Biology subjects

Sasagawa, Y.

Publications and source records attributed to Sasagawa, Y..

7 recordsLinked to original sources

bioRxiv2026

Cycling persister clones with elevated NR2F1-mediated cholesterol biosynthesis cause chemotherapy resistance

While a subpopulation termed cycling persisters (CPs) that is characterized by sustained proliferation, even under chemotherapy drug exposure, contributes more directly to tumor relapse, the molecular basis for the emergence of CPs has been unclear. Here, we used the human tongue cancer organoid (TCO) library to continuously track the in vitro fate of individual cancer cell clones during and after chemotherapy exposure using time-lapse imaging. Among the heterogeneous clones, we identified CPs that formed larger clusters than the others, which barely grew and remained small (non-CPs). Using differences in cell cluster size as an indicator, thousands of CP and non-CP clones were directly sampled from 3D matrix organoid cultures and were analyzed. Notably, tumor-intrinsic interferon (IFN) signaling and hypoxic pathways were inactivated, whereas the NR2F1-mediated cholesterol biosynthesis pathway was distinctly activated in CPs compared to non-CPs. Indeed, inhibiting cholesterol biosynthesis with simvastatin significantly suppressed the appearance of CP clones, showing that elevated cholesterol biosynthesis is essential for the emergence of CPs. These findings suggest that clonal-level variations in the intensity of these signaling pathways determine the fate of individual tumor cells exposed to chemotherapeutic agents, which may provide insights into cancer relapse mechanisms and identify potential molecular targets of CPs.

cancer biology↗

bioRxiv2025

Elucidation of the mechanism of byproduct DNA synthesis in whole-transcript amplification by poly-A tagging and development of its specific suppression

Poly-A tagging is a whole-transcript amplification method that uses terminal transferase to add poly-A to single-stranded cDNA in a non-template-dependent manner, and is used in highly sensitive single-cell RNA-seq. However, this method has long been reported to amplify byproduct DNA derived from reverse-transcription primers, particularly under conditions of low RNA abundance, leading to experimental complexity and reduced accuracy in quantifying gene expression. In this study, we elucidated the mechanism by which byproduct DNA is amplified at the molecular level and developed a new strategy of suppressing it named Suppression Tagging (SupTag). SupTag makes use of TdT, which has extremely low extension efficiency at the 3' recessive end, to stabilize dA/dT base pairs and inhibit poly-A extension of oligo-dT primers, thereby preventing the formation of precursor DNA for byproduct DNA. We demonstrated that SupTag can specifically suppress byproduct DNA using chemicals such as tetramethylammonium chloride (TMAC), modified nucleotides (e.g., 2F-dATP), and Super T-modified reverse-transcription primers. SupTag improves usability by streamlining purification processes and prevents reductions in sequence read output and quantitative sensitivity caused by byproduct DNA. Additionally, SupTag demonstrated the ability to suppress byproduct DNA amplification without using suppression PCR and is compatible with cDNA amplification introducing different adapter sequences at both ends. This study presents a novel suppression method based on the mechanism of amplification of byproduct DNA in the poly-A tagging method, providing a practical technology enabling high-precision transcriptomic analysis from low-abundance RNA.

molecular biology↗

bioRxiv2025

MitoDelta: identifying mitochondrial DNA deletions at cell-type resolution from single-cell RNA sequencing data

