Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. (c) 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry

p53 dynamics control cell fate.

Cells transmit information through molecular signals that often show complex dynamical patterns. The dynamic behavior of the tumor suppressor p53 varies depending on the stimulus; in response to double-strand DNA breaks, it shows a series of repeated pulses. Using a computational model, we identified a sequence of precisely timed drug additions that alter p53 pulses to instead produce a sustained p53 response. This leads to the expression of a different set of downstream genes and also alters cell fate: Cells that experience p53 pulses recover from DNA damage, whereas cells exposed to sustained p53 signaling frequently undergo senescence. Our results show that protein dynamics can be an important part of a signal, directly influencing cellular fate decisions

A Switch in p53 Dynamics Marks Cells That Escape from DSB-Induced Cell Cycle Arrest

© 2020 The Author(s) Cellular responses to stimuli can evolve over time, resulting in distinct early and late phases in response to a single signal. DNA damage induces a complex response that is largely orchestrated by the transcription factor p53, whose dynamics influence whether a damaged cell will arrest and repair the damage or will initiate cell death. How p53 responses and cellular outcomes evolve in the presence of continuous DNA damage remains unknown. Here, we have found that a subset of cells switches from oscillating to sustained p53 dynamics several days after undergoing damage. The switch results from cell cycle progression in the presence of damaged DNA, which activates the caspase-2-PIDDosome, a complex that stabilizes p53 by inactivating its negative regulator MDM2. This work defines a molecular pathway that is activated if the canonical checkpoints fail to halt mitosis in the presence of damaged DNA

CellProfiler 3.0: Next-generation image processing for biology

<div><p>CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.</p></div

Segmentation steps for the quantification of transcripts per cell within a 3D blastocyst.

<p>Images were captured of a mouse embryo blastocyst cell membrane stained with WGA and FISH for GAPDH transcripts. (A) Original 3D image of blastocyst cell membrane prior to analysis. (B) CellProfiler 3.0 image processing modules used for membrane image processing. Figure labels: RH (“RemoveHoles”), Close (“Closing”), Erode (“Erosion”), Mask (“MaskImage”), Math (“ImageMath”), EorS Features (“EnhanceOrSuppressFeatures”). (C) Nuclei after segmentation by CellProfiler, as viewed in Fiji. (D) Segmentation of cells after setting nuclei as seeds by CellProfiler, as viewed in Fiji. (E) Segmentation of GAPDH transcript foci using CellProfiler, as viewed in Fiji. (F) Examples of analysis that can be done by CellProfiler: (top) cell volume relative nucleus volume, (middle) GAPDH transcript quantity in each cell using CellProfiler’s “RelateObjects” module, (bottom) number of GAPDH transcripts in Z-plane (bin size = 2.5 μm). The underlying measurements may be downloaded as <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s012" target="_blank">S1 File</a>. <i>Images were provided by Javier Frias Aldeguer and Nicolas Rivron from Hubrecht Institute</i>, <i>Netherlands</i>, <i>and are available from the Broad Bioimage Benchmark Collection (<a href="https://data.broadinstitute.org/bbbc/BBBC032/" target="_blank">https://data.broadinstitute.org/bbbc/BBBC032/</a></i>). 3D, three-dimensional; FISH, fluorescent in situ hybridization; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; WGA, wheat germ agglutinin.</p

Segmentation and analysis of 3D hiPSC images using CellProfiler 3.0.

<p>DNA channel showing nuclei (A), CellMaskDeepRed channel showing membrane (B), and GFP channel showing beta-actin (C) at the center (left) and edge (right) of the hiPSC colony. (D) Various measurements obtained from the samples are shown; note that cells touching the edge of each image are excluded from this analysis. The underlying measurements may be downloaded as <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s013" target="_blank">S2 File</a>. <i>Images are from the Allen Institute for Cell Science</i>, <i>Seattle</i>, <i>and are available from the Broad Bioimage Benchmark Collection (<a href="https://data.broadinstitute.org/bbbc/BBBC034/" target="_blank">https://data.broadinstitute.org/bbbc/BBBC034/</a>)</i>. 3D, three-dimensional; GFP, green fluorescent protein; hiPSC, human induced pluripotent stem cell.</p

Examples of 3D image segmentation produced by CellProfiler 3.0, across two experimental systems and two sets of synthesized images.

<p>Three focal planes shown for each. Raw images (left) and CellProfiler outputs (right) showing nuclei of mouse embryo blastocyst (A), mouse trophoblast stem cells (B), and synthetic images of HL60 cell lines (C) and (D). More information about segmentation steps used for these images can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s002" target="_blank">S2</a>–<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s005" target="_blank">S5</a> Figs. (E) Comparison of the segmentation accuracy of CellProfiler 3.0 and Fiji’s plugin MorphoLibJ, based on the Rand index of the processed image and its ground truth (out of a total of 1.0). Object accuracy comparisons of these same images may be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s017" target="_blank">S6 Fig</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2005970#pbio.2005970.s014" target="_blank">S3</a> File. 3D, three-dimensional; hiPSC, human induced pluripotent stem cell.</p