Cell fate decision is essential for the emergence of a complex multicellular organism starting from a single pluripotent zygote, and perpetually occurs during adult life to maintain organ tissues. The robustness of cell fate decision is remarkable given the stochastic interaction of hundreds of thousands of molecules in each cell. It has remained a central open question how signals from the microenvironment are integrated with stochastic processes in a cell to control cell fate decision in normal tissues and upon perturbations due to disease or tissue damage. Our lab investigates these questions in the context of immune cell differentiation by combining single-cell resolution experimental methods with computational methods involving machine learning and mathematical modeling.
We are focusing on development and homeostatic turnover of immune cells and organ tissues to understand the driving forces and control mechanisms of cell fate decision. We investigate a variety of systems which prominently show constant turnover, lineage plasticity, and response to perturbations in terms of cell differentiation dynamics. For example, we investigate the role of the hematopoietic niche, i.e., the effect of a varying microenvironments, such as fetal liver and bone marrow, on blood cell differentiation, and compare to aberrant cell fate decision upon diseases such as leukemia.
As a complementary system we are studying plasticity and turnover of epithelial cells and their crosstalk with tissue-resident immune cells in the liver, the central metabolic organ of the human body. The liver exhibits high regenerative capacity, driven by complex interactions with the immune, endothelial, and mesenchymal cell compartment.
In our studies we are aiming to address basic research questions, such as the identification of the molecular mechanisms underpinning robust cell differentiation, but also aim at translating our findings into clinically relevant novel therapeutic approaches.
With the recent emergence of powerful single-cell resolution experimental methods, including single-cell RNA-seq and ATAC-seq and multiplexed single-molecule-resolution imaging, it has become possible to profile the composition of tissues at the level of individual cells. By quantifying the genome-wide transcriptional profiles and the state of the chromatin, cell states and types can be annotated and differentiation trajectories as well as entire lineage trees can be inferred. Moreover, spatial methods based on in situ sequencing or highly multiplexed microscopic imaging permit the quantification of the expression of hundreds of genes in tissue sections, enabling the reconstruction of spatial tissue architecture and the inference of molecular interactions between co-localized cells. Paired with computational machine learning techniques tailored to such multimodal, large datasets, these approaches facilitate the integrative analysis of systematic and stochastic gene expression variability (i.e., biological noise) within cells, which represent intrinsic cell fate determinants, as well as signaling pathways mediating interactions of neighboring cells and representing extrinsic cell fate determinants.
Substantial variability of mRNA levels across cells of the same type has been observed in any organism studied, ranging from bacteria and yeast to mammals. Gene expression variability can have diverse origins. Heterogeneity of cell states following extrinsic or intrinsic stimuli, e. g., due to cell cycle, or changes in the microenvironment, can be one reason for cell-to-cell transcriptome differences. Moreover, many genes are transcribed in bursts rather than at a constant rate, leading to substantially different transcript numbers in individual cells of the same type, commonly addressed as gene expression noise. Gene expression variability causes fluctuations in protein levels, and can entail physiological differences between cells.
In our lab we investigate how stem cells robustly maintain their multipotent state and reliably execute differentiation programs with spatial and temporal precision in the presence of gene expression noise. We propose a model in which stem cells can exist in different metastable states, which are primed towards distinct lineages by subtle transcriptome modulations (Figure 1). While transitions between these stages occur in the multipotent state, reinforcement of a primed state by random fluctuations of lineage determining factors or signalling events from the microenvironment lead to commitment towards a terminal fate. We explore the molecular mechanisms underpinning the transition from a plastic multipotent state towards a lineage-restricted committed state. We have a particular focus on the integration of external cell fate determinants, i.e., signals from the microenvironment, with cell-intrinsic driving forces of fate bias such as gene expression noise.
To understand how gene expression is regulated during differentiation of a stem cell into all its daughter cell types one has to be able to discriminate distinct cell types and states in a complex mixture, such as a tissue or an organ.
