Cheeseman, B. | bioRxiv Science

bioRxiv2023

Efficient image analysis for large-scale next generation histopathology using pAPRica

The large size of imaging datasets generated by next-generation histology methods limits the adoption of those approaches in research and the clinic. We propose pAPRica (pipelines for Adaptive Particle Representation image compositing and analysis), a framework based on the Adaptive Particle Representation (APR) to enable efficient analysis of large microscopy datasets, scalable up to petascale on a regular workstation. pAPRica includes stitching, merging, segmentation, registration, and mapping to an atlas as well as visualization of the large 3D image data, achieving 100+ fold speedup in computation and commensurate data-size reduction.

bioinformatics↗

bioRxiv2017

Variation in traction forces during cell cycle progression

Tissue morphogenesis results from the interplay between cell growth and mechanical forces. While the impact of forces on cell proliferation has been fairly well characterized, the inverse relationship is much less understood. Here we investigated how traction forces vary during cell cycle progression. Cell shape was constrained on micropatterned substrates in order to distinguish variations in cell contractility from cell size increase. We performed traction force measurements of asynchronously dividing cells expressing a cell-cycle reporter, to obtain measurements of contractile forces generated during cell division. We found that forces tend to increase as cells progress through G1, before reaching a plateau in S phase, and then decline during G2. This biphasic behaviour revealed a previously undocumented specific and opposite regulation of cell contractility during each cell cycle stage.

cell biology↗