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The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 with a next-generation file format such as Zarr will satisfy the majority of use cases in bioimaging. Critically, a common metadata format used in all these vessels can deliver truly findable, accessible, interoperable and reusable bioimaging data.OME’s next-generation file format (OME-NGFF) provides a cloud-native complement to OME-TIFF and HDF5 for storing and accessing bioimaging data at scale and works toward the goal of findable, accessible, interoperable and reusable bioimaging data.

Autophagy is a highly conserved process from yeast to mammals in which intracellular materials are engulfed by a double-membrane organelle called autophagosome and degrading materials by fusing with the lysosome. The process of autophagy is regulated by sequential recruitment and function of autophagy-related (Atg) proteins. Genetic hierarchical analyses show that the ULK1 complex comprised of ULK1-FIP200-ATG13-ATG101 translocating from the cytosol to autophagosome formation sites as a most upstream ATG factor; this translocation is critical in autophagy initiation. However, how this translocation occurs remains unclear. Here, we show that ULK1 is palmitoylated by palmitoyltransferase ZDHHC13 and translocated to the autophagosome formation site upon autophagy induction. We find that the ULK1 palmitoylation is required for autophagy initiation. Moreover, the ULK1 palmitoylated enhances the phosphorylation of ATG14L, which is required for activating PI3-Kinase and producing phosphatidylinositol 3-phosphate, one of the autophagosome membrane’s lipids. Our results reveal how the most upstream ULK1 complex translocates to the autophagosome formation sites during autophagy. It was unknown how the most upstream Atg protein transits from the cytosol to autophagosome formation sites. Here, the authors show that ULK1 palmitoylation by ZDHHC13 recruits the complex to the formation site and enhances ATG14L phosphorylation.

A sphingomyelin-binding motif is identified in the membrane-spanning domain of p24, a COPI machinery protein. Sphingolipid recognition by membrane proteins Sphingolipids are structural components of membranes, and some of them also act as intracellular second messengers. This work shows that one sphingomyelin species, known as SM18, directly and specifically interacts with the transmembrane domain of the COPI machinery protein p24. The interaction depends on a motif (VXXTLXXIY) within the membrane-spanning domain of p24, and bioinformatic analyses predict that this motif represents a conserved sphingolipid-binding cavity in a variety of other mammalian membrane proteins. Functioning and processing of membrane proteins critically depend on the way their transmembrane segments are embedded in the membrane 1 . Sphingolipids are structural components of membranes and can also act as intracellular second messengers. Not much is known of sphingolipids binding to transmembrane domains (TMDs) of proteins within the hydrophobic bilayer, and how this could affect protein function. Here we show a direct and highly specific interaction of exclusively one sphingomyelin species, SM 18, with the TMD of the COPI machinery protein p24 (ref. 2 ). Strikingly, the interaction depends on both the headgroup and the backbone of the sphingolipid, and on a signature sequence (VXXTLXXIY) within the TMD. Molecular dynamics simulations show a close interaction of SM 18 with the TMD. We suggest a role of SM 18 in regulating the equilibrium between an inactive monomeric and an active oligomeric state of the p24 protein 3 , 4 , which in turn regulates COPI-dependent transport. Bioinformatic analyses predict that the signature sequence represents a conserved sphingolipid-binding cavity in a variety of mammalian membrane proteins. Thus, in addition to a function as second messengers, sphingolipids can act as cofactors to regulate the function of transmembrane proteins. Our discovery of an unprecedented specificity of interaction of a TMD with an individual sphingolipid species adds to our understanding of why biological membranes are assembled from such a large variety of different lipids.

A significant challenge for developmental systems biology is balancing throughput with controlled conditions that minimize experimental artifacts. Large-scale developmental screens such as unbiased mutagenesis surveys have been limited in their applicability to embryonic systems, as the technologies for quantifying precise expression patterns in whole animals has not kept pace with other sequencing-based technologies. Here, we outline an open-source semi-automated pipeline to chemically fixate, stain, and 3D-image Drosophila embryos. Central to this pipeline is a liquid handling robot, Flyspresso , which automates the steps of classical embryo fixation and staining. We provide the schematics and an overview of the technology for an engineer or someone equivalently trained to reproduce and further improve upon Flyspresso, and highlight the Drosophila embryo fixation and colorimetric or antibody staining protocols. Additionally, we provide a detailed overview and stepwise protocol for our adaptive-feedback pipeline for automated embryo imaging on confocal microscopes. We demonstrate the efficiency of this pipeline compared to classical techniques, and how it can be repurposed or scaled to other protocols and biological systems. We hope our pipeline will serve as a platform for future research, allowing a broader community of users to build, execute, and share similar experiments.

