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642 results for “Multiplexing”
Multiplexed single-cell characterization of alternative polyadenylation regulators (HEK293FT & K562 Perturb-seq data)
<p>This site provides access to datasets from the CPA-Perturb-seq <a href="https://www.biorxiv.org/content/10.1101/2023.02.09.527751v1">manuscript</a> Kowalski*, Wessels*, Linder* et al., including processed Perturb-seq datasets from HEK293FT and K562. We release these data as Seurat objects, where each object contains single-cell quantifications of gene expression (RNA assay), and in addition, quantifications of polyA site usage (polyA site assay). To explore these data, please install the <a href="https://github.com/satijalab/PASTA">PASTA</a> (PolyA Site analysis using relative Transcript Abundance) package, which provides infrastructure and analytical tools to explore alternative polyadenylation at single-cell resolution. For each dataset, we also include a fragment file which enables visualization of read coverage plots across groups of cells. </p> <p>The files include:</p> <p>1. CPA_K562.Rds : Seurat object containing the K562 CPA-Perturb-seq dataset </p> <p>2. CPA_K562_fragments.tsv.gz : Fragment file for the K562 dataset </p> <p>3. CPA_K562_fragments.tsv.gz.tbi : Fragment file index for the K562 dataset </p> <p> </p> <p>R code below:</p> <pre><code>library(PASTA) k562 <- readRDS("CPA_K562.Rds") # Add fragments for plotting Fragments(k562) <- CreateFragmentObject(path = "download/CPA_K562_blocks.tsv.gz", cells = Cells(k562)) # visualize polyA site usage PolyACoveragePlot(k562, region ="chr7-26212195-26213351")</code></pre>
Wavelength-division multiplexing optical Ising simulator enabling fully programmable spin couplings and external magnetic fields
<p>Recently various physical systems have been proposed for modeling Ising spin Hamiltonians appealing to solve combinatorial optimization problems with remarkable performance. However, how to implement arbitrary spin-spin interactions is a critical and challenging problem in various kinds of unconventional Ising machines. Here we propose a general gauge transformation scheme to enable arbitrary spin-spin interactions and external magnetic fields as well, by decomposing an Ising Hamiltonian into multiple Mattis-type interactions. Based on this scheme, a wavelength-division multiplexing spatial photonic Ising machine (SPIM) is developed to show the programmable capability of general spin coupling interactions. We exploit the wavelength-division multiplexing SPIM to simulate three spin systems: pm J models, Sherrington-Kirkpatrick models, and only locally connected J1/J2 models and observe the phase transitions among the spin-glass, the ferromagnetic, the paramagnetic and the stripe-antiferromagnetic phases. We also demonstrate the ground state search for solving the Max-Cut problem with the wavelength-division multiplexing SPIM. These results promise the realization of ultrafast-speed and high-power-efficiency Boltzmann sampling of a generalized large-scale Ising model.</p>
Antibody panel used for multiplexed antibody-based imaging of Human Pancreas Analysis Program (HPAP) samples by CODEX
<p>This data file details antibodies applied to human pancreas tissue samples from the Human Pancreas Analysis Program (HPAP; RRID:SCR_016202) of the <a href="https://hirnetwork.org/">Human Islet Research Network</a> (HIRN; RRID:SCR_014393). Images will be uploaded for interactive analysis on <a href="https://pancreatlas.org/datasets">Pancreatlas</a> (RRID:SCR_018567) and made available for download via <a href="https://hpap.pmacs.upenn.edu/">PANC-DB</a>. Workflow is documented on protocols.io: <a href="https://dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1">dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1</a>.</p><p>Table format adapted from Radtke AJ, Quardokus EM, Saunders DC (2022), <a href="https://doi.org/10.5281/zenodo.7386417">SOP: Construction of Organ Mapping Antibody Panels for Multiplexed Antibody-Based Imaging of Human Tissues</a>. See also: Saunders D; Reihsmann R. <a href="https://doi.org/10.48539/HBM754.BHVR.258">OMAP-13: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX, v1.0</a>.</p>
Multiplexed imaging analysis of human pancreatic islets from donors with and without type 2 diabetes
