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Helmholtz Munich Summer internship on Epigenetics Without Barriers: environment, AI, data science and stem cells.

Our progam is a unique opportunity for students to experience the daily life of scientists live and to gain practical experience working in wet and/or dry laboratories.

Come and discover with us a truly interdisciplinary, cross-departmental research adventure during the summer! New learning, new experience, new tools, new knowledge!

We are thrilled to announce the launch of our Helmholtz Munich Summer internship in Epigenetics, offering an unparalleled experience for students eager to explore the dynamic world of epigenetics and life-long health. This year's program is exceptionally diverse, covering a spectrum of research areas within epigenetics, such as histone modifications, environmental stress, nuclear architecture, image processing, metabolic diseases, cell fate decisions, deep learning, AI, totipotency and spatial transcriptomics.

Our Helmholtz Munich SUMMER INTERNSHIP ON EPIGENETICS WITHOUT BARRIERS: environment, AI, data science and stem cells is a unique opportunity for students, who are interested in pursuing a career in the life sciences to experience the daily life of scientists live and gain practical experience working in wet and/or dry laboratories.

Projects: Explore a wide range of projects, spanning from fundamental epigenetic mechanisms to cutting-edge applications like deep learning and spatial transcriptomics.

Experience: Immerse yourself in the daily routine of a prestigious research institution, obtaining hands-on experience in wet and dry lab environments.

Collaboration: Collaborate with world-leading researchers, contributing to ground-breaking projects over an intensive 8-week period.

The international program welcomes highly talented and motivated undergraduate and first-year master's students to work alongside leading researchers for 8 weeks on cutting-edge projects. Techniques include biochemistrymolecular biologyimagingstem cell biology, and implementing computational approaches including artificial intelligence algorithms according to the expertise of the host lab. We offer an 8-week program, which will give you the opportunity to learn how to:

  • ask scientific questions
  • plan and perform experiments and/or
  • develop and apply computational and machine-learning approaches
  • discuss your data with our experts
  • enjoy a dynamic, international research environment

During your research stay, you will work alongside leading scientists on cutting-edge projects and participate in all activities of the epigenetics@HelmholtzMunich research groups.

Projects 2024

In our lab, we aim at understanding how environmental cues, including metabolic perturbations, impact on different aspects of chromatin organization, by using the model organism Caenorhabditis elegans.

Chromatin modifying enzymes use metabolites (Acetyl-CoA and SAM among others) to generate histone post-translation modifications (PTMs), such as methylation and acetylation. Thus, metabolites availability is critical for chromatin regulation and epigenetic processes. Yet, the study of how intracellular metabolism shapes chromatin function is in its infancy and many questions are unanswered.

In this 2024 summer internship project, the student will contribute to a PhD project that aims to investigate the role specific metabolic enzymes in regulating heterochromatin silencing. During his/her time in the lab, the student will learn the basics of C. elegans growth and maintenance and combine genetics (crosses, RNAi-mediated gene knock down) and confocal live imaging to investigate how specific metabolic perturbations affect canonical and non-canonical histone PTMs.

Supervisor: Dr. Daphne Cabianca

Institute: Institute of Functional Epigenetics

 

Single-locus specific chromatin isolation as a tool to study nucleosome positioning at the single-molecule level

Chromatin presents the natural substrate of the essential and complex machineries dedicated to transcription, replication and repair. These fundamental processes require major chromatin rearrangements to access and make use of the genome. Thus, to understand the molecular basis of these DNA transactions, it is critical to define the collective changes of the chromatin structure at precise genomic regions where these machineries assemble and drive biological reactions.

This project will make use of an established affinity purification protocol to enrich specific chromosomal domains with high yields and purity (Weiβ et al, 2023; Chanou et al, 2023; Sajid et al., JOVE 2023), allowing us to purify a single-copy gene locus of interest in its native chromatin context. As part of this project, we will take advantage of this method and combine it with methylation-footprinting combined with Nanopore sequencing to determine at the single-molecule level the heterogeneity and influence of nucleosome positioning and occupancy on the functional state of the locus of interest. In addition, we will establish a novel tool named expansion microscopy that allows physical enlarging of the isolated molecules in order to visualize the native chromatin domains potentially with single nucleosome particle resolution.

