Label-free, simultaneous quantification of starch, protein and triacylglycerol in single microalgal cells

Molecular mechanism and application of emerging technologies in study of bacterial persisters

Since the discovery of antibiotics, they have served as a potent weapon against bacterial infections; however, natural evolution has allowed bacteria to adapt and develop coping mechanisms, ultimately leading to the concerning escalation of multidrug resistance. Bacterial persisters are a subpopulation that can survive briefly under high concentrations of antibiotic treatment and resume growth after lethal stress. Importantly, bacterial persisters are thought to be a significant cause of ineffective antibiotic therapy and recurrent infections in clinical practice and are thought to contribute to the development of antibiotic resistance. Therefore, it is essential to elucidate the molecular mechanisms of persister formation and to develop precise medical strategies to combat persistent infections. However, there are many difficulties in studying persisters due to their small proportion in the microbiota and their non-heritable nature. In this review, we discuss the similarities and differences of antibiotic resistance, tolerance, persistence, and viable but non-culturable cells, summarize the molecular mechanisms that affect the formation of persisters, and outline the emerging technologies in the study of persisters.

Plant and algal lipidomes: Analysis, composition, and their societal significance

Plants and algae play a crucial role in the earth's ecosystems. Through photosynthesis they convert light energy into chemical energy, capture CO2 and produce oxygen and energy-rich organic compounds. Photosynthetic organisms are primary producers and synthesize the essential omega 3 and omega 6 fatty acids. They have also unique and highly diverse complex lipids, such as glycolipids, phospholipids, triglycerides, sphingolipids and phytosterols, with nutritional and health benefits. Plant and algal lipids are useful in food, feed, nutraceutical, cosmeceutical and pharmaceutical industries but also for green chemistry and bioenergy. The analysis of plant and algal lipidomes represents a significant challenge due to the intricate and diverse nature of their composition, as well as their plasticity under changing environmental conditions. Optimization of analytical tools is crucial for an in-depth exploration of the lipidome of plants and algae. This review highlights how lipidomics analytical tools can be used to establish a complete mapping of plant and algal lipidomes. Acquiring this knowledge will pave the way for the use of plants and algae as sources of tailored lipids for both industrial and environmental applications. This aligns with the main challenges for society, upholding the natural resources of our planet and respecting their limits.

Symbiodiniaceae and Ruegeria sp. Co-Cultivation to Enhance Nutrient Exchanges in Coral Holobiont

The symbiotic relationship between corals and their associated microorganisms is crucial for the health of coral reef eco-environmental systems. Recently, there has been a growing interest in unraveling how the manipulation of symbiont nutrient cycling affects the stress tolerance in the holobiont of coral reefs. However, most studies have primarily focused on coral-Symbiodiniaceae-bacterial interactions as a whole, neglecting the interactions between Symbiodiniaceae and bacteria, which remain largely unexplored. In this study, we proposed a hypothesis that there exists an inner symbiotic loop of Symbiodiniaceae and bacteria within the coral symbiotic loop. We conducted experiments to demonstrate how metabolic exchanges between Symbiodiniaceae and bacteria facilitate the nutritional supply necessary for cellular growth. It was seen that the beneficial bacterium, Ruegeria sp., supplied a nitrogen source to the Symbiodiniaceae strain Durusdinium sp., allowing this dinoflagellate to thrive in a nitrogen-free medium. The Ruegeria sp.-Durusdinium sp. interaction was confirmed through 15N-stable isotope probing-single cell Raman spectroscopy, in which 15N infiltrated into the bacterial cells for intracellular metabolism, and eventually the labeled nitrogen source was traced within the macromolecules of Symbiodiniaceae cells. The investigation into Symbiodiniaceae loop interactions validates our hypothesis and contributes to a comprehensive understanding of the intricate coral holobiont. These findings have the potential to enhance the health of coral reefs in the face of global climate change.

Effect of N/P ratio on attached microalgae growth and the differentiated metabolism along the depth of biofilm

Attached microalgae cultivation coupled with wastewater treatment could convert pollutants into bioresource with high efficiency and low cost. Nitrogen to phosphorus ratio (N/P ratio) is considered as an important factor on microalgae growth. Due to spatially heterogeneous distribution of nutrient, how N/P ratio affected attached microalgae growth in both macro- and micro-scopes was explored in this study. The findings revealed that an optimal N/P ratio of 10:1 promoted attached microalgae growth, while unsuitable ratios hampered algal growth by inhibiting photosynthesis, lowering oxidative resistance and decreasing metabolism activity. Long-term cultivation with improper N/P ratios resulted in a gradual decrease in actual photosynthetic rates, implying 50 days as the upper culture time limit for high-efficiency growth. Moreover, the study highlighted the uneven distribution of light and nutrients in algal biofilms, causing cells in different biofilm layers with variability of metabolism and composition. However, the 15N isotopic distribution demonstrated that even bottom cells were equally capable of nitrogen assimilation.

