Anne Straube

Here we describe the development of ShapeSpaceExplorer, an interactive software for the extraction, visualisation and analysis of complex 2D shape series. We also demonstrate its application to the analysis of cell morphology changes during cell migration. Cell migration is essential for many physiological and pathological processes. Intracellular force generation and transmission of these forces to extracellular structures or neighbouring cells drives cell migration. The emergent property of the processes driving cell migration is a change in cell shape. We describe a machine learning approach to understand the relationship of cell shape dynamics and cell migration behaviour. Our algorithm analyses cell shape from time-lapse images and learns the intrinsic low-dimensional structure of cell shape space. We use the resultant shape space map to visualise differences in cell shape distribution following perturbation experiments and to analyse the quantitative relationships between shape and migration behaviour. The core of our algorithm is a new, rapid, and landmark-free shape difference measure that allows unbiased analysis of the widely varying morphologies exhibited by migrating mesenchymal cells. We used our method to predict cell turning from dynamic cell shape information. ShapeSpaceExplorer can be applied widely to visualise and analyse cell morphology changes during development, the cell cycle and stress response, but also to the outlines of clusters, tissues and inanimate objects.

Here we describe the development of ShapeSpaceExplorer, an interactive software for the extraction, visualisation and analysis of complex 2D shape series. We also demonstrate its application to the analysis of cell morphology changes during cell migration. Cell migration is essential for many physiological and pathological processes. Intracellular force generation and transmission of these forces to extracellular structures or neighbouring cells drives cell migration. The emergent property of the processes driving cell migration is a change in cell shape. We describe a machine learning approach to understand the relationship of cell shape dynamics and cell migration behaviour. Our algorithm analyses cell shape from time-lapse images and learns the intrinsic low-dimensional structure of cell shape space. We use the resultant shape space map to visualise differences in cell shape distribution following perturbation experiments and to analyse the quantitative relationships between shape and migration behaviour. The core of our algorithm is a new, rapid, and landmark-free shape difference measure that allows unbiased analysis of the widely varying morphologies exhibited by migrating mesenchymal cells. We used our method to predict cell turning from dynamic cell shape information. ShapeSpaceExplorer can be applied widely to visualise and analyse cell morphology changes during development, the cell cycle and stress response, but also to the outlines of clusters, tissues and inanimate objects.

Cellular cargos move bidirectionally on microtubules by recruiting opposite polarity motors dynein and kinesin. These motors show codependence, where one requires the activity of the other, although the mechanism is unknown. Here we show that kinesin-3 KIF1C acts as both an activator and a processivity factor for dynein, using in vitro reconstitutions of human proteins. Activation requires only a fragment of the KIF1C nonmotor stalk binding the cargo adapter HOOK3. The interaction site is separate from the constitutive factors FTS and FHIP, which link HOOK3 to small G-proteins on cargos. We provide a structural model for the autoinhibited FTS–HOOK3–FHIP1B (an FHF complex) and explain how KIF1C relieves it. Collectively, we explain codependency by revealing how mutual activation of dynein and kinesin occurs through their shared adapter. Many adapters bind both dynein and kinesins, suggesting this mechanism could be generalized to other bidirectional complexes.

Cellular cargos move bidirectionally on microtubules due to the presence of opposite polarity motors dynein and kinesin. Many studies show these motors are co-dependent, whereby one requires the activity of the other, although the mechanism is unknown. Here, using in vitro motility assays, we show that the kinesin-3 KIF1C acts both as an activator and a processivity factor for dynein. Activation only requires a fragment of the non-motor tail of KIF1C (KIF1C-stalk) to bind the cargo adaptor HOOK3. Cryo-EM, crosslinking mass spectrometry and AlphaFold2 predictions reveal this binding site to be separate from that of two constitutive factors (FTS and FHIP), which link HOOK3 to small G-proteins on cargos. We provide a structural model for how the FTS-HOOK3-FHIP1B (FHF) complex is auto-inhibited and explain how the KIF1C-stalk relieves this inhibition. Collectively, our work provides a molecular explanation for co-dependency by revealing that the mutual activation of dynein and kinesin is mediated through their shared adaptor. Many adaptors bind both dynein and kinesins, suggesting this mechanism could be generalised to other bidirectional complexes.

