces provided by micro and nanochannels allow rapid mixing and promote interactions between different phases. As such, faster reaction rates and better yields can be obtained using systems that are easy to operate. In addition, unique reaction mechanisms can be achieved based on the specific properties exhibited only by nanospaces. Although it remains challenging to balance high efficiency with a suitable production volume, new super-high-performance reactors allowing well-controlled processes with suitable productivity are anticipated in future. This advanced technology will represent significant progress in the areas not only of analytical chemistry and bioanalytical chemistry, but also chemical and biochemical engineering.

Accurate prediction of compound-protein interactions (CPIs) is crucial for chemical biology and drug discovery. Despite recent advancements, existing deep learning (DL)-based CPI models often struggle to simultaneously achieve high generalization performance, quantify prediction confidence, and ensure explainability. Here, we propose ChemGLaM, a chemical genomics language model designed to address these three crucial challenges, thereby enabling reliable and explainable CPI predictions. ChemGLaM integrates independently pre-trained chemical and protein language models through an interaction block with a cross-attention mechanism, achieving near state-of-the-art performance in predicting novel CPIs at a low computational cost. Incorporating uncertainty estimation and attention visualization enables ChemGLaM to enhance the success rate of virtual screening and to provide molecular insights into CPIs. To demonstrate the practical impact of ChemGLaM, we constructed a publicly available database containing large-scale CPI predictions for every possible pairing between all 20,434 human proteins and all 11,455 drugs and validated its practical applicability in a case study on amyotrophic lateral sclerosis. ChemGLaM marks an important step forward in addressing the challenges of AI-driven CPI exploration and drug discovery.Scientific ContributionThis study established a unified CPI prediction framework that simultaneously achieves high generalization performance, confidence quantification, and explainability. We leveraged this framework to create a community resource by constructing a comprehensive CPI database and demonstrated its practical utility by successfully prioritizing hit compounds and deconvoluting their targets in a phenotypic screening for amyotrophic lateral sclerosis.

Research on biomarkers for predicting psychiatric disorders from resting-state functional connectivity (FC) is advancing. While the focus has primarily been on the discriminative performance of biomarkers by machine learning, identification of abnormal FCs in psychiatric disorders has often been treated as a secondary goal. However, it is crucial to investigate the effect size and robustness of the selected FCs because they can be used as potential targets of neurofeedback training or transcranial magnetic stimulation therapy. Here, we incorporated approximately 5,000 runs of resting-state functional magnetic resonance imaging from six datasets, including individuals with three different psychiatric disorders (major depressive disorder [MDD], schizophrenia [SCZ], and autism spectrum disorder [ASD]). We demonstrated that a PCA-based feature selection method can robustly extract FCs related to psychiatric disorders compared with other conventional supervised feature selection methods. We found that our proposed method robustly extracted FCs with larger effect sizes from the validation dataset compared with different types of feature selection methods based on supervised learning for MDD (Cohen's d = 0.40 vs. 0.25), SCZ (0.37 vs. 0.28), and ASD (0.17 vs. 0.16). We found 78, 69, and 81 essential FCs for MDD, SCZ, and ASD, respectively, and these FCs were mainly thalamic and motor network FCs. The current study showed that the PCA-based feature selection method robustly identified abnormal FCs in psychiatric disorders consistently across datasets. The discovery of such robust FCs will contribute to understanding neural mechanisms as abnormal brain signatures in psychiatric disorders.

Nanoscale modifications of apical extracellular matrix (ECM) have created various functional surfaces with distinct physical properties, exemplified by structural coloration and superhydrophobicity in animals and plants. To reveal the mechanisms, we investigated cuticle morphogenesis in Drosophila olfactory organs, where hundreds of ~50-nanometer nanopores in the cuticle covering the olfactory neurons permit selective odorant entry. We showed that zona pellucida domain (ZPD) proteins form the cell type–specific ECM compartments before cuticle secretion, and its disruption leads to less and irregularly sized nanopores. The ZPD protein Dusky-like controls the formation of the outermost layer of the cuticle, the envelope. Trynity, Nyobe, Neo, and Morpheyus form matrices with specific mixing and sorting properties, termed “cloud ECM,” which restrict cell growth and movement. This work identifies a previously unidentified role for ZPD proteins as modular units that establish the mechanical environment essential for nanoscale ECM morphogenesis, opening a previously unexplored context for these biomimetically important structures.

Long noncoding RNAs (lncRNAs) are abundantly transcribed in eukaryotes, but most of their physiological roles, especially in neural development, remain unclear due to limited invivo studies. Here we show that Lobe-less (LOL) lncRNA of Drosophila is expressed in developing neuronal cells and is required for the development of the mushroom body, a center of memory and learning in the insect brain. lol mutant flies exhibit defective morphology of the mushroom body and axon branch patterns, as well as misregulation of neurogenic genes. LOL RNA forms nuclear puncta and genetically interacts with Polycomb group genes in the regulation of homeotic genes. These findings demonstrate that this long ncRNA plays a critical role in the epigenetic control of neural circuit formation.

Single-cell sequencing has advanced our understanding of cell-type diversity and heterogeneity. However, existing single-cell multi-omics methods lack the ability to monitor gene expression dynamics during S-phase progression. Here, we introduce single-cell (sc)Repli-RamDA-seq (scRR-seq), a multi-omics method that enables high-resolution DNA replication profiling and full-length total RNA sequencing from the same single cell in a haplotype-specific manner. scRR-seq generates DNA replication and RNA sequencing data comparable to individually obtained scRepli-seq and scRamDA-seq data, respectively. Unlike other scDNA/RNA-seq methods, scRR-seq allows one to tell the S-phase stage of a given cell based on the percentage of the replicated genome derived from scRepli-seq data. This facilitates analysis of gene expression dynamics during S-phase progression, enabling the identification of S-phase progression markers. scRR-seq also detects copy-number variation in non-S-phase cells and outperforms other scDNA/RNA-seq methods in various measures. Taken together, scRR-seq is a robust single-cell multi-omics method with promising potential for comprehensive genome/transcriptome analysis.