Culture-free identification of fast-growing cyanobacteria cells by Raman-activated gravity-driven encapsulation and sequencing

By directly converting solar energy and carbon dioxide into biobased products, cyanobacteria are promising chassis for photosynthetic biosynthesis. To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales, exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance. To simplify and accelerate the screening for fast-growing cyanobacteria strains, a method called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS) was established. We show that the 13C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations, thus greatly accelerating the process screening for fastest-growing cells. The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing. Therefore, this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories.

Microbial persisters and host: recent advances and future perspectives

Microbial persisters are defined as the tiny sub-population of microorganisms that develop intrinsic strategies for survival with high tolerance to various antimicrobials. Currently, persister research remains in its infancy, and it is indeed a great challenge to precisely distinguish persister cells from other drug tolerant ones. Notably, the existence of persisters crucially contributes to prolonged antibiotic exposure time and treatment failure, yet there is the formation of antibiotic-resistant mutants. Further understanding on persisters is of profound importance for effective prevention and control of chronic infections/inflammation. The past two decades have witnessed rapid advances on the science, technologies and methodologies for persister investigations, along with deep knowledge about persisters and numerous anti-persister approaches developed. Whereas, various critical issues remain unsolved, such as what are the potential interaction profiles of persisters and host cells, and how to apply what we know about persisters to translational studies and clinical practice. Importantly, it is highly essential to better understand the multifaceted and complex cross-talk of microbial persisters with the host to develop novel tackling strategies for precision healthcare in the near future.

Robust Spontaneous Raman Flow Cytometry for Single-Cell Metabolic Phenome Profiling via pDEP-DLD-RFC

A full-spectrum spontaneous single-cell Raman spectrum (fs-SCRS) captures the metabolic phenome for a given cellular state of the cell in a label-free, landscape-like manner. Herein a positive dielectrophoresis induced deterministic lateral displacement-based Raman flow cytometry (pDEP-DLD-RFC) is established. This robust flow cytometry platform utilizes a periodical positive dielectrophoresis induced deterministic lateral displacement (pDEP-DLD) force that is exerted to focus and trap fast-moving single cells in a wide channel, which enables efficient fs-SCRS acquisition and extended stable running time. It automatically produces deeply sampled, heterogeneity-resolved, and highly reproducible ramanomes for isogenic cell populations of yeast, microalgae, bacteria, and human cancers, which support biosynthetic process dissection, antimicrobial susceptibility profiling, and cell-type classification. Moreover, when coupled with intra-ramanome correlation analysis, it reveals state- and cell-type-specific metabolic heterogeneity and metabolite-conversion networks. The throughput of ≈30-2700 events min-1 for profiling both nonresonance and resonance marker bands in a fs-SCRS, plus the >5 h stable running time, represent the highest performance among reported spontaneous Raman flow cytometry (RFC) systems. Therefore, pDEP-DLD-RFC is a valuable new tool for label-free, noninvasive, and high-throughput profiling of single-cell metabolic phenomes.

An attempt to simultaneously quantify the polysaccharide, total lipid, protein and pigment in single Cyclotella cryptica cell by Raman spectroscopy

BACKGROUND

At present, the conventional methods for determining photosynthetic products of microalgae are usually based on a large number of cell mass to reach the measurement baseline, and the result can only reveal the average state at the population level, which is not feasible for large-scale and rapid screening of specific phenotypes from a large number of potential microalgae mutants. In recent years, single-cell Raman spectra (SCRS) has been proved to be able to rapidly and simultaneously quantify the biochemical components of microalgae. However, this method has not been reported to analyze the biochemical components of Cyclotella cryptica (C. cryptica). Thus, SCRS was first attempt to determine these four biochemical components in this diatom.

RESULTS

The method based on SCRS was established to simultaneously quantify the contents of polysaccharide, total lipids, protein and Chl-a in C. cryptica, with thirteen Raman bands were found to be the main marker bands for the diatom components. Moreover, Partial Least Square Regression (PLSR) models based on full spectrum can reliably predict these four cellular components, with Pearson correlation coefficient for these components reached 0.949, 0.904, 0.801 and 0.917, respectively. Finally, based on SCRS data of one isogenic sample, the pairwise correlation and dynamic transformation process of these components can be analyzed by Intra-ramanome Correlation Analysis (IRCA), and the results showed silicon starvation could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.

CONCLUSIONS

First, method for the simultaneous quantification of the polysaccharide, total lipid, protein and pigment in single C. cryptica cell are established. Second, the instant interconversion of intracellular components was constructed through IRCA, which is based on data set of one isogenic population and more precision and timeliness. Finally, total results indicated that silicon deficiency could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.

Advances in bioreactor control for production of biotherapeutic products

Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

Structured population balances to support microalgae-based processes: Review of the state-of-art and perspectives analysis

Design and optimization of microalgae processes have traditionally relied on the application of unsegregated mathematical models, thus neglecting the impact of cell-to-cell heterogeneity. However, there is experimental evidence that the latter one, including but not limited to variation in mass/size, internal composition and cell cycle phase, can play a crucial role in both cultivation and downstream processes. Population balance equations (PBEs) represent a powerful approach to develop mathematical models describing the effect of cell-to-cell heterogeneity. In this work, the potential of PBEs for the analysis and design of microalgae processes are discussed. A detailed review of PBE applications to microalgae cultivation, harvesting and disruption is reported. The review is largely focused on the application of the univariate size/mass structured PBE, where the size/mass is the only internal variable used to identify the cell state. Nonetheless, the need, addressed by few studies, for additional or alternative internal variables to identify the cell cycle phase and/or provide information about the internal composition is discussed. Through the review, the limitations of previous studies are described, and areas are identified where the development of more reliable PBE models, driven by the increasing availability of single-cell experimental data, could support the understanding and purposeful exploitation of the mechanisms determining cell-to-cell heterogeneity.

