Postdoctoral Fellow in Single-Cell and Spatial Bioinformatics

Postdoctoral Fellow in Single-Cell and Spatial Bioinformatics

Location:
University of Pennsylvania, Penn Dental Medicine
Open Date:
Aug 6, 2026
Description:
The Opportunity

A postdoctoral position is available in the laboratory of Dr. Dana T. Graves at the University of Pennsylvania. The fellow will investigate inflammatory mechanisms affecting the skin, mucosa, skeleton, and periodontium in the context of diabetes, aging, and other pathologic conditions. The position comprises two primary, complementary components: (1) leading bioinformatic analyses of single-cell RNA sequencing (scRNA-seq) and 10x Genomics Xenium spatial transcriptomic datasets; and (2) validation through techniques such as multiplex immunofluorescence and quantitative image-analysis to establish spatial distribution and molecular, cellular and tissue organization of in vivo specimens. The goal is to define mechanisms of disease and identify potential therapeutic targets.

Research Focus

Our research investigates how diabetes and other factors alter cell signaling, differentiation, and immune-stromal interactions. Projects span multiple tissues, disease models, species, and experimental platforms. Component 1 - Single-cell and spatial genomics bioinformatics: The fellow will lead bioinformatic analyses of scRNA-seq and 10x Genomics Xenium spatial transcriptomic datasets to identify disease-associated cell states, transcriptional programs, and spatially organized cellular responses. Component 2 – Other Opportunities. The fellow will have opportunities to develop additional laboratory skills, including orthogonal validation approaches such as immunofluorescence and quantitative image analysis. These activities will focus on characterizing spatial distribution, molecular and cellular localization, and tissue organization in vivo specimens. Across the bioinformatics component, the fellow will investigate spatial signaling networks, cell-cell communication, and temporal changes in cell state. Projects may include regulatory network inference, pseudotime analysis, and machine-learning approaches when scientifically appropriate. When informative, findings from the bioinformatics and image-analysis components may be integrated to relate molecular and cellular states to adhesion-molecule distribution.

Key Responsibilities

• Lead bioinformatic analyses of 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 rigorous, reproducible computational workflows.

• Perform quality control, data integration, cell annotation, and differential expression analyses.

• 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.

• Generate clear figures and communicate findings to computational and experimental collaborators.

• Contribute to analytical strategy and biological interpretation.

• Present findings 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 computational environment uses R, Seurat, and related tools. Other validated methods may be used when they improve analytical rigor or interpretation. The image-analysis component will use quantitative imaging and spatial-analysis tools appropriate to the specimens, imaging modalities, and scientific questions.

Research Environment and Career Development

The Graves laboratory integrates computational discovery with in vivo models, human specimens, histology, flow cytometry, immunofluorescence, and in vitro validation. Experimental systems include genetically engineered mouse models, models of diabetes and aging, primary mouse and human cell cultures, and molecular perturbation studies. The fellow will have substantial intellectual ownership of both components, including leadership of scRNA-seq and Xenium bioinformatic analyses and project leadership for the image-analysis study. Responsibilities include selecting analytical approaches, leading analysis, interpreting and presenting findings, preparing first-author manuscripts and participation in grant writing. The development of independent fellowship grants is also encouraged. Dr. Graves will provide direct scientific mentoring and regular project guidance. The fellow will also collaborate with investigators and shared-resource specialists across the University of Pennsylvania. Penn core facilities support 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.

Qualifications:
Required Qualifications

• A PhD, MD, DMD, DVM, or equivalent doctoral degree in a relevant field.

• Hands-on experience with bioinformatic analysis of scRNA-seq or spatial transcriptomic data, sufficient to lead the single-cell/Xenium bioinformatics component.

• Strong proficiency in R or Python and modern single-cell analysis workflows.

• Ability to interpret analytical results in biological and disease contexts.

• Ability to work independently and collaborate effectively across disciplines.

• Strong scientific writing and communication skills.

Relevant fields include 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.

Preferred Qualifications Include Experience in the Following Areas:

• Spatial transcriptomics.

• Seurat and related R packages.

• Multiomic, multi-species, or cross-cohort integration.

• 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, with experience sufficient to independently lead an image-analysis project.

• In vitro or in vivo validation experiments.

• Integration of molecular and 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. 

Application Instructions:
Funding: The principal investigator has grant support through 2031.

Start date: Available immediately following interviews and reference review.

Application materials: Submit a curriculum vitae, a brief statement describing research experience and future interests, and the names and contact information of three references.

Contact: Jen East, jeneast@upenn.edu

Equal Employment Opportunity Statement:

The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.