Ocean coastal ecosystems are changing, and global shifts in temperature lead to the expansion and intensification of harmful algae. In conjunction with anthropogenic effects it may result in future exacerbation of harmful algal blooms. Here we use the 2002-2020 years record of surface ocean temperature data retrieved from Sentinel-2 satellite to examine the recent temperature trend in Avacha Bay, Kamchatka Peninsula. Satellite analysis demonstrated a temperature increase trend in ocean surface water during spring and summer months and detected algal bloom in July 2020 preceding a mass death of marine benthic life in September-October 2020. Using 16S rRNA and 18S rRNA gene amplicon nanopore-based sequencing, we analyzed microbial and microalgal communities in the water samples from area of 2020 algal blooms. Our results suggest the presence of potentially toxic and bloom-forming algae from genera related to former HABs (harmful algal blooms) in the Avacha Bay region. A better understanding of the potentially toxic algae phytoplankton composition in the shifting temperature environment and time-series monitoring of HABs is of utmost importance for scientific community. We suggest that satellite analysis in combination with eDNA monitoring by nanopore-based sequencing represents promising option to detect potentially toxic algae and follow bloom development.

Regulated cell death (RCD) is central to the development, integrity, and functionality of multicellular organisms. In the last decade, evidence has accumulated that RCD is a universal phenomenon in all life domains. Cyanobacteria are of specific interest due to their importance in aquatic and terrestrial habitats and their role as primary producers in global nutrient cycling. Current knowledge on cyanobacterial RCD is based mainly on biochemical and morphological observations, often by methods directly transferred from vertebrate research and with limited understanding of the molecular genetic basis. However, the metabolism of different cyanobacteria groups relies on photosynthesis and nitrogen fixation, whereas mitochondria are the central executioner of cell death in vertebrates. Moreover, cyanobacteria chosen as biological models in RCD studies are mainly colonial or filamentous multicellular organisms. On the other hand, unicellular cyanobacteria have regulated programs of cellular survival (RCS) such as chlorosis and post-chlorosis resuscitation. The co-existence of different genetically regulated programs in cyanobacterial populations may have been a top engine in life diversification. Development of cyanobacteria-specific methods for identification and characterization of RCD and wider use of single-cell analysis combined with intelligent image-based cell sorting and metagenomics would shed more light on the underlying molecular mechanisms and help us to address the complex colonial interactions during these events. In this review, we focus on the functional implications of RCD in cyanobacterial communities.

Fluorescence methods are widely used for the study of marine and freshwater phytoplankton communities. However, the identification of different microalgae populations by the analysis of autofluorescence signals remains a challenge. Addressing the issue, we developed a novel approach using the flexibility of spectral flow cytometry analysis (SFC) and generating a matrix of virtual filters (VF) which allowed thorough examination of autofluorescence spectra. Using this matrix, different spectral emission regions of algae species were analyzed, and five major algal taxa were discriminated. These results were further applied for tracing particular microalgae taxa in the complex mixtures of laboratory and environmental algal populations. An integrated analysis of single algal events combined with unique spectral emission fingerprints and light scattering parameters of microalgae can be used to differentiate major microalgal taxa. We propose a protocol for the quantitative assessment of heterogenous phytoplankton communities at the single-cell level and monitoring of phytoplankton bloom detection using a virtual filtering approach on a spectral flow cytometer (SFC-VF).