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4,330 results for “Melanoma”
MALDI-IMS files of 53 experiments over sections of nevus, primary melanoma and melanoma metastasis in imzml format
<p>MALDI-IMS files of 53 experiments over sections of nevus, primary melanoma and melanoma metastasis in imzml format. The experiments were recorded in negative-ion mode with a MALDI LTQ-orbitrap XL (ThermoFisher) at 25 um/pixel of spatial resolution. We also uploaded three .ppt with a comparison between the eosin-hematoxilin images, the IHC (MelanA and HMB45) and the segmentation images</p>
Dabrafenib With Trametinib in the Adjuvant Treatment of High-risk BRAF V600 Mutation-positive Melanoma (COMBI-AD).
ClinicalTrials.gov study NCT01682083. IPD Sharing: YES. Countries: 25. Publications: 9.
Study to Investigate the Objective Response Rate of Dabrafenib in Combination With Trametinib in Subjects With BRAF V600 Mutation-Positive Melanoma
ClinicalTrials.gov study NCT02083354. IPD Sharing: UNDECIDED. Countries: 5. Publications: 2.
A Study of the Anti-PD1 Antibody PDR001, in Combination With Dabrafenib and Trametinib in Advanced Melanoma
ClinicalTrials.gov study NCT02967692. IPD Sharing: YES. Countries: 29. Publications: 3.
Dabrafenib Plus Trametinib vs Vemurafenib Alone in Unresectable or Metastatic BRAF V600E/K Cutaneous Melanoma
ClinicalTrials.gov study NCT01597908. IPD Sharing: YES. Countries: 28. Publications: 5.
A Phase II Study of the Interleukin-6 Receptor Inhibitor Tocilizumab in Combination With Ipilimumab and Nivolumab in Patients With Unresectable Stage III or Stage IV Melanoma
ClinicalTrials.gov study NCT03999749. IPD Sharing: YES. Countries: 1. Publications: 1.
Study of Dabrafenib+Trametinib in the Adjuvant Treatment of Stage III BRAF V600+ Melanoma After Complete Resection to Evaluate the Impact on Pyrexia Related Outcomes
ClinicalTrials.gov study NCT03551626. IPD Sharing: YES. Countries: 23. Publications: 1.
Genomic, clinical data, and scripts for PD-1 blockade resistance in metastatic melanoma
Open the record for dataset details and reuse information.
Recombinant Costimulatory Fusion Proteins as Functional Immunomodulators Enhance Antitumor Activity in Murine B16F10 Melanoma
<p>Cancer immunotherapy has emerged as an important therapeutic modality in preclinical and clinical oncology. Blocking inhibitory signaling and engaging stimulatory signaling enable some patients to produce an efficient antitumor response. In this work, we demonstrate unique and unexpected immunomodulatory features of different costimulatory ligand proteins with no combination of tumor-associated products, which bridge innate and adaptive immune responses against cancer in preventive as well as therapeutic efficacy.</p>
Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma
<p>Processed data:</p><p>inferCNV_output_WM4237.zip, inferCNV_output_WM4007.zip - InferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_cnv_WM4237.h5ad, ad_all_human_clustered_cnv_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of the inferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_im_st_WM4237.h5ad, ad_all_human_clustered_im_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for 12 Visium samples of model WM4237 or WM4007. "_im" for imaging, "_st" for Visium.</p><p>ad_all_human_clustered_im_ad_WM4237_m.h5ad, ad_all_human_clustered_im_ad_WM4007_m.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for additional H&E slides (non-Visium) of model WM4237 or WM4007. "_im" for imaging, "_m" for Macenko normalization of the H&E slides with STQ.</p><p>ids_WM4237_AD.txt, ids_WM4007_AD.txt, ids_WM4237_ST.txt, ids_WM4237_ST.txt - list of identifiers of all samples (tissue sections).</p><p>ad_all_human_clustered_st_WM4237.h5ad, ad_all_human_clustered_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of RNA profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>CNV_burden_WM4237.csv, CNV_burden_WM4007.csv - Per-spot values of InferCNV-derived CNV burden for samples of models WM4237 or WM4007.</p><p>ad_all_scaled_filtered_st_WM4237.h5ad, ad_all_scaled_filtered_st_WM4007.h5ad - AnnData objects containing concatenated and pre-processed samples of WM4237 or WM4007.