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Abstract. This paper presents the quantitative imaging datasets collected during the Tara Pacific expedition (2016–2018) carried out on the schooner Tara. The datasets cover a wide range of plankton sizes, from microphytoplankton (> 20 µm in size) to mesozooplankton (a few centimetres in size), and non-living particles such as plastic and detrital particles. It consists of surface samples collected across the North Atlantic and the North and South Pacific Ocean from open-ocean stations (a total of 357 samples) and from stations located in coastal waters, lagoons or reefs of 32 Pacific islands (a total of 228 samples). As this expedition involved long distances and long sailing times, we designed two sampling systems to collect plankton while sailing at speeds of up to 9 knots. To sample microplankton, surface water was pumped aboard using a customised pumping system and filtered through a 20 µm mesh size plankton net (hereafter referred to as the deck net – DN). A high-speed net (HSN; 330 µm mesh size) was developed to sample the mesoplankton. In addition, a manta net (330 µm) was also used, when possible, to collect mesoplankton and plastics simultaneously. We could not deploy these nets at the reef and lagoon stations of islands. Instead, two bongo nets (20 µm) attached to an underwater scooter were used to sample microplankton. In addition to describing and presenting the datasets, the complementary aim of this paper is to investigate and quantify the potential sampling biases associated with these two high-speed sampling systems and the different net types, in order to improve further ecological interpretations. Regarding the imaging techniques, microplankton (20–200 µm) from the DN and bongo net were imaged directly aboard Tara using a FlowCam instrument (Fluid Imaging Technologies), whereas mesoplankton (>200 µm) from the HSN and manta net were analysed in the laboratory with a ZooScan system (back on land). Organisms and other particles were taxonomically and morphologically classified using the automatic sorting tools of the EcoTaxa web application; following this, validation or correction was carried out by taxonomic experts. For microplankton smaller than 45 µm, a subsample of 30 % of the annotations was 100 % visually validated by experts. More than 300 different taxonomic and morphological groups were identified. The datasets include the metadata and the raw data from which morphological traits such as size (equivalent spherical diameter) and biovolume were calculated for each particle as well as a number of quantitative descriptors of the surface plankton communities. These descriptors include abundance, biovolumes, the Shannon diversity index and normalised biovolume size spectrum, allowing the study of their structures (e.g. taxonomic, functional, size and trophic structures) according to a wide range of environmental parameters at the basin scale (https://doi.org/10.5281/zenodo.6445609, Lombard et al., 2023). 

Transcription factors associate with architectural proteins to regulate genome organization and three-dimensional gene regulation. 

Abstract Single-molecule tracking (SMT) allows the study of transcription factor (TF) dynamics in the nucleus, giving important information regarding the diffusion and binding behavior of these proteins in the nuclear environment. Dwell time distributions obtained by SMT for most TFs appear to follow bi-exponential behavior. This has been ascribed to two discrete populations of TFs—one non-specifically bound to chromatin and another specifically bound to target sites, as implied by decades of biochemical studies. However, emerging studies suggest alternate models for dwell-time distributions, indicating the existence of more than two populations of TFs (multi-exponential distribution), or even the absence of discrete states altogether (power-law distribution). Here, we present an analytical pipeline to evaluate which model best explains SMT data. We find that a broad spectrum of TFs (including glucocorticoid receptor, oestrogen receptor, FOXA1, CTCF) follow a power-law distribution of dwell-times, blurring the temporal line between non-specific and specific binding, suggesting that productive binding may involve longer binding events than previously believed. From these observations, we propose a continuum of affinities model to explain TF dynamics, that is consistent with complex interactions of TFs with multiple nuclear domains as well as binding and searching on the chromatin template.