Review of methods to probe single cell metabolism and bioenergetics

Gaining insights into the responses of individual yeast cells to ethanol fermentation using Raman tweezers and chemometrics

Saccharomyces cerevisiae is the most common microbe used for the industrial production of bioethanol, and it encounters various stresses that inhibit cell growth and metabolism during fermentation. However, little is currently known about the physiological changes that occur in individual yeast cells during ethanol fermentation. Therefore, in this work, Raman spectroscopy and chemometric techniques were employed to monitor the metabolic changes of individual yeast cells at distinct stages during high gravity ethanol fermentation. Raman tweezers was used to acquire the Raman spectra of individual yeast cells. Multivariate curve resolution-alternating least squares (MCR-ALS) and principal component analysis were employed to analyze the Raman spectra dataset. MCR-ALS extracted the spectra of proteins, phospholipids, and triacylglycerols and their relative contents in individual cells. Changes in intracellular biomolecules showed that yeast cells undergo three distinct physiological stages during fermentation. In addition, heterogeneity among yeast cells significantly increased in the late fermentation period, and different yeast cells may respond to ethanol stress via different mechanisms. Our findings suggest that the combination of Raman tweezers and chemometrics approaches allows for characterizing the dynamics of molecular components within individual cells. This approach can serve as a valuable tool in investigating the resistance mechanism and metabolic heterogeneity of yeast cells during ethanol fermentation.

Applications and opportunities of click chemistry in plant science

The Nobel Prize in Chemistry for 2022 was awarded to the pioneers of Lego-like 'click chemistry': combinatorial chemistry with remarkable modularity and diversity. It has been applied to a wide variety of biological systems, from microorganisms to plants and animals, including humans. Although click chemistry is a powerful chemical biology tool, comparatively few studies have examined its potential in plant science. Here, we review click chemistry reactions and their applications in plant systems, highlighting the activity-based probes and metabolic labeling strategies combined with bioorthogonal click chemistry to visualize plant biological processes. These applications offer new opportunities to explore and understand the underlying molecular mechanisms regulating plant composition, growth, metabolism, defense, and immune responses.

Shared metabolic shifts in endothelial cells in stroke and Alzheimer's disease revealed by integrated analysis

Since metabolic dysregulation is a hallmark of both stroke and Alzheimer's disease (AD), mining shared metabolic patterns in these diseases will help to identify their possible pathogenic mechanisms and potential intervention targets. However, a systematic integration analysis of the metabolic networks of the these diseases is still lacking. In this study, we integrated single-cell RNA sequencing datasets of ischemic stroke (IS), hemorrhagic stroke (HS) and AD models to construct metabolic flux profiles at the single-cell level. We discovered that the three disorders cause shared metabolic shifts in endothelial cells. These altered metabolic modules were mainly enriched in the transporter-related pathways and were predicted to potentially lead to a decrease in metabolites such as pyruvate and fumarate. We further found that Lef1, Elk3 and Fosl1 may be upstream transcriptional regulators causing metabolic shifts and may be possible targets for interventions that halt the course of neurodegeneration.

Directed evolution of linker helix as an efficient strategy for engineering LysR-type transcriptional regulators as whole-cell biosensors

Whole-cell biosensors based on transcriptional regulators are powerful tools for rapid measurement, high-throughput screening, dynamic metabolic regulation, etc. To optimize the biosensing performance of transcriptional regulator, its effector-binding domain is commonly engineered. However, this strategy is encumbered by the limitation of diversifying such a large domain and the risk of affecting effector specificity. Molecular dynamics simulation of effector binding of LysG (an LysR-type transcriptional regulator, LTTR) suggests the crucial role of the short linker helix (LH) connecting effector- and DNA-binding domains in protein conformational change. Directed evolution of LH efficiently produced LysG variants with extended operational range and unaltered effector specificity. The whole-cell biosensor based on the best LysG^E58V variant outperformed the wild-type LysG in enzyme high-throughput screening and dynamic regulation of l-lysine biosynthetic pathway. LH mutations are suggested to affect DNA binding and facilitate transcriptional activation upon effector binding. LH engineering was also successfully applied to optimize another LTTR BenM for biosensing. Since LTTRs represent the largest family of prokaryotic transcriptional regulators with highly conserved structures, LH engineering is an efficient and universal strategy for development and optimization of whole-cell biosensors.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications

Cells are inherently dynamic, whether they are responding to environmental conditions or simply at equilibrium, with biomolecules constantly being made and destroyed. Due to their small volumes, the chemical reactions inside cells are stochastic, such that genetically identical cells display heterogeneous behaviors and gene expression profiles. Studying these dynamic processes is challenging, but the development of microfluidic methods enabling the tracking of individual prokaryotic cells with microscopy over long time periods under controlled growth conditions has led to many discoveries. This review focuses on the recent developments of one such microfluidic device nicknamed the mother machine. We overview the original device design, experimental setup, and challenges associated with this platform. We then describe recent methods for analyzing experiments using automated image segmentation and tracking. We further discuss modifications to the experimental setup that allow for time-varying environmental control, replicating batch culture conditions, cell screening based on their dynamic behaviors, and to accommodate a variety of microbial species. Finally, this review highlights the discoveries enabled by this technology in diverse fields, such as cell-size control, genetic mutations, cellular aging, and synthetic biology.

Microbial metabolites in the marine carbon cycle

One-quarter of photosynthesis-derived carbon on Earth rapidly cycles through a set of short-lived seawater metabolites that are generated from the activities of marine phytoplankton, bacteria, grazers and viruses. Here we discuss the sources of microbial metabolites in the surface ocean, their roles in ecology and biogeochemistry, and approaches that can be used to analyse them from chemistry, biology, modelling and data science. Although microbial-derived metabolites account for only a minor fraction of the total reservoir of marine dissolved organic carbon, their flux and fate underpins the central role of the ocean in sustaining life on Earth.

New insights into phenotypic heterogeneity for the distinct lipid accumulation of Schizochytrium sp. H016

BACKGROUND

Schizochytrium sp. is a marine heterotrophic protist and an important sustainable resource for high value-added docosahexaenoic acid in the future. The production of different phenotypes during the continuous subculture of Schizochytrium sp. results in a serious reduction in lipid yield and complicates the used of this strain in scientific research and industrial production. Hence, obtaining an improved understanding of the phenotypic differences and molecular mechanisms underlying the cell-to-cell heterogeneity of Schizochytrium sp. is necessary.

RESULTS

After continuous culture passage, Schizochytrium sp. H016 differentiated into two subpopulations with different morphologies and showed decreased capacity for lipid production. The presence of cell subpopulations with degraded lipid droplets led to a substantial decrease in overall lipid yield. Here, a rapid screening strategy based on fluorescence-activated cell sorting was proposed to classify and isolate subpopulations quickly in accordance with their lipid-producing capability. The final biomass and lipid yield of the subpopulation with high cell lipid content (i.e., H016-H) were 38.83 and 17.22 g/L, respectively, which were 2.07- and 5.38-fold higher than those of the subpopulation with low lipid content (i.e., H016-L), respectively. Subsequently, time‑resolved transcriptome analysis was performed to elucidate the mechanism of phenotypic heterogeneity in different subpopulations. Results showed that the expression of genes related to the cell cycle and lipid degradation was significantly upregulated in H016-L, whereas the metabolic pathways related to fatty acid synthesis and glyceride accumulation were remarkably upregulated in H016-H.

CONCLUSION

This study innovatively used flow cytometry combined with transcriptome technology to provide new insights into the phenotypic heterogeneity of different cell subpopulations of Schizochytrium sp. Furthermore, these results lay a strong foundation for guiding the breeding of oleaginous microorganisms with high lipid contents.

Human metabolite detection by surface-enhanced Raman spectroscopy

Metabolites are important biomarkers in human body fluids, conveying direct information of cellular activities and physical conditions. Metabolite detection has long been a research hotspot in the field of biology and medicine. Surface-enhanced Raman spectroscopy (SERS), based on the molecular “fingerprint” of Raman spectrum and the enormous signal enhancement (down to a single-molecule level) by plasmonic nanomaterials, has proven to be a novel and powerful tool for metabolite detection. SERS provides favorable properties such as ultra-sensitive, label-free, rapid, specific, and non-destructive detection processes. In this review, we summarized the progress in recent 10 years on SERS-based sensing of endogenous metabolites at the cellular level, in tissues, and in biofluids, as well as drug metabolites in biofluids. We made detailed discussions on the challenges and optimization methods of SERS technique in metabolite detection. The combination of SERS with modern biomedical technology were also anticipated.