References:

Chanou A, Weiβ M, Holler K, Straub T, Krietsch J, Sanchi A, Ummethum H, Lee CSK, Kruse E, Trauner M, et al (2023) Single molecule MATAC-seq reveals key determinants of DNA replication origin efficiency. Nucleic Acids Research, gkad1022, doi.org/10.1093/nar/gkad1022

Sajid A, Hamperl S. (2023), Single-Copy Gene Locus Chromatin Purification in Saccharomyces cerevisiae. JOVE, accepted

Weiβ M, Chanou A, Schauer T, Tvardovskiy A, Meiser S, König A-C, Schmidt T, Kruse E, Ummethum H, Trauner M, et al (2023) Single-copy locus proteomics of early- and late-firing DNA replication origins identifies a role of Ask1/DASH complex in replication timing control. Cell Rep 42: 112045

 

Supervisor: Dr. Stephan Hamperl

Institute: Institute of Epigenetics and Stem Cells

 

Self-supervised generation of synthetic leukemia blood smear data

Diagnosing acute leukemia from blood cell morphologies is a challenging task where artificial intelligence can help tremendously. Multiple instance learning approaches can look into a pool of single cell data, identify the relevant hallmark cells, and extract the necessary information for diagnosis thanks to the attention pooling mechanism. From embedding patients and not just single cells, we now want to generate synthetic data to better understand disease ​​subclasses and infer possible disease trajectories.

Supervisor: Prof. Carsten Marr

Institute: Institute of Artifical Intelligence for Health

Understanding nucleoid number control and mitochondrial DNA regulation in S. cerevisiae

Mitochondrial DNA (mtDNA) is essential to mitochondria functions, and defects in mtDNA maintenance underlie severe diseases, including Parkinson’s, Alzheimer’s and cancer. mtDNA encodes for essential subunits of the respiratory chain, and it is organised in nucleoproteins called nucleoids, which are distributed throughout the mitochondrial network. Despite the fundamental importance, the mechanisms controlling nucleoid number, nucleoid distribution, and mtDNA replication and maintenance are poorly understood.

To analyse the complex 3D distribution of the nucleoids and mitochondria network in single cells for our recent study1  we developed two bioimage analysis tools: 1) Cell-ACDC2 that employs state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data, and 2) spotMAX (manuscript in preparation), a deep-learning based 3D detection and quantification of fluorescent foci. Thanks to these studies we identified important mechanisms for mtDNA copy number regulation with cell volume. However, recent unpublished results suggest that nucleoid number control is uncoupled from mtDNA copy number control.

This leaves many open questions: how is nucleoid number control achieved? Is it uncoupled from mtDNA content regulation? Building on the image analysis tools that we developed, with this project will address these questions by employing cutting-edge live-cell imaging techniques, machine-learning image analysis, and genetic manipulation of yeast cells to unravel the molecular mechanisms underlying the co-regulation of nucleoid number and mtDNA content in S. cerevisiae.

The proposed project will provide fundamental insights into the mechanisms underlying mtDNA maintenance and replication, shedding light on an open question in cell biology and a central feature in mtDNA-related genetic diseases.

References:

1. Seel, A. et al. Regulation with cell size ensures mitochondrial DNA homeostasis during cell growth. Nat. Struct. Mol. Biol.30, 1549–1560 (2023).

2. Padovani, F., Mairhörmann, B., Falter-Braun, P., Lengefeld, J. & Schmoller, K. M. Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC. BMC Biol.20, 174 (2022).

Unravelling the function of chromatin modifications and their role in disease 

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 in chromatin components (e.g. 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 chromatin modifications and their role in disease processes. 

