Advancing single-cell proteomics and metabolomics with microfluidic technologies

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

Microfluidic Blood Separation: Key Technologies and Critical Figures of Merit

Blood is a complex sample comprised mostly of plasma, red blood cells (RBCs), and other cells whose concentrations correlate to physiological or pathological health conditions. There are also many blood-circulating biomarkers, such as circulating tumor cells (CTCs) and various pathogens, that can be used as measurands to diagnose certain diseases. Microfluidic devices are attractive analytical tools for separating blood components in point-of-care (POC) applications. These platforms have the potential advantage of, among other features, being compact and portable. These features can eventually be exploited in clinics and rapid tests performed in households and low-income scenarios. Microfluidic systems have the added benefit of only needing small volumes of blood drawn from patients (from nanoliters to milliliters) while integrating (within the devices) the steps required before detecting analytes. Hence, these systems will reduce the associated costs of purifying blood components of interest (e.g., specific groups of cells or blood biomarkers) for studying and quantifying collected blood fractions. The microfluidic blood separation field has grown since the 2000s, and important advances have been reported in the last few years. Nonetheless, real POC microfluidic blood separation platforms are still elusive. A widespread consensus on what key figures of merit should be reported to assess the quality and yield of these platforms has not been achieved. Knowing what parameters should be reported for microfluidic blood separations will help achieve that consensus and establish a clear road map to promote further commercialization of these devices and attain real POC applications. This review provides an overview of the separation techniques currently used to separate blood components for higher throughput separations (number of cells or particles per minute). We present a summary of the critical parameters that should be considered when designing such devices and the figures of merit that should be explicitly reported when presenting a device's separation capabilities. Ultimately, reporting the relevant figures of merit will benefit this growing community and help pave the road toward commercialization of these microfluidic systems.

Application of flow cytometry for rapid, high-throughput, multiparametric analysis of environmental microbiomes

Quantification of the abundance and understanding of the dynamics of the microbial communities is essential to establish a basis for microbiome characterization. The conventional techniques used for the quantification of microbes are complicated and time-consuming. With scientific advancement, many techniques evolved and came into account. Among them, flow cytometry is a robust, high-throughput technique through which microbial dynamics, morphology, microbial distribution, physiological characteristics, and many more attributes can be studied in a high-throughput manner with comparatively less time and resources. Flow cytometry, when combined with other omics-based methods, offers a rapid and efficient platform to analyze and understand the composition of microbiome at the cellular level. The microbial diversity observed through flow cytometry will not be equivalent to that obtained by sequencing methods, but this integrated approach holds great potential for high throughput characterization of microbiomes. Flow cytometry is regarded as an established characterization tool in haematology, oncology, immunology, and medical microbiology research; however, its application in environmental microbiology is yet to be explored. This comprehensive review aims to delve into the diverse environmental applications of flow cytometry across various domains, including but not limited to bioremediation, landfills, anaerobic digestion, industrial bioprocesses, water quality regulation, and soil quality regulation. By conducting an in-depth analysis, this article seeks to shed light on the potential benefits and challenges associated with the utilization of flow cytometry in addressing environmental concerns.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery

The identification of tumor-specific antigens (TSAs) is critical for developing effective cancer immunotherapies. Mass spectrometry (MS)-based immunopeptidomics has emerged as a powerful tool for identifying TSAs as physical molecules. However, current immunopeptidomics platforms face challenges in measuring low-abundance TSAs in a precise, sensitive, and reproducible manner from small needle-tissue biopsies (<1 mg). Inspired by recent advances in single-cell proteomics, microfluidics technology offers a promising solution to these limitations by providing improved isolation of human leukocyte antigen (HLA)-associated peptides with higher sensitivity. In this context, we highlight the challenges in sample preparation and the rationale for developing microfluidics technology in immunopeptidomics. Additionally, we provide an overview of promising microfluidic methods, including microchip pillar arrays, valved-based systems, droplet microfluidics, and digital microfluidics, and discuss the latest research on their application in MS-based immunopeptidomics and single-cell proteomics.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

Recent Advances in Methods for Circulating Tumor Cell Detection

Circulating tumor cells (CTCs) are released from primary tumors and transported through the body via blood or lymphatic vessels before settling to form micrometastases under suitable conditions. Accordingly, several studies have identified CTCs as a negative prognostic factor for survival in many types of cancer. CTCs also reflect the current heterogeneity and genetic and biological state of tumors; so, their study can provide valuable insights into tumor progression, cell senescence, and cancer dormancy. Diverse methods with differing specificity, utility, costs, and sensitivity have been developed for isolating and characterizing CTCs. Additionally, novel techniques with the potential to overcome the limitations of existing ones are being developed. This primary literature review describes the current and emerging methods for enriching, detecting, isolating, and characterizing CTCs.

From the Catastrophic Objective Irreproducibility of Cancer Research and Unavoidable Failures of Molecular Targeted Therapies to the Sparkling Hope of Supramolecular Targeted Strategies

The unprecedented non-reproducibility of the results published in the field of cancer research has recently come under the spotlight. In this short review, we try to highlight some general principles in the organization and evolution of cancerous tumors, which objectively lead to their enormous variability and, consequently, the irreproducibility of the results of their investigation. This heterogeneity is also extremely unfavorable for the effective use of molecularly targeted medicine. Against the seemingly comprehensive background of this heterogeneity, we single out two supramolecular characteristics common to all tumors: the clustered nature of tumor interactions with their microenvironment and the formation of biomolecular condensates with tumor-specific distinctive features. We suggest that these features can form the basis of strategies for tumor-specific supramolecular targeted therapies.

