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For the seminar on the 2nd of December at 4 pm Robin Xu invited Leif Ludwig (MDC/BIH) to talk about his research. If you are interested in joining write an e-Mail to compcancer at charite dot de to receive the zoom link.

From human lineage tracing to mitochondrial genetics.

Abstract:

Somatic mutations enable lineage and clonal tracing of human cells to reconstruct cellular population dynamics. Here, I will review our efforts to leverage somatic mitochondrial DNA mutations and single-cell multi-omics for studying clonal dynamics in human malignancies, as well as in the innate immune system and more broadly hematopoiesis. Further, I will discuss how mitochondrial genetic variation contributes to cellular heterogeneity and human phenotypes, including in the immune system and cancer. For example, we reveal pathogenic variants to be selected against in T cell subsets, suggestive of distinct metabolic vulnerabilities of human immune cells.

Bio:

Leif S. Ludwig graduated with a Master of Science (Diploma) and PhD (Dr. rer. nat.) in Biochemistry from the Freie Universität Berlin and MD from the Charite Universitätsmedizin Berlin. During his PhD he worked in the laboratory of Harvey Lodish at the Whitehead Institute for Biomedical Research, functionally investigating how human genetic variation affects human traits and phenotypes, in particular in the context of congenital blood disorders. During his postdoc in the laboratories of Aviv Regev and Vijay Sankaran at the Broad Institute of MIT and Harvard he established single-cell sequencing approaches leveraging natural mitochondrial sequence variation to enable the clonal tracing of human cells in a physiologic human context. In November 2020, he started his Emmy Noether research group at the Berlin Institute of Health at Charité and Berlin Institute for Medical Systems Biology at the MDC, where he and his team develop and apply single-cell technologies to investigate fundamental properties of mitochondrial genetics and stem cell dynamics in human hematopoiesis.

Professor Itai Yanai is the Director of the Institute for Computational Medicine and Professor of Biochemistry and Molecular Pharmacology at NYU Grossman School of Medicine. His research focus is mainly on gene expression profiling and transcriptome. He will be our next speaker on 16.11.2022 at 3pm Berlin Time. If you are interested in joining write an e-Mail to compcancer at charite dot de to receive the zoom link.

Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment

Goal:
Propose a unified catalog of gene modules that could underpin the recurrent cancer cell states by using scRNA-Seq, spatial transcriptomics and CODEX over 15 cancer types.

Abstract:
Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

For the seminar on the 19th of October Sinduja Chandrasekaran invited Jan Koster to talk about the R2 tool. If you are interested in joining write an e-Mail to compcancer at charite dot de to receive the zoom link.

R2: Genomics Analysis and Visualization Platform guided by stories from the neuroblastoma research

Overall goal

Development of a dedicated bioinformatics platform, loaded with tools and methods which allow wetlab biologists to interpret their own high-throughput experiments (gene expression data, chip-seq, whole genome sequencing, whole exome, methylation, etc ). 

Reaching this goal

R2 is our in house developed web-based bioinformatics analysis and visualization platform that enables analysis of probable targets in the context of (tumor) gene expression data (url: http://r2.amc.nl). Specifically focusing at biologists and biomedical scientists, R2 aids in the analysis and visualization of gene expression data (tumor series, experiments) and their annotated features (such as clinical information). R2 contains an expanding set of analyses which are heavily inter-connected, allowing users to quickly hop from one view (representation of the data) to another. Currently R2 supports all major platforms for human, mouse and rat and harbors more than 1.000.000 public samples (divided over more than 1800 datasets) and in addition also restricted datasets from research groups and consortia. R2 is also being employed in the integration, analysis and visualization of copy numbe / ChIP-seq / Whole genome sequence information in a wide array of research collaborations that include both research groups from around the world, as well as consortia in which we actively participate.

For the seminar on the 21st of September Mădălina Giurgiu invited Evangelia Petsalaki to talk about her research. If you are interested in joining write an e-Mail to compcancer at charite dot de to receive the zoom link.

