Job Description

The Spraggins group develops integrated molecular imaging technologies to elucidate the molecular basis of health and disease. Modern instrumentation and computing capabilities have enabled researchers to move beyond reductionist biology and, instead, probe how the components of biological entities (e.g., molecules, cells, and tissues) interact globally to reveal the underlying biology of disease. This systems biology approach has been accelerated by advancements in high-throughput ‘omics’ technologies, however, genetic and molecular information are only part of the story. The challenge lies in understanding how these parts interact and how perturbations to the system relate to disease. To address this challenge, we are advancing instrumental capabilities and developing the computational tools necessary to integrate and mine multimodal data sets that bring together 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 consists of two research groups, including the Spraggins and Schey groups, and three cores that offer analytical services in proteomics, small molecules, and tissue imaging using mass spectrometry. The MSRC conducts collaborative research programs with investigators in nearly every Center and department in the Medical Center and Vanderbilt University as well as many trans-institutional initiatives. 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 a collection of commercial and custom software for the analysis of imaging, multi-omics, and microscopy data. 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).

Key Functions and Expected Performance:

Data Analysis and Method Development

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 acquired 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 generated by imaging and ‘omics assays and to inform experimental design.

Produce data visualizations and publication-quality figures for presentations, manuscripts, and grant applications.

Data and Software Management

Management of large imaging and multi-omics datasets, including organization, storage, backup, and metadata capture.

Execution of analysis workflows in high-performance and cloud computing environments.

Use of version control and reproducible research practices for all analysis code.

Preparation and submission of data and derived products to consortium data portals and public repositories in accordance with FAIR data standards.

Record-keeping and documentation of analytical protocols.

Collaborations and Education

Assist team members with implementing biocomputational, single-cell, and spatial transcriptomics workflows.

Train students, postdoctoral fellows, and staff on biocomputational tools and analysis methods.

Effective delivery of technical progress reports and presentations in written and oral form to research staff, faculty, and consortium working groups.

Communicate regularly, effectively, and professionally with the Principal Investigator, research team, and internal and external collaborators.

Supervisory Relationships:

This position does not have formal supervisory responsibility.

This position reports directly to Dr. Spraggins.

This position is expected to provide technical guidance and day-to-day project direction to students, interns, and junior staff.

Education and Certifications:

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.

Experience and Qualifications:

2 years of relevant research experience or the equivalent is required.

4 years of relevant research experience 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.

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 other 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.