A User-Friendly Platform for Single-Cell Raman Spectroscopy Analysis

The optimization of Raman instruments greatly expands our understanding of single-cell Raman spectroscopy. The improvement in the speed and sensitivity of the instrument and the implementation of advanced data mining methods help to reveal the complex chemical and biological information within the Raman spectral data. Here we introduce a new Matlab Graphical User-Friendly Interface (GUI), named "CELL IMAGE" for the analysis of cellular Raman spectroscopy data. The three main steps of data analysis embedded in the GUI include spectral processing, pattern recognition and model validation. Various well-known methods are available to the user of the GUI at each step of the analysis. Herein, a new subsampling optimization method is integrated into the GUI to estimate the minimum number of spectral collection points. The introduction of the signal-to-noise ratio (SNR) of the analyte in the binomial statistical model means the new subsampling model is more sophisticated and suitable for complicated Raman cell data. These embedded methods allow "CELL IMAGE" to transform spectral information into biological information, including single-cell visualization, cell classification and biomolecular/ drug quantification.

Micro-Raman spectroscopy of the light-harvesting pigments in Chlamydomonas reinhardtii under salinity stress

Microalgae are a rich source of carotenoids with enhanced yields during biotic or abiotic stresses, which often impose survival challenges on the cells. Using a non-invasive pigment profiling approach with micro-Raman spectroscopy, we have analyzed the effect of salinity stress on carotenoids in autotrophic Chlamydomonas reinhardtii. Raman spectral analysis of ν(C = C) mode indicates an increase in the carotenoids with lower conjugation length (lutein and zeaxanthin) compared to β-carotene, as the function of culture age and salinity stress, but especially when salinity stress was imposed in two-stage mode (stress imposed on 2nd day, D2_100, and 4th day, D4_100, during exponential phase). Population-scale heterogeneities in carotenoid Raman mode peak center, quantified with heterogeneity index (HI), were highest during the stationary phase of the cultures and under salinity stress. Although the Raman signal was obtained from a randomly selected small focal volume in the cell, a decrease in chlorophyll Raman mode intensities with age and salinity stress was well corroborated by single-cell population fraction measurements by microscopy. Raman intensity fluctuations (I_f) were high for both chlorophyll and carotenoid modes under salinity stress, which can arise due to variations in chlorophyll/carotenoid content and composition, or conformational changes in the pigments in C. reinhardtii cells. Interestingly, in all growth conditions, chlorophyll a Raman mode intensity was found to show a high correlation to that of β-carotene, pointing out a high degree of cooperativity in the light-harvesting complex pigments even during salinity stress. Thus, we demonstrate the usefulness of non-invasive pigment profiling with micro-Raman spectroscopy for developing an optimization for salinity stress conditions for high biomass yield and proper harvest time to obtain carotenoids with desired chemical composition.

Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems

Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.

A rapid fluorescence approach on differentiation of typical dinoflagellate of East China Sea

Detecting the marine phytoplankton by the means of absorption or fluorescence spectra were successfully deployed in the past decades, however, the differentiation are mainly limited in levels of class, such as bacillariophytas, dinophytas, raphidophytes, chlorophytes, cyanobacteria, etc. which are characterized by their specific composition of photosynthetic pigments. To further differentiate the typical dinoflagellate Prorocentrum donghaiense, Amphidinium carterae, Scrippsiella trochoidea, Karenia mikimotoi out of the common diatom Skeletonema costatum and haptonema Phaeocystis globosa at East China Sea, a rapid 3D-fluorescence method equipped with CHEMTAX model were conducted. Initial fluorescence excitation spectra of each algal species (under variable environmental conditions) were captured by 3D-fluorometer first. Then fingerprints of each algae were characterized by ten-point discrete excitation spectrum with the excitation wavelengths of 405, 420, 435, 470, 490, 505, 535, 555, 570 and 590 nm, which closely reflecting the difference of photosynthetic pigments. By equipping with CHEMTAX model, the standard spectra and norm spectra were constructed for FS-CHEMTAX (Fluorescence spectra-CHEMTAX) model to further identify the algal species and estimate the cell density. The developed method performed a better way of identifying the toxic species Amphidinium carterae, Phaeocystis globosa, and Karenia mikimotoi out of the non-toxic ones, with the identification accuracy rates of 83.3%, 90% and 100%, in monocultures, and 77.8%, 90% and 100%, in the bi-mixed cultures, respectively. Meanwhile, the detection limits for the three toxic species were found as low as 250, 1,400 and 120 cells/mL. The concentrations estimated are in good agreement with the microscopic cell counts for all the algae groups (correlation coefficients (R²) exceed 0.8). The relative error of predict concentration was lowest for small cells, i.e., Phaeocystis globosa (10.0%) and Amphidinium carterae (21.1%), but the highest for big cells, i.e. Karenia mikimotoi (41.8%) when the target algae become the dominant species. The overall concentration detection error was no more than one order of magnitude, indicating that this method could provide an important technical support for monitoring the related harmful algal blooms.

