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.