Biology subjects

Dommann, N.

Publications and source records attributed to Dommann, N..

3 recordsLinked to original sources

bioRxiv2025

De novo H3.3K27M-altered Diffuse Midline Glioma in human brainstem organoids to dissect GD2 CAR T cell function

Diffuse midline glioma (DMG) is a rare yet highly aggressive paediatric cancer primarily arising in the pontine region of the brainstem, necessitating the development of scalable patient-representative models for treatment advance1,2. Here, we developed an FGF4-driven human brainstem organoid model, with high representation of pontine glial lineages. By genetically engineering de novo H3.3K27M-altered DMG, we show that this brainstem glial specification is essential for driving DMG tumorigenesis, resulting in tumours that recapitulate the infiltrative nature and molecular heterogeneity of patient samples. By performing prolonged GD2 CAR T cell treatment in this model, we could mirror variable treatment outcomes as observed in the clinic3,4 and demonstrate a high level of CAR T cell transcriptional heterogeneity. From these CAR T cell functional states, we could identify the most potent effector population and validated NCAM1 as a selection marker for their enrichment. In contrast, NCAM1- cells were linked to a cellular stress response, previously associated to immunotherapy resistance5. Furthermore, incorporating the brain-resident myeloid compartment resulted in DMG-specific, largely immunosuppressive microglia subtypes6. These disease-representative microglia reduced GD2 CAR T cell treatment efficacy and we identified the functional profiles most susceptible to this microglia-dependent immune modulation. Thus, we present a scalable human DMG model with critical applications towards understanding CAR T cell functionality to aid therapy development for this detrimental disease.

cancer biology↗

bioRxiv2021

Single cell chronoatlas of regenerating mouse livers reveals early Kupffer cell proliferation

The liver is exemplar to study tissue regeneration due to its inherent ability of repair and regrowth. It replaces its lost or injured tissue by the proliferation, interaction and temporal coordination of multiple residential cell types. Until now we lacked a detailed description of the specific contributions of each cell type to the regenerative process, and therefore analyzed mouse livers 0, 3, 6, and 24 hours following two-thirds partial hepatectomy (PHx) by single cell RNA-sequencing (scRNA-seq) and mass cytometry. Our resulting genome wide temporal atlas contains the time dependent transcriptional changes in hepatocytes, endothelial cells, bone marrow-derived macrophages (BMDM) and Kupffer cells. In addition, it describes the cell specific contribution of mitogenic growth factors from biliary epithelial, endothelial and stellate cells as well as chemokines and cytokines from BMDM and granulocytes. And interestingly, Kupffer cells as opposed to hepatocytes emerged as the first cell to proliferate presenting a new dynamic in the liver following PHx. Here, we provide a robust data set at cellular resolution to uncover new elements and revisit current dogmas on the mechanisms underlying liver regeneration. To facilitate access to the data, we have launched the portal www.phxatlas.ch in which the scRNA-seq data can be visualized.

developmental biology↗

bioRxiv2019

Bayesian Correlation is a robust similarity measure for single cell RNA-seq data

Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq.\n\nRecently, a Bayesian correlation scheme, that assigns low similarity to genes that have low confidence expression estimates, has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering 3 ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression.\n\nWe demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population.\n\nWe conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.

bioinformatics↗