DOI: 10.5281/zenodo.4271918
title: Quantified dataset: Chimeric overexpression

description: This repository contains the quantified single cell dataset for the chimeric overexpression spheroid experiment described in: "A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids" https://www.biorxiv.org/content/10.1101/2020.07.24.219659v1 This is an export of the processed dataset after quality control. Please consult the README bellow for a description of the data. An example script to browse this data using Python can be found here: https://github.com/BodenmillerGroup/SpheroidPublication/blob/oexp_analysis/workflow/notebooks/99_browse_export_data.py.ipynb Export Oexp Analysis by Vito Zanotelli et al, Bodenmiller Lab UZH, 2020 This is the export of the overexpression dataset from the paper: "A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids" Raw data: 10.5281/zenodo.4055780 Please cite the paper if you use this data! Experimental design (More details in the paper): Overexpressing 51 signaling constructs, 4 control contstructs (2x GFP, 1x HcRed, 1x Luciferase) and 5x 'empty' mock transfection controls grown in 6 replicates on 6 different plates. Most signaling constructs have a GFP tag. Typically only a subset of cells per sphere were overexpresing. 4 plates were pooled into one block with 240 well barcoding, 2 plates in one block with 120 well barcoding. A pellet of each pool was generated and cut into several 6um thick sections A subset of these sections (='sites') were stained with an IMC pane and acquired as 1 or more 'acquisitions' containing multiple spheres each. Spheres in these acquisitions were identified via computer vision and croped into individual 'images' In each image the following 'objects' were identified via computer vision: 'cell's (cell sections) 'nucleiexp' (slighly expanded cell centers around nuclei) 'cyto' (cytoplasm, cell region without nuclei) -&gt; In the manuscript only 'cell' level data was used. The data was exported using the 'anndata' csv format: https://anndata.readthedocs.io/en/stable/anndata.AnnData.html Some notes on the files and their columns: <strong>{object}_X.csv</strong>: The data matrix Shape: #objects x #features The measurements of this export contain both compensated MeanIntensities (MeanIntensityComp) of an IMC image stack (FullStackFiltered). In the case of 'cell' objects there are also measurements of min/max and mean Intensities from a pixel-wise compensated IMC image (FullStackComp) as well an Imunofluorescent stack image (IfStack, Dapi+GFP channel) and a pixel-probability stack (ProbPos, channels: prop-pos, prop-neg), column metadata: <strong>{object}_var.csv</strong> table row metadata: <strong>{object}_obs.csv</strong> table <strong>{object}_var.csv</strong>: Variable metadata Shape: #features x #columns Columns: measurement_id: unique measurement id measurement_name: Name of measurement (this export: all compensated mean intensity) measurement_type: Type of measurement (this export: only Intensity features) channel_name, metal: Isotope name stack_name: multicolor image stack containing this channel ref_plane_number: position of the measured channel in it's image stack goodname: The name of the marker<br> no prefix: total protein<br> p-: phopho protein<br> []: phospho residue<br> BC: barcoding metal Antibody Clone: antibody clone name is_cc: bool, indication if this marker is considered a classical cell cycle marker working: bool, indicates if the markers are working and of biological value. I would only look at the marker with working=1 Not important: scale: scale of raw data (data is already scaled) plane_id: database id for image plane. <strong>{object}_obs.csv</strong>: Object (cell/nuclei/cytoplasma section) level metadata Shape: #objects x #columns Columns: object_id: Unique object id (unique also accross object types) image_id: The key linking to the 'image_meta.csv' table object_number: id corresponding to the object value in the segmentation mask distrim: Estimated distance to sphere border -&gt; unit 'um' Center_X/Y: Centroid of object in image -&gt; unit 'um' dist-sphere: distance to estimated spheroid section border dist-other: distance to other spheroid section in image dist-bg: distance to background pixels <strong>relations<em>{source}</em>{target}.csv</strong>: Cell relationship graphs Shape: #relations x #columns Encoding relations between objects: cell_neighbors: Neighbourhood graph: object_id_cell: id of cell object_id_neighbour: id of neighbor cell_nuclei: Relationship between cells and nuclei object_id_cell object_id_nucleiexp -&gt; This is not necessarily a 1:1 correspondence -cell_cyto: Relationship between cells and cytoplasm object_id_cell object_id_cyto -&gt; This is not necessarily a 1:1 correspondence <strong>image_meta.csv</strong>: Image (=spheroid section) metadata Shape: #images x #columns Columns: Image metadata: image_id: The unique key of this table. Each row corresponds to a single spheroid section image_shape_h/w: width/heigh of image in pixels/um acquisition_id: unique id of IMC acquisition this image was cropped from site_id: unique id of the section this sphere cut comes from.<br> All cuts in the same section were stained together. slide_id: unique id for a single slide containing 1 or more sites sampleblock_id: unique id of the sample block this sphere was pooled and processed in. Not important: image_number: original cellprofiler image number crop_number: object number of the sphere that was used for this crop image_pos_x/y: top left coordinate of crop of sphere from original acquisition bc_depth: cells within this distance from border were considered for debarcoding bc_invalid: number of invalid debarcoded objects in this sphere crop bc_highest_count: number of cells assigned to the main barcode of this crop bc_second_count: number of cells assigned to the second most frequent barcode of this crop barcode: dictionary containing the barcode bc_plate, bc_x, bc_y: barcode metadata acquisition_mcd_acid: original MCD aquisition id site_mcd_panoramaid: original MCD panorama id acquisition_mcd_roiid: original MCD roiid slideac_id/name: unique id for each aquisition of a slide. Corresponds to a single mcd file slide_number: original number of slide this acquisition comes from Experimental metadata: condition_id: id of the physical spheroid the slice belongs to. Unique to each sphere replicate. condition_name: name of the growth condition this sphere came from plate_id: id of the plate the spheroid was grown in well_name: position of the well the spheroid was grown in sampleblock_id/sampleblock_name: id/name of the pooled block the spheroid was processed in site_id: corresponds to the site the spheroid slice was located on. All spheroid slices in the same site were stained together. file_name: filename of the segmentation mask found in masks_cell Filenames: mask<em>filename</em>{object}: filename of the object mask corresponding to this image image_stack<em>filename</em>{imagestack}: filename of the image stack with this name. Note: all mean intensity measurements are usually done in the 'FullStackFiltered' (raw image with only filtered for strong outliers) and then compensated for metal impurities (as recomended in Chevrier, Zanotelli and Crowell 2018). For visualization and Min/Max measurements 'FullStackComp' can be used as there the image was corrected for metal impurities. The channel order is the same for both stacks. Folder <strong>masks</strong>: Folder containing the segmentation masks (See image_meta -&gt; Filenames) Folder <strong>images</strong>: Folder containing the image stacks (See image_meta -&gt; Filenames) The mapping between channels and image planes number is given through the 'ref_plane_number' from the {object}_var.csv metadata.