3D Spatial Transcriptome of Primate Early Organogenesis - Analysis Code

This repository contains the bioinformatics analysis code supporting the manuscript "Reconstruction of a three-dimensional spatial transcriptome of primate early organogenesis". This study represents the first comprehensive 3D spatial transcriptomic atlas of cynomolgus monkey embryos at Carnegie Stages 9 and 10, capturing key events of early organogenesis.

Project Overview

Scientific Context

Early organogenesis is a critical phase of embryogenesis that establishes the foundation for organ development. This study utilized Stereo-seq spatial transcriptomics to create a comprehensive 3D molecular atlas of primate embryos during this crucial developmental window.

Key Findings

• 13 major tissue clusters identified across CS9 and CS10 embryos
• Tissue-specific subclustering revealing specialized cell populations in:
  • Heart tube development (5 subclusters)
  • Gut tube regionalization (5 subclusters)
  • Neural tissue organization (8 subclusters)
  • Somitogenesis processes (6 subclusters)
• Spatially-resolved gene regulatory networks via SCENIC analysis
• Cell-cell communication networks during organogenesis
• Developmental trajectory analysis capturing temporal transitions

Methodological Innovations

• 3D reconstruction using Spateo alignment of consecutive tissue sections
• Integration of spatial transcriptomics with published scRNA-seq datasets
• Spatial cell communication analysis using CellChat
• Multi-modal trajectory analysis combining RNA velocity and pseudotime

Repository Structure

├── clustering_annotation/          # Major cluster identification and annotation
├── integration_analysis/           # Spatial-scRNA integration and correlation
├── subclustering/                  # Tissue-specific subclustering analysis
├── gene_regulatory_networks/       # SCENIC transcription factor analysis
├── trajectory_analysis/            # Developmental trajectory inference
├── cell_communication/             # CellChat ligand-receptor analysis
├── comparative_analysis/           # CS9 vs CS10 temporal comparisons
└── functional_analysis/            # GO enrichment and pathway analysis

System Requirements

Software Dependencies

• R version: 4.3.1 or higher
• Key R packages:
  • Seurat (spatial and single-cell analysis)
  • Harmony (batch correction and integration)
  • CellChat (cell-cell communication)
  • SCENIC (gene regulatory networks)
  • Monocle3 (trajectory analysis)
  • velocyto.R (RNA velocity)
  • clusterProfiler (functional enrichment)

Computing Environment

• Memory: Minimum 64GB RAM recommended for large spatial datasets
• Storage: At least 100GB free space for intermediate files
• Environment: Conda environment with R 4.3.1 recommended

Data Requirements

Input Data Formats

• Spatial transcriptomics: Stereo-seq bin50 resolution data
• Reference datasets: Published scRNA-seq datasets for integration
• Metadata: Cell type annotations and developmental stage information

Usage Examples

Basic Workflow

1. Clustering and Annotation

   source("clustering_annotation/initial_clustering.R")

2. Integration Analysis

   source("integration_analysis/scrna_spatial_integration.R")

3. Tissue-Specific Analysis

   source("subclustering/heart_tube_subclustering.R")

Key Parameters

• Clustering resolution: 0.5-0.8 for major clusters, 0.3-0.6 for subclusters
• Integration method: Harmony for batch correction
• Trajectory analysis: RNA velocity + Monocle3 pseudotime

Analysis Overview

Clustering and Annotation

Identification of 13 major tissue clusters using UMAP dimensionality reduction and marker gene-based annotation with spatial validation.

Integration Analysis

Cross-modal integration of spatial transcriptomics with published scRNA-seq datasets using Harmony batch correction and correlation analysis.

Subclustering Analysis

Tissue-specific subclustering to identify specialized cell populations within major lineages, focusing on heart, gut, neural, and somite development.

Gene Regulatory Networks

SCENIC analysis to identify transcription factor regulons and regulatory networks governing tissue specification and development.

Trajectory Analysis

RNA velocity and pseudotime analysis to capture developmental transitions and cell fate decisions during organogenesis.

Cell Communication

CellChat analysis to map ligand-receptor interactions and signaling pathways coordinating tissue development.

Comparative Analysis

Temporal comparison between CS9 and CS10 to identify stage-specific gene expression changes and developmental progression.

Functional Analysis

Gene Ontology enrichment and pathway analysis to understand functional significance of identified gene signatures.

Citation

If you use this code in your research, please cite:

[Manuscript citation will be added upon publication]

Data Availability

The spatial transcriptomics datasets supporting this study are available at:

• [Data repository information will be added upon publication]

Contact

For questions about the analysis methods or code, please submit an issue on GitHub. This includes:

• Technical problems and bug reports
• Feature requests and suggestions
• Questions about implementation
• Scientific inquiries about the analysis methods

License

This code is provided under the MIT License.