</p><p>WM4237_3_AD_m-Imaging-STQ.zip, WM4007_3_AD_m-Imaging-STQ.zip - Imaging portion of STQ pipeline output derived from additional (non-Visium) H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237_ST-Imaging-STQ.tar.gz, WM4007_ST-Imaging-STQ.tar.gz - Imaging portion of STQ pipeline output derived from Visium H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237-ST-downstream-output.tar.gz, WM4007-ST-downstream-output.tar.gz - ST-downstream-processing pipeline output for Visium samples of model WM4237 or WM4007.</p><p>WM4237-STQ-sequencing.tar.gz, WM4007-STQ-sequencing.tar.gz - RNA sequencing portion of STQ pipeline output derived from Visium samples of model WM4237 or WM4007.</p><p>rna-pseudotime-ordered.zip - Pseudotime ordering of RNA profiles of spots for each time point (T0, T1, T2, T3, T4, TC) for samples of models WM4237 and WM4007.</p>
Multiomics analyses reveal a Type III-associated immune response in immunotherapy-induced toxicity in melanoma and identifies potential new therapeutic targets
<p>Immune checkpoint inhibitors (ICIs) are standard-of-care for the treatment of advanced melanoma, but their use is limited by immune-related adverse events (irAEs). Proteomic analyses and multiplex cytokine/chemokine assays from serum at baseline and at irAEs onset in melanoma patients indicated aberrant T-cell activity with differential expression of Type I and III immune signatures. This was in line with an increase in the proportions of monocytes and decrease of IL-17A-producing CD4+ T-cells in the peripheral blood using single cell RNA sequencing. Multiplex immunohistochemistry and spatial transcriptomics on ICI-induced skin rash and colitis showed an increase in the proportion of CD4+ T-cells with IL-17A expression. </p> <p>Anti-IL-17A mAbs were administered in two patients with myocarditis, colitis and skin rash with resolution of the irAE. This study highlights the potential role of Type III CD4+ T-cells in irAEs development and provides proof-of-principle evidence for a corresponding clinical trial using anti-IL17A for treating irAEs.</p> <p>Code to generate the figures are located at https://github.com/pcheng84/AE_analysis/</p> <p> </p>
CODEX multiplexed imaging of immunotherapy in human and mouse melanomas
<p>Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into melanoma tumors in both murine models and human samples. CODEX imaging involves an iterative process of annealing and stripping fluorophore-labeled oligonucleotide barcodes, complementing the barcodes attached to over 40 antibodies used for tissue staining. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue.</p> <p>Our datasets comprise individual cells as rows, each characterized by 40+ antibody fluorescence values quantified from various markers evaluated for each study. These markers correspond to the antibodies targeting specific proteins within the tissue, quantified at the single-cell level. The values represent per-cell/area-averaged fluorescent intensities, z-normalized along each column. Each cell is mapped with its cell type and cellular neighborhood, defined by x and y coordinates representing pixel locations in the original image. Refer to the table in the "Usage Notes" section below for further details. The CODEX multiplexed imaging data is organized into three distinct files, each representing key aspects of our research and the studies detailed in our manuscript.</p> <p>We then used this data investigate the major cellular organization of the tumor sections we imaged, with downstream spatial statistics and analyses like cellular neighborhood analysis and cell-cell interaction analysis. These data could be used to understand the cellular interactions, composition, and structure of anti-tumor melanoma responses induced by antigen-specific immunotherapy either with adoptive T cell transfer for checkpoint blockade immunotherapy. These datasets offer valuable insights for researchers interested in anti-tumor microenvironments, immune responses, and therapeutic interventions such as T cell therapies.