Genome-wide profiling of gene expression in individual cells, e.g., by single-cell RNA-seq, reveals an unbiased sample of all cell types in a complex mixture. The transcriptome of a cell can be looked at as a fingerprint revealing its identity. We use single cell mRNA sequencing to investigate the transcriptome and profile other molecular readouts such as chromatin accessibility. Since such methods require tissue digestion, and, hence, spatial context is lost, we are pairing these single-cell sequencing methods with microscopic imaging-based in situ single-cell analysis utilizing highly multiplexed single-molecule FISH. Integrating these approaches enables the reconstruction of tissue architecture at single-cell resolution and facilitates the derivation of cell-cell interactions based on molecular pathway activity in co-localized cells. Our lab develops computational methods to integrate such multimodal data for the inference of differentiation trajectories, the quantification of gene expression noise within cells, and for the derivation of molecular interactions between co-localized cells within the tissue (Grün et al., Nature, 2015; Grün# et al., Cell Stem Cell, 2016; Herman et al., Nature Methods, 2018; Grün, Nature Methods, 2020) (Figure 2). We are particularly interested in rare cell types, since those are oftentimes overlooked and can have crucial functions. For example, the stem cells themselves often occur at low frequencies.
Our strategy allows to derive lineage trees de novo and to revise current models for cell differentiation in well-studied systems, such as the bone marrow. Moreover, marker genes for cell types and states can be identified with high specificity, which permits the purification of these cells and subsequent population-based assays, e. g. ChiP-seq, to measure epigenetic marks and transcription factor binding. The ultimate goal is the derivation and functional validation of a mechanistic model of gene regulation during differentiation by combining these population-based measurements with single cell gene expression data, and functional experiments, e.g., in mouse or organoid models.
A specific focus of the lab is the role of gene expression variability across single cells during differentiation (Grün et al., Nature Methods, 2014; Grün, Nature Methods, 2020). It has been shown that transcription is frequently not a continuous process but occurs in bursts. This induces substantial cell-to-cell variability of mRNA levels, and the role of this so-called biological noise during cellular differentiation is not well understood (Figure 3).
Using single cell transcriptomics in conjunction with population based assays we try to elucidate how biological gene expression noise changes during differentiation and how it is mechanistically regulated. Since single cell sequencing still suffers from substantial technical noise we also utilize microscopic imaging of individual mRNAs in single cells (single molecule FISH) to investigate gene expression variability with high specificity and sensitivity.
As a model of multi-lineage differentiation we are studying cells of the hematopoietic system with a focus on lymphocytes. Years of intense research have revealed the major hematopoietic cell types as well as multipotent progenitor populations with the help of flow cytometry on the basis of a relatively small number of cell surface markers. The development of sensitive high-throughput single-cell sequencing has revolutionized the identification of cell types and differentiation trajectories, and recent studies are changing our model of hematopoietic differentiation (Figure 4).
We are studying differentiation of murine hematopoietic cell populations in primary lymphoid organs, e.g., bone marrow and fetal liver, as well as differentiation of tissue-resident cells of the innate immune system (Herman et al., Nature Methods, 2018; Sagar et al., The EMBO Journal, 2020; Zeis et al., Immunity, 2020). Our aim is to elucidate the dependence of hematopoietic cell fate decision and lineage output on the local microenvironment, in order to explain differences in hematopoiesis across tissues, as well as temporal heterogeneity of immune cell differentiation during life. In more detail, we are aiming to identify tissue-specific progenitor states and regulatory mechanisms underpinning local lineage choice, and to understand the role of gene expression variability in this context. By means of single-cell sequencing analysis we are creating high-resolution map of cell states and the differentiation trajectories connecting these states. These maps are used to generate hypotheses on progenitor states and mechanisms of fate commitment, which are functionally analysed with the help of mouse models and in vitro culture systems. To obtain information on the interactions of hematopoietic cells with neighbouring cells in the local microenvironment, we are integrating single-cell sequencing methods with spatial imaging-based analysis using highly multiplexed single-molecule FISH in tissue sections.
To facilitate translation of our research into biomedically relevant therapeutic approaches, we are combining the analysis of mouse models and human patient material.
The liver is the major metabolic organ of the body. Liver malignancies, such as fatty liver disease, liver cancer, or Hepatitis, are on the rise, in particular in western societies as a consequence of an unhealthy lifestyle. To repair tissue damage, the liver has developed and extraordinary regenerative capacity. This is best exemplified by the ability of the liver to regrow to its full size after surgical removal of up to 75% of its original mass. In the past, a large body of research has emerged revealing distinct pathways of regeneration. For instance, liver resection and acute liver damage was found to initiate proliferation of hepatocytes, while upon chronic liver damage or suppression of hepatocyte proliferation parenchymal cells regenerate from the bile duct epithelium. However, our understanding of the molecular and cellular processes controlling cell state changes in the liver during health and disease is far from complete. With the recent availability of single-cell resolution methods, in particular single-cell RNA-seq, it became possible to study liver cell types with high resolution.
