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1,952 results for “Monocyte”
Code and data for "Bordetella adenylate cyclase toxin elicits chromatin remodeling and transcriptional reprogramming that blocks differentiation of monocytes into macrophages"
<p># Code and data for "Bordetella adenylate cyclase toxin elicits chromatin remodeling and transcriptional reprogramming that blocks differentiation of monocytes into macrophages"</p> <p><br> - `pipeline` contains scripts for processing raw reads with `nf-core/rnaseq` Nextflow pipeline<br> - `setup_conda.sh` sets up a conda environment for Nextflow<br> - `samplesheet.csv` describes the samples<br> - `rna-seq-nextflow.sh` runs the analysis<br> - `data` contains the quantifications by Salmon from the pipeline and data for the CRE motif (see `analysis.Rmd` for more details)<br> - `analysis.Rmd` an R notebook containing a code to reproduce conclusions and figures in the paper and additional comments.</p> <p>The R environment (all packages) is reproducibly reconstructed via `renv` - run `renv::restore()` after loading the project.</p> <p>The generated figures and tables are stored in the `outputs` directory.</p> <p> </p>
Intraperitoneal Infusion of Autologous Monocytes With Sylatron (Peginterferon Alfa-2b) and Actimmune (Interferon Gamma-1b) in Women With Recurrent or Refractory Ovarian Cancer, Fallopian Tube Cancer o
ClinicalTrials.gov study NCT02948426. IPD Sharing: YES. Countries: 1. Publications: 6.
Monocyte Distribution Width (MDW) in the General Population of Emergency Department Patients With and Without Bacteremia
ClinicalTrials.gov study NCT05296590. IPD Sharing: NO. Countries: 1. Publications: 9.
Monocyte Biomarkers in Moderate to Severe Plaque Psoriasis Subjects Treated With Apremilast
ClinicalTrials.gov study NCT03442088. IPD Sharing: NO. Countries: 1. Publications: 45.
Antigen-specific CD4+ T cells promote monocyte recruitment and differentiation into glycolytic lung macrophages to control Mycobacterium tuberculosis
Open the record for dataset details and reuse information.
Dataset related to article "Senescent thyrocytes and thyroid tumor cells induce M2-like macrophage polarization of human monocytes via a PGE2-dependent mechanism."
<p>BACKGROUND:</p> <p>Thyroid carcinoma includes several variants characterized by different biological and clinical features: from indolent microcarcinoma to undifferentiated and aggressive anaplastic carcinoma. Inflammation plays a critical role in thyroid tumors. Conditions predisposing to cancer, as well as oncogene activity, contribute to the construction of an inflammatory microenvironment that facilitates thyroid tumor progression. Moreover, oncogene-induced senescence, a mechanism tightly connected with inflammation, and able to restrain or promote cancer progression, is involved in thyroid cancer. The interactions between thyroid tumor cells and the microenvironment are not completely clarified.</p> <p>METHODS:</p> <p>We characterize in vitro the interplay between macrophages and senescent thyrocytes and tumor-derived cell lines, modeling early and late thyroid tumor stages, respectively. Purified peripheral blood-derived human monocytes were exposed to thyroid cell-derived conditioned medium (CM) and assessed for phenotype by flow cytometry. The factors secreted by thyroid cells and macrophages were identified by gene expression analysis and ELISA. The protumoral effect of macrophages was assessed by wound healing assay on K1 thyroid tumor cells. The expression of PTGS2 and M2 markers in thyroid tumors was investigated in publicly available datasets.</p> <p>RESULTS:</p> <p>Human monocytes exposed to CM from senescent thyrocytes and thyroid tumor cell lines undergo M2-like polarization, showing high CD206 and low MHC II markers, and upregulation of CCL17 secretion. The obtained M2-like macrophages displayed tumor-promoting activity. Among genes overexpressed in polarizing cells, we identified the prostaglandin-endoperoxide synthase enzyme (PTGS2/COX-2), which is involved in the production of prostaglandin E2 (PGE2). By using COX-2 inhibitors we demonstrated that the M2-like polarization ability of thyroid cells is related to the production of PGE2. Co-expression of PTGS2 and M2 markers is observed a significant fraction of human thyroid tumors.</p> <p>CONCLUSIONS:</p> <p>Our results demonstrate that both senescent thyrocytes and thyroid tumor cell lines trigger M2-like macrophage polarization that is related to PGE2 secretion. This suggests that the interaction with the microenvironment occurs at both early and late thyroid tumor stages, and favors tumor progression. The co-expression of PTGS2 gene and M2 markers in human thyroid carcinoma highlights the possibility to counteract tumor growth through COX-2 inhibition.</p>
