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6,650 results for “pancreatitis”
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Data for: 3D in vitro modeling of the exocrine pancreatic unit using tomographic volumetric bioprinting
<p><strong>Abstract</strong></p> <div> <div> <p><span><span>Pancreatic ductal adenocarcinoma (PDAC) is the most frequent type of pancreatic cancer, one of the leading causes of cancer-related deaths worldwide. The first lesions associated with PDAC occur within the functional units of exocrine pancreas</span><span>. T</span><span>he crosstalk between PDAC cells and stromal cells plays a key role in tumor progression.</span><span> Thus,</span> <span>i</span></span><span><span>n vitro</span></span><span><span>, fully human models of the pancreatic cancer microenvironment are needed to foster the development of new, more effective therapies</span><span>.</span> <span>However,</span><span> it is challenging to make these models anatomically and functionally relevant. Here, we used tomographic volumetric bioprinting, a novel method to fabricate </span><span>three-dimensional </span><span>cell-laden constructs</span><span>,</span><span> to produce a </span><span>portion</span><span> of the </span><span>complex convoluted </span><span>exocrine pancreas</span> </span><span><span>in vitro</span></span><span><span>.</span><span> Human fibroblast-laden gelatin methacrylate-based pancreatic models were processed to reassemble the </span><span>tubuloacinar</span><span> structures of the exocrine pancreas and, then human pancreatic ductal epithelial (HPDE) cells overexpressing the KRAS oncogene (HPDE-KRAS) were seeded in the acinar lumen to reproduce the pathological exocrine pancreatic tissue. The growth and organization of HPDE cells within the structure was evaluated and the formation of a thin epithelium which covered the acini inner surfaces in a physiological way inside the 3D model was</span> <span>successfully</span> <span>demonstrated</span><span>. Interestingly, immunofluorescence assays revealed a significantly higher expressions of alpha smooth muscle </span><span>actin</span><span> (α-SMA) vs. </span><span>actin</span><span> in the fibroblasts co-cultured with cancerous than with wild-type HPDE cells. Moreover, α-SMA expression increased with time, and it was found to be higher in fibroblasts that laid closer to HPDE cells than in those </span><span>laying </span><span>deeper into the model. Increased levels of interleukin (IL)-6 were also quantified in supernatants from co-cultures of stromal and HPDE-KRAS cells. These findings correlate with inflamed tumor-associated fibroblast behavior, thus being relevant biomarkers to </span><span>monitor</span><span> the early progression of the disease and to target drug efficacy. </span></span><span> </span></p> </div> <div> <p><span><span>To our knowledge, this is the first</span> <span>demonstration of a </span><span>3D </span><span>bioprinted</span> <span>portion</span><span> of </span><span>pancreas that</span> <span>rec</span><span>apit</span><span>ulates</span> <span>its</span> <span>true 3-dimensional </span><span>microanatomy</span><span>,</span><span> and which shows </span><span>tumor triggered </span><span>inflammation</span><span>. </span></span><span> </span></p> </div> </div> <p> </p> <p><strong>Contents</strong></p> <p>This repository contains the raw data, materials list, protocols, and code necessary to reproduce the work in the namesake preprint.</p> <p> </p>
Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines
<p><strong>SUMMARY OF THE STUDY</strong></p> <p>We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk.</p> <p><strong>DESCRIPTION OF FILES:</strong></p> <ul> <li>Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response - also in GSE205853</li> <li>Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853</li> <li>Supplementary Data 6. Variants selected for SNP-SELEX assay </li> <li>Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX</li> <li>Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853</li> <li>Supplementary Data 9. CRISPR-KO screen results - also in GSE205853</li> <li>Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853</li> <li>Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 18. Luciferase assay rs10483809</li> <li>Supplementary Data 19. SOCS1 knockdown qPCR results</li> <li>Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results</li> </ul> <p><strong>Raw data deposited at GEO, accessions GSE205853 and GSE118725.</strong></p> <p><em>Please refer to publication and GEO for details on methods.</em></p>
