Automated Online Flow Cytometry Advances Microalgal Ecosystem Management as in situ, High-Temporal Resolution Monitoring Tool

Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition

The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method's prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Development of an Automated Online Flow Cytometry Method to Quantify Cell Density and Fingerprint Bacterial Communities

Cell density is an important factor in all microbiome research, where interactions are of interest. It is also the most important parameter for the operation and control of most biotechnological processes. In the past, cell density determination was often performed offline and manually, resulting in a delay between sampling and immediate data processing, preventing quick action. While there are now some online methods for rapid and automated cell density determination, they are unable to distinguish between the different cell types in bacterial communities. To address this gap, an online automated flow cytometry procedure is proposed for real-time high-resolution analysis of bacterial communities. On the one hand, it allows for the online automated calculation of cell concentrations and, on the other, for the differentiation between different cell subsets of a bacterial community. To achieve this, the OC-300 automation device (onCyt Microbiology, Zürich, Switzerland) was coupled with the flow cytometer CytoFLEX (Beckman Coulter, Brea, USA). The OC-300 performs the automatic sampling, dilution, fixation and 4',6-diamidino-2-phenylindole (DAPI) staining of a bacterial sample before sending it to the CytoFLEX for measurement. It is demonstrated that this method can reproducibly measure both cell density and fingerprint-like patterns of bacterial communities, generating suitable data for powerful automated data analysis and interpretation pipelines. In particular, the automated, high-resolution partitioning of clustered data into cell subsets opens up the possibility of correlation analysis to identify the operational or abiotic/biotic causes of community disturbances or state changes, which can influence the interaction potential of organisms in microbiomes or even affect the performance of individual organisms.

Automated flow cytometry as a tool to obtain a fine-grain picture of marine prokaryote community structure along an entire oceanographic cruise

On a standard oceanographic cruise, flow cytometry data are usually collected sparsely through a bottle-based sampling and with stations separated by kilometers leading to a fragmented view of the ecosystem; to improve the resolution of the datasets produced by this technique here it is proposed the application of an automatic method of sampling and staining. The system used consists of a flow-cytometer (Accuri-C6) connected to an automated continuous sampler (OC-300) that collects samples of marine surface waters every 15 min. We tested this system for five days during a brief Mediterranean cruise with the aim of estimating the abundance, relative size and phenotypic diversity of prokaryotes. Seawater was taken by a faucet linked to an inlet pump (ca. 5 m depth). Once the sample was taken, the Oncyt-300 stained it and sent it to the flow cytometer. A total of 366 samples were collected, effectively achieving a fine-grained scale view of microbial community composition both through space and time. A significative positive relationship was found comparing data obtained with the automatic method and 10 samples collected from the faucet but processed with the standard protocol. Abundance values retrieved varied from 3.56·10⁵ cell mL^-1 in the coastal area till 6.87 10⁵ cell mL^-1 in open waters, exceptional values were reached in the harbor area where abundances peaked to 1.28 10⁶ cell mL^-1. The measured features (abundance and size) were associated with metadata (temperature, salinity, conductivity) also taken in continuous, of which conductivity was the one that better explained the variability of abundance. A full 24 h measurement cycle was performed resulting in slightly higher median bacterial abundances values during daylight hours compared to night. Alpha diversity, calculated using computational cytometry techniques, showed a higher value in the coastal area above 41° of latitude and had a strong inverse relationship with both salinity and conductivity. This is the first time to our knowledge that the OC-300 is directly applied to the marine environment during an oceanographic cruise; due to its high-resolution, this set-up shows great potential both to cover large sampling areas, and to monitor day-night cycles in situ.

Optimized Protocol for Microalgae DNA Staining with SYTO9/SYBR Green I, Based on Flow Cytometry and RSM Methodology: Experimental Design, Impacts and Validation

Multiple fluorochromes are extensively used to investigate different microalgal aspects, such as viability and physiology. Some of them can be used to stain nucleic acids (DNA). Well-known examples are SYBR Green I and SYTO 9, the latter of which offers several advantages, especially when combined with flow cytometry (FCM)—a powerful method for studying microalgal population heterogeneity and analyzing their cell cycles. However, the effects of these dyes on the microalgae cell physiology have not been fully elucidated yet. A statistical experimental design, using response surface methodology (RSM) with FCM was applied in this study to optimize the DNA staining of a non-conventional microalgae, Chromochloris zofingiensis, with SYBR Green I and SYTO 9, and to optimize the variables affecting staining efficiency, i.e., the dye concentration, incubation time and staining temperature. We found that none of these factors affects the staining efficiency, which was not less than 99.65%. However, for both dyes, the dye concentration was shown to be the most significant factor causing cell damage (p-values: 0.0003; <0.0001) for SYBR Green I and SYTO 9, respectively. The staining temperature was only significant for SYTO 9 (p-value: 0.0082), and no significant effect was observed regarding the incubation time for both dyes. The values of the optimized parameters (0.5 µM, 05 min and 25 °C) for SYTO 9 and (0.5 X, 5 min and 25 °C) for SYBR Green I resulted in the maximum staining efficiency (99.8%; 99.6%), and the minimum damaging effects (12.86%; 13.75%) for SYTO 9 and SYBR Green I, respectively. These results offer new perspectives for improving the use of DNA staining fluorochromes and provides insights into their possible side effects on microalgae.

How does the Internet of Things (IoT) help in microalgae biorefinery?

Microalgae biorefinery is a platform for the conversion of microalgal biomass into a variety of value-added products, such as biofuels, bio-based chemicals, biomaterials, and bioactive substances. Commercialization and industrialization of microalgae biorefinery heavily rely on the capability and efficiency of large-scale cultivation of microalgae. Thus, there is an urgent need for novel technologies that can be used to monitor, automatically control, and precisely predict microalgae production. In light of this, innovative applications of the Internet of things (IoT) technologies in microalgae biorefinery have attracted tremendous research efforts. IoT has potential applications in a microalgae biorefinery for the automatic control of microalgae cultivation, monitoring and manipulation of microalgal cultivation parameters, optimization of microalgae productivity, identification of toxic algae species, screening of target microalgae species, classification of microalgae species, and viability detection of microalgal cells. In this critical review, cutting-edge IoT technologies that could be adopted to microalgae biorefinery in the upstream and downstream processing are described comprehensively. The current advances of the integration of IoT with microalgae biorefinery are presented. What this review discussed includes automation, sensors, lab-on-chip, and machine learning, which are the main constituent elements and advanced technologies of IoT. Specifically, future research directions are discussed with special emphasis on the development of sensors, the application of microfluidic technology, robotized microalgae, high-throughput platforms, deep learning, and other innovative techniques. This review could contribute greatly to the novelty and relevance in the field of IoT-based microalgae biorefinery to develop smarter, safer, cleaner, greener, and economically efficient techniques for exhaustive energy recovery during the biorefinery process.