Isobe, K. | bioRxiv Science

bioRxiv2021

A spike-sorting method based on hierarchical clustering to discriminate extracellularly recorded simple spikes and complex spikes from cerebellar Purkinje cells

BackgroundIn the cerebellar cortex, Purkinje cells are the only output neurons and exhibit two types of discharge. Most Purkinje cell discharges are simple spikes, which are commonly appearing action potentials exhibiting a rich variety of firing patterns with a rate of up to 400 Hz. More infrequent discharges are complex spikes, which consist of a short burst of impulses accompanied by a massive increase in dendritic Ca2+ with a firing rate of around 1 Hz. The discrimination of these spikes in extracellular single-unit recordings is not always straightforward, as their waveforms vary depending on recording conditions and intrinsic fluctuations. New MethodTo discriminate complex spikes from simple spikes in the extracellular single-unit data, we developed a semiautomatic spike-sorting method based on divisive hierarchical clustering. ResultsQuantitative evaluation using parallel in vivo two-photon Ca2+ imaging of Purkinje cell dendrites indicated that 96.6% of the complex spikes were detected using our spike-sorting method from extracellular single-unit recordings obtained from anesthetized mice. Comparison with Existing Method(s)No reports have conducted a quantitative evaluation of spike-sorting algorithms used for the classification of extracellular spikes recorded from cerebellar Purkinje cells. ConclusionsOur method could be expected to contribute to research in information processing in the cerebellar cortex and the development of a fully automatic spike-sorting algorithm by providing ground-truth data useful for deep learning. HighlightsO_LIA spike-sorting algorithm on hierarchical clustering was developed. C_LIO_LIIt was applied to extracellular recordings from cerebellar Purkinje cells. C_LIO_LIComplex and simple spikes were discriminated based on their waveforms. C_LIO_LISpike-sorting performance was quantified using in vivo Ca2+ imaging data. C_LIO_LIThe algorithm isolated over 96.6% of complex spikes. C_LI

neuroscience↗

bioRxiv2021

Mega-fire in Redwood Tanoak Forest Reduces Bacterial and Fungal Richness and Selects for Pyrophilous Taxa and Traits that are Phylogenetically Conserved

Mega-fires of unprecedented size, intensity, and socio-economic impacts have surged globally due to climate change, fire suppression, and development. Soil microbiomes are critical for post-fire plant regeneration and nutrient cycling, yet how mega-fires impact the soil microbiome remains unclear. We had a serendipitous opportunity to obtain pre- and post-fire soils from the same sampling locations because the 2016 Soberanes Fire, a mega-fire burning >500 Km2, burned with high severity throughout several of our established redwood-tanoak plots. This makes our study the first to examine microbial fire response in redwood-tanoak forests. We re-sampled soils immediately post-fire from two burned plots and one unburned plot to elucidate the effect of mega-fire on soil microbiomes. We used Illumina MiSeq sequencing of 16S and ITS1 to determine that both bacterial and fungal richness were reduced by 38-70% in burned plots, with richness unchanged in the unburned plot. Fire altered composition by 27% for bacteria and 24% for fungi, whereas the unburned plots experienced no change in fungal and negligible change in bacterial composition. We observed several pyrophilous taxa previously observed in Pinaceae forests, indicating that these microbes are likely general fire-responders across forest types. Further, the pyrophilous taxa that positively responded to fire were phylogenetically conserved, suggesting shared evolutionary traits. For bacteria, fire selected for increased Firmicutes and Actinobacteria. For fungi, fire selected for the Ascomycota classes Pezizomycetes and Eurotiomycetes and for a Basidiomycota class of heat-resistant Geminibasidiomycete yeasts. We hypothesize that microbes share analogous fire response to plants and propose a trait-based conceptual model of microbial response to fire that builds from Grimes Competitor-Stress tolerator-Ruderal framework (C-S-R) and its recent applications to microbes. Using this framework and established literature on several microbial species, we hypothesize some generalizable principals to predict which microbial taxa will respond to fire.

ecology↗