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Birrer, M.

Publications and source records attributed to Birrer, M..

4 recordsLinked to original sources

Single-cell analysis of ovarian myeloid cells identifies aging associated changes in macrophages and signaling dynamics

The aging of mammalian ovary is accompanied by an increase in tissue fibrosis and heightened inflammation. Myeloid cells, including macrophages, monocytes, dendritic cells, and neutrophils, play pivotal roles in shaping the ovarian tissue microenvironment and regulating inflammatory responses. However, a comprehensive understanding of the roles of these cells in the ovarian aging process is lacking. To bridge this knowledge gap, we utilized single-cell RNA sequencing (scRNAseq) and flow cytometry analysis to functionally characterize CD45+ CD11b+ myeloid cell populations in young (3 months old) and aged (14-17 months old) murine ovaries. Our dataset unveiled the presence of five ovarian macrophage subsets, including a Cx3cr1lowCd81hi subset unique to the aged murine ovary. Most notably, our data revealed significant alterations in ANNEXIN and TGF{beta} signaling within aged ovarian myeloid cells, which suggest a novel mechanism contributing to the onset and progression of aging-associated inflammation and fibrosis in the ovarian tissue.

immunology↗

Single cell compendium of the muscle microenvironment in peripheral artery disease reveals capillary endothelial heterogeneity and activation of resident macrophages

AO_SCPLOWBSTRACTC_SCPLOWPeripheral artery disease (PAD) results from atherosclerosis and chronic narrowing of lower limb arteries leading to decreased muscle perfusion. Current treatments are suboptimal, partly due to limited understanding of PAD muscle pathology. Here we use scRNAseq and spatial transcriptomics to analyze the composition of the muscle microenvironment in non-ischemic and PAD patients. We identified ATF3/4+ endothelial cells (ECs) that exhibit altered angiogenic and immune regulatory profiles during PAD and confirmed that ATF4 signaling in ECs is required for effective ischemia recovery. Also, capillary ECs display features of endothelial-to-mesenchymal transition. Furthermore, LYVE1hiMHCIIlow macrophages are the dominant macrophage population in human muscle, adopting a more pro-inflammatory profile during PAD. Finally, we analyzed alterations in intercellular communication within the muscle microenvironment during PAD and confirmed that EC-derived factors can influence macrophage polarization. This dataset deeply characterizes the PAD muscle microenvironment and provides a resource for exploration of targeted therapies.

cell biology↗

Multi-omic analysis supports a developmental hierarchy of molecular subtypes in high-grade serous ovarian carcinoma

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but these have yet to impact clinical practice. Interpretation and translation of HGSOC subtypes are complicated by tumor evolution and polyclonality accompanied by accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. The chronology of HGSOC subtype evolution was examined in the context of these factors by a novel integrative analysis of bulk absolute somatic copy number analysis and gene expression in The Cancer Genome Atlas, complemented by single-cell RNA-seq analysis of six independent tumors. The approach was validated by contrast to soft-tissue sarcoma. Genomic lesions associated with HGSOC subtypes tend to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations is particularly evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors are characterized by largely intact genome integrity, high immune infiltration, and younger patient age. We propose an alternative model to discrete subtypes of HGSOC, in which tumors develop from an early differentiated spectrum to a late proliferative spectrum, along a timeline characterized by increasing genomic instability and subclonal expansion. The proposed methods provide a new approach to investigating tumor evolution through multi-omic analysis. Statement of SignificanceThis study proposes a method to infer whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are therefore potentially appropriate targets for therapy, and demonstrates that this is not the case for high-grade serous ovarian carcinoma (HGSOC). Significant findings for HGSOC include: O_LITumor purity, ploidy, and subclonality can be reliably inferred from different genomic platforms and show marked differences between subtypes C_LIO_LIRecurrent DNA alterations are associated with subtypes and tend to occur more frequently in subclones C_LIO_LISingle-cell sequencing of 42,000 tumor cells reveals widespread heterogeneity in tumor cell type composition that drives bulk subtype calls, but demonstrates a lack of intrinsic subtypes among tumor epithelial cells C_LIO_LIFindings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tissue of origin and tumor stage, and evolution between properties previously associated with discrete subtypes C_LI

cancer biology↗

Consensus on Molecular Subtypes of Ovarian Cancer

INTRODUCTIONVarious computational methods for gene expression-based subtyping of high-grade serous (HGS) ovarian cancer have been proposed. This resulted in the identification of molecular subtypes that are based on different datasets and were differentially validated, making it difficult to achieve consensus on which definitions to use in follow-up studies. We assess three major subtype classifiers for their robustness and association to outcome by a meta-analysis of publicly available expression data, and provide a classifier that represents their consensus.\n\nMETHODSWe use a compendium of 15 microarray datasets consisting of 1,774 HGS ovarian tumors to assess 1) concordance between published subtyping algorithms, 2) robustness of those algorithms to re-clustering across datasets, and 3) association of subtypes with overall survival. A consensus classifier is trained on concordantly classified samples, and validated by leave-one-dataset-out validation.\n\nRESULTSEach subtyping classifier identified subsets significantly differing in overall survival, but were not robust to re-fitting in independent datasets and grouped only approximately one third of patients concordantly into four subtypes. We propose a consensus classifier to identify the minority of unambiguously classifiable tumors across multiple gene expression platforms, using a 100-gene signature. The resulting consensus subtypes correlate with patient age, survival, tumor purity, and lymphocyte infiltration.\n\nCONCLUSIONSOur analysis demonstrates that most HGS ovarian cancers are not able to be subtyped. A minority of tumors can be classified and our proposed consensus classifier consolidates and improves on the robustness of three previously proposed subtype classifiers. It provides reliable stratification of patients with HGS ovarian tumors of clearly defined subtype, and will assist in studying the role of polyclonality in the majority of tumors that are not robustly classifiable.

cancer biology↗