Datasets: Natural killer cells associate with malignant epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment
<p>The following are necessary data files for the manuscript "Natural killer cells associate with malignant epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment":</p> <ul> <li>.zip files for TMA_1, TMA_2, TMA_3, and TMA_4 are .mcd files acquired from imaging mass cytometry (IMC) for each slide of the pancreas TMA slide series</li> <li>pancreas_TMA_sample_info.xlxs includes info on all samples of the TMA slide series that were imaged by IMC</li> <li>custom_gates_0.zip includes histoCAT-derived single cell data files from all IMC samples in the pancreas TMA to be used for single cell analyses in R</li> <li>PDAC_IMC.RDS is a Seurat object of the IMC-derived PDAC single cell data to use for single cell and spatial analyses</li> <li>PDAC_sce is a SingleCellExperiment object of IMC-derived PDAC single cell data to use for spatial analyses</li> <li>mat.RDS is a distance matrix of PDAC cell types to use in R to generate network graph (Figure 2)</li> </ul> <p> </p> <p> </p>
CyTOF data of PBMC samples of patients with metastatic pancreatic ductal adenocarcinoma
<p>These two CyTOF datasets are a part of the manuscript by M. Baretti "E<span>ntinostat in combination with nivolumab in metastatic pancreatic ductal adenocarcinoma: a phase 2 clinical trial" accepted in Nature Communications. The datasets contain FCS files of PBMCs samples of patients with metastatic pancreatic ductal adenocarcinoma treated with entinostat and nivolumab. PBMC samples were run with myeloid- and lymphoid-oriented panels.<br></span></p>
Characterization of Metabolism Associated with Outcomes in Severe Acute Pancreatitis: Insights from Serum Metabolomic Analysis
<p>1H NMR spectra data of SAP patients (Survivors/ Non-survivors). The spectra were binned as 0.02 ppm spectral buckets. The chemical shift regions corresponding to the water region and TSP were excluded to avoid spectral interference. This dataset was used for the metabolomics related study to highlight the dysregulation of metabolites in the study group.</p> <p> </p>
Meta analysis of prognostic scoring systems for pancreatitis
Open the record for dataset details and reuse information.
Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis
<p>Genetic studies have identified ≥240 loci associated with risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterize the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into <em>cis</em>- and <em>trans</em>-acting genetic components and mapping <em>cis</em>-acting loci associated with miRNA expression (miRNA-eQTLs). We find (i) 84 heritable miRNAs, primarily regulated by <em>trans</em>-acting genetic effects, and (ii) 5 miRNA-eQTLs. We also use several different strategies to identify T2D-associated miRNAs. First, we colocalize miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersect miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and find 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we perform differential expression analysis and identify 14 miRNAs associated with T2D status—including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p—and 4 miRNAs associated with a polygenic score for HbA1c levels—miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.</p>
Transcriptomic profiles of resected pancreatic adenocarcinoma, whole-slide match
<p>RNA was extracted from the whole-slide tumor regions of 100 pancreatic adenocarcinomas, consecutively resected at the Beaujon hospital (Clichy, FRANCE). Tumors were sequenced in two batches, using 3' RNA-sequencing for FFPE compatibility.</p>
MALDI FTICR MS imaging data of pancreatic mouse tissue
<p>Preprocessed imaging mass spectrometry data (.imzML format) for mouse pancreatic Islets of Langerhans. Detailed information is given in the publication by Prade & Kunzke et al. "De novo discovery of metabolic heterogeneity with immunophenotype-guided imaging mass spectrometry" (currently in revision).</p>
Aberrant development of pancreatic beta cells derived from human iPSCs with FOXA2 deficiency