</p> <p><em>1. Time-course of tumor microenvironment following antigen-specific T cell therapy in mice</em></p> <p>We investigate the dynamic interplay between immune responses, antigen-specific T cell interactions, and tumor progression in a murine melanoma model. We activated PMEL CD8+ T cells with cognate antigen gp100 and IL-2 for 10 days ex vivo and transferred into mice with established B16-F10 tumors. Tumors were harvested and imaged with CODEX imaging at 0-, 1-, 3-, 5-, and 12-days post-treatment (n=3-7 per time point). Our 42-plex CODEX antibody panel characterizes immune cell types, T cell phenotypes, stromal cell types, and tumor cell phenotypes, resulting in a rich dataset of 1,052,125 cells across 42 marker channels.</p> <p><em>2. Tumor microenvironment following antigen-specific T cell therapies with different phenotypes in mice</em></p> <p>We delve deeper into the modulation of the tumor microenvironment by manipulating T cell phenotypes. By comparing activated T cells stimulated with and without 2-hydroxycitrate (2HC), a metabolic inhibitor of acetyl CoA production, we explore the impact of phenotype on tumor progression. Our datasets from mice treated with 2HC T cells or T cells provide insights into the role of T cell phenotype manipulation in the tumor microenvironment (n=4-7 per group).</p> <p><em>3. Tumor microenvironment before and after checkpoint blockade in human melanoma patients of both responders and non-responders</em></p> <p>Our research extends to human melanoma patients with advanced, metastatic, stage IV tumors. We examine 12 FFPE tumor samples from six patients, each with samples taken before and after checkpoint inhibitor therapy. Our CODEX multiplexed imaging, using a panel of 58 antibodies, reveals changes in immune, stromal, and tumor compartments. We segmented 5,019,159 individual cells from the 12 CODEX images, facilitating unsupervised clustering to identify 39 major cell types based on their expression profiles. Our accompanying donor metadata table links donor IDs to essential clinical information, including treatment response, demographics, and sample details.</p>
Melanoma Histopathology Dataset with Tissue and Nuclei Annotations
<h2><strong>Description</strong>:</h2> <p>This dataset is designed for development of deep learning models for segmentation of nuclei and tissue in melanoma H&E stained histopathology. Existing nuclei segmentation models that are trained on non-melanoma specific datasets have low performance due to the ability of melanocytes to mimic other cell types, whereas existing melanoma specific models utilize older, sub-optimal techniques. Moreover, these models do not provide tissue annotations necessary for determining the localization of tumor-infiltrating lymphocytes, which may hold value for predictive and prognostic tasks. To address this, we created a melanoma specific dataset with nuclei and tissue annotations. <br><br></p> <h2><strong>Methodology</strong>:</h2> <p><strong>Sample Collection</strong>:</p> <p>Regions of interest (ROIs) were sampled from H&E stained slides of 103 primary melanoma specimens and 102 metastatic melanoma specimens, scanned using a Hamamatsu scanner at 40× magnification (0.23 μm per pixel). All slides were obtained from regular diagnostic procedures.<br>From each specimen, a 40× magnified ROI of 1024×1024 pixels was selected for annotation. Additionally, a context ROI of 5120×5120 pixels was sampled to provide information about the broader context for the annotation process. Selection was performed by a trained medical expert (M.S.) and subsequently verified by a dermatopathologist (W.B.). Manual ROI selection ensured the inclusion of diverse tissue and nuclei types.</p> <p><strong>Annotation Process</strong>:</p> <ul> <li><strong>Nuclei segmentation<br></strong>Nuclei segmentations were generated using Hover-Net pretrained on the PanNuke dataset. Manual annotation adjustments were performed by author M.S. using QuPath, with the following nuclei categories: tumor, stroma, vascular endothelium, histiocyte, melanophage, lymphocyte, plasma cell, neutrophil, apoptotic cell, and epithelium. All annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.).</li> <li><strong>Tissue segmentation<br></strong>Tissue segmentations were created manually using QuPath by M.S., with the following categories: tumor, stroma, epidermis, necrosis, blood vessel, and background. Annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.).