Cross-Species Single-Cell RNA-Seq Integration for Neuroscience Target Discovery

1. Scientific Context & Challenge

An academic neuroscience group had generated a large multi-sample single-cell RNA-sequencing (scRNA-seq) dataset across experimental models, but faced an impending grant deadline without internal bioinformatics capacity to run rigorous doublet/ambient-RNA QC, cross-species batch integration, and translational target prioritization.

2. Methodological Approach

We engineered a containerized, fully reproducible Seurat/Scanpy pipeline handling sample-level QC, ambient RNA decontamination, and cross-species canonical correlation / Harmony integration. Cell-type and disease-state clusters were annotated against validated reference atlases, followed by differential abundance, receptor-ligand communication, and pathway perturbation scoring to rank druggable targets.

3. Solution Architecture

Integrated computational biology and PhD scientific writing — transforming raw sequencing matrices into defensible biological figures, methods sections, and target-validation narratives.

4. Verified Outcome

Handed over the complete version-controlled analysis repository, interactive cell-state embeddings, prioritized candidate target tables, and publication-quality multi-panel figures ahead of the grant submission deadline, establishing the quantitative foundation for both the grant application and subsequent manuscript.

Engagement Specifications

Timeline / Date

8–10 weeks

Scientific Team

2 PhD Scientists (Bioinformatics + Medical Writing)

Scope Classification

End-to-end scRNA-seq analysis, cross-species integration & grant/manuscript figure architecture

Capabilities Applied