Single-cell Raman spectroscopy identifies Escherichia coli persisters and reveals their enhanced metabolic activities

Microbial persisters are the featured tiny sub-population of microorganisms that are highly tolerant to multiple antimicrobials. Currently, studies on persisters remain a considerable challenge owing to technical limitations. Here, we explored the application of single-cell Raman spectroscopy (SCRS) in the investigation of persisters. Escherichia coli (ATCC 25922) cells were treated with a lethal dosage of ampicillin (100 μg/mL, 32 × MIC, 4 h) for the formation of persisters. The biochemical characters of E. coli and its persisters were assessed by SCRS, and their metabolic activities were labeled and measured with D₂O-based single-cell Raman spectroscopy (D₂O-Ramanometry). Notable differences in the intensity of Raman bands related to major cellular components and metabolites were observed between E. coli and its ampicillin-treated persisters. Based on their distinct Raman spectra, E. coli and its persister cells were classified into different projective zones through the principal component analysis and t-distributed stochastic neighbor embedding. According to the D₂O absorption rate, E. coli persisters exhibited higher metabolic activities than those of untreated E. coli. Importantly, after the termination of ampicillin exposure, these persister cells showed a temporal pattern of D₂O intake that was distinct from non-persister cells. To our knowledge, this is the first report on identifying E. coli persisters and assessing their metabolic activities through the integrated SCRS and D₂O-Ramanometry approach. These novel findings enhance our understanding of the phenotypes and functionalities of microbial persister cells. Further investigations could be extended to other pathogens by disclosing microbial pathogenicity mechanisms for developing novel therapeutic strategies and approaches.

Exploring a blue-light-sensing transcription factor to double the peak productivity of oil in Nannochloropsis oceanica

Oleaginous microalgae can produce triacylglycerol (TAG) under stress, yet the underlying mechanism remains largely unknown. Here, we show that, in Nannochloropsis oceanica, a bZIP-family regulator NobZIP77 represses the transcription of a type-2 diacylgycerol acyltransferase encoding gene NoDGAT2B under nitrogen-repletion (N+), while nitrogen-depletion (N−) relieves such inhibition and activates NoDGAT2B expression and synthesis of TAG preferably from C16:1. Intriguingly, NobZIP77 is a sensor of blue light (BL), which reduces binding of NobZIP77 to the NoDGAT2B-promoter, unleashes NoDGAT2B and elevates TAG under N−. Under N+ and white light, NobZIP77 knockout fully preserves cell growth rate and nearly triples TAG productivity. Moreover, exposing the NobZIP77-knockout line to BL under N− can double the peak productivity of TAG. These results underscore the potential of coupling light quality to oil synthesis in feedstock or bioprocess development.

Microalgae are promising feedstock for oil production. The authors report that a transcription factor NobZIP77 can regulate oil synthesis by sensing the blue light, and explore these findings to greatly enhance oil productivity via genetic and process engineering in Nannochloropsis oceanica.

Intracellular polyphosphate length characterization in polyphosphate accumulating microorganisms (PAOs): Implications in PAO phenotypic diversity and enhanced biological phosphorus removal performance

Polyphosphate (polyP) accumulating organisms (PAOs) are the key agent to perform enhanced biological phosphorus removal (EBPR) activity, and intracellular polyP plays a key role in this process. Potential associations between EBPR performance and the polyP structure have been suggested, but are yet to be extensively investigated, mainly due to the lack of established methods for polyP characterization in the EBPR system. In this study, we explored and demonstrated that single-cell Raman spectroscopy (SCRS) can be employed for characterizing intracellular polyPs of PAOs in complex environmental samples such as EBPR systems. The results, for the first time, revealed distinct distribution patterns of polyP length (as Raman peak position) in PAOs in lab-scale EBPR reactors that were dominated with different PAO types, as well as among different full-scale EBPR systems with varying configurations. Furthermore, SCRS revealed distinctive polyP composition/features among PAO phenotypic sub-groups, which are likely associated with phylogenetic and/or phenotypic diversity in EBPR communities, highlighting the possible resolving power of SCRS at the microdiversity level. To validate the observed polyP length variations via SCRS, we also performed and compared bulk polyP length characteristics in EBPR biomass using conventional polyacrylamide gel electrophoresis (PAGE) and solution ³¹P nuclear magnetic resonance (³¹P-NMR) methods. The results are consistent with the SCRS findings and confirmed the variations in the polyP lengths among different EBPR systems. Compared to conventional methods, SCRS exhibited advantages as compared to conventional methods, including the ability to characterize in situ the intracellular polyPs at subcellular resolution in a label-free and non-destructive way, and the capability to capture subtle and detailed biochemical fingerprints of cells for phenotypic classification. SCRS also has recognized limitations in comparison with ³¹P-NMR and PAGE, such as the inability to quantitatively detect the average polyP chain length and its distribution. The results provided initial evidence for the potential of SCRS-enabled polyP characterization as an alternative and complementary microbial community phenotyping method to facilitate the phenotype-function (performance) relationship deduction in EBPR systems.

Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells

To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications.

Culture-Free Identification and Metabolic Profiling of Microalgal Single Cells via Ensemble Learning of Ramanomes

Microalgae are among the most genetically and metabolically diverse organisms on earth, yet their identification and metabolic profiling have generally been slow and tedious. Here, we established a reference ramanome database consisting of single-cell Raman spectra (SCRS) from >9000 cells of 27 phylogenetically diverse microalgal species, each under stationary and exponential states. When combined, prequenching ("pigment spectrum" (PS)) and postquenching ("whole spectrum" (WS)) signals can classify species and states with 97% accuracy via ensemble machine learning. Moreover, the biosynthetic profile of Raman-sensitive metabolites was unveiled at single cells, and their interconversion was detected via intra-ramanome correlation analysis. Furthermore, not-yet-cultured cells from the environment were functionally characterized via PS and WS and then phylogenetically identified by Raman-activated sorting and sequencing. This PS-WS combined approach for rapidly identifying and metabolically profiling single cells, either cultured or uncultured, greatly accelerates the mining of microalgae and their products.

One-Cell Metabolic Phenotyping and Sequencing of Soil Microbiome by Raman-Activated Gravity-Driven Encapsulation (RAGE)

Soil harbors arguably the most metabolically and genetically heterogeneous microbiomes on Earth, yet establishing the link between metabolic functions and genome at the precisely one-cell level has been difficult. Here, for mock microbial communities and then for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) platform, which identifies, sorts, and sequences precisely one bacterial cell via its anabolic (incorporating D from heavy water) and physiological (carotenoid-containing) functions. We showed that (i) metabolically active cells from numerically rare soil taxa, such as Corynebacterium spp., Clostridium spp., Moraxella spp., Pantoea spp., and Pseudomonas spp., can be readily identified and sorted based on D₂O uptake, and their one-cell genome coverage can reach ∼93% to allow high-quality genome-wide metabolic reconstruction; (ii) similarly, carotenoid-containing cells such as Pantoea spp., Legionella spp., Massilia spp., Pseudomonas spp., and Pedobacter spp. were identified and one-cell genomes were generated for tracing the carotenoid-synthetic pathways; and (iii) carotenoid-producing cells can be either metabolically active or inert, suggesting culture-based approaches can miss many such cells. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at exactly one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint “who is doing what” in complex ecosystems.

IMPORTANCE Soil is home to an enormous and complex microbiome that features arguably the highest genomic diversity and metabolic heterogeneity of cells on Earth. Their in situ metabolic activities drive many natural processes of pivotal ecological significance or underlie industrial production of numerous valuable bioactivities. However, pinpointing “who is doing what” in a soil microbiome, which consists of mainly yet-to-be-cultured species, has remained a major challenge. Here, for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) method, which identifies, sorts, and sequences at the resolution of precisely one microbial cell via its catabolic and anabolic functions. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint “who is doing what” in complex ecosystems.

Genome engineering of Nannochloropsis with hundred-kilobase fragment deletions by Cas9 cleavages

Industrial microalgae are promising photosynthetic cell factories, yet tools for large-scale targeted genome engineering are limited. Here for the model industrial oleaginous microalga Nannochloropsis oceanica, we established a method to precisely and serially delete large genome fragments of ~100 kb from its 30.01 Mb nuclear genome. We started by identifying the 'non-essential' chromosomal regions (i.e. low expression region or LER) based on minimal gene expression under N-replete and N-depleted conditions. The largest such LER (LER1) is ~98 kb in size, located near the telomere of the 502.09-kb-long Chromosome 30 (Chr 30). We deleted 81 kb and further distal and proximal deletions of up to 110 kb (21.9% of Chr 30) in LER1 by dual targeting the boundaries with the episome-based CRISPR/Cas9 system. The telomere-deletion mutants showed normal telomeres consisting of CCCTAA repeats, revealing telomere regeneration capability after losing the distal part of Chr 30. Interestingly, the deletions caused no significant alteration in growth, lipid production or photosynthesis (transcript-abundance change for < 3% genes under N depletion). We also achieved double-deletion of both LER1 and LER2 (from Chr 9) that total ~214 kb at maximum, which can result in slightly higher growth rate and biomass productivity than the wild-type. Therefore, loss of the large, yet 'non-essential' regions does not necessarily sacrifice important traits. Such serial targeted deletions of large genomic regions had not been previously reported in microalgae, and will accelerate crafting minimal genomes as chassis for photosynthetic production.

