Markus Wolfien

The project addresses the generation and establishment of programmed pacemaker cells for an in vitro drug testing possibility to perform predictive tests. This may lead to an improved treatment of cardiac arrhythmias or an accurate identification of potential drug molecules at a very early stage of development. Important benefits will arise in verifying the safety of a wide variety of medicines while reducing animal testing. For more information you may visit our project website at ...

The gut–brain axis (GBA) provides a bidirectional homeostatic communication between the gastrointestinal tract and the central nervous system. The interdisciplinary collaboration is going to fully explore a first comprehensive GBA cross-disease map of genetic, expression and regulatory changes associated with ulcerative colitis and schizophrenia disease entities.

Dysfunction of this axis can have pathophysiological consequences. Genome-wide association studies (GWAS) have identified >300 ...

Aims: The immune response is important for mediating the benefit of cardiac cell therapies. The role of varied immune responses in influencing the outcome of cardiomyocyte cell transplantation after myocardial infarction was investigated. Methods and Results: Cardiac flow cytometric analysis of C57BL/6J and T- and B cell deficient Rag2del mice revealed varied CD11b, natural killer and dendritic cell responses following sham injection and a disparate macrophage response after myocardial infarction. ...

This study contains the singele nuclei data analysis part of the Bl6 and Rag2del comparison. Here, we used Seurat, harmony, and monocle for an in-depth analysis.

Single-cell RNA-sequencing (scRNA-seq) provides high-resolution insights into complex tissues. Cardiac tissue, however, poses a major challenge due to the delicate isolation process and the large size of mature cardiomyocytes. Regardless of the experimental technique, captured cells are often impaired and some capture sites may contain multiple or no cells at all. All this refers to “low quality” potentially leading to data misinterpretation. Common standard quality control parameters involve the ...

This study contains our snRNA-Seq based comparison of whole hearts from Fzt.DU and Bl6 mice published in Cardiovascular Research.

This study includes the single snRNA-seq in whole adult murine hearts from an inbred (C57BL/6NRj) and an outbred (Fzt:DU) mouse strain in comparison to publicly available scRNA-seq data of the tabula muris project.

This study contains our single nuclei characterisation of whole hearts from Fzt.DU mice published in Cells.

Here, we conduct a proof of principle by comparing a 2D and 3D fluorescent image analysis based approach on unlabeled cardiomyocytes. Based on the CellProfiler software, we extracted high-dimensional features of individual cells and nuclei, which are subsequently down-sampled and clustered. These clusters are furthermore benchmarked via different machine learning classifiers (e.g., AdaBoost, Gradient Boosting, Random Forest) as the ground truth for our proposed approach.

Application of the LoRAS oversampling approach on single-cell/single-nuclei data to annotate/identify specific cell populations in new data based on previously, manually curated data.

For scRNA-Seq, iSABs were dissociated using the Primary Cardiomyocyte Isolation Kit (Thermo Fisher Scientific) before library preparation was performed using the 10xGenomics system with subsequent sequencing on the HighSeq4000 (Illumina). The mouse-SAN scRNA-Seq protocol is described in Goodyer et al. Preprocessing of raw sequencing data from iSABs relied on tools of the Cell Ranger Software (v.6.1.0) as was the procedure in Goodyer et al. Downstream analyses were conducted similar for both ...

Visualization of the workflow demonstrating a step-by-step explanation for a sc-SynO analysis. a) Several or one snRNA-Seq or scRNA-Seq fastq datasets can be used as an input. Here, we identify our cell population of interest and provide raw or normalized read counts of this specific population to sc-SynO for training. b) Further information for cluster annotation and processed count data are serving as input for the core algorithm. c) Based on the data input, we utilize the LoRAS synthetic ...

Validation of the sc-SynO model for the first use case of cardiac glial cell annotation. UMAP representation of the manually clustered Bl6 dataset of Wolfien et al. (2020) Precicted cells of sc-SynO are highlighted in blue, cells not chosen are grey. UMAP representation of the manually clustered dataset of Vidal (2019). Precicted cells of sc-SynO are highlighted in blue, cells not chosen are grey. Average expression of the respective top five cardiac glial cell marker genes for both validation ...

Validation of the sc-SynO model for the second use case of proliferative cardiomyocytes annotation. a) UMAP representation of the manually clustered single-nuclei dataset of Linscheid et al. (2019) Precicted cells of sc-SynO are highlighted in blue (based on top 20 selected features in the training model), red (based on top 100 selected features in the training model) cells not chosen are grey. b) UMAP representation of the manually clustered dataset of Vidal et al. (2020). PPrecicted cells of ...

This pdf file contains all figures of the article "Integrative Cluster Analysis of Whole Hearts Reveals Proliferative Cardiomyocytes in Adult Mice" in high resolution.

File contains the detailed cluster names for each data set, number of nuclei per cluster, average reads per nucleus, and average reads per cluster.

This dot-plot represents only the most significant genes per identified cell type cluster.

This file contains the top markers for the identified cell types.

This pdf contains the top 10 markers for each identified cell type as a dot-plot.

This file contains the R-script to analyse single nuclei data previously processed with kallisto and bustools. The analyses utilizes the Seurat, harmony and RNAvelocity package.

This file contains the detailed UNIX commands for mm10 index file creation (suitable for RNAvelocity) for kallisto as well as the commands used for the alignment with kallisto and the subsequent quantification with bustools.

This file contains the IDs, adj. p-values and official gene names of the top 100 marker genes (where applicable) for each of the identified cluster.

Underlying R script for the investigation of immune cells. Script contains basic data processing, as well as a DE and monocle analysis.

Here, we describe the index file generation of the mm10 genome, the genome alignment with kallisto, and quantification with bustools to obtain the used spliced / unspliced transcript input.

Here is the detailed R script to generate the input needed by scSynO for synthetic cell generation and classification model training.

The code that can be embedded into any other Seurat data processing workflow is:

cell_expression_target_cluster <- as.matrix(GetAssayData(seuratobject, slot = "data")[, WhichCells(seuratobject, ident = "target_cluster_number")]) cell_expression_all_other_clusters <- as.matrix(GetAssayData(seuratobject, slot = "data")[, WhichCells(seuratobject, ident = ...

Single nuclei transcriptomics data as .csv files from the Allen Brain atlas data set of mus musculus (https://celltypes.brain-map.org/) have been utilized as an input for scSynO. The underlying analysis is part of the manuscript entitled "Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling". Data anaylsis and visalizations were mainly generated with the Seurat R package (https://satijalab.org/seurat/archive/v3.2/spatial_vignette.html)

This file contains the detailed experimental protocol for the cultivation of "induced sinoatrial bodies (iSABs), the scRNA-Seq procedure as well as the general computational workflow for data processing. The R-script is provided separately.

This file contains the detailed experimental protocol to isolate nuclei from murine hearts.

This R-script contains the single nuclei analysis for our Fzt:DU and BL6 data, namely bustools file integration, preprocessing, scaling, clustering, marker indentification and annotation of markers and RNAvelocity computation.

Sehr geehrte Damen und Herren,

mit diesem Informationsflyer erfahren Sie mehr über das iRhythmics Projekt unseres Konsortiums.

Mit freundlichen Grüßen

Anna Skorska Projektmanagerin iRhythmics

This file contains a detailed description of the underlying experimental procedure and the computational analysis.

This PDF contains a dotplot indicating the gene expression of the top 100 markers (where applicable) for each of the identified 23 clusters.