</li> </ul> <p><strong>Quality Control</strong>: <br>To assess the reliability of the annotations, intra- and interobserver agreement (by pathologist G.B.) were determined on 12 randomly selected ROIs.</p> <ul> <li><strong>Nuclei segmentation<br></strong>The intraobserver overall precision was 84.89%, with a recall of 86.45%, and an F1 score of 85.66%. Interobserver overall precision was 80.34%, with a recall of 80.62%, and an F1 score of 80.20%. These results are based on the sum of all true positive, false positive, and false negative counts for the 12 ROIs.</li> <li><strong>Tissue segmentation<br></strong>The DICE score was determined on the same 12 randomly selected ROIs. The average intraobserver DICE score was 0.90, and the interobserver DICE score was also 0.90.</li> </ul> <p> </p> <p><strong>Version 3</strong>:<br>Removed sample "training_set_metastatic_roi_103" due to inconsistencies in annotation file.</p> <p><strong>Version 4:<br></strong>Sample training_set_metastatic_roi_088 missed one color annotation for a nuclei_apoptosis in the geojson file rendering it qupath uncompatible. This is fixed in the new version. </p> <p><strong>Version 5:<br></strong>Addition of correct sample of training_set_metastatic_roi_103" after deadline of <a href="https://puma.grand-challenge.org/">panoptic segmentation of nuclei and tissue in advanced melanoma challenge</a> test phase. </p>
POTENCIALES CIRCUITOS REGULATORIOS ENTRE miRNAs, MITF Y HIF1α EN LA LÍNEA CELULAR DE MELANOMA B16 BAJO CONDICIONES DE SUPRESIÓN DE CRECIMIENTO CELULAR E HIPOPIGMENTACIÓN INDUCIDOS.
<p>El melanoma es un cáncer de melanocitos, células especializadas en la síntesis de melanina, que muestra variación en la expresión de moléculas como RNAs mensajeros (mRNAs), factores de transcripción y microRNAs (miRNAs), entre otras. Los miRNAs son RNAs pequeños no codificantes entre los 18-22 nt, asociados con la regulación de cerca del 30% de los genes que codifican para proteínas, incluidos factores de transcripción, tanto en condiciones fisiológicas, como fisiopatológicas. A su vez, los factores de transcripción regulan la expresión de cientos de genes, incluidos los miRNAs; sin embargo, a la fecha, la asociación funcional entre estos tres actores moleculares y el fenotipo tumoral no es del todo clara. El objetivo de la presente investigación fue evaluar la expresión de miRNAs y de los mRNAs de los factores de transcripción Mitf y Hif1α en la línea celular de melanoma B16F1 expuesta a la 5-bromo-2´-deoxiuridina (5-Br-2’-dU), y plantear potenciales circuitos regulatorios asociados al fenotipo de hipopigmentación y disminución de la proliferación celular. Para ello, células B16F1 fueron expuestas a 5-Br-2’-dU 2.5 µg.mL-1 durante 72 horas, en medio DMEM (Dulbecco Modified Eagle Medium) a 37°C y 5% de CO2. Mediante small RNAseq se determinó la expresión diferencial de miRNAs maduros, mientras que la predicción de sus blancos moleculares, Mitf y Hif1α se realizó por TargetScanMouse versión 7.2. La expresión fue confirmada para los miRNAs miR-211-5p, miR-138-5p, miR-129-5p, miR-470-5p y miR-470-3p por RT-qPCR stem loop con un N= 3 y establecida para Mitf y Hif1α por RT-qPCR con un N=4; en todos los casos, el nivel de significación (*) fue determinado mediante una prueba t-Student a dos colas con corrección del Welch y las diferencias fueron consideradas como estadísticamente significativas para un valor p <0.05, ns: no significativo. Usando DEseq2, se analizaron los conteos obtenidos por small RNAseq y se determinó la expresión diferencial de 33 miRNAs por exposición a 5-Br-2’-dU, 11 sobre-expresados y 21 sub-expresados. Se confirmó por RT-qPCR el nivel de expresión de cinco miRNAs, dos de ellos a la baja (miR-211-5p (2.2X) y 138-5p (2.8X) y tres al alta (miR-129-5p (1.5X), miR-470-5p (22.7X) y miR-470-3p (124X)). El nivel de expresión de los mRNAs Mitf y Hif1α, se encontró para ambos a la baja (3.48X y, 4.58X, respectivamente). El análisis bioinformático por TargetScanMouse facilitó la construcción de potenciales circuitos regulatorios entre los miRNAs diferencialmente expresados y los factores Mitf y Hif1α para el fenotipo de hipopigmentación y disminución de la proliferación celular inducidos por la 5-Br-2’-dU. Los resultados obtenidos, plantean nuevos escenarios en el estudio del melanoma y mejoran nuestra comprensión de las potenciales asociaciones funcionales entre conjuntos de miRNAs, factores de transcripción como MITF y HIF1α y algunos genes que participan en el control de la pigmentación y la proliferación celular; sin embargo, se requiere establecer mediante ensayos de gen reportero de la luciferasa y otros ensayos funcionales, donde se usen estrategias como miRNAs miméticos y antimiRs, si para los circuitos regulatorios planteados, existe asociación funcional.</p>
