Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
108
datasets available to search
ShareScore release 0.9.0
Dataset results
108 results for “spatial gene expression”
The spatial landscape of gene expression isoforms in tissue sections
<p><strong>This upload provides raw in situ sequencing (ISS) data used to validate Spatial Isoform Transcriptomics (SiT), as well as R scripts required for SiT analysis.</strong></p> <p><strong>GenePlots.zip and Reads.zip are ISS data </strong><strong>generated and collected by the CARTANA ISS service</strong>. <strong>The following data description is cited from the report provided by CARTANA ISS service:</strong></p> <p><em>"Folder "Reads" contains coordinates and gene information of segmented spots.<br> The coordinates are in pixel unit. Scaling factor is 0.32 um/pixel. (0,0) is at northwest (top-left corner).<br> With Low/High Threshold, we refer to the quality thresholding. Our technology is fluorescence based, i.e. with the thresholding one can balance how certain the signals are.</em></p> <p><em>Files ending with _LowThreshold: reads not matching with any known barcode were already discarded.</em></p> <p><em>Files ending with _HighThreshold: has information only about spots that passed additional quality check.</em></p> <p><em>Folder "GenePlots" has plotted images in static .png format, fully zoomed out. LowThreshold and HighThreshold follow the same thresholding strategy as in reads files."</em></p> <p> </p> <p><strong>SiT-master.zip is a download of the GitHub repository </strong><a href="https://github.com/ucagenomix/SiT">https://github.com/ucagenomix/SiT</a>, <strong>providing figures and analysis scripts for SiT.</strong></p> <p> </p> <p><strong>Related SiT data are deposited through GEO, accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153859">GSE153859</a></strong></p>
Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network
<p><strong>Aims:</strong> The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped.</p> <p><strong>Methods and Results:</strong> In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner.</p> <p><strong>Conclusions:</strong> The experimental results show that deep learning models reveal regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.</p>
Gene expression dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution
<p>Here are the gene expression datasets of RIBOmap included in "<strong>Spatially Resolved Single-cell Translatomics at Molecular Resolution</strong>" from Zeng et al. Please refer to the README file for more detailed information. </p> <p> </p> <p><strong>Abstract</strong></p> <p>The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.</p>
Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms
<p>Data extracted from confocal microscopy associated with the paper "Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms"</p> <p>Stochastic pulsing of gene expression can generate phenotypic diversity in a genetically identical population of cells, but it is unclear whether it has a role in the development of multicellular systems. Here, we show how stochastic pulsing of gene expression enables spatial patterns to form in a model multicellular system, Bacillus subtilis bacterial biofilms. We use quantitative microscopy and time-lapse imaging to observe pulses in the activity of the general stress response sigma factor σ<sup>B</sup> in individual cells during biofilm development. Both σ<sup>B</sup> and sporulation activity increase in a gradient, peaking at the top of the biofilm, even though σ<sup>B</sup> represses sporulation. As predicted by a simple mathematical model, increasing σ<sup>B</sup> expression shifts the peak of sporulation to the middle of the biofilm. Our results demonstrate how stochastic pulsing of gene expression can play a key role in pattern formation during biofilm development.</p>
Mimulus cardinalis plasticity analyses and R scripts for: Spatial variation in high temperature-regulated gene expression predicts evolution of plasticity with climate change in the scarlet monkeyflower
<p>A major way that organisms can adapt to changing environmental conditions is by evolving increased or decreased phenotypic plasticity. In the face of current global warming, more attention is being paid to the role of plasticity in maintaining fitness as abiotic conditions change over time. However, given that temporal data can be challenging to acquire, a major question is whether evolution in plasticity across space can predict adaptive plasticity across time. In growth chambers simulating two thermal regimes, we generated transcriptome data for western North American scarlet monkeyflowers (<i>Mimulus cardinalis</i>) collected from different latitudes and years (2010 and 2017) to test hypotheses about how plasticity in gene expression is responding to increases in temperature, and if this pattern is consistent across time and space. Supporting the genetic compensation hypothesis, individuals whose progenitors were collected from the warmer-origin northern 2017 descendant cohort showed lower thermal plasticity in gene expression than their cooler-origin northern 2010 ancestors. This was largely due to a change in response at the warmer (40ºC) rather than cooler (20ºC) treatment. A similar pattern of reduced plasticity, largely due to a change in response at 40ºC, was also found for the cooler-origin northern versus the warmer-origin southern population from 2017. Our results demonstrate that reduced phenotypic plasticity can evolve with warming and that spatial and temporal changes in plasticity predict one another.</p>
Spatial gene expression in the mouse colon during experimental colitis measured with MERFISH
Open the record for dataset details and reuse information.
