Computational master internships on single cell data analysis

Currently, there are three computational master internships on single cell data analysis is available in the Basak lab and collaborators.

Internship projects

Project 1: Integration of single cell transcriptomic atlases of the adult mammalian midbrain

Background: Recent advances in single cell genomics technology allows determination of the molecular make-up of adult mammalian brain at unprecedented resolution. Several studies have profiled the adult mouse midbrain and whole-brain atlases; These extensive atlases reach up to 10 million combined total cells/nuclei as well additional cells from spatial atlases. The Basak lab is creating a detailed molecular atlas of the ventral tegmental area (VTA) in the adult mouse and human brains, aiming to use it for various studies, including gene therapy. However, a gap in the field exists concerning the comparison of cell type annotations from different studies, necessitating data harmonization, integration, and hierarchical comparison, as demonstrated in a recent hypothalamus study.

Aim and objectives: In this project, we aim to integrate published molecular atlases of the adult mouse and human single cell atlases midbrain to form a unifying atlas, midMap. The student will use harmonized datasets, which we have put together, to determine which cell types from different studies correspond to each other, which are unique, and which are in fact a collection of smaller cell types from another study. In parallel, she/he will perform a midbrain-focused cross species comparison including the rat and macaque single cell datasets. Finally, we will integrate the publicly available spatial transcriptomics data with the midMap.

Techniques to be employed: Bash/shell script on hpc, R/python based single cell analysis tools such as Seurat, scanpy, scArches and TACTiCS

Project 2: Several project on single cell analysis of human hippocampal neurogenesis (t/w the Salta lab, NIN)

Tosoni et al, 2023, Neuron
Tosoni et al, 2023, Neuron

Background: The existence of adult hippocampal neurogenesis (AHN) in the human brain and its putative contribution to the Alzheimer’s disease (AD) susceptibility or resilience have been under intense debate over the last years. In this ongoing project, the Salta lab employ single-nucleus RNA sequencing (snRNA-seq) technology in the human adult hippocampal neurogenic niche to profile cell type-specific molecular signatures of AHN and their changes during AD pathology and in resilient brains.

Aim and objectives: Multiple projects are available. For more info you can contact us. Briefly:

1) Integration across datasets: Your contribution to our project will involve the integration of our in-house dataset of the Salta lab with recently published human (Franjic et al. 2022, Zhou et al. 2022, Wang et al. 2022), mouse (Hochgerner et al. 2018) and macaque (Hao et al. 2022) hippocampal datasets to form a unifying atlas and perform cross species comparison.

Techniques to be employed: You will mainly use Ensembl BioMart for orthologous mapping, Seurat and Scanpy to test various data integration methods such as Harmony, fastMNN, LIGER, Scanorama, scVI, scANVI, SeuratV4CCA and SeuratV4RPCA (Song Y., 2023).

2) Cross-Comparative Analysis of Adult and Fetal Brain scRNA-seq Data through Machine Learning: As ‘positive control’ to identify (rare and previously uncharacterized) neurogenic cell types in the adult human brain we also generated a fetal dataset of the same brain region, where neurogenic cell types are abundant and therefore easily identifiable. In this project you will using supervised machine learning approaches for cross-comparative analysis of snRNA-seq data of adult and fetal human brain tissues.                 

Techniques to be employed: You will mainly use Seurat, Scanpy, scikit-learn, and apply machine learning models, including Random Forest or deep learning techniques, to find similarities between adult and fetal cellular populations. Python libraries like scikit-learn or PyTorch will be utilized for machine learning.

3)Integration of snRNA-seq and genetics data in Alzheimer’s disease and neurogenesis Your contribution to our project will consist of evaluating the association between cell type-specific differentially expressed genes (DEGs) and AD susceptibility (risk) genes. To understand in which cell type(s) DEGs are most vulnerable to AD pathology, we will link them with GWAS summary statistics and evaluate the association between cell type specific DEGs and AD risk genes.

Techniques to be employed: You will mainly use Seurat, Scanpy, CELLECT, MAGMA, scLinker, linkage disequilibrium score regression (LDSC) and covariate analysis

Location: This collaborative project will be primarily performed at the Netherlands Institute of Neuroscience at the Salta group under the supervision of E. Salta, G. Tosoni and D. Ayyildiz.

Project 3: Predicting cell-cell interactions during mouse interneuron development (t/w the Gomes lab)

Lepiemme et al, 2022, Science
Lepiemme et al, 2022, Science

Background: Brain development is a product of complex cell fate decisions. Both intrinsic properties of cells as well as intercellular communication play a role in ensuring proper spatiotemporal development. Interneurons are born in the ventral forebrain and migrate into the cortex over weeks to integrate into specific circuits. They interact with other cell types, such as oligodendrocyte precursors and microglia, to determine their fate. However, our knowledge on the mechanisms of these interactions remains poorly understood. Recent single cell atlases provide us with rich resources on the molecular make-up of the developing forebrain.

Aim and objectives: In this project, we aim to computationally determine the cell-cell interaction between interneuron subpopulations during their migration with cortical cell types using published single cell atlases. The student will use a collection of machine learning tools for predicting cell-cell interactions from single cell datasets using three different mouse single cell atlases. We will focus on interneuron interaction with the radial glia, oligodendrocyte precursor cells and microglia. Finally, a literature search will be performed to validate potential interactions identified.

Techniques to be employed: Bash/shell script on hpc, R/python based single cell analysis tools such as Seurat, Scanpy, cell-cell interaction prediction using LIANA

Location: This project is a collaboration between the Gomes and Basak labs at the translational neuroscience department of the UMC Brain Center

Identity of the intern: The applicant must be enrolled in a master program in the Netherlands.

Requirements: Basic level of computational skills in R/python/bash are expected. Students will be given advanced training on the topic relevant to the project. Knowledge in Neuroscience, single cell data analysis or machine learning tools is highly appreciated.

Duration and start date: Jan/Feb ’24 onwards. Project 1 is preferably for a major internship, while projects 2/3 can be minor internships.

If you fit in the profile and are interested in one of the projects, please contact or the above mentioned group leaders indicated in respective projects for more information. Please include a CV in your application, and keep in mind that we may ask for references. 

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