Monitoring melanoma patients on treatment reveals a distinct macrophage population driving targeted therapy resistance
<p><span>Resistance to targeted therapy remains a major clinical challenge in melanoma patients. To uncover resistance mechanisms, we performed single cell RNA sequencing of fine needle aspirates from resistant and responding tumors from melanoma patients before and during BRAFi/MEKi treatment. Among the genes most differentially expressed between malignant cells from resistant vs. responding tumors was POSTN encoding the secreted factor periostin. POSTN was predicted to predominantly signal to a macrophage population associated with targeted therapy resistance (TTR). Accordingly, tumors from patients with fast disease progression after targeted therapy exhibited high POSTN expression levels as well as high numbers of TTR macrophages, and POSTN was able to polarize macrophages towards a TTR phenotype. In a mouse model in vivo, POSTN expression protected melanoma from targeted therapy, which was associated with a phenotype change of intratumroal macrophages. Finally, polarized TTR macrophages protected melanoma cells from MEKi-induced killing through CD44 receptor expression on melanoma cells. Thus, interfering with the protective activity of TTR macrophages might represent a strategy to overcome resistance to targeted therapy in melanoma.</span></p>
Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case)
<p>Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case). The first GWAS meta-analysis combines GWAS summary statistics of invasive melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The second GWAS meta-analysis combines GWAS summary statistics of in situ melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The third GWAS meta-analysis combines GWAS summary statistics of in situ vs invasive (case-case; in situ code 0, invasive code 1) melanoma from UK Biobank (as of August 2022), QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>Columns</p> <p>CHR Chromosome</p> <p>SNP rsid</p> <p>POS Base position HG Build 37</p> <p>A1 effect allele</p> <p>A2 Non-effect allele</p> <p>A1FREQ Allele frequency of effect allele</p> <p>BETA effect estimate of effect allele</p> <p>SE standard error of effect estimate</p> <p>PVAL two-tailed p value</p> <p>DIRECTION the Direction of effect of the SNP in each cohort ( in the order UKBB, FINNGEN, QSKIN, QMEGA 610k, QMEGA OMNI)</p> <p>N sample size</p> <p>See </p>
A machine learning model reveals expansive downregulation of ligand-receptor interactions enhancing lymphocyte infiltration in melanoma with developed resistance to Immune Checkpoint Blockade
<p>Data repository containing the data to reproduce the results and findings that are published in:</p> <p>Sahni, S., Wang, B., Wu, D. <em>et al.</em> A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. <em>Nat Commun</em> <strong>15</strong>, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4</p>
Collected Colorimetric Microscopy (C-Microscopy) Images of Melanocytes and Melanoma 3D Spheroids Irradiated with Different Type of Proton Beam as Used in Proton Radiotherapy
