Journal of Mucosal Immunology Research
Open Access

Fibrosis; Innate immunity; Heart failure

Introduction

The typical organ and tissue reaction to almost all chronic, recurrent traumas is fibrosis. 1 Extracellular matrix (ECM) deposition can result in maladaptive remodelling and impairment of organ function, even if the initial fibrotic response is essential for tissue healing and preservation of organ integrity. As a result, it is believed that fibrosis is to blame for up to 45% of all fatalities in the industrialised world. 2 Myofibroblasts are thought to be the principal causes of fibrotic illness and organ failure since they synthesise the majority of the ECM. Current research demonstrates that resident mesenchymal cells like fibroblasts and pericytes are the source of the vast majority of myofibroblasts. 3,4,5,6 Despite these recent discoveries, we still don’t fully comprehend the molecular and cellular signals that start mesenchymal cell activation [1,2].

Using ECM regulator scoring, a subgroup of macrophages with profibrotic characteristics is discovered

To characterize and disentangle profibrotic immune cell populations, we sub-clustered leukocytes from a publicly available murine scRNA-seq time course of left ventricular myocardial infarction (MI) .23 Clustering and annotation revealed all major immune cell populations in MI. While the early, inflammatory phase (day 1–3) after MI was characterized by expansion of Ly6c2hi monocytes and granulocytes, the later remodeling phase (day 3–7) was marked by the expansion of resident-like macrophages, as well as a second macrophage cluster with high expression of Spp1, Arg1, and Fn1 (hereafter Spp1+ macrophages).

Using a profibrotic ECM regulator gene set identified by the matrisome project, we graded cells in order to objectively identify profibrotic immune cell types. 24 We identified these signatures and discovered that Spp1+ macrophages, which were additionally identified by the expression of profibrotic genes (Spp1, Fn1), exhibited the greatest levels of ECM regulator expression. Despite having the highest ECM regulator scores, MPC did not express the core ECM components at a significant level (core matrisome: collagens, glycoproteins, and proteoglycans) as expected, indicating that their function in fibrosis is more regulatory [3,4].

We carried out pseudotime trajectory inference analysis utilising PHATE dimensionality reduction technique to better define Spp1+ macrophages as well as genes that drive their differentiation.

We predicted that Spp1+ macrophages are monocyte-derived based on Spp1+ macrophage dynamics following MI and recent literature37 and consequently subsetted infiltrating MPC (Ly6c2hi monocytes, Ifn macrophages, and Spp1+ macrophages) for further study. With the aid of these three clusters, dimensionality reduction using PHATE and Slingshot analysis was able to pinpoint one trajectory for Ly6c2hi monocyte to Spp1+ macrophage differentiation using Ifn Mac as an intermediary cluster. It was discovered by imputing differentially expressed genes along pseudotime that Spp1+ macrophage differentiation was linked to the elevation of profibrotic genes (Spp1, Timp2) and the downregulation of inflammatory genes (Il1b, S100a8, Ifitm3). We found that the platelet factor 4 gene, also known as chemokine (C-X-C motif) ligand 4 (Cxcl4), ranked among the top differentially expressed genes along pseudotime [5,6].

We linked ECM regulator scores with gene expression across all immune cells to confirm Cxcl4 as a potential driver of a profibrotic immune cell signature independent of our assumption on Spp1+ macrophage ontogeny for trajectory inference analysis. Cxcl4 again rated among the top genes (rank = 7), which is consistent with a profibrotic ECM regulator profile. According to these findings, Cxcl4 was only co-expressed in macrophages with high ECM regulator signatures, with Spp1+ macrophages showing the highest expression.

Harmony was combined with reference datasets to provide a new UMAP embedding that enables Symphony reference-mapping. To provide a fair reference-mapping, the original authors’ cluster annotations were preserved. Symphony was used to map the datasets of macrophages following myocardial infarction, 23 Cxcl4/ mice following Sham or IRI surgery, and MPC in human heart failure50 to the corresponding reference single-cell datasets. 39 A k-NN classifier was used to annotate the query samples with the reference labels. The Figures display the percentage of each query cell type’s cells [7,8].

Discussion

Nf-core/rnaseq (version 3.1), Star (version 2.7.9a) for read alignment, Salmon (version 1.5.0) for read quantification, Trimgalore (version 0.6.6) for read trimming, and Gencode (version 38) for gene annotation were used for preprocessing in accordance with the nf-core nextflow pipeline (version 21.04.1). 81 Salmon was used to generate the count matrix file, which was then filtered to exclude mitochondrial and ribosomal genes (specified as “Mt tRNA,” “rRNA,” “Mt rRNA,” or “rRNA pseudogene” in the column gene type). Low expressed genes were then deleted using HTSFilter (version 1.32.0). 82 Genes (beginning with “Gm”) lacking a conventional gene name were also excluded from consideration. The filtered count matrix file was used to calculate differentially expressed genes using DESeq2 (version 1.32.0). 83 Finally, the following conclusions on the activity of the DoRothEA transcription factor and the PROGENy pathway were drawn [9,10].

Conclusion

Using the murine version of PROGENy (version 1.16.0), we estimated PROGENy pathway activity for single-cell and singlenuclear RNA sequencing data based on the top 500 most responsive genes as suggested by a benchmark research. We deduced PROGENy pathway activity for analysis of bulk RNA sequencing data as previously disclosed. 55 On the basis of gene t-values discovered using DE-seq analysis, original pathway activity scores were, in essence, inferred. The null distribution was created by repeatedly permuting t-values (10,000 times), and the original pathway scores were scaled to the resulting null distribution to provide a normalised pathway activity score.

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