Biology subjects

Vorobjev, I. A.

Publications and source records attributed to Vorobjev, I. A..

6 recordsLinked to original sources

bioRxiv2025

Analysis of microalgae autofluorescence using full-spectrum cytometry to discriminate and monitor microalgae and bacteria

AbstractThe autofluorescence of algal pigments allows for non-invasive, high-throughput characterization of microalgae at a single-cell resolution. We applied full-spectrum cytometry, imaging flow cytometry, and cell sorting to analyze the spectral and morphological diversity among major microalgal groups and 102 Chlorophyta strains. The distinct spectral signatures from chlorophylls, carotenoids, and phycobiliproteins enabled us to achieve taxonomic resolution with clear separation of phycobiliprotein-containing taxa. Furthermore, principal component analysis of Volvocales revealed three spectral clusters supported by corresponding differences in cell size and shape. Additionally, in Gonium cultures, we observed that spectral signatures in the yellow-green region were altered by bacterial presence, suggesting that interactions between algal host and bacteria affect pigment-related fluorescence. Spectral heterogeneity observed within monocultures was linked to pigment accumulation, cell size, and morphological variability. These findings establish full-spectrum cytometry as a powerful method for profiling pigment composition, physiology, and structural diversity in microalgae, with broad applications in microbial ecology, environmental monitoring, and biotechnology.

microbiology↗

bioRxiv2025

Deep Learning-enabled Sperm Morphology Analysis of Bovine Sperm for label-free Imaging Flow Cytometry

Data analysis of sperm morphology is critical for evaluating bull fertility, yet it is often performed using light microscopy and staining techniques in a highly subjective and manual manner. In this study, we introduce a scalable, high-resolution approach combining label-free Imaging Flow Cytometry (IFC) with deep learning for automated classification of bovine sperm morphology. We analyzed 436,374 single-cell images obtained from three prominent bull breeds in Kazakhstan - Kazakh Whitehead, Auliekol, and Simmental from fresh and cryopreserved sperm - providing a uniquely large and diverse dataset. The dataset was used for training and evaluation of deep learning models, among which the convolutional neural network (CNN) MobileNetV4 yielded superior results, achieving 92.3% accuracy and a 0.91 F1-score after training with a Layer-wise Pretraining and Fine-Tuning (LP-FT) strategy. The model classified spermatozoa into eight distinct morphological categories. The CNN-based pipeline ensured consistent, observer-independent classification across all samples. Testing across different conditions and breeds resulted in a 5-10% drop in generalization performance, highlighting the impact of domain-specific biases and underscoring the need for larger, standardized datasets. The proportion of morphologically abnormal spermatozoa varied between seasons and after cryopreservation. This study highlights the advantages of integrating IFC and artificial intelligence (AI) algorithms for robust, high-throughput, and objective label- free spermatozoa morphology assessment in fresh and cryopreserved sperm, offering a promising tool for improving fertility diagnostics and breeding strategies in veterinary practice.

systems biology↗

bioRxiv2024

Assessing Aral Sea residual lake system: impact of fluctuating salinity on phytoplankton communities

The Aral Sea was once the fourth-largest inland water body in the world. However, the lake rapidly shrank over the past six decades, mainly due to the loss of inflow from one of its tributaries, the Amu Darya River. Lakes and reservoirs are traditionally characterized by static chemical and morphological parameters, leaving untouched a dynamic impact of phytoplankton changes. We used an integrated approach combining traditional microscopy and FlowCam-based imaging flow cytometry to study phytoplankton communities during the 2018 and 2019 expeditions in the Aral Sea remnant lakes system. The residual Aral Sea water bodies experienced different environmental conditions, forming hypersaline South Aral, North Aral Sea that is constantly getting freshwater, and brackish Chernyshev Bay and Tushchybas Lake with 2-8 times amplitude of salinity changes attributed to the variability in the precipitation and periodical influx of freshwater. The salinity fluctuations had an impact on the phytoplankton communities in Chernyshev Bay, making it similar to the phytoplankton of North Aral in 2018 while resembling the hypersaline South Aral phytoplankton assemblages in 2019. Multivariate analysis revealed that salinity, water temperature, ammonium, and nitrates were major contributors to explaining the variance in the sampling data. We conclude that drastic phytoplankton fluctuations occur in the two brackish water bodies in the middle of the former Aral Sea, reflecting changes in salinity.