Droplet microfluidics for CTC-based liquid biopsy: a review

Circulating tumor cells (CTCs) are important biomarkers of liquid biopsy. The number and heterogeneity of CTCs play an important role in cancer diagnosis and personalized medicine. However, owing to the low-abundance biomarkers of CTCs, conventional assays are only able to detect CTCs at the population level. Therefore, there is a pressing need for a highly sensitive method to analyze CTCs at the single-cell level. As an important branch of microfluidics, droplet microfluidics is a high-throughput and sensitive single-cell analysis platform for the quantitative detection and heterogeneity analysis of CTCs. In this review, we focus on the quantitative detection and heterogeneity analysis of CTCs using droplet microfluidics. Technologies that enable droplet microfluidics, particularly high-throughput droplet generation and high-efficiency droplet manipulation, are first discussed. Then, recent advances in detecting and analyzing CTCs using droplet microfluidics from the different aspects of nucleic acids, proteins, and metabolites are introduced. The purpose of this review is to provide guidance for the continued study of droplet microfluidics for CTC-based liquid biopsy.

Microfluidic systems in clinical diagnosis

The use of microfluidic devices is highly attractive in the field of biomedical and clinical assessments, as their portability and fast response time have become crucial in providing opportune therapeutic treatments to patients. The applications of microfluidics in clinical diagnosis and point-of-care devices are continuously growing. The present review article discusses three main fields where miniaturized devices are successfully employed in clinical applications. The quantification of ions, sugars, and small metabolites is examined considering the analysis of bodily fluids samples and the quantification of this type of analytes employing real-time wearable devices. The discussion covers the level of maturity that the devices have reached as well as cost-effectiveness. The analysis of proteins with clinical relevance is presented and organized by the function of the proteins. The last section covers devices that can perform single-cell metabolomic and proteomic assessments. Each section discusses several strategically selected recent reports on microfluidic devices successfully employed for clinical assessments, to provide the reader with a wide overview of the plethora of novel systems and microdevices developed in the last 5 years. In each section, the novel aspects and main contributions of each reviewed report are highlighted. Finally, the conclusions and future outlook section present a summary and speculate on the future direction of the field of miniaturized devices for clinical applications.

Raman spectroscopy differ leukemic cells from their healthy counterparts and screen biomarkers in acute leukemia

Precision medicine is important in the treatment of acute leukemia (AL). The target therapies of AL provide an opportunity to reduce the mortality of AL. How AL cells differ from their healthy counterparts is the basis for the development of therapies and the outcome of AL patients. Therefore, a label-free and noninvasive single-cell Raman platform was used to characterize cell molecular profiles and found potential biomarkers from three healthy people and twelve AL patients with more than 90% accuracy. We analyzed myeloblasts, abnormal promyelocytes, monoblasts and B-ALL cells respectively, compared with their healthy counterparts, which could be distinguished by their intrinsic phenotypic Raman spectra using orthogonal partial least squares discriminate analysis (OPLS-DA). Most importantly, we selected statistically significant markers of the four leukemia models. Further analysis of leukemic granulocytes, we found that a combination of the 1003, 1341 and 1579 cm^-1 Raman peaks could discriminate myeloblasts and abnormal promyelocytes from normal granulocytes. The assignments of 1579 cm^-1 gave us a clue to find potential important variables myeloperoxidase related with AL diagnosis. Our study demonstrates the capability of the Raman platform to characterize leukemia cells with non-invasively probing metabolites. The biomarker we identified could be extensible to other blood cells and potentially have a high impact on leukemia therapy.

New opportunities and challenges of natural products research: When target identification meets single-cell multiomics

Natural products, and especially the active ingredients found in traditional Chinese medicine (TCM), have a thousand-year-long history of clinical use and a strong theoretical basis in TCM. As such, traditional remedies provide shortcuts for the development of original new drugs in China, and increasing numbers of natural products are showing great therapeutic potential in various diseases. This paper reviews the molecular mechanisms of action of natural products from different sources used in the treatment of inflammatory diseases and cancer, introduces the methods and newly emerging technologies used to identify and validate the targets of natural active ingredients, enumerates the expansive list of TCM used to treat inflammatory diseases and cancer, and summarizes the patterns of action of emerging technologies such as single-cell multiomics, network pharmacology, and artificial intelligence in the pharmacological studies of natural products to provide insights for the development of innovative natural product-based drugs. Our hope is that we can make use of advances in target identification and single-cell multiomics to obtain a deeper understanding of actions of mechanisms of natural products that will allow innovation and revitalization of TCM and its swift industrialization and internationalization.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Single cell metabolism: current and future trends

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Femtoliter-Droplet Mass Spectrometry Interface Utilizing Nanofluidics for Ultrasmall and High-Sensitivity Analysis

