QasimHussain/Human_Immune_Multiomics_Atlas

Human Immune Multi-Omics Atlas: Decoding the Epigenomic-Transcriptomic Manifold at Single-Cell Resolution

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Abstract

To decode the functional state of human immune cells, quantifying transcriptomic output is no longer sufficient without interrogating the underlying epigenomic architecture that dictates regulatory potential. This repository presents the Human Immune Multi-Omics Atlas, a comprehensive, production-grade analytical pipeline that seamlessly integrates single-cell RNA (scRNA-seq) and chromatin accessibility (scATAC-seq) data. By utilizing joint Weighted Nearest Neighbor (WNN) graph embeddings, rigorous cross-modal validation, and pseudotemporal trajectory inference, this resource provides computational biologists with a unified framework to map the chromatin-to-transcript regulatory circuitry governing peripheral immune cell fate and function. The source dataset captures approximately 10,000 peripheral blood mononuclear cells (PBMCs) from a healthy human donor, assayed via the 10x Genomics Chromium Single Cell Multiome platform.

Introduction

Before the human immune system can effectively defend the host, its entire functional architecture must be rigorously educated and regulated by complex genomic and epigenomic interactions. Characterizing the high-dimensional phenotypic states of circulating leukocytes requires transitioning beyond unimodal analyses. Single-cell multi-omics technologies enable the simultaneous measurement of chromatin accessibility and gene expression from the same nucleus, providing an unprecedented opportunity to observe how distal cis-regulatory elements directly dictate transcriptomic output. This atlas serves as a computational infrastructure to dissect these relationships within human PBMCs, establishing a benchmark for exploring the regulatory mechanisms mapping naive immune pools towards terminally differentiated effector states.

Materials and Methods

Data Acquisition and Preprocessing

Raw sequencing data was generated using the 10x Genomics Chromium Next GEM Single Cell Multiome ATAC + Gene Expression v1.0 chemistry. The starting matrices comprize gene expression counts and ATAC fragment intervals mapped to the GRCh38 (hg38) reference genome. Initial quality control filters remove doublets, dead cells, and low-complexity libraries, ensuring only high-fidelity nuclei proceed to integration. Modalities are partitioned and managed via MuData representations for computational efficiency prior to Seurat integration.

Weighted Nearest Neighbor Integration

To resolve the transcriptomic-epigenomic axis, independent linear and non-linear dimensional reductions are performed. The transcriptomic assay undergoes Principal Component Analysis (PCA), while the epigenomic assay is subjected to Latent Semantic Indexing (LSI). These modalities are subsequently integrated utilizing a Weighted Nearest Neighbor (WNN) algorithm, which dynamically learns cell-specific modality weights. This composite graph informs all downstream clustering and pseudo-dimensional projections, including three-dimensional UMAP topologies.

Cross-Modal Concordance and Trajectory Inference

Robust biological inference demands that the detected RNA accurately reflects the underlying chromatin architecture. Independent Gene Activity scores are mathematically deduced from the scATAC-seq signal by aggregating transposase insertions across gene bodies and promoters. Subsequent density-based Pearson concordance analyses validate the integration. Cellular differentiation pathways are modeled via Monocle3, implementing continuous branching trajectories over the multi-omics manifold to order cells by geodesic pseudotime sequence.

Results

The pipeline execution yields a multi-phase dissection of the immune landscape:

1. The Three-Dimensional Multi-Omics Manifold: Projection of the WNN graph into three-dimensional coordinate space mathematically recovers intricate spatial boundaries and branching trajectories between myeloid and lymphoid compartments, overcoming the artificial compression inherent in two-dimensional schemas.
2. Deep Immunophenotyping: Dual-assay differential expression and differential accessibility modeling establishes simultaneous evidence for cell identity, mapping canonical meta-signatures across T cells, B cells, Monocytes, and Natural Killer populations.
3. Integrated Regulatory Cross-Talk: Genome-wide density profiles highlight the rigorous mathematical concordance between direct RNA transcript frequency and inferred chromatin gene activity.
4. Differentiation Trajectories: Continuous pseudotemporal mapping captures the developmental transitions of naive immune reservoirs maturing into effector and memory phenotypes.
5. Epigenomic Complexity Clustering: Extraction of three-dimensional coordinate centroids enables the direct macro-mapping of metabolic properties, proliferative indexing, and library complexities across the distinct cell types.

Discussion

The establishment of the Human Immune Multi-Omics Atlas delivers a highly robust computational methodology for single-cell multi-omic profiling. By providing curated, multi-omics profiles of the immune ecosystem—hardened against computational instability via rigorous error handling and index-verification architectures—this pipeline is structured for researchers building advanced inference models of host-microbe immunity, autoimmune dysregulation, and cellular fate.

Data Availability and Execution

Repository Structure

To ensure deterministic reproduction while adhering to standard version control size limitations, massive binary datasets (>100MB) are strategically ignored via .gitignore, preserving only the reproducible architectural scaffold:

Human_Immune_Multiomics_Atlas/
├── data/
│   ├── raw/                          # Raw inputs (e.g., filtered_feature_bc_matrix.h5)
│   ├── interim/                      # Computable intermediate binaries (e.g., pbmc_multiome.h5mu)
│   └── curated/                      # Final structured outputs
├── notebooks/
│   ├── Human_Immune_Multiomics_Atlas.Rmd    # Main analysis R Markdown logic
│   └── Human_Immune_Multiomics_Atlas.ipynb  # Primary compiled Jupyter Notebook
├── scripts/
│   ├── 01_fetch_data.py              # Download module for original 10x data
│   ├── 02_build_mudata.py            # H5 to MuData extraction processing
│   ├── 03_wnn_integration.R          # Base mathematical Seurat WNN calculations
│   └── 04_visualize_wnn.R            # Standalone visual rendering scripts
└── README.md                         # Protocol manuscript (this document)

Deployment Protocol

This pipeline has been hardened specifically for cloud execution on platforms such as Kaggle.

1. Mount the associated dataset containing the primary Multiome_WNN_Seurat.rds artifact.
2. Load notebooks/Human_Immune_Multiomics_Atlas.ipynb into the computational environment.
3. Execute the kernel to sequentially reconstruct the manifold, trajectories, and cross-modal statistical arrays.

References

1. Hao, Y., et al. (2024). Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology.
2. Stuart, T., et al. (2021). Single-cell chromatin state analysis with Signac. Nature Methods.
3. Cao, J., et al. (2019). The single-cell transcriptional landscape of mammalian organogenesis. Nature.
4. 10x Genomics. (2021). PBMC from a Healthy Donor -- Granulocytes Removed Through Cell Sorting (10k).