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54,099 results for “Expression”
Performance analysis of micro-expression recognition over different sample image sizes.
<p>Performance of micro-expression recognition accuracy analyzed using different sample sizes with motion and geometric features. The sample sizes analyzed are: 140x170, 280x340, 560x680 and 1120x1360 using SMIC, CASMEII, CAS(ME)^2 and SAMM. The experiments were conducted using three different feature extraction setups: (i) optimized BiWOOF, (ii) full-face graph, and (iii) full-face graph with amplitude-based emotion magnification method (A-EMM). The results were compared with the results of the original BiWOOF presented in [1] as the baseline study. These files are published under CC0 license.</p> <p> </p> <p>References</p> <p>[1] Liong, S. T., See, J., Wong, K., & Phan, R. C. W. (2018). Less is more: Micro-expression recognition from video using apex frame. <em>Signal Processing: Image Communication</em>, <em>62</em>, 82-92.</p>
Text-fig. 2. A – First small excavation trench in the newly discovered fossiliferous outcrop area of the Ploužnice lake horizon, locality "Small Ravine" south of the village Ploužnice on the slope of road No. 286. Typical are the red, violet and varicoloured tuffaceous siltstones. Semily Formation, Late Stephanian, Krkonoše Piedmont Basin. B – Basal part of fossiliferous 12 cm to14 cm thick reddish to purple-red tuffaceous siltstone bed from the trench in Text-fig. 2A. Internal bedding is expressed as horizontal but with diffuse changes in grain size from fine silt to coarse, fine sandy silt. Intercalated in places are 3 to 5 mm thick layers (indicated by arrows) with patchy concentrations of isolated fish remains, especially in the first 3 centimetres of this siltstone layer. The large white spot at 2.5 cm is a coprolite fragment. in Fossil Fauna And Flora Of A Re-Discovered Locality In The Late Carboniferous Ploužnice Horizon Of The Krkonoše Piedmont Basin, Bohemian Massif
Text-fig. 2. A – First small excavation trench in the newly discovered fossiliferous outcrop area of the Ploužnice lake horizon, locality "Small Ravine" south of the village Ploužnice on the slope of road No. 286. Typical are the red, violet and varicoloured tuffaceous siltstones. Semily Formation, Late Stephanian, Krkonoše Piedmont Basin. B – Basal part of fossiliferous 12 cm to14 cm thick reddish to purple-red tuffaceous siltstone bed from the trench in Text-fig. 2A. Internal bedding is expressed as horizontal but with diffuse changes in grain size from fine silt to coarse, fine sandy silt. Intercalated in places are 3 to 5 mm thick layers (indicated by arrows) with patchy concentrations of isolated fish remains, especially in the first 3 centimetres of this siltstone layer. The large white spot at 2.5 cm is a coprolite fragment.
Dataset for "Seismological Expression of the Iron Spin Crossover in Ferropericlase in the Earth's Lower Mantle"
<p>The folder contains data related to the publication: Shephard, Houser, Hernlund, Trønnes, Valencia-Cardona, Wentzkovitch (2021) Seismological Expression of the Iron Spin Crossover in Ferropericlase in the Earth’s Lower Mantle. Accepted for publication in Nature Communications.</p> <p>It include numerical grids, a simple plotting script, colour maps, and images (jpg and ps files). There is a README file explaining the content and uses of the these additional data files.</p>
RNA-seq data of "Transcriptome analyses of leaves reveal that hexanoic acid priming differentially regulate gene expression in contrasting Coffea arabica cultivars"
