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.
1,952
datasets available to search
ShareScore release 0.9.0
Dataset results
1,952 results for “monocytes”
Monocyte class switch and hyperinflammation characterise severe COVID-19 in type 2 diabetes
<p>raw and source data for manuscript EMM-2020-13038 under revision and preprint doi: https://doi.org/10.1101/2020.06.02.20119909</p>
Differentiation of human monocytes into macrophages (RNA-seq, Salmon 1.4.0, GENCODE 36)
<p>RNA-seq of differentiation of human monocytes into macrophages as described in:</p> <p>Phanstiel et al "Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development" Molecular Cell, Volume 67, Issue 6, 2017, Pages 1037-1048.e6.</p> <p>https://doi.org/10.1016/j.molcel.2017.08.006</p> <p>See publication for full author list.</p> <p>Data from publication was reprocessed by Michael Love. Paired end reads were quantified with Salmon 1.4.0 and GENCODE 36 human transcripts.</p>
RNA-Seq read counts from monocyte- and ips-derived macrophages
<p>This file contains raw RNA-Seq read counts from monocyte-derived macrophages (MDMs) and induced pluripotent stem cell-derived macrophages (IPSDMs) before and after 6 hours stimulation after LPS.</p> <p>The file was created using featureCounts. The first two columns are:</p> <ul> <li>gene_id: Ensembl 74 gene id.</li> <li>length: Length of the gene in bp.</li> </ul> <p>The MDM samples are:</p> <ul> <li>B1_ctrl</li> <li>B1_LPS</li> <li>B4_ctrl</li> <li>B4_LPS</li> <li>B5_ctrl</li> <li>B5_LPS</li> <li>B2_ctrl</li> <li>B2_LPS</li> <li>B3_ctrl</li> <li>B3_LPS</li> </ul> <p>The IPSDM samples are:</p> <ul> <li>CRL1_ctrl</li> <li>CRL1_LPS</li> <li>S7RE_ctrl</li> <li>S7RE_LPS</li> <li>FSPS10C_ctrl</li> <li>FSPS10C_LPS</li> <li>FSPS11B_ctrl</li> <li>FSPS11B_LPS</li> </ul> <p> </p>
FIB-SEM tomography datasets of entire single-cell human monocyte derived macrophages
<p>FIB-SEM tomography was used to determine the absolute number of internalized gold nanoparticles in GM-CSF and M-CSF human monocyte-derived macrophages at the single-cell level. All experiments were performed using a Thermo Scientific Scios 2 Dual Beam microscope (Thermo Fisher Scientific, Waltham, MA, USA). A selected cell was protected with a platinum (Pt) layer of 1 µm thickness (30 kV and current of 1 nA). Then, a trench of 5 µm in depth was milled on the front face and each side of the cell of interest using the ion beam at 7 nA current. Finally, the front face of the volume of interest was polished with an ion beam current of 1 nA until the beginning of the cell was visible to optimize the focus and contrast. FIB-SEM tomography of the whole cell was performed using the FEI Slice and View software (Thermo Fisher Scientific, Waltham, MA, USA, version 4.1). The electron beam acceleration voltage was set to 5 kV, the current to 0.4 nA, the resolution to 1536 × 1024 pixels, and the dwell time to 30 µs. Images were acquired in immersion mode with the backscattered electron detector, yielding clear signals from AuNPs due to the detector's sensitivity to backscattered electrons, which correlates with the atomic number of Au (Z = 79). The ion beam operating with a current of 1 nA current at 30 kV was used to slice through the cell of interest at an interval of 18 nm and a depth of 5 µm. The datasets available on the repository have been aligned and cropped.</p>
Interferon-induced activation of dendritic cells and monocytes by yellow fever vaccination correlates with early antibody responses
<p>Bulk RNA-seq analysis of sorted subpopulations isolated from PBMC of yellow fever vaccinees from before and 3, 7, 14 and 28 days after vaccination and single cell RNA-seq analysis of sorted DC and monocytes fractions isolated from PBMC of of yellow fever vaccinees from before and 3 and 7 days after vaccination.</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>
Supporting data for "Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche"