Probing the Biogenesis of Polysaccharide Granules in Algal Cells at Sub-Organellar Resolution via Raman Microscopy with Stable Isotope Labeling

Phototrophs assimilate CO₂ into organic compounds that accumulate in storage organelles. Elucidation of the carbon dynamics of storage organelles could enhance the production efficiency of valuable compounds and facilitate the screening of strains with high photosynthetic activity. To comprehensively elucidate the carbon dynamics of these organelles, the intraorganellar distribution of the carbon atoms that accumulate at specific time periods should be probed. In this study, the biosynthesis of polysaccharides in storage organelles was spatiotemporally probed via stimulated Raman scattering (SRS) microscopy using a stable isotope (¹³C) as the tracking probe. Paramylon granules (a storage organelle of β-1,3-glucan) accumulated in a unicellular photosynthetic alga, Euglena gracilis, were investigated as a model organelle. The carbon source of the culture medium was switched from NaH¹²CO₃ to NaH¹³CO₃ during the production of the paramylon granules; this resulted in the distribution of the ¹²C and ¹³C constituents in the granules, so that the biosynthetic process could be tracked. Taking advantage of high-resolution SRS imaging and label switching, the localization of the ¹²C and ¹³C constituents inside a single paramylon granule could be visualized in three dimensions, thus revealing the growth process of paramylon granules. We propose that this method can be used for comprehensive elucidation of the dynamic activities of storage organelles.

Metabolic therapy and bioenergetic analysis: The missing piece of the puzzle

BACKGROUND

Aberrant metabolism is recognized as a hallmark of cancer, a pillar necessary for cellular proliferation. Regarding bioenergetics (ATP generation), most cancers display a preference not only toward aerobic glycolysis ("Warburg effect") and glutaminolysis (mitochondrial substrate level-phosphorylation) but also toward other metabolites such as lactate, pyruvate, and fat-derived sources. These secondary metabolites can assist in proliferation but cannot fully cover ATP demands.

SCOPE OF REVIEW

The concept of a static metabolic profile is challenged by instances of heterogeneity and flexibility to meet fuel/anaplerotic demands. Although metabolic therapies are a promising tool to improve therapeutic outcomes, either via pharmacological targets or press-pulse interventions, metabolic plasticity is rarely considered. Lack of bioenergetic analysis in vitro and patient-derived models is hindering translational potential. Here, we review the bioenergetics of cancer and propose a simple analysis of major metabolic pathways, encompassing both affordable and advanced techniques. A comprehensive compendium of Seahorse XF bioenergetic measurements is presented for the first time.

MAJOR CONCLUSIONS

Standardization of principal readouts might help researchers to collect a complete metabolic picture of cancer using the most appropriate methods depending on the sample of interest.

A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data

The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

Single Cell Protein: A Potential Substitute in Human and Animal Nutrition

Single cell protein (SCP) is the first product of the fermentation process and has proven to be a good protein alternative. Food competition is becoming more intense as the world’s population continues to grow. Soon, SCP may be able to compensate for a protein deficit. Various global businesses are focusing on SCP production, and the scope of its application is expanding as time and knowledge increases. High quantities of SCP can be produced by microorganisms, such as algae, yeast, fungi and bacteria, due to their fast development rate and the significant level of protein in their chemical structure. Beside proteins, SCP contains carbohydrates, nucleic acids, lipids, minerals, vitamins and several important amino acids. SCP has been an effective substitute for more expensive protein sources such as fish and soybean products. In conclusion, SCP can easily replace traditional protein sources in human and animal feed without detrimental effects. Potential substrate candidates and optimization strategies for SCP production have been extensively studied. This review article focuses on the various aspects of SCP, from its production, using different substrates, player microorganisms and nutritional benefits, to its economic aspects.