Many bioimage analysis projects produce quantitative descriptors of regions of interest in images. Associating these descriptors with visual characteristics of the objects they describe is a key step in understanding the data at hand. However, as many bioimage data and their analysis workflows are moving to the cloud, addressing interactive data exploration in remote environments has become a pressing issue. To address it, we developed the Image Data Explorer (IDE) as a web application that integrates interactive linked visualization of images and derived data points with exploratory data analysis methods, annotation, classification and feature selection functionalities. The IDE is written in R using the shiny framework. It can be easily deployed on a remote server or on a local computer. The IDE is available at https://git.embl.de/heriche/image-data-explorer and a cloud deployment is accessible at https://shiny-portal.embl.de/shinyapps/app/01ᵢmage-data-explorer.

Centromeres are defined by a self-propagating chromatin structure based on stable inheritance of CENP-A containing nucleosomes. Here, we present a genetic screen coupled to pulse-chase labeling that allow us to identify proteins selectively involved in deposition of nascent CENP-A or in long-term transmission of chromatin-bound CENP-A. These include factors with known roles in DNA replication, repair, chromatin modification, and transcription, revealing a broad set of chromatin regulators that impact on CENP-A dynamics. We further identify the SUMO-protease SENP6 as a key factor, not only controlling CENP-A stability but virtually the entire centromere and kinetochore. Loss of SENP6 results in hyper-SUMOylation of CENP-C and CENP-I but not CENP-A itself. SENP6 activity is required throughout the cell cycle, suggesting that a dynamic SUMO cycle underlies a continuous surveillance of the centromere complex that in turn ensures stable transmission of CENP-A chromatin. Centromeres are a self-propagating chromatin structure that feature nucleosomes containing histone H3 variant CENP-A. Here, the authors screen for factors that play a role in CENP-A chromatin maintenance, finding that SUMO-protease SENP6 controls inheritance of chromatin bound CENP-A and is required for the maintenance of the centromere and kinetochore complex.

Many bioimage analysis projects produce quantitative descriptors of regions of interest in images. Associating these descriptors with visual characteristics of the objects they describe is a key step in understanding the data at hand. However, as many bioimage data and their analysis workflows are moving to the cloud, addressing interactive data exploration in remote environments has become a pressing issue. To address it, we developed the Image Data Explorer (IDE) as a web application that integrates interactive linked visualization of images and derived data points with exploratory data analysis methods, annotation, classification and feature selection functionalities. The IDE is written in R using the shiny framework. It can be easily deployed on a remote server or on a local computer. The IDE is available at https://git.embl.de/heriche/image-data-explorer and a cloud deployment is accessible at https://shiny-portal.embl.de/shinyapps/app/01_image-data-explorer .

