Proposal

1 Research question

Alcohol-associated liver disease (ALD) disrupts normal immune and metabolic processes in the liver. In this project, we analyze single-cell transcriptomic data to investigate how pathway activity shifts across cell types, examining evidence of immune suppression and metabolic reprogramming in immune cells.

2 Dataset(s)

ALD Dataset

  • Source: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE236382&utm_source (ALD Data)
  • Sample size: n = 5
  • Key variables: Single-cell gene expression counts per cell; disease status (ALD); cell barcode and sample of origin
  • License/usage notes: Public Access (NCBI)

Control Dataset

  • Source: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115469 (Control Data)
  • Sample size: n = 5
  • Key variables: Singe-cell gene expression counts per cell
  • License/usage notes: Public Access (NCBI)

3 Planned analysis/Methods

The pipeline below shows the full planned/executed workflow from raw data to final figures:

Project Workflow Pipeline

Cleaning / QC: Cells were filtered by minimum gene count (≥200), maximum gene count (≤6,000), and mitochondrial content (≤20%) to remove empty droplets, doublets, and damaged cells. Highly variable genes were identified and data were log-normalized.

Integration: Because the two datasets originated from different studies, Seurat integration was applied to remove batch effects before joint clustering (Hao et al. 2021).

Visualizations: UMAP plots were used to visualize cell clusters. Dot plots and feature plots were used to confirm cell-type annotations based on canonical marker genes.

Statistical/ML approach: Differential expression was performed within each annotated cell type using a Wilcoxon rank-sum test (adjusted p < 0.05, |log2FC| > 0.25). GSEA was performed using the fgsea package with MSigDB Hallmark gene sets, ranked by log2 fold-change (Subramanian et al. 2005; Liberzon et al. 2015).

Validation: Results were validated by cross-referencing marker gene patterns with published human liver single-cell atlases (MacParland et al. 2018; Aizarani et al. 2019). Cell-type proportion results were checked for consistency with known ALD immune phenotypes from the literature (Ju and Mandrekar 2015; Krenkel and Tacke 2017).

4 Deliverables

  • Website sections
  • Key figures/tables
  • Any optional stretch goals

5 References

Aizarani, N., A. Saviano, Sagar, et al. 2019. “A Human Liver Cell Atlas Reveals Heterogeneity and Epithelial Progenitors.” Nature 572: 199–204. https://doi.org/10.1038/s41586-019-1373-2.
Hao, Y., S. Hao, E. Andersen-Nissen, et al. 2021. “Integrated Analysis of Multimodal Single-Cell Data.” Cell 184 (13): 3573–87. https://doi.org/10.1016/j.cell.2021.04.048.
Ju, C., and P. Mandrekar. 2015. “Macrophages and Alcohol-Related Liver Inflammation.” Alcohol Research: Current Reviews 37 (2): 251–62.
Krenkel, O., and F. Tacke. 2017. “Liver Macrophages in Tissue Homeostasis and Disease.” Nature Reviews Immunology 17 (5): 306–21. https://doi.org/10.1038/nri.2017.11.
Liberzon, A., C. Birger, H. Thorvaldsdóttir, M. Ghandi, J. P. Mesirov, and P. Tamayo. 2015. “The Molecular Signatures Database Hallmark Gene Set Collection.” Cell Systems 1 (6): 417–25. https://doi.org/10.1016/j.cels.2015.12.004.
MacParland, S. A., J. C. Liu, X. Z. Ma, et al. 2018. “Single Cell RNA Sequencing of Human Liver Reveals Distinct Intrahepatic Macrophage Populations.” Nature Communications 9: 4383. https://doi.org/10.1038/s41467-018-06318-7.
Subramanian, A., P. Tamayo, V. K. Mootha, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.