The project for the summer student will focus on how (novel) 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) and get insights in the fascinating field of chromatin modifications and how the 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

 

Computational reconstruction of 3D gene expression patterns by Graphical Modelling

Spatial transcriptomics is revolutionizing the study of molecular tissue architecture. However, the advent of these techniques has also created a demand for modeling such data in a meaningful way. Over the years, much work has been dedicated to developing new statistical methods to address the problem of modeling the count/intensity distribution of spatial transcriptomic data, identifying genes with high spatial variation, clustering specimens into distinct domains, and deconvolving the cell type composition.

The tomography-inspired spatial transcriptomics technique called TOMO-seq allows cost-efficient genome- and organ-level scaling of spatial transcriptomic studies, albeit at the cost of lower spatial resolution. Consequently, complementing TOMO-seq with a robust and scalable bioinformatic pipeline can mitigate the effects of lower spatial resolution, making it an attractive experimental strategy for future studies (Junker et al., 2014).

Tomographer, a state-of-the-art model for TOMO-seq reconstruction, uses Laplace and Exponential priors to reconstruct a sparse and smooth image (Schede et al., 2021). Such models, however, assume that their realizations are i.i.d and, hence, do not account for possible spatial dependencies (Bardsley, 2012). Graphical models, on the other hand, inherently consider the spatial neighborhood relationships and, thanks to their mathematical properties, can be factored into conditionally independent structures, reducing the runtime of the inference (Koller & Friedman, 2009). Numerous tools developed for traditional spatial transcriptomics analyses utilize the power of graphical models (Dries et al., 2021; Kats et al., 2021; Sun et al., 2020).

The student will have the chance to work with various types of spatial omics datasets, will be involved in discussions of state-of-the-art techniques in spatial reconstruction, and assist our lab in graphical model specification within a hierarchical Bayesian framework.

 

References

Bardsley, J. M. (2012). Laplace-distributed increments, the Laplace prior, and edge-preserving regularization. Journal of Inverse and Ill-Posed Problems, 20(3), 271–285. doi.org/10.1515/jip-2012-0017

Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., George, R. E., Pierson, N., Cai, L., & Yuan, G.-C. (2021). Giotto: A toolbox for integrative analysis and visualization of spatial expression data. Genome Biology, 22(1), 78. doi.org/10.1186/s13059-021-02286-2

Junker, J. P., Noël, E. S., Guryev, V., Peterson, K. A., Shah, G., Huisken, J., McMahon, A. P., Berezikov, E., Bakkers, J., & van Oudenaarden, A. (2014). Genome-wide RNA Tomography in the Zebrafish Embryo. Cell, 159(3), 662–675. doi.org/10.1016/j.cell.2014.09.038

Kats, I., Vento-Tormo, R., & Stegle, O. (2021). SpatialDE2: Fast and localized variance component analysis of spatial transcriptomics (p. 2021.10.27.466045). bioRxiv. doi.org/10.1101/2021.10.27.466045

Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. MIT Press.

Moses, L., & Pachter, L. (2022). Museum of spatial transcriptomics. Nature Methods, 19(5), Article 5. doi.org/10.1038/s41592-022-01409-2

Schede, H. H., Schneider, C. G., Stergiadou, J., Borm, L. E., Ranjak, A., Yamawaki, T. M., David, F. P. A., Lönnerberg, P., Tosches, M. A., Codeluppi, S., & La Manno, G. (2021). Spatial tissue profiling by imaging-free molecular tomography. Nature Biotechnology, 39(8), Article 8. doi.org/10.1038/s41587-021-00879-7

Sun, S., Zhu, J., & Zhou, X. (2020). Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nature Methods, 17(2), Article 2. doi.org/10.1038/s41592-019-0701-7

Velten, B., & Stegle, O. (2023). Principles and challenges of modeling temporal and spatial omics data. Nature Methods, 20(10), Article 10. doi.org/10.1038/s41592-023-01992-y

 

Supervisor: Dr. Antonio Scialdone

Institute: Institute for Epigenetics and Stem Cells

The environmental epigenetic group studies the influence of paternal health on pregnancy and offspring phenotypes.

