PubMed HealthSearch

Biomedical subjects

Peter K Sorger

Publications and source records attributed to Peter K Sorger.

3 recordsLinked to original sources

Spatial Integration of Protein and Chromosomal States Reveals Early Copy-Number Changes and Genotype-Associated Immune Neighborhoods in Serous Ovarian Cancer Evolution.

UNLABELLED: Detecting chromosomal copy-number alterations together with protein-defined cell states in intact tissue is critical for understanding early clonal evolution and microenvironmental interactions in cancer. We developed ORION-FISH, which integrates high-plex tissue imaging with a morphology-preserving DNA fluorescence in situ hybridization (DNA-FISH) workflow and single-cell registration, yielding measurements concordant with clinical FISH. In high-grade serous ovarian carcinoma (HGSOC), ORION-FISH recapitulated known chromosomal changes while revealing subclonal heterogeneity missed by targeted sequencing. Applied to serous tubal intraepithelial carcinomas, precursors of HGSOC, ORION-FISH identified intermixed epithelial cells with MYC or CCNE1 copy-number gains, as well as concurrent alterations associated with distinct immune microenvironments. In addition, epithelial cells with MYC and CCNE1 copy-number gains were detected in morphologically normal fallopian tube epithelium, along with rare MDM4 increases across epithelial lineages. Together, ORION-FISH provides a framework linking chromosomal copy-number states to protein-defined phenotypes within preserved tissue architecture, enabling context-aware interrogation of early copy-number diversification at single-cell resolution. SIGNIFICANCE: We introduce ORION-FISH, a spatially resolved workflow integrating multiplexed protein imaging with DNA-FISH to map genomic alterations within intact tissues. Applying this approach to ovarian cancer precursors reveals early copy-number diversification and associations with the local immune context, providing a foundation for studying how genomic and microenvironmental states coevolve during tumor initiation.

Female

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Epidermal Growth Factor

The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging.

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.

Computational Biology