Better Research Software Tools to Elevate the Rate of Scientific Discovery -- or why we need to invest in research software engineering

In the past decade, enormous progress has been made in advancing the state-of-the-art in bioimage analysis - a young computational field that works in close collaboration with the life sciences on the quantitative analysis of scientific image data. In many cases, tremendous effort has been spent to package these new advances into usable software tools and, as a result, users can nowadays routinely apply cutting-edge methods to their analysis problems using software tools such as ilastik [1], cellprofiler [2], Fiji/ImageJ2 [3,4] and its many modern plugins that build on the BigDataViewer ecosystem [5], and many others. Such software tools have now become part of a critical infrastructure for science [6]. Unfortunately, overshadowed by the few exceptions that have had long-lasting impact, many other potentially useful tools fail to find their way into the hands of users. While there are many reasons for this, we believe that at least some of the underlying problems, which we discuss in more detail below, can be mitigated. In this opinion piece, we specifically argue that embedding teams of research software engineers (RSEs) within imaging and image analysis core facilities would be a major step towards sustainable bioimage analysis software.Comment: 8 pages, 0 figure

bAIoimage analysis: elevating the rate of scientific discovery -- as a community

The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue in a way most useful for stimulating scientific progress, it will require our multidisciplinary community to work together, establish FAIR data sharing and deliver usable, reproducible analytical tools.Comment: 5 pages, 1 figure, opinio

Knockdown of Bardet-Biedl Syndrome Gene BBS9/PTHB1 Leads to Cilia Defects

Bardet-Biedl Syndrome (BBS, MIM#209900) is a genetically heterogeneous disorder with pleiotropic phenotypes that include retinopathy, mental retardation, obesity and renal abnormalities. Of the 15 genes identified so far, seven encode core proteins that form a stable complex called BBSome, which is implicated in trafficking of proteins to cilia. Though BBS9 (also known as PTHB1) is reportedly a component of BBSome, its direct function has not yet been elucidated. Using zebrafish as a model, we show that knockdown of bbs9 with specific antisense morpholinos leads to developmental abnormalities in retina and brain including hydrocephaly that are consistent with the core phenotypes observed in syndromic ciliopathies. Knockdown of bbs9 also causes reduced number and length of cilia in Kupffer's vesicle. We also demonstrate that an orthologous human BBS9 mRNA, but not one carrying a missense mutation identified in BBS patients, can rescue the bbs9 morphant phenotype. Consistent with these findings, knockdown of Bbs9 in mouse IMCD3 cells results in the absence of cilia. Our studies suggest a key conserved role of BBS9 in biogenesis and/or function of cilia in zebrafish and mammals

Self-organizing spots get under your skin.

Sixty-five years after Turing first revealed the potential of systems with local activation and long-range inhibition to generate pattern, we have only recently begun to identify the biological elements that operate at many scales to generate periodic patterns in nature. In this Primer, we first review the theoretical framework provided by Turing, Meinhardt, and others that suggests how periodic patterns could self-organize in developing animals. This Primer was developed to provide context for recent studies that reveal how diverse molecular, cellular, and physical mechanisms contribute to the establishment of the periodic pattern of hair or feather buds in the developing skin. From an initial emphasis on trying to disambiguate which specific mechanism plays a primary role in hair or feather bud development, we are beginning to discover that multiple mechanisms may, in at least some contexts, operate together. While the emergence of the diverse mechanisms underlying pattern formation in specific biological contexts probably reflects the contingencies of evolutionary history, an intriguing possibility is that these mechanisms interact and reinforce each other, producing emergent systems that are more robust

Shroom3 is required downstream of FGF signalling to mediate proneuromast assembly in zebrafish

During development, morphogenetic processes require a precise coordination of cell differentiation, cell shape changes and, often, cell migration. Yet, how pattern information is used to orchestrate these different processes is still unclear. During lateral line (LL) morphogenesis, a group of cells simultaneously migrate and assemble radially organized cell clusters, termed rosettes, that prefigure LL sensory organs. This process is controlled by Fibroblast growth factor (FGF) signalling, which induces cell fate changes, cell migration and cell shape changes. However, the exact molecular mechanisms induced by FGF activation that mediate these changes on a cellular level are not known. Here, we focus on the mechanisms by which FGFs control apical constriction and rosette assembly. We show that apical constriction in the LL primordium requires the activity of non-muscle myosin. We demonstrate further that shroom3, a well-known regulator of non-muscle myosin activity, is expressed in the LL primordium and that its expression requires FGF signalling. Using gain-and loss-of-function experiments, we demonstrate that Shroom3 is the main organizer of cell shape changes during rosette assembly, probably by coordinating Rho kinase recruitment and non-muscle myosin activation. In order to quantify morphogenesis in the LL primordium in an unbiased manner, we developed a unique trainable 'rosette detector'. We thus propose a model in which Shroom3 drives rosette assembly in the LL downstream of FGF in a Rho kinase-and non-muscle myosin-dependent manner. In conclusion, we uncovered the first mechanistic link between patterning and morphogenesis during LL sensory organ formation

Polarization and migration in the zebrafish posterior lateral line system

<div><p>Collective cell migration plays an important role in development. Here, we study the posterior lateral line primordium (PLLP) a group of about 100 cells, destined to form sensory structures, that migrates from head to tail in the zebrafish embryo. We model mutually inhibitory FGF-Wnt signalling network in the PLLP and link tissue subdivision (Wnt receptor and FGF receptor activity domains) to receptor-ligand parameters. We then use a 3D cell-based simulation with realistic cell-cell adhesion, interaction forces, and chemotaxis. Our model is able to reproduce experimentally observed motility with leading cells migrating up a gradient of CXCL12a, and trailing (FGF receptor active) cells moving actively by chemotaxis towards FGF ligand secreted by the leading cells. The 3D simulation framework, combined with experiments, allows an investigation of the role of cell division, chemotaxis, adhesion, and other parameters on the shape and speed of the PLLP. The 3D model demonstrates reasonable behaviour of control as well as mutant phenotypes.</p></div

Analysis of mutual inhibition dynamics leads to model insights.

<p>The scaled mutual inhibition <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005451#pcbi.1005451.e015" target="_blank">Eq (7)</a> can have one of four possible behaviours represented by nullclines in the <i>W</i>_<i>R</i> <i>F</i>_<i>R</i> phase plane. (<i>F</i>_<i>R</i> nullcline in black, <i>W</i>_<i>R</i> nullcline in red.) (a) Bistability, in which either a high Wnt receptor (<i>W</i>_<i>R</i>) or high FGF (<i>F</i>_<i>R</i>) receptor expressing state can result, depending on initial conditions. (b) A Wnt-receptor expressing state always result, (c) a coexistence-state, with both Wnt and FGF receptors expressed, and (d) an FGF only receptor expression state exists. When the Hill coefficients <i>n</i>, <i>m</i> are large, the steady states (appropriately scaled) occur approximately at a subset of the points {(1, 0), (1, 1), (0, 1)} and transitions between the four qualitative outcomes displayed in this figure can be summarized by simple inequalities in terms of aggregate quantities <i>ϕ</i> and <i>ω</i>, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005451#pcbi.1005451.e017" target="_blank">Eq (7c)</a>.</p