Microtubules (MTs) are central to the organisation of the eukaryotic intracellular space and are involved in the control of cell morphology. For these purposes, MT polymerisation dynamics are tightly regulated. Using automated image analysis software, we investigate the spatial dependence of MT dynamics in interphase fission yeast cells with unprecedented statistical accuracy. We find that MT catastrophe frequencies (switches from polymerisation to depolymerisation) strongly depend on intracellular position. We provide evidence that compressive forces generated by MTs growing against the cell pole locally reduce MT growth velocities and enhance catastrophe frequencies. Furthermore, we find evidence for an MT length‐dependent increase in the catastrophe frequency that is mediated by kinesin‐8 proteins (Klp5/6). Given the intrinsic susceptibility of MT dynamics to compressive forces and the widespread importance of kinesin‐8 proteins, we propose that similar spatial regulation of MT dynamics plays a role in other cell types as well. In addition, our systematic and quantitative data should provide valuable input for (mathematical) models of MT organisation in living cells. Synopsis Microtubules (MTs) are dynamic protein polymers that change their length by switching between growing and shrinking states in a process termed ‘dynamic instability’ (Mitchison and Kirschner, 1984 ; Desai and Mitchison, 1997 ). MTs are central to the organisation of the eukaryotic intracellular space and are involved in the control of cell morphology (Kirschner and Mitchison, 1986 ; Hayles and Nurse, 2001 ). To better understand how MTs control the organisation of the intracellular space, it is important to quantitatively understand how dynamic instability is regulated, because this affects MT length (Verde et al , 1992 ; Dogterom and Leibler, 1993 ) as well as the ability of MTs to exert pushing and pulling forces (Inoue and Salmon, 1995 ; Dogterom et al , 2005 ). Several proteins have been characterised that globally affect MT growth, shrinkage, catastrophes (switches from growth to shrinkage) and rescues (switches from shrinkage to growth) (Howard and Hyman, 2007 ), but it is a largely open question how such regulation is achieved locally, in response to spatially varying biochemical cues and/or mechanical effects induced by the shape and size of cells. In fact, the precise and spatially resolved measurement of MT catastrophe and rescue frequencies is a challenging task, because those appear to be stochastic events that are governed by an average rate (Odde, 1995 ; Howell et al , 1997 ). This has two consequences: statistical accuracy is a serious issue when investigating catastrophe frequencies, and, the stochastic nature of the process makes it very difficult to avoid picking a subset of events when examining data by visual inspection. In this article, we present quantitative investigations of spatial MT catastrophe regulation in interphase fission yeast cells ( Schizosaccharomyces pombe ) with high statistical accuracy (Hayles and Nurse, 2001 ). Fission yeast is an excellent model system, because MTs are well organised and the rigid cylindrical cell wall makes it possible to accurately assign catastrophes to specific locations within the cell (Figure 1A and B ) (Hagan, 1998 ). MT minus ends are generally found close to the nucleus within the central overlap zone of the MTs, whereas dynamic plus tips grow and shrink between the nucleus and the cell poles (Drummond and Cross, 2000 ; Tran et al , 2001 ). Catastrophe events are mainly restricted to the regions of the two cell poles by an unknown mechanism. This local regulation of MT catastrophes is, however, crucial for the maintenance of correct fission yeast morphology and intracellular organisation (Beinhauer et al , 1997 ; Mata and Nurse, 1997 ; Browning et al , 2000 ; Brunner and Nurse, 2000 ; Hayles and Nurse, 2001 ; Tran et al , 2001 ; Sawin and Snaith, 2004 ; Tolic‐Norrelykke et al , 2005 ; Daga et al , 2006). To obtain good statistics and to ensure unbiased observations, we developed fully automated image analysis software that generates spatially resolved maps of MT dynamics from movies of GFP‐labelled MTs in fission yeast. Our spatially resolved measurements show that there is both local enhancement of the catastrophe frequency specifically at cell poles as well as long‐range modulation before the cell pole is reached (Figure 2 ). We find several indications that the local regulation at the cell pole is (at least in part) due to compressive forces that build up when bundle tips hit the cell pole (Figure 4 ). In addition, we find evidence that the long‐range catastrophe regulation is an MT length‐dependent effect mediated by the kinesin‐8 proteins Klp5/6. As physical boundaries and kinesin‐8 proteins are also present in other eukaryotic systems, we think that the relevance of our findings reaches beyond the fission yeast model system. Specifically, it is interesting to note that f cat is also enhanced at the boundaries of animal cells (Komarova et al , 2002 ; Mimori‐Kiyosue et al , 2005 ) by a yet unknown mechanism. Our findings in fission yeast, taken together with earlier in vitro observations (Dogterom and Yurke, 1997 ; Janson et al , 2003 ; Janson and Dogterom, 2004 ), suggest that there are intrinsic relations between polymerisation force, v g and f cat that help terminate MT growth at physical boundaries. In a living cell, the spatial extent of the cell and the length of its MT cytoskeleton must be well adapted to each other. There has been evidence that MT dynamics play a role in establishing cell shape (Kirschner and Mitchison, 1986 ; Hayles and Nurse, 2001 ). Our data indicate that, vice versa, the shape of a cell also influences MT dynamics. Thus, cell shape and MT organisation may not be separable components but should be viewed as one system. Moreover, Klp5/Klp6 are part of the kinesin‐8 family, comprising Kip3 ( Saccharomyces cerevisiae ), KLP67A ( Drosophila melanogaster ), Kif18A ( Homo sapiens ) and KipB ( Aspergillus nidulans ), which have in common that mutants show defects in mitosis (Garcia et al , 2002 ; West et al , 2002 ; Rischitor et al , 2004 ; Tytell and Sorger, 2006 ; Mayr et al , 2007 ; Stumpff et al , 2008 ). In this context, it has been speculated that a kinesin‐8‐mediated increase in f cat with MT length could contribute to proper chromosome centring and spindle length regulation (Gardner et al , 2008 ; Stumpff et al , 2008 ). Our data provide good experimental evidence that kinesin‐8 proteins indeed specifically enhance f cat of long MTs. Finally, we would like to point out a related article in Molecular Systems Biology by Foethke et al . In this article, the authors perform a 3D simulation of the self‐organisation of dynamic MTs within the physical confinement of a fission yeast cell. Specifically, following results of earlier in vitro experiments (Dogterom and Yurke, 1997 ; Janson et al , 2003 ; Janson and Dogterom, 2004 ), the authors investigate a model in which MT growth and catastrophe frequencies depend on compressive physical forces. The authors find that such force dependence is sufficient to reproduce most, but not all, of the known experimental data on the spatiotemporal organisation of MTs inside interphase fission yeast cells. Interestingly, evoking in addition an MT length dependence of the catastrophe frequency, as provided evidence for by our measurements, allows the model to reproduce further observations on MT dynamics in fission yeast that were so far considered to be signatures of more complicated processes. This indicates that a susceptibility of MT dynamics to physical forces and to MT length are simple yet very efficient mechanisms for adapting MT dynamics to cell size and shape. We developed fully automated image analysis software to investigate the spatial regulation of microtubule dynamics in interphase fission yeast cells with high statistical accuracy. Our data provide evidence that compressive forces generated by microtubules growing against the cell pole locally reduce microtubule growth velocities and enhance catastrophes frequencies. We also find evidence for a microtubule length‐dependent increase in the catastrophe frequency that is mediated by kinesin‐8 motor proteins (Klp5/6). The inclusion of these experimental findings in a 3‐D simulation of interphase fission yeast cells by Foethke et al. allows the authors to reproduce many observations on MT dynamics in fission yeast that were so far considered to be signatures of more complicated processes.