<p>This dataset represent FASTQ gziped files from the study "Transcriptome analyses of leaves reveal that hexanoic acid priming differentially regulate gene expression in contrasting <em>Coffea arabica</em> cultivars" (<a href="https://doi.org/10.3389/fsufs.2021.735893">https://doi.org/10.3389/fsufs.2021.735893</a>). Sequencing was done using an Illumina Novaseq 6000 instrument, paired-sequencing (2 X150 bp). Sample details are also available at https://www.ebi.ac.uk/ena/browser/view/ERA6282544.</p> <p> </p> <p>All filenames have the following naming scheme:</p> <p>LCS7609_DS_AAA_leafBBB_(R1 or R2).fq.gz</p> <p>AAA stands for the abbreviations:</p> <p>- CC (Coffea arabica cv Catuai control)</p> <p>- CHx (Coffea arabica cv Catuai exposed to Hexanoic acid)</p> <p>- OC (Coffea arabica cv Obatã control)</p> <p>- OHx (Coffea arabica cv Obatã exposed to Hexanoic acid)</p> <p>BBB stands for the number of biological replicate (1, 2 or 3).</p> <p> </p> <p> </p> <p> </p>
Significance of the relationship between BIRC5 and the expression of other genes in AML patients
<p>BIRC5 expression levels were examined in relationship to the expression of other genes in the MILE dataset.</p>
Dataset of Russian multiword expressions for a DSM
<p>The dataset consists of Russian multiword expressions selected according to the presence or absence of a 'categorical shift' between the meaning of a noun phrase and that of its head. The items with the categorical shift are intended for inclusion in the vocabulary of a distributional-semantic model (DSM) as single items. The dataset consists of 2 parts, 173 positive and 173 negative examples. The entries are additionally labeled for further categorization.</p>
Extracellular vesicles from Fusarium graminearum contain protein effectors expressed during infection of corn
<p><em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>) is a devastating filamentous fungal pathogen that causes diseases in cereals, while producing mycotoxins that are toxic for humans and animals, and render grains unusable. Low efficiency in managing <em>Fgr</em> poses a constant need for identifying novel control mechanisms. Evidence that fungal extracellular vesicles (EVs) from pathogenic yeast have a role in human disease led us to question whether this is also true for fungal plant pathogens. We separated EVs from <em>Fgr</em> and performed a proteomic analysis to determine if EVs carry proteins with potential roles in pathogenesis. We revealed that protein effectors, which are crucial for fungal virulence, were detected in EV preparations and some of them did not contain predicted secretion signals. Furthermore, a transcriptomic analysis of corn (<em>Zea</em> <em>mays</em>) plants infected by <em>Fgr</em> revealed that the genes of some of the effectors were highly expressed in vivo, suggesting that the <em>Fgr</em> EVs are a mechanism for the unconventional secretion of effectors and virulence factors. Our results expand the knowledge on fungal EVs in plant pathogenesis and cross-kingdom communication, and may contribute to the discovery of new antifungals.</p> <p>The following are available online at www.mdpi.com/xxx/s1, Figure S1. Controls for the separation of EVs from <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>) by SEC. Figure S2. The superoxide dismutase [Cu-Zn] (SOD1) from F. <em>graminearum</em> (<em>Fgr</em>) contains a diacidic amino acid motif implicated in unconventional secretion. Figure S3. Sequence alignment of the chitinase GH18 domain. Figure S4. Computational prediction of effector candidates detected in EV samples from <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>). Table S1. List of proteins detected in EVs from <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>). Table S2. List of proteins employed in the computational effector prediction analysis. Table S3. Proteins identified in the secretome from <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>). Table S4. List of transcripts identified in corn (<em>Zea</em> <em>mays</em>) infected by <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>). Table S5. Gene expression values per biological replicate.</p>
DATASET of INTEGRADDE Expression of Interest, including aggregated and anonymized results.
