Epigenomic tomography for probing spatially-defined chromatin state in the brain
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE215972 | GEO | 2023/10/18
REPOSITORIES: GEO
Similar Datasets
Project description:In 10X Genomics Visium Spatial Gene Expression (ST), the resolution for distinguishing neighboring cells can be improved using data integration with single-cell/single-nuclei transcriptomics profiles. Besides, depending on the cell type and tissue, nuclei size may vary significantly to an extent that it may exceed the thickness of tissue slices. This may jeopardize capturing full transcriptomics profile of single slice due to the improper/incomplete incision of nuclei during cryosectioning process and this may cause drawbacks in downstream analysis. To monitor the probable consequences, we monitored the effect of consecutive slices data integration (CSDI) on improvement of cell type clustering and annotation through transferring cell labels from a single-nuclei transcriptomics dataset to ST. To do so, two consecutive slices from the orbitofrontal neocortex and temporal neocortex of two post mortem brain samples were obtained and their spatial transcriptomics profiles were retrieved using 10x Genomics Visium Spatial Gene Expression protocol. Using CSDI, not only the number of identified clusters were increased and the inconsistency between the pattern of clusters in consecutive slices was resolved, but the layered-structure of gray matter was unveiled. Besides, only after CSDI the transferred annotations from single-nuclei transcriptomics to ST could match the microscopic results. CSDI can improve the ST clustering and cell type annotation by providing the full signals coming from all cell types of single slice of tissue. The codes in R programming language are publicly available at https://github.com/ElyasMo/ST_snRNA-seq
2023-05-12 | GSE184510 | GEO
Project description:Advancing our understanding of embryonic development is heavily dependent on identification of novel pathways or regulators. While genome-wide techniques such as RNA sequencing are ideally suited for discovering novel candidate genes, they are unable to yield spatially resolved information in embryos or tissues. Microscopy-based approaches, using for example in situ hybridization, can provide spatial information about gene expression, but are limited to analyzing one or a few genes at a time. Here, we present a method where we combine traditional histological techniques with low-input RNA sequencing and mathematical image reconstruction to generate a high-resolution genome-wide 3D atlas of gene expression in the zebrafish embryo at three developmental stages. We also demonstrate that our technique is suitable for spatially-resolved differential expression analysis in wildtype and Gli3 mutant mouse forelimbs. Importantly, our method enables searching for genes that are expressed in specific spatial patterns without manual image annotation. We envision broad applicability of RNA tomography as an accurate and sensitive approach for spatially resolved transcriptomics in whole embryos and dissected organs. To generate spatially-resolved RNA-seq data for zebrafish embryos (shield stage, 10 somites, 15 somites, 18 somites) and mouse forelimbs (E10.5), we cryosectioned samples, extracted RNA from the individual sections, and amplified and barcoded mRNA using the CEL-seq protocol (Hashimshony et al., Cell Reports, 2012) with a few modifications. Libraries were sequenced on Illumina HiSeq 2500 using 50bp paired end sequencing. Selected zebrafish libraries were sequenced on MiSeq 250bp paired-end to improve 3' annotations.
2014-10-23 | E-GEOD-59873 | biostudies-arrayexpress
Project description:Advancing our understanding of embryonic development is heavily dependent on identification of novel pathways or regulators. While genome-wide techniques such as RNA sequencing are ideally suited for discovering novel candidate genes, they are unable to yield spatially resolved information in embryos or tissues. Microscopy-based approaches, using for example in situ hybridization, can provide spatial information about gene expression, but are limited to analyzing one or a few genes at a time. Here, we present a method where we combine traditional histological techniques with low-input RNA sequencing and mathematical image reconstruction to generate a high-resolution genome-wide 3D atlas of gene expression in the zebrafish embryo at three developmental stages. We also demonstrate that our technique is suitable for spatially-resolved differential expression analysis in wildtype and Gli3 mutant mouse forelimbs. Importantly, our method enables searching for genes that are expressed in specific spatial patterns without manual image annotation. We envision broad applicability of RNA tomography as an accurate and sensitive approach for spatially resolved transcriptomics in whole embryos and dissected organs.