Data from: Spatial analysis of mitochondrial gene expression reveals dynamic translation hubs and remodeling in stress
Open the record for dataset details and reuse information.
Mimulus cardinalis plasticity analyses and R scripts for: Spatial variation in high temperature-regulated gene expression predicts evolution of plasticity with climate change in the scarlet monkeyflower
Open the record for dataset details and reuse information.
Multimodal contrastive learning for spatial gene expression prediction using histology images
<p>we employed two human breast cancer datasets and one human cutaneous squamous cell carcinoma (cSCC) dataset.</p>
Maintenance of spatial gene expression by Polycomb-mediated repression after formation of a vertebrate body plan
<p>This dataset contains zebrafish (<em>Danio rerio</em>) raw RNA and ChIP sequencing data:</p> <ul> <li>RNA-seq: <ul> <li>RNAseq_Wildtype_rep[12].fastq.gz: 2 biological replicates of single-end RNA-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>RNAseq_Wildtype_rep[3-6].fastq.gz 4 biological replicates of paired-end RNA-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> </ul> </li> <li>ChIP-seq: <ul> <li>lane1_MPZezh2WT-24hpf-Ezh2__R[12].fastq.gz: 1 sample of paired-end Ezh2 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>lane1_MPZezh2WT-24hpf-Rnf2__R[12].fastq.gz: 1 sample of paired-end Rnf2 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>lane1_MPZezh2WT-24hpf-H3K27me3__R[12].fastq.gz: 1 sample of paired-end H3K27me3 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>*MPZezh2WT-24hpf-H3K4me3*: 2 biological replicates of paired-end H3K4me3 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>MPZezh2WT-24hpf-Ezh2-spikein-13277_R[12].fastq.gz: 1 sample of paired-end Ezh2 ChIP-seq data (with Drosophila H2Ay spike in) from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>MPZezh2WT-24hpf-H3K27A[cC]*: 2 biological replicates of paired-end H3K27ac ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>MPZezh2WT-24hpf-H3K27me3-spikein-13275_R[12].fastq.gz: 1 sample of paired-end H3K27me3 ChIP-seq data (with Drosophila H2Ay spike in) from 24hpf wild-type (TU/TL background) whole embryo lysates</li> </ul> </li> </ul>
Single-cell and spatial transcriptomics of vulvar lichen sclerosus reveal multi-compartmental alterations in gene expression and signaling cross-talk
GEO Series GSE274068. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
The spatial position of budding yeast chromosomes affects gene expression
GEO Series GSE108261. Saccharomyces cerevisiae. 42 samples. Type: Expression profiling by high throughput sequencing.
Mouse Skin Xenium In Situ Spatial Gene Expression
GEO Series GSE276273. Mus musculus. 2 samples. Type: Other.
Visium spatial gene expression analyses of ovarian clear cell carcinoma (OCCC) [visium OCCC]
GEO Series GSE224335. Homo sapiens. 2 samples. Type: Other.
Sequencing mRNA from cryo-sliced Drosophila embryos to determine genome-wide spatial patterns of gene expression
GEO Series GSE43506. Drosophila melanogaster. 122 samples. Type: Expression profiling by high throughput sequencing.
Spatial hepatocyte plasticity of gluconeogenic gene expression and gluconeogenic activity during the metabolic transitions between fed, fasted and starvation states [scRNA-Seq]
GEO Series GSE263418. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Positive and Negative Spatial Gradients of High Wall Shear Stress Have Different Effects on Endothelial Gene Expression
GEO Series GSE37127. Bos taurus. 12 samples. Type: Expression profiling by array.
Spatial gene expression of canine veins during carotid-cartoid vein bypass implantation
GEO Series GSE263281. Canis lupus familiaris. 8 samples. Type: Other.
Single-cell and spatial transcriptomics of vulvar lichen sclerosus reveal multi- compartmental alterations in gene expression and signaling cross-talk
GEO Series GSE274837. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
RNA tomography reveals spatial gene expression maps of Arabidopsis thaliana roots infected with Heterodera schachtii
GEO Series GSE297648. Arabidopsis thaliana. 2 samples. Type: Other.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.