Biology subjects

Amarasinghe, S. L.

Publications and source records attributed to Amarasinghe, S. L..

6 recordsLinked to original sources

bioRxiv2024

Long-lived chondroprogenitors are generated by fetal-limb Gli1+ cells and are replenished upon mosaic cell-cycle arrest in the cartilage

The growth-plate cartilage of the developing long bones is a well-known system of spatially segregated stem/progenitor, transient amplifying and terminally differentiated cells. However, the regulation of the number and activity of long-lived cartilage progenitors (LLCPs) is poorly understood, despite its relevance for understanding human-height variation, the evolution of limb size and proportions and the aetiology of skeletal growth disorders. Moreover, whether their behaviour can adapt to developmental perturbations, generating robustness, has not been explored. Here, we show that Gli1+ cells are the fetal precursors of postnatal LLCPs. Moreover, while during normal growth they remain mostly dormant until postnatal stages, in response to cartilage-targeted cell-cycle arrest they increase their cartilage contribution, compensating for the perturbation. We further show that, during the response to challenge, some of the reparative Gli1+ cells originate from Pdgfra+ cells outside the cartilage anlage. Elucidating how stromal cells become Gli1+ LLCPs could shed light on developmental robustness and lead to growth-boosting therapies.

developmental biology↗

bioRxiv2023

scPipe: An extended preprocessing pipeline for comprehensive single-cell ATAC-Seq data integration in R/Bioconductor

scPipe is a flexible R/Bioconductor package originally developed to analyse platform-independent single-cell RNA-Seq data. To expand its preprocessing capability to accommodate new single-cell technologies, we further developed scPipe to handle single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data. After executing multiple data cleaning steps to remove duplicated reads, low abundance features and cells of poor quality, a SingleCellExperiment object is created that contains a sparse count matrix with features of interest in the rows and cells in the columns. Quality control information (e.g. counts per cell, features per cell, total number of fragments, fraction of fragments per peak) and any relevant feature annotations are stored as metadata. We demonstrate that scPipe can efficiently identify "true" cells and provides flexibility for the user to fine-tune the quality control thresholds using various feature and cell-based metrics collected during data preprocessing. Researchers can then take advantage of various downstream single-cell tools available in Bioconductor for further analysis of scATAC-Seq data such as dimensionality reduction, clustering, motif enrichment, differential accessibility and cis-regulatory network analysis. The scPipe package enables a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.

bioinformatics↗

bioRxiv2022

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures

The current lack of benchmark datasets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs ("sequins"). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that, StringTie2 and bambu outperformed other tools from the 6 isoform detection tools tested, DESeq2, edgeR and limma-voom were best amongst the 5 differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the 5 tools compared, which suggests further methods development is needed for this application.

bioinformatics↗

bioRxiv2022

Arginine-rich C9ORF72 ALS proteins stall ribosomes in a manner distinct from a canonical ribosome-associated quality control substrate

Hexanucleotide expansion mutations in C9ORF72 are a cause of familial amyotrophic lateral sclerosis. We previously reported that long arginine-rich dipeptide repeats (DPR), mimicking abnormal proteins expressed from the hexanucleotide expansion, caused translation stalling when expressed in cell culture models. Whether this stalling provides a mechanism of pathogenicity remains to be determined. Here we explored the molecular features of DPR-induced stalling and examined whether known regulatory mechanisms of ribosome quality control (RQC) are involved to sense and resolve the stalls. We demonstrate that arginine-containing DPRs lead to stalling in a length dependent manner, with lengths longer than 40 repeats invoking severe translation arrest. Mutational screening of 40xGly-Xxx DPRs shows that stalling is most pronounced where Xxx are positively charged amino acids (Arg or Lys). Through a genome-wide knockout screen we find that genes regulating stalling on polyadenosine mRNA coding for poly-Lys, a canonical RQC substrate, respond differently to the readthrough of arginine-rich DPRs. Indeed, we find evidence that DPR-mediated stalling has no natural regulatory responses even though the stalls may be sensed, as evidenced by an upregulation of RQC gene expression. These findings therefore implicate arginine-rich DPR-mediated stalled ribosomes as posing a particular danger to cellular health and viability.

biochemistry↗

bioRxiv2020

Comprehensive characterization of single cell full-length isoforms in human and mouse with long-read sequencing

Alternative splicing shapes the phenotype of cells in development and disease. Long-read RNA-sequencing recovers full-length transcripts but has limited throughput at the single-cell level. Here we developed single-cell full-length transcript sequencing by sampling (FLT-seq), together with the computational pipeline FLAMES to overcome these issues and perform isoform discovery and quantification, splicing analysis and mutation detection in single cells. With FLT-seq and FLAMES, we performed the first comprehensive characterization of the full-length isoform landscape in single cells of different types and species and identified thousands of unannotated isoforms. We found conserved functional modules that were enriched for alternative transcript usage in different cell populations, including ribosome biogenesis and mRNA splicing. Analysis at the transcript-level allowed data integration with scATAC-seq on individual promoters, improved correlation with protein expression data and linked mutations known to confer drug resistance to transcriptome heterogeneity. Our methods reveal previously unseen isoform complexity and provide a better framework for multi-omics data integration.

genomics↗

bioRxiv2020

AtCIPK16 Mediates Salt Stress Potentially Through Phytohormones and Transcription Factors

Soil salinity causes large productivity losses for agriculture worldwide. "Next-generation crops" that can tolerate salt stress are required for the sustainability of global food production. Previous research in Arabidopsis thaliana aimed at uncovering novel factors underpinning improved plant salinity tolerance identified the protein kinase AtCIPK16. Overexpression of AtCIPK16 enhanced shoot Na+ exclusion and increased biomass in both Arabidopsis and barley. Here, a comparative transcriptomic study on Arabidopsis lines expressing AtCIPK16 was conducted in the presence and absence of salt stress, using an RNA-Seq approach, complemented by AtCIPK16 interaction and localisation studies. We are now able to provide evidence for AtCIPK16 activity in the nucleus. Moreover, the results manifest the involvement of a transcription factor, AtTZF1, phytohormones and the ability to quickly reach homeostasis as components important for improving salinity tolerance in transgenics overexpressing AtCIPK16. Furthermore, we suggest the possibility of both biotic and abiotic tolerance through AtCIPK16, and propose a model for the salt tolerance pathway elicited through AtCIPK16.

plant biology↗