Jubayer Hossain - Multiomics and Machine Learning https://mdjubayerhossain.com/blog/ Academic Website for Jubayer Hossain https://mdjubayerhossain.com/files/profiles/jubayer.jpg Jubayer Hossain - Multiomics and Machine Learning https://mdjubayerhossain.com/blog/ quarto-1.10.18 Thu, 19 Feb 2026 18:00:00 GMT Introduction to Single-Cell RNA Sequencing Jubayer Hossain https://mdjubayerhossain.com/blog/posts/intro-scrna-seq/

Why Single-Cell?

Bulk RNA sequencing measures gene expression averaged across thousands of cells, masking the diversity within complex tissues. Single-cell RNA sequencing (scRNA-seq) overcomes this limitation by profiling transcriptomes at individual-cell resolution, enabling the discovery of rare cell types, transitional states, and cell-type-specific responses to perturbation.

Key Steps in a Typical scRNA-seq Workflow

  1. Tissue dissociation & cell capture — Droplet-based platforms (10x Genomics Chromium) or plate-based methods (Smart-seq2/3).
  2. Library preparation & sequencing — cDNA synthesis, amplification, and Illumina sequencing.
  3. Raw data processing — Demultiplexing, alignment (STARsolo, Cell Ranger), and count matrix generation.
  4. Quality control — Filtering low-quality cells based on mitochondrial content, gene counts, and doublet detection.
  5. Downstream analysis — Normalization, dimensionality reduction (PCA, UMAP), clustering, differential expression, and trajectory inference.

Tools of the Trade

The scverse ecosystem — including Scanpy, AnnData, scvi-tools, and Muon — provides a comprehensive Python framework for single-cell analysis. R users often rely on Seurat and Bioconductor packages.

Looking Ahead

Stay tuned for deeper dives into specific applications: microglial profiling in neurodegeneration, spatial transcriptomics in the tumor microenvironment, and multi-modal single-cell approaches.

]]> Single-Cell scRNA-seq Methods https://mdjubayerhossain.com/blog/posts/intro-scrna-seq/ Thu, 19 Feb 2026 18:00:00 GMT Microglial Activation States in Alzheimer’s Disease Jubayer Hossain https://mdjubayerhossain.com/blog/posts/microglial-states-alzheimers/

Microglia: The Brain’s Immune Sentinels

Microglia are the resident macrophages of the central nervous system, constantly surveying the brain parenchyma for damage and pathogens. In Alzheimer’s disease (AD), microglia adopt a range of activation states that can be both neuroprotective and neurotoxic.

Disease-Associated Microglia (DAM)

Single-cell RNA-seq studies in AD mouse models and human post-mortem tissue have identified a conserved transcriptional program termed disease-associated microglia (DAM). Key features include:

  • Downregulation of homeostatic genes (P2ry12, Cx3cr1, Tmem119)
  • Upregulation of lipid metabolism and phagocytosis genes (Apoe, Lpl, Trem2)
  • A TREM2-dependent transition from stage 1 to stage 2 DAM

Beyond the DAM Binary

Recent high-resolution atlases have expanded the picture beyond a simple homeostatic-vs-DAM dichotomy, revealing:

  • Interferon-responsive microglia enriched near amyloid plaques
  • Proliferating microglia associated with disease progression
  • Lipid-droplet-accumulating microglia (LDAM) linked to aging

Implications for Therapeutics

Understanding microglial heterogeneity is critical for designing targeted therapies. TREM2 agonists, for example, aim to enhance protective DAM responses while minimizing pro-inflammatory signaling.

Further Reading

  • Keren-Shaul et al. (2017). Cell. DOI: 10.1016/j.cell.2017.05.018
  • Mathys et al. (2019). Nature. DOI: 10.1038/s41586-019-1195-2
]]> Neurogenomics Microglia Alzheimer's Disease Single-Cell https://mdjubayerhossain.com/blog/posts/microglial-states-alzheimers/ Sat, 14 Feb 2026 18:00:00 GMT Spatial Transcriptomics in Cancer Research Jubayer Hossain https://mdjubayerhossain.com/blog/posts/spatial-transcriptomics-cancer/

Why Space Matters

Standard scRNA-seq dissociates tissues, losing spatial context. Spatial transcriptomics technologies preserve the physical location of gene expression, revealing how cells communicate within their native tissue architecture.

Technology Landscape

Platform Resolution Approach
10x Visium ~55 µm spots Capture-based
MERFISH Subcellular Imaging-based
Slide-seq ~10 µm beads Capture-based
CODEX Single-cell (protein) Imaging-based

Applications in Cancer

  • Tumor-immune interface mapping — Identifying spatial patterns of immune infiltration and exclusion
  • Tertiary lymphoid structures — Characterizing organized immune aggregates associated with immunotherapy response
  • Invasion fronts — Transcriptional programs at the leading edge of tumor expansion
  • Cell-cell communication — Ligand-receptor analysis with spatial constraints

Computational Challenges

Spatial data require specialized tools: Squidpy for spatial statistics, cell2location and DestVI for deconvolution, and NICHE-NET for spatially-aware cell communication inference.

Outlook

As spatial technologies achieve higher resolution and multiplexing, they will become essential for understanding treatment resistance and designing combination therapies.

]]> Spatial Transcriptomics Cancer Genomics Methods https://mdjubayerhossain.com/blog/posts/spatial-transcriptomics-cancer/ Mon, 09 Feb 2026 18:00:00 GMT Single-Cell Dissection of the Tumor Microenvironment Jubayer Hossain https://mdjubayerhossain.com/blog/posts/scrna-tumor-microenvironment/

The Tumor Microenvironment (TME)

Tumors are not just masses of malignant cells — they are complex ecosystems comprising cancer cells, immune cells, fibroblasts, endothelial cells, and extracellular matrix. The composition and state of this tumor microenvironment (TME) profoundly influences disease progression and treatment response.

What scRNA-seq Reveals

Single-cell profiling of the TME has uncovered:

Immune Compartment

  • CD8+ T cell exhaustion gradients — Progressive loss of effector function marked by TOX, LAG3, and PDCD1 expression
  • Regulatory T cells (Tregs) — Immunosuppressive populations enriched in the tumor core
  • Tumor-associated macrophages (TAMs) — Polarization along a spectrum from M1-like (anti-tumor) to M2-like (pro-tumor)

Stromal Compartment

  • Cancer-associated fibroblasts (CAFs) — Multiple subtypes with distinct roles in immune exclusion, matrix remodeling, and angiogenesis
  • Endothelial heterogeneity — Tumor vasculature with abnormal transcriptional programs

Clinical Implications

Understanding TME composition at single-cell resolution enables:

  • Biomarker discovery for immunotherapy response prediction
  • Rational combination therapy design targeting multiple TME components
  • Patient stratification based on immune cell states rather than bulk signatures

Key Resources

  • Comprehensive human tumor atlases (e.g., HTAN, Tabula Sapiens)
  • Pan-cancer single-cell studies providing cross-tumor-type insights
]]> Cancer Genomics Single-Cell Tumor Microenvironment https://mdjubayerhossain.com/blog/posts/scrna-tumor-microenvironment/ Wed, 04 Feb 2026 18:00:00 GMT