Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks

SND-ID: 2024-175. Version: 1. DOI: https://doi.org/10.48723/srtg-ss33

Citation

Creator/Principal investigator(s)

Osheen Sharma - Karolinska Institutet, Department of Oncology-Pathology orcid

Brinton Seashore-Ludlow - Karolinska Institutet, Department of Oncology and Pathology orcid

Research principal

Karolinska Institutet - Department of Oncology and Pathology rorId

Description

This dataset provides detailed imaging data from various co-culture assays of ovarian cancer and fibroblast cell lines, treated with a wide range of drugs. The structured organization and comprehensive naming conventions allow for easy navigation and analysis of the data. The images are treated with 528 drugs from FIMM Oncology Library to study the drug effect on cancer cell morphology in presence of fibroblasts.

The dataset comprises images from 2D coculture high-content screening data in .tiff format. The images were acquired using the Opera Phenix at a 10x magnification. It includes a total of 245,760 raw images (including 4 field of views), each with a resolution of 1080x1080 pixels. For initial analysis, the images were read directly into CellProfiler, a software platform designed for high-throughput image analysis. To facilitate neural network processing, each image was converted into a NumPy array using Python 3 and the Python Imaging Library (PIL).

The data set is available for download in five separate ZIP archives, Kuramochi_BjhTERT.zip (93.68 GB), Kuramochi_WI38.zip (93.50 GB), M

... Show more..
This dataset provides detailed imaging data from various co-culture assays of ovarian cancer and fibroblast cell lines, treated with a wide range of drugs. The structured organization and comprehensive naming conventions allow for easy navigation and analysis of the data. The images are treated with 528 drugs from FIMM Oncology Library to study the drug effect on cancer cell morphology in presence of fibroblasts.

The dataset comprises images from 2D coculture high-content screening data in .tiff format. The images were acquired using the Opera Phenix at a 10x magnification. It includes a total of 245,760 raw images (including 4 field of views), each with a resolution of 1080x1080 pixels. For initial analysis, the images were read directly into CellProfiler, a software platform designed for high-throughput image analysis. To facilitate neural network processing, each image was converted into a NumPy array using Python 3 and the Python Imaging Library (PIL).

The data set is available for download in five separate ZIP archives, Kuramochi_BjhTERT.zip (93.68 GB), Kuramochi_WI38.zip (93.50 GB), MH_BjhTERT.zip (62.22 GB), OvCar3_BjhTERT.zip (84.98 GB), OvCar8_WI38.zip (91.89 GB).

For a description on the file structure, see associated documentation file Dataset_Description.pdf. Show less..

Data contains personal data

No

Language

Method and outcome

Unit of analysis

Population

This dataset consists of images of ovarian cancer cell lines and fibroblasts cell lines grown together (2D coculture) in a 384 well microtiter plate (in vitro). In our analysis each single cell is an object. These cocultures are used to study how cancer and fibroblasts cells interact with each other and upon drug purturbation and how the morphology of cancer cells changes.

Time Method

Study design

Experimental study

Sampling procedure

Data format / data structure

Data collection
Geographic coverage
Administrative information

Responsible department/unit

Department of Oncology and Pathology

Contributor(s)

Greta Gudoitytė - Karolinska Institutet, Department of Oncology and Pathology

Funding 1

  • Funding agency: Swedish Research Council rorId
  • Funding agency's reference number: 2021-03420

Funding 2

  • Funding agency: Åke Wibergs Stiftelse rorId
  • Funding agency's reference number: M19-0271

Funding 3

  • Funding agency: Karolinska Institutet rorId
  • Funding agency's reference number: KID, 2020-01096
  • Funding information: Karolinska Institutet Doctoral Student Funding, KID-funding
Topic and keywords

Research area

Bioinformatics (computational biology) (Standard för svensk indelning av forskningsämnen 2011)

Computer vision and robotics (autonomous systems) (Standard för svensk indelning av forskningsämnen 2011)

Cell biology (Standard för svensk indelning av forskningsämnen 2011)

Bioinformatics and systems biology (Standard för svensk indelning av forskningsämnen 2011)

Cancer and oncology (Standard för svensk indelning av forskningsämnen 2011)

Publications

License

CC BY 4.0

Versions

Version 1. 2025-01-16

Version 1: 2025-01-16

DOI: https://doi.org/10.48723/srtg-ss33

Contact for questions about the data

Brinton Seashore-Ludlow

brinton.seashore-ludlow@ki.se

Published: 2025-01-16