Biology subjects

Wanchai, V.

Publications and source records attributed to Wanchai, V..

7 recordsLinked to original sources

bioRxiv2026

A transcriptomic-driven segmentation and cell simulation framework for high-resolution spatial transcriptomics and cell-cell communication

The Visium HD spatial transcriptomics platform enables transcriptome-wide profiling at near-single-cell resolution. However, accurate segmentation of cells to define spatial boundaries relies heavily on histological images. Previous approaches struggle to define cells when the tissues have high cell density, are inflamed, or are mineralized, leading to transcriptomic bleed-through and inaccurate clustering. To address this, we developed TENGU (Transcript-signal Enrichment and Grouping Unit), a comprehensive end-to-end bioinformatic software package. Unlike existing tools, TENGU employs a transcript-first segmentation approach, prioritizing transcript-signal density as the primary modality and utilizing histological images only as a secondary supplement in unresolved regions. These initial boundaries are further optimized through a novel transcriptomic-driven cell simulation algorithm. Iterative refinement of boundaries based on localized gene expression probabilities effectively minimizes spatial scattering and preserves biologically distinct molecular signatures. The pipeline seamlessly integrates tissue segmentation, high-resolution cell-type annotation, and basic spatially aware cell-cell communication (CCC) analysis. We rigorously benchmarked TENGU against the 10X Genomics and Bin2cell pipelines for cell segmentation across diverse and technically challenging microenvironments. TENGU demonstrated superior transcriptomic distinctness in the murine brain, successfully captured matrix-embedded osteocytes, and localized critical osteoimmune CCC networks (Tgfb and Il1a) in a murine model of osteomyelitis. TENGU also resolved species-specific, pro-tumorigenic signaling hubs (MDK-SDC4) within a highly compacted human colorectal cancer xenograft. By mitigating the constraints of traditional image-dependent segmentation, TENGU provides a highly adaptable and robust computational framework that empowers researchers to accurately decode the complex functional micro-anatomy of both healthy and pathological tissues.

bioinformatics↗

bioRxiv2025

An Enhancement of Extrachromosomal Circular DNA Enrichment and Amplification to Address the Extreme Low Overlap Between Replicates

Extrachromosomal circular DNA (eccDNA) of chromosomal origin is present in all eukaryotic organisms and tissues that have been tested. Populations of eccDNA exhibit immense diversity and a characteristically low degree of overlap between samples, suggesting low inheritance of eccDNA between cells or a deficiency in the methods by which eccDNA is detected. This study revisits the Circle-seq approach for enrichment of eccDNA to address these limitations, hypothesizing that experimental procedures significantly contribute to the observed low eccDNA overlap. We optimized the protocol by reducing the time needed to complete the procedure. Linear DNA is digested by increasing Exonuclease V activity. We employed CRISPR-Cas9 for mitochondrial linearization, which proved superior to using restriction enzymes. A key finding is the critical role of random hexamer primer concentration and genomic DNA input in Rolling circle amplification (RCA) for generating high-quality long amplicons from eccDNA (concatemeric tandem copy [CTC]), essential for confident de novo eccDNA construction from long-read sequencing data. Lower primer concentrations substantially increased the percentage of CTC-derived eccDNA and improved the overlap of identified eccDNAs in technical replicates. Applying this revised approach to human myeloma and breast cancer cell lines, as well as xenograft models, demonstrated >50% overlap in detected eccDNA, a substantial improvement over the <1% overlap observed in previous studies. Additionally, the oncogenic signature of eccDNAs can be identified across all replicates. These findings provide guidelines for developing standardized procedures for eccDNA profiling, advancing our understanding of eccDNA biology, and its potential clinical applications.

genomics↗

bioRxiv2023

A framework for defining mesenchymal cell types associated with murine periosteal and endosteal bone

