SCOG Virtual Workshop: "Temporal Single Cell Analysis" - September 2020

The one-day workshop is hosted by Fabian Theis (Institute of Computational Biology, Helmholtz Munich) and Emmanuel Saliba (Helmholtz Institute for RNA-based infection research, Würzburg) and will cover the new exciting fields of RNA metabolic labeling and velocity. Motivation: A major obstacle to understanding dynamical processes in biology is that they happen in the scale of a few hours and are highly heterogeneous and asynchronous across cells. We are currently lacking a clear molecular, temporal and spatial picture of transition processes. Recent advances in single-cell and computational technologies are making addressing these challenges possible. Thus, this workshop will bring together experts in temporal single-cell technologies from the experimental and computational sides that are developing novel methods to uncover cellular transitions.

Alexander van Oudenaarden (Hubrecht Institute, Utrecht): "Integration of multiple lineage measurements from the same single cell reconstructs parallel tumor evolution"
Caroline Uhler (ETH, Zurich): "Multi-Domain Data Integration: From Observations to Mechanistic Insights"
Dana Pe'er (MSKCC, New York City): "Trajectories and Gene Regulation"
Volker Bergen (Helmholtz Munich): "RNA velocity state-of-the-art and future directions"
Florian Erhard (University of Würzburg): "infect me if you can: Studying virus infection in single cells"

SCOG Virtual Lecture Series

11 September 2020: Dominic Grün (MPI-IE, Freiburg): "Revealing Dynamics of Gene Expression Variability in Cell State Space "

The availability of high-throughput single-cell RNA-sequencing methods has facilitated the identification of cell types in organs and tissues, allowing the creation of tissue cell type atlases. In these efforts typically tens of thousands of cell are sequenced, frequently at low sequencing depth, and cell type identification relies on computational methods sensitive to highly expressed genes. To understand cell differentiation and cell state transitions in general it is crucial to quantify weak expression of lineage determining factors, requiring computational methods sensitive to variability of lowly expressed genes. We here introduce VarID, a computational method that identifies locally homogenous neighborhoods in cell state space, permitting the quantification of local gene expression variability. By controlling for the variance-mean dependence of transcript counts VarID delineates neighborhoods with differential variability and reveals pseudo-temporal dynamics of gene expression variability during differentiation. VarID recovers the stochastic activity of lineage-associated transcription factor networks in murine hematopoietic multipotent progenitors, and reveals increased variability of transcription factors associated with secretory lineages in mouse intestinal epithelial stem cells. In conclusion, our approach enables the investigation of stochastic gene activity, a previously understudied aspect, with the help of single-cell RNA-seq data and can provide novel insights in the regulation of cell fate decision.

Register for the Virtual Lecture Series here.

28 August 2020: Uwe Ohler (BIMSB/MDC, Berlin): "Single-cell decoding of gene regulatory programs"

In the 7th SCOG Virtual Lecture, Uwe Ohler from the BIMSB/MDC presented ongoing research and recently published data. The title of the talk is “Single-cell decoding of gene regulatory programs” and you can find references to the published papers on the slides.

Register for the Virtual Lecture Series here.

03 July 2020: Julio Saez-Rodriguez (Heidelberg University): "Extracting mechanistic insight from single-cell and spatial transcriptomics"

Single-cell technologies generate large datasets that allow us to study biomedical processes with unprecedented detail. These data could, in principle, shed light on the cellular communication mechanisms that control homeostasis in multi-cellular systems, and how these mechanisms go wrong in diseases ranging from cancer to heart failure. We have developed a set of tools for the analysis transcriptomic data, ranging from a meta-resource of biological knowledge (Omnipath) to methods to infer pathway and transcription factor activities (PROGENy and DoRothEA, respectively) from gene expression and subsequently infer causal paths among them (CARNIVAL). We have recently adopted these tools to single-cell data, supported by a comprehensive benchmark with in silico and real data. We have also developed approaches to analyze transcriptomic data with spatial resolution. In particular, our method MISTy is a flexible, scalable, and explainable machine learning framework for extracting interactions from spatial omics data. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We have also integrated the aforementioned to estimate the activities of pathways and transcription factors in MISTy, enabling us the analysis of intercellular signaling in a spatial context. All our methods are freely available as R-packages. I will illustrate these strategies in ongoing studies of with unpublished data in various disease contexts that we are performing with clinical collaborators.

Register for the Virtual Lecture Series here.

