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Computational Spatial Transcriptomics Jobs in Pennsylvania

Computational Spatial Transcriptomics information

What are some typical challenges faced when working in computational spatial transcriptomics, and how can new team members prepare for them?

Professionals in computational spatial transcriptomics often encounter challenges related to handling and analyzing large, complex datasets that combine spatial and gene expression information. Integrating data from different technologies and ensuring data quality can be demanding, requiring strong programming skills and familiarity with bioinformatics pipelines. New team members can prepare by strengthening their skills in statistical analysis, programming languages like Python or R, and staying updated on the latest spatial transcriptomics techniques. Collaborating closely with experimental biologists and data scientists is also key to overcoming these challenges and driving successful research outcomes.

What is the difference between Computational Spatial Transcriptomics vs Computational Biologist?

AspectComputational Spatial TranscriptomicsComputational Biologist
Required CredentialsAdvanced degrees in bioinformatics, computational biology, or related fields; experience with spatial data analysisTypically a PhD or Master's in biology, bioinformatics, or related disciplines; strong programming skills
Work EnvironmentResearch labs, biotech companies, academic institutions focusing on spatial genomicsResearch institutions, biotech firms, academia working on biological data analysis
Industry UsageSpecialized in spatial transcriptomics techniques and data interpretationBroad biological data analysis across various fields

Computational Spatial Transcriptomics focuses on analyzing spatial gene expression data within tissues, requiring specialized skills in spatial data processing. In contrast, Computational Biologists work on a wider range of biological data types. While both roles involve bioinformatics expertise, the former emphasizes spatial data analysis techniques specific to transcriptomics.

What is computational spatial transcriptomics?

Computational spatial transcriptomics is a field that combines advanced computational methods with spatial transcriptomics, a technique that measures gene expression within the physical context of tissue samples. It involves processing and analyzing large datasets to map where specific genes are active within tissues, helping researchers understand how cells interact and function in their native environments. This approach is crucial for studies in developmental biology, cancer research, and neuroscience, as it provides insights into cellular organization and tissue architecture. Computational tools help extract meaningful patterns from complex data, enabling discoveries that were previously impossible with traditional methods.

What are the key skills and qualifications needed to thrive as a computational spatial transcriptomics scientist, and why are they important?

To excel in Computational Spatial Transcriptomics, you need a strong background in bioinformatics, genomics, and statistical data analysis, typically supported by advanced degrees in computational biology or related fields. Familiarity with programming languages (such as R and Python), spatial transcriptomics platforms (like 10x Genomics Visium), and high-throughput sequencing data analysis tools is essential. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting complex datasets and collaborating with multidisciplinary teams. These competencies ensure accurate data interpretation, innovative research, and successful integration of spatial transcriptomics insights into biological and clinical applications.
What are popular job titles related to Computational Spatial Transcriptomics jobs in Pennsylvania? For Computational Spatial Transcriptomics jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Computational Spatial Transcriptomics jobs in Pennsylvania look for? The top searched job categories for Computational Spatial Transcriptomics jobs in Pennsylvania are:

Postdoctoral Position in Skeletal Regeneration, Diabetes, and Spatial Biology

University of Pennsylvania

Penn, PA • On-site

$43K - $59K/yr

Full-time

Posted 15 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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