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

This postdoctoral position in the laboratory of Dr. Dana T. Graves at the University of Pennsylvania focuses on studying inflammatory processes and their impact on the skin, mucosa, skeleton, and periodontium in the context of diabetes, aging, or other pathologic conditions.

About the Role

The fellow will engage in two primary, complementary research components:

• Leading bioinformatic studies using single-cell RNA sequencing (scRNA-seq) and 10x Genomics Xenium spatial transcriptomic datasets.
• 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 conducting complementary experimental studies and will have substantial intellectual ownership of both research areas. The goal is to identify mechanisms of disease and potential therapeutic targets.

Research Focus

Our research examines how diabetes alters cell signaling, differentiation, immune-stromal interactions, and tissue repair. Projects span multiple 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 involves developing and applying quantitative image-analysis approaches and interpreting 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 where scientifically appropriate. Results from bioinformatics and image-analysis components may be integrated to relate molecular and cellular states to adhesion-molecule distribution.

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, the primary focus will be on analyzing 10x Genomics Xenium spatial transcriptomic and scRNA-seq datasets using 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 based on 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 studies.

The fellow will have substantial intellectual ownership of both major components of the position, including leadership of scRNA-seq and Xenium bioinformatic studies and project leadership for the image-analysis study. This includes selecting analytical approaches, leading data analysis, presenting findings, and writing first-author papers. Dr. Graves will provide direct scientific mentoring and regular project guidance. The fellow will also work with collaborators and shared-resource specialists across the University of Pennsylvania.

Penn core facilities provide support in single-cell and spatial genomics, biostatistics, imaging, histology, and quantitative analysis. The position offers training at the interface of computational biology, genomics, diabetes, inflammation, tissue repair, mouse genetics, and translational research. The goal is to support scientific independence, strong publications, grant development, and preparation for an academic or industry career.

Requirements

• A PhD, MD, DMD, DVM, or equivalent doctoral degree in a relevant field such as bioinformatics, computational biology, genomics, biostatistics, systems biology, molecular or cell biology, immunology, bioengineering, diabetes biology, skeletal biology, computer science, statistics, data science, or a related discipline.
• Hands-on experience with bioinformatic analysis of scRNA-seq or spatial transcriptomic data, with experience applicable to leadership of the single-cell/Xenium bioinformatics component.
• Strong skills in R and modern single-cell analysis workflows.
• Ability to interpret results in a biological and disease context.
• Ability to work independently and collaborate across disciplines.
• Clear scientific writing and communication skills.

Preferred Qualifications

• Experience with spatial transcriptomics.
• Proficiency with Seurat and related R packages.
• Experience with multiomic, multi-species, or cross-cohort integration.
• Experience with trajectory or pseudotime analysis, cell-cell communication analysis, or regulatory network inference.
• Quantitative or spatial image analysis of immunofluorescence, histologic, or related in vivo imaging datasets, particularly experience suitable for independently leading an image-analysis project.
• Experience with in vitro or in vivo validation experiments.
• Experience integrating molecular data with imaging data.

Selected Publications

• Diabetes exacerbates destructive inflammation by activating the CD137L-CD137 axis. Journal of Clinical Investigation. PMID: 41379565.
• Single Cell Sequencing Identifies Distinct Cellular Alterations in Impaired Aged and Diabetic Wounds. Aging Cell. PMID: 41189300.
• Ko KI et al. NF-kappaB perturbation reveals unique immunomodulatory functions in Prx1-positive fibroblasts that promote development of atopic dermatitis. Science Translational Medicine. 2022. PMID: 35108061.

The principal investigator has grant support through 2031. The position is available immediately following interviews and reference review.