Discovery of NRG1-VII: a novel myeloid-derived class of NRG1 isoforms

Berrocal-Rubio, Miguel Ángel; Prawer, Yair D J; Dinevska, Marija; Paoli-Iseppi, Ricardo De; Widodo, Samuel S; Rajab, Nadia; Nardo, William De; Hallab, Jeannette C.; Li, Anran; Mantamadiotis, Theo; Clark, Michael B.; Wells, Christine A.

doi:10.1101/2023.02.02.525781

Cited by 2 publications

(1 citation statement)

References 65 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These genes are immune activating, and loss of the monocytes expressing these genes is an indication of an immunosuppressive milieu within the leukemic bone marrow. [27][28][29][30][31] Our communication results therefore point at the reduced communication within the immune compartment, potentially as a consequence of the immune suppression caused by the tumor.…”

Section: Changes In Communication In Aml Vs Healthy Bone Marrowmentioning

confidence: 63%

Communityassesses differential cell communication using large multi-sample case-control scRNAseq datasets

Solovey,

Celik,

Salcher

et al. 2024

Preprint

View full text Add to dashboard Cite

Cell-cell communication is essential for physiological tissue function. In disease, this communication often gets disbalances by changes in the tissue cell type composition, fraction of cell engaged in communication and the rising or dropping expression levels of ligands, receptors and adhesion molecules. The changes in all these components of communication can be studied using single cell RNA-sequencing (scRNAseq) methods. With dropping sequencing costs, it is now possible to perform scRNAseq studies in larger cohorts of case and control samples to better address the heterogeneity of diseases. Here we present community, an R-based tool that is designed to perform differential communication analysis using scRNAseq between large cohorts of cases and controls. Community is able to reconstruct communication between different cell types both in the case and the control cohort of a dataset, and subsequently analyze which communication channels are affected in disease. Community is the first tool that integrates cell type abundance into the calculation of an interaction strength. Community is also able to disentangle the mechanisms underlying these changes, as well as detect interactions that are kept compensated by a sender or a receiver despite the disbalanced signaling from the counterpart. We tested community on two disease entities, ulcerative colitis and acute myeloid leukemia, using published scRNAseq datasets. We compared the performance of our tool to other differential communication pipelines, which community outperformed in speed and robustness. Overall, community is a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets disentangling the driving mechanisms of communication shifts in disease.

show abstract

Section: Changes In Communication In Aml Vs Healthy Bone Marrowmentioning

confidence: 63%

Communityassesses differential cell communication using large multi-sample case-control scRNAseq datasets

Solovey,

Celik,

Salcher

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Community: component based differential cell communication analysis in large multi-sample case-control scRNAseq datasets

Solovey,

Celik,

Salcher

et al. 2024

Preprint

View full text Add to dashboard Cite

Cell-cell communication is essential for physiological tissue function. In disease, communication often gets disbalanced by changes in the tissue cell type composition, fraction of cells engaged in communication, and changes in expression levels of ligands, receptors and adhesion molecules. Single cell RNAseq analyses allow to measure these parameters in healthy and diseased tissues. Here we present community, an R-based tool that is designed to perform differential communication analysis using scRNAseq data between large cohorts of cases and controls. Community performs differential analysis to identify communication channels affected in disease by reconstructing the communication between different cell types using three components: cell type abundance, fraction of active cells, and ligand/receptor expression levels, both in cases and controls. This approach allows to not only identify up- or down-regulated interactions, but also detect cases of compensation, where a shift in one component gets compensated by a counter-shift in another component, keeping the levels of communication stable. The component analysis enables us to better understand the underlying biological processes leading to changes in communication. We demonstrate the performance of community by using two disease entities, ulcerative colitis and acute myeloid leukemia. We compared the performance of our tool to other differential communication pipelines, which community outperformed in robust identification of up- and down-regulated interactions, as well as its unique feature of identifying compensated communication shifts. Overall, community is a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets, and disentangle the driving mechanisms of communication shifts in disease.

show abstract

Discovery of NRG1-VII: a novel myeloid-derived class of NRG1 isoforms

Cited by 2 publications

References 65 publications

Communityassesses differential cell communication using large multi-sample case-control scRNAseq datasets

Communityassesses differential cell communication using large multi-sample case-control scRNAseq datasets

Community: component based differential cell communication analysis in large multi-sample case-control scRNAseq datasets

Contact Info

Product

Resources

About