Harshini Suresha

Single-Cell Transcriptomic Profiling of Mouse Fibroblast Heterogeneity in Scleroderma

Fibroblastic Single-Cell Transcriptomics

Year2026
LocationNCBS
Tags10x Genomics, Monocle3, CellChat, GSEA

10x Genomics scRNA-seq of wild and phenotype fibroblasts across ages: integration and batch correction, disease-state signatures, CNV inference, Monocle3 trajectories and CellChat / CellPhoneDB communication networks.

Jun 2026 – Sep 2026 at NCBS, Bangalore, supervised by Dr. Tanay Bhatt.

Workflow figureOpen full size ↗
Figure for Single-Cell Transcriptomic Profiling of Mouse Fibroblast Heterogeneity in Scleroderma
Methods
  1. 01
    Single-cell data

    10x Genomics scRNA-seq of wild-type and phenotype mouse fibroblasts across several age points.

    10x Genomics, Cell Ranger
  2. 02
    Integration & batch correction

    Integrated samples across timepoints and corrected batch effects to separate real fibroblast subpopulations from technical variation.

    Seurat, Batch correction
  3. 03
    Disease-associated states

    Characterised fibroblast states with marker genes and pathway enrichment, scoring ECM, collagen, TGF-β, inflammatory, interferon, senescence and myofibroblast signatures.

    GO, KEGG, Reactome, GSEA
  4. 04
    Cell cycle & CNV

    Cell-cycle scoring, copy-number variation inference and gene-set pathway activity to describe proliferative states and genomic variation.

    Cell-cycle scoring, CNV inference
  5. 05
    Trajectories

    Reconstructed fibroblast state transitions and pseudotime to follow how gene expression shifts along developmental trajectories.

    Monocle3, Pseudotime
  6. 06
    Cell–cell communication

    Mapped ligand–receptor interactions, signalling pathways, niches and transcription-factor regulons across age points.

    CellChat, CellPhoneDB, Cell2cell
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