iRhythmics

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.

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
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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
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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
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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.

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.

A CellProfiler pipeline for 3D analysis of cardiomyocytes

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.