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Biology subjects

Ribback, S.

Publications and source records attributed to Ribback, S..

3 recordsLinked to original sources

High-throughput differentiation of human blood vessel organoids reveals overlapping and distinct functions of the cerebral cavernous malformation proteins

Cerebral cavernous malformations (CCMs) are clusters of thin-walled enlarged blood vessels in the central nervous system that are prone to recurrent hemorrhage and can occur in both sporadic and familial forms. The familial form results from loss-of-function variants in the CCM1, CCM2, or CCM3 gene. Despite a better understanding of CCM pathogenesis in recent years, it is still unclear why CCM3 mutations often lead to a more aggressive phenotype than CCM1 or CCM2 variants. By combining high-throughput differentiation of blood vessel organoids from human induced pluripotent stem cells (hiPSCs) with a CCM1, CCM2, or CCM3 knockout, single-cell RNA sequencing, and high-content imaging, we uncovered both shared and distinct functions of the CCM proteins. While there was a significant overlap of differentially expressed genes in fibroblasts across all three knockout conditions, inactivation of CCM1, CCM2, or CCM3 also led to specific gene expression patterns in neuronal, mesenchymal, and endothelial cell populations, respectively. Taking advantage of the different fluorescent labels of the hiPSCs, we could also visualize the abnormal expansion of CCM1 and CCM3 knockout cells when differentiated together with wild-type cells into mosaic blood vessel organoids. In contrast, CCM2 knockout cells showed even reduced proliferation. These observations may help to explain the less severe clinical course in individuals with a pathogenic variant in CCM2 and to decode the molecular and cellular heterogeneity in CCM disease. Finally, the ability to differentiate blood vessel organoids in a 96-well format will further facilitate their use in drug discovery and other biomedical research studies. STATEMENTS AND DECLARATIONSO_ST_ABSConflicts of interest statementC_ST_ABSThe authors declare no competing interests. The here described protocol for high-throughput organoid synthesis has been filed as a patent application at the European Patent Office (Process number: EP24213596.0) Author contribution statementMR, DSk, and UF designed the study. DSk, VS, LM, and RAP performed most of the functional experiments. SH and TA performed the CAM assays. SR performed the immunohistochemical stainings. SB, DSi, DSk, and VS performed the confocal microscopy and high-content imaging analyses. AE, CB, and EMB performed the scRNA sequencing analysis. AW and CAH performed and analyzed the karyotyping of the hiPSC clones. DSk, RAP, VS, KC, MR, and SB analyzed the data. DSk, VS, LM, and MR prepared figures. All authors contributed to the interpretation of the results. DSk, RAP, VS, and MR drafted the manuscript, and all authors contributed to writing. Ethics statementThis study does not involve human participants or animal subjects. Availability of data and materialsAll relevant data are published within the paper and the supplementary files. ScRNA sequencing data can be accessed through the Gene Expression Omnibus (GEO) database (record number: GSE276497).

molecular biology↗

scoMorphoFISH: A Deep-Learning enabled toolbox for single-cell single-mRNA quantification and correlative (ultra-)morphometry

Increasing the information depth of single kidney biopsies can improve diagnostic precision, personalized medicine and accelerate basic kidney research. Until now, information on mRNA abundance and morphologic analysis has been obtained from different samples, missing out on the spatial context and single-cell correlation of findings. Herein, we present scoMorphoFISH, a modular toolbox to get spatial single-cell single-mRNA expression data optimized for routinely generated kidney biopsies. Deep-Learning was used to virtually dissect tissue sections in tissue compartments and cell types to which single-cell expression data was assigned. Furthermore, we show correlative and spatial single-cell expression quantification with super-resolved podocyte foot process morphometry on the same histological section. In contrast to bulk analysis methods, this approach will help to identify local transcription changes even in less frequent kidney cell types on a spatial single-cell level with single-mRNA resolution. As this method performs well with standard formalin-fixed paraffin-embedded samples and we provide pretrained DL-networks embedded in a comprehensive image analysis workflow, this method can be applied immediately in a variety of settings.

pathology↗

Transcriptomic and Proteomic analysis of clear cell foci (CCF) in the human non-cirrhotic liver identifies several differentially expressed genes and proteins with functions in cancer cell biology and glycogen metabolism.

Clear cell foci (CCF) of the liver are considered to be pre-neoplastic lesions of hepatocellular adenomas and carcinomas. They are hallmarked by glycogen-overload and activation of AKT/mTOR-signaling. Here we report the transcriptome and proteome of CCF extracted from human liver biopsies by laser capture microdissection. We mainly found genes and proteins with lower expression in CCF compared to control samples. Of 14 differentially expressed transcripts only 3 were overexpressed and belong to previously uncharacterized long non-coding RNAs. At the protein level, 3 proteins were at least 1.5-fold higher expressed in CCF than in control samples, while 22 proteins were at least 1.5-fold less abundant. Using immunohistochemistry, the reduced expressions of STBD1, USP28, monad/WDR92, CYB5B and HSPE1 were validated in CCF. Knockout of STBD1, the gene coding for Starch-binding domain-containing protein 1, in mice did not have an effect on liver glycogen levels. Similiarly, Usp28 knockout in mice did not affect glycogen storage in diethylnitrosamine-induced liver carcinoma. Thus, these data indicate that reduced expression of STBD1 or USP28 in CCF is unlikely to explain glycogen overload. Our study revealed several novel proteins and RNAs that are differentially expressed in CCF and have functions relevant for carcinogenesis or the glycogen metabolism.

cancer biology↗