In the fields of biology and medicine, comprehensive protein analysis at the single-cell level utilizing mass spectrometry (MS) with pL sample volumes and zmol to amol sensitivity is required. Our group has developed nanofluidic analytical pretreatment methods that exploit nanochannels for downsizing chemical unit operations to fL-pL volumes. In the field of analytical instruments, mass spectrometers have advanced to achieve ultrahigh sensitivity. However, a method to interface between fL-pL pretreatments and mass spectrometers without sample loss and dispersion is still challenging. In this study, we developed an MS interface utilizing nanofluidics to achieve high-sensitivity detection. After charging analyte molecules by an applied voltage through an electrode, the liquid sample was converted to fL droplets by a nanofluidic device. Considering the inertial force that acts on the droplets, the droplets were carried with a controlled trajectory, even in turbulent air flow, and injected into a mass spectrometer with 100% efficiency. A module for heat transfer was designed and constructed, by which all of the injected droplets were vaporized to produce gas-phase ions. The detection of caffeine ions was achieved at a limit of detection of 1.52 amol, which was 290 times higher than a conventional MS interface by electrospray ionization with sample dispersion combined with a similar mass spectrometer. Therefore, sensitivity that was 2 orders of magnitude higher could be realized due to the 100% sample injection rate. The present study provides a new methodology for the analysis of ultrasmall samples with high-sensitivity, such as protein molecules produced from a single cell.

MIMAS: microfluidic platform in tandem with MALDI mass spectrometry for protein quantification from small cell ensembles

Understanding cell-to-cell variation at the molecular level provides relevant information about biological phenomena and is critical for clinical and biological research. Proteins carry important information not available from single-cell genomics and transcriptomics studies; however, due to the minute amount of proteins in single cells and the complexity of the proteome, quantitative protein analysis at the single-cell level remains challenging. Here, we report an integrated microfluidic platform in tandem with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the detection and quantification of targeted proteins from small cell ensembles (> 10 cells). All necessary steps for the assay are integrated on-chip including cell lysis, protein immunocapture, tryptic digestion, and co-crystallization with the matrix solution for MALDI-MS analysis. We demonstrate that our approach is suitable for protein quantification by assessing the apoptotic protein Bcl-2 released from MCF-7 breast cancer cells, ranging from 26 to 223 cells lysed on-chip (8.75 nL wells). A limit of detection (LOD) of 11.22 nM was determined, equivalent to 5.91 × 10⁷ protein molecules per well. Additionally, the microfluidic platform design was further improved, establishing the successful quantification of Bcl-2 protein from MCF-7 cell ensembles ranging from 8 to 19 cells in 4 nL wells. The LOD in the smaller well designs for Bcl-2 resulted in 14.85 nM, equivalent to 3.57 × 10⁷ protein molecules per well. This work shows the capability of our approach to quantitatively assess proteins from cell lysate on the MIMAS platform for the first time. These results demonstrate our approach constitutes a promising tool for quantitative targeted protein analysis from small cell ensembles down to single cells, with the capability for multiplexing through parallelization and automation.

High-Throughput, Living Single-Cell, Multiple Secreted Biomarker Profiling Using Microfluidic Chip and Machine Learning for Tumor Cell Classification

Secreted proteins provide abundant functional information on living cells and can be used as important tumor diagnostic markers, of which profiling at the single-cell level is helpful for accurate tumor cell classification. Currently, achieving living single-cell multi-index, high-sensitivity, and quantitative secretion biomarker profiling remains a great challenge. Here, a high-throughput living single-cell multi-index secreted biomarker profiling platform is proposed, combined with machine learning, to achieve accurate tumor cell classification. A single-cell culture microfluidic chip with self-assembled graphene oxide quantum dots (GOQDs) enables high-activity single-cell culture, ensuring normal secretion of biomarkers and high-throughput single-cell separation, providing sufficient statistical data for machine learning. At the same time, the antibody barcode chip with self-assembled GOQDs performs multi-index, highly sensitive, and quantitative detection of secreted biomarkers, in which each cell culture chamber covers a whole barcode array. Importantly, by combining the K-means strategy with machine learning, thousands of single tumor cell secretion data are analyzed, enabling tumor cell classification with a recognition accuracy of 95.0%. In addition, further profiling of the grouping results reveals the unique secretion characteristics of subgroups. This work provides an intelligent platform for high-throughput living single-cell multiple secretion biomarker profiling, which has broad implications for cancer investigation and biomedical research.

Accelerated protein digestion and separation with picoliter volume utilizing nanofluidics

Single cell analyses can provide critical biological insight into cellular heterogeneity. In particular, the proteome, which governs cell functions, is much more difficult to analyze because it is principally impossible to amplify proteins compared to nucleic acids. The most promising approach to single cell proteomics is based on the liquid chromatography mass spectrometry (LC-MS) platform. However, pretreatments before MS detection have two critical issues for single cell analysis: analyte loss as a result of adsorption and artifacts due to the duration of analysis. This is a serious problem because single cells have a limited number of protein molecules and a small volume. To solve these issues, we developed an integrated nanofluidic device to manipulate samples on a femtoliter to picoliter (fL-pL) scale to achieve high-throughput analysis via suppressing analyte loss. This device can perform tryptic digestion, chromatographic separation, and non-labeled detection with high consistency. In addition, we introduced an open/close valve by physical deformation of glass on a nanometer scale to independently modify the nanochannel surfaces and control sample aliquots. The injection system equipped with this valve achieved an injection volume of 1.0 ± 0.1 pL. By using this integrated device, we found that the chromatogram of bulk-digestion for 12 hours resembled that of 15 min-digestion in the nanochannel, which indicated that these conditions reached a similar state of digestion. Therefore, an integrated device for ultra-fast protein analysis was developed on a 1 pL scale for the first time.