Single-cell RNA sequencing has led to numerous novel designations for mesenchymal cell types associated with bone. Consequently, there are now multiple designations for what appear to be the same cell type. In addition, existing datasets contain relatively small numbers of mature osteoblasts and osteocytes and there has been no comparison of periosteal bone cells to those at the endosteum and trabecular bone. The main goals of this study were to increase the amount of single cell RNA sequence data for osteoblasts and osteocytes, to compare cells from the periosteum to those inside bone, and to clarify the major categories of cell types associated with murine bone. To do this, we created an atlas of murine bone-associated cells by harmonizing published datasets with in-house data from cells targeted by Osx1-Cre and Dmp1-Cre driver strains. Cells from periosteal bone were analyzed separately from those isolated from the endosteum and trabecular bone. Over 100,000 mesenchymal cells were mapped to reveal 11 major clusters designated fibro-1, fibro-2, chondrocytes, articular chondrocytes, tenocytes, adipo-CAR, osteo-CAR, pre-osteoblasts, osteoblasts, osteocytes, and osteo-X, the latter defined in part by Postn expression. Osteo-X, osteo-CAR, and pre-osteoblasts were closely associated with osteoblasts at the trabecular bone surface. Wnt16 was expressed in multiple cell types from the periosteum but not in any cells from endocortical or cancellous bone. Fibro-2 cells, which express markers of skeletal stem cells, localized to the periosteum but not trabecular bone in adult mice. Suppressing bone remodeling eliminated osteoblasts and altered gene expression in pre-osteoblasts but did not change the abundance or location of osteo-X or osteo-CAR cells. These results provide a framework for identifying bone cell types in murine single cell RNA sequencing datasets and suggest that osteoblast progenitors reside near the surface of remodeling bone. Author SummaryThe skeleton of vertebrate animals is produced by bone forming cells known as osteoblasts. New osteoblasts are continually produced throughout life to maintain the skeleton. However, the identity of the cells from which new osteoblasts are derived is not clear. Single cell RNA sequencing provides a gene expression profile of individual cells and allows them to be grouped into clusters of cells with similar profiles. In many cases, distinct clusters represent distinct cell types. Recent single cell RNA sequencing studies of mouse bones by several different laboratories have identified many novel cell types, some of which may be osteoblast progenitors. In many cases, different laboratories have created different names for what appear to be the same cell type. To help clarify this situation, we performed single cell RNA sequencing studies and combined our results with those from several published studies to create a harmonized map of the cell types associated with mouse bones. We also identified gene signatures for each cell type that can be used to improve the consistency of cell type designation in future single cell RNA sequencing studies of mouse bone.

genomics↗

bioRxiv2022

Accurate Identification of Extrachromosomal Circular DNA from Long-read Sequences.

Extrachromosomal circular DNA (eccDNA) of chromosomal origin is found in a range of eukaryotic species and cell type including cancer where eccDNA with oncogenes appear to drive tumorigenesis. Most studies of eccDNA employ short-read sequencing to identify for their identification. However, short-read sequencing cannot resolve the complexity of genomic repeats, which can lead to missing eccDNA identification. An alternative is the long-read sequencing technologies that can potentially be used to construct complete eccDNA. We present a software suite, Construction-based Rolling-circle amplification for eccDNA Sequence Identification and Location (CReSIL) 2.0, to identify and characterize eccDNA from long-read sequences. CReSILs performance in the identification of eccDNA, with a minimum F1 score of 0.98, is superior to the other bioinformatic tools based on simulated data. CReSIL provides many useful features for genomic annotation, which can used to infer eccDNA function and Circos visualization for eccDNA architecture investigation. We demonstrated CReSILs capability in many of the long-read sequencing datasets. This includes datasets enriched for eccDNA as well as whole genome datasets from many cells that contained large eccDNA. CReSIL suite software will be a versatile tool to deeply investigate eccDNA in biological samples.

bioinformatics↗

bioRxiv2019

What can we learn from over 100,000 Escherichia coli genomes?