19 June 2020: Joachim L. Schultze (DZNE, Bonn): "Single Cell Omics on COVID-19 in DeCOI"

The current pandemic crisis triggered by SARS-CoV-2 leads to very different disease courses of COVID-19 ranging from very mild to very severe  with multi-organ failure. A significant number of patients with severe symptoms even succumb the infection. The host’s cellular reaction seems to be of utmost importance and responsible for this heterogenous response to the virus. Clearly, single cell omics can significantly contribute to our understanding of cellular reactions of infected cells and the response of the immune cells towards the infected cells. Over the last weeks, the German COVID-19 OMICS Initiative was established to coordinate NGS-based research efforts in COVID-19 research in Germany and together with colleagues world-wide. I will present initial results from single cell omics studies that are conducted on patient samples derived from COVID-19 patients within DeCOI.

Register for the Virtual Lecture Series here.

SCOG Workshop Computational Single Cell Genomics, March 2019, Munich

Watch the workshop sessions following the links. Thanks to the Chan Zuckerberg Initiative for sponsoring the recordings.

Christoph BockCeMM Research Center, ViennaCRISPR single-cell sequencing: Toward functional biology in high throughput
Joachim SchultzeDZNE, BonnFrom single cells to patients: A LifeTime journey
Sam RiesenfeldBroad Institute of MIT and Harvard, BostonUsing topic models to analyze the transcriptional spectrum of innate lymphoid cells
Clément CochainComprehensive Heart Failure Center, WürzburgTime series single-cell analysis of cardiac myeloid cells after acute myocardial infarction in mice
Heiko LickertHelmholtz MunichDeciphering mechanisms of β-cell failure and regeneration by single cell RNA Sequencing
Peter KharchenkoHarvard Medical School, BostonWiring together large single-cell RNA-seq collections
Vahid ShahrezaeiImperial College LondonBayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data
Amit FrishbergTel Aviv UniversityCell composition analysis of bulk genomics using single-cell data
David FischerHelmholtz Munichdiffxpy: Scalable differential expression analysis for single-cell RNA-seq data
Angela GoncalvesDKFZ HeidelbergDetermining KIR repertoires using single cell transcriptomics
Maria Colomé-TatchéHelmholtz MunichEpiscanpy: epigenomic single cell analysis
Sara AibarVIB-KU LeuvenExploiting single-cell transcriptomics and epigenomics to reconstruct gene-regulatory networks

Helmholtz Horizons 2018

The Helmholtz Horizons Symposium 2018, hosted by the President of the Association, highlights scientific breakthroughs of Helmholtz’s established researchers, showcases talented early career researchers, and gives a platform to the entrepreneurs and innovators, who ensure that Helmholtz research has societal impact.

Visit the Helmholtz Horizons Website.

Video production: BIOCOM

Fabian Theis: ‘The Digital (R)Evolution in Science’ – Berlin, 2018 talking about ‘Learning cell lineages using artificial intelligence’.

Christin Sünkel @ Helmholtz Horizons 2018: ‘The Digital (R)Evolution in Science’ – Berlin, 2018

“Single Cells in Time and Space”

Single cell RNA sequencing allows us to study all genes that are being used in a single cell. However powerful this method is, it leaves us with thousands of data points for each cell and we study thousands of cells per experiment. Hence, there is a big need to develop computational methods that can deal with this kind of “big data”.

In her presentation Christin also introduces the LifeTime initiative for a new FET-Flagship which is coordinated  by Nikolaus Rajewsky, Head of the Berlin Institute for Medical Systems Biology at the  Max-Delbrück Center for Molecular Medicine.

Christin Sünkel studied biochemistry and molecular biology at the University of Bayreuth. As a student intern at the Rockefeller University in New York City, in the laboratory of Thomas Tuschl she worked on the role of certain RNA-binding proteins. Her studies were supported by the Studienstiftung des deutschen Volkes. For her master thesis she moved to the laboratory of Nikolaus Rajewsky at Max-Delbrück Center for Molecular Medicine / Berlin Institute for Medical Systems Biology in Berlin to develop in vitro methods for the study of circular RNAs. Now she is pursuing her PhD in Nikolaus Rajewsky’s lab focusing on the function of circular RNAs and on the development of a method to study single cell gene expression in a spatial context. Her PhD work is supported by the Boehringer Ingelheim Fonds, Signgene and MDC BOOST.

Single Cell Data Analysis - Overview

This video has been made for bioinformatics students in Cairo University, Egypt; to discuss the main Single Cell Data Analysis challenges.

Hemberg lab course:

Abdelrahman Mahmoud @ DKFZ – Heidelberg, 2018.