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opencc-by-4.0Dec 2021View details →

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

opencc-by-4.0Dec 2021View details →

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

opencc-by-4.0Dec 2021View details →

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.

dryad36/100

Raw data for: Dynamic instability of dendrite tips generates the highly branched morphologies of sensory neurons

The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell types, as well as to branching at the subcellular and tissue levels.

opencc-zeroMay 2022View details →

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-4.0Jan 2023View details →

Supplementary Data to "Simulation of dendritic-eutectic growth with the phase-field method" by Seiz et al.

Video files for several simulations conducted for the paper, showing more of the dynamic time evolution than possible in the paper itself.   Update 24/04/2023: A few additional simulations were conducted to test for the applicability of the theory delineating the dendritic-eutectic regime from the eutectic regime. Videos of these plus some additional data is deposited at   https://zenodo.org/record/7858461   All videos show the Cu composition field, with the color ranging from 0.02 (pure black) to 0.33 (pure white). Thus black represents the fcc Al crystal, whitish-grey the Al2Cu intermetallic phase and grey shades in between the liquid melt, with lighter shades being richer in Cu. Excluding the complete directional solidification videos (full*webm), all videos show regions of 280x250um^2, with the far-field to the right being cut off to emphasize the structure. {close,far}_d+e.webm: Complete simulations resulting in a eutectic structure either growing close to the dendrite tip or far from it, cropped to slightly above the solidification front. The same speed v=160um/s and melt composition c_0=0.12 are used, but two different gradients: 99K/mm for close growth and 24.7K/mm for far growth; at the even smaller gradient the eutectic is no longer in the moving window. traveling_oscillation.webm: Complete simulation resulting in a eutectic with traveling oscillations. (v=160um/s, c_0=0.13, G=6.18K/mm) jump_d+e_e.webm: Jumps from v = 160um/s to 320um/s at simulation start in order to move from a dendritic-eutectic morphology to a eutectic morphology. After a eutectic morphology is obtained, the jump is reversed (around 17s into the video) and only a coarsening of the eutectic is observed. jump_e_d+e.webm: Jumps from v=320um/s to 20um/s at simulation start in order to move from a eutectic morphology to a dendritic-eutectic morphology. full_cropped*webm: Complete directional solidification for different alloy compositions and processing conditions yielding different structures. Cropped to slightly above the final maximum position of any solid phase, showing a 970x500um^2 domain. A scaling to 50% of the original resolution is performed as some players/browsers have trouble with large resolutions. e: primarily eutectic (v=320um/s, G=24.7K/mm, c_0 = 0.12) d+e: dendritic-eutectic (v=160um/s, G=24.7K/mm, c_0 = 0.12) full_d.webm: Same as above, only non-cropped as the structure fills the entire simulation box (1500x500um^2). (v=320um/s, G=24.7K/mm, c_0 = 0.08)

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.

opencc-by-4.0Oct 2022View details →

Dynamic interactome of the MHC I peptide loading complex in human dendritic cells - Source II

Source data underlying MS data set (SFig.2). Datafile comprises MS raw files + MaxQuant output files.

opencc-by-4.0Oct 2022View details →

Dynamic interactome of the MHC I peptide loading complex in human dendritic cells - Source III

Source data underlying Raji cell data set (SFig.3). Datafile comprises MS raw data + MaxQuant output files.

opencc-by-4.0Feb 2023View details →

dataset of papers from PubMed related to Dendritic potential as immune contraception

This is the dataset from PubMed. It consist of two files: 1. Abstract of each previous studies used in form of txt file 2. Details of each previous studies used in form of CSV file

CT7, MAGE-A3, and WT1 mRNA-electroporated Autologous Langerhans-type Dendritic Cells as Consolidation for Multiple Myeloma Patients Undergoing Autologous Stem Cell Transplantation

ClinicalTrials.gov study NCT01995708. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Blockade of PD-1 in Conjunction With the Dendritic Cell/AML Vaccine Following Chemotherapy Induced Remission

ClinicalTrials.gov study NCT01096602. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Phase II Feasibility Study of Dendritic Cell Vaccination for Newly Diagnosed Glioblastoma Multiforme

ClinicalTrials.gov study NCT00323115. IPD Sharing: Not stated. Countries: 1. Publications: 1.

A Pilot Study of a Dendritic Cell Vaccine in HIV-1 Infected Subjects

ClinicalTrials.gov study NCT00833781. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Dendritic Cell Vaccines + Dasatinib for Metastatic Melanoma

ClinicalTrials.gov study NCT01876212. IPD Sharing: Not stated. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

Dendritic Cell/Myeloma Fusion Vaccine for Multiple Myeloma (BMT CTN 1401)

ClinicalTrials.gov study NCT02728102. IPD Sharing: YES. Countries: 1. Publications: 1.

Dendritic Cell Vaccine Study (DC/PC3) for Prostate Cancer

ClinicalTrials.gov study NCT00345293. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Tagraxofusp in Patients With Blastic Plasmacytoid Dendritic Cell Neoplasm or Acute Myeloid Leukemia

ClinicalTrials.gov study NCT02113982. IPD Sharing: Not stated. Countries: 1. Publications: 3.