Using quantitative single-cell methods we are investigating the cellular composition of the liver in mouse liver damage models and human samples derived from healthy and diseased individuals. Our goal is to decipher pathways of cellular differentiation maintaining the tissue under homeostatic conditions and to elucidate perturbations of these pathways upon liver disease such as damage-induced cell type plasticity (Aizarani et al., Nature, 2020). We study fundamental processes of regeneration and organ growth in the mouse model and use liver organoids derived from human samples for functional studies of these pathways.
Our strategy has a particular focus on the role of cell-cell communication across the epithelial, immune, and mesenchymal compartments involved in the control of cellular differentiation and cell state plasticity in the liver.
Grün link pubmed: https://pubmed.ncbi.nlm.nih.gov
LIST OF PUBLICATIONS
(*co-first authors, #co-corresponding authors, **Lead author)
- Hensel N, Gu Z, Sagar, Wieland D, Jechow K, Kemming J, Llewellyn-Lacey S, Gostick E, Sogukpinar O, Emmerich F, Price DA, Bengsch B, Boettler T, Neumann-Haefelin C, Eils R, Conrad C, Bartenschlager R, Grün D, Ishaque N, Thimme R, Hofmann M. (2021) Memory-like HCV-specific CD8 + T cells retain a molecular scar after cure of chronic HCV infection. Nature Immunology doi: 10.1038/s41590-020-00817-w
- Olah M, Menon V, Habib N, Taga MF, Ma Y, Yung CJ, Cimpean M, Khairallah A, Coronas-Samano G, Sankowski R, Grün D, Kroshilina AA, Dionne D, Sarkis RA, Cosgrove GR, Helgager J, Golden JA, Pennell PB, Prinz M, Vonsattel JPG, Teich AF, Schneider JA, Bennett DA, Regev A, Elyaman W, Bradshaw EM, De Jager PL. (2020) Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer's disease. Nature Communications 11(1):6129
- Probst S, Sagar, Tosic J, Schwan C, Grün D, Arnold SJ. (2020) Spatiotemporal sequence of mesoderm and endoderm lineage segregation during mouse gastrulation. Development 148(1):dev193789
- Ramamoorthy S, Kometani K, Herman JS, Bayer M, Boller S, Edwards-Hicks J, Ramachandran H, Li R, Klein-Geltink R, Pearce EL, Grün D, Grosschedl R. (2020) EBF1 and Pax5 safeguard leukemic transformation by limiting IL-7 signaling, Myc expression, and folate metabolism. Genes & Development 34(21-22):1503-19
- Zeis P, Lian M, Fan X, Herman JS, Hernandez DC, Gentek R, Elias S, Symowski C, Knöpper K, Peltokangas N, Friedrich C, Doucet-Ladeveze R, Kabat AM, Locksley RM, Voehringe D, Bajenoff M, Rudensky AY, Romagnani C, Grün#,** D, Gasteiger G#. (2020) In situ maturation and tissue adaptation of type 2 innate lymphoid cell progenitors. Immunity 53(4):775-792.e9
- Pessoa Rodrigues C, Herman JS, Herquel B, Valsecchi CIK, Stehle T, Grün D, Akhtar A. (2020) Temporal expression of MOF acetyltransferase primes transcription factor networks for erythroid fate. Science Advances 6(21): eaaz4815
- Sagar, Pokrovskii M, Herman JS, Naik S, Sock E, Lausch U, Wegner M, Tanriver Y, Littman DR, Grün D. (2020) Deciphering the Regulatory Landscape of γδ T Cell Development by Single-Cell RNA-Sequencing. EMBO Journal 39(13): e104159
- Sheikh BN, Guhathakurta S, Tsang TH, Schwabenland M, Renschler G, Herquel B, Bhardwaj V, Holz H, Stehle T, Bondareva O, Aizarani N, Mossad O, Kretz O, Reichardt W, Chatterjee A, Braun LJ, Thevenon J, Sartelet H, Blank T, Grün D, von Elverfeldt D, Huber TB, Vestweber D, Avilov S, Prinz M, Buescher JM, Akhtar A. (2020) Neural metabolic imbalance induced by MOF dysfunction triggers pericyte activation and breakdown of vasculature. Nature Cell Biology 22(7): 828-841