<p>This data repository contains several datasets supplementing the paper “Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche” by Fetahu, Esser-Skala, Dnyansagar et al. (2023).</p> <ul> <li>HOMER_Results.zip: detailed results of the HOMER analysis</li> <li>nblast_scopen_gene_activity_normalized_motifs_added.rds: Seurat object with scATAC-seq data</li> <li>snp_array.tgz: SNP array data</li> <li>R_data_generated.tgz: Files generated by the scRNA-seq analysis scripts in the GitHub repository associated with the publication.</li> </ul>
DATASET: Replication-competent HIV-1 in human alveolar macrophages and monocytes despite nucleotide pools with elevated dUTP
<p>Complete data set to support content of the study: Cui, et al (2022) <strong>Replication-competent HIV-1 in human alveolar macrophages and monocytes despite nucleotide pools with elevated dUTP</strong></p>
Fig 2 in Soluble total antigen derived from Toxoplasma gondii RH strain prevents apoptosis, but induces anti-apoptosis in human monocyte cell line
Fig 2. This schematic overview suggests signaling pathways of apoptosis. Apoptosis is induced through either the extrinsic pathway or intrinsic pathway. Toxoplasma gondii can prevent cell apoptosis via inhibition of cytochrome c release from the mitochondria into the cytosol, inhibition of cas-3, -6, -7, and -9 via blocking the mitochondrial pathway, and blocking cas-8 by external pathways. The parasite may also suppress pro-apoptotic protein (Bax) and induce anti-apoptotic Bcl-2 proteins and is able to activate transcription factors STAT6 and STAT3-miR-17-92-Bim pathway to inhibit apoptosis. Some proteins of T. gondii such as ROP18 plays a role in Fas/ CD95-mediated apoptosis to inhibit this process. Similarly ROP16 phosphorylates STAT3 and STAT6 to inhibit cell death. Original (S. Nemati).
Fig 1 in Soluble total antigen derived from Toxoplasma gondii RH strain prevents apoptosis, but induces anti-apoptosis in human monocyte cell line
Fig 1. The comparison of the expression levels of Bcl-2 (A), Mcl-1 (B), Cas-7 (C), Cas-3 (D), and Bax (E) in THP-1 cell line regarding different concentrations of the soluble total antigen of Toxoplasma gondii (Nicolle et Manceaux, 1908) STAg. NS: not significant; * P value <0.05; ** P value <0.01; *** P value <0.001.
Raw data to "Specialized pro-resolving lipid mediators are differentially altered in peripheral blood of patients with multiple sclerosis and attenuate monocyte and blood-brain barrier dysfunction"
<p>Background: Lack of resolution of inflammation may be considered a critical player for the onset and progression of multiple sclerosis. To demonstrate this we extracted lipids from plasma samples of healthy donors and MS patients and we quantified over 65 lipid mediators (LMs) through LC-MS-MS using signature diagnostic ions via multiple reaction monitoring.</p> <p>Results: Out of the 65 lipid mediators analyzed, only 42 were detected and out of those only 27 were finally revealed to show differences between healthy subjects and MS patients. These 27 LMs belonged to the arachidonic (AA), docosahexaenoic (DHA) or eicosapentaenoic (EPA) acid metabolomes and we could clusterize each form of MS into a specific profile by means of principal component analysis. Altogether, compared to healthy subjects, MS patients showed a strong production of several AA-derived eicosanoids (i.e. PGE2, PGD2 and PGF2a) (Fig.1D) and a little production of two DHA-derived pro-resolving mediators (SPMs), i.e. Protectin D1 (D1) and protectin DX (PDX) (Fig.1A). However, no production of DHA-derived resolvins and maresins (Fig. 1B) as well as EPA-derived resolvins (Fig.1 C) was observed.</p> <p>When stratifying MS patients according to disease form, both relapsing MS patients showed production of only two pro-resolving mediators (SPMs), i.e. Resolvin D1 (RvD1) and Protectin D1 (D1) compared to healthy subjects, whereas remitting MS patients showed a production of only few AA- and DHA-derived metabolic pathway markers and progressive MS patients a strong production of several eicosanoids as well as other metabolic pathway markers.</p> <p>Conclusions: These data suggest that along disease progression, there is a lack of production of anti-inflammatory and pro-resolving lipid mediators associated to a higher production of pro-inflammatory ones.</p>