High-throughput glycolytic inhibitor discovery targeting glioblastoma by graphite dots-assisted LDI mass spectrometry

Malignant tumors will become vulnerable if their uncontrolled biosynthesis and energy consumption engaged in metabolic reprogramming can be cut off. Here, we report finding a glycolytic inhibitor targeting glioblastoma with graphite dots-assisted laser desorption/ionization mass spectrometry as an integrated drug screening and pharmacokinetic platform (GLMSD). We have performed high-throughput virtual screening to narrow an initial library of 240,000 compounds down to the docking of 40 compounds and identified five previously unknown chemical scaffolds as promising hexokinase-2 inhibitors. The best inhibitor (Compd 27) can regulate the reprogrammed metabolic pathway in U87 glioma cells (median inhibitory concentration ~ 11.3 μM) for tumor suppression. Highly effective therapy against glioblastoma has been demonstrated in both subcutaneous and orthotopic brain tumors by synergizing Compd 27 and temozolomide. Our glycolytic inhibitor discovery can inspire personalized medicine targeting reprogrammed metabolisms of malignant tumors. GLMSD enables large, high-quality data for next-generation artificial intelligence-aided drug development.

Droplet-based microfluidics in biomedical applications

Droplet-based microfluidic systems have been employed to manipulate discrete fluid volumes with immiscible phases. Creating the fluid droplets at microscale has led to a paradigm shift in mixing, sorting, encapsulation, sensing, and designing high throughput devices for biomedical applications. Droplet microfluidics has opened many opportunities in microparticle synthesis, molecular detection, diagnostics, drug delivery, and cell biology. In the present review, we first introduce standard methods for droplet generation (i.e., passive and active methods) and discuss the latest examples of emulsification and particle synthesis approaches enabled by microfluidic platforms. Then, the applications of droplet-based microfluidics in different biomedical applications are detailed. Finally, a general overview of the latest trends along with the perspectives and future potentials in the field are provided.

Proteomics as a Complementary Technique to Characterize Bladder Cancer

Simple Summary

Although immunohistochemistry is a routine technique in clinics, and genomics has been rapidly incorporated, proteomics is a step behind. This general situation is also the norm in bladder cancer research. This review shows the contributions of proteomics to the molecular classification of bladder cancer, and to the study of histopathology due to tissue insults caused by tumors. Furthermore, the importance of proteomics for understanding the cellular and molecular changes as a consequence of the therapy of bladder cancer cannot be neglected.

Abstract

Bladder cancer (BC) is the most common tumor of the urinary tract and is conventionally classified as either non-muscle invasive or muscle invasive. In addition, histological variants exist, as organized by the WHO-2016 classification. However, innovations in next-generation sequencing have led to molecular classifications of BC. These innovations have also allowed for the tracing of major tumorigenic pathways and, therefore, are positioned as strong supporters of precision medicine. In parallel, immunohistochemistry is still the clinical reference to discriminate histological layers and to stage BC. Key contributions have been made to enlarge the panel of protein immunomarkers. Moreover, the analysis of proteins in liquid biopsy has also provided potential markers. Notwithstanding, their clinical adoption is still low, with very few approved tests. In this context, mass spectrometry-based proteomics has remained a step behind; hence, we aimed to develop them in the community. Herein, the authors introduce the epidemiology and the conventional classifications to review the molecular classification of BC, highlighting the contributions of proteomics. Then, the advances in mass spectrometry techniques focusing on maintaining the integrity of the biological structures are presented, a milestone for the emergence of histoproteomics. Within this field, the review then discusses selected proteins for the comprehension of the pathophysiological mechanisms of BC. Finally, because there is still insufficient knowledge, this review considers proteomics as an important source for the development of BC therapies.

Automation of single-cell proteomic sample preparation

Molecular heterogeneity exists at different spatial scales in biological samples and is an important parameter in the development of pathologies and resistances to therapies. When aiming to reach molecular heterogeneity of cells at extremely low spatial scales, single-cell analysis can be the ultimate choice. Proteomics performed in bulk population of cells (macroproteomics) is prone to mask molecular heterogeneity. Mass spectrometry-based single cell proteomics (SCP-MS) is the right solution to overcome this issue. Three main problems can be identified using SCP-MS: (i) analytical loss during sample preparation, (ii) inefficient microinjection/delivery of proteins/peptides from samples to MS and (iii) low analytical throughput. Technologies for automation of SCP have recently gained attention to improve methods accuracy, sensitivity, throughput and in-depth and low-biased proteome analysis. In this minireview, we therefore overview the state-of-the-art of automation of SCP-MS sample preparation approaches.