- Mereu E, Lafzi A, Moutinho C, Ziegenhain C, McCarthy DJ, Álvarez-Varela A, Batlle E, Grün D, Lau JK, Boutet SC, Sanada C, Ooi A, Jones RC, Kaihara K, Brampton C, Talaga Y, Sasagawa Y, Tanaka K, Hayashi T, Braeuning C, Fischer C, Sauer S, Trefzer T, Conrad C, Adiconis X, Nguyen LT, Regev A, Levin JZ, Parekh S, Janjic A, Wange LE, Bagnoli JW, Enard W, Gut M, Sandberg R, Nikaido I, Gut I, Stegle O, Heyn H. (2020) Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nature Biotechnology 38(6): 747-755
- Sagar, Grün D. (2020) Deciphering cell fate decision by integrated single-cell sequencing analysis. Annual Review of Biomedical Data Science 3:1-22
- Derecka M, Herman JS, Cauchy P, Ramamoorthy S, Lupar E, Grün D, Grosschedl R. (2020) EBF1-deficient bone marrow stroma elicits persistent changes in HSC potential. Nature Immunology 21(3): 261-273
- Grün D. (2020) Revealing Dynamics of Gene Expression Variability in Cell State Space. Nature Methods 17(1): 45-49
- Sankowski R, Böttcher C, Masuda T, Geirsdottir L, Sagar, Sindram R, Seredenina T, Muhs A, Scheiwe C, Shah MJ, Heiland DH, Schnell O, Grün D#, Priller J#, Prinz M#. (2019) Mapping microglia diversity in the human brain through the integration of high-dimensional techniques. Nature Neuroscience 22(12): 2098-2110
- Honkoop H, de Bakker DE, Aharonov A, Kruse F, Shakked A, Nguyen PD, de Heus C, Garric L, Muraro MJ, Shoffner A, Tessadori F, Peterson JC, Noort W, Bertozzi A, Weidinger G, Posthuma G, Grün D, van der Laarse WJ, Klumperman J, Jaspers RT, Poss KD, van Oudenaarden A, Tzahor E, Bakkers J (2019) Single-cell analysis uncovers that metabolic reprogramming by ErbB2 signaling is essential for cardiomyocyte proliferation in the regenerating heart. Elife 8: e50163
- Sheikh BN, Bondareva O, Guhathakurta S, Tsang TH, Sikora K, Aizarani N, Sagar, Holz H, Grün D, Hein L, Akhtar A (2019) Systematic Identification of Cell-Cell Communication Networks in the Developing Brain. iScience 21: 273-287
- Hummel JF, Zeis P, Ebert K, Fixemer J, Konrad P, Schachtrup C, Arnold SJ, Grün D, Tanriver Y (2019) Single-cell RNA-sequencing identifies the developmental trajectory of C-Myc-dependent NK1.1- T-bet+ intraepithelial lymphocyte precursors. Mucosal Immunology 13(2): 257-270
- Aizarani A, Saviano A, Sagar, Mailly L, Durand S, Herman JS, Pessaux P, Baumert TF#, Grün D# (2019) A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors. Nature, 572(7768): 199-204
- Kolter J, Feuerstein R, Zeis P, Hagemeyer N, Paterson N, d'Errico P, Baasch S, Amann L, Masuda T, Lösslein A, Gharun K, Meyer-Luehmann M, Waskow C, Franzke CW, Grün D, Lämmermann T, Prinz M, Henneke P (2019) A Subset of Skin Macrophages Contributes to the Surveillance and Regeneration of Local Nerves. Immunity 50(6):1482-1497.e7
- Sagar, Grün D (2019) Lineage Inference and Stem Cell Identity Prediction Using Single-Cell RNA-Sequencing Data. Methods in Molecular Biology 1975:277-301
- Masuda T, Sankowski R, Staszewski O, Böttcher C, Amann L, Sagar, Scheiwe C, Nessler S, Kunz P, van Loo G, Coenen VA, Reinacher PC, Michel A, Sure U, Gold R, Grün D, Priller J, Stadelmann C, Prinz M. (2019) Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566 (7744): 388-392
- Costa Jordão MJ, Sankowski R, Brendecke SM, Sagar, Locatelli G, Tai YH, Tay TL, Schramm E, Armbruster S, Hagemeyer N, Groß O, Mai D, Çiçek Ö, Falk T, Kerschensteiner M, Grün D, Prinz M (2019) Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363 (6425). pii: eaat7554
