Biology subjects

Chau, T. N.

Publications and source records attributed to Chau, T. N..

5 recordsLinked to original sources

bioRxiv2026

A chromosome-scale reference genome and single-nucleus root atlas reveal the cellular and molecular basis of coastal adaptation in sand bean (Strophostyles helvola)

Plants adapted to saline coastal habitats provide valuable systems for understanding how natural selection reshapes stress-response programs, yet the cellular and molecular basis of this adaptation remains poorly resolved. Here, we generated a chromosome-scale reference genome and a single-nucleus transcriptomic atlas of roots from sand bean (Strophostyles helvola), a wild legume represented by salt-tolerant Beach and salt-sensitive Inland ecotypes. The atlas comprised 87,901 nuclei assigned to 25 transcriptional clusters representing 16 major root cell types. Salt exposure elicited cell-type-specific transcriptional responses that differed between Beach and Inland roots. Beach roots preferentially maintained respiration-, energy metabolism-, and protein-homeostasis-associated functions, whereas Inland roots showed stronger induction of canonical abiotic-stress, ABA, water-deficit, hypoxia, and oxidative-stress programs. Integrating baseline ecotype differences with salt-responsive expression revealed that many genes induced by salt in Inland roots were already expressed at higher levels in untreated Beach roots. This baseline-enriched configuration comprised a broadly distributed regulatory backbone, including ERF-family transcription factors and RING-type E3 ubiquitin ligases, together with cell-type-associated modules involving ion and water transport at the root-soil interface, redox and dehydration protection in outer-root tissues, endodermal barrier-associated genes, and vascular regulatory candidates. These findings support a model in which Beach salt tolerance is associated not with entirely distinct stress-response pathways, but with the pre-existing and spatially organized deployment of conserved protective programs that remain responsive to salt exposure. Our study establishes genomic and cellular resources for sand beans and provides a framework for investigating how natural variation in the regulation and cellular organization of conserved pathways contributes to environmental stress tolerance.

plant biology↗

bioRxiv2026

Single nucleus transcriptome analysis of Arabidopsis thaliana roots infected with Phytophthora. capsici

Understanding how plant roots coordinate immune responses at the cellular level is key to unraveling host-pathogen interactions. Using single-nucleus RNA sequencing (snRNA-seq) of Arabidopsis thaliana roots 24 hours after Phytophthora. capsici(P. capsici)inoculation, we captured the transcriptional landscape of early infection at single-cell resolution. Four libraries (two infected and two mock-treated) were generated with approximately 26,000 high-quality nuclei with consistent sequencing depth and viability. A reference-based pipeline distinguished host and pathogen transcripts, enabling species-resolved mapping and host-focused single-nucleus transcriptomic analysis. Integration and clustering identified 12 transcriptionally distinct root cell types, encompassing major tissues such as the meristem, cortex, endodermis, and vasculature. Cluster-specific marker analysis confirmed cell-type identities, while differential expression and Gene Ontology enrichment revealed a global transcriptional shift from metabolic and translational processes in mock samples to defense-, stress-, and pathogen-response pathways upon infection. Hormone-related enrichment indicated broad salicylic acid activation across root tissues, spatially confined ethylene signaling in vascular-associated clusters, and localized jasmonic acid responses in cortex and phloem. Together, these results provide a high-resolution view of Arabidopsis root immunity, highlighting a coordinated yet tissue-specific defense architecture in which salicylic acid underpins systemic protection, ethylene modulates vascular defense, and jasmonic acid contributes targeted reinforcement during early P. capsici infection.

bioinformatics↗

bioRxiv2025

PLM-OMG: Protein Language Model-Based Ortholog Detection for Cross-Species Cell Type Mapping

Understanding conserved and divergent cell types across plant species is essential for ad- vancing comparative genomics and improving crop traits. Accurate and scalable ortholog detection is central to this goal, particularly in cross-species single-cell analysis. However, conventional methods are time-consuming and perform poorly with distantly related species, limiting their effectiveness. To address these limitations, we introduce PLM-OMG, a protein language model-based framework for orthogroup classification and cross-species cell type mapping. We benchmark five deep learning models including ESM2, ProGen2, ProteinBERT, ProtGPT2, and LSTM, using a curated 15-species dataset and large-scale monocot and dicot datasets from PLAZA. Transformer-based models, particularly ProtGPT2 and ESM2, achieve superior accuracy and generalization across evolutionary distances. Our results show that PLM-OMG enables scalable and reusable orthogroup detection without recomputing existing groups, significantly reducing computational overhead and highlighting its potential to transform cross-species transcriptomic analysis in plant genomics.

bioinformatics↗

bioRxiv2025

scCoBench: Benchmarking single cell RNA-seq co-expression using promoter-reporter lines

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for uncovering transcriptomic heterogeneity and reconstructing gene regulatory networks in complex tissues. However, the sparsity, high noise levels, and dropout events inherent to scRNA-seq data pose challenges for accurate inference of gene-gene relationships. In this study scCoBench, we systematically benchmark correlation metrics, pseudo bulk analysis, and imputation methods using promoter-reporter and native gene pairs as internal controls to evaluate the performance of ten widely used gene-gene co-expression measurements. Interestingly, we found that commonly used data scaling and normalization approaches lead to lower correlation between promoter reporter and native gene pairs in most of the co-expression methods. Moreover, we assess the impact of five popular imputation techniques, including scImpute, SAVER, Autoencoder (AE), Variational Autoencoder (VAE), and Generative Adversarial Network (GAN) on recovering biologically relevant co-expression patterns. Our results demonstrate that imputation models not only markedly enhance correlation between each promoter-reporter and native gene pair but also increase the number of cells co-expressing both genes. Imputation also improved transcription factor target gene correlations and revealed stronger associations among genes within the same protein complex. This work highlights the utility of promoter-reporter systems for benchmarking computational methods and underscores the potential of deep learning-based imputation to improve the biologically relevant signals of scRNA-seq data.

bioinformatics↗

bioRxiv2025

Cell and nuclear size are associated with chromosomal instability and tumorigenicity in cancer cells that undergo whole genome doubling

Whole genome doubling (WGD) is a frequent event in cancer evolution associated with chromosomal instability, metastasis, and poor prognosis. While the genomic consequences of WGD are well documented, the effects of non-genetic alterations that accompany WGD, such as changes to cell and nuclear size, on tetraploid (4N) cancer cell physiology are less understood. Here, we show that cell and nuclear volume do not always scale with DNA content after WGD in cancer cells, resulting in 4N cells that differ in size. We find that small size is associated with enhanced cell fitness, mitotic fidelity, and tumorigenicity in 4N cancer cells and with poor patient survival in WGD-positive human cancers. Overall, these results suggest that cell and nuclear size contribute to the tumorigenic potential of 4N cancer cells and could be an important prognostic marker in human tumors that undergo WGD. Statement of SignificanceWe report that WGD generates tetraploid cancer cells that vary in size, with larger cells displaying high chromosomal instability and smaller cells exhibiting high fitness and tumorigenicity. Furthermore, WGD status and cancer cell nuclear size in human tumors correlated with patient survival, demonstrating the clinical relevance of this association.

cancer biology↗