4.5 Article

Data science in cell imaging

Journal

JOURNAL OF CELL SCIENCE
Volume 134, Issue 7, Pages -

Publisher

COMPANY BIOLOGISTS LTD
DOI: 10.1242/jcs.254292

Keywords

Data science; Deep learning; Imaging; Machine learning; Microscopy

Categories

Funding

  1. Israeli Council for Higher Education (CHE) via the Data Science Research Center at Ben-Gurion University of the Negev, Israel
  2. National Institutes of Health [K99GM123221]
  3. Lyda Hill Foundation

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Cell imaging has entered the 'Big Data' era, with new technologies leading to an explosion in high content, dynamic, and multidimensional imaging data. Data science plays a crucial role in processing, analyzing, and mining these vast amounts of data in the field of cell imaging.
Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools - democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

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