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Spatial Transcriptomics Jobs in Pennsylvania (NOW HIRING)

Spatial Transcriptomics information

What is spatial transcriptomics?

Spatial transcriptomics is an advanced technique that allows scientists to measure gene expression within the spatial context of tissue samples. Unlike traditional RNA sequencing, which loses information about where each gene is expressed, spatial transcriptomics preserves the physical location of gene activity in tissues. This helps researchers better understand how cells function within their native environments and interact with neighboring cells, which is especially valuable in fields like cancer research, neuroscience, and developmental biology. The method combines microscopy, molecular biology, and computational analysis to produce detailed maps of gene expression.

What are some common challenges faced by professionals working in spatial transcriptomics, and how can they be addressed?

Professionals in spatial transcriptomics often encounter challenges related to handling large, complex datasets and integrating spatial information with gene expression data. Ensuring high-quality sample preparation and mastering advanced imaging or sequencing technologies are also frequent hurdles. These challenges can be addressed by collaborating closely with multidisciplinary teams—including bioinformaticians, molecular biologists, and imaging specialists—and staying up-to-date with the latest software tools and protocols. Continuous learning and effective communication within the team are key to overcoming technical and analytical obstacles in this rapidly evolving field.

What are the key skills and qualifications needed to thrive as a spatial transcriptomics scientist?

To thrive as a Spatial Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a life science field. Familiarity with spatial transcriptomics platforms (such as 10x Genomics Visium), next-generation sequencing (NGS) technologies, and data analysis tools like R or Python is essential. Strong problem-solving skills, attention to detail, and effective communication are important soft skills for collaborating on interdisciplinary research projects. These skills and qualities are crucial for generating high-quality spatial gene expression data and translating findings into meaningful biological insights.
What are popular job titles related to Spatial Transcriptomics jobs in Pennsylvania? For Spatial Transcriptomics jobs in Pennsylvania, the most frequently searched job titles are:
What cities in Pennsylvania are hiring for Spatial Transcriptomics jobs? Cities in Pennsylvania with the most Spatial Transcriptomics job openings:
Infographic showing various Spatial Transcriptomics job openings in Pennsylvania as of August 2026, with employment types broken down into 72% Full Time, 24% Part Time, 1% Temporary, and 3% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution.

Postdoctoral Position in Skeletal Regeneration, Diabetes, and Spatial Biology

University of Pennsylvania

Penn, PA • On-site

$43K - $59K/yr

Full-time

Posted 16 days ago


University Of Pennsylvania rating

8.1

Company rating: 8.1 out of 10

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Job description

Description
A postdoctoral position is available in the laboratory of Dr. Dana Graves at the University of Pennsylvania's School of Dental Medicine, Department of Periodontics, to investigate a newly identified mechanism through which diabetes impairs fracture healing and to develop a locally delivered therapeutic strategy to restore skeletal repair.
The project is supported by strong preliminary evidence demonstrating that lineage-specific deletion of FOXO1 in chondrocytes or osteoblasts reverses diabetes-impaired fracture healing. We have also found that disruption of primary cilia in these skeletal lineages reproduces defining features of defective repair in diabetes. Together, these findings identify and strongly support a previously unrecognized FOXO1-primary cilia signaling axis as an important regulator of skeletal regeneration under diabetic conditions.
The successful candidate will define how diabetes-induced FOXO1 activity alters ciliogenesis, cellular differentiation, and regenerative signaling in chondrocytes and osteoblasts. The studies will integrate conditional mouse models targeting FOXO1, IFT80, and combined FOXO1/IFT80 deletion with fracture-healing models of type 1 and type 2 diabetes to establish the cellular and molecular events that impair skeletal regeneration.
A major emphasis will be resolution of the fracture-healing microenvironment at spatial and single-cell resolution. Experimental approaches will include 10x Genomics Xenium spatial transcriptomics, single-cell RNA sequencing, computational analysis using R and Seurat, histology, immunofluorescence, semi-automated image analysis, and microcomputed tomography. These complementary methods will identify lineage-specific transcriptional programs, spatially organized signaling networks, and cellular interactions that distinguish effective from impaired skeletal repair and determine how these programs are altered by diabetes, FOXO1 activity, and loss of primary cilia.
The project also includes a translational component focused on a newly developed IGF-1 mimetic-containing nanofiber hydrogel designed for controlled local delivery at the fracture site. The candidate will examine its effects on inflammation and the sequential formation of immature mesenchymal tissue, cartilage, and bone, and determine whether the hydrogel restores cilia-dependent regenerative signaling, limits pathological FOXO1 activity, and improves structural and functional fracture healing in type 1 and type 2 diabetes. This work directly connects discovery of a previously unexplored regulatory pathway with preclinical evaluation of a locally delivered, mechanism-based therapy.
Professional Development and Research Environment
The fellow will be expected to take substantial intellectual ownership of the project, including development of experimental directions, leadership of spatial-transcriptomic and computational analyses, presentation of findings, and preparation of first-author manuscripts. The position provides multidisciplinary training at the interface of skeletal biology, diabetes, mouse genetics, spatial and single-cell genomics, computational biology, and translational biomaterials research. The fellow will receive direct scientific mentoring from Dr. Graves, regular project-based guidance, and opportunities to work with collaborators and shared-resource specialists across the University of Pennsylvania. Access to Penn core facilities and collaborative expertise will support spatial transcriptomics, single-cell genomics, imaging, histology, and quantitative analysis. Guided training in R, Seurat, and analysis of Xenium and single-cell datasets will be available to candidates who have strong experimental backgrounds but require additional computational experience. The research plan is designed to support intellectual independence, high-quality first-author publications, and development of a competitive platform for subsequent fellowship, faculty, or industry applications.
Qualifications
Applicants should hold a PhD, MD, DMD, DVM, or equivalent degree in skeletal biology, cell biology, molecular biology, bioengineering, diabetes biology, immunology, computational biology, or a related field.
Experience in one or more of the following areas is desirable: mouse genetics and disease models, bone or cartilage biology, fracture healing, spatial transcriptomics, single-cell RNA sequencing, computational analysis using R and Seurat, image analysis, molecular and cellular assays, histology, or microcomputed tomography. Candidates with strong experimental backgrounds who wish to develop expertise in osseous and regenerative biology, spatially resolved molecular analysis, and single-cell transcriptomics are encouraged to apply. Evidence of scientific rigor, clear communication, and the ability to work both independently and collaboratively will be important.
Application Instructions
Funding duration: The position if grant supported through 2028 and the PI has other grant support through 2031.
Anticipated start date: Available immediately following interviews and feedback from references.
Application materials: Please 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

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The University of Pennsylvania, the largest private employer in Philadelphia, is a world-renowned leader in education, research, and innovation. This historic, Ivy League school consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn's distinctive interdisciplinary approach to scholarship and learning. As an employer Penn has been ranked nationally on many occasions with the most recent award from Forbes who named Penn one of America's Best Employers By State in 2021.

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