<p>This record contains tabular data from traditional and multiplexed immunohistochemistry experiments presented in the manuscript <i>Genetic risk converges on regulatory networks mediating early type 2 diabetes</i> (<a href="https://doi.org/10.1038/s41586-023-06693-2">Walker, Saunders & Rai et al., <i>Nature</i> 2023</a>), a body of work that includes tissue imaging, <a href="https://theparkerlab.shinyapps.io/Islet-RNAseq-WGCNA/">sorted islet cell transcriptomics</a>, and islet functional analysis of donors with early-stage type 2 diabetes (T2D) and control donors. Images can be viewed interactively on Pancreatlas (RRID:SCR_018567): <a href="https://pancreatlas.org/datasets/904/explore">https://pancreatlas.org/datasets/904/explore</a>.</p><p>All immunohistochemistry was performed on lightly PFA-fixed human pancreatic tissue (sample characteristics available in Supplementary Table 1). For traditional immunohistochemistry, islets were imaged at 20× with 2× digital zoom using a FV3000 confocal laser scanning microscope (Olympus) or full cross-sections were scanned on a ScanScope FL (Leica/Aperio). Quantitative analysis was carried out using HALO™ (Indica Labs) or Metamorph (Molecular Devices) software. For multiplexed immunohistochemistry, images were acquired using the PhenoCycler (CODEX) Open system (Akoya Biosciences) integrated with a BZ-X810 epifluorescence microscope (Keyence) with a CFI plan Apo I 20x/0.75 objective (Nikon). Image alignment, stitching, background subtraction, and deconvolution were performed using the CODEX Processor v1.7.0.6 (Akoya Biosciences). Cell segmentation and cell type annotations were generated using the HALO HighPlex FL v3.2.1 module (Indica Labs). For cell neighborhood (CN) analysis, two methods were applied in parallel to CODEX data from annotated islets: a community detection method, termed <i>Dynamic CF-IDF</i>, and a <i>k</i>-means approach. Packages used for cell neighborhood analyses are published in <a href="http://github.com/liu-bioinfo-lab/Cellular-Neighborhood-Analysis">Github</a>.</p>
Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer
<p>All data supporting the publication: "Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer."</p><p>1. Fully_Processed_OME.TIFF: This folder contains the OME.TIFF files with all markers after compensation and hot pixel removal for visualization of the data. These can be opened with QuPath and other software. </p><p>2. PDAC_IMC_Seurat_FINAL.rds: Seurat object of all cells included in the analysis with cell type and neighborhood annotations, and unintegrated and rPCA-integrated UMAP reductions. </p><p>3. Raw_Data_TIFF_Files: All raw individual TIFF files from the image acquisition</p><p>4. ROI_Selection: Brightfield and IHC images of individual samples showing where the ROIs for each sample are collected </p><p>5. Segmentation_Files: All relevant segmentation files from Mesmer for nuclear and whole cell segmentation. </p><p>6. H&E Images for each case scanned at 40x </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>
Characterization of the tumor-immune microenvironment in hepatocellular carcinoma by highly multiplexed imaging mass cytometry
<p>Imaging mass cytometry data of 54 HCC patients. </p> <ul> <li>DC_img_normalized: Preprocessed and normalized multistack .tiff images. Each stack represents one channel. Channel annotations are stored in the ICICohort_panel.csv file. ROIs are located in the tumor, interface and adjacent liver as indicated in the file name.</li> <li>DC_cellmasks: Masks identifying individual cells on the images.</li> <li>DC_stromamasks: Masks identifying stromal and parenchymal regions on the image.</li> <li>DCCohort_panel.csv: table containing channel information (metal tag and marker).</li> </ul> <p>Patient metadata may be found as supplementary table 2 of DOI <a href="https://doi.org/10.1136/gutjnl-2024-332837" target="_blank" rel="noopener noreferrer"> 10.1136/gutjnl-2024-332837 </a>.</p>
First insights into population structure and genetic diversity versus host specificity in trypanorhynch tapeworms using multiplexed shotgun genotyping
<p>Theory predicts relaxed host specificity and high host vagility should contribute to reduced genetic structure in parasites while strict host specificity and low host vagility should increase genetic structure. Though these predictions are intuitive, they have never been explicitly tested in a population genomic framework. Trypanorhynch tapeworms, which parasitize sharks and rays (elasmobranchs) as definitive hosts, are the only order of elasmobranch tapeworms that exhibit considerable variability in their definitive host specificity. This allows for unique combinations of host use and geographic range, making trypanorhynchs ideal candidates for studying how these traits influence population-level structure and genetic diversity. Multiplexed shotgun genotyping (MSG) datasets were generated to characterize component population structure and infrapopulation diversity for a representative of each trypanorhynch suborder: the ray-hosted <em>Rhinoptericola megacantha</em> (Trypanobatoida) and the shark-hosted Callitetrarhynchus gracilis (Trypanoselachoida). Adults of <em>R. megacantha</em> are more host-specific and less broadly distributed than adults of <em>C. gracilis</em>, allowing correlation between these factors and genetic structure. Replicate tapeworm specimens were sequenced from the same host individual, from multiple conspecific hosts within and across geographic regions, and from multiple definitive host species. For <em>R. megacantha</em>, population structure coincided with geography rather than host species. For <em>C. gracilis</em>, limited population structure was found, suggesting a potential link between degree of host specificity and structure. Conspecific trypanorhynchs from the same host individual were found to be as, or more, genetically divergent from one another as from conspecifics from different host individuals. For both species, high levels of homozygosity and positive FIS values were documented.</p>
Label-free multiplexed detection of diabetic retinopathy biomarkers using fiber optic biosensors: towards lab-in-the-tear
<p>Raw experimental data on label-free detection of diabetic retinopathy biomarkers using fiber optic biosensors. This data contains information on the multiplexed and separate detection of LCN1 and VEGF diabetic retinopathy biomarkers in artificial tears. </p>
Raw data of multiplex assay on cyto-/chemokine secretion from human monocyte-derived dendritic cells
<p>Raw data of <em>in vitro</em> investigations on immune activation by bare and surface-functionalized SiO<sub>2</sub> NP-allergen conjugates using human monocyte-derived dendritic cells as model antigen-presenting cells. Data repository for Punz B. et al., 2022.</p>
Supplementary material - Fingerprint multiplex CARS at high speed based on supercontinuum generation in bulk media and deep learning spectral denoising
<p>Supplementary material</p>
A risk-reward examination of sample multiplexing reagents for Single Cell RNA-Seq
<div> <h2>A Risk-reward Examination of Sample Multiplexing Reagents for Single Cell RNA-Seq</h2> <br> <div>The publication is available open access at https://doi.org/10.1016/j.ygeno.2024.110793</div> </div> <p>Be sure to check the md5 checksum: checklist.chk</p>
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks
<p>Data used to train multiclass and binary neural networks to analyse SiPM (Silicon Photomultiplier) signals in a multiplexed array of 16 detectors and detect the signal detector origin. Data acquired using an oscilloscope. Results compared with previous anger logic methods. </p> <p>Dataset used in the publication</p> <p>"Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks"</p> <p>https://doi.org/10.1088/2057-1976/ad4f73</p>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
<p>Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.</p> <p>The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user's system or to familiarize oneself with the pipeline.</p>
Rapid Identification of Bacterial isolates Using Microfluidic Adaptive Channels and Multiplexed Fluorescence Microscopy
<p>Dataset for: Rapid Identification of Bacterial isolates Using Microfluidic Adaptive Channels and Multiplexed Fluorescence Microscopy</p> <p>doi: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4LC00325J">10.1039/D4LC00325J</a></p>
Multiplex immunofluorescence ROIs related to Van Hijfte et al. 2024
<p>Multiplex immunofluorescence ROIs from IDH1-R132H mutant astrocytomas. </p> <p>For more information see associated publication. </p> <p>Panel:</p> <ul> <li>Opal 520: CD3</li> <li>Opal 540: CD68</li> <li>Opal 570: CD8</li> <li>Opal 620: CD56</li> <li>Opal 650: CD20</li> <li>Opal 690: IDH1-R132H</li> </ul>
Cell type labels for all clustering and normalization combinations compared for CODEX multiplexed imaging