ecology↗

bioRxiv2024

Spectral algal fingerprinting and long sequencing in synthetic algal-microbial communities

O_LISynthetic biology has made progress in creating artificial microbial and algal communities, but technical and evolutionary complexities still pose significant challenges. C_LIO_LITraditional methods for studying microbial and algal communities, such as microscopy and pigment analysis, are limited in throughput and resolution. In contrast, advancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on their size, complexity, and spectral fingerprints, offering more precise and comprehensive analysis than conventional flow cytometry. C_LIO_LIThis study demonstrates the use of full-spectrum cytometry for analyzing synthetic algal-microbial communities, facilitating rapid species identification and enumeration. The workflow involves recording individual spectral signatures from monocultures, utilizing autofluorescence to distinguish them from noise, and subsequent creation of a spectral library for further analysis. The obtained library is used then to analyze mixtures of unicellular cyanobacteria and synthetic phytoplankton communities, revealing differences in spectral signatures. The synthetic consortium experiment monitored algal growth, comparing results from different instruments and highlighting the advantages of the spectral virtual filter system for precise population separation and abundance tracking. This approach demonstrated higher flexibility and accuracy in analyzing multi-component algal-microbial assemblages and tracking temporal changes in community composition. C_LIO_LIBy capturing the complete emission spectrum of each cell, this method enhances the understanding of algal-microbial community dynamics and responses to environmental stressors. With development of standardized spectral libraries, our work demonstrates an improved characterization of algal communities, advancing research in synthetic biology and phytoplankton ecology. C_LI

ecology↗

bioRxiv2023

Species-level classification provides new insights into the biogeographical patterns of microbial communities in shallow saline lakes

Saline lakes are rapidly drying out across the globe, particularly in Central Asia, due to climate change and anthropogenic activities. We present the results of a long-read next generation sequencing analysis of the 16S rRNA-based taxonomic structure of bacteriomes of the Tengiz-Korgalzhyn lakes system. We found that the shallow endorheic, mostly saline lakes of the system show unusually low bacterioplankton dispersal rates at species-level taxonomic resolution. The major environmental factor structuring the lakes microbial communities was salinity. The dominant bacterial phyla of the lakes with high salinity included a significant proportion of marine and halophilic species. In sum, these results, which can be applied to other lake systems of the semi-arid regions, improve our understanding of the factors influencing lake microbiomes undergoing salinization in response to climate change and other anthropogenic factors. Our results show that finer taxonomic classification can provide new insights and improve our understanding of the environmental factors influencing the microbiomes of lakes undergoing salinization in response to climate change and other anthropogenic factors.

ecology↗

bioRxiv2019

Probing complexity of microalgae mixtures with novel spectral flow cytometry approach and \"virtual filtering\"

Fluorescence methods are widely applied for the study of the marine and freshwater phytoplankton communities. However, identification of different microalgae populations by autofluorescent pigments remains a challenge because of the very strong signal from chlorophyll. Addressing the issue we developed a novel approach using the flexibility of spectral flow cytometry analysis (SFC) and generated a matrix of virtual filters (VF) capable to of differentiating non-chlorophyll parts of the spectrum. Using this matrix 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 further used to differentiate major microalgal taxa. Our results demonstrate that spectral flow cytometer (SFC-VF) and virtual filtering approach can provide a quantitative assessing of heterogenous phytoplankton communities at single cell level spectra and be helpful in the monitoring of phytoplankton blooms.\n\nO_LIThe research was partly presented during AQUAFLUO II Colloquium 2017, Sydney, Australia and ISAC Congress, Boston 2017\nC_LI

ecology↗