Project description:Raw data for Metabolomics Studies of Cell-Cell Interactions using Single Cell Mass Spectrometry Combined with Fluorescence Microscopy
Project description:Melanoma is the deadliest type of skin cancer, characterized by high cellular heterogeneity which contributes to therapy resistance and unpredictable disease outcome. Recently, by correlating Reflectance-Confocal-Microscopy (RCM) morphology with histopathological type, we identified four distinct melanoma-subtypes: dendritic-cell (DC), round-cell (RC), dermal-nest (DN), and combined-type (CT) melanomas. Our results demontsrate that each melanoma subtypes has a distinct biological and gene expression profile, related to tumor aggressiveness, confirming that RCM can be a dependable tool for in vivo detecting different types of melanoma and for early diagnostic screening
Project description:Current spatial transcriptomics methods identify cell types and states in a spatial context but lack morphological information. Electron microscopy, in contrast, provides structural details at nanometer resolution without decoding the diverse cellular states and identity. STEM address this limitation by correlating multiplexed error-robust FISH with electron microscopy from adjacent tissue sections. Using STEM to characterize demyelinated lesions in mice, we were able to bridge spatially resolved transcriptional data with morphological information on cell identities. This approach allowed us to link the morphology of foamy microglia and interferon-response microglia with their transcriptional signatures.
Project description:To study cancer cells heterogeneity at the single cell level we grew cancer cells as spheroids and extracted their RNA preform SmartSeq3xpress. We grew MDA-MB-231 cells on agar coated plates for 5-10 days in DMEM 10% FBS. The spheroids were incubated for 2 hours with Calcein AM and Vybrant Dye 10uM at 37C and washed twice with PBS. After dissociation with trypsinLE 0.25% the cells were facs sorted and the fluorescence intensity for each cell was recorded. The RNA were extracted and the cDNA libraries were built according to the SmartSeq3xpress protocol.
Project description:Gene regulatory interactions that shape developmental processes can often can be inferred from microarray analysis of gene expression, but most computational methods used require extensive datasets that can be difficult to generate. Here, we show that maximumentropy network analysis allows extraction of genetic interactions from limited microarray datasets. Maximum-entropy networks indicated that the inflammatory cytokine TNF-_ plays a pivotal role in Schwann cell–axon interactions, and these data suggested that TNF mediates its effects by orchestrating cytoplasmic movement and axon guidance. In vivo and in vitro experiments confirmed these predictions, showing that Schwann cells in TNF_/_ peripheral sensory bundles fail to envelop axons efficiently, and that recombinant TNF can partially correct these defects. These data demonstrate the power of maximum-entropy network-based methods for analysis of microarray data, and they indicate that TNF-_ plays a direct role in Schwann cell–axon communication.