For this project, the student will study the placental immune compartment. She/he will work with primary mouse placenta, isolate and characterise placental immune cells. She/he will gain experience with FACS analysis and basic molecular biology techniques for protein and rna isolation and analysis. The goal is to understand whether and how paternal health at conception influences the placental immune compartment and thereby offspring health.

Supervisor: Dr. Raffaele Teperino

Institute: Institute of Environmental Epigenetics

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

In our lab, we aim at understanding how environmental cues, including metabolic perturbations, impact on different aspects of chromatin organization, by using the model organism Caenorhabditis elegans.

Chromatin modifying enzymes use metabolites (Acetyl-CoA and SAM among others) to generate histone post-translation modifications (PTMs), such as methylation and acetylation. Thus, metabolites availability is critical for chromatin regulation and epigenetic processes. Yet, the study of how intracellular metabolism shapes chromatin function is in its infancy and many questions are unanswered.

In this 2024 summer internship project, the student will contribute to a PhD project that aims to investigate the role specific metabolic enzymes in regulating heterochromatin silencing. During his/her time in the lab, the student will learn the basics of C. elegans growth and maintenance and combine genetics (crosses, RNAi-mediated gene knock down) and confocal live imaging to investigate how specific metabolic perturbations affect canonical and non-canonical histone PTMs.

Supervisor: Dr. Daphne Cabianca

Institute: Institute of Functional Epigenetics

 

Single-locus specific chromatin isolation as a tool to study nucleosome positioning at the single-molecule level

Chromatin presents the natural substrate of the essential and complex machineries dedicated to transcription, replication and repair. These fundamental processes require major chromatin rearrangements to access and make use of the genome. Thus, to understand the molecular basis of these DNA transactions, it is critical to define the collective changes of the chromatin structure at precise genomic regions where these machineries assemble and drive biological reactions.

This project will make use of an established affinity purification protocol to enrich specific chromosomal domains with high yields and purity (Weiβ et al, 2023; Chanou et al, 2023; Sajid et al., JOVE 2023), allowing us to purify a single-copy gene locus of interest in its native chromatin context. As part of this project, we will take advantage of this method and combine it with methylation-footprinting combined with Nanopore sequencing to determine at the single-molecule level the heterogeneity and influence of nucleosome positioning and occupancy on the functional state of the locus of interest. In addition, we will establish a novel tool named expansion microscopy that allows physical enlarging of the isolated molecules in order to visualize the native chromatin domains potentially with single nucleosome particle resolution.

References:

Chanou A, Weiβ M, Holler K, Straub T, Krietsch J, Sanchi A, Ummethum H, Lee CSK, Kruse E, Trauner M, et al (2023) Single molecule MATAC-seq reveals key determinants of DNA replication origin efficiency. Nucleic Acids Research, gkad1022, doi.org/10.1093/nar/gkad1022

Sajid A, Hamperl S. (2023), Single-Copy Gene Locus Chromatin Purification in Saccharomyces cerevisiae. JOVE, accepted

Weiβ M, Chanou A, Schauer T, Tvardovskiy A, Meiser S, König A-C, Schmidt T, Kruse E, Ummethum H, Trauner M, et al (2023) Single-copy locus proteomics of early- and late-firing DNA replication origins identifies a role of Ask1/DASH complex in replication timing control. Cell Rep 42: 112045

 

Supervisor: Dr. Stephan Hamperl

Institute: Institute of Epigenetics and Stem Cells

 

Self-supervised generation of synthetic leukemia blood smear data

Diagnosing acute leukemia from blood cell morphologies is a challenging task where artificial intelligence can help tremendously. Multiple instance learning approaches can look into a pool of single cell data, identify the relevant hallmark cells, and extract the necessary information for diagnosis thanks to the attention pooling mechanism. From embedding patients and not just single cells, we now want to generate synthetic data to better understand disease ​​subclasses and infer possible disease trajectories.