Increased sCD163 and sCD14 plasmatic levels and depletion of peripheral blood pro-inflammatory monocytes, myeloid and plasmacytoid dendritic cells in patients with severe COVID-19 pneumonia
<p><strong>Background: </strong>Emerging evidence argues that monocytes, circulating innate immune cells, are principal players in COVID-19 pneumonia. The study aimed to investigate the role of soluble (s)CD163 and sCD14 plasmatic levels in predicting disease severity and characterize peripheral blood monocytes and dendritic cells (DCs), in patients with COVID-19 pneumonia (COVID-19 subjects). </p> <p><strong>Methods:</strong> On admission, in COVID-19 subjects sCD163 and sCD14 plasmatic levels, and peripheral blood monocyte and DC subsets were compared to healthy donors (HDs). According to clinical outcome, COVID-19 subjects were divided into ARDS and non-ARDS groups. </p> <p><strong>Results:</strong> Compared to HDs, COVID-19 subjects showed higher sCD163 (p<0.0001) and sCD14 (p<0.0001) plasmatic levels. We observed higher sCD163 plasmatic levels in the ARDS group compared to the non-ARDS one (p=0.002). The cut-off for sCD163 plasmatic level greater than 2032 ng/ml was predictive of disease severity (AUC: 0.6786, p=0.0022; sensitivity 56.7% [CI: 44.1-68.4] specificity 73.8% [CI: 58.9-84.7]). Positive correlation between plasmatic levels of sCD163, LDH and IL-6 and between plasmatic levels of sCD14, D-dimer and ferritin were found. Compared to HDs, COVID-19 subjects showed lower percentages of non-classical (p=0.0012) and intermediate monocytes (p=0.0447), slanDCs (p<0.0001), myeloid DCs (mDCs, p<0.0001) and plasmacytoid DCs (pDCs, p=0.0014). Compared to the non-ARDS group, the ARDS group showed lower percentages of non-classical monocytes (p=0.0006), mDCs (p=0.0346) and pDCs (p=0.0492). </p> <p><strong>Conclusions:</strong> The increase in sCD163 and sCD14 plasmatic levels, observed on hospital admission in COVID-19 subjects, especially in those who developed ARDS, and the correlations of these monocyte/macrophage activation markers with typical inflammatory markers of COVID-19 pneumonia, underline their potential use to assess the risk of progression of the disease. In an early stage of the disease, the assessment of sCD163 plasmatic levels could have clinical utility in predicting the severity of COVID-19 pneumonia. </p>
Data from: Reduced internalization of TNF-ɑ/TNFR1 down-regulates caspase dependent phagocytosis induced cell death (PICD) in neonatal monocytes
Phagocytosis-induced cell death (PICD) is diminished in cord blood monocytes (CBMO) as compared to cells from adults (PBMO) due to differences in the CD95-pathway. This may support a prolonged pro-inflammatory response with sequels of sustained inflammation as seen in neonatal sepsis. Here we hypothesized that TNF-α mediated induction of apoptosis is impaired in CBMO due to differences in the TNFR1-dependent internalization. Monocytes were infected with Escherichia coli-GFP (E. coli-GFP). Monocyte phenotype, phagocytic activity, induction of apoptosis, and TNF-α/TNF-receptor (TNFR) -expression were analysed. In the course of infection TNF-α-secretion of CBMO was reduced to 40% as compared to PBMO (p<0.05). Neutralization of TNF-α by an αTNF-α antibody reduced apoptotic PICD in PBMO four-fold (p < 0.05 vs. infection with E. coli). PICD in CBMO was reduced 5-fold compared to PBMO and showed less responsiveness to αTNF-α antibody. CBMO expressed less pro-apoptotic TNFR1, which, after administration of TNF-α or infection with E. coli was internalized to a lesser extent. With similar phagocytic capacity, reduced TNFR1 internalization in CBMO was accompanied by lower activation of caspase-8 (p < 0.05 vs. PBMO). Stronger caspase-8 activation in PBMO caused more activation of effector caspase-3 and apoptosis (all p < 0.05 vs. PBMO). Our results demonstrate that TNFR1 internalization is critical in mediating PICD in monocytes after infection with E.coli and is reduced in CBMO.
