Summer Internship Program in Epigenetics, Stem Cells, and Artificial Intelligence

In vivo study of chromatin organization upon stress in C. elegans

Multiple layers of regulation are required to establish and maintain appropriate gene expression patterns. These include chromatin modifications and higher order architecture of the genome. Epigenetic mechanisms can link the environment and the genome, yet how epigenomes respond to environmental perturbations remains largely unknown.

In our team, we study how environmental inputs affect the state, spatial compartmentation and function of heterochromatin within an intact developing organism, namely the roundworm C. elegans. Worms develop rapidly, are genetically manipulatable and are transparent, a feature that gives us the unique possibility to monitor fluorescently labeled chromatin within the cells of an intact animal. In this internship, you will join a running project aimed at investigating the potential role of mitochondrial stress in regulating heterochromatin organization. More specifically, you will i) familiarize with C. elegans handling and genetics, for example by performing crosses and RNAi-mediated knock down of genes of interest, ii) image animals under a microscope and quantify the results. Also, you will help with preparing samples for western blot and IP, thus gaining experience in molecular biology techniques.

Supervisor: Dr. Daphne Cabianca

Institute: Institute of Functional Epigenetics

Identification of chromatin structures preventing cell fate changes during reprogramming

Vertebrate eggs have the remarkable ability to induce nuclear reprogramming of somatic cells to enable the production of any other cell type of an organism upon nuclear transfer. During this process, the ‘memory’ of cells, which is stabilized by epigenetic mechanisms, can be fully erased to generate totipotent cells. However, the molecular mechanisms that enable, drive, or hinder the conversion of a differentiated cell to totipotency remain elusive. Your project will address the pressing question of how differentiated nuclei resist reprogramming to totipotency.

Reprogramming via nuclear transfer to eggs of the frog Xenopus laevis provides an excellent model for understanding how the memory of a specialized cell hinders the generation of totipotent cells. In your project, we will combine multi-omics approaches (RNAseq, ChIPseq) with biochemical and cell biological assays to identify the chromatin signatures that prevent cell fate changes during reprogramming and to address the underlying molecular mechanisms.

Supervisor: Dr. Eva Hörmanseder

Institute: Institute for Epigenetics and Stem Cells

AI-based single-cell profiling 

Supervisor: Prof. Carsten Marr

Institute: Institute of Artifical Intelligence for Health

Analyzing in vitro models of embryonic development with machine learning and physics

We have trillions of cells in our body, which can be classified into hundreds of types, each carrying out specific functions. This high level of complexity arises from a single cell, the zygote, through a process called embryogenesis, by which cell types emerge at the right time and place in the forming embryo. While key discoveries have been made by observing how embryos develop, many open questions remain due to the technical and ethical issues arising when working with embryos.

Recently, a bottom-up approach has harnessed the ability of stem cells to self-organize into structures resembling embryos when proper chemical and mechanical cues are provided. This has led to the generation of tri-dimensional in vitro models of embryonic development that are allowing researchers to investigate previously inaccessible stages of human development. While several models are available, it is unclear which aspects of development each reproduces best and how they can be improved.

The project aims to find similarities and differences between embryos and their 3D models and identify strategies to improve such models. To this aim, single-cell omics and imaging data will be analyzed with machine learning techniques. Moreover, when appropriate, the data analysis will be aided by physical modelling that give insights into the mechanisms cells use to self-organize in these 3D structures.

Supervisor: Dr. Antonio Scialdone

Institute: Institute for Epigenetics and Stem Cells

Deciphering the function of novel histone modifications and their role in diseases

Our aim is to identify novel pathways regulating chromatin function in order to discover new therapy targets and unique diagnostic or prognostic markers. We want to understand how covalent modifications of histones regulate cellular functions and how their deregulation results in diseases such as cancer or diabetes.  One of our main research lines are novel types and sites of histone modifications and their role in disease processes.  

The project for the summer student will focus on how (novel) histone modifications can regulate genome function (e.g. transcription or differentiation) and epigenetic inheritance. It will be performed together with an experienced supervisor. This includes the possibility to be part of a very dynamic and international team, learn state-of-the-art technologies (such as imaging techniques and/or ChIP/CUT&RUN/CUT&Tag assays as well as data analysis), and get insights into the fascinating field of histone modifications and how they regulate cellular processes.  Join us to harness the power of epigenetics to promote a healthier society in a rapidly changing world.

