Postdoctoral Fellow in Single-Cell and Spatial Bioinformatics and Quantitative Image Analysis

A postdoctoral position is available in the laboratory of Dr. Dana T. Graves at the University of Pennsylvania. The fellow will study inflammatory processes and how they impact the skin, mucosa, skeleton and periodontium in the context of diabetes, aging or other pathologic conditions. The position has two primary, complementary components: (1) leading bioinformatic studies using single-cell RNA sequencing (scRNA-seq) and 10x Genomics Xenium spatial transcriptomic datasets;

and (2) serving as project leader for a quantitative image-analysis study examining the spatial distribution and tissue organization of adhesion molecules in in vivo specimens. The fellow will work closely with investigators who conduct complementary experimental studies and will have substantial intellectual ownership of both areas of research. The goal is to identify mechanisms of disease and potential therapeutic targets.

 Research Focus  

Our research examines how diabetes changes cell signaling, differentiation, immune-stromal interactions, and tissue repair. Projects span several tissues, disease models, species, and experimental platforms. Component 1 - Single-cell and spatial genomics bioinformatics:
The fellow will lead bioinformatic studies using scRNA-seq and 10x Genomics Xenium spatial transcriptomic datasets to identify disease-associated cell states, transcriptional programs, and spatially organized cellular responses. Component 2 - Quantitative image analysis:
The fellow will serve as project leader for a study examining the spatial distribution, cellular localization, and tissue organization of adhesion molecules in in vivo specimens. This component will involve development and application of quantitative image-analysis approaches and interpretation of spatial relationships within tissues.

Across the bioinformatics component, the fellow will address spatial signaling networks, cell-cell communication, and changes in cell state over time. Projects may include regulatory network inference, pseudotime analysis, and machine-learning approaches when these methods are scientifically appropriate. Where scientifically informative, results from the bioinformatics and image-analysis components may be integrated to relate molecular and cellular states to adhesion-molecule distribution.

Key Responsibilities  

- Lead bioinformatic studies using scRNA-seq and 10x Genomics Xenium spatial transcriptomic datasets.

- Serve as project leader for quantitative image analysis of in vivo specimens to characterize the spatial distribution, cellular localization, and tissue organization of adhesion molecules.

- Develop clear, reproducible computational workflows.

- Perform quality control, data integration, cell annotation, and differential expression analysis.

- Conduct pathway, trajectory, state-transition, and ligand-receptor analyses.

- Integrate multiomic, cross-species, and cross-cohort datasets.

- Integrate transcriptomic data with imaging, histologic, and phenotypic measurements.

- Create clear figures and communicate results to computational and experimental collaborators.

- Help define analytical strategy and interpret biological findings.

- Present results and prepare first-author manuscripts.

- Contribute to grant development and collaborative studies.

 Computational Environment  

For the single-cell and spatial genomics bioinformatics component, a major focus will be analysis of 10x Genomics Xenium spatial transcriptomic and scRNA-seq datasets. The primary environment uses R, Seurat, and related tools. The fellow may use other validated methods when they improve the analysis. The image-analysis component will use appropriate quantitative imaging and spatial-analysis tools selected according to the specimens, imaging modalities, and scientific questions.

Research Environment and Career Development  

The Graves laboratory combines computational discovery with in vivo models, human specimens, histology, flow cytometry, immunofluorescence, and in vitro validation. Relevant experimental systems include genetically engineered mouse models, diabetic and aging models, primary mouse and human cell cultures, and molecular perturbation…