Dataset related to publication: "Bromo-substituted indirubins for inhibition of protein kinase-mediated signalling involved in inflammatory mediator release in human monocytes"
<p>Dataset related to publication: Bachmann et al. "Bromo-substituted indirubins for inhibition of protein kinase-mediated signalling involved in inflammatory mediator release in human monocytes". Bioorganic Chemistry (2024) https://doi.org/10.1016/j.bioorg.2024.107470 </p> <ul> <li>Full Desmond MD simulation trajectories of the 10 replica simulations of compounds <strong>1</strong> and <strong>2</strong> bound to GSK-3beta (.zip files contain the -out.cms file and the trajectory)</li> <li>Maestro Glide XP docking output pose viewer files of compounds <strong>1 </strong>and <strong>2</strong> for CDKs (PDB IDs in the filenames)(.maegz -files)</li> </ul>
Defective interferon signaling in the circulating monocytes of type 2 diabetic mice
<h1>Data Repository for scRNA-seq and Cell Communication Analysis in T2DM Stroke Model</h1> <p>This repository contains the data and Seurat/CellChat objects generated for analyzing the transcriptomic response of peripheral monocytes to Type 2 Diabetes Mellitus (T2DM) and ischemic stroke in a mouse model. Data were generated using single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, and analysis with NicheNet and CellChat. This study aims to reveal molecular and cellular changes in diabetic and ischemic conditions, particularly focusing on interferon signaling and inflammation.</p> <h2>Repository Structure</h2> <h3>Root Files</h3> <ul> <li> <p><code>README.md</code>: This file, describing the repository and data files.</p> </li> <li> <p><code>cellchat_object/</code>: Contains RDA files for CellChat analysis.</p> </li> <li> <p><code>scrna_sequencing_raw/</code>: Raw scRNA-seq data files.</p> </li> <li> <p><code>seurat_object/</code>: Contains Seurat objects for clustering and visualization.</p> </li> <li> <p><code>trajectory_object/</code>: Contains Cell Data Set (CDS) objects for trajectory and pseudotime analysis.</p> </li> </ul> <h3>File Descriptions</h3> <h4><code>cellchat_object/</code></h4> <ul> <li> <p><strong>dbdb_case_cellchat.rda</strong>: CellChat object for the db/db mice with ischemic stroke. Analysis was conducted to identify cell-cell communication patterns altered due to stroke in diabetic conditions.</p> </li> <li> <p><strong>dbdb_control_cellchat.rda</strong>: CellChat object for db/db control (no stroke) mice. Provides baseline data for diabetic conditions.</p> </li> <li> <p><strong>dbp_case_cellchat.rda</strong>: CellChat object for the db/+ (normoglycemic) mice with ischemic stroke, representing the stroke model in non-diabetic conditions.</p> </li> <li> <p><strong>dbp_control_cellchat.rda</strong>: CellChat object for db/+ control (no stroke) mice. Baseline for non-diabetic, non-stroke conditions.</p> </li> </ul> <p><strong>Methods</strong>: CellChat analysis identifies ligand-receptor interactions to reveal cross-talk between monocyte subtypes, focusing on pathways affected by T2DM and stroke. These data provide insights into thromboinflammatory responses, interferon signaling, and immune suppression in diabetic mice.</p> <h4><code>scrna_sequencing_raw/</code></h4> <ul> <li> <p><strong>db_db-DMCAO.zip</strong>: Raw scRNA-seq data for db/db mice post-distal middle cerebral artery occlusion (DMCAO).</p> </li> <li> <p><strong>db_db-Sham.zip</strong>: Raw scRNA-seq data for db/db mice with sham surgery (control).</p> </li> <li> <p><strong>db_pos-Sham.zip</strong>: Raw scRNA-seq data for db/+ mice with sham surgery.</p> </li> <li> <p><strong>db_pos-dMCAO.zip</strong>: Raw scRNA-seq data for db/+ mice post-DMCAO.</p> </li> </ul> <p><strong>Methods</strong>: scRNA-seq was performed using 10X Genomics GemCode Technology. Data were processed with Cell Ranger (v1.3), and differential gene expression analysis was conducted in Seurat with normalization based on UMI counts.</p> <h4><code>seurat_object/</code></h4> <ul> <li> <p><strong>bloodstroke.rda</strong>: Seurat object containing processed scRNA-seq data of peripheral blood mononuclear cells (PBMCs) from both diabetic and normoglycemic mice under stroke and sham conditions. Contains cell clusters annotated using SingleR and the ImmGen database.</p> </li> <li> <p><strong>monocytes.rda</strong>: Seurat object specifically for monocytes, enriched through re-clustering in Seurat. Enables focused analysis of monocyte subsets and their responses to diabetic and ischemic conditions.