Microfluidic Single-Cell Proteomics Assay Chip: Lung Cancer Cell Line Case Study

Cancer is a dynamic disease involving constant changes. With these changes, cancer cells become heterogeneous, resulting in varying sensitivity to chemotherapy. The heterogeneity of cancer cells plays a key role in chemotherapy resistance and cancer recurrence. Therefore, for effective treatment, cancer cells need to be analyzed at the single-cell level by monitoring various proteins and investigating their heterogeneity. We propose a microfluidic chip for a single-cell proteomics assay that is capable of analyzing complex cellular signaling systems to reveal the heterogeneity of cancer cells. The single-cell assay chip comprises (i) microchambers (n = 1376) for manipulating single cancer cells, (ii) micropumps for rapid single-cell lysis, and (iii) barcode immunosensors for detecting nine different secretory and intracellular proteins to reveal the correlation among cancer-related proteins. Using this chip, the single-cell proteomics of a lung cancer cell line, which may be easily masked in bulk analysis, were evaluated. By comparing changes in the level of protein secretion and heterogeneity in response to combinations of four anti-cancer drugs, this study suggests a new method for selecting the best combination of anti-cancer drugs. Subsequent preclinical and clinical trials should enable this platform to become applicable for patient-customized therapies.

Circulating Tumor Cells (CTCs): A Unique Model of Cancer Metastases and Non-invasive Biomarkers of Therapeutic Response

Late-stage cancer metastasis remains incurable in the clinic and is the major cause death in patients. Circulating tumor cells (CTCs) are thought to be metastatic precursors shed from the primary tumor or metastatic deposits and circulate in the blood. The molecular network regulating CTC survival, extravasation, and colonization in distant metastatic sites is poorly defined, largely due to challenges in isolating rare CTCs. Recent advances in CTC isolation and ex vivo culture techniques facilitates single-cell omics and the development of related animal models to study CTC-mediated metastatic progression. With these powerful tools, CTCs can potentially be used as non-invasive biomarkers predicting therapeutic response. These studies may open a new avenue for CTC-specific drug discoveries. In this short review, we aim to summarize recent progress in the characterization of CTCs and their clinical relevance in various cancers, setting the stage for realizing personalized therapies against metastases.

Cationization of neutral small molecules by site-specific carboxylation of 10-phenyl-10H-phenothiazine in laser desorption/ionization

It is a pre-requisite to ionize analyte molecules efficiently for detection by laser desorption/ionization mass spectrometry. Here, we report a conceptual demonstration of cationizing neutral small molecules which are typically difficult to be ionized with the traditional organic matrices due to their low proton/cation affinity values. Our strategy features generating radical cations from site-specifically carboxylated 10-(4-carboxyphenyl)-10H-phenothiazine-3,7-dicarboxylic acid (PTZ(A)₂-Ph(A)) with a laser, and anchoring the chlorine ion from NaCl through covalent bond-like bridging interactions with the N/S atoms in the heterocyclic structure. This "Maverick" design allows a dramatic change of the energy landscape of analyte sodiation with an enhanced efficiency. We have synthesized two families of compounds based on the model structures of phenothiazine (PTZ) and phenoxazine (PXZ) and their carboxylated derivatives, and performed comparison between them or against the traditional organic matrices in a systematic format. We have demonstrated that PTZ(A)₂-Ph(A) is outstanding as a novel MALDI matrix for the detection of oligosaccharides and amino acids, with an ultra-clean background baseline and high signal-to-noise ratios (up to dozens of times better than the traditional matrices). This work provides a new method for the cationization of neutral small molecules in a distinct mechanism, inspiring the development of next-generation matrices for sensitive detection of hard-to-be-ionized molecules by MALDI MS.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Blood-based protein biomarkers in bladder urothelial tumors

Bladder cancer (BC) is the fifth most common cancer with a high prevalence rate. It is classically classified in two groups, namely non-muscle invasive (NMIBC) and muscle invasive (MIBC). NMIBC accounts for 75% of cases and has a better prognosis than MIBC. However, 30-50% of the NMIBC patients will show recurrences throughout their lives, and about 10-20% of them will progress to MIBC, with frequent metastasis and a reduced survival rate. The diagnosis of bladder cancer is confirmed by direct visualization of the tumour and other mucosal abnormalities with endoscopic excision using cystoscopy and transurethral resection of the bladder (TURBT). An adequate TURBT requires complete resection of all visible tumour with appropriate sampling of the bladder to assess the depth of invasion. However, for many years, researchers have attempted to identify and utilise urinary markers for bladder cancer detection. Voided urine cytology has been the mainstay of urine-based diagnosis of bladder cancer since originally described by Papanicolau and Marshall. Nonetheless, urine cytology has several drawbacks, including a poor sensitivity for low-grade/stage tumours, a lack of interobserver consistency and a variable range of readings (e.g., atypical, atypical-suspicious, non-diagnostic). These shortcomings have inspired the search for more sensitive bladder cancer biomarkers. To bring precision medicine to genitourinary oncology, the analysis of the plasma/serum wide genome and proteome offers promising possibilities.

Single-cell proteomics: A treasure trove in neurobiology

Single-cell analysis came to change the way we look at cell populations. RNA sequencing of single cells allowed us to appreciate the diversity of cell types in the human brain in an unprecedented manner and its power to reveal cell-type specific changes in cell populations has just begun to be explored. In this context, looking at the proteome of single cells promises to bring functional information and contribute to completing the picture. The potential of single cell proteome, in developing a better understanding of the intricate connections between the very diverse cell populations in the brain, is huge. Whereas early approaches to address single-cell proteome have identified hundreds of proteins, today, techniques combining isobaric labelling and LC-MS can lead to the identification of thousands of proteins. In this review, we describe methods which have been used to identify and quantify proteins from single cells and propose that the application of isobaric labeling and label-free quantitative proteomics approach for single-cell analysis is ready to provide useful information for the neurobiology field.