FACS output files for neutrophils and monocytes
<p>This dataset contains FACS output files of the studies of the apoptotic rate in neutrophils and monocytes under aflotoxin B1 exposure and treatment with Schiff base derivatives. Following groups of animals were studied:</p> <p>CNTRL – intact animals;</p> <p>PLP, PLT and NLT intact animals received 10-day oral treatment with corresponding Schiff bases at 10mg/kg dosage;</p> <p>AFB1 – rats treated with AFB1 for 21 days at 25μg/kg dosage;</p> <p>AFB1+PLP, AFB1+PLT, AFB1+NLT – AFB1 exposed rats treated with corresponding Schiff bases (mycotoxin and Schiff base dosages were similar in all treated groups). </p> <p>AFB1 - aflatoxin B1, PLP, picolinyl-L-phenylalaninate; PLT, picolinyl-L-tryptophanate; NLT, nicotinyl-L-tryptophanate.</p>
QPCTL regulates macrophage and monocyte abundance and inflammatory signatures in the tumor microenvironment
<p><strong>The enzyme glutaminyl-peptide cyclotransferase-like protein (QPCTL) catalyzes the formation of pyroglutamate residues at the NH<sub>2</sub>-terminus of proteins, thereby influencing their biological properties. A number of studies have implicated QPCTL in the regulation of chemokine stability. Furthermore, QPCTL activity has recently been shown to be critical for the formation of the high affinity SIRPa binding site of the CD47 “don’t-eat-me” protein. Based on the latter data, interference with QPCTL activity—and hence CD47 maturation—may be proposed as a means to promote anti-tumor immunity. However, the pleiotropic activity of QPCTL makes it difficult to predict the effects of QPCTL inhibition on the tumor microenvironment (TME). Using a syngeneic mouse melanoma model, we demonstrate that QPCTL deficiency alters the intra-tumoral monocyte-to-macrophage ratio, results in a profound increase in the presence of pro-inflammatory cancer-associated fibroblasts (CAFs) relative to immunosuppressive TGF-b1-driven CAFs, and leads to an increased IFN and decreased TGF-b transcriptional response signature in tumor cells. Importantly, the functional relevance of the observed TME remodeling is demonstrated by the synergy between QPCTL deletion and anti PD-L1 therapy, sensitizing an otherwise refractory melanoma model to anti-checkpoint therapy. Collectively, these data provide support for the development of strategies to interfere with QPCTL activity as a means to promote tumor-specific immunity.</strong></p>
Raw data of multiplex assay on cyto-/chemokine secretion from human monocyte-derived dendritic cells
<p>Raw data of <em>in vitro</em> investigations on immune activation by bare and surface-functionalized SiO<sub>2</sub> NP-allergen conjugates using human monocyte-derived dendritic cells as model antigen-presenting cells. Data repository for Punz B. et al., 2022.</p>
Phenotype profile of monocytes differentiated in the presence of semen Fucosylated Clusterin
<p>Having shown that fucosylated clusterin effectively interacts with macrophages via DC-SIGN, we then analyzed its influence on the phenotype of macrophages. In these experiments, monocytes were cultured for 5 days with M-CSF, in the absence or presence of semen fucosylated clusterin. Then, the cells were cultured for additional 24 hours with or without LPS and the phenotype were analyzed. Fucosylated clusterin-treated macrophages failed to upregulate the expression of HLA-DR and CD86 induced by LPS but not the expression of CD40, CD80 and PDL-1. These results are represented on Figure 6 from the publication: Aberrant fucosylation enables breast cancer clusterin to interact with dendritic cell-specific ICAM-grabbing non-integrin (DC-SIGN). </p>
Data from: Vaginal lactobacilli produce anti-inflammatory beta-carboline (BC) compounds. RNAseq dataset of BC-treated human monocytes.
