Biology subjects

Pir, P.

Publications and source records attributed to Pir, P..

4 recordsLinked to original sources

bioRxiv2026

A transcriptional continuum from clinically normal to lesional skin: Single-cell trajectory analysis of psoriasis

Psoriasis is a chronic inflammatory skin disease characterized by immune dysregulation and complex cellular interactions involving keratinocytes (KCs), T cells, and endothelial cells. Single-cell studies have mapped the cellular diversity of psoriatic plaques, yet most analyses treat skin as either healthy or diseased and therefore overlook the graded changes that precede a visible lesion. We asked whether psoriasis instead progresses along a transcriptional continuum and whether clinically normal skin from patients already carries a measurable disease signature. Using single-cell RNA sequencing (scRNA-seq) of healthy skin (NS), clinically normal skin adjacent to lesions (PN), and lesional skin (PP), we combined two complementary designs: a complete set spanning all three states and a within-patient paired set of matched PN and PP skin biopsies that removes inter-individual variability. Trajectory analysis using a novel pseudotime approach reveals transcriptional changes within cell populations and dynamic cellular states underlying psoriatic pathology. To distinguish lesion-driven effects from disease-associated transcriptional changes, we applied a differential expression strategy comparing PP and PN psoriatic tissue with NS and integrating these contrasts. This approach enabled the identification of disease-specific gene signatures while minimizing lesion-specific bias. As a result, we identified a coherent, cell-type-resolved continuum of transcriptional states in which endothelial cells, IL20+ keratinocytes (KCs: IL20), and KGF+ keratinocytes (KCs: KGF) undergo trajectory-dependent state transitions. These trajectories converge on features including epidermal thickening, barrier dysfunction, and plaque maintenance. Endothelial cells transition from an inflamed, angiogenic state to a mature, barrier-stabilized phenotype, while KCs: IL20 and KCs: KGF shift from PN states toward hyperproliferative, stress-adapted lesional phenotypes. These findings reframe psoriasis as a dynamic, multicellular continuum and identify candidate temporal windows for early intervention.

bioinformatics↗

bioRxiv2026

Basophilic Erythroblast Emerges as the Key Turning Point in Polycythemia Vera

Abstract Polycythemia vera (PV) is a rare, chronic myeloproliferative neoplasm driven by the JAK2V617F mutation and characterized by uncontrolled erythroid proliferation. Although the mutation arises in hematopoietic stem cells, the differentiation stage at which its transcriptional consequences first become biologically meaningful has remained undefined. Using a multi-layer transcriptomics integration approach that combined differential gene expression, NicheNet ligand-receptor analysis, pseudotime trajectory inference, and CNV profiling on scRNA seq data, alongside bulk transcriptome validation, we identified basophilic erythroblasts as the critical transition point at which JAK2V617F shifts from a genomically present but transcriptionally silent state to an actively trajectory-altering and treatment-responsive disease driver. Differential expression revealed a qualitatively distinct disease signature at this stage, including ERFE-mediated iron dysregulation, MAP2K2-driven RAS/MAPK co-activation, and epigenetic reprogramming. NicheNet showed the establishment of a TGF{beta} superfamily and chemokine-driven niche-remodeling axis, and pseudotime analysis demonstrated that basophilic erythroblasts are the first erythroid population to exhibit condition-dependent trajectory divergence, whereas earlier progenitors showed none despite carrying the mutation. Interferon- treatment showed its broadest counterresponse at this stage but declined sharply thereafter, identifying basophilic erythroblasts as both the principal therapeutic target and the point of maximum vulnerability in PV.

bioinformatics↗

bioRxiv2020

Transcriptional response of signalling pathways to SARS-CoV-2 infection in normal human bronchial epithelial cells

SARS-CoV-2 virus, the pathogen that causes Covid-19 disease, emerged in Wuhan region in China in 2019, infected more than 4M people and is responsible for death of at least 300K patients globally as of May 2020. Identification of the cellular response mechanisms to viral infection by SARS-CoV-2 may shed light on progress of the disease, indicate potential drug targets, and make design of new test methods possible. In this study, we analysed transcriptomic response of normal human bronchial epithelial cells (NHBE) to SARS-CoV-2 infection and compared the response to H1N1 infection. Comparison of transcriptome of NHBE cells 24 hours after mock-infection and SARS-CoV-2 infection demonstrated that most genes that respond to infection were upregulated (320 genes) rather than being downregulated (115 genes).While upregulated genes were enriched in signalling pathways related to virus response, downregulated genes are related to kidney development. We mapped the upregulated genes on KEGG pathways to identify the mechanisms that mediate the response. We identified canonical NF{kappa}B, TNF and IL-17 pathways to be significantly upregulated and to converge to NF{kappa}B pathway via positive feedback loops. Although virus entry protein ACE2 has low expression in NHBE cells, pathogen response pathways are strongly activated within 24 hours of infection. Our results also indicate that immune response system is activated at the early stage of the infection and orchestrated by a crosstalk of signalling pathways. Finally, we compared transcriptomic SARS-CoV-2 response to H1N1 response in NHBE cells to elucidate the virus specificity of the response and virus specific extracellular proteins expressed by NHBE cells.

bioinformatics↗

bioRxiv2020

Comparison of visualisation tools for single-cell RNAseq data

In the last decade, single cell RNAseq (scRNAseq) datasets have grown from a single cell to millions of cells. Due to its high dimensionality, the scRNAseq data contains a lot of valuable information, however, it is not always feasible to visualise and share it in a scientific report or an article publication format. Recently, a lot of interactive analysis and visualisation tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review and compare several of the currently available analysis and visualisation tools and benchmark those that allow to visualize the scRNAseq data on the web and share it with others. To address the problem of format compatibility for most visualisation tools, we have also developed a user-friendly R package, sceasy, which allows users to convert their own scRNAseq datasets into a specific data format for visualisation.

bioinformatics↗