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

[Methods and applications of single-cell proteomics analysis based on mass spectrometry]

The cell is the smallest unit of living organisms. Although cells often assemble to serve a common function, intercellular heterogeneity often exists due to different genetic and environmental effects. Therefore, single-cell analysis has been regarded as an indispensable means to investigate cell heterogeneity, especially when researching cell differentiation, disease diagnosis, and therapy. As the chief factors influencing cell and biological activities, proteins have long been a major concern in biochemistry. However, due to their intrinsic lack of amplification characteristics, wide species variety, low abundance, and wide dynamic range, proteins are scarcely studied in single-cell research when compared with other biological macromolecules. Therefore, ultra-sensitive single-cell proteomics analysis methods are urgently required. Among all general measurement techniques, fluorescence methods possess high sensitivity and a capability of dynamic tracing, but low target numbers impose restrictions on their broad application in real "proteomic" studies. Similarly, electrochemical methods adapt to electrochemically active molecules, which miss the majority of proteins. Mass spectrometry (MS), as the core approach of proteomic studies, provides high-sensitivity and high-throughput analysis of proteins together with abundant structural information, which is unique in all the analytical instruments and has made great progress in single-cell proteomic research. Herein, the representative research methods for single-cell proteomics based on MS are reviewed. According to the different protein separation methods used prior to MS analysis, they are divided into three categories, including capillary electrophoresis (CE), liquid chromatography (LC), and direct infusion without the need for separation. First, CE has been widely used in the separation and analysis of complex biological samples owing to its low cost, high analysis speed, and high separation efficiency. Its unique feature is the extraction and transfer of contents from cellular or subcellular regions using capillaries smaller than a single cell size. This sampling method also offers less substrate interference and negligible oxidative damage to the cells. Nonetheless, single-cell analysis based on CE-MS mainly focuses on proteomic studies of large cells because of the considerable sample loss, interface instability, and reproducibility issues. Compared with CE, LC, especially nanoLC, is more widely used in single-cell proteomic research, which mainly depends on its good reproducibility, nanoliter injection volume, low flow rate, low sample loss, and good compatibility with mass spectrometry. In recent years, it has been increasingly applied in the study of large-volume embryos, germ cells, and even somatic cells. More than 1000 proteins have been identified in single HeLa cells using this state-of-the-art single-cell proteomics method. It is worth noting that the single-cell sampling volume based on LC gradually reduces to the nanoliter level, and that the sample loss can be reduced by integrating a series of proteomic sampling processes into small volumes, setting sealing conditions, and reducing washing steps. However, the adequacy of cell lysis, the completeness and efficiency of protein pretreatment, and the labeling of peptide segments are important factors affecting the number and types of protein identification. Compared with protein separation using CE or LC prior to MS analysis, the direct MS analysis, assisted by labelling and signal transformation, eliminates complicated sample pretreatment and simplifies the operation by reducing enzymatic hydrolysis and separation. It also renders higher resolution as well as multi-omics compatibility. So far, the number of proteins detected using this method is limited due to the complexity of the samples. In conclusion, the aspects of throughput, sensitivity, identified protein species, and applications are summarized for each method mentioned above, and the prospect of single-cell proteomic research based on MS in the future is also discussed.

PepS: An Innovative Microfluidic Device for Bedside Whole Blood Processing before Plasma Proteomics Analyses

Immunoassays have been used for decades in clinical laboratories to quantify proteins in serum and plasma samples. However, their limitations make them inappropriate in some cases. Recently, mass spectrometry (MS) based proteomics analysis has emerged as a promising alternative method when seeking to assess panels of protein biomarkers with a view to providing protein profiles to monitor health status. Up to now, however, translation of MS-based proteomics to the clinic has been hampered by its complexity and the substantial time and human resources necessary for sample preparation. Plasma matrix is particularly tricky to process as it contains more than 3000 proteins with concentrations spanning an extreme dynamic range (10¹⁰). To address this preanalytical challenge, we designed a microfluidic device (PepS) automating and accelerating blood sample preparation for bottom-up MS-based proteomics analysis. The microfluidic cartridge is operated through a dedicated compact instrument providing fully automated fluid processing and thermal control. In less than 2 h, the PepS device allows bedside plasma separation from whole blood, volume metering, depletion of albumin, protein digestion with trypsin, and stabilization of tryptic peptides on solid-phase extraction sorbent. For this first presentation, the performance of the PepS device was assessed using discovery proteomics and targeted proteomics, detecting a panel of three protein biomarkers routinely assayed in clinical laboratories (alanine aminotransferase 1, C-reactive protein, and myoglobin). This innovative microfluidic device and its associated instrumentation should help to streamline and simplify clinical proteomics studies.