BackgroundDeletion variants in mitochondrial DNA (mtDNA) are associated with various diseases, such as mitochondrial disorders and neurodegenerative diseases. Traditionally, mtDNA deletions have been studied using bulk DNA sequencing, but bulk methods average signals across cells, thereby masking the cell-type-specific mutational landscapes. Resolving mtDNA deletions at single-cell resolution is beneficial for understanding how these mutations affect distinct cell populations. To date, no specialized method exists for detecting cell-type-specific mtDNA deletions from single-cell RNA sequencing data. Notably, mtDNA possesses unique molecular features: a high copy number, stable transcription, and compact structure of the mitochondrial genome. This results in a relatively high abundance of mtDNA-derived reads even in single-cell RNA sequencing data, suggesting the possibility of detecting mtDNA deletion variants directly from transcriptomic data. ResultsHere, we present MitoDelta, a computational pipeline that enables the detection of mtDNA deletions at cell-type resolution solely from single-cell RNA sequencing data. MitoDelta combines a sensitive alignment strategy with robust statistical filtering based on a beta-binomial model, allowing accurate identification of deletion events even from noisy single-cell transcriptomes. To capture cell-type-specific deletion patterns, MitoDelta analyzes reads pooled by annotated cell types, enabling quantification of deletion burden across distinct cellular populations. We benchmarked MitoDelta against existing mtDNA deletion detection tools and demonstrated superior overall performance. As a practical application, we applied MitoDelta to a published single-nucleus RNA sequencing dataset for Parkinsons disease and revealed distinct mtDNA deletion burdens across neuronal subtypes. ConclusionsMitoDelta enables the transcriptome-integrated, cell-type-specific detection of mtDNA deletions from single-cell RNA sequencing data alone, offering a valuable framework for reanalyzing public datasets and studying mitochondrial genome alterations at cell-type resolution. This integrated approach enables insights into how mtDNA deletions are distributed across specific cell types and cellular states, providing new opportunities to investigate the role of mtDNA deletions in cell-type-specific disease mechanisms. The tool is available at https://github.com/NikaidoLaboratory/mitodelta.

bioinformatics↗

bioRxiv2022

Distinctiveness and continuity in transcriptome and connectivity in the anterior-posterior axis of the paraventricular nucleus of thalamus

The paraventricular nucleus of the thalamus (PVT) projects axons to multiple areas and mediates a wide range of behaviors. Heterogeneity of functions and axonal projections in PVT have been reported, but what cell types exist in PVT and how different they are have not been addressed. We applied single-cell RNA sequencing to depict transcriptomic characteristics of mouse PVT neurons. The transcriptome of PVT neurons had a continuous distribution with the largest variance corresponding to the anterior-posterior axis. Although the single-cell transcriptome classified PVT neurons into four types, transcriptomic and histological analyses showed their continuity. Similarly, anterior and posterior subpopulations had nearly non-overlapping axon projection patterns, while another population showed intermediate patterns. In addition, they responded differently to appetite-related neuropeptides, and their chemogenetic activation showed opposing effects in food consumption. Our studies showed contrasts and continuity of PVT neurons underlying their function as a behavior-modulating hub.

neuroscience↗

bioRxiv2019

The local state of chromatin compaction at transcription start sites controls transcription levels

A defined amount of transcript is produced from transcription start sites (TSSs) of each gene, suggesting that the binding frequency of RNA polymerase varies among genes. However, what structure in chromatin controls this frequency remains elusive. We established a method to fractionate chromatin according to its degree of three-dimensional compaction. Nucleosomes were evenly detected through all of the fractions, but histone H1 was more highly enriched in the more compact chromatin fractions. Similarly, HP1 and MBD2b were more abundant in more compact chromatin, while the levels of tri-methylated H3 (Lys9) and 5-methyl cytosine subtly increased. Via genome-wide analyses, nearly the entire genome was found to exist in compact chromatin without variations between repeat and non-repeat sequences; however, active TSSs were rarely found in compact chromatin. Based on a correlation between weak compaction and RNA polymerase binding at TSSs, it appears that local states of chromatin compaction determine transcription levels.

molecular biology↗

bioRxiv2019

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects

Single-cell RNA sequencing (scRNA-seq) is the leading technique for charting the molecular properties of individual cells. The latest methods are scalable to thousands of cells, enabling in-depth characterization of sample composition without prior knowledge. However, there are important differences between scRNA-seq techniques, and it remains unclear which are the most suitable protocols for drawing cell atlases of tissues, organs and organisms. We have generated benchmark datasets to systematically evaluate techniques in terms of their power to comprehensively describe cell types and states. We performed a multi-center study comparing 13 commonly used single-cell and single-nucleus RNA-seq protocols using a highly heterogeneous reference sample resource. Comparative and integrative analysis at cell type and state level revealed marked differences in protocol performance, highlighting a series of key features for cell atlas projects. These should be considered when defining guidelines and standards for international consortia, such as the Human Cell Atlas project.

genomics↗

bioRxiv2017

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts (at a rate of 30%-50%) and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes in total from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.

genomics↗