<p><strong>Project manager(s)</strong><strong>: </strong>Essam M. Abdelalim</p> <p>Induced pluripotent stem cells (iPSCs) were generated from a patient with a heterozygous deletion of the short arm of chromosome 20 at bands p11.22 to p11.21 (~969 kb deletion), which contains only one gene, <em>FOXA2 </em>(<em>FOXA2<sup>+/-</sup></em>iPSCs) as well as healthy controls (Ctr1 iPSCs and Ctr2 iPSCs). <em>FOXA2<sup>+/-</sup></em>iPSCs were differentiated into different stages of beta cell development to understand the role of FOXA2 during pancreatic beta cell development as described in the article entitled "<strong>Aberrant development of pancreatic beta cells derived from human iPSCs with <em>FOXA2</em> deficiency" by Elsayed et al</strong>. The dataset represents RNA-seq data generated from pancreatic progenitors (PP2) and endocrine progenitors (EPs) derived from Ctr1 iPSCs, Ctr2 iPSCs, and three clones of <em>FOXA2<sup>+/-</sup></em>iPSCs. </p> <p>The file name is: Sample name _overall sample number_read direction_001 where:</p> <p>- PP2-Ctr 1: pancreatic progenitors (PP2) derived from Ctr1 iPSCs (healthy control 1)</p> <p>- PP2-Ctr 2: pancreatic progenitors (PP2) derived from Ctr2 iPSCs (healthy control 2)</p> <p>- PP2-FOX1, PP2-FOX2, and PP2-FOX3: pancreatic progenitors (PP2) derived from three different clones of patient-derived <em>FOXA2<sup>+/-</sup></em>iPSCs.</p> <p>- EP-Ctr1: endocrine progenitors (Eps) derived from Ctr1 iPSCs (healthy control 1)</p> <p>- EP-Ctr2: endocrine progenitors (Eps) derived from Ctr2 iPSCs (healthy control 2)</p> <p>- EP-FOX R1, EP-FOX R2, and EP-FOX R3: endocrine progenitors (EPs) derived from three different clones of patient-derived <em>FOXA2<sup>+/-</sup></em>iPSCs.</p> <p>- The RNA-Seq data were generated from two Ctr-iPSC lines and three FOXA2<sup>+/-</sup>iPSC lines.</p> <p>- Read direction: R1 (Forward), R2 (Reverse).</p>
Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer
<p>Data supporting the findings of "<a href="https://doi.org/10.1158/2326-6066.CIR-23-0873" target="_blank" rel="noopener">Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer</a>" publication. Files include patient and tissue region metadata (in metadata folder) and output of multiplex immunohistochemistry computational image processing workflow for each tissue region (in mIHC_files folder). The code used to produce the results of this study is available at: <a href="https://github.com/kblise/PDAC_mIHC_paper">https://github.com/kblise/PDAC_mIHC_paper</a>.</p>
Availability of results of trials studying pancreatic adenocarcinoma over the past ten years.
<p>Dataset underlying our work "Availability of results of trials studying pancreatic adenocarcinoma over the past ten years".</p> <p>Data has been extracted either through AACT (Clinical Trials Transformation Initiative) or by the authors</p> <p>Trials are listed and organized by their NCT number (ClinicalTrials.gov)</p>
Integrated Data of Single cell RNA sequencing for Human Pancreatic Adenocarcinoma
<p>These data are collected and integrated from five available deposit data and one original data of single cell RNA sequencing from human pancreatic adenocarcinoma. Further analyses data for bulk transcriptomics (such as TCGA )using scRNAseq data and re-clustering for ductal epithelial cells and fibroblasts are also stored in step by step. Moreover, all R code is uploaded.</p>
Pancreatic adecarcinoma fibroblast subtype using RNAseq
<p>Cancer-associated fibroblasts (CAFs) are orchestrators of the pancreatic ductal adenocarcinoma (PDAC) microenvironment. Previously we described four CAF subtypes with specific molecular and functional features. Here, we have refined our CAF subtype signatures using RNAseq and immunostaining with the goal to define bioinformatically the phenotypic stromal and tumor epithelial states associated with CAF diversity. We used primary CAF cultures grown from patient PDAC tumors, human datasets (in-house and public, including single-cell analyses), genetically engineered mouse PDAC tissues, and patient-derived xenografts (PDX) grown in mice. We found that CAF subtype RNAseq signatures correlated with immunostaining. Tumors rich in periostin-positive CAFs were significantly associated with shorter overall survival of patients. Periostin-positive CAFs were characterized by high proliferation and protein synthesis rates, low αSMA expression, and were found in peri-/pre-tumoral areas. They were associated with highly cellular tumors and with macrophage infiltrates. Podoplanin-positive CAFs were associated with immune-related signatures and recruitment of dendritic cells. Importantly, we showed that the combination of periostin-positive CAFs and podoplanin-positive CAFs was associated with specific tumor microenvironment features in terms of stromal abundance and immune cell infiltrates. Podoplanin-positive CAFs identified an iCAF-like subset whereas periostin-positive CAFs were not correlated with the published myCAF/iCAF classification.</p> <p>Taken together, these results suggest that a periostin-positive CAF is an early, activated CAF, associated with aggressive tumors, whereas a podoplanin-positive CAF is associated with an immune-related phenotype. These two subpopulations cooperate to define specific tumor microenvironment and patient prognosis, and are of putative interest for future therapeutic stratification of patients.