<p>This dataset corresponds to the one generated with information about those applying to the Expression of Interest (EOI) including participants’ attributes, e.g. country of origin, TRLs, type of applicants, scores obtained in the evaluation process, and funding status after the funnel process. All of this, for statistical purposes and analysis of the performance of the Expression of Interest</p>
Online Supplemental Tables - An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index
<p>Summary statistics of metabolite-gene expression associations and mediation analyses of effects on body mass index.</p> <p>The corresponding publication is currently under revision.</p> <ul> <li><strong>Online Supplemental Table 1</strong>: Gene expression-metabolite association summary statistics from 97 metabolites and metabolite ratios and up to 15175 genes calculated seperately in the LIFE-Adult, LIFE-Heart, LIFE-AMI and the Sorb studies. Associations were adjusted for six covariates.</li> <li><strong>Online Supplemental Table 2</strong>: Random-effects meta-analyzed gene expression-metabolite association summary statistics. P-Values were adjusted for multiple testing using a hierarchical adjustment procedure both on local (within phenotypes) and on global (across phenotypes) level.</li> <li><strong>Online Supplemental Table 3</strong>: Single-study association results needed for checking mediation analysis assumptions and for calculating mediation statistics. Only gene expression probe-metabolite pairs that associated significantly at hierarchical FDR=5% in the gene-expression-metabolite association meta-analysis qualified for these associations.The following Associations were tested: gene expression probes ~ metabolites, log-BMI ~ metabolites log-BMI ~ gene expression, log-BMI ~ gene expression + metabolite. Associations were adjusted for six covariates. P-values</li> <li><strong>Online Supplemental Table 4</strong>: Meta-analyzed association results needed for checking mediation analysis assumptions and for calculation mediation statistics.</li> <li><strong>Online Supplemental Table 5: </strong>Mediation analysis summary statistics. Mediations of gene expression effects (exposure) via metabolite effects (mediatior) and of metabolite effects (exposure) via gene expression effects (mediator) on body mass index (outcome) were tested.</li> </ul> <p> </p>
Expression of terminal deoxynucleotidyl transferase (TdT) identifies lymphoid-primed progenitors in human bone marrow
<p><span>With emerging single-cell techniques in the human hematopoiesis, discrepancies between the traditional cell-surface-marker-based cell-type identification and their single-cell level molecular phenotype are observed. To better associate lymphoid identity with protein-level cell features, we examined the protein expression of terminal deoxynucleotidyl transferase (TdT), a specialized DNA polymerase intrinsic to VDJ recombination, and detected TdT expression within CD34+ progenitors prior to B/T cell emergence. While these TdT+ cells coincided with granulocyte-monocyte progenitor (GMP) immunophenotype, their accessible chromatin regions showed enrichment for lymphoid-associated transcription factor (TF) motifs. TdT expression on GMPs was inversely related to the SLAM family member CD84. Prospective isolation of CD84lo GMPs demonstrated robust lymphoid potential ex vivo, while still retaining significant myeloid differentiation capacity, akin to LMPPs. This multi-omic study identifies previously unappreciated lymphoid-primed progenitors, redefining the lympho-myeloid axis in human hematopoiesis.</span></p>
Supplementary Data for 'A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA).'
<p>Supplementary Data for 'Castoldi, M., Schmidt, S., Benes, V., Noerholm, M., Kulozik, A.E., Hentze, M.W. and Muckenthaler, M.U., 2006. A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA). <em>Rna</em>, <em>12</em>(5), pp.913-920.'</p>
Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, low seq depth
<p><strong>File description:</strong></p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a>, and the split and non-split counts contain only the annotated junctions from the same release.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Number of samples:</strong> 127<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34</p> <p><strong>Median mapped reads:</strong> 71 million<br> <strong>Disease</strong> (ICD-10: N): E88: 84, NONE: 12, F89: 6, G31: 3, R27: 3, E72: 3, G40: 2, R16: 2, K72: 2, P94: 2, E77: 1, E75: 1, G71: 1, G93: 1, Q78: 1, G82: 1, R29: 1, Q02: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True</p> <p><strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p> <p> </p>
Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, high seq depth