The NanDeSyn database for Nannochloropsis systems and synthetic biology

Nannochloropsis species, unicellular industrial oleaginous microalgae, are model organisms for microalgal systems and synthetic biology. To facilitate community-based annotation and mining of the rapidly accumulating functional genomics resources, we have initiated an international consortium and present a comprehensive multi-omics resource database named Nannochloropsis Design and Synthesis (NanDeSyn; http://nandesyn.single-cell.cn). Via the Tripal toolkit, it features user-friendly interfaces hosting genomic resources with gene annotations and transcriptomic and proteomic data for six Nannochloropsis species, including two updated genomes of Nannochloropsis oceanica IMET1 and Nannochloropsis salina CCMP1776. Toolboxes for search, Blast, synteny view, enrichment analysis, metabolic pathway analysis, a genome browser, etc. are also included. In addition, functional validation of genes is indicated based on phenotypes of mutants and relevant bibliography. Furthermore, epigenomic resources are also incorporated, especially for sequencing of small RNAs including microRNAs and circular RNAs. Such comprehensive and integrated landscapes of Nannochloropsis genomics and epigenomics will promote and accelerate community efforts in systems and synthetic biology of these industrially important microalgae.

Data mining in Raman imaging in a cellular biological system

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Working flow of data mining in Raman imaging of cell system described.

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Pre-processing, pattern recognition and validation discussed.

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Machine learning methods applied at each step discussed.

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Single-cell visualization, cell type classification and quantification applications.

The distribution and dynamics of biomolecules in the cell is of critical interest in biological research. Raman imaging techniques have expanded our knowledge of cellular biological systems significantly. The technological developments that have led to the optimization of Raman instrumentation have helped to improve the speed of the measurement and the sensitivity. As well as instrumental developments, data mining plays a significant role in revealing the complicated chemical information contained within the spectral data. A number of data mining methods have been applied to extract the spectral information and translate them into biological information. Single-cell visualization, cell classification and biomolecular/drug quantification have all been achieved by the application of data mining to Raman imaging data. Herein we summarize the framework for Raman imaging data analysis, which involves preprocessing, pattern recognition and validation. There are multiple methods developed for each stage of analysis. The characteristics of these methods are described in relation to their application in Raman imaging of the cell. Furthermore, we summarize the software that can facilitate the implementation of these methods. Through its careful selection and application, data mining can act as an essential tool in the exploration of information-rich Raman spectral data.

Positive dielectrophoresis-based Raman-activated droplet sorting for culture-free and label-free screening of enzyme function in vivo

The potential of Raman-activated cell sorting (RACS) is inherently limited by conflicting demands for signal quality and sorting throughput. Here, we present positive dielectrophoresis-based Raman-activated droplet sorting (pDEP-RADS), where a periodical pDEP force was exerted to trap fast-moving cells, followed by simultaneous microdroplet encapsulation and sorting. Screening of yeasts for triacylglycerol (TAG) content demonstrated near-theoretical-limit accuracy, ~120 cells min^-1 throughput and full-vitality preservation, while sorting fatty acid degree of unsaturation (FA-DU) featured ~82% accuracy at ~40 cells min^-1. From a yeast library expressing algal diacylglycerol acyltransferases (DGATs), a pDEP-RADS run revealed all reported TAG-synthetic variants and distinguished FA-DUs of enzyme products. Furthermore, two previously unknown DGATs producing low levels of monounsaturated fatty acid-rich TAG were discovered. This first demonstration of RACS for enzyme discovery represents hundred-fold saving in time consumables and labor versus culture-based approaches. The ability to automatically flow-sort resonance Raman-independent phenotypes greatly expands RACS' application.

Phenome-Genome Profiling of Single Bacterial Cell by Raman-Activated Gravity-Driven Encapsulation and Sequencing

The small size and low DNA amount of bacterial cells have hindered establishing phenome-genome links in a precisely indexed, one-cell-per-reaction manner. Here, Raman-Activated Gravity-driven single-cell Encapsulation and Sequencing (RAGE-Seq) is presented, where individual cells are phenotypically screened via single-cell Raman spectra (SCRS) in an aquatic, vitality-preserving environment, then the cell with targeted SCRS is precisely packaged in a picoliter microdroplet and readily exported in a precisely indexed, "one-cell-one-tube" manner. Such integration of microdroplet encapsulation to Raman-activated sorting ensures high-coverage one-cell genome sequencing or cultivation that is directly linked to metabolic phenotype. For clinical Escherichia coli isolates, genome assemblies derived from precisely one cell via RAGE-Seq consistently reach >95% coverage. Moreover, directly from a urine sample of urogenital tract infection, metabolic-activity-based antimicrobial susceptibility phenotypes and genome sequence of 99.5% coverage are obtained simultaneously from precisely one cell. This single-cell global mutation map corroborates resistance phenotype and genotype, and unveils epidemiological features with high specificity and sensitivity. The ability to profile and correlate bacterial metabolic phenome and high-quality genome sequences at one-cell resolution suggests broad application of RAGE-Seq.

Integration of proteome and transcriptome refines key molecular processes underlying oil production in Nannochloropsis oceanica

BACKGROUND

Under nitrogen deficiency situation, Nannochloropsis spp. accumulate large amounts of lipids in the form of triacylglycerides (TAG). Mechanisms of this process from the perspective of transcriptome and metabolome have been obtained previously, yet proteome analysis is still sparse which hinders the analysis of dynamic adaption to nitrogen deficiency. Here, proteomes for 3 h, 6 h, 12 h, 24 h, 48 h and 10th day of nitrogen deplete (N-) and replete (N+) conditions were obtained and integrated with previous transcriptome data for N. oceanica.

