iRhythmics

Projects that do not fall under current programmes.

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.

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 ...

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.

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.

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.

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

The file contains an UMAP representation illustrating the expression of the stem cell marker Cd34 in all identified cell clusters.

The file contains FeaturePlots of 4 different cardiomyocyte markers (Actn2, Tnnc1, Actc1 and Ryr2) to demonstrate how annotation of clusters needs to be based on several markers and approaches, which in the aggregate allow for more reliable conclusions/results.

The file contains the absolute and relative cell/nuclei numbers per cluster for each individual dataset used in the integrated analysis. Subpopulations were grouped together for the representation of relative cell/nuclei numbers.

This file contains the top 100 transcripts for the identified cell types in the integrated dataset.

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.

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:

cellexpressiontarget_cluster

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 script for the downstream scRNA-Seq analysis including quality control using the barcode ranking method together with the tool DropletUtils to exclude empty droplets and undetected genes as well as standard data processing (normalisation, variable feature identification, scaling, and dimensionality reduction) using tools of Seurat (v.3.2.2). After cluster annotation the %mtDNA was plotted for both datasets.

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 R-script to analyse single nuclei and single cell data of Bl6 and Fzt:DU mice previously processed with cellranger. The analyses utilizes the Seurat and harmony package to integrate three datasets before subsequent downstream analysis characterizing proliferative cardiomyocytes.

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.

This file contains Violin plots for mitochondrial gene transcripts (%mtRNA), cardiac marker (Tnnt2), and pace-maker marker (Hcn4) for identified cell clusters in iSABS and sinoatrial node region (results obtained from a data reanalysis of Goodyer et al.).

This file contains several Scatter plots illustrating the correlation of %mtDNA and (a) Tnnt2 (b) Hcn4 (c) total number of gene transcripts per cell In Tnnt2 positive cells and (d) total number of gene transcripts per cell In Hcn4 positive cells.

Sehr geehrte Damen und Herren,

wir laden Sie herzlich zum unseren 2. Statusmeeting des Forschungsverbundes iRhythmicsein, welches Live via Webex *Anbieter am *10.06.2021 übertragen wird. Anbei finden Sie den Flyer mit dem gesamten Programm.

Informationen zum Konsortium erfahren Sie unter: https://irhythmics.med.uni-rostock.de/konsortium/

Bei Interesse wenden Sie sich bitte an die Projektmanegerin via anna.skorska@med.uni-rostock.de Somit werden Ihnen die Zugangsdaten für die Webex ...

Graphical abstract for the manuscript "Quality control in scRNA-Seq can discriminate pacemaker cells – the mtDNA bias" by Anne-Marie Galow#, Sophie Kussauer, Markus Wolfien, Robert David, and Andreas Hoeflich#

This file contains a UMAP plot generated for the Goodyer dataset illustrating the various cell populations and their proportions as well as the original t-SNE plot of the respective data published by Goodyer et al. in 2019 (doi: 10.1161/CIRCRESAHA.118.314578).

This file contains a UMAP plot for the iSABs dataset illustrating the various cell populations and their proportions.

A CellProfiler pipeline for 3D analysis of cardiomyocytes