High-throughput optofluidic screening for improved microbial cell factories via real-time micron-scale productivity monitoring

The industrial synthetic biology sector has made huge investments to achieve relevant miniaturized screening systems for scalable fermentation. Here we present the first example of a high-throughput (>10³ genotypes per week) perfusion-based screening system to improve small-molecule secretion from microbial strains. Using the Berkeley Lights Beacon® system, the productivity of each strain could be directly monitored in real time during continuous culture, yielding phenotypes that correlated strongly (r² > 0.8, p < 0.0005) with behavior in industrially relevant bioreactor processes. This method allows a much closer approximation of a typical fed-batch fermentation than conventional batch-like droplet or microplate culture models, in addition to rich time-dependent data on growth and productivity. We demonstrate these advantages by application to the improvement of high-productivity strains using whole-genome random mutagenesis, yielding mutants with substantially improved (by up to 85%) peak specific productivities in bioreactors. Each screen of ∼5 × 10³ mutants could be completed in under 8 days (including 5 days involving user intervention), saving ∼50-75% of the time required for conventional microplate-based screening methods.

Single Cell Metabolite Detection Using Inertial Microfluidics-Assisted Ion Mobility Mass Spectrometry

Single-cell metabolite measurement remains highly challenging due to difficulties related to single cell isolation, metabolite detection, and identification of low levels of metabolites. Here, as a first step of the technological development, we propose a novel strategy integrating spiral inertial microfluidics and ion mobility mass spectrometry (IM-MS) for single-cell metabolite detection and identification. Cells in methanol suspension are inertially focused into a single stream in the spiral microchannel. This stream of separated cells is delivered to the nanoelectrospray needle to be lysed and ionized and subsequently analyzed in real time by IM-MS. This analytical system enables six to eight single-cell metabolic fingerprints to be collected per minute, including gas-phase collisional cross section (CCS) measurements as an additional molecular descriptor, giving increased confidence in metabolite identification. As a proof of concept, the metabolic profiles of three types of cancer cells (U2OS, HepG2, and HepG2.215) were successfully screened, and 19 distinct lipids species were identified with CCS value filtering. Furthermore, principal component analysis (PCA) showed differentiation of the three cancer cell lines, mainly due to cellular surface phospholipids. Taken together, our technology platform offers a simple and efficient method for single-cell lipid profiling, with additional ion mobility separation of lipids significantly improving the confidence toward identification of metabolites.

High-throughput screening for high-efficiency small-molecule biosynthesis

Systems metabolic engineering faces the formidable task of rewiring microbial metabolism to cost-effectively generate high-value molecules from a variety of inexpensive feedstocks for many different applications. Because these cellular systems are still too complex to model accurately, vast collections of engineered organism variants must be systematically created and evaluated through an enormous trial-and-error process in order to identify a manufacturing-ready strain. The high-throughput screening of strains to optimize their scalable manufacturing potential requires execution of many carefully controlled, parallel, miniature fermentations, followed by high-precision analysis of the resulting complex mixtures. This review discusses strategies for the design of high-throughput, small-scale fermentation models to predict improved strain performance at large commercial scale. Established and promising approaches from industrial and academic groups are presented for both cell culture and analysis, with primary focus on microplate- and microfluidics-based screening systems.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Bacterial metabolic heterogeneity: origins and applications in engineering and infectious disease

Bacteria within an isoclonal population display significant heterogeneity in metabolism, even under tightly controlled environmental conditions. Metabolic heterogeneity enables influential functions not possible or measurable at the ensemble scale. Several molecular and cellular mechanisms are likely to give rise to metabolic heterogeneity including molecular noise in metabolic enzyme expression, positive feedback loops, and asymmetric partitioning of cellular components during cell division. Dissection of the mechanistic origins of metabolic heterogeneity has been enabled by recent developments in single-cell analytical tools. Finally, we provide a discussion of recent studies examining the importance of metabolic heterogeneity in applied settings such as infectious disease and metabolic engineering.

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.

Next-generation physiology approaches to study microbiome function at single cell level

The function of cells in their native habitat often cannot be reliably predicted from genomic data or from physiology studies of isolates. Traditional experimental approaches to study the function of taxonomically and metabolically diverse microbiomes are limited by their destructive nature, low spatial resolution or low throughput. Recently developed technologies can offer new insights into cellular function in natural and human-made systems and how microorganisms interact with and shape the environments that they inhabit. In this Review, we provide an overview of these next-generation physiology approaches and discuss how the non-destructive analysis of cellular phenotypes, in combination with the separation of the target cells for downstream analyses, provide powerful new, complementary ways to study microbiome function. We anticipate that the widespread application of next-generation physiology approaches will transform the field of microbial ecology and dramatically improve our understanding of how microorganisms function in their native environment.

