Experimental and Computational Tools to Study P53 Dynamics at the Single-Cell Level

One of the most commonly mutated genes found in cancer is the tumor suppressor p53. p53 is a transcription factor capable of inducing cell-cycle arrest, apoptosis, senescence, and other cellular processes thought to halt the progression of a nascent cancer. As part of a stress signaling pathway, p53 is acutely activated by ionizing radiation and the formation of DNA double-strand breaks. The appearence of this DNA damage causes the concentration of p53 within the nucleus to fluctuate and pulse regularly, which can be observed in single cells using fluorescence time-lapse microscopy. From the time this was first discovered, the connection between these p53 dynamics and p53 function has been speculated upon. A key insight into this connection came from a Lahav Lab publication that demonstrated the act of pulsing, itself, controls p53-dependent transcription and cell fate. The mechanisms and molecular details behind this relationship are now an area of intense study. Another area of high interest is the broader characterization of p53 dynamics in different time-scales, genetic backgrounds, and stresses. These lines of research each depend upon single-cell measurements that are often time consuming, noisy, and yield small sample sizes. The ongoing development of experiemental and computational tools for single-cell biology is needed to overcome these limitations. In the publication referenced earlier, a novel method was created to measure p53 dynamics and gene expression in the same cell. In a seperate study characterizing p53 dynamics over long time-scales, semi-automated tracking software aided in the discovery of new p53 dynamics: sustained elevation of p53 levels that follow a period of pulsing. Population measurements showing similarly elevated p53 levels on the same time-scale are shown to depend on the late induction of the p53-target PIDD.Systems Biolog

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

BIN1 protein isoforms are differentially expressed in astrocytes, neurons, and microglia: neuronal and astrocyte BIN1 are implicated in tau pathology

Background Identified as an Alzheimer’s disease (AD) susceptibility gene by genome wide-association studies, BIN1 has 10 isoforms that are expressed in the Central Nervous System (CNS). The distribution of these isoforms in different cell types, as well as their role in AD pathology still remains unclear. Methods Utilizing antibodies targeting specific BIN1 epitopes in human post-mortem tissue and analyzing mRNA expression data from purified microglia, we identified three isoforms expressed in neurons and astrocytes (isoforms 1, 2 and 3) and four isoforms expressed in microglia (isoforms 6, 9, 10 and 12). The abundance of selected peptides, which correspond to groups of BIN1 protein isoforms, was measured in dorsolateral prefrontal cortex, and their relation to neuropathological features of AD was assessed. Results Peptides contained in exon 7 of BIN1’s N-BAR domain were found to be significantly associated with AD-related traits and, particularly, tau tangles. Decreased expression of BIN1 isoforms containing exon 7 is associated with greater accumulation of tangles and subsequent cognitive decline, with astrocytic rather than neuronal BIN1 being the more likely culprit. These effects are independent of the BIN1 AD risk variant. Conclusions Exploring the molecular mechanisms of specific BIN1 isoforms expressed by astrocytes may open new avenues for modulating the accumulation of Tau pathology in AD

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

TFEB Transcriptional Responses Reveal Negative Feedback by BHLHE40 and BHLHE41

Transcription factor EB (TFEB) activates lysosomal biogenesis genes in response to environmental cues. Given implications of impaired TFEB signaling and lysosomal dysfunction in metabolic, neurological, and infectious diseases, we aim to systematically identify TFEB-directed circuits by examining transcriptional responses to TFEB subcellular localization and stimulation. We reveal that steady-state nuclear TFEB is sufficient to activate transcription of lysosomal, autophagy, and innate immunity genes, whereas other targets require higher thresholds of stimulation. Furthermore, we identify shared and distinct transcriptional signatures between mTOR inhibition and bacterial autophagy. Using a genome-wide CRISPR library, we find TFEB targets that protect cells from or sensitize cells to lysosomal cell death. BHLHE40 and BHLHE41, genes responsive to high, sustained levels of nuclear TFEB, act in opposition to TFEB upon lysosomal cell death induction. Further investigation identifies genes counter-regulated by TFEB and BHLHE40/41, adding this negative feedback to the current understanding of TFEB regulatory mechanisms. Employing RNA sequencing, genome-wide CRISPR screening, and high-content subcellular imaging, Carey et al. systematically unravel localization- and stimulation-specific transcriptional responses to TFEB, including target gene activation at steady state. The authors further uncover a negative feedback loop by BHLHE40 and BHLHE41 that counteracts a TFEB transcriptional signature induced by lysosomal stress

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