</p> <p> </p> <p><strong>Material and methods</strong></p> <p>Total RNA was extracted from FFPE sections using a high pure FFPE RNA isolation kit (Roche®, Basel, Switzerland) following the manufacturer’s protocol. RNA yield and quality was determined using a NanoDrop™ One spectrophotometer and fragment size was analyzed using an RNA ScreenTape assay run on a 4200 Bioanalyzer (Agilent Technologies®, Santa Clara, CA, USA . DV200 values representing the percentage of RNA fragments above 200 nucleotides in length were estimated, and cases with DV200 more than 30% were included for library preparation.<br> Library preparation was performed using QuantSeq 3’ mRNA-Seq REV (Lexogen® , Vienna, Austria) with an input of 150 ng of total FFPE RNA. The pool was sequenced on a NovaSeq 6000 system flow cell SP (Illumina Inc., San Diego, CA) using a 75-cycle, paired-end protocol providing approximately 10 million reads per sample. Base call files were converted to fastq format using Bcl2Fastq (Illumina®, San Diego, CA). All RNA-seq reads were aligned to the human reference genome (GRCh37, hg19) using STAR (version 2.6.1a_08-27), quantified using FeatureCount and Upper-Quartile normalized.<br> </p>
Survival-associated cellular response maintained in pancreatic ductal adenocarcinoma (PDAC) switched between soft and stiff 3D microgel culture
<div> <div> <div> <div> <p>Pancreatic ductal adenocarcinoma (PDAC) accounts for about 90% of all pancreatic cancer cases. Five-year survival rates have remained below 12% since the 1970s, in part due to the difficulty in detection before metastasis (migration and invasion into neighboring organs and glands). Mechanical memory is a concept that has emerged over the past decade that may provide a path towards understanding how invading PDAC cells "remember" the mechanical properties of their diseased ("stiff," elastic modulus, E ≈ 10 kPa) microenvironment even whilst invading a healthy ("soft," E ≈ 1 kPa) microenvironment. Here, we investigated the role of mechanical priming by culturing a dilute suspension of PDAC (FG) cells within a 3D, rheologically tunable microgel platform from hydrogels with tunable mechanical properties. We conducted a suite of acute (short-term) priming studies where we cultured PDAC cells in either a soft (E ≈ 1 kPa) or stiff (E ≈ 10 kPa) environment for 6 h, then removed and placed them into a new soft or stiff 3D environment for another 18 h. Following these steps, we conducted RNA-seq analyses to quantify gene expression. Initial priming in 3D culture showed persistent gene expression for the duration of the study, regardless of the subsequent environments (stiff or soft). Stiff 3D culture was associated with the down-regulation of tumor suppressors (LATS1, BCAR3, CDKN2C ), as well as the up-regulation of cancer-associated genes (RAC3). Immunofluorescence staining (BCAR3, RAC3) further supported the persistence of this cellular response, with BCAR3 upregulated in soft culture, and RAC3 upregulated in stiff-primed culture. Stiff-primed genes were stratified against patient data found in The Cancer Genome Atlas (TCGA). Upregulated genes in stiff-primed 3D culture were associated with decreased survival in patient data, suggesting a link between patient survival and mechanical priming.</p> </div> </div> </div> </div>
VISION Invited lecture - Future approaches of pancreatic ductal adenocarcinoma
<p>Recording and presentation of the invited lecture that took place online on 14 October 2020 - <strong>Prof Alfredo Carrato - Future approaches of pancreatic ductal adenocarcinoma.</strong></p> <p>Although pancreatic ductal adenocarcinoma (PDAC) is not so frequent, it is the third leading cause of cancer death. As it shows non-specific symptoms it is diagnosed late and only 20% of patients are surgery candidates. Tumor recurrs locally or distantly after surgery in two thirds of them. Only 5% of PDAC patients survive 10 years. Targeted therapies have not yet proven their efficacy and treatment prescribed consists of chemotherapy combinations.</p> <p>PDAC has a dense stroma that reaches an 80% of the tumor, helping PDAC epithelial tumor cells to evade the immune system and growth, invade and metastasize through a crosstalk among PDAC cells and fibroblasts, macrophages, pericytes, stroma, etc. Targeting the stromal constituents may result in a step forward a better treatment efficacy.</p> <p>PDAC microbiome is unique and has been identified into the cancer cells and the local immune cells. Wisely management of the different resident microbial species could also result in prevention and another alternative for treatment.</p> <p>The identification of the PDAC high-risk population and the development of a convenient screening program is an objective to be reached for an earlier diagnosis and a potential advantage as more patients will be candidates for surgery, but to know in depth and detail the biology of the tumor and its interaction with the host will lead to a better treatment design and a real benefit of our patients.</p>
