The covariance environment defines cellular niches for spatial inference
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PROVIDER: PRJNA1032867 | ENA |
REPOSITORIES: ENA
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Project description:The tsunami of new multiplexed spatial profiling technologies has opened a range of computational challenges focused on leveraging these powerful data for biological discovery. A key challenge underlying computation is a suitable representation for features of cellular niches. Here, we develop the covariance environment (COVET), a representation that can capture the rich, continuous multivariate nature of cellular niches by capturing the gene-gene covariate structure across cells in the niche, which can reflect the cell-cell communication between them. We define a principled optimal transport-based distance metric between COVET niches and develop a computationally efficient approximation to this metric that can scale to millions of cells. Using COVET to encode spatial context, we develop environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA-seq data into a latent space. Two distinct decoders either impute gene expression across spatial modality, or project spatial information onto dissociated single-cell data. We show that ENVI is not only superior in the imputation of gene expression but is also able to infer spatial context to disassociated single-cell genomics data.
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Project description:Cellular function is strongly dependent on surrounding cells and environmental factors. Current technologies are limited in characterizing the spatial location and unique gene-programs of cells in less structured and dynamic niches. Here we developed a method (NICHE-seq) that combines photoactivatable fluorescent reporters, two-photon microscopy and single-cell RNA-seq to infer the cellular and molecular composition of niches. We applied NICHE-seq to examine the high-order assembly of immune cell networks. NICHE-seq is highly reproducible in spatial tissue reconstruction, enabling identification of rare niche-specific immune subpopulations and unique gene-programs, including natural killer cells within infected B cell follicles and distinct myeloid states in the marginal zone. This study establishes NICHE-seq as a broadly applicable method for elucidating high-order spatial organization of cell types and their molecular pathways.
2017-12-22 | GSE104054 | GEO
Project description:Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches
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Project description:We generated a paired snRNA-seq (n= 15) and spatial transcriptomics (n=19) dataset from subcortical chronic active and chronic inactive MS lesions, identifying spatial niches and key cell interactions driving inflammation and disease progression at the lesion rim. This repository offers access to all the trancriptomics data that was used in the paper. It includes, all FASTQ files for both transcriptomics, along with the necessary files for running spatial transcriptomic samples (H&E images and JSON files), as well as the curated atlas, all derived cell subtype atlases from the main atlas and all curated ST slides.
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Project description:A theoretical framework for the function of the medial temporal lobe system in memory defines differential contributions of the hippocampal subregions with regard to pattern recognition retrieval processes and encoding of new information. To investigate molecular programs of relevance, we designed a spatial learning protocol to engage a pattern separation function to encode new information. After background training, two groups of animals experienced the same new training in a novel environment, however only one group was provided spatial information and demonstrated spatial memory in a retention test. Global transcriptional analysis of the microdissected subregions of the hippocampus exposed a CA3 pattern that was sufficient to clearly segregate spatial learning animals from control. Individual gene and functional group analysis anchored these results to previous work in neural plasticity. From a multitude of expression changes, increases in camk2a, rasgrp1 and nlgn1 were confirmed by in situ hybridization. Furthermore, siRNA inhibition of nlgn1 within the CA3 subregion impaired spatial memory performance, pointing to mechanisms of synaptic remodeling as a basis for rapid encoding of new information in long-term memory. Experiment Overall Design: RNA samples from animals subjected to a spatial learning paradigm were compared to controls using Affymetirx RAE230a chips. An N of 7 was used in each of the two experimental conditions.
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Project description:A theoretical framework for the function of the medial temporal lobe system in memory defines differential contributions of the hippocampal subregions with regard to pattern recognition retrieval processes and encoding of new information. To investigate molecular programs of relevance, we designed a spatial learning protocol to engage a pattern separation function to encode new information. After background training, two groups of animals experienced the same new training in a novel environment, however only one group was provided spatial information and demonstrated spatial memory in a retention test. Global transcriptional analysis of the microdissected subregions of the hippocampus exposed a CA3 pattern that was sufficient to clearly segregate spatial learning animals from control. Individual gene and functional group analysis anchored these results to previous work in neural plasticity. From a multitude of expression changes, increases in camk2a, rasgrp1 and nlgn1 were confirmed by in situ hybridization. Furthermore, siRNA inhibition of nlgn1 within the CA3 subregion impaired spatial memory performance, pointing to mechanisms of synaptic remodeling as a basis for rapid encoding of new information in long-term memory. Experiment Overall Design: RNA samples from animals subjected to a spatial learning paradigm were compared to controls using Affymetirx RAE230a chips. An N of 6 was used in each of the two experimental conditions.
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Project description:Using Multiome and previously published sc/snRNA-seq data, we studied eight anatomical regions of the human heart including left and right ventricular free walls (LV and RV), left and right atria (LA and RA), left ventricular apex (AX), interventricular septum (SP), sino-atrial node (SAN) and atrioventricular node (AVN). For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors and cell-cell interactions. We map the identified cells to spatial transcriptomic data to discover cellular niches within the eight regions of the heart.
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