Preparation of neuronal co-cultures with single cell precision

Microfluidic embodiments of the Campenot chamber have attracted great interest from the neuroscience community. These interconnected co-culture platforms can be used toinvestigate a variety of questions, spanning developmental and functional neurobiology to infection and disease propagation. However, conventional systems require significant cellular inputs (many thousands per compartment), inadequate for studying low abundance cells, such as primary dopaminergic substantia nigra, spiral ganglia and Drosophilia melanogaster neurons, and impractical for high throughput experimentation. The dense cultures are also highly locally entangled, with few outgrowths (<10%) interconnecting the two cultures. In this paper straightforward microfluidic and patterning protocols are described which address these challenges: (i) a microfluidic single neuron arraying method, and (ii) a water masking method for plasma patterning biomaterial coatings to register neurons and promote outgrowth between compartments. Minimalistic neuronal co-cultures were prepared with high-level (>85%) inter-compartment connectivity and can be used for high throughput neurobiology experiments with single cell precisio

Micropatterning neuronal networks

Spatially organised neuronal networks have wide reaching applications, including fundamental research, toxicology testing, pharmaceutical screening and the realisation of neuronal implant interfaces. Despite the large number of methods catalogued in the literature there remains the need to identify a method that delivers high pattern compliance, long-term stability and is widely accessible to neuroscientists. In this comparative study, aminated (polylysine/polyornithine and aminosilanes) and cytophobic (poly(ethylene glycol) (PEG) and methylated) material contrasts were evaluated. Backfilling plasma stencilled PEGylated substrates with polylysine does not produce good material contrasts, whereas polylysine patterned on methylated substrates becomes mobilised by agents in the cell culture media which results in rapid pattern decay. Aminosilanes, polylysine substitutes, are prone to hydrolysis and the chemistries prove challenging to master. Instead, the stable coupling between polylysine and PLL-g-PEG can be exploited: Microcontact printing polylysine onto a PLL-g-PEG coated glass substrate provides a simple means to produce microstructured networks of primary neurons that have superior pattern compliance during long term (>1 month) cultur

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Funding: H2020 European Research Council (Grant Number(s): GA 101044975, GA 101098020).Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables: Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain: Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain: The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement: The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format: csv and. SQL.Peer reviewe

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Motivation. Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included. The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain. Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain. The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement. The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format. csv and. SQL

Correction: Micropatterning neuronal networks

Correction for ‘Micropatterning neuronal networks’ by Heike Hardelauf, et al., Analyst, 2014, 139, 3256–3264.</p

BioTIME:a database of biodiversity time series for the Anthropocene

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of two, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology andcontextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1 000 000 000 000 cm2).Time period and grain: BioTIME records span from 1874 to 2016. The minimum temporal grain across all datasets in BioTIME is year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton, and terrestrial invertebrates to small and large vertebrates.Software format: .csv and .SQ