The explosion of microbial genome sequences in public databases allows for large-scale population genomic studies of bacterial species, such as Escherichia coli. In this study, we examine and classify more than one hundred thousand E. coli and Shigella genomes. After removing outliers, a semi-automated Mash-based analysis of 10,667 assembled genomes reveals 14 distinct phylogroups. A representative genome or medoid identified for each phylogroup serves as a proxy to classify more than 95,000 unassembled genomes. This analysis shows that most sequenced E. coli genomes belong to 4 phylogroups (A, C, B1 and E2(O157)). Authenticity of the 14 phylogroups described is supported by pangenomic and phylogenetic analyses, which show differences in gene preservation between phylogroups. A phylogenetic tree constructed with 2,613 single copy core genes along with a matrix of phylogenetic profiles is used to confirm that the 14 phylogroups change at different rates of gene gain/loss/duplication. The methodology used in this work is able to identify previously uncharacterized phylogroups in E. coli species. Some of these new phylogroups harbor clonal strains that have undergone a process of genomic adaptation to the acquisition of new genomic elements related to virulence or antibiotic resistance. This is, to our knowledge, the largest E. coli genome dataset analyzed to date and provides valuable insights into the population structure of the species.

genomics↗

bioRxiv2018

Decoding the Epitranscriptional Landscape from Native RNA Sequences

Sequencing of native RNA and corresponding cDNA was performed using Oxford Nanopore Technology. The % Error of Specific Bases (%ESB) was higher for native RNA than for cDNA, which enabled detection of ribonucleotide modification sites. Based on %ESB differences of the two templates, a bioinformatic tool ELIGOS was developed and applied to rRNAs of E. coli, yeast and human cells. ELIGOS captured 91%, 95%, [~]75%, respectively, of the known variety of RNA methylation sites in these rRNAs. Yeast transcriptomes from different growth conditions were also compared, which identified an association between metabolic adaptation and inferred RNA modifications. ELIGOS was further applied to human transcriptome datasets, which identified the well-known DRACH motif containing N6-methyadenine being located close to 3-untranslated regions of mRNA. Moreover, the RNA G-quadruplex motif was uncovered by ELIGOS. In summary, we have developed an experimental method coupled with bioinformatic software to uncover native RNA modifications and secondary-structures within transcripts.

genetics↗

bioRxiv2017

dBBQs : dataBase of Bacterial Quality scores

BackgroundIt is well-known that genome sequencing technologies are becoming significantly cheaper and faster. As a result of this, the exponential growth in sequencing data in public databases allows us to explore ever growing large collections of genome sequences. However, it is less known that the majority of available sequenced genome sequences in public databases are not complete, drafts of varying qualities. We have calculated quality scores for around 100,000 bacterial genomes from all major genome repositories and put them in a fast and easy-to-use database.\n\nResultsProkaryotic genomic data from all sources were collected and combined to make a non-redundant set of bacterial genomes. The genome quality score for each was calculated by four different measurements: assembly quality, number of rRNA and tRNA genes, and the occurrence of conserved functional domains. The dataBase of Bacterial Quality scores (dBBQs) was designed to store and retrieve quality scores. It offers fast searching and download features which the result can be used for further analysis. In addition, the search results are shown in interactive JavaScript chart framework using DC.js. The analysis of quality scores across major public genome databases find that around 68% of the genomes are of acceptable quality for many uses.\n\nConclusionsdBBQs (available at http://arc-gem.uams.edu/dbbqs) provides genome quality scores for all available prokaryotic genome sequences with a user-friendly Web-interface. These scores can be used as cut-offs to get a high-quality set of genomes for testing bioinformatics tools or improving the analysis. Moreover, all data of the four measurements that were combined to make the quality score for each genome, which can potentially be used for further analysis. dBBQs will be updated regularly and is freely use for non-commercial purpose.

bioinformatics↗