Cancer clone evolution takes place within tissue ecosystem habitats. But, how exactly tumors arise from a few malignant cells within an intact epithelium is a central, yet unanswered question. This is mainly due to the inaccessibility of this process to longitudinal imaging together with a lack of systems that model the progression of a fraction of transformed cells within a tissue. Here, we developed a new methodology based on primary mouse mammary epithelial acini, where oncogenes can be switched on in single cells within an otherwise normal epithelial cell layer. We combine this stochastic breast tumor induction model with inverted light-sheet imaging to study single-cell behavior for up to four days and analyze cell fates utilizing a newly developed image-data analysis workflow. The power of this integrated approach is illustrated by us finding that small local clusters of transformed cells form tumors while isolated transformed cells do not. There are now drugs to treat many types of cancer, but questions still remain around how these diseases start in the first place. Researchers think that tumor growth begins when a single cell suffers damage to certain sites in its DNA that eventually cause it to divide uncontrollably. That damaged cell, and its descendants, go on to form a lump, or tumor. The trouble with proving this theory is that it is hard to watch it happening in real time. Doctors usually only meet people with cancer when their tumors start to cause health problems. By this point, the tumors contain millions of cells. A way to watch the very beginnings of a cancer could reveal risk factors within a tissue that foster the growth of a tumor. But first, researchers need to test their theory about how the disease begins in the first place. One way to do this is to surround a single cancer cell with healthy cells and watch what happens next. To do this, Alladin, Chaible et al. took healthy cells from the breast tissue of mice and grew them in the laboratory into mini-organs called organoids. These organoids share a lot of features with actual mouse breast tissue; they can even make milk if given the right hormones. Once the organoids were ready, Alladin, Chaible et al then started modifying a small number of single cells inside them by switching on genes called oncogenes, which are known to drive cancer formation in humans. Using fluorescent proteins and a sheet of laser light it was possible to watch what happened to the cells over time. This revealed that, even though all the oncogene-driven single cells received the same signals, not all of them started to divide uncontrollably. In fact, a single modified cell had a low chance of forming a tumor on its own. The more oncogene-driven cells there were near to each other, the more likely they were to form tumors. Alladin, Chaible et al. think that this is because the healthy tissue interacts with the modified, oncogene-driven cells to suppress tumor formation. It is only when a larger number of modified cells group together and start to communicate with each other that they can override the inhibitory messages of the healthy tissue. How healthy tissue stops single modified cells from forming tumors is not yet clear. But, with this new mini-organ system, researchers now have the tools to investigate. In the future, this could lead to new strategies to stop cancer before it has a chance to get started.