Cellular cargos move bidirectionally on microtubules due to the presence of opposite polarity motors dynein and kinesin. Many studies show these motors are co-dependent, whereby one requires the activity of the other, although the mechanism is unknown. Here, using in vitro motility assays, we show that the kinesin-3 KIF1C acts both as an activator and a processivity factor for dynein. Activation only requires a fragment of the non-motor tail of KIF1C (KIF1C-stalk) to bind the cargo adaptor HOOK3. Cryo-EM, crosslinking mass spectrometry and AlphaFold2 predictions reveal this binding site to be separate from that of two constitutive factors (FTS and FHIP), which link HOOK3 to small G-proteins on cargos. We provide a structural model for how the FTS-HOOK3-FHIP1B (FHF) complex is auto-inhibited and explain how the KIF1C-stalk relieves this inhibition. Collectively, our work provides a molecular explanation for co-dependency by revealing that the mutual activation of dynein and kinesin is mediated through their shared adaptor. Many adaptors bind both dynein and kinesins, suggesting this mechanism could be generalised to other bidirectional complexes.

I am delighted to introduce this Special Issue on the ‘Cell Biology of Motors’. Molecular motors are fascinating proteins, and we can observe the motility that they produce – the twitching of muscles, the beating of flagella, cytoplasmic flows – with our naked eye or with very simple microscopes. However, it is not immediately clear how a bunch of proteins can generate such motility. During the past two decades, structural biology, single-molecule imaging and force spectroscopy approaches have revealed in detail how myosin, kinesin and dynein step along cytoskeletal tracks, coupling each step to the hydrolysis of ATP. The attention of the field is now focussed on the mechanisms underlying how motor activity is controlled and coordinated, understanding how different motors specialise in modifying and organising tracks, and how cargo transport specificity arises.My own research group aims to understand how microtubule motors are activated and loaded with cargo and how they contribute to the organisation of the underlying network of microtubule tracks. To do this, we combine quantitative cell biology with biochemistry, in vitro reconstitution and single-molecule biophysics. This combination of techniques allows the thorough testing of mechanistic hypotheses that can explain how motors function in cellular processes. Likewise, many studies featured in this special issue combine cell biology with in vitro reconstitution or mathematical modelling. The issue covers the full range of molecular motors, including different types of myosins, kinesins and dyneins, as well as a variety of experimental systems, including mammalian cells, mice, Drosophila pupae, Xenopus tadpoles, sea urchin eggs and fission yeast. Motors are investigated during cell division, intraflagellar transport, ciliary beating, cargo sorting, organelle homeostasis, viral egress, cell migration and morphogenesis. As a personal highlight, Xu et al. reveal a surprising answer to one of the key unresolved questions in the field, namely how many motors bind to one cargo in a cell (Xu et al., 2023). For vaccinia virus, it's in the order of 100–300 motors, suggesting that some viruses assemble an entire coat of kinesins. Previous studies have suggested that only a few motors per cargo are engaged with the microtubule at any one time (Levi et al., 2006; Hill et al., 2004; Miller and Lasek, 1985). If both sets of observations are true, the number of motors that accompany cargo as passive passengers might exceed those that are at work by at least one order of magnitude. Thus, the common view that cargo binding equates with motor activation might require re-evaluation, and the principle that autoinhibited dynein is packed onto anterograde intraflagellar transport trains might apply more generically to cytoplasmic cargoes.The special issue also includes a number of comprehensive and thoughtful review-based articles that summarise and discuss some of the most exciting and recent developments in the field. This includes indirect cargo transport via bulk cytoplasm flow generated by motors sliding along microtubules, the coordination of bidirectional transport, the recruitment and turnover of motors at cargoes and subcellular structures, and understanding diseases caused by mutations in genes encoding motor proteins. It is in those areas where big strides are being made to understand how molecular motors work in their natural habitat in living cells.I hope you will find something in the special issue that excites you and inspires your research. I'd like to thank all the authors who contributed to the special issue, as well as the reviewers, who provided expert help. I also encourage you to submit your next article on this exciting topic to Journal of Cell Science – a community-focussed journal. Do also come along to the ‘Motors in Quarantine’ webinar series (https://mechanochemistry.org/whatson/MiQ), which provides the motor community with an opportunity to discuss the latest findings in the field.