Project description:Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based, transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial mRNA. To overcome this challenge, we combine 1000-fold volumetric expansion with multiplexed error robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH, a method enabling high-throughput, spatially resolved profiling of thousands of operons within individual bacteria. Using bacterial-MERFISH, we dissect the response of E. coli to carbon starvation, systematically map subcellular RNA organization, and chart the adaptation of B. thetaiotaomicron to micron-scale niches in the mammalian colon. We envision bacterial-MERFISH could prove useful in the study of bacterial single-cell decisions made in diverse, spatially structured, and native environments.
Project description:These data were used in the spatial transcriptomics analysis of the article titled \\"Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging\\".
Project description:To investigate spatial heterogeneities in the axolotl forebrain, a coronal section of it was obtained for spatial transcriptomics using Visium V1.
Project description:Current spatial transcriptomics methods provide molecular and spatial information but no morphological readout. Here, we present STEM - a method that correlates multiplexed error-robust FISH with electron microscopy from neighboring tissue sections of the same sample. STEM links transcriptional and spatial organization of single cells with ultrastructural morphology of the tissue in vivo. Using STEM to characterize demyelinated white-matter lesions allowed us to link morphology of myelin-laden foamy microglia to transcriptional signature. Moreover, we revealed that interferon-response microglia have unique morphology and are enriched near CD8 T-cells.
Project description:In this study, we present a multiplexed version of Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm2 with spots of 50 µm×50 µm. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared with previous reports. Furthermore, the introduction of alignment markers allows seamless registration of images and spatial transcriptomic spot coordinates. Together with technological advances, we provide an open-source computational pipeline to transform raw sequencing data from xDBiT experiments into the AnnData format. The functionality of xDBiT was demonstrated by the acquisition of 16 spatially resolved transcriptomic datasets from five different murine organs, including cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of xDBiT spatial transcriptomes allowed for in-depth characterization of the murine kidney.
Project description:Knowledge of the expression profile and spatial landscape of the transcriptome in individual cells is essential for understanding the rich repertoire of cellular behaviors. Here we report multiplexed error-robust fluorescence in situ hybridization (MERFISH), a single-molecule imaging approach that allows the copy numbers and spatial localizations of thousands of RNA species to be determined in single cells. Using error-robust encoding schemes to combat single-molecule labeling and detection errors, we demonstrated the imaging of 100 – 1000 unique RNA species in hundreds of individual cells. Correlation analysis of the ~10^4 – 10^6 pairs of genes allowed us to constrain gene regulatory networks, predict novel functions for many unannotated genes, and identify distinct spatial distribution patterns of RNAs that correlate with properties of the encoded proteins. A single sample is analyzed
Project description:Spatial organization of different cell types within prenatal skin across various anatomical sites is not well understood. To address this, here we have generated spatial transcriptomics data from prenatal facial and abdominal skin obtained from a donor at 10 post conception weeks. This in combination with our prenatal skin scRNA-seq dataset has helped us map the location of various identified cell types.