RESULTS

Physiological adaptations to N- not apparent from transcriptome data were unveiled: (a) abundance of proteins related to photosynthesis only slightly decreased in the first 48 h, indicating that photosynthesis is still working efficiently, and protein amounts adjust gradually with reduction in chloroplast size. (b) Most proteins related to the TCA cycle were strongly upregulated after 48 h under N-, suggesting that respiration is enhanced after 48 h and that TCA cycle efflux supports the carbon required for lipid synthesis. (c) Proteins related to lipid accumulation via the Kennedy pathway increased their abundance at 48 h, synchronous with the previously reported diversification of fatty acids after 48 h.

CONCLUSIONS

This study adds a proteome perspective on the major pathways for TAG accumulation in Nannochloropsis spp. Temporal changes of proteome exhibited distinct adaptation phases that are usually delayed relative to transcriptomic responses. Notably, proteome data revealed that photosynthesis and carbon fixation are still ongoing even after 48 h of N-. Moreover, sometimes completely opposite trends in proteome and transcriptome demonstrate the relevance of underexplored post-transcriptional regulation for N- adaptation.

Assessment of recombinant protein production in E. coli with Time-Gated Surface Enhanced Raman Spectroscopy (TG-SERS)

Time-Gated Surface-Enhanced Raman spectroscopy (TG-SERS) was utilized to assess recombinant protein production in Escherichia coli. TG-SERS suppressed the fluorescence signal from the biomolecules in the bacteria and the culture media. Characteristic protein signatures at different time points of the cell cultivation were observed and compared to conventional continuous wave (CW)-Raman with SERS. TG-SERS can distinguish discrete features of proteins such as the secondary structures and is therefore indicative of folding or unfolding of the protein. A novel method utilizing nanofibrillar cellulose as a stabilizing agent for nanoparticles and bacterial cells was used for the first time in order to boost the Raman signal, while simultaneously suppressing background signals. We evaluated the expression of hCNTF, hHspA1, and hHsp27 in complex media using the batch fermentation mode. HCNTF was also cultivated using EnBase in a fed-batch like mode. HspA1 expressed poorly due to aggregation problems within the cell, while hCNTF expressed in batch mode was correctly folded and protein instabilities were identified in the EnBase cultivation. Time-gated Raman spectroscopy showed to be a powerful tool to evaluate protein production and correct folding within living E. coli cells during the cultivation.

Development of Novel Bioreactor Control Systems Based on Smart Sensors and Actuators

Bioreactors of various forms have been widely used in environmental protection, healthcare, industrial biotechnology, and space exploration. Robust demand in the field stimulated the development of novel designs of bioreactor geometries and process control strategies and the evolution of the physical structure of the control system. After the introduction of digital computers to bioreactor process control, a hierarchical structure control system (HSCS) for bioreactors has become the dominant physical structure, having high efficiency and robustness. However, inherent drawbacks of the HSCS for bioreactors have produced a need for a more consolidated solution of the control system. With the fast progress in sensors, machinery, and information technology, the development of a flat organizational control system (FOCS) for bioreactors based on parallel distributed smart sensors and actuators may provide a more concise solution for process control in bioreactors. Here, we review the evolution of the physical structure of bioreactor control systems and discuss the properties of the novel FOCS for bioreactors and related smart sensors and actuators and their application circumstances, with the hope of further improving the efficiency, robustness, and economics of bioprocess control.

The Composition and Implications of Polyphosphate-Metal in Enhanced Biological Phosphorus Removal Systems

The individual cellular level and quantitative Polyphosphate (PolyP)-metal compositions in EBPR (enhanced biological phosphorus removal) systems have hardly been investigated and its potential link to EBPR performance therefore remain largely unknown. In this study, we applied scanning electron microscopy combined with energy dispersive X-ray spectroscopy (SEM/EDX) method that enabled detection and semiquantification of metal elemental compositions in intact intracellular PolyP granules in individual PAO (polyphosphate accumulating organism) cells. We, for the first time, revealed diverse and dynamic distributions of different metals ions in the PolyP-metal granules in different EBPR systems operated with the same influent metal composition but varying SRT of 5-30 days. We further demonstrated that the PolyP-metal composition diversity correlated with 16S rRNA gene based PAO phylogenetic diversity, suggesting the possible phylogeny-dependent PolyP-metal composition variation. The impact of PolyP metal composition in EBPR system, especially the Mg content in PolyP granules, was evidenced by the significant and strong positive correlation between PolyP-Mg content and the long-term stability of the four EBPR systems with varying SRTs. The PolyP-Mg content can therefore possibly serve as an indicator for EBPR performance monitoring. The results demonstrated that phenotyping techniques, such as PolyP-metal-based profiling, in compliment, or combined with genotyping techniques such as phylogenetic and functional gene sequencing, can provide more insights into the mechanisms and performance prediction of this important microbial ecosystem.

