A5072205610- Single-cell and spatial transcriptomics
- Gene expression and cancer classification
- Fault Detection and Control Systems
- Molecular Biology Techniques and Applications
- Cell Image Analysis Techniques
- Real-time simulation and control systems
- Gene Regulatory Network Analysis
- Erythrocyte Function and Pathophysiology
- Immune Cell Function and Interaction
- Advanced Fluorescence Microscopy Techniques
- Birth, Development, and Health
- Reproductive System and Pregnancy
- Hematopoietic Stem Cell Transplantation
- Prenatal Screening and Diagnostics
- Pregnancy and preeclampsia studies
Berlin Institute of Health at Charité - Universitätsmedizin Berlin
2021-2025
Freie Universität Berlin
2024-2025
ETH Zurich
2019
Imaging-based spatially resolved transcriptomics can localise transcripts within cells in 3D. Cell egmentation precedes assignment of to and nnotation cell function. However, segmentation is usually performed 2D, thus unable deal with spatial oublets arising from overlapping cells, resulting segmented containing originating multiple cell-types. Here we present a computational tool called ovrlpy that identifies tissue folds inaccurate cell-segmentation.
BACKGROUND: Preeclampsia is a severe hypertensive disorder in pregnancy that causes preterm delivery, maternal and fetal morbidity, mortality, life-long sequelae. Understanding the pathogenesis of preeclampsia critical first step toward protecting mother child from this syndrome increased risk cardiovascular disease later life. However, effective early predictive tests therapies for are scarce. METHODS: To identify novel markers signaling pathways onset preeclampsia, we profiled human...
The combination of a cell’s transcriptional profile and location defines its function in spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay choice for characterizing cells situ . SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with is correct aggregation mRNA molecules into cells. Traditionally, aggregating cell-based features begins identification via segmentation nucleus or cell membrane. However,...
Abstract Spatially resolved transcriptomics has become the method of choice to characterise complexity biomedical tissue samples. Until recently, scientists have been restricted profiling methods with high spatial resolution but for a limited set genes or that can profile transcriptome-wide at low resolution. Through recent developments, there are now which offer subcellular and full transcriptome coverage. However, utilizing gene these new remains elusive due several factors including...
Abstract Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden algorithm of choice in community. However, field spatial has been considered a non-spatial method. Here, we show by integrating embeddings rendered into computationally highly performant, spatially aware method compares well state-of-the art methods.
Abstract Spatially resolved transcriptomics (SRT) has become the method of choice for characterising complexity biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set target genes at high spatial resolution or transcriptome‐wide but low resolution. Through recent developments, there are now offer both subcellular and full transcriptome coverage. However, utilising these new methods' gene remains elusive due several factors,...
1 Abstract The combination of a cell’s transcriptional profile and location defines its function in spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay choice for characterizing cells situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with is correct aggregation mRNA molecules into cells. Traditionally, aggregating cell-based features begins identification via segmentation nucleus or cell membrane....