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Schiller, C.

Publications and source records attributed to Schiller, C..

4 recordsLinked to original sources

Highly multiplexed imaging recovers immune and metabolic niches in multiple myeloma associated with disease progression and bone involvement

Multiple myeloma (MM) is a cancer arising from genetically aberrant plasma cells (PCs) that remain dependent on the bone marrow (BM) microenvironment for disease establishment and progression. Here, we spatially mapped BM biopsies from patients with MM, smoldering MM and monoclonal gammopathy of undetermined significance by imaging mass cytometry. We found that PCs near bone surfaces were quiescent and demonstrated a distance-to-bone-dependent upregulation of IL-32 and HIF1 in patients with bone disease, consistent with their role in promoting bone loss in MM. We further identified two distinct PC neighborhoods termed PC_OXPHOS characterized by focal PC growth, enrichment of endothelial cells and elevated oxidative phosphorylation; and PC_MYELOID, characterized by PCs interspersed with immune cells and featuring a glycolytic phenotype. Spatial interactions between PCs and immune correlated with shorter progression free survival. Notably, a strong neighbor preference between PCs and CD4+ T cells was an independent predictor for disease progression. Our work underscores spatial context as a key factor in understanding MM pathogenesis and the potential for spatial analyses to improve MM risk stratification. SignificanceThis study demonstrates that MM cells in different neighborhoods experience unique metabolic conditions and immune environments, and that neighbor preference of CD4+ T cells and plasma cell is associated with disease progression. These features were not captured by non-spatial metrics, highlighting the value of spatially resolved analyses in uncovering pathophysiological mechanisms of MM.

cancer biology↗

Comparison and Optimization of Cellular Neighbor Preference Methods for Quantitative Tissue Analysis

Studying the spatial distribution of cell types in tissues is essential for understanding their function in health and disease. A widely used spatial feature for quantifying tissue organization is the pairwise neighbor preference (NEP) of cell types, commonly referred to as co-occurrence or colocalization. Various methods to infer NEP have proved their utility in spatial omics studies, but despite their broad usage, no clear guidelines exist for selecting one method over the other. In this paper, we deconstruct frequently used methods into their underlying analysis steps and evaluate their optimal combination. We studied the methods on two aspects: (1) their discriminatory power to distinguish different tissue architectures and (2) their ability to recover the directionality of NEPs. We compared existing as well as our in-house developed method (conditional z-score (COZI)) and compared their performance using in silico tissue simulations and demonstrated its biological applicability in a myocardial infarction dataset. Overall, our study serves as a comprehensive guide for users and method developers in spatial omics analysis and offers a novel approach (COZI), which outperforms existing methods, to performing NEP analysis.

bioinformatics↗

QuAPPro: An R/shiny app for Quantification and Alignment of Polysome Profiles

Polysome profiling is a powerful technique to study mRNA translation. After separation of ribosomal subunits from monosomes and polysomes by ultracentrifugation on sucrose density gradients, a UV absorbance profile is recorded during elution. This profile can be used to assess global translational activity, or reveal changes in ribosome biogenesis or translation elongation. In parallel to UV absorbance profiles, it is also possible to record fluorescence to measure the association of fluorescently tagged proteins with ribosomes or polysomes. To this end, the area under subsections of the UV/fluorescence profiles needs to be quantified carefully. In addition, alignment of profiles in one graph helps to visualize differences. With QuAPPro, we present the first interactive web app that allows quantification and alignment of polysome profiles, independently of the device or software that was used to generate the profiles. This user-friendly tool does not only speed up the analysis of polysome profiles but also facilitates reproducibility and documentation of the process.

bioinformatics↗

Cell division tracing combined with single-cell transcriptomics reveals new cell types and differentiation paths in the regenerating mouse lung

Understanding the molecular and cellular processes involved in lung epithelial regeneration may fuel the development of new therapeutic approaches for lung diseases. We combined new mouse models that allow diphtheria toxin (DTA)-mediated depletion of specific epithelial cell types and GFP-labeling of dividing cells with single-cell transcriptomics to characterize the regeneration of the distal lung. We uncovered new cell types, some of which likely represent epithelial precursors, propose goblet cells as progenitor cells, and provide evidence that adventitial fibroblasts act as supporting cells in epithelial regeneration. We also found that DTA-expressing cells can persist in the lung, express specific inflammatory factors, and resemble a previously undescribed population in the lungs of COVID-19 patients. Our study provides a comprehensive single-cell atlas of the distal lung that characterizes early transcriptional and cellular responses to defined epithelial injury, encompassing proliferation, differentiation, and cell-to-cell interactions.

cell biology↗