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

University of Pennsylvania; United States; On-site; USD 52,000 - 70,000; Full time

Job summary

University of Pennsylvania invites applications for a postdoctoral fellow to study inflammatory processes related to diabetes, aging, and tissue health. The role combines leading scRNA-seq and Xenium bioinformatics with a project-led image-analysis study of adhesion molecules in vivo.

The fellow will work across computational and experimental teams, gaining substantial intellectual ownership, and contributing to translational research in a collaborative Penn environment.

Qualifications

• PhD/MD/DMD/DVM or equivalent doctoral degree in a relevant field.
• Experience bioinformatic analysis of scRNA-seq or spatial transcriptomic data, with leadership potential for the single-cell/Xenium 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.

Responsibilities

• Lead bioinformatic studies using scRNA-seq and Xenium datasets.
• Serve as project leader for quantitative image analysis of adhesion molecules in vivo.
• Develop and apply analytical approaches, interpret spatial relationships within tissues.
• Present findings and write first-author papers; collaborate with experimental colleagues.

Skills

Bioinformatics leadership

R programming

Single-cell analysis workflows

Interpretation of biological data

Independent collaboration

Scientific writing

Education

PhD/MD/DMD/DVM or equivalent

Tools

Seurat

R packages

Job description

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.

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 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.

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, 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 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, particularly experience suitable for independently leading an image-analysis project.
• In vitro or in vivo validation experiments.
• 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.

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, [e-mail]

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.