Spatiotemporal monitoring of intracellular metabolic dynamics by resonance Raman microscopy with isotope labeling

We probed production process of a cellular metabolite with a stable isotope-labeled substrate exposed to various conditions.

A digital microfluidic system with 3D microstructures for single-cell culture

Despite the precise controllability of droplet samples in digital microfluidic (DMF) systems, their capability in isolating single cells for long-time culture is still limited: typically, only a few cells can be captured on an electrode. Although fabricating small-sized hydrophilic micropatches on an electrode aids single-cell capture, the actuation voltage for droplet transportation has to be significantly raised, resulting in a shorter lifetime for the DMF chip and a larger risk of damaging the cells. In this work, a DMF system with 3D microstructures engineered on-chip is proposed to form semi-closed micro-wells for efficient single-cell isolation and long-time culture. Our optimum results showed that approximately 20% of the micro-wells over a 30 × 30 array were occupied by isolated single cells. In addition, low-evaporation-temperature oil and surfactant aided the system in achieving a low droplet actuation voltage of 36V, which was 4 times lower than the typical 150 V, minimizing the potential damage to the cells in the droplets and to the DMF chip. To exemplify the technological advances, drug sensitivity tests were run in our DMF system to investigate the cell response of breast cancer cells (MDA-MB-231) and breast normal cells (MCF-10A) to a widely used chemotherapeutic drug, Cisplatin (Cis). The results on-chip were consistent with those screened in conventional 96-well plates. This novel, simple and robust single-cell trapping method has great potential in biological research at the single cell level.

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology.

Novel computational model of gastrula morphogenesis to identify spatial discriminator genes by self-organizing map (SOM) clustering

Deciphering the key mechanisms of morphogenesis during embryonic development is crucial to understanding the guiding principles of the body plan and promote applications in biomedical research fields. Although several computational tissue reconstruction methods using cellular gene expression data have been proposed, those methods are insufficient with regard to arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Here, we report SPRESSO, a new in silico three-dimensional (3D) tissue reconstruction method using stochastic self-organizing map (stochastic-SOM) clustering, to estimate the spatial domains of cells in tissues or organs from only their gene expression profiles. With only five gene sets defined by Gene Ontology (GO), we successfully demonstrated the reconstruction of a four-domain structure of mid-gastrula mouse embryo (E7.0) with high reproducibility (success rate = 99%). Interestingly, the five GOs contain 20 genes, most of which are related to differentiation and morphogenesis, such as activin A receptor and Wnt family member genes. Further analysis indicated that Id2 is the most influential gene contributing to the reconstruction. SPRESSO may provide novel and better insights on the mechanisms of 3D structure formation of living tissues via informative genes playing a role as spatial discriminators.

Quantification of Drug Molecules in Live Single Cells Using the Single-Probe Mass Spectrometry Technique

Analyzing cellular constituents on the single-cell level through mass spectrometry (MS) allows for a wide range of compounds to be studied simultaneously. However, there is a need for quantitative single-cell mass spectrometry (qSCMS) methods to fully characterize drug efficacy from individual cells within cell populations. In this study, qSCMS experiments were carried out using the Single-probe MS technique. The method was successfully used to perform rapid absolute quantifications of the anticancer drug irinotecan in individual mammalian cancer cells under ambient conditions in real time. Traditional liquid chromatography/mass spectrometry (LC/MS) quantifications of irinotecan in cell lysate samples were used to compare the results from Single-probe qSCMS. This technique showcases heterogeneity of drug efficacy on the single-cell level.

Advancing single-cell proteomics and metabolomics with microfluidic technologies

Recent advances in single-cell analysis have unraveled substantial heterogeneity among seemingly identical cells at genomic and transcriptomic levels. These discoveries have urged scientists to develop new tools that are capable of investigating single cells from a broader set of "omics". Proteomics and metabolomics, for instance, are of particular interest as they are closely correlated with a dynamic picture of cellular behaviors and phenotypic identities. The development of such tools requires highly efficient isolation and processing of a large number of individual cells, where techniques such as microfluidics are extremely useful. Here, we review the recent advances in single-cell proteomics and metabolomics, with a focus on microfluidics-based platforms. We highlight a vast array of emerging microfluidic formats for single-cell isolation and manipulation, and how the state-of-the-art analytical tools are coupled with such platforms for proteomic and metabolomic profiling.

