Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Associated code and data for "Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization (doi: 10.3389/fcell.2021.74635)"

This deposit contains the data, code, and analysis to reproduce the results in the manuscript - Lai X, Keller C, Santos-Rosales G, Schaft N, Dörrie J, Vera J. Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization. Frontiers in Cell and Developmental Biolology. 2022; 9:746359; <a href="https://www.researchgate.net/publication/358461035_Multi-Level_Computational_Modeling_of_Anti-Cancer_Dendritic_Cell_Vaccination_Utilized_to_Select_Molecular_Targets_for_Therapy_Optimization">doi:10.3389/fcell.2021.746359</a>. If you have used the code for your research, please cite the original publication. Thank you very much.

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.   Data: 1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis 2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis

opencc-by-4.0Apr 2024View details →

Correlative microscopy of rat cultured hippocampal pyramidal cell from 40x confocal imaging to super-resolution 93x 3D STED of dendritic spines

This dataset contain multi-scale image of rat hippocampal pyramidal cell related to our paper "From tissues to segmentation: a modular framework for multi-scale neuron isolation" by Cauzzo et al. Nature Comm (2024).

openSep 2024View details →

Interferon-induced activation of dendritic cells and monocytes by yellow fever vaccination correlates with early antibody responses

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.

opencc-by-4.0Sep 2021View details →

Dataset part one to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"

This study was supported in parts by research funding from the Swiss National Science Foundation (grant number 310030_189144), the Thurgauische Stiftung für Wissenschaft und Forschung, and the State Secretariat for Education, Research and Innovation to DFL.

opencc-by-4.0Jun 2019View details →

Raw data related to: "Resiquimod-Mediated Activation of Plasmacytoid Dendritic Cells Is Amplified in Multiple Sclerosis"

Introduction Multiple sclerosis (MS) is a chronic inflammatory autoimmune disease of the central nervous system. The cause of multiple sclerosis is unknown but there are several evidences that associate the genetic basis of the disease with environmental causes. An important association between viral infection and development of MS is clearly demonstrated. Viruses have a strong impact on innate immune cells. In particular, myeloid dendritic cells (mDCs) and plasmacytoid dendritic cells (pDCs), are able to respond to viruses and to activate the adaptive immune response. Methods  In this study we mimic viral infection using synthetic single-strand RNA, Resiquimod, and we compared the response of both DC subsets derived from healthy donors and MS patients by characterizing the expression of costimulatory molecules on the DC surface. Results We found that pDCs from MS patients express higher levels of OX40-L. Moreover, we found that blood cells from MS patients and healthy donors upon Resiquimod-stimulation are enriched in a subpopulation of pDCs, characterized by a high amount of costimulatory molecules. Conclusion Overall, these results indicate that activation of pDCs is enhanced in MS, likely due to a latent viral infection, and that costimulatory molecules expressed on pDCs could mediate a protective response against the viral trigger of autoimmunity.

opencc-zeroApr 2016View details →

Dendritic cell subsets in oral mucosa of allergic and healthy subjects

Abstract Immunohistochemistry was used to identify, enumerate, and describe the tissue distribution of Langerhans type (CD1a and CD207), myeloid (CD1c and CD141), and plasmacytoid (CD303 and CD304) dendritic cell subsets in oral mucosa of allergic and non-allergic individuals. Allergic individuals have more CD141+ myeloid cells in epithelium and more CD1a+ Langerhans cells in the lamina propria compared to healthy controls, but similar numbers for the other DC subtypes. Our data are the first to describe the presence of CD303+ plasmacytoid DCs in human oral mucosa and a dense intraepithelial network of CD141+ DCs. The number of Langerhans type DCs (CD1a and CD207) and myeloid DCs (CD1c), was higher in the oral mucosa than in the nasal mucosa of the same individual independent of the atopic status.

opencc-by-4.0Aug 2026View details →

Pan-cancer analyses refine the single-cell portrait of tumor-infiltrating dendritic cells

This is the dataset for "Pan-cancer Analyses Refine the Single-Cell Portrait of Tumor-Infiltrating Dendritic Cells". File "panDC_all_h5ad.gz" contains processed expression .h5ad data. File "panDC_metadata.csv" contains the meta data for this study.

dryad36/100

Data from: Illuminating T cell-dendritic cell interactions in vivo by FlAsHing antigens

Delineating the complex network of interactions between antigen-specific T cells and antigen presenting cells (APCs) is crucial for effective precision therapies against cancer, chronic infections, and autoimmunity. However, the existing arsenal for examining antigen-specific T cell interactions is restricted to a select few antigen-T cell receptor pairs, with limited in situ utility. This lack of versatility is largely due to the disruptive effects of reagents on the immune synapse, which hinder real-time monitoring of antigen-specific interactions. To address this limitation, we have developed a novel and versatile immune monitoring strategy by adding a short cysteine-rich tag to antigenic peptides that emits fluorescence upon binding to thiol-reactive biarsenical hairpin compounds. Our findings demonstrate the specificity and durability of the novel antigen-targeting probes during dynamic immune monitoring in vitro and in vivo. This strategy opens new avenues for biological validation of T-cell receptors with newly identified epitopes by revealing the behavior of previously unrecognized antigen-receptor pairs, expanding our understanding of T cell responses.

opencc-zeroJan 2024View details →

opencc-by-4.0May 2023View details →

Respiratory Complex I Regulates Dendritic Cell Maturation in Explant Model of Human Tumor Immune Microenvironment

Source data for the paper, "Respiratory Complex I Regulates Dendritic Cell Maturation in Explant Model of Human Tumor Immune Microenvironment." This includes nanostring gene expression profiling of primary human tumor material  ("fresh" in the data, these samples are taken directly after enzymatic digestion) and the corresponding 3D Patient-Derived Explant Culture (PDEC).  transfer_257851_files_0a119f4d.zip refers to the mouse spatial transcriptomics data. DE_results_Metformin/LPS files refer to differentially expressed genes of human monocyte-derived dendritic cells of 6 individual donors to control untreated dendritic cells after 24hr treatment.   Also includes the original Seurat.rds file for the scSEQ of primary tumor material (fresh) vs. PDEC

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data : 1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.   Data: 1. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data: cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data:  MDAlab_cDC1_maturation.tar : Docker image used for the analysis

opencc-by-4.0Apr 2022View details →

Raw data of multiplex assay on cyto-/chemokine secretion from human monocyte-derived dendritic cells

Raw data of in vitro investigations on immune activation by bare and surface-functionalized SiO2 NP-allergen conjugates using human monocyte-derived dendritic cells as model antigen-presenting cells. Data repository for Punz B. et al., 2022.

opencc-by-4.0Oct 2024View details →

Flow cytometry data and image data -A dendritic cell vaccine for both vaccination and neoantigen-reactive T cell preparation for cancer immunotherapy in mice

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2021View details →

Dataset part two to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"

Additional dataset to dataset part one (doi: 10.5281/zenodo.4719596) to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"

opencc-by-4.0Feb 2023View details →

Non-targeted metabolome profiling in splenic monocyte-derived dendritic cells from Plasmodium chabaudi-infecetd mice

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.