<p><strong>File description:</strong></p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p> </p> <p>The gene counts were originated using the GTF file from release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a>, and the split and non-split counts contain only the annotated junctions from the same release.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Number of samples:</strong> 135<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34</p> <p><strong>Median mapped reads:</strong> 116 million<br> <strong>Disease</strong> (ICD-10: N): E88: 112, G31: 8, NONE: 5, K72: 2, G71: 2, E72: 1, G93: 1, I42: 1, F82: 1, E75: 1, F89: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True<br> <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p> <p> </p>
Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech
<p>Transcriptomics, the quantification of gene expression, provides a versatile tool for ecological monitoring. Here, we show that through genome-guided profiling of transcripts mapping to 33,042 loci, gene expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck imposed due to large-scale post-Columbian deforestation, the SNP-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our time series of expression analyses during spring-summer and summer-fall transitions for two consecutive years involved 4197 differentially expressed protein coding genes. A global comparison of 12 seasons has revealed that spring gene expression sets the pace for the rest of the growing season. Using \textit{Populus} orthologs of the differentially expressed genes, we reconstructed a protein-protein interactome as a representation of the leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed GO terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of terms that could be putatively attributed to phenological shifts due to late spring. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture ranges of responses arising from other factors including air quality, chronic disease as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms needed for the survival of long living trees such as the American beech.</p>
Expression Profile of CD157 Reveals Functional Heterogeneity of Capillaries in Human Dermal Skin
<p>CD157 acts as a receptor, regulating leukocyte trafficking and the binding of extracellular matrix components. However, the expression pattern and the role of CD157 in human blood (BEC) and the lymphatic endothelial cells (LEC) of human dermal microvascular cells (HDMEC), remain elusive. We demonstrated constitutive expression of CD157 on BEC and LEC, in fetal and juvenile/adult skin, in situ, as well as in isolated HDMEC. Interestingly, CD157 epitopes were mostly localized on BEC, co-expressing high levels of CD31 (CD31<sup>High</sup>), as compared to CD31<sup>Low</sup> BEC, whereas the podoplanin expression level on LEC did not affect CD157. Cultured HDMEC exhibited significantly higher numbers of CD157-positive LEC, as compared to BEC. Interestingly, separated CD157<sup>-</sup> and CD157<sup>+</sup> HDMEC demonstrated no significant differences in clonal expansion in vitro, but they showed distinct expression levels of cell adhesion molecules, before and after cytokine stimulation in vitro. In particular, we proved the enhanced and specific adherence of CD11b-expressing human blood myeloid cells to CD157<sup>+</sup> HDMEC fraction, using an in vitro immune-binding assay. Indeed, CD157 was also involved in chemotaxis and adhesion of CD11b/c monocytes/neutrophils in prevascularized dermo-epidermal skin substitutes (vascDESS) in vivo. Thus, our data attribute specific roles to endothelial CD157, in the regulation of innate immunity during inflammation.</p> <p> </p>
A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3' UTR region, denoting expression levels, are associated with Atopic Dermatitis
<p>The publication "A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3’ UTR region, denoting expression levels, are associated with Atopic Dermatitis" contains analysis from two different cohorts: Genetics in Atopic Dermatitis (GAD), which is the main dataset, and Pediatric Eczema Elective Registry (PEER), which is the replication cohort. Included herein are the HLA Class II genotypes for both the GAD and PEER cohorts at 2-field resolution, which forms the basis for the analysis included in the publication. (DOI: 10.3389/fgene.2023.1004138)</p>
Recombinant Expression and Chemical Amidation of Isotopically Labeled Native Melittin
<p><strong>Table S1. </strong>Amidated cationic membrane-lytic and antimicrobial peptides. There are myriad cationic membrane-lytic and antimicrobial peptides consisting of a C-terminal amidation post-translational modification that are of high interest to researchers and which our recombinant expression followed by chemical amidation could be immediately applied to. Below is a short representative list of such peptides.</p> <p><strong>Table S2</strong>. Melittin backbone chemical shifts (ppm). Deuterium isotope shift corrections were applied to all chemical shifts and pressure corrections were applied to the pressure denatured monomer chemical shifts. All shifts were measured on a 1.0-mM <sup>2</sup>H,<sup>13</sup>C,<sup>15</sup>N-labeled melittin sample in 25-mM potassium phosphate buffer, pH 7.0, 50-mM NaCl, 3% D2O. The folded tetramer peaks were measured at atmospheric pressure, while the pressure denatured monomer resonances were measured at 2.25 kbar. </p> <p><strong>Table S3.