</p> </li> </ul> <p><strong>Methods</strong>: Filtering was performed for cells with fewer than 500 detected genes, and data were normalized using log-normalization. Clustering was carried out with PCA and visualized with UMAP.</p> <h4><code>trajectory_object/</code></h4> <ul> <li> <p><strong>dbdb_control_cds.rda</strong>: Cell Data Set (CDS) object for db/db control monocytes, representing baseline differentiation trajectory under diabetic conditions.</p> </li> <li> <p><strong>dbdb_stroke_cds.rda</strong>: CDS for db/db stroke monocytes, used to observe pseudotime trajectories and differentiation in diabetic and ischemic states.</p> </li> <li> <p><strong>dbp_control_cds.rda</strong>: CDS for db/+ control monocytes, baseline data for normoglycemic mice.</p> </li> <li> <p><strong>dbp_stroke_cds.rda</strong>: CDS for db/+ stroke monocytes, tracking pseudotime trajectory in response to ischemic stroke.</p> </li> <li> <p><strong>monocytes_cds.rda</strong>: CDS focusing on monocyte trajectory across all experimental conditions, facilitating the comparison of diabetic and normoglycemic trajectories.</p> </li> </ul> <p><strong>Methods</strong>: Trajectory analysis was conducted in Monocle 3 to explore cellular differentiation paths. Trajectories reveal potential maturation stages and highlight changes due to T2DM and ischemic conditions.</p> <h2>Methodology Overview</h2> <ol> <li> <p><strong>scRNA-seq and Bulk RNA-seq</strong>: Mononuclear cells were isolated and sequenced using 10X Genomics protocols, with analysis in Seurat to determine cell clusters, particularly monocytes. Bulk RNA-seq was used for pathway validation in broader cell populations.</p> </li> <li> <p><strong>CellChat Analysis</strong>: Ligand-receptor interactions were identified in CellChat, analyzing how cell communication changes with T2DM and stroke. Pathways of interest include the Anxa1-Fpr2 axis for thromboinflammation and MHCII-CD4 for immune activation.</p> </li> <li> <p><strong>Trajectory Analysis</strong>: Using Monocle 3, we investigated monocyte differentiation paths to uncover the effects of T2DM and stroke on cellular progression. Results demonstrate disrupted monocyte differentiation and increased proinflammatory markers in diabetic stroke conditions.</p> </li> <li> <p><strong>NicheNet Analysis</strong>: Identified key ligands affecting monocyte gene expression in diabetic versus non-diabetic conditions, highlighting interferon signaling and inflammatory markers such as Ccl6, Ccl9, and Ifng.</p> </li> </ol> <h2>Notes</h2> <ul> <li> <p>Ensure all analyses respect data filtering thresholds as set in Seurat and Monocle.</p> </li> <li> <p>Refer to the manuscript for further details on experimental setup, animal models, and additional context.</p> </li> </ul> <h2>License</h2> <p>Data and scripts are available for non-commercial use under a Creative Commons License.</p>
Association of Peripheral Monocytic-Myeloid-Derived Suppressor Cells with Molecular Subtypes in Single Center Endometrial Cancer Patients Receiving Carboplatin + Paclitaxel/Avelumab (MITO END-3 Trial)
<p><span>The MITO-END3 trial compared carboplatin and paclitaxel (</span><span>CP</span><span>) with avelumab plus carboplatin and paclitaxel (</span><span>CPA)</span><span> as first-line treatment in endometrial cancer (EC) patients and </span><span>demonstrated a significant interaction between avelumab response and mismatch repair status. To investigate prognostic/predictive biomarker, </span><span>twenty-nine MITO-END3-EC patients were evaluated at pre-treatment (B1) and at the end of CP/CPA treatment (B2) for </span><span>peripheral Myeloid derived suppressor cells (MDSC) and Tregs. </span><span>At B2, <span>effector</span> Tregs frequency was significantly higher in patients treated with CPA as compared to CP (p=0.038). Both treatments (CP/CPA) induced significant decrease in peripheral M-MDSC (-5.41%) in TCGA 2-MSI-High as compared to TCGA-category 4 tumors (p=0.004). In accordance, both treatments induced M-MDSCs (+5.34%) in MSS patients as compared to MSI-High patients (p=0.001). Moreover, in a subgroup of patients, </span><span>primary tumors were highly infiltrated by M-MDSCs in MSS as compared to MSI-high ECs. </span><span>A post hoc analysis displayed higher frequeny of M-MDSCs (p=0.020) and lower frequency of CD4+ (p<0.005) at pretreatment in EC patients as compared to healthy donors. </span><span>In conclusion, </span><span>the</span><span> peripheral evaluation of MDSCs and Tregs correlated with molecular features in EC treated with CP/CPA and may add insights in identifying EC patients responder to first line chemo/chemo-immunotherapy. </span></p>