Supervisor: Prof. Robert Schneider

Institute: Institute of Functional Epigenetics

Modeling similarity between imaging-based spatial transcriptomics and single-cell transcriptomics

Spatial transcriptomics (ST; named Nature Methods method of the year in 2020¹) provides a novel spatial dimension to the study of single-cell gene expression. Imaging based ST methods capture the topology of cell types and states in tissues at single cell- and subcellular resolution by measuring the expression of a predefined set of genes^2–4. As spatial data can be challenging to analyse, we typically use matched single cell RNA sequencing (scRNA-seq) data, which we know how to analyse, as reference. Even though both modalities measure the same molecules, the resulting data can look very different⁵. In this project we want to understand these differences and provide guidelines to data generators and analysts to help them generate more consistent cellular representations.

There are multiple factors that can lead to discrepancies between the modalities including technological effects (probe efficiency, optical crowding, dissociation protocol), experimental setup (gene panel, tissue type, additional cell stainings), and spatial data processing (segmentation errors, normalization). It is not well understood how the different parameters affect the similarity between spatial and scRNAseq modalities and how experimental design and processing of the spatial data can be improved to increase the similarity. To answer these questions we've set up a computational pipeline that runs multiple spatial preprocessing schemes and measures a set of similarity and spatial quality metrics to compare these schemes. The metrics cover different similarity and quality aspects which can be modeled based on characteristics/covariates of the input datasets (e.g., cell density, spatial technology, gene scores or pathways, segmentation expansion) and the preprocessing options. 

In this project, we aim to model and interpret the similarity of single cell and spatial modalities based on multiple covariates. We will formulate guidelines for scRNAseq reference-based analysis of spatial data following our similarity analysis, lay foundations to optimize spatial data preprocessing methods, and compare the quality of different spatial technologies.

Goals: Find main effects that lead to discrepancies between scRNAseq and imaging based spatial transcriptomics by modeling and interpreting similarity metrics.

Data set: matched sc/snRNAseq and imaging based spatial transcriptomics datasets for developmental heart (ISS), fetal liver (MERFISH), brain (ISS, MERFISH), and more.^6–9

Methods: Application and adaptation of our existing spatial preprocessing and evaluation pipeline; preparation of additional datasets and metadata; statistical analysis/modeling

Literature:

1. Marx, V. Method of the Year: spatially resolved transcriptomics.Nat. Methods18, 9–14 (2021).

2. Aldridge, S. & Teichmann, S. A. Single-cell transcriptomics comes of age. Nat. Commun.11, 4307 (2020).

3. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.Science348, aaa6090 (2015).

4. Gyllborg, D. et al.Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue.Nucleic Acids Res.48, e112 (2020).

5. Liu, J. et al.Concordance of MERFISH Spatial Transcriptomics with Bulk and Single-cell RNA Sequencing. Preprint at doi.org/10.1101/2022.03.04.483068.

6. Asp, M. et al.A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart.Cell179, 1647–1660.e19 (2019).

7. Lu, Y. et al.Spatial transcriptome profiling by MERFISH reveals fetal liver hematopoietic stem cell niche architecture.Cell Discov7, 47 (2021).

8. Zhang, M. et al.Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH.Nature598, 137–143 (2021).

9. Qian, X. et al.Probabilistic cell typing enables fine mapping of closely related cell types in situ.Nat. Methods17, 101–106 (2020).

Supervisor: Louis Kuemmerle, Malte Luecken, Prof. Fabian Theis

Institute: Institute of Computational Biology

Analysis of chromatin and transcription factors that regulate stem cell identity

The project proposed aims at identifying the molecular players and epigenetic mechanisms that allow early embryonic cells to acquire their high plasticity or totipotency. Cellular plasticity is the capacity of a cell to give rise to different cell types upon differentiation.

 In particular, the student will work with cell culture models for pluripotency and totipotency. They will implement cell biology and molecular biology approaches to understand the function of candidate proteins and genomic elements to regulate gene expression in pluripotent and totipotent cells.

The student will be able to grow and culture embryonic stem cells and manipulate the cells using RNAi, transfection, CrisprCas9-based epigenetic engineering and ectopic expression of chromatin modifiers.

Overall, the project proposed aims to uncover the epigenetic mechanisms behind the establishment of totipotency and to provide insights into the origin of the first pluripotent stem cells to form.

Supervisor: Prof. Maria-Elena Torres-Padilla

Institute: Institute for Epigenetics and Stem Cells