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. Subsequently images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), and single cell segmentation. Output of this process was a dataframe of nearly 130,000 cells with fluorescence values quantified from each marker. We used this dataframe as input to 1 of the 5 normalization techniques of which we compared z, double-log(z), min/max, and arcsinh normalizations to the original unmodified dataset. We used these normalized dataframes as inputs for 4 unsupervised clustering algorithms: k-means, leiden, X-shift euclidian, and X-shift angular.</p> <p>From the clustering outputs, we then labeled the clusters that resulted for cells observed in the data producing 20 unique cell type labels. We also labeled cell types by hiearchical hand-gating data within cellengine (cellengine.com). We also created another gold standard for comparison by overclustering unormalized data with X-shift angular clustering. Finally, we created one last label as the major cell type call from each cell from all 21 cell type labels in the dataset. </p> <p>Consequently the dataset has individual cells segmented out in each row. Then there are columns for the X, Y position in pixels in the overall montage image of the dataset. There are also columns to indicate which region the data came from (4 total). The rest are labels generated by all the clustering and normalization techniques used in the manuscript and what were compared to each other. These also were the data that were used for neighborhood analysis for the last figure of the manuscript. These are provided at all four levels of cell type level granularity (from 7 cell types to 35 cell types). </p>
Data from: Cerebellar complex spikes multiplex complementary behavioral information
<p><span>Purkinje<b> </b>cell (PC) discharge, the only output of cerebellar cortex, involves two types of action potentials, high-frequency simple spikes (SSs) and low-frequency complex spikes (CSs). While there is consensus that SSs convey information needed to optimize movement kinematics, the function of CSs, determined by the PC´s climbing fibre input, remains controversial. While initially thought to be specialized in reporting information on motor error for the subsequent amendment of behavior, CSs seem to contribute to other aspects of motor behavior as well. When faced with the bewildering diversity of findings and views unraveled by highly specific tasks one may wonder if there is just one true function with all the other attributions wrong? Or is the diversity of findings a reflection of distinct pools of PCs, each processing specific streams of information conveyed by climbing fibres? With these questions in mind, we recorded CSs from the monkey oculomotor vermis deploying a repetitive saccade task that entailed sizable motor errors as well as small amplitude saccades, correcting them. We demonstrate that in addition to carrying error-related information, CSs carry information on the metrics of both primary and small corrective saccades in a time-specific manner, with changes in CS firing probability coupled with changes in CS duration. Furthermore, we also found CS activity that seemed to predict the upcoming events. </span>Hence PCs receive a multiplexed climbing fibre input that merges complementary streams of information on the behavior, separable by the recipient PC because they are staggered in time.</p>
Raw data and media: Tetraspanins are unevenly distributed across single extracellular vesicles and bias sensitivity to multiplexed cancer biomarkers
<p>Raw datasets and media accompanying the manuscript: T<strong>etraspanins are unevenly distributed across single extracellular vesicles and bias sensitivity to multiplexed cancer biomarkers</strong>, published in the Journal of Nanobiotechnology </p>
Highly-multiplexed mass cytometry screen of human bone marrow hematopoietic stem and progenitor cells
<p>In contrast to the rich single-cell transcriptomic and epigenetic data, the corresponding protein level information of human hematopoietic stem and progenitor cell (HSPC) populations is still missing. We used a highly-multiplexed single-cell screen to quantify the protein expression of 353 surface molecules and 79 functional intracellular molecules (TFs, chromatin regulators, and metabolic enzymes) with mass cytometry. In doing this, we created a core panel with probes against functional protein molecules associated with specific lineage potentials to better illuminate the differentiation potentials of the progenitors. In total, we analyzed 556,226 CD34+ bone marrow HSPCs across three individuals. Our analysis identified ten distinct clusters among HSPCs by unsupervised method and defined their unique proteomic composition. We compare our data-driven populations to the canonical HSPC cell types identified by cell surface proteins and observe discrepancies, especially in the lympho-myeloid axis. Overall, we supply a quantified summary of the proteomes of human HSPCs and create a framework to redefine progenitor populations with unique functional states along hematopoiesis. </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.