Data-driven approaches towards studying context-specific cell signalling

Our group aims to understand and describe the organisation principles of cell signalling that allow the diverse and context-specific cell responses and phenotypes.

It is well established that signalling responses happen through complex networks. However, most signalling research still uses linear pathways as the ground truth. Moreover, signalling responses are highly dependent on context, such as tissue type, genetic background etc and therefore these static pathways are not always suitable. There is also a high bias in the literature towards kinases and pathways for which reagents and prior knowledge is readily available. This leaves a huge dark space in our understanding of cell signalling and significantly hinders studies of its general principles.

In this talk I will present two projects where we try to mitigate some of the above issues. For the first one I will present CEN-tools, an integrative webserver and python package, that allows users to navigate the contexts of different gene essentialities. I will demonstrate examples of its use in discovering new gene-gene relationships and important putative signalling targets for different cancers. I will also describe an extension of this project whereby we have developed a linear model to deconvolute the effect of different tissue types and driver cancer mutations on context specific essentiality. For the second one I will present a method that combines paired transcriptomics and imaging data to extract context-specific signalling networks, with the context in this case cell shape in breast cancer. The method is generalisable to any paired transcriptomics/phenotype data.

Short biography of Evangelia Petsalaki

I am originally from Athens Greece, where I also completed my undergraduate degree in Biology at the University of Athens, with a thesis on using neural networks for the prediction of protein subcellular location. I then moved to the EMBL in Heidelberg, where under the supervision of Rob Russell I did a PhD (2009) on structural bioinformatics predicting peptide binding on protein structures. My postdoc was done under the supervision of Tony Pawson and Fritz Roth where I worked on a diverse array of projects from Rho signalling, proteomics, phosphoproteomics to yeast genetics. Since 2017 I am a group leader at the EMBL-EBI and my group focuses on the study of the principles underpinning context specific cell signalling responses in humans.

The upcoming CompCancer Seminar will be hosted by Eleonora Usatikova. Find the invitation below. The link is available from compcancer at charite dot de.

Dear all,

I would like to invite you to the next CompCancer seminar, which will take place on 23.02. at 3 pm.

Our guest speaker will be Benjamin Auerbach, a Ph.D. student from the Laboratory for Statistical and Translational Genomics at the University of Pennsylvania. He will present his unpublished work on circadian phase inference in single cells.

Title of the talk: “Unsupervised Circadian Phase Inference in Single-Cell RNA-Sequencing Data”

The circadian clock is a 24-hour cellular timekeeping mechanism that temporally regulates human physiology. Single-cell RNA-sequencing (scRNA-seq) has been increasingly adopted to study circadian transcription. Nonetheless, scRNA-seq has mainly been applied to data generated over light-dark cycle time courses, in which cell collection time in the light-dark cycle is presumed to be a direct proxy for cell circadian time. This assumption limits using these data for the discovery of cell phase heterogeneity, its determinants (e.g. spatial location within a tissue), and its role in other cellular processes (e.g. gating cellular differentiation). Moreover, investigators may be interested in conducting single-sample scRNA-seq of unsynchronized cell populations, for which sample collection time is not a meaningful proxy of cell phase. One approach to break the reliance on sample collection time as a proxy for cell phase is to estimate cell phase from scRNA-seq data directly, a task referred to as unsupervised phase inference. While existing approaches have been developed for similar problems, such as cell cycle phase inference in scRNA-seq and circadian phase inference from bulk RNA-seq samples, these approaches yield poor circadian phase estimates in scRNA-seq. Moreover, existing approaches do not quantify estimation uncertainty, which is essential for result interpretation from highly sparse scRNA-seq data. We’ve developed an unsupervised phase inference algorithm, Tempo, to predict cell circadian time from scRNA-seq expression. Based on Bayesian variational inference, Tempo incorporates domain knowledge of the circadian clock to yield state-of-the-art circadian phase estimates and well-calibrated uncertainty quantifications. We further demonstrate these properties generalize to the cell cycle.