<p>Primary human monocytes were isolated from peripheral blood mononuclear cells via CD14+ magnetic selection. Monocytes were then treated with 3 beta-carboline compounds isolated from the supernatant of vaginal <em>Lactobacillus crispatus</em> strain MV-1A-US (HM-637). The beta-carbolines are labled 322/BC1; 325/BC3 and 361/BC6. We find that BC6 suppresses inflammatory signaling genes in untreated and in LPS-treated monocytes. FIles include raw reads, count table and code for DESeq2-generated differentially expressed genes. </p>
Clinical outcome prediction in COVID-19 patients by lymphocyte subsets analysis and monocytes' iTNF-α expression
<p>Raw Box & Whiskers plot related to a manuscript submitted to "Biology" journal - MDPI - https://www.mdpi.com/journal/biology - https://doi.org/10.3390/biology10080735</p> <p><strong>Manuscript Title</strong>: Clinical outcome prediction in COVID-19 patients by lymphocyte subsets analysis and monocytes’ iTNF-α expression</p> <p><strong>Authors: G</strong>abriele Madonna <sup>1†</sup>, Silvia Sale <sup>2†</sup>, Mariaelena Capone <sup>1</sup>, Chiara De Falco <sup>2</sup>, Valentina Santocchio <sup>2</sup>, Tiziana Di Matola <sup>2</sup>, Giuseppe Fiorentino <sup>3</sup>, Caterina Pirozzi <sup>2</sup>, Anna D’Antonio <sup>2</sup>, Rocco Sabatino <sup>2</sup>, Lidia Atripaldi<sup>4</sup>, Umberto Atripaldi <sup>4</sup>, Marcello Raffone <sup>5</sup>, Marcello Curvietto<sup> 1</sup>, Antonio Maria Grimaldi <sup>1</sup>, Vito Vanella <sup>1</sup>, Lucia Festino <sup>1</sup>, Luigi Scarpato <sup>1</sup>, Marco Palla <sup>1</sup>, Michela Spatarella <sup>6</sup>, Francesco Perna <sup>7</sup>, Pellegrino Cerino <sup>8</sup>, Gerardo Botti <sup>9</sup>, Roberto Parrella <sup>10</sup>, Vincenzo Montesarchio <sup>11</sup>, Paolo Antonio Ascierto <sup>1††* </sup>and Luigi Atripaldi <sup>2††</sup></p> <p><strong>Affiliations:</strong></p> <p>1 Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy</p> <p>2 UOC Biochimica Clinica, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>3 UOC Fisiopatologia e Riabilitazione respiratoria, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>4 University of Campania "Luigi Vanvitelli", Naples, Italy</p> <p>5 UOC Microbiologia e Virologia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>6 UOSD di Farmacia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>7 Università degli Studi di Napoli "Federico II", Naples, Italy.</p> <p>8 Istituto Zooprofilattico Sperimentale del Mezzogiorno, Portici (Na), Italy.</p> <p>9 Scientific Direction, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy</p> <p>10 UOC Malattie Infettive ad Indirizzo Respiratorio, AORN Ospedali dei Colli - Monaldi – Cotugno -CTO, Napoli, Italy</p> <p>11 UOC Oncologia, AORN Ospedali dei Colli - Monaldi – Cotugno - CTO, Napoli, Italy</p> <p>†These authors have contributed equally to this work and share first authorship</p> <p>††These authors have contributed equally to this work and share senior authorship</p> <p>*Correspondence:</p> <p><strong>Abstract of submitted Manuscript:</strong> T</p> <p>In December 2019 a novel coronavirus, “SARS-CoV-2”, was recognized as the cause of Coronavirus disease-2019 (COVID-19-disease). Several studies have explored the changes and the role of inflammatory cells and cytokines in the immunopathogenesis of disease, but until today the results have been controversial. Based on these premises, we carried out a retrospective assessment of monocytes’ intracellular TNF-α expression (iTNF-α) and on frequencies of lymphocyte sub-populations in twenty-five patients with moderate/severe COVID-19-disease. We found lymphopenia in all COVID-19 infected subjects compared with healthy subjects. On initial observation, in patients with favorable outcome we detected high absolute eosinophils count and high CD4+/CD8+ T lymphocytes ratio, while in the exitus group we observed high neutrophils and CD8+ T lymphocytes counts. During infection, in patients with favorable outcome we observed a rise in lymphocyte count, in monocytes and in Treg lymphocytes counts, in CD4+ and in CD8+ T lymphocytes count but a reduction in CD4+/CD8+ T lymphocytes ratio. Instead, in the exitus group we observed a reduction in Treg lymphocytes counts and a decrease in iTNF-α expression. Our preliminary findings point to a modulation of the different cellular mediators of immune system, which probably have a key role in the outcome of the COVID-19-disease.</p> <p><strong>Funding:</strong> This work was supported by Grants from “Regione Campania” through “POR FESR CAMPANIA 2014-2020”, CUP H64I20000300002.</p> <p><strong>Cite</strong>: Madonna, G.; Sale, S.; Capone, M.; De Falco, C.; Santocchio, V.; Di Matola, T.; Fiorentino, G.; Pirozzi, C.; D’Antonio, A.; Sabatino, R.; Atripaldi, L.; Atripaldi, U.; Raffone, M.; Curvietto, M.; Grimaldi, A.M.; Vanella, V.; Festino, L.; Scarpato, L.; Palla, M.; Spatarella, M.; Perna, F.; Cerino, P.; Botti, G.; Parrella, R.; Montesarchio, V.; Ascierto, P.A.; Atripaldi, L. Clinical Outcome Prediction in COVID-19 Patients by Lymphocyte Subsets Analysis and Monocytes’ iTNF-α Expression. <em>Biology</em> <strong>2021</strong>, <em>10</em>, 735. doi: <a href="https://doi.org/10.3390/biology10080735">10.3390/biology10080735</a></p> <p> </p>
