Quantified dataset: 4 cell lines

This repository contains the quantified single cell dataset for the '4 cell line' spheroid physiology 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/phys_analysis/workflow/notebooks/99_browse_export_data.py.ipynb

or be interactively tried on Google Colab:

https://colab.research.google.com/github/BodenmillerGroup/SpheroidPublication/blob/phys_analysis/workflow/notebooks/99_browse_export_data.py.ipynb

Export Phys Analysis

by Vito Zanotelli et al, Bodenmiller Lab UZH, 2020

This is the export of the 4 cell line 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):

• 4 Cell lines grown with 3 seeding densities for two timepoints

• Each timepoint was separately harvested and pooled into a 60 well barcoding plate

• 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) -> 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:

• {object}_X.csv:

  • The data matrix
  • Shape: #objects x #features
  • column metadata: {object}_var.csv table
  • row metadata: {object}_obs.csv table

• {object}_var.csv:

  • Variable metadata

  • For the paper analyses mainly the 'MeanIntensityComp' (compensated mean intensity) and 'NbMeanMeanIntensityComp' (Average intensity of neighbouring cells) of the FullStackFiltered was used. This export further contains additionally mean, max, min, std of the compensated images (FullStackComp) as well as area and location features. Other important features:

      - distrim: Estimated distance to sphere border -> unit 'um'
      - Center_X/Y: Centroid of object in image -> 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
    

  • Shape: #features x #columns

  • Columns:
    • measurement_id: unique measurement id
    • measurement_name: Name of measurement
    • measurement_type: Type of measurement
    • 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

       no prefix: total protein
       p-: phopho protein
       []: phospho residue
       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.

• {object}_obs.csv:

  • Object (cell/nuclei/cytoplasma section) level metadata. For the paper only 'cell' level data was used.
  • 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

• relations{source}{target}.csv:

  • 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 -> This is not necessarily a 1:1 correspondence -cell_cyto: Relationship between cells and cytoplasm
      • object_id_cell
      • object_id_cyto -> This is not necessarily a 1:1 correspondence

• image_meta.csv:

  • 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.

          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
      • concentration: Relative seeding concentrations. Correlated to spheroid size.
      • time_point: spheroid growth time in hours
      • cellline: Cell line
      • plate_id: id of the plate the spheroid was grown in
      • well_name: position of the well the spheroid was grown in
      • hastelox: bool, indicates if the telox2 hypoxia assay was performed in this sphere. Only spheres with hastelox=True should have signal in the Telox channel (Te125)
      • 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:

      • maskfilename{object}: filename of the object mask corresponding to this image
      • image_stackfilename{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 masks:

  • Folder containing the segmentation masks (See image_meta -> Filenames)

• Folder images:

  • Folder containing the image stacks (See image_meta -> Filenames)
  • The mapping between channels and image planes number is given through the 'ref_plane_number' from the {object}_var.csv metadata.