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206 results for “FFPE”
OMAP-23: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of vermiform appendix (FFPE) with MICS on MACSima
<p><strong>Description</strong></p> <p> OMAP-23 was designed for MICS (MACSima imaging cyclic staining) imaging of FFPE human vermiform appendix sample. Tissue fixation and antigen retrieval is described in (<a href="https://www.biorxiv.org/content/biorxiv/early/2023/11/07/2023.10.27.564191.full.pdf">Spatial protein and RNA analysis on the same tissue section using MICS technology</a>). The MACSima technology is described in detail in the following publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors</a>). All, antibodies in this panel are recombinant antibodies with a mutated human IgG1 constant region, removing Fc receptor binding capacity of human IgG1, eliminating the need for additional blocking steps and reducing non-specific binding. The use of human IgG1 recombinant antibodies allows for the addition of uncoupled monoclonal antibodies from other species followed by a fluorescence labelled secondary reagent specific for the species of the monoclonal antibody. The multiplex system has been described already for OMAP-10 and OMAP-21. A new dye VioB515 is used for one reagents. The fluorescence is removed by cleavage. The panel contains 28 antibodies and the nuclear marker DAPI for image alignment and nuclear segmentation. This OMAP provides a spatial context for six anatomical structures and most cell types present in the vermiform appendix (link to ASCT+B table added). OMAP-23 follows OMAP-21, with fewer antibodies but adding antibodies for non-immune cells.</p> <p>All reagents are from Miltenyi Biotec and have been rigorously tested through an internal quality control system to have minimal variation between lots. For this reason, lot information is not included in this table. Analysis was performed by an accompanied software package MACSIQ View Analysis also described in the MACSima publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">https://doi.org/10.1038/s41598-022-05841-4</a>). The MACSima system is continuously evolving, this is the third OMAP for the MACSima system. A representative dataset created using OMAP-23 can be found here 10.5281/zenodo.14008816 .The AVRs for the dataset can be found here (to be added).</p>
Visium HD Human Colorectal Cancer (FFPE) data release pathologist annotation
<p>10X Genomics released a spatial <a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc">transcriptomic dataset of human colorectal cancer collected on the Visium HD platform. </a></p> <p>The dataset was divided into different spatial domains based on the accompanying HE stain and the expression of characteristic marker genes.</p> <p>The pathologist's annotation was added with the help of Napari and the Spatialdata python package.</p> <p>Every .csv file contains the Visium HD bin barcode and annotation for the respective level of binning.</p> <p>In addition the HE image was segmented and bins were assigned to Nuclei for a pseudo single cell resolution, as described <a href="https://www.10xgenomics.com/analysis-guides/segmentation-visium-hd">here.</a></p> <p>This work was carried out for the <a href="https://github.com/SpatialHackathon/SpaceHack2023">SpaceHack2023 project </a>and the data shared here is licensed CC0. </p> <p> </p>
Data from: MicroRNA stability in FFPE tissue samples: dependence on GC content
MicroRNAs (miRNAs) are small non-coding RNAs responsible for fine-tuning of gene expression at post-transcriptional level. The alterations in miRNA expression levels profoundly affect human health and often lead to the development of severe diseases. Currently, high throughput analyses, such as microarray and deep sequencing, are performed in order to identify miRNA biomarkers, using archival patient tissue samples. MiRNAs are more robust than longer RNAs, and resistant to extreme temperatures, pH, and formalin-fixed paraffin-embedding (FFPE) process. Here, we have compared the stability of miRNAs in FFPE cardiac tissues using next-generation sequencing. The mode read length in FFPE samples was 11 nucleotides (nt), while that in the matched frozen samples was 22 nt. Although the read counts were increased 1.7-fold in FFPE samples, compared with those in the frozen samples, the average miRNA mapping rate decreased from 32.0% to 9.4%. These results indicate that, in addition to the fragmentation of longer RNAs, miRNAs are to some extent degraded in FFPE tissues as well. The expression profiles of total miRNAs in two groups were highly correlated (0.88
SAMPLER representations of FFPE TCGA-lung WSIs using tile-level features of the MMIL-Transformer model
<p>Here we provide single-scale SAMPLER representations of the FFPE TCGA-lung (LUAD and LUSC) WSIs using tile-level features provided in https://github.com/hustvl/MMIL-Transformer. To learn more about SAMPLER please visit https://github.com/TheJacksonLaboratory/SAMPLER.</p><p>The SAMPLER representations are provided as a single python pickle file. This pickle file contains a dictionary where each key is a WSI ID and each entry is the SAMPLER representation of the WSI.</p>
Meningioma FFPE samples
<p>RNA-sequencing of meningioma samples from FFPE</p>
Sample data for analysis of FFPE sequencing data