Non-targeted metabolome profiling in splenic monocyte-derived dendritic cells from Plasmodium chabaudi-infecetd mice
<p>Spleens from mice infected with Plasmodium chabaudi were processed and stained for localization of monocyte-derived dendritic cells (MODCs) by flow cytometry. The markers utilized were: Live/Dead, F4/80, CD11b, DCSign, MHCII, CD11c and CD3. After sorted out, MODCs were frozen in liquid nitrogen, followed by metabolite extraction, as recommended by The Metabolomics Facility at MD Anderson. Metabolites were extracted using ice-cold 0.1% Ammonium hydroxide in 80/20 (v/v) methanol/water. Extracts were centrifuged at 17,000 g for 5 min at 4°C, and supernatants were transferred to clean tubes, followed by evaporation to dryness under nitrogen. Dried extracts were reconstituted in deionized water, and 5 μL was injected for analysis by ion chromatography (IC)-MS. IC mobile phase A (MPA; weak) was water, and mobile phase B (MPB; strong) was water containing 100 mM KOH. A Thermo Scientific Dionex ICS-5000+ system included a Thermo IonPac AS11 column (4 µm particle size, 250 x 2 mm) with column compartment kept at 30°C. The autosampler tray was chilled to 4°C. The mobile phase flow rate was 350 µL/min and gradient from 1mM to 100mM KOH was used. The total run time was 60 min. To assist the desolvation for better sensitivity, methanol was delivered by an external pump and combined with the eluent via a low dead volume mixing tee. Data were acquired using a Thermo Orbitrap Fusion Tribrid Mass Spectrometer under ESI negative ionization mode at a resolution of 240,000.</p>
Transcriptome profiling of human monocytes from accute phase and convalescent patients infected with Plasmodium vivax malaria
<p> RNA sequencing raw data of monocytes obtained from five patients in acute phase of <em>Plasmodium vivax </em>malaria and 45 days after treatment. Individuals from Rondônia (Brazil) were between 18 and 60 years and attended to the exclusion criteria: chronic inflammatory or infectious diseases, pregnancy, and breastfeeding. Diagnostic of malaria by clinical symptoms and thick blood smear was also confirmed by qPCR. According to the recommended protocol of the Brazilian Ministry of Health patients were treated with chloroquine associated to primaquine. Protocols used were approved by the Ethical Committees on Human Experimentation from Instituto Rene ́ Rachou, Fiocruz, and National Ethical Council (CAAE: 59902816.7.0000.5091). </p> <p> Sequencing was performed in a NextSeq 500 system using the NextSeq 500/550 High Output Kit v2.5 for 75 cycles (both from Illumina). Before sequencing total mRNA samples were prepared using TruSeq Stranded mRNA Kit (Illumina) according to the manufacturer’s protocol. Files are in FASTQ format, just as they were downloaded from Illumina's cloud environment (BaseSpace) right after sequencing. Files are identified by patient number (1 to 5) and health condition: acute phase of the disease or convalescent (45 days after treatment).</p>
Mapping the epigenomic landscape of human monocytes following innate immune activation reveals context-specific mechanisms driving endotoxin tolerance
<p><strong>Processed datasets for publication at <em>BMC Genomics</em>.</strong></p> <p>We exposed human primary monocytes from healthy donors (n=6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2hr LPS; LPS2) and two models of endotoxin tolerance: repeated stimulations (6+6hr; LPS6:6) and prolonged exposure to endotoxin (24hr LPS; LPS24). Another subset of monocytes was left untreated (naïve; UT). We performed total RNA-seq and ATAC-seq for monocytes across these treatment conditions.</p> <p>The following processed datasets include the raw and normalised count data for each gene or ATAC peak, and the bedGraph and bigWig format for genome-wide signal data. [ bigWig files for RNA-seq (monocytes_*_mean.bw); bigWig files for ATAC-seq (Monocyte_ATAC_*_mean_normalised.by.1/size.factor.bw); bedgraph files for eRNA visualization (*RPKM_FR.bedgraph.gz); raw and normalised count files for ATAC-seq and RNA-seq data (Monocyte.featureCounts_RNAseq.txt.gz; Monocyte_filtered_log2.normalized_RNAseq_counts.txt.gz; Meta.file.txt.gz; Monocyte_raw.counts_ATACseq.txt.gz.).]</p> <p><strong>Data curator:</strong></p> <p>Ping Zhang</p> <p> </p>
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.