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Meritxell (Meri) Huch is a Director and Group Leader at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, where her group uses 3D organoid models to study the maintenance and repair of adult tissues, as well as the mechanisms by which their dysregulation contributes to disease. This year, Meri is a Guest Editor for Development's special issue on lifelong development, which highlights how developmental processes and pathways are used throughout animal lifespans. We caught up with Meri over Teams to discuss the importance of using organoid models to study tissue regeneration and pathology, as well as some of the most exciting research questions that her lab is trying to answer.

Long-range intracellular transport is facilitated by motor proteins, such as kinesin-1 and cytoplasmic dynein, moving along microtubules (MTs). These motors often work in teams for the transport of various intracellular cargos. Although transport by multiple kinesin-1 motors has been studied extensively in the past, collective effects of cytoplasmic dynein are less well understood. On the level of single molecules, mammalian cytoplasmic dynein is not active in the absence of dynactin and adaptor proteins. However, when assembled into a team bound to the same cargo, processive motility has been observed. The underlying mechanism of this activation is not known. Here, we found that in MT gliding motility assays the gliding velocity increased with dynein surface density and MT length. Developing a mathematical model based on single-molecule parameters, we were able to simulate the observed behavior. Integral to our model is the usage of an activation term, which describes a mechanical activation of individual dynein motors when being stretched by other motors. We hypothesize that this activation is similar to the activation of single dynein motors by dynactin and adaptor proteins.This article has an associated First Person interview with the first author of the paper.

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Recent studies have clearly demonstrated a shift towards collaborative research and team science approaches across a spectrum of disciplines. Such collaborative efforts have also been acknowledged and nurtured by popular extramurally funded programs including the Clinical Translational Science Award (CTSA) conferred by the National Institutes of Health. Since its inception, the number of CTSA awardees has steadily increased to 60 institutes across 30 states. One of the objectives of CTSA is to accelerate translation of research from bench to bedside to community and train a new genre of researchers under the translational research umbrella. Feasibility of such a translation implicitly demands multi-disciplinary collaboration and mentoring. Networks have proven to be convenient abstractions for studying research collaborations. The present study is a part of the CTSA baseline study and investigates existence of possible community-structure in Biomedical Research Grant Collaboration (BRGC) networks across data sets retrieved from the internally developed grants management system, the Automated Research Information Administrator (ARIA) at the University of Arkansas for Medical Sciences (UAMS). Fastgreedy and link-community community-structure detection algorithms were used to investigate the presence of non-overlapping and overlapping community-structure and their variation across years 2006 and 2009. A surrogate testing approach in conjunction with appropriate discriminant statistics, namely: the modularity index and the maximum partition density is proposed to investigate whether the community-structure of the BRGC networks were different from those generated by certain types of random graphs. Non-overlapping as well as overlapping community-structure detection algorithms indicated the presence of community-structure in the BRGC network. Subsequent, surrogate testing revealed that random graph models considered in the present study may not necessarily be appropriate generative mechanisms of the community-structure in the BRGC networks. The discrepancy in the community-structure between the BRGC networks and the random graph surrogates was especially pronounced at 2009 as opposed to 2006 indicating a possible shift towards team-science and formation of non-trivial modular patterns with time. The results also clearly demonstrate presence of inter-departmental and multi-disciplinary collaborations in BRGC networks. While the results are presented on BRGC networks as a part of the CTSA baseline study at UAMS, the proposed methodologies are as such generic with potential to be extended across other CTSA organizations. Understanding the presence of community-structure can supplement more traditional network analysis as they're useful in identifying research teams and their inter-connections as opposed to the role of individual nodes in the network. Such an understanding can be a critical step prior to devising meaningful interventions for promoting team-science, multi-disciplinary collaborations, cross-fertilization of ideas across research teams and identifying suitable mentors. Understanding the temporal evolution of these communities may also be useful in CTSA evaluation.