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Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples
<p>This repository contains 411,890 unique image patches derived from histological images of colorectal cancer and gastric cancer patients in the TCGA cohort (original whole slide SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this repository are derived from formalin-fixed paraffin-embedded (FFPE) diagnostic slides ("DX" at the GDC data portal). This is explained well in this blog: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing</strong></p> <p>All SVS slides were preprocessed as follows</p> <p>1. automatic detection of tumor</p> <p>2. resizing to 224 px x 224 px at a resolution of 0.5 µm/px</p> <p>4. color normalization with the Macenko method (Macenko et al., 2009, http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf)</p> <p>5. assignment of patients to either "MSS" (microsatellite stable) or "MSIMUT" (microsatellite instable or highly mutated)</p> <p>6. randomization of patients to training and testing sets (~70% and ~30%). Randomization was done on a patient level rather than on a slide or tile level</p> <p>7. equilibration of training sets by undersampling (removing excess tiles in MSS class in a random way)</p> <p><strong>File description</strong></p> <p>1. STAD_TRAIN_MSS - training images (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 50285 unique image patches; FFPE samples</p> <p>2. STAD_TRAIN_MSIMUT - training images ( (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 50285 unique image patches; FFPE samples</p> <p>3. STAD_TEST_MSS - test images (~30% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 90104 unique image patches; FFPE samples</p> <p>4. STAD_TEST_MSIMUT - test images ( ~30% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 27904 unique image patches; FFPE samples</p> <p>5. CRC_DX_TEST_MSIMUT - test images (~30% of all patients) for colorectal cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 29335 unique image patches; FFPE samples</p> <p>6. CRC_DX_TEST_MSS - test images (~30% of all patients) for colorectal cancer TCGA patients with MSS (microsatellite stable) tumors, 70569 unique image patches; FFPE samples</p> <p>7. CRC_DX_TRAIN_MSIMUT - training images (~70% of all patients) for colorectal cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 46704 unique image patches; FFPE samples</p> <p>8. CRC_DX_TRAIN_MSS - training images (~70% of all patients) for colorectal cancer TCGA patients with MSS (microsatellite stable) tumors, 46704 unique image patches; FFPE samples</p>
Data from: MicroRNA stability in FFPE tissue samples: dependence on GC content
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SAMPLER representations of frozen and FFPE TCGA-KICH WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of frozen and FFPE TCGA-KIRC WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of frozen and FFPE TCGA-LUSC WSIs using an InceptionV3 backbone pretrained on imagenet
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Data from: Validation of targeted next-generation sequencing for RAS mutation detection in FFPE colorectal cancer tissues: comparison with Sanger sequencing and ARMS-Scorpion real-time PCR
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FFPE-samples from cavitational ultrasonic surgical aspirates are suitable for RNA profiling of gliomas
GEO Series GSE166696. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Identification of mRNAs and lincRNAs associated with lung cancer progression using next-generation RNA sequencing from laser micro-dissected archival FFPE tissue specimens
GEO Series GSE52248. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
MicroRNA expression profiling on formalin-fixed paraffin-embedded (FFPE) tissue blocks of human adrenal pheochromocytomas
GEO Series GSE21767. Homo sapiens. 24 samples. Type: Non-coding RNA profiling by array.
Focused gene expression microarray of tumors from 596 men with prostate cancer using RNA from archival FFPE tissue
GEO Series GSE10645. Homo sapiens. 1192 samples. Type: Expression profiling by array.
miRNA expression differences between FFPE prostate cancer and adjacent normal tissues using real-time quantitative PCR array analysis
GEO Series GSE48430. Homo sapiens. 20 samples. Type: Expression profiling by RT-PCR.
Single-Cell RNA Sequencing Identifies Molecular Biomarkers Predicting Late Progression to CDK4/6 Inhibition in Patients with HR+/HER2- Metastatic Breast Cancer [FFPE]
GEO Series GSE274141. Homo sapiens. 54 samples. Type: Expression profiling by high throughput sequencing.
Germinal Center Dark Zone harbors ATR-dependent determinants of T-cell exclusion that are also identified in aggressive lymphoma – Visium_FFPE_Spatial_Trasciptomics_project
GEO Series GSE260998. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Whole genome copy number profiles of FFPE samples from patients with metasatic colorectal cancer (mCRC)
GEO Series GSE110785. Homo sapiens. 154 samples. Type: Genome variation profiling by genome tiling array.
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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.
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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.