Single-cell RNA sequencing of CNS-infiltrating HSC-derived phagocytes of Ms4a3Ai14, BM chimeric mice (CD45.2 Csf2rb-/-: CD45.1 Csf2rb+/+ and CD45.2 Ifngr1-/-: CD45.1 Ifngr1+/+) using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of CNS-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE, BM chimeric mice (CD45.2 <em>Csf2rb</em><sup>-/-</sup>: CD45.1 <em>Csf2rb</em><sup>+/+</sup> and CD45.2 <em>Ifngr1<sup>-/-</sup></em>: CD45.1 <em>Ifngr1<sup>+/+</sup></em>) using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Zbtb14 regulates monocyte and macrophage development through inhibiting pu.1 expression in zebrafish
<p>To elucidate the mechanism underlying the aberrant monocyte/macrophage development of <em>zbtb14<sup>-/- </sup></em>mutant, GFP-positive cells were isolated from either wild-type <em>Tg(mpeg1.1:eGFP)</em> or <em>zbtb14 <sup>-/-</sup> //Tg(mpeg1.1:eGFP) </em>larvae at 2dpf by FACS. mRNA was extracted from sorted cells using RNeasy Micro (Qiagen, Manchester, UK) and mRNA libraries were constructed using NEBNext Ultra RNA Library Prep Kit for52 Illumina and sequenced under Illumina HiSeq X Ten with pair end 150bp (PE150). </p>
StarDist_BF_Monocytes_dataset
<p>This repository includes a StarDist deep learning model and its training and validation datasets for detecting mononucleated cells perfused over an endothelial cell monolayer. The model was trained on 27 manually annotated images and achieved an average F1 Score of 0.941. The dataset and model are helpful for biomedical research, especially in studying interactions between mononucleated and endothelial cells.</p> <h3>Specifications</h3> <ul> <li> <p>Model: StarDist for mononucleated cell detection on endothelial cells</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Number of Images: 27 paired brightfield microscopy images and label masks</p> </li> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images with manually segmented masks</p> </li> <li> <p>File Format: TIFF (.tif)</p> </li> <ul> <li> <p>Brightfield Images: 16-bit</p> </li> <li> <p>Masks: 8-bit</p> </li> </ul> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> </ul> <li> <p>Training Parameters:</p> </li> <ul> <li> <p>Epochs: 400</p> </li> <li> <p>Patch Size: 992 x 992 pixels</p> </li> <li> <p>Batch Size: 2</p> </li> </ul> <li> <p>Performance:</p> </li> <ul> <li> <p>Average F1 Score: 0.941</p> </li> <li> <p>Average IoU: 0.831</p> </li> </ul> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
Data from: Monocyte recruitment to the dermis and differentiation to dendritic cells increases the targets for Dengue virus replication
Dengue virus (DENV) causes the most prevalent arthropod-borne viral disease in humans. Although Aedes mosquitoes transmit DENV when probing for blood in the skin, no information exists on DENV infection and immune response in the dermis, where the blood vessels are found. DENV suppresses the interferon response, replicates, and causes disease in humans but not wild-type mice. Here, we used mice lacking the interferon-α/β receptor (Ifnar–/–), which had normal cell populations in the skin and were susceptible to intradermal DENV infection, to investigate the dynamics of early DENV infection of immune cells in the skin. CD103+ classical dendritic cells (cDCs), Ly6C– CD11b+ cDCs, and macrophages in the steady-state dermis were initial targets of DENV infection 12-24 hours post-inoculation but then decreased in frequency. We demonstrated recruitment of adoptively-transferred Ly6Chigh monocytes from wild-type and Ifnar–/– origin to the DENV-infected dermis and differentiation to Ly6C+ CD11b+ monocyte-derived DCs (moDCs), which became DENV-infected after 48 hours, and were then the major targets for virus replication. Ly6Chigh monocytes that entered the DENV-infected dermis expressed chemokine receptor CCR2, likely mediating recruitment. Further, we show that ~100-fold more hematopoietic cells in the dermis were DENV-infected compared to Langerhans cells in the epidermis. Overall, these results identify the dermis as the main site of early DENV replication and show that DENV infection in the skin occurs in two waves: initial infection of resident cDCs and macrophages, followed by infection of monocytes and moDCs that are recruited to the dermis. Our study reveals a novel viral strategy of exploiting monocyte recruitment to increase the number of targets for infection at the site of invasion in the skin and highlights the skin as a potential site for therapeutic action or intradermal vaccination.
