Project description:Raw data for Metabolomics Studies of Cell-Cell Interactions using Single Cell Mass Spectrometry Combined with Fluorescence Microscopy
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.
Project description:Glioblastoma (GBM) remains the most malignant primary brain tumor, with a median survival rarely exceeding 2 years. Tumor heterogeneity and an immunosuppressive microenvironment are key factors contributing to the poor response rates of current therapeutic approaches. GBM-associated macrophages (GAMs) often exhibit immunosuppressive features that promote tumor progression. However, their dynamic interactions with GBM tumor cells remain poorly understood. Here, we used patient-derived GBM stem cell cultures and combined single-cell RNA sequencing of GAM-GBM co-cultures and real-time in vivo monitoring of GAM-GBM interactions in orthotopic zebrafish xenograft models to provide insight into the cellular, molecular, and spatial heterogeneity. Our analyses revealed substantial heterogeneity across GBM patients in GBM-induced GAM polarization and the ability to attract and activate GAMs – features that correlated with patient survival. Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified LGALS1 as a primary regulator of immunosuppression. Overall, our work highlights that GAM-GBM interactions can be studied in a clinically relevant way using co-cultures and avatar models, while offering new opportunities to identify promising immune-modulating targets.
Project description:Background. Heterogeneity in vascular smooth muscle cells (VSMCs) has been classically defined with a small set of predefined markers. Single cell genomics provides a unique unbiased approach to evaluate VSMC phenotypes. Objectives. The goal of this study was to define the heterogeneity of primary murine VSMCs grown in culture. VSMCs grown in culture are routinely used as a model of VSMCs in the vessel wall. We wanted to better understand the assumptions of this model. RNA sequencing was performed on single aortic VSMCs from 3-4 month old male mice at passages 1 and 2 (P1 and P2). Single cell contractility measurements were performed using Traction Force microscopy.
Project description:Use of single-cell transcriptomics to test early HD selective vulnerability by comparing CTRL and HD telencephalic organoids at day 45 and 120 of differentiation. To test the influence and the interactions between healthy and HD cells, chimeric organoids composed of CTRL and HD cells juxtaposed within the same organoid were grown and analyzed by scRNAseq at day 120.
Project description:We combined the Single-probe single cell MS(SCMS) experimental technique with a bioinformatics software package, SinCHet-MS (Single Cell Heterogeneity for Mass Spectrometry), to characterize changes of tumor heterogeneity, quantify cell subpopulations, and prioritize the metabolite biomarkers of each subpopulation.
Project description:Live cell imaging allows direct observation and monitoring of phenotypes that are difficult to infer from the transcriptome. However, existing methods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability. Here, we describe an upgraded version of Single Cell Optical Phenotyping and Expression (SCOPE-seq2), which builds on our earlier efforts to combine single-cell imaging and expression profiling, with substantial improvements in throughput, molecular capture efficiency, linking accuracy, and compatibility with standard microscopy instrumentation. We introduce improved optically decodable mRNA capture beads and implement a more scalable and simplified optical decoding process. We demonstrated the utility of SCOPE-seq2 for fluorescence, morphological, and expression profiling of individual primary cells from a human glioblastoma (GBM) surgical sample, revealing relationships between simple imaging features and cellular identity, particularly among malignantly transformed tumor cells.
Project description:We have developed a high-throughput method for the detection and quantification of a wide range of phosphatidylcholine (PC) and sphingomyelin (SM) species from single cells that combines fluorescence-assisted cell sorting (FACS) with automated chip-based nanoelectrospray ionization (nanoESI) and shotgun lipidomics. Using this method we can detect and perform relative quantitation on more than >50 different PC and SM species from immortalised human cells, and can easily distinguish between tumorigenic and non-tumorigenic prostate cell lines.
Project description:Large-scale chromosome structure and spatial nuclear arrangement have been linked to control of gene expression and DNA replication and repair. Genomic techniques based on chromosome conformation capture assess contacts for millions of loci simultaneously, but do so by averaging chromosome conformations from millions of nuclei. Here we introduce single cell Hi-C, combined with genome-wide statistical analysis and structural modeling of single copy X chromosomes, to show that individual chromosomes maintain domain organisation at the megabase scale, but show variable cell-to-cell chromosome territory structures at larger scales. Despite this structural stochasticity, localisation of active gene domains to boundaries of territories is a hallmark of chromosomal conformation, affecting most domains in most nuclei. Single cell Hi-C data bridge current gaps between genomics and microscopy studies of chromosomes, demonstrating how modular organisation underlies dynamic chromosome structure, and how this structure is probabilistically linked with genome activity patterns. Mouse Th1 single-cell Hi-C maps were produced and paired-end sequenced. 10 single-cell samples and a multi-sample pool together with a population Hi-C sample are included.