</strong> Melittin experimental <sup>15</sup>N-<sup>1</sup>H isotropic J-couplings, (J+<em><sup>1</sup>D<sub>NH</sub></em>) anisotropic couplings and <em><sup>1</sup>D<sub>NH</sub></em> residual dipolar couplings. All couplings and errors are reported in Hz. Isotropic J-couplings were not measured for G3 and A4. In these cases, an average J-coupling of -93.3 Hz was used with an uncertainty of 1.1 Hz..</p> <p><strong>Table S4</strong>. Analysis of the AlphaFold-Multimer structural models with respect to the melittin RDCs measured with the Pf1 alignment media. SVD fitting of the RDC alignment tensor parameters was done for residues 3-23 in the AF-M models and residues 3-25 in the 2MLT crystal structure. The AF-M model confidence score is a prediction of the similarity between the AF-M model and the true oligomeric structure. The RMSD listed in the final column is the backbone RMSD between the AF-M structural models and the 2MLT X-ray crystal structure tetramer for residues 1-23.</p> <p><strong>Table S5</strong>. Analysis of the AlphaFold-Multimer structural models with respect to the melittin RDCs measured with a stretched polyacrylamide gel alignment media. SVD fitting of the RDC alignment tensor parameters was done for residues 4-23 in the AF-M models and residues 4-25 in the 2MLT crystal structure. The AF-M model confidence score is a prediction of the similarity between the AF-M model and the true oligomeric structure. The RMSD listed in the final column is the backbone RMSD between the AF-M structural models and the 2MLT X-ray crystal structure tetramer for residues 1-23.</p> <p><strong>Table S6.</strong> RDC based ranking of melittin AlphaFold-Multimer structural models and the 2MLT crystal structure. Models are ranked according to the average Q factor in the two alignment media used in this study: Pf1 and a positively charged stretched polyacrylamide gel. The AF-M model confidence score is a prediction of the similarity between the AF-M model and the true oligomeric structure. The RMSD listed in the final column is the backbone RMSD between the AF-M structural models and the 2MLT X-ray crystal structure tetramer for residues 1-23.</p> <p> <strong>Table S7</strong>. 2MLT crystal structure and AlphaFold-Multimer atomic coordinates. The X-ray crystal structure was retrieved from the protein data bank (PDB), then it was symmetry expanded into a tetramer, residues were renumbered as described in <em>3.3.3 Comparison of <sup>1</sup>D<sub>NH</sub> couplings with the crystal structure and AlphaFold-Multimer model,</em> and finally protons were added to the structure with DYNAMO. The C-terminus column lists the C-terminal modifications for the AF-M structures in comparison to melittin’s native -CONH<sub>2</sub> C-terminus. </p> <p>Atomic coordinates in the form of .pdb files for all RDC SVD fitting done in this study:</p> <table> <tbody> <tr> <td>2mlt.pdb</td> </tr> <tr> <td>af1.pdb</td> </tr> <tr> <td>af2.pdb</td> </tr> <tr> <td>af3.pdb</td> </tr> <tr> <td>af4.pdb</td> </tr> <tr> <td>af5.pdb</td> </tr> <tr> <td>afd1.pdb</td> </tr> <tr> <td>afd2.pdb</td> </tr> <tr> <td>afd3.pdb</td> </tr> <tr> <td>afd4.pdb</td> </tr> <tr> <td>afd5.pdb</td> </tr> <tr> <td>afg1.pdb</td> </tr> <tr> <td>afg2.pdb</td> </tr> <tr> <td>afg3.pdb</td> </tr> <tr> <td>afg4.pdb</td> </tr> <tr> <td>afg5.pdb</td> </tr> <tr> <td>afk1.pdb</td> </tr> <tr> <td>afk2.pdb</td> </tr> <tr> <td>afk3.pdb</td> </tr> <tr> <td>afk4.pdb</td> </tr> <tr> <td>afk5.pdb</td> </tr> <tr> <td>afr1.pdb</td> </tr> <tr> <td>afr2.pdb</td> </tr> <tr> <td>afr3.pdb</td> </tr> <tr> <td>afr4.pdb</td> </tr> <tr> <td>afr5.pdb</td> </tr> </tbody> </table> <p> </p>
CORE: Gene Expression-Cancer Knowledge Base
<p>This repository contains the Gene Expression-Cancer Knowledge Base generated by the CORE system.<br> The 'schema.owl' file contains the KB schema, whereas the 'data.ttl' file contains the actual data.</p>
Germinal Center and Cultured B cell expression libraries
<p>Comparison of gene expression profiles between in vitro mitogen-activated spleen B lymphocytes and FACS-sorted Germinal Center B lymphocytes isolated from intestinal PeyerPatches of C57BL/6 mice. In order to explore gene expression signatures that may be orchestrating somatic hypermutation (SHM, also known as affinity maturation) during antigen-induced B lymphocyte differentiation in Germinal Centers (GC), we profiled whole transcriptome gene expression of ex vivo, commensal microflora-activated GC B-cells that exhibit efficient SHM, with in vitro activated B-cell blasts, which fail to undergo SHM. In In particular, we examine our hypothesis that differential expression or the DNA endonucleases apex1 and apex2 regulate SHM. Using Templated Oligo Sequencing (TempO-Seq®) in conjunction with conventional intracellular FACS staining and B lymphocyte cell sorting, we compare gene expression profiles of in vitro mitogen-activated (LPS and anti-IgD-dextran), short-term (48h) cultured B cell blasts with Germinal Center B-cells isolated from intestinal Peyer Patches (PP, identified by surface staining B220+, CD95+, GL7+), compared to each other and to naive PP B-cells (B220+, CD95-, GL7-). We find that unique, distinct transcriptional signatures dominate the DNA repair processes in GC B-cells or in vitro B-cell blast, shedding light on genomic processes that orchestrate efficient affinity maturation of B-cells during GC differentiation. We use Templated Oligo Sequencing (TempO-Seq), a RNA motif-specific short tandem Oligo DNA hybridization technique to quantitatively measure whole transcriptome mRNA in conjunction with conventional surface membrane, fixable live/dead discriminator dye, and intracytoplasmic fluorescence antibody staining followed by FACS to isolate B cell populations of interest.</p>
Gene expression in monocytes, neutrophils and whole blood after stroke
<p>Dataset from:</p> <p>Carmona-Mora, P., Knepp, B., Jickling, G.C. <em>et al.</em> Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. <em>BMC Med</em> <strong>21</strong>, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1</p> <p>All methods available in the publication above.</p> <p>Abstract</p> <p>Background After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. Peripheral blood cells display distinctive gene expression signatures post IS and these transcriptional programs reflect changes in immune responses to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of immune and clotting responses at the molecular and cellular level that are involved in acute brain injury and may assist with time-targeted, cell-specific therapy.</p> <p>Methods The transcriptomic profiles from peripheral monocytes, neutrophils, and whole blood from 38 ischemic stroke patients and 18 controls were analyzed with RNAseq as a function of time and etiology after stroke. Differential expression analyses were performed at 0-24 h, 24-48 h, and >48 h following stroke.</p> <p>Results Unique patterns of temporal gene expression and pathways were distinguished for monocytes, neutrophils and whole blood with enrichment of interleukin signaling pathways for different timepoints and stroke etiologies. Compared to control subjects, gene expression was generally up-regulated in neutrophils and generally down- regulated in monocytes over all times for cardioembolic, large vessel and small vessel strokes. Self-Organizing Maps identified gene clusters with similar trajectories of gene expression over time for different stroke causes and sample types. Weighted Gene Co- expression Network Analyses identified modules of co-expressed genes that significantly varied with time after stroke and included hub genes of immunoglobulin genes in whole blood.</p> <p>Conclusions Altogether, the identified genes and pathways are critical for understanding how the immune and clotting systems change over time after stroke. This study identifies potential time- and cell-specific biomarkers and treatment targets.</p> <p>clinical_parameters_MON.txt: Clinical parameters from cohort used from monocyte samples.</p> <p>clinical_parameters_NEU.txt: Clinical parameters from cohort used from neutrophil samples.</p> <p>clinical_parameters_WB.txt: Clinical parameters from cohort used from whole blood samples.</p> <p>MON_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from monocyte samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=40 reads were excluded).</p> <p>NEU_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from neutrophil samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=40 reads were excluded).</p> <p>WB_gene_counts_filtered-WGCNA.txt: Filtered counts of each annotated gene from whole blood samples, cohort used for WGCNA analyses, (TPM normalized, non-log, filtered features where maximum <=80 reads were excluded).</p> <p>MON_gene_raw_counts.txt: raw counts for cohort used of monocyte samples.</p> <p>NEU_gene_raw_counts.txt: raw counts for cohort used of neutrophil samples.</p> <p>WB_gene_raw_counts.txt: raw counts for cohort used of whole blood samples.</p> <p>MON_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in monocyte samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p>NEU_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in neutrophil samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p>WB_Time_Course_filtered_normalized_counts_ready.txt: matrix counts of each annotated gene used for differential expression analyses of time points in whole blood samples. (TPM normalized, filtered features where maximum <=30 reads were excluded).</p> <p> </p> <p>All methods to generate the above files are available in the publication:</p> <p>Carmona-Mora, P., Knepp, B., Jickling, G.C. <em>et al.</em> Monocyte, neutrophil, and whole blood transcriptome dynamics following ischemic stroke. <em>BMC Med</em> <strong>21</strong>, 65 (2023). https://doi.org/10.1186/s12916-023-02766-1</p> <p> </p>
ScienceDex guides
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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.