Biology subjects

Minoura, K.

Publications and source records attributed to Minoura, K..

5 recordsLinked to original sources

bioRxiv2024

mmVelo: A deep generative model for estimating cell state-dependent dynamics across multiple modalities

Single-cell multiomics provides unique insight into the regulatory relationships across different biological layers such as the transcriptome and regulome. However, single-cell multiomics is limited by its ability to capture only static snapshots at the time of observation, restricting the reflection of dynamic state changes orchestrated across modalities. RNA velocity analysis of single cells allows for the prediction of temporal changes in the transcriptome; however, the inferred dynamics cannot be applied across all biological layers, specifically in the regulome. Therefore, to address this limitation, we developed multimodal velocity of single cells (mmVelo), a deep generative model designed to estimate cell state-dependent dynamics across multiple modalities. mmVelo estimates cell state dynamics based on spliced and unspliced mRNA expression, and uses multimodal representation learning to project these dynamics onto chromatin accessibility, inferring chromatin velocity at a single-peak resolution. We applied mmVelo to single-cell multiomics data from a developing mouse brain and validated the accuracy of the estimated chromatin accessibility dynamics. Furthermore, using the estimated dynamics, we identified the transcription factors that are crucial for chromatin accessibility regulation in mouse skin. Finally, using multiomics data as a bridge, we demonstrated that during human brain development, the dynamics of missing modalities can be inferred from single-modal data via cross-modal generation. Overall, mmVelo enhances our understanding of the dynamic interactions between modalities, offering insights into the regulatory relationships across molecular layers.

bioinformatics↗

bioRxiv2024

LineageVAE: Reconstructing Historical Cell States and Transcriptomes toward Unobserved Progenitors

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors. This approach transforms scRNA-seq observations with an identical lineage barcode into sequential trajectories toward a common progenitor in a latent cell state space. Using hematopoiesis and reprogrammed fibroblast datasets, we demonstrate the capability of LineageVAE to reconstruct unobservable cell state transitions, historical transcriptome, and regulatory dynamics toward progenitor cell states at single-cell resolution.

bioinformatics↗

bioRxiv2021

scMM: Mixture-of-experts multimodal deep generative model for single-cell multiomics data analysis.

AO_SCPLOWBSTRACTC_SCPLOWThe recent development in single-cell multiomics analysis has enabled simultaneous detection of multiple traits at the single-cell level, thus providing deeper insights into the cellular phenotypes and functions in diverse tissues. However, currently, it is challenging to infer the joint representations and learn relationships among multiple modalities from complex multimodal single-cell data. Herein, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint representations and cross-modal generation. scMM addresses the complexity of data by leveraging a mixture-of-experts multimodal variational autoencoder. The pseudocell generation strategy of scMM compensates for the limited interpretability of deep learning models and discovered multimodal regulatory programs associated with latent dimensions. Analysis of recently produced datasets validated that scMM facilitates high-resolution clustering with rich interpretability. Furthermore, we show that cross-modal generation by scMM leads to more precise prediction and data integration compared with the state-of-the-art and conventional approaches.

bioinformatics↗

bioRxiv2019

Model-based cell clustering and population tracking for time-series flow cytometry data

MotivationModern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on \"manual gating\" by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis.\n\nTherefore, there is a great need for tools to systematically analyze time-series flow cytometry data.\n\nResultsWe propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior.\n\nConclusionsOur results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.

systems biology↗

bioRxiv2018

Antibacterial effects of nanoimprinted moth-eye film in practical settings

Recent studies report that surfaces displaying micrometer-or nanometer-sized undulating structures exhibit antibacterial effects. In previous work, we described the use of an advanced nanofabrication technique to generate an artificial biomimetic moth-eye film by coating a polyethylene terephthalate (PET) film with nanoscale moth-eye protrusions made from a hydrophilic resin. This moth-eye film exhibited enhanced antibacterial effects in in vitro experiments. The aim of the present study was to verify the antibacterial efficacy of the Moth-eye film in practical environments. Three types of films (Moth-eye film, Flat film, and PET film) were used to compare antibacterial effects. Sample films were pasted onto hand-wash sinks at the testing locations. After several hours of elapsed time, bacteria from the surface of sample films were collected using one of three kinds of culture media stampers (to permit identification of bacterial species). The stampers were incubated for 48 hours at 35 {degrees}C, and the numbers of colonies were counted.\n\nThe number of common bacteria including E. coli and S. aureus from the Moth-eye film was significantly lower than that from the PET film (p<0.05) and that from the Flat film at 1 hour (p<0.05). This study found that the Moth-eye film had durability of antibacterial effect and the Moth-eye structure (PET coated with nanoscale cone-shaped pillars) had a physical antibacterial effect from the earlier time points. Therefore, the Moth-eye film might be useful for general-purpose applications in practical environments.

microbiology↗