Particle Timing Control and Alignment in Microchannel Flow by Applying Periodic Force Control Using Dielectrophoretic Force

In this study, a technique for particle streamwise timing, spacing and velocity control (alignment) in microchannel flow by controlling the forces exerted on the particle in space and time, was developed. In the present technique, the timing of particles crossing a certain position in microchannel flow with a specific interval and the particle velocity are controlled by applying acceleration and deceleration forces periodically in the streamwise direction and activating them periodically. The force is produced by a dielectrophoretic force using ladder-type electrodes embedded in the microfluidic device and is turned on and off in a cycle. The timing of particles crossing a certain position can be changed by adjusting the phase of the on-off cycle, i.e., the phase of the voltage signal. In the experiment, timing and velocity were measured at the inlet and outlet of ladder-type regions for Jurkat cells and particles of some variation in size, and probability density functions for the deviation of these values from the equilibrium (aligned) state were evaluated. Further, we will discuss the motion characteristics of the particles numerically and experimentally to understand the mechanism and evaluate the performance of the particle timing control and alignment using the present technique. The results confirm that the particles randomly distributed at the inlet of ladder-type electrode regions are controlled to flow with even spacing at a specific velocity. Moreover, the timing of the particles passing a specific location in the ladder-type electrode region was synchronized with the activated/nonactivated cycle of the applied force and could be specified.

Self-Folding Hybrid Graphene Skin for 3D Biosensing

Biological samples such as cells have complex three-dimensional (3D) spatio-molecular profiles and often feature soft and irregular surfaces. Conventional biosensors are based largely on 2D and rigid substrates, which have limited contact area with the entirety of the surface of biological samples making it challenging to obtain 3D spatially resolved spectroscopic information, especially in a label-free manner. Here, we report an ultrathin, flexible skinlike biosensing platform that is capable of conformally wrapping a soft or irregularly shaped 3D biological sample such as a cancer cell or a pollen grain, and therefore enables 3D label-free spatially resolved molecular spectroscopy via surface-enhanced Raman spectroscopy (SERS). Our platform features an ultrathin thermally responsive poly( N-isopropylacrylamide)-graphene-nanoparticle hybrid skin that can be triggered to self-fold and wrap around 3D micro-objects in a conformal manner due to its superior flexibility. We highlight the utility of this 3D biosensing platform by spatially mapping the 3D molecular signatures of a variety of microparticles including silica microspheres, spiky pollen grains, and human breast cancer cells.

Design of a multi-sensor platform for integrating extracellular acidification rate with multi-metabolite flux measurement for small biological samples

Rates of cellular oxygen consumption (OCR) and extracellular acidification (ECAR) are widely used proxies for mitochondrial oxidative phosphorylation (OXPHOS) and glycolytic rate in cell metabolism studies. However, ECAR can result from both oxidative metabolism (carbonic acid formation) and glycolysis (lactate release), potentially leading to erroneous conclusions about metabolic substrate utilization. Co-measurement of extracellular glucose and lactate flux along with OCR and ECAR can improve the accuracy and provide better insight into cellular metabolic processes but is currently not feasible with any commercially available instrumentation. Herein, we present a miniaturized multi-sensor platform capable of real-time monitoring of OCR and ECAR along with extracellular lactate and glucose flux for small biological samples such as single equine embryos. This multiplexed approach enables validation of ECAR resulting from OXPHOS versus glycolysis, and expression of metabolic flux ratios that provide further insight into cellular substrate utilization. We demonstrate expected shifts in embryo metabolism during development and in response to OXPHOS inhibition as a model system for monitoring metabolic plasticity in very small biological samples. Furthermore, we also present a preliminary interference analysis of the multi-sensor platform to allow better understanding of sensor interference in the proposed multi-sensor platform. The capability of the platform is illustrated with measurements of multi-metabolites of single-cell equine embryos for assisted reproduction technologies. However, this platform has a wide potential utility for analyzing small biological samples such as single cells and tumor biopsies for immunology and cancer research applications.

Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging

Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; however, how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Here, we introduce a single-cell strategy for quantifying the trade-offs between triacylglycerol production and growth in the oleaginous microorganism Yarrowia lipolytica. The strategy relies on high-throughput quantitative-phase imaging and, enabled by nanoscale secondary ion mass spectrometry analyses and dedicated image processing, allows us to image how resources are partitioned between growth and productivity. Enhanced precision over population-averaging biotechnologies and conventional microscopy demonstrates how cellular noise impacts growth and productivity differently. As such, subpopulations with distinct metabolic trade-offs emerge, with notable impacts on strain performance and robustness. By quantifying the self-degradation of cytosolic macromolecules under nutrient-limiting conditions, we discover the cell-to-cell heterogeneity in protein and fatty-acid recycling, unmasking a potential bet-hedging strategy under starvation.

Single-cell assay on microfluidic devices

Advances in microfluidic techniques have prompted researchers to study the inherent heterogeneity of single cells in cell populations.

True one cell chemical analysis: a review

The constantly growing field of True One Cell (TOC) analysis has provided important information on the direct chemical composition of various cells and cellular components.

Microfluidics and single-cell microscopy to study stochastic processes in bacteria

Bacteria have molecules present in low and fluctuating numbers that randomize cell behaviors. Understanding these stochastic processes and their impact on cells has, until recently, been limited by the lack of single-cell measurement methods. Here, we review recent developments in microfluidics that enable following individual cells over long periods of time under precisely controlled conditions, and counting individual fluorescent molecules in many cells. We showcase discoveries that were made possible using these devices in various aspects of microbiology, such as antibiotic tolerance/persistence, cell-size control, cell-fate determination, DNA damage response, and synthetic biology.

Dean flow assisted cell ordering system for lipid profiling in single-cells using mass spectrometry

A Dean flow assisted ordering system connected to an ESI-MS to identify single-cells in a subpopulation by lipid profiling.

Cell-Type Specific Metabolic Flux Analysis: A Challenge for Metabolic Phenotyping and a Potential Solution in Plants

Stable isotope labelling experiments are used routinely in metabolic flux analysis (MFA) to determine the metabolic phenotype of cells and tissues. A complication arises in multicellular systems because single cell measurements of transcriptomes, proteomes and metabolomes in multicellular organisms suggest that the metabolic phenotype will differ between cell types. In silico analysis of simulated metabolite isotopomer datasets shows that cellular heterogeneity confounds conventional MFA because labelling data averaged over multiple cell types does not necessarily yield averaged flux values. A potential solution to this problem—the use of cell-type specific reporter proteins as a source of cell-type specific labelling data—is proposed and the practicality of implementing this strategy in the roots of Arabidopsis thaliana seedlings is explored. A protocol for the immunopurification of ectopically expressed green fluorescent protein (GFP) from Arabidopsis thaliana seedlings using a GFP-binding nanobody is developed, and through GC-MS analysis of protein hydrolysates it is established that constitutively expressed GFP reports accurately on the labelling of total protein in root tissues. It is also demonstrated that the constitutive expression of GFP does not perturb metabolism. The principal obstacle to the implementation of the method in tissues with cell-type specific GFP expression is the sensitivity of the GC-MS system.

Beyond the bulk: disclosing the life of single microbial cells

Microbial single cell analysis has led to discoveries that are beyond what can be resolved with population-based studies. It provides a pristine view of the mechanisms that organize cellular physiology, unbiased by population heterogeneity or uncontrollable environmental impacts. A holistic description of cellular functions at the single cell level requires analytical concepts beyond the miniaturization of existing technologies, defined but uncontrolled by the biological system itself. This review provides an overview of the latest advances in single cell technologies and demonstrates their potential. Opportunities and limitations of single cell microbiology are discussed using selected application-related examples.

