Time-Resolved Quantification of Centrosomes by Automated Image Analysis Suggests Limiting Component to Set Centrosome Size in C. Elegans Embryos

The centrosome is a dynamic organelle found in all animal cells that serves as a microtubule organizing center during cell division. Most of the centrosome components have been identified by genetic screens over the last decade, but little is known about how these components interact with each other to form a functional centrosome. Towards a better understanding of the molecular organization of the centrosome, we investigated the mechanism that regulates the size of the centrosome in the early C. elegans embryo. For this, we monitored fluorescently labeled centrosomes in living embryos and developed a suite of image analysis algorithms to quantify the centrosomes in the resulting 3D time-lapse images. In particular, we developed a novel algorithm involving a two-stage linking process for tracking entrosomes, which is a multi-object tracking task. This fully automated analysis pipeline enabled us to acquire time-resolved data of centrosome growth in a large number of embryos and could detect subtle phenotypes that were missed by previous assays based on manual image analysis. In a first set of experiments, we quantified centrosome size over development in wild-type embryos and made three essential observations. First, centrosome volume scales proportionately with cell volume. Second, beginning at the 4-cell stage, when cells are small, centrosome size plateaus during the cell cycle. Third, the total centrosome volume the embryo gives rise to in any one cell stage is approximately constant. Based on our observations, we propose a ‘limiting component’ model in which centrosome size is limited by the amounts of maternally derived centrosome components. In a second set of experiments, we tested our hypothesis by varying cell size, centrosome number and microtubule-mediated pulling forces. We then manipulated the amounts of several centrosomal proteins and found that the conserved centriolar and pericentriolar material protein SPD-2 is one such component that determines centrosome size

Identification of Z-Tyr-Ala-CHN 2, a Cathepsin L Inhibitor with Broad-Spectrum Cell-Specific Activity against Coronaviruses, including SARS-CoV-2.

The ongoing COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is partly under control by vaccination. However, highly potent and safe antiviral drugs for SARS-CoV-2 are still needed to avoid development of severe COVID-19. We report the discovery of a small molecule, Z-Tyr-Ala-CHN 2, which was identified in a cell-based antiviral screen. The molecule exerts sub-micromolar antiviral activity against SARS-CoV-2, SARS-CoV-1, and human coronavirus 229E. Time-of-addition studies reveal that Z-Tyr-Ala-CHN 2 acts at the early phase of the infection cycle, which is in line with the observation that the molecule inhibits cathepsin L. This results in antiviral activity against SARS-CoV-2 in VeroE6, A549-hACE2, and HeLa-hACE2 cells, but not in Caco-2 cells or primary human nasal epithelial cells since the latter two cell types also permit entry via transmembrane protease serine subtype 2 (TMPRSS2). Given their cell-specific activity, cathepsin L inhibitors still need to prove their value in the clinic; nevertheless, the activity profile of Z-Tyr-Ala-CHN 2 makes it an interesting tool compound for studying the biology of coronavirus entry and replication

Time-Resolved Quantification of Centrosomes by Automated Image Analysis Suggests Limiting Component to Set Centrosome Size in C. Elegans Embryos

The centrosome is a dynamic organelle found in all animal cells that serves as a microtubule organizing center during cell division. Most of the centrosome components have been identified by genetic screens over the last decade, but little is known about how these components interact with each other to form a functional centrosome. Towards a better understanding of the molecular organization of the centrosome, we investigated the mechanism that regulates the size of the centrosome in the early C. elegans embryo. For this, we monitored fluorescently labeled centrosomes in living embryos and developed a suite of image analysis algorithms to quantify the centrosomes in the resulting 3D time-lapse images. In particular, we developed a novel algorithm involving a two-stage linking process for tracking entrosomes, which is a multi-object tracking task. This fully automated analysis pipeline enabled us to acquire time-resolved data of centrosome growth in a large number of embryos and could detect subtle phenotypes that were missed by previous assays based on manual image analysis. In a first set of experiments, we quantified centrosome size over development in wild-type embryos and made three essential observations. First, centrosome volume scales proportionately with cell volume. Second, beginning at the 4-cell stage, when cells are small, centrosome size plateaus during the cell cycle. Third, the total centrosome volume the embryo gives rise to in any one cell stage is approximately constant. Based on our observations, we propose a ‘limiting component’ model in which centrosome size is limited by the amounts of maternally derived centrosome components. In a second set of experiments, we tested our hypothesis by varying cell size, centrosome number and microtubule-mediated pulling forces. We then manipulated the amounts of several centrosomal proteins and found that the conserved centriolar and pericentriolar material protein SPD-2 is one such component that determines centrosome size

Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids

In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation

S4_File: 3D image stack, LNCaP cancer spheroids in co-culture with CAFs, control sample (DMSO), well A05, field 3, stack 335

3D image stack of 3D fluorescent multi-cellular spheroid culture existing out of LNCaP human prostate cancer cells (ATCC, Rockville, USA) and CAF-PF179T human cancer associated fibroblasts (a cell line obtained from the Weizmann Institute, via the PREDECT consortium)

S3_File: ground truth manual segmentation data on 2D MIP images

This is a zip-file containing manually segmented 2D ground truth labeled masks of 3D spheroid cultures. The cultures are LNCaP cancer spheroids in co-culture with CAF stromal cells (untreated, DMSO samples). For the manual segmentation 2D maximum intensity projections (MIPs) were used of the original 3D stacks. The spheroids are labeled in five distinct classes: (1) well separated, (2) overlapping with brighter spheroids in the MIP (rendering it non-separable), (3) overlapping with less bright spheroids in the MIP (rendering it well separable), (4) merely touching other spheroids, and (5) touching the border of the 2D projection of the image. Opening of the images in FIJI (ImageJ) with the ROI Manager allows visual inspection of the data. For this purpose the cases (1) to (5) are color-coded in red, green, magenta, cyan, and blue, respectively

ReadMe

The ReadMe file describes the 3D image data contained in this data repository