Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches (spatial)
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PROVIDER: PRJNA793008 | ENA |
REPOSITORIES: ENA
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Project description:Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches
| PRJNA793005 | ENA
Project description:Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches (single cells)
| PRJNA793009 | ENA
Project description:Analysis of CITE-seq data , Nuclei RNA-seq data and single cell RNA-seq data on CD45+ and CD45- cells isolated from the livers of mice fed a standard diet (SD) or western diet (WD; fat, cholesterol and sugar), from healthy and steatotic human livers, from hamster liver, pig liver, chicken liver, monkey liver and zebrafish liver. We also performed Spatial Transcriptomics analysis on heatlhy mouse livers, NAFLD mouse livers, healthy human livers and steatotic human livers.
2022-01-11 | GSE192741 | GEO
Project description:Analysis of CITE-seq data , Nuclei RNA-seq data and single cell RNA-seq data on CD45+ and CD45- cells isolated from the livers of mice fed a standard diet (SD) or western diet (WD; fat, cholesterol and sugar), from healthy and steatotic human livers, from hamster liver, pig liver, chicken liver, monkey liver and zebrafish liver. We also performed Spatial Transcriptomics analysis on heatlhy mouse livers, NAFLD mouse livers, healthy human livers and steatotic human livers.
2022-01-11 | GSE192740 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below. For metadata files please visit https://livercellatlas.org
2022-01-11 | GSE192742 | GEO
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.
2024-03-08 | GSE246395 | GEO
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:Extensive changes in replication timing occur during early mouse development, but their biological significance remains uncertain. To identify evolutionarily conserved features of replication timing and their relationships to epigenetic properties in humans, we profiled replication timing genome-wide in four human embryonic stem cell (hESC) lines, hESC-derived neural precursor cells (NPCs), lymphoblastoid cells, and two independently derived human induced pluripotent stem cell lines (hiPSCs). Results confirm the conservation of coordinately replicated megabase-sized units of chromosomes (replication domains) with stable cell type specific molecular boundaries that consolidate into larger replication domains during differentiation. Replication timing changes encompassed units of 400-800 kb and were coordinated with changes in transcription similar to mouse. Moreover, significant cell-type specific conservation of replication timing profiles was observed across regions of conserved synteny, despite significant species variation in the alignment of replication timing to isochore GC/LINE-1 content. Replication profiling also revealed a closer genome-wide epigenetic alignment of hESCs to mouse epiblast-derived stem cells (mEpiSCs) than to mouse ESCs. Finally, we identify a signature of chromatin modifications marking the boundaries of early replicating domains and a remarkably strong link between spatial proximity of chromatin as measured by Hi-C analysis and replication timing. Together, our results reveal evolutionarily conserved elements of the replication program in mammalian early development, demonstrate the power of replication profiling to identify important epigenetic distinctions between closely related stem cell populations (e.g. ESCs vs. EpiSCs), and strengthen the hypothesis that replication domains are structural and functional units of 3D chromosomal architecture. 8 cell types, with a total of 13 individual replicates (i.e. 5 in duplicates, 3 in single replicates)
2010-03-23 | E-GEOD-20027 | biostudies-arrayexpress
Project description:Proteogenomics methods have identified many non-annotated protein-coding genes in the human genome. Many of the newly discovered protein-coding genes encode peptides and small proteins, referred to collectively as microproteins. Microproteins are produced through ribosome translation of small open reading frames (smORFs). The discovery of many smORFs reveals a blind spot in traditional gene-finding algorithms for these genes. Biological studies have found roles for microproteins in cell biology and physiology, and the potential that there exists additional bioactive microproteins drives the interest in detection and discovery of these molecules. A key step in any proteogenomics workflow is the assembly of RNA-Seq data into likely mRNA transcrips that are then used to create a searchable protein databases. Here we demonstrate that specific features of the assembled transcriptome impact microprotein detection by shotgun proteomics. By tailoring transcript assembly for downstream mass spectrometry searching, we show that we can detect more than double the number of high-quality microprotein candidates and introduce a novel open-source mRNA assembler for proteogenomics (MAPS) that incorporates all of these features. By integrating our specialized assembler, MAPS, and a popular generalized assembler into our proteogenomics pipeline, we detect 45 novel human microproteins from a high quality proteogenomics dataset of a human cell line. We then characterize the features of the novel microproteins, identifying two classes of microproteins. Our work highlights the importance of specialized transcriptome assembly upstream of proteomics validation when searching for short and potentially rare and poorly conserved proteins.
2017-12-20 | GSE92659 | GEO