“Multiplex Imaging of Spatial Biology in Single Cells”
NOTE The location for BME Seminars in the spring 2019 semester will be 012 Brauer Hall.
Host: Barani Raman (WashU Biomedical Engineering)
Abstract Spatial organization of cells and subcellular variations in tissues can be considered as a quantitative metric in determining the health and disease states. Single cell analyses of molecular profiles with in-situ detection methods dissect spatial heterogeneity of distinct cell types. Such detailed cellular maps shed light on spatial regulation mechanisms of diseases. In this talk, I will introduce multiplex imaging modalities to quantify up to hundred markers at macromolecular resolution in single cells. First, we have devised a “spatial genomics” method (seqFISH) to visualize 3D gene expression profiles in single cells. Using computational modeling of seqFISH data, we have formulated correlation FISH (corrFISH) to resolve dense RNA distributions in immune organs. CorrFISH identified cell-type specific gene expression of ribosomal proteins, a supporting evidence for specialized ribosome theory. Second, we have developed a “subcellular metabolomics” approach, Ion Beam Tomography (IBT), to quantify time-encoded 4D dynamics of chromatin, replication, and transcription at 65-nm lateral and 5-nm axial resolution. IBT revealed spatial segregation of transcription and replication in subcellular volumes of aberrant immune cells, a phase separation model for metabolic regulation of single cells. Lastly, we have demonstrated a “subcellular proteomics” scheme to determine 5D cancer-type specific profiles of nuclear markers in immune/cancer cells within archival patient samples. Super-resolved CODEX protein maps at 100-nm isotropic (x-y-z) resolution in FFPE tissues suggested spatial hierarchical ordering of epigenetics markers, intracellular signaling molecules, and cellular phenotypes in blood cancers.
Together, image-based molecular profiling, when combined with advanced mathematical
theories and computation, has the potential to decode high-dimensional dynamics at the
subcellular and molecular level in complex tissues and organs. Automated machine learning
algorithms in this single cell big data impact the biomedical practice and clinical care.
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