<p>Collected Colorimetric Microscopy (C-Microscopy) images, color calibrated (D65 illuminant), of melanocytes and melanoma 3D spheroids, irradiated with different type of proton beam as used in proton radiotherapy.<br> <br>The data are supplement to:</p> <p>Martyna Durak-Kozica, Ewa Stępień, Jan Swakoń, Benedykt R. Jany, Kamil Kawoń, Damian Wróbel, Sebastian Kusyk, Małgorzata Grzesiak, Katarzyna Knapczyk-Stwora, Andrzej Wróbel, Joanna Chwiejand Paweł Moskal, Short-term response of melanoma spheroids and melanocytes to FLASH proton therapy - colorimetric and FTIR microscopy study, Pol J Med Phys Eng 2024;30(4):263-268 (2024) <a href="https://doi.org/10.2478/pjmpe-2024-0031">https://doi.org/10.2478/pjmpe-2024-0031</a></p> <p> </p> <p><br>HEMA-Spheroids-C-Microscopy.zip - melanocytes 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 435.87 microns</p> <p><br>WM-Spheroids-C-Microscopy.zip - melanoma 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 1089.68 microns</p> <p><br>WM-Spheroids-Texture-C-Microscopy.zip - surface texture of melanoma 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 108.97 microns</p> <p> </p> <p>Proton Beam Radiotherapy Irradiation Conditions:</p> <p>C - Control</p> <p>CC - Control minus 7days</p> <p>LP - conventional proton radiotherapy (CONV) final dose 3Gy (dose rate about 0.140 Gy/s)</p> <p>F - FLASH proton radiotherapy final dose 3Gy (dose rate >60 Gy/s)</p> <p>F20 - FLASH proton radiotherapy final dose 20Gy (dose rate >60 Gy/s)</p> <p>F40 - FLASH proton radiotherapy final dose 40Gy (dose rate >60 Gy/s)</p> <p> </p> <p><br>The details about Colorimetric Microscopy (C-Microscopy) approach could be found in:</p> <p>Benedykt R. Jany, Quantifying Colors at Micrometer Scale by Colorimetric Microscopy (C-Microscopy) Approach, Micron 176, 103557 (2024) <a href="https://doi.org/10.1016/j.micron.2023.103557">https://doi.org/10.1016/j.micron.2023.103557</a></p>
Single-cell RNA-Seq and TCR-Seq analysis of PD-1+ CD8+ T-cells responding to anti-PD-1 and anti-PD-1/CTLA-4 immunotherapy in melanoma
<p><strong>This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference) </strong></p> <p> </p> <p><strong>*experimental design</strong></p> <p> Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*extract protocol</strong></p> <p> PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.</p> <p> </p> <p><strong>*library construction protocol</strong></p> <p> Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*library strategy</strong></p> <p> scRNA-seq and scTCR-seq</p> <p> </p> <p><strong>*data processing step</strong></p> <p> Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.</p> <p> Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).</p> <p> </p> <p> <strong>*genome build/assembly</strong></p> <p> Alignment was performed using prebuilt Cell Ranger human reference GRCh38.</p> <p> </p> <p><strong>*processed data files format and content</strong></p> <p> RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format.</p> <p>Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>file name</strong></p> </td> <td> <p><strong>file checksum</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>da2e006d2b39485fd8cf8701742c6d77</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>e125fc5031899bba71e1171888d78205</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>927241805d507204fbe9ef7045d0ccf4</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>8ca544d27f06e66592b567d3ab86551e</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>*processed data file </strong></p> </td> <td> <p><strong>antibodies/tags</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M1_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M1_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - C1_base_combined_therapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - C1_post_combined_therapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C2_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C2_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M2_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M2_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - M3_base_monotherapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - M3_post_monotherapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C3_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C3_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> </tbody> </table> <p> </p>
ICOSLG is associated to anti-PD1 and concomitant antihistamine treatment response in advanced melanoma
<p>Dataset of manuscript: ICOSLG is associated to anti-PD1 and concomitant antihistamine treatment response in advanced melanoma</p>
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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.
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.