2014-10-23 | GSE59873 | GEO
Project description:Both cis- and trans-acting changes could accumulate and participate in complex interactions, so to isolate the cis-regulatory component of patterning evolution, we measured allele-specific spatial gene expression patterns in Drosophila melanogaster × D. simulans hybrid embryos. RNA-seq of cryosectioned slices revealed 55 genes with strong spatially-varying allele-specific expression, and several hundred more with weaker but significant spatial divergence. Combined with mathematical modeling and regulatory locus editing, we determine the SNP responsible for the allele-specific expression observed in the gene hunchback.
2017-08-04 | GSE102233 | GEO
Project description:To better understand how individual cells function within an anatomical space, we developed XYZeq, a novel scRNA-seq workflow that uses combinatorial indexing in microwells to encode spatial metadata into scRNA-seq libraries. We used XYZeq to profile heterotopic mouse liver and spleen tumor models to capture transcriptomes from tens of thousands of cells across a total of eight tissue slices.
2021-01-08 | GSE164430 | GEO
Project description:Our understanding of metabolic interactions between small symbiotic animals and bacteria or parasitic eukaryotes that reside within their bodies is extremely limited. This gap in knowledge originates from a methodological challenge, namely to connect histological changes in host tissues induced by beneficial and parasitic (micro)organisms to the underlying metabolites. We addressed this challenge and developed chemo-histo-tomography (CHEMHIST), a culture-independent approach to connect anatomic structure and metabolic function in millimeter-sized symbiotic animals. CHEMHIST combines chemical imaging of metabolites based on mass spectrometry imaging (MSI) and microanatomy-based micro-computed X-ray tomography (micro-CT) on the same animal. Both high-resolution MSI and micro-CT allowed us to correlate the distribution of metabolites to the same animal's three-dimensional (3D) histology down to submicrometer resolutions. Our protocol is compatible with tissue-specific DNA sequencing and fluorescence in situ hybridization for the taxonomic identification and localization of the associated micro(organisms). Building CHEMHIST upon in situ imaging, we sampled an earthworm from its natural habitat and created an interactive 3D model of its physical and chemical interactions with bacteria and parasitic nematodes in its tissues. Combining MSI and micro-CT, we present a methodological groundwork for connecting metabolic and anatomic phenotypes of small symbiotic animals that often represent keystone species for ecosystem functioning.
2021-07-06 | MTBLS2639 | MetaboLights
Project description:We developed a novel framework to assess comprehensive endogenous gene expression maps in a broad and isotropic 3D-anatomical context using a relatively small number of original data points: transcriptome tomography. Using this method, we produced a dataset of gene expression maps of the whole mouse brain, computationally reconstructed from fraction data.
2012-09-21 | GSE36408 | GEO
Project description:Genome-wide RNA tomography in the zebrafish embryo
| PRJNA256975 | ENA
Project description:We developed a novel framework to assess comprehensive endogenous gene expression maps in a broad and isotropic 3D-anatomical context using a relatively small number of original data points: transcriptome tomography. Using this method, we produced a dataset of gene expression maps of the whole mouse brain, computationally reconstructed from fraction data. A total of 61 fraction samples obtained with body axes-based slicing preparation conjugated with anatomical image acquisition were analysed. There are two sets of body axes-based preparation series: the S series, composed of 9 fractions/ C of 13 fractions/ H of 6 fractions , and the So series, composed of 10 fractions/ Co of 16 fractions/ Ho of 7 fractions.
2012-09-21 | E-GEOD-36408 | biostudies-arrayexpress
Project description:We have developed an imaging-free framework to localize nucleic acids within a tissue by combining a compressed sensing tissue-sampling strategy based on multi-angle-sectioning and an associated image reconstruction algorithm. Initially, the tissue is cut into consecutive thin slices. Subsequently these are further sliced along an orthogonal plane at predefined orientations resulting in tissue strips that are subject to RNA sequencing. We implemented this framework to transform a single-cell RNA sequencing protocol into an imaging-free spatial transcriptomics technique. The method was validated by profiling the transcriptome of the Murine brain, and used to spatially profile the brain transcriptome of the Australian Central bearded dragon, Pogona vitticeps.
2020-08-02 | GSE152989 | GEO