Epigenetic changes in inflammatory arthritis monocytes contribute to disease and can be targeted by JAK inhibition
<p><strong>Objectives</strong> How the local inflammatory environment regulates epigenetic changes in the context of inflammatory arthritis remains unclear. Here we assessed the transcriptional and active enhancer profile of monocytes derived from the inflamed joints of Juvenile Idiopathic Arthritis (JIA) patients, a model well-suited for studying inflammatory arthritis.<br> <strong>Methods </strong>RNA-sequencing and H3K27me3 chromatin immunoprecipitation sequencing (ChIP-seq) were used to analyze the transcriptional and epigenetic profile, respectively, of JIA synovial fluid-derived monocytes.<br> <strong>Results </strong>Synovial-derived monocytes display an activated phenotype, which is regulated on the epigenetic level. IFN signaling-associated genes are increased and epigenetically altered in synovial monocytes, indicating a driving role for IFN in establishing the local inflammatory phenotype. Treatment of synovial monocytes with the Janus-associated kinase (JAK) inhibitor ruxolitinib, which inhibits IFN signaling, transformed the activated enhancer landscape and reduced disease-associated gene expression, thereby inhibiting the inflammatory phenotype.<br> <strong>Conclusion </strong>This study provides novel insights into epigenetic regulation of inflammatory arthritis patient-derived monocytes and highlights the therapeutic potential of epigenetic modulation for the treatment of inflammatory rheumatic diseases.</p> <p><br> <strong>Data deposited</strong></p> <ul> <li>H3K27ac ChIP-seq peak coordinates of healthy control peripheral blood-derived monocytes and JIA peripheral blood- and synovial fluid-derived monocytes (Figure 2 in article).</li> <li>Individual and merged peak coordinates of H3K27ac and H3K4me1 ChIP-seq on JIA monocytes with and without Ruxolitinib and IFN gamma (Figure 4 in article).</li> <li>Gene expression data of JIA monocytes treated with and without JQ1 (Figure 2 in article).</li> <li>Gene expression data of Ruxolitinib- and IFN-stimulated JIA monocytes (Figure 5 in article).</li> </ul>
Clinically used broad-spectrum antibiotics compromise inflammatory monocyte-dependent antibacterial defense in the lung
<p>Code and data for the preprint "Clinically used broad-spectrum antibiotics compromise inflammatory monocyte-dependent antibacterial defense in the lung". </p> <p><strong>Metagenomic analysis</strong> </p> <p>- uses `<a href="https://github.com/ropensci/targets">targets</a>` (workflow manager) and `<a href="https://github.com/rstudio/renv">renv</a>` (environment manager) R packages. </p> <p>- calling `targets::tar_make()`, once the environment is configured using renv and a separate project has been created (with the exact file structure including the data), should run the entire pipeline, creating a _targets folder with all the .rds files </p> <p>- independent objects can be explored using `targets::tar_load(obj)` or `targets::tar_read(plot)` </p> <p><a href="https://books.ropensci.org/targets/">Targets user manual </a>for more information. Preprocessing/QC scripts can be provided upon reasonable request. </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.