Research Specialist, Senior

About the Role

The Spraggins group develops integrated molecular imaging technologies to elucidate the molecular basis of health and disease. Modern instrumentation and computing capabilities enable researchers to probe how biological components (e.g., molecules, cells, and tissues) interact globally to reveal underlying disease biology. This systems biology approach is accelerated by high-throughput ‘omics’ technologies, but understanding how these parts interact and how perturbations relate to disease remains a challenge. The group advances instrumental capabilities and develops computational tools to integrate and mine multimodal datasets combining imaging mass spectrometry, highly multiplexed immunofluorescence microscopy, and spatial transcriptomics.

The Spraggins laboratory is part of the Mass Spectrometry Research Center (MSRC) and Department of Cell & Developmental Biology at Vanderbilt University. The MSRC includes two research groups and three cores offering analytical services in proteomics, small molecules, and tissue imaging. Team members have access to state-of-the-art instrumentation, including 6 imaging mass spectrometers, a CODEX highly multiplexed immunofluorescence platform, 2 fluorescence slide scanners, a laser capture microdissection system, a Xenium in situ platform, and a GeoMx spatial transcriptomics instrument, as well as commercial and custom software for imaging, multi-omics, and microscopy data analysis.

The group’s research is embedded in large national consortia, including the Human BioMolecular Atlas Program (HuBMAP), the Kidney Precision Medicine Project (KPMP), and the Human Tumor Atlas Network (HTAN).

Responsibilities

• Develop, validate, document, and maintain computational pipelines for multimodal biomedical imaging data, including preprocessing, quality control, normalization, image registration, cell segmentation, and feature extraction.
• Analyze single-cell and spatial transcriptomics data, including clustering, marker-based cell type annotation, neighborhood enrichment, and cell–cell interaction analysis.
• Integrate imaging mass spectrometry, multiplexed immunofluorescence microscopy, and spatial transcriptomics data from the same or serial tissue sections into common coordinate frameworks.
• Apply statistical and machine learning approaches to identify molecular and spatial features associated with disease state, progression, or treatment response.
• Design and execute analyses independently, selecting appropriate methods and evaluating model performance and robustness to confounding variables.
• Work closely with team members to interpret data and inform experimental design.
• Produce data visualizations and publication-quality figures for presentations, manuscripts, and grant applications.
• Manage large imaging and multi-omics datasets, including organization, storage, backup, and metadata capture.
• Execute analysis workflows in high-performance and cloud computing environments.
• Use version control and reproducible research practices for all analysis code.
• Prepare and submit data and derived products to consortium data portals and public repositories in accordance with FAIR data standards.
• Assist team members with implementing biocomputational, single-cell, and spatial transcriptomics workflows.
• Train students, postdoctoral fellows, and staff on biocomputational tools and analysis methods.
• Deliver technical progress reports and presentations to research staff, faculty, and consortium working groups.
• Communicate regularly and professionally with the Principal Investigator, research team, and internal and external collaborators.
• Provide technical guidance and day-to-day project direction to students, interns, and junior staff.

Requirements

• Bachelor’s degree in a biological, physical, computational, or engineering discipline is required.
• Master’s degree or higher in bioinformatics, computational biology, biomedical engineering, data science, or a related field is preferred.
• 2 years of relevant research experience or the equivalent is required; 4 years is preferred.
• Proficiency in Python and/or R for scientific data analysis is required.
• Demonstrated track record of independently executing complex computational analyses of biological data is required.

Skills

• Experience with single-cell and/or spatial transcriptomics analysis (e.g., Scanpy, Seurat, Squidpy, scimap) is preferred.
• Experience with biomedical image analysis and cell segmentation (e.g., QuPath, Napari, StarDist, Mesmer, scikit-image, OpenCV) is preferred.
• Experience with whole-slide image handling and cross-modality image registration is preferred.
• Experience with machine learning and deep learning frameworks (e.g., scikit-learn, PyTorch) is preferred.
• Practical knowledge of Linux commands, shell scripting, and high-performance computing schedulers (e.g., Bash, SLURM) is preferred.
• Practical knowledge of version control and collaborative software development (e.g., Git and GitHub) is preferred.
• Experience integrating multimodal or multi-omic biomedical datasets is preferred.
• Experience working within a multi-institutional research consortium or large collaborative research program is preferred.
• Record of scientific communication through publications, preprints, posters, or conference presentations is preferred.
• Prior experience mentoring or training students, interns, or junior staff is preferred.
• Strong organizational skills and the ability to manage multiple concurrent projects and deadlines.
• Ability to work independently and take ownership of analytical projects from design through publication.
• Ability to learn and assist in the development of new methods, protocols, and technologies in a rapidly evolving field.
• Strong written and oral scientific communication skills, including the ability to communicate technical results to interdisciplinary audiences.
• Ability to work collaboratively as part of a large, multidisciplinary team.