A global sampler of single particle tracking solutions for single molecule microscopy.

The dependence on model-fitting to evaluate particle trajectories makes it difficult for single particle tracking (SPT) to resolve the heterogeneous molecular motions typical of cells. We present here a global spatiotemporal sampler for SPT solutions using a Metropolis-Hastings algorithm. The sampler does not find just the most likely solution but also assesses its likelihood and presents alternative solutions. This enables the estimation of the tracking error. Furthermore the algorithm samples the parameters that govern the tracking process and therefore does not require any tweaking by the user. We demonstrate the algorithm on synthetic and single molecule data sets. Metrics for the comparison of SPT are generalised to be applied to a SPT sampler. We illustrate using the example of the diffusion coefficient how the distribution of the tracking solutions can be propagated into a distribution of derived quantities. We also discuss the major challenges that are posed by the realisation of a SPT sampler

Correlative multi-scale cryo-imaging unveils SARS-CoV-2 assembly and egress.

Funder: Medical Research CouncilSince the outbreak of the SARS-CoV-2 pandemic, there have been intense structural studies on purified viral components and inactivated viruses. However, structural and ultrastructural evidence on how the SARS-CoV-2 infection progresses in the native cellular context is scarce, and there is a lack of comprehensive knowledge on the SARS-CoV-2 replicative cycle. To correlate cytopathic events induced by SARS-CoV-2 with virus replication processes in frozen-hydrated cells, we established a unique multi-modal, multi-scale cryo-correlative platform to image SARS-CoV-2 infection in Vero cells. This platform combines serial cryoFIB/SEM volume imaging and soft X-ray cryo-tomography with cell lamellae-based cryo-electron tomography (cryoET) and subtomogram averaging. Here we report critical SARS-CoV-2 structural events - e.g. viral RNA transport portals, virus assembly intermediates, virus egress pathway, and native virus spike structures, in the context of whole-cell volumes revealing drastic cytppathic changes. This integrated approach allows a holistic view of SARS-CoV-2 infection, from the whole cell to individual molecules

EGFR oligomerization organizes kinase-active dimers into competent signalling platforms

Epidermal growth factor receptor (EGFR) signalling is activated by ligand-induced receptor dimerization. Notably, ligand binding also induces EGFR oligomerization, but the structures and functions of the oligomers are poorly understood. Here, we use fluorophore localization imaging with photobleaching to probe the structure of EGFR oligomers. We find that at physiological epidermal growth factor (EGF) concentrations, EGFR assembles into oligomers, as indicated by pairwise distances of receptor-bound fluorophore-conjugated EGF ligands. The pairwise ligand distances correspond well with the predictions of our structural model of the oligomers constructed from molecular dynamics simulations. The model suggests that oligomerization is mediated extracellularly by unoccupied ligand-binding sites and that oligomerization organizes kinase-active dimers in ways optimal for auto-phosphorylation in trans between neighbouring dimers. We argue that ligand-induced oligomerization is essential to the regulation of EGFR signalling

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Cooperation and Interplay between EGFR Signalling and Extracellular Vesicle Biogenesis in Cancer

Epidermal growth factor receptor (EGFR) takes centre stage in carcinogenesis throughout its entire cellular trafficking odyssey. When loaded in extracellular vesicles (EVs), EGFR is one of the key proteins involved in the transfer of information between parental cancer and bystander cells in the tumour microenvironment. To hijack EVs, EGFR needs to play multiple signalling roles in the life cycle of EVs. The receptor is involved in the biogenesis of specific EV subpopulations, it signals as an active cargo, and it can influence the uptake of EVs by recipient cells. EGFR regulates its own inclusion in EVs through feedback loops during disease progression and in response to challenges such as hypoxia, epithelial-to-mesenchymal transition and drugs. Here, we highlight how the spatiotemporal rules that regulate EGFR intracellular function intersect with and influence different EV biogenesis pathways and discuss key regulatory features and interactions of this interplay. We also elaborate on outstanding questions relating to EGFR-driven EV biogenesis and available methods to explore them. This mechanistic understanding will be key to unravelling the functional consequences of direct anti-EGFR targeted and indirect EGFR-impacting cancer therapies on the secretion of pro-tumoural EVs and on their effects on drug resistance and microenvironment subversion

Mechanisms of Action of EGFR Tyrosine Kinase Receptor Incorporated in Extracellular Vesicles

EGFR and some of the cognate ligands extensively traffic in extracellular vesicles (EVs) from different biogenesis pathways. EGFR belongs to a family of four homologous tyrosine kinase receptors (TKRs). This family are one of the major drivers of cancer and is involved in several of the most frequent malignancies such as non-small cell lung cancer, breast cancer, colorectal cancer and ovarian cancer. The carrier EVs exert crucial biological effects on recipient cells, impacting immunity, pre-metastatic niche preparation, angiogenesis, cancer cell stemness and horizontal oncogene transfer. While EV-mediated EGFR signalling is important to EGFR-driven cancers, little is known about the precise mechanisms by which TKRs incorporated in EVs play their biological role, their stoichiometry and associations to other proteins relevant to cancer pathology and EV biogenesis, and their means of incorporation in the target cell. In addition, it remains unclear whether different subtypes of EVs incorporate different complexes of TKRs with specific functions. A raft of high spatial and temporal resolution methods is emerging that could solve these and other questions regarding the activity of EGFR and its ligands in EVs. More importantly, methods are emerging to block or mitigate EV activity to suppress cancer progression and drug resistance. By highlighting key findings and areas that remain obscure at the intersection of EGFR signalling and EV action, we hope to cross-fertilise the two fields and speed up the application of novel techniques and paradigms to both

Hydrophobic fluorescent probes introduce artifacts into single molecule tracking experiments due to non-specific binding.

Single-molecule techniques are powerful tools to investigate the structure and dynamics of macromolecular complexes; however, data quality can suffer because of weak specific signal, background noise and dye bleaching and blinking. It is less well-known, but equally important, that non-specific binding of probe to substrates results in a large number of immobile fluorescent molecules, introducing significant artifacts in live cell experiments. Following from our previous work in which we investigated glass coating substrates and demonstrated that the main contribution to this non-specific probe adhesion comes from the dye, we carried out a systematic investigation of how different dye chemistries influence the behaviour of spectrally similar fluorescent probes. Single-molecule brightness, bleaching and probe mobility on the surface of live breast cancer cells cultured on a non-adhesive substrate were assessed for anti-EGFR affibody conjugates with 14 different dyes from 5 different manufacturers, belonging to 3 spectrally homogeneous bands (491 nm, 561 nm and 638 nm laser lines excitation). Our results indicate that, as well as influencing their photophysical properties, dye chemistry has a strong influence on the propensity of dye-protein conjugates to adhere non-specifically to the substrate. In particular, hydrophobicity has a strong influence on interactions with the substrate, with hydrophobic dyes showing much greater levels of binding. Crucially, high levels of non-specific substrate binding result in calculated diffusion coefficients significantly lower than the true values. We conclude that the physic-chemical properties of the dyes should be considered carefully when planning single-molecule experiments. Favourable dye characteristics such as photostability and brightness can be offset by the propensity of a conjugate for non-specific adhesion