Flow Cytometry and Sorting in Arabidopsis

Flow cytometry and sorting represents a valuable and mature experimental platform for the analysis of cellular populations. Applications involving higher plants started to emerge around 40 years ago and are now widely employed both to provide unique information regarding basic and applied questions in the biosciences and to advance agricultural productivity in practical ways. Further development of this platform is being actively pursued, and this promises additional progress in our understanding of the interactions of cells within complex tissues and organs. Higher plants offer unique challenges in terms of flow cytometric analysis, first since their organs and tissues are, almost without exception, three-dimensional assemblies of different cell types held together by tough cell walls, and, second, because individual plant cells are generally larger than those of mammals.This chapter, which updates work last reviewed in 2014 [Galbraith DW (2014) Flow cytometry and sorting in Arabidopsis. In: Sanchez Serrano JJ, Salinas J (eds) Arabidopsis Protocols, 3rd ed. Methods in molecular biology, vol 1062. Humana Press, Totowa, pp 509-537], describes the application of techniques of flow cytometry and sorting to the model plant species Arabidopsis thaliana, in particular emphasizing (a) fluorescence labeling in vivo of specific cell types and of subcellular components, (b) analysis using both conventional cytometers and spectral analyzers, (c) fluorescence-activated sorting of protoplasts and nuclei, and (d) transcriptome analyses using sorted protoplasts and nuclei, focusing on population analyses at the level of single protoplasts and nuclei. Since this is an update, details of new experimental methods are emphasized.

Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

3D projection electrophoresis for single-cell immunoblotting

Immunoassays and mass spectrometry are powerful single-cell protein analysis tools; however, interfacing and throughput bottlenecks remain. Here, we introduce three-dimensional single-cell immunoblots to detect both cytosolic and nuclear proteins. The 3D microfluidic device is a photoactive polyacrylamide gel with a microwell array-patterned face (xy) for cell isolation and lysis. Single-cell lysate in each microwell is "electrophoretically projected" into the 3rd dimension (z-axis), separated by size, and photo-captured in the gel for immunoprobing and confocal/light-sheet imaging. Design and analysis are informed by the physics of 3D diffusion. Electrophoresis throughput is > 2.5 cells/s (70× faster than published serial sampling), with 25 immunoblots/mm2 device area (>10× increase over previous immunoblots). The 3D microdevice design synchronizes analyses of hundreds of cells, compared to status quo serial analyses that impart hours-long delay between the first and last cells. Here, we introduce projection electrophoresis to augment the heavily genomic and transcriptomic single-cell atlases with protein-level profiling.

Advances in Label-Free Detections for Nanofluidic Analytical Devices

Nanofluidics, a discipline of science and engineering of fluids confined to structures at the 1-1000 nm scale, has experienced significant growth over the past decade. Nanofluidics have offered fascinating platforms for chemical and biological analyses by exploiting the unique characteristics of liquids and molecules confined in nanospaces; however, the difficulty to detect molecules in extremely small spaces hampers the practical applications of nanofluidic devices. Laser-induced fluorescence microscopy with single-molecule sensitivity has been so far a major detection method in nanofluidics, but issues arising from labeling and photobleaching limit its application. Recently, numerous label-free detection methods have been developed to identify and determine the number of molecules, as well as provide chemical, conformational, and kinetic information of molecules. This review focuses on label-free detection techniques designed for nanofluidics; these techniques are divided into two groups: optical and electrical/electrochemical detection methods. In this review, we discuss on the developed nanofluidic device architectures, elucidate the mechanisms by which the utilization of nanofluidics in manipulating molecules and controlling light-matter interactions enhances the capabilities of biological and chemical analyses, and highlight new research directions in the field of detections in nanofluidics.

Developing and validating protocols for mechanical isolation of guard-cell enriched epidermal peels for omics studies

Stomata, which are microscopic valves on the leaf surface formed by two guard cells (GC), play a critical role in the regulation of leaf water and gas exchange and, hence, determine plant adaptive potential. However, little data is available on GC biochemistry, protein abundance and gene expression, mainly due to technical difficulties and challenges in isolating sufficient amounts of high-quality pure GC. In the present study we applied some modifications to the mechanical isolation of guard-cell to generalise this method for diverse growth conditions as well as plant species. Epidermal peel fragments enriched in guard cells were mechanically isolated from quinoa, spinach and sugar beet leaves grown at two conditions (normal and salt stress). Multiple analysis was performed to confirm the suitability and superiority of the modified technique to the original method. At the first step, the viability and purity of GC-enriched epidermal fragments were assessed under the microscope. Then, the RNA integrity, gene expression, and 1D SDS-PAGE tests were performed to validate the suitability of this technique for omics studies. The data revealed a wide range of proteins as well as a high integrity of RNA extracted from guard cell samples. The expression level of several GC-specific genes and mesophyll-dominant genes were investigated using a comparative analysis of transcriptome datasets of GC and whole-leaf samples. We found that Rubisco and photosynthesis-related proteins such as chlorophyll a/b binding protein were substantially higher in the whole leaf compared with the GCs. More importantly, GC-specific genes such as OST1, SLAC1, MYB60, FAMA and HT1 were highly expressed in the GCs, confirming that our guard cell preparation was highly enriched in GC gene transcripts. Real-time quantitative reverse transcription PCR further confirmed the efficacy of the GC isolation technique for exploring responses of GC to diverse types of stress at the molecular level.