- Tay TL, Sagar, Dautzenberg J, Grün D#, Prinz M# (2018) Unique microglia recovery population revealed by single-cell RNAseq following neurodegeneration. Acta Neuropathologica Communications 6(1): 87
- Costa F, Grün D#, Backofen R# (2018) GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge. Nature Communications 9(1): 3685
- Roovers EF, Kaaij LJT, Redl S, Bronkhorst AW, Wiebrands K, de Jesus Domingues AM, Huang HY, Han CT, Riemer S, Dosch R, Salvenmoser W, Grün D, Butter F, van Oudenaarden A, Ketting RF (2018) Tdrd6a Regulates the Aggregation of Buc into Functional Subcellular Compartments that Drive Germ Cell Specification. Developmental Cell 46(3): 285-301
- Grün D (2018) Revealing routes of cellular differentiation by single-cell RNA-seq. Current Opinion in Systems Biology 11: 9-17
- Boisset JC, Vivié J, Grün D, Muraro M, Lyubimova A, van Oudenaarden A (2018) Mapping the physical network of cellular interactions. Nature Methods 15(7): 547-553
- Lu TT, Heyne S, Dror E, Casas E, Leonhardt L, Boenke T, Yang CH, Sagar, Arrigoni L, Dalgaard K, Teperino R, Enders L, Selvaraj M, Ruf M, Raja SJ, Xie H, Boenisch U, Orkin SH, Lynn FC, Hoffman BG, Grün D, Vavouri T, Lempradl AM, Pospisilik JA (2018) The Polycomb-Dependent Epigenome Controls β Cell Dysfunction, Dedifferentiation, and Diabetes. Cell Metabolism 27(6): 1294-1308
- Herman JS, Sagar, Grün D (2018) FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nature Methods 15(5): 379-386
- Sagar, Herman JS, Pospisilik JA, Grün D (2018) High-Throughput Single-Cell RNA-Sequencing and Data Analysis. Methods in Molecular Biology 1766: 257-283
- Felmlee D, Grün D, Baumert T (2017) Zooming in on liver zonation. Hepatology 67(2): 784-787
- Tay TL, Mai D, Dautzenberg J, Fernández-Klett F, Lin G, Sagar, Datta M, Drougard A, Stempfl T, Ardura-Fabregat A, Staszewski O, Margineanu A, Sporbert A, Steinmetz LM, Pospisilik JA, Jung S, Priller J, Grün D, Ronneberger O, Prinz M. (2017) A new fate mapping system reveals context-dependent random or clonal expansion of microglia. Nature Neuroscience 20(6): 793-803
- Herrtwich L, Nanda I, Evangelou K, Nikolova T, Horn V, Sagar, Erny D, Stefanowski J, Rogell R, Klein C, Gharun K, Follo M, Seidl M, Kremer B, Münke N, Senges J, Fliegauf M, Aschman T, Pfeifer D, Sarrazin S, Sieweke MH, Wagner D, Dierks C, Haaf T, Ness T, Zaiss MM, Voll RE, Deshmukh SD, Prinz M, Goldmann T, Hölscher C, Hauser AE, Lopez-Contreras AJ, Grün D, Gorgoulis V, Diefenbach A, Henneke P, Triantafyllopoulou, A (2016) DNA Damage Signaling Instructs Polyploid Macrophage Fate in Granulomas. Cell 167(5): 1264-1280
- Muraro MJ, Dharmadhikari G, Grün D, Groen N, Dielen T, Jansen E, van Gurp L, Engelse MA, Carlotti F, de Koning EJ, van Oudenaarden A (2016) A Single-Cell Transcriptome Atlas of the Human Pancreas. Cell Systems 3(4): 385-394
- Grün D#, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J, Jansen E, Clevers H, de Koning EJP, van Oudenaarden A# (2016) De Novo Prediction of Stem Cell Identity Using Single-Cell Transcriptome Data. Cell Stem Cell 19(2): 266-77
- Wu CC, Kruse F, Vasudevarao MD, Junker JP, Zebrowski DC, Fischer K, Noël ES, Grün D, Berezikov E, Engel FB, van Oudenaarden A, Weidinger G, Bakkers J (2016) Spatially Resolved Genome-wide Transcriptional Profiling Identifies BMP Signaling as Essential Regulator of Zebrafish Cardiomyocyte Regeneration. Developmental Cell 36(1): 36-49
- Grün D and van Oudenaarden A. (2015) Design and analysis of single cell sequencing experiments. Cell 163(4): 799-810
- Grün D*, Lyubimova A*, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A. (2015) Single-cell mRNA sequencing reveals rare intestinal cell types. Nature 525: 251-255