Supervisor: Prof. Carsten Marr

Institute: Institute of Artifical Intelligence for Health

Understanding nucleoid number control and mitochondrial DNA regulation in S. cerevisiae

Mitochondrial DNA (mtDNA) is essential to mitochondria functions, and defects in mtDNA maintenance underlie severe diseases, including Parkinson’s, Alzheimer’s and cancer. mtDNA encodes for essential subunits of the respiratory chain, and it is organised in nucleoproteins called nucleoids, which are distributed throughout the mitochondrial network. Despite the fundamental importance, the mechanisms controlling nucleoid number, nucleoid distribution, and mtDNA replication and maintenance are poorly understood.

To analyse the complex 3D distribution of the nucleoids and mitochondria network in single cells for our recent study1  we developed two bioimage analysis tools: 1) Cell-ACDC2 that employs state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data, and 2) spotMAX (manuscript in preparation), a deep-learning based 3D detection and quantification of fluorescent foci. Thanks to these studies we identified important mechanisms for mtDNA copy number regulation with cell volume. However, recent unpublished results suggest that nucleoid number control is uncoupled from mtDNA copy number control.

This leaves many open questions: how is nucleoid number control achieved? Is it uncoupled from mtDNA content regulation? Building on the image analysis tools that we developed, with this project will address these questions by employing cutting-edge live-cell imaging techniques, machine-learning image analysis, and genetic manipulation of yeast cells to unravel the molecular mechanisms underlying the co-regulation of nucleoid number and mtDNA content in S. cerevisiae.

The proposed project will provide fundamental insights into the mechanisms underlying mtDNA maintenance and replication, shedding light on an open question in cell biology and a central feature in mtDNA-related genetic diseases.

References:

1. Seel, A. et al. Regulation with cell size ensures mitochondrial DNA homeostasis during cell growth. Nat. Struct. Mol. Biol.30, 1549–1560 (2023).

2. Padovani, F., Mairhörmann, B., Falter-Braun, P., Lengefeld, J. & Schmoller, K. M. Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC. BMC Biol.20, 174 (2022).

Unravelling the function of chromatin modifications and their role in disease 

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 in chromatin components (e.g. 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 chromatin modifications and their role in disease processes. 

The project for the summer student will focus on how (novel) 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) and get insights in the fascinating field of chromatin modifications and how the 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

 

Computational reconstruction of 3D gene expression patterns by Graphical Modelling

Spatial transcriptomics is revolutionizing the study of molecular tissue architecture. However, the advent of these techniques has also created a demand for modeling such data in a meaningful way. Over the years, much work has been dedicated to developing new statistical methods to address the problem of modeling the count/intensity distribution of spatial transcriptomic data, identifying genes with high spatial variation, clustering specimens into distinct domains, and deconvolving the cell type composition.

The tomography-inspired spatial transcriptomics technique called TOMO-seq allows cost-efficient genome- and organ-level scaling of spatial transcriptomic studies, albeit at the cost of lower spatial resolution. Consequently, complementing TOMO-seq with a robust and scalable bioinformatic pipeline can mitigate the effects of lower spatial resolution, making it an attractive experimental strategy for future studies (Junker et al., 2014).

Tomographer, a state-of-the-art model for TOMO-seq reconstruction, uses Laplace and Exponential priors to reconstruct a sparse and smooth image (Schede et al., 2021). Such models, however, assume that their realizations are i.i.d and, hence, do not account for possible spatial dependencies (Bardsley, 2012). Graphical models, on the other hand, inherently consider the spatial neighborhood relationships and, thanks to their mathematical properties, can be factored into conditionally independent structures, reducing the runtime of the inference (Koller & Friedman, 2009). Numerous tools developed for traditional spatial transcriptomics analyses utilize the power of graphical models (Dries et al., 2021; Kats et al., 2021; Sun et al., 2020).