The sensitive detection of single-cell secreted lactic acid for glycolytic inhibitor screening with a microdroplet biosensor

Lactic acid (LA) plays an important role in the tumor metabolism and malignant progression of various cancers. Herein, we have developed a one-step, wash-free microfluidic approach with droplet biosensors for the sensitive detection of LA secreted by a single tumor cell. Our assay integrates the enzyme-assisted chemical conversion of LA in small-volume (4.2 nL) droplets for fluorescence signal readout. The microdroplet assay achieved a limit of detection of 1.02 μM and was more sensitive than the commercial ELISA kit by nearly two orders of magnitude. A good specificity has been demonstrated for this assay by testing various ions and biomolecules from the culture medium. This droplet assay allows us to acquire the profiles of the lactic acid secretion of tumor cells under the influence of glycolytic inhibitors at the single-cell level. It offers a useful research tool to study the cell-to-cell differences of LA secretion and glycolytic inhibitor screening for cancer research.

Leveraging nature's biomolecular designs in next-generation protein sequencing reagent development

Next-generation approaches for protein sequencing are now emerging that could have the potential to revolutionize the field in proteomics. One such sequencing method involves fluorescence-based imaging of immobilized peptides in which the N-terminal amino acid of a polypeptide is readout sequentially by a series of fluorescently labeled biomolecules. When selectively bound to a specific N-terminal amino acid, the NAAB (N-terminal amino acid binder) affinity reagent identifies the amino acid through its associated fluorescence tag. A key technical challenge in implementing this fluoro-sequencing approach is the need to develop NAAB affinity reagents with the high affinity and selectivity for specific N-terminal amino acids required for this biotechnology application. One approach to develop such a NAAB affinity reagent is to leverage naturally occurring biomolecules that bind amino acids and/or peptides. Here, we describe several candidate biomolecules that could be considered for this purpose and discuss the potential for developability of each. Key points • Next-generation sequencing methods are emerging that could revolutionize proteomics. • Sequential readout of N-terminal amino acids by fluorescent-tagged affinity reagents. • Native peptide/amino acid binders can be engineered into affinity reagents. • Protein size and structure contribute to feasibility of reagent developability.

Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective

The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

Stable Isotopes for Tracing Mammalian-Cell Metabolism In Vivo

Metabolism is at the cornerstone of all cellular functions and mounting evidence of its deregulation in different diseases emphasizes the importance of a comprehensive understanding of metabolic regulation at the whole-organism level. Stable-isotope measurements are a powerful tool for probing cellular metabolism and, as a result, are increasingly used to study metabolism in in vivo settings. The additional complexity of in vivo metabolic measurements requires paying special attention to experimental design and data interpretation. Here, we review recent work where in vivo stable-isotope measurements have been used to address relevant biological questions within an in vivo context, summarize different experimental and data interpretation approaches and their limitations, and discuss future opportunities in the field.

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs

In late 2012 it was evidenced that most of the human genome is transcribed but only a small percentage of the transcripts are translated. This observation supported the importance of non-coding RNAs and it was confirmed in several organisms. The most abundant non-translated transcripts are long non-coding RNAs (lncRNAs). In contrast to protein-coding RNAs, they show a more cell-specific expression. To understand the function of lncRNAs, it is fundamental to investigate in which cells they are preferentially expressed and to detect their subcellular localization. Recent improvements of techniques that localize single RNA molecules in tissues like single-cell RNA sequencing and fluorescence amplification methods have given a considerable boost in the knowledge of the lncRNA functions. In recent years, single-cell transcription variability was associated with non-coding RNA expression, revealing this class of RNAs as important transcripts in the cell lineage specification. The purpose of this review is to collect updated information about lncRNA classification and new findings on their function derived from single-cell analysis. We also retained useful for all researchers to describe the methods available for single-cell analysis and the databases collecting single-cell and lncRNA data. Tables are included to schematize, describe, and compare exposed concepts.

Single-molecule measurements in microwells for clinical applications

The ability to detect and analyze proteins, nucleic acids, and other biomolecules is critical for clinical diagnostics and for understanding the underlying mechanisms of disease. Current detection methods in clinical and research laboratories rely upon bulk measurement techniques such as immunoassays, polymerase chain reaction, and mass spectrometry to detect these biomarkers. However, many potentially useful protein or nucleic acid biomarkers in blood, saliva, or other biofluids exist at concentrations well below the detection limits of current methods, necessitating the development of more sensitive technologies. Single-molecule measurements are poised to address this challenge, vastly improving sensitivity for detecting low abundance biomarkers and rare events within a population. Microwell arrays have emerged as a powerful tool for single-molecule measurements, enabling ultrasensitive detection of disease-relevant biomolecules in easily accessible biofluids. This review discusses the development, fundamentals, and clinical applications of microwell-based single-molecule methods, as well as challenges and future directions for translating these methods to the clinic.

Hydrogel Microparticles Functionalized with Engineered Escherichia coli as Living Lactam Biosensors

Recently there has been an increasing need for synthesizing valued chemicals through biorefineries. Lactams are an essential family of commodity chemicals widely used in the nylon industry with annual production of millions of tons. The bio-production of lactams can substantially benefit from high-throughput lactam sensing strategies for lactam producer screening. We present here a robust and living lactam biosensor that is directly compatible with high-throughput analytical means. The biosensor is a hydrogel microparticle encapsulating living microcolonies of engineered lactam-responsive Escherichia coli. The microparticles feature facile and ultra-high throughput manufacturing of up to 10,000,000 per hour through droplet microfluidics. We show that the biosensors can specifically detect major lactam species in a dose-dependent manner, which can be quantified using flow cytometry. The biosensor could potentially be used for high-throughput metabolic engineering of lactam biosynthesis.