Single-cell transcriptomics of the aged human hippocampus in Alzheimer’s disease and resilience

The Salta group studies the cellular and molecular mechanisms underlying brain resilience and neurodegeneration, with a particular focus on adult hippocampal neurogenesis and Alzheimer’s disease. Our research combines different omics, iPSC cultures, imaging, and integrative computational approaches to understand how the brain responds to aging and disease, and whether these mechanisms can be harnessed to improve cognitive function.

Project Description

The student will join an ongoing project investigating cellular and molecular signatures of the aged human hippocampus in the context of Alzheimer’s disease and resilience, building on our recent single-nucleus RNA sequencing work (Tosoni, Giorgia et al. “Transcriptional profiles of immature neurons in aged human hippocampus track Alzheimer’s pathology and cognitive resilience.” Cell stem cell vol. 33,5 (2026): 763-783.e9. doi:10.1016/j.stem.2026.04.002).

The project is primarily computational and centered on the analysis of single-nucleus RNA-seq data. The student will work closely with a bioinformatician and participate in discussions on analysis design, data interpretation, and scientific literature.

Activities

The student will:

• Analyze single-nucleus RNA-seq data
• Perform statistical analyses
• Contribute to the interpretation of biological findings
• Read and discuss relevant scientific literature
• Attend and participate in lab meetings and journal clubs
• Present project progress and findings

The student will develop and pursue an independent research question within the project, taking ownership of a specific aspect of the study from data analysis to interpretation and presentation of results.

Requirements

We are looking for a highly motivated student with a background in neuroscience, bioinformatics, computational biology, or a related field.

Required:

• Hands-on experience with bulk RNA-seq analysis
• Proficiency in R programming (preferred); familiarity with bash or Python is a plus
• Understanding of applied statistics and data visualization
• Appreciated but not strictly required:
• Prior experience with single-cell/single-nucleus RNA-seq (e.g., Seurat, Bioconductor, scanpy)

The student should be available full-time for at least 5 months. Longer periods are appreciated. We understand that some students may need to combine the internship with other work to support themselves, and we will take this into account when planning the project and timeline.

Conditions This internship is unpaid and does not include financial support from the institute.