The student will have the chance to work with various types of spatial omics datasets, will be involved in discussions of state-of-the-art techniques in spatial reconstruction, and assist our lab in graphical model specification within a hierarchical Bayesian framework.

 

References

Bardsley, J. M. (2012). Laplace-distributed increments, the Laplace prior, and edge-preserving regularization. Journal of Inverse and Ill-Posed Problems, 20(3), 271–285. doi.org/10.1515/jip-2012-0017

Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., George, R. E., Pierson, N., Cai, L., & Yuan, G.-C. (2021). Giotto: A toolbox for integrative analysis and visualization of spatial expression data. Genome Biology, 22(1), 78. doi.org/10.1186/s13059-021-02286-2

Junker, J. P., Noël, E. S., Guryev, V., Peterson, K. A., Shah, G., Huisken, J., McMahon, A. P., Berezikov, E., Bakkers, J., & van Oudenaarden, A. (2014). Genome-wide RNA Tomography in the Zebrafish Embryo. Cell, 159(3), 662–675. doi.org/10.1016/j.cell.2014.09.038

Kats, I., Vento-Tormo, R., & Stegle, O. (2021). SpatialDE2: Fast and localized variance component analysis of spatial transcriptomics (p. 2021.10.27.466045). bioRxiv. doi.org/10.1101/2021.10.27.466045

Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. MIT Press.

Moses, L., & Pachter, L. (2022). Museum of spatial transcriptomics. Nature Methods, 19(5), Article 5. doi.org/10.1038/s41592-022-01409-2

Schede, H. H., Schneider, C. G., Stergiadou, J., Borm, L. E., Ranjak, A., Yamawaki, T. M., David, F. P. A., Lönnerberg, P., Tosches, M. A., Codeluppi, S., & La Manno, G. (2021). Spatial tissue profiling by imaging-free molecular tomography. Nature Biotechnology, 39(8), Article 8. doi.org/10.1038/s41587-021-00879-7

Sun, S., Zhu, J., & Zhou, X. (2020). Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nature Methods, 17(2), Article 2. doi.org/10.1038/s41592-019-0701-7

Velten, B., & Stegle, O. (2023). Principles and challenges of modeling temporal and spatial omics data. Nature Methods, 20(10), Article 10. doi.org/10.1038/s41592-023-01992-y

 

Supervisor: Dr. Antonio Scialdone

Institute: Institute for Epigenetics and Stem Cells

The environmental epigenetic group studies the influence of paternal health on pregnancy and offspring phenotypes.

For this project, the student will study the placental immune compartment. She/he will work with primary mouse placenta, isolate and characterise placental immune cells. She/he will gain experience with FACS analysis and basic molecular biology techniques for protein and rna isolation and analysis. The goal is to understand whether and how paternal health at conception influences the placental immune compartment and thereby offspring health.

Supervisor: Dr. Raffaele Teperino

Institute: Institute of Environmental Epigenetics

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

Application Information

Call for Program 2024 IS CLOSED

Eligibility

Helmholtz Munich

You are eligible to apply for our program if you |

∞ have a background in biological sciences, bioinformatics, or related discipline. |
∞ are an undergraduate who has completed at least two years of university study. |
∞ have finished your undergraduate studies, but you are still enrolled at your university |
∞ are a first-year post-graduate Master's student |
∞ have a very good academic record |
∞ have good English language skills (written and spoken) |

How to apply

Helmholtz Munich

How you can apply for our program |

∞ Submit your application in English via our online application system |
∞ Fill out the contact details (everything marked with a red star is mandatory). |
∞ Desired Salary: put in 0€. |
∞ Upload the required documents (see below) combined in ONE pdf file |
∞ Your application was successful after you got the automated confirmation email. |

Contact Office

Epigenetics Office