Synthetic biology for manufacturing chemicals: constraints drive the use of non-conventional microbial platforms

Genetically modified microbes have had much industrial success producing protein-based products (such as antibodies and enzymes). However, engineering microbial workhorses for biomanufacturing of commodity compounds remains challenging. First, microbes cannot afford burdens with both overexpression of multiple enzymes and metabolite drainage for product synthesis. Second, synthetic circuits and introduced heterologous pathways are not yet as "robust and reliable" as native pathways due to hosts' innate regulations, especially under suboptimal fermentation conditions. Third, engineered enzymes may lack channeling capabilities for cascade-like transport of metabolites to overcome diffusion barriers or to avoid intermediate toxicity in the cytoplasmic environment. Fourth, moving engineered hosts from laboratory to industry is unreliable because genetic mutations and non-genetic cell-to-cell variations impair the large-scale fermentation outcomes. Therefore, synthetic biology strains often have unsatisfactory industrial performance (titer/yield/productivity). To overcome these problems, many different species are being explored for their metabolic strengths that can be leveraged to synthesize specific compounds. Here, we provide examples of non-conventional and genetically amenable species for industrial manufacturing, including the following: Corynebacterium glutamicum for its TCA cycle-derived biosynthesis, Yarrowia lipolytica for its biosynthesis of fatty acids and carotenoids, cyanobacteria for photosynthetic production from its sugar phosphate pathways, and Rhodococcus for its ability to biotransform recalcitrant feedstock. Finally, we discuss emerging technologies (e.g., genome-to-phenome mapping, single cell methods, and knowledge engineering) that may facilitate the development of novel cell factories.

Microfluidics in systems biology-hype or truly useful?

Systems biology often relies on large-scale measurements and model-building to understand how complex biological systems function. Microfluidic technology has been touted as a tool for high-throughput experiments and has been a valuable tool to some systems biology research. This review focuses on applications where microfluidics can enhance experimental sensitivity and throughput, particularly in recent development in single-cell analyses and analyses on multi-cellular or complex biological entities. We conclude that microfluidics is not necessarily always useful for systems biology, but when used appropriately can greatly enhance experimentalists' ability to measure and control, and thereby enhance the understanding of and expand the utility of biological systems.

Microtechnologies for Cell Microenvironment Control and Monitoring

A great breadth of questions remains in cellular biology. Some questions cannot be answered using traditional analytical techniques and so demand the development of new tools for research. In the near future, the development of highly integrated microfluidic analytical platforms will enable the acquisition of unknown biological data. These microfluidic systems must allow cell culture under controlled microenvironment and high throughput analysis. For this purpose, the integration of a variable number of newly developed micro- and nano-technologies, which enable control of topography and surface chemistry, soluble factors, mechanical forces and cell–cell contacts, as well as technology for monitoring cell phenotype and genotype with high spatial and temporal resolution will be necessary. These multifunctional devices must be accompanied by appropriate data analysis and management of the expected large datasets generated. The knowledge gained with these platforms has the potential to improve predictive models of the behavior of cells, impacting directly in better therapies for disease treatment. In this review, we give an overview of the microtechnology toolbox available for the design of high throughput microfluidic platforms for cell analysis. We discuss current microtechnologies for cell microenvironment control, different methodologies to create large arrays of cellular systems and finally techniques for monitoring cells in microfluidic devices.

Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements

High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.

Exploiting Bioprocessing Fluctuations to Elicit the Mechanistics of De Novo Lipogenesis in Yarrowia lipolytica

Despite substantial achievements in elucidating the metabolic pathways of lipogenesis, a mechanistic representation of lipid accumulation and degradation has not been fully attained to-date. Recent evidence suggests that lipid accumulation can occur through increases of either the cytosolic copy-number of lipid droplets (LDs), or the LDs size. However, the prevailing phenotype, or how such mechanisms pertain to lipid degradation remain poorly understood. To address this shortcoming, we employed the-recently discovered-innate bioprocessing fluctuations in Yarrowia lipolytica, and performed single-cell fluctuation analysis using optical microscopy and microfluidics that generate a quasi-time invariant microenvironment. We report that lipid accumulation at early stationary phase in rich medium is substantially more likely to occur through variations in the LDs copy-number, rather than the LDs size. Critically, these mechanistics are also preserved during lipid degradation, as well as upon exposure to a protein translation inhibitor. The latter condition additionally induced a lipid accumulation phase, accompanied by the downregulation of lipid catabolism. Our results enable an in-depth mechanistic understanding of lipid biogenesis, and expand longitudinal single-cell fluctuation analyses from gene regulation to metabolism.