VISION Invited lecture - Advances in familial pancreatic cancer
<p>Recording and presentation of the invited lecture that took place online on 28 October 2020 - <strong>Dr Julie Earl - Advances in familial pancreatic cancer.</strong></p> <p>The prognosis of patients diagnosed with pancreatic cancer (PC) is dismal with a 5 year survival rate of around 5% as the majority of patients present with advanced disease. Very few risk factors have been identified, although there is good evidence to suggest that smoking, obesity, a family history of pancreatic cancer, pancreatitis and diabetes increase pancreatic cancer risk. Sporadic PC occurs worldwide at an approximate frequency of 1 in 10,000 people. However, the risk of developing PC increases according to the number of affected family members, the standard incidence ratio is 4.6 with one affected family member to 32 with three affected family members. Familial pancreatic cancer (FPC) is defined as a family with at least one pair of affected first degree relatives and an estimated 4-10% of pancreatic cancers diagnosed have a familial background. Approximately 10–13% of FPC families carry germline mutations in BRCA2, PALB2, ATM, CHEK2, CDKN2A, Lynch syndrome mismatch repair genes, Fanconi anaemia related genes and PRSS1 and SPINK2 (hereditary pancreatitis), among others. The understanding of genetic basis of hereditary pancreatic cancer has important implications for the identification of true high-risk individuals in order to optimise secondary screening strategies.</p>
VISION Invited lecture - Objective: Personalised Prevention of Pancreatic Cancer
<p>Recording of the invited lecture that took place online on 16 December 2021 - <strong>Núria Malats, MD, MPH, PhD - Objective: Personalised Prevention of Pancreatic Cancer.</strong></p> <p>Identifying the population at high risk of developing non-hereditary pancreatic cancer in order to offer them to enter screening programs and diagnose cancer in early stages, is a must. To this end, we integrate information on the risk factors for this tumor with biomarkers determined at the omics level, including genomics and microbiome. All this information comes from a large study carried out in 28 centres from six European countries. For the integration of this big molecular data with epidemiological and clinical data, we use artificial intelligence tools. In the lecture, I will go through the most recent achievements of these collective efforts.</p>
An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic Supplementary Information Histology Slides Part 2
<p>H&E and Gram stained whole slide scans for blinded cohorts from publication titled ‘An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-modified <em>Clostridium novyi-</em>Non Toxic’ published in PLOS One 2023.</p> <p>Excerpt from methods section of publication detailing acquisition of this data: "Formalin-fixed tissue samples were processed for paraffin embedding by the means of dehydration, clearing and paraffin infiltration (Lynx II Tissue Processor). The paraffin embedded tissue samples were sectioned using a Leica Rotatory Microtome RM2125 RTS at 5um thickness. Designated tissue sections were subsequently de-paraffined and stained with H&E and gram staining as per standard histology protocols (Leica Autostainer XL). Whole slide scanning (WSI) was performed by a Panoramic 250 whole slide scanner at 20x magnification (3D Histech) using a Carl-Zeiss Plan-Apochromat 20x / NA 0.8 objective."</p> <p>Please note when downloading the image files that the folder containing all of the .data files <strong>cannot</strong> contain the corresponding .mxrs file. The .mxrs and the folder containing the .data files <strong>must</strong> have identical names and be at the same file level (as uploaded). Files of this type can be opened by the open source software FIJI or QuPath, among others.</p> <p>Cohorts have been uploaded with viewers blinded to treatment groups to allow for unbiased review should it be desired, with a cohort key uploaded separately (doi: ##). Files are grouped so that all major organs from a single mouse are in the same compressed folder.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.