- Grün D*, Kester L*, van Oudenaarden A. (2014) Validation of noise models for single-cell transcriptomics enables genome-wide quantification of stochastic gene expression. Nature Methods 11(6): 637-40
- Stoeckius M*, Grün D*, Kirchner M, Ayoub S, Torti F, Piano F, Herzog M, Selbach M, Rajewsky N. (2014) Global characterization of the oocyte‐to‐embryo transition in Caenorhabditis elegans uncovers a novel mRNA clearance mechanism. EMBO Journal 33(16): 1751-66
- Stoeckius M*, Grün D*, Rajewsky N. (2014) Paternal RNA contributions in the C. elegans zygote. EMBO Journal 33(16): 1740-50
- Grün D*, Kirchner M*, Thierfelder N*, Stoeckius M, Selbach M, Rajewsky N. (2014) Conservation of mRNA and protein expression during development of C. elegans. Cell Reports 6(3): 565-77
- Kim DH, Grün D, van Oudenaarden A. (2013) Dampening of expression oscillations by synchronous regulation of a microRNA and its target. Nature Genetics 45(11): 1337-44
- Onal P*, Grün D*, Adamidi C*, Rybak A, Solana J, Wang Y, Rahn HP, Chen W, Ziebold U, Rajewsky N (2012) Molecular determinants of pluripotency are deeply conserved between mammalian and planarian stem cells. EMBO Journal 31(12): 2755-69
- Jungkamp AC, Stoeckius M, Mecenas D, Grün D, Mastrobuoni G, Kempa S, Rajewsky N (2011) In vivo and transcriptome-wide identification of RNA-binding protein target sites. Molecular Cell 44 (5): 828-40
- Adamidi C*, Wang Y*, Grün D*, Mastrobuoni G*, You X*, Tolle D, Dodt M, Mackowiak SD, Gogol-Doering A, Oenal P, Rybak A, Ross E, Alvarado AS, Kempa S, Dieterich C, Rajewsky N, Chen W (2011) De novo assembly and validation of planaria transcriptome by massive parallel sequencing and shotgun proteomics. Genome Research 21 (7): 1193-1200
- Grün D, Rajewsky N (2008) Computational prediction of microRNA targets in vertebrates, fruitflies and nematodes. MicroRNAs: From Basic Science to Disease Biology: 172-186
- Lall S*, Grün D*, Krek A, Chen K, Wang YL, Dewey CN, Sood P, Colombo T, Bray N, Macmenamin P, Kao HL, Gunsalus KC, Pachter L, Piano F, Rajewsky N (2006) A genome-wide map of conserved microRNA targets in C. elegans. Current Biology 16 (5): 460-471
- Grün D, Wang YL, Langenberger D, Gunsalus KC, Rajewsky N (2005) microRNA target predictions across seven Drosophila species and comparison to mammalian targets. PLoS Computational Biology 1 (1): e13
- Krek A*, Grün D*, Poy MN*, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N (2005) Combinatorial microRNA target predictions. Nature Genetics 37 (5): 495-500
Algorithm for rare cell type identification and differentiation trajectory inference from single-cell RNA-seq data
Grün, Nature Methods, 2020; Herman et al., Nature Methods, 2018; Grün et al., Cell Stem Cell, 2016; Grün et al., Nature 2015
Algorithm for the inference of cell fate bias in multipotent progenitors from singe-cell RNA-seq data
Herman et al., Nature Methods, 2018
Web interface for the visualization of single-cell RNA-seq data of murine hematopoietic progenitors
Herman et al., Nature Methods, 2018
Web interface for the Human Liver Cell Atlas
Aizarani et al., Nature, 2019
Zeis et al., Immunity, 2020
We investigates how signals from the microenvironment are integrated with stochastic processes in a cell to control cell fate decision in normal tissues and upon perturbations due to disease or tissue damage by combining single-cell resolution experimental methods with computational methods involving machine learning and mathematical modeling.