Proteomic study uncovers molecular principles of single-cell-level phenotypic heterogeneity in lipid storage of Nannochloropsis oceanica

BACKGROUND

Nannochloropsis oceanica belongs to a large group of photoautotrophic eukaryotic organisms that play important roles in fixation and cycling of atmospheric CO2. Its capability of storing solar energy and carbon dioxide in the form of triacylglycerol (TAG) of up to 60% of total weight under nitrogen deprivation stress sparked interest in its use for biofuel production. Phenotypes varying in lipid accumulation among an N. oceanica population can be disclosed by single-cell analysis/sorting using fluorescence-activated cell sorting (FACS); yet the phenomenon of single cell heterogeneity in an algae population remains to be fully understood at the molecular level. In this study, combination of FACS and proteomics was used for identification, quantification and differentiation of these heterogeneities on the molecular level.

RESULTS

For N. oceanica cultivated under nitrogen deplete (-N) and replete (+N) conditions, two groups differing in lipid content were distinguished. These differentiations could be recognized on the population as well as the single-cell levels; proteomics uncovered alterations in carbon fixation and flux, photosynthetic machinery, lipid storage and turnover in the populations. Although heterogeneity patterns have been affected by nitrogen supply and cultivation conditions of the N. oceanica populations, differentiation itself seems to be very robust against these factors: cultivation under +N, -N, in shaker bottles, and in a photo-bioreactor all split into two subpopulations. Intriguingly, population heterogeneity resumed after subpopulations were separately recultivated for a second round, refuting the possible development of genetic heterogeneity in the course of sorting and cultivation.

CONCLUSIONS

This work illustrates for the first time the feasibility of combining FACS and (prote)-omics for mechanistic understanding of phenotypic heterogeneity in lipid-producing microalgae. Such combinatorial method can facilitate molecular breeding and design of bioprocesses.

Toward Better Understanding of EBPR Systems via Linking Raman-Based Phenotypic Profiling with Phylogenetic Diversity

This study reports a proof-of concept study to demonstrate the novel approach of phenotyping microbial communities in enhanced biological phosphorus removal (EBPR) systems using single cell Raman microspectroscopy and link it with phylogentic structures. We use hierarchical clustering analysis (HCA) of single-cell Raman spectral fingerprints and intracellular polymer signatures to separate and classify the functionally relevant populations in EBPR systems, namely polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), as well as other microbial populations. We then investigated the link between Raman-based community phenotyping and 16S rRNA gene-based phylogenetic characterization of four lab-scale EBPR systems with varying solid retention time (SRT) to gain insights into possible genotype-function relationships. Combined and simultaneous phylogenetic and phenotypic evaluation of EBPR ecosystems revealed SRT-dependent phylogenetic and phenotypic characteristics of the PAOs and GAOs, and their association with EBPR performance. The phenotypic diversity and plasticity of PAO populations, which otherwise could not be obtained with phylogenetic analysis alone, showed complex but potentially crucial association with EBPR process stability.

Single cell assessment of yeast metabolic engineering for enhanced lipid production using Raman and AFM-IR imaging

BACKGROUND

Biodiesel is a valuable renewable fuel made from derivatized fatty acids produced in plants, animals, and oleaginous microbes. Of the latter, yeasts are of special interest due to their wide use in biotechnology, ability to synthesize fatty acids and store large amounts of triacylglycerols while utilizing non-food carbon sources. While yeast efficiently produce lipids, genetic modification and indeed, lipid pathway metabolic engineering, is usually required for cost-effective production. Traditionally, gas chromatography (GC) is used to measure fatty acid production and to track the success of a metabolic engineering strategy in a microbial culture; here we have employed vibrational spectroscopy approaches at population and single cell level of engineered yeast while simultaneously investigating metabolite levels in subcellular structures.

RESULTS

Firstly, a strong correlation (r² > 0.99) was established between Fourier transform infrared (FTIR) lipid in intact cells and GC analysis of fatty acid methyl esters in the differently engineered strains. Confocal Raman spectroscopy of individual cells carrying genetic modifications to enhance fatty acid synthesis and lipid accumulation revealed changes to the lipid body (LB), the storage organelle for lipids in yeast, with their number increasing markedly (up to tenfold higher); LB size was almost double in the strain that also expressed a LB stabilizing gene but considerable variation was also noted between cells. Raman spectroscopy revealed a clear trend toward reduced unsaturated fatty acid content in lipids of cells carrying more complex metabolic engineering. Atomic force microscopy-infrared spectroscopy (AFM-IR) analysis of individual cells indicated large differences in subcellular constituents between strains: cells of the most highly engineered strain had elevated lipid and much reduced carbohydrate in their cytoplasm compared with unmodified cells.

CONCLUSIONS

Vibrational spectroscopy analysis allowed the simultaneous measurement of strain variability in metabolite production and impact on cellular structures as a result of different gene introductions or knockouts, within a lipid metabolic engineering strategy and these inform the next steps in comprehensive lipid engineering. Additionally, single cell spectroscopic analysis measures heterogeneity in metabolite production across microbial cultures under genetic modification, an emerging issue for efficient biotechnological production.