Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analyses

Monitoring the Metabolic Activity of a Single Bacterial Cell Based on Scattering Intensity

Cell activity is evaluated using the number of colonies formed on a medium or the number of live cells in a suspension or by staining nuclei with fluorescent dyes to determine whether cells are dead. However, the culture methods generally require extended culturing times, and damage to the cell membranes observed using fluorescent dyes is not necessarily related to cell survival or activity. Hence, accurately determining the activities of individual cells is impossible. Therefore, we developed a method for quantitatively evaluating the metabolic activities of single cells by focusing on the optical and chemical properties of formazan dye, i.e., 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). The oxidized form of MTT is soluble and highly permeable to cell membranes, but it is reduced to insoluble MTT formazan upon reaction with intracellular metabolic products. Single-cell observation using dark-field microscopy revealed that insoluble formazan aggregates within the cells formed particles that emitted characteristic scattered light. The formazan-derived scattered light component extracted via peak fitting was related to metabolic activity, demonstrating its usefulness as a parameter indicating the activity of an individual cell. This method enables the real-time evaluation of the activities of single cells, which should lead to not only the acceleration of bacterial screening and microbial control but also the development of antibiotics and suppression of drug-resistant bacteria.

Application of bioinformatic tools in cell type classification for single-cell RNA-seq data

The advancements in single-cell RNA sequencing (scRNAseq) technology have significantly transformed genomics research, enabling the handling of thousands of cells in each experiment. As of now, 32,068 research studies have been cataloged in the Pubmed database. The primary aim of scRNAseq investigations is to identify cell types, understand the antitumor immune response, and identify new and uncommon cell types. Traditional techniques for identifying cell types include microscopy, histology, and pathological characteristics. However, the complexity of instruments and the need for precise experimental design make it difficult to fully capture the overall heterogeneity. Unsupervised clustering and supervised classification methods have been used to solve this task. Supervised cell type classification methods have gained popularity as large-scale, high-quality, well-annotated and more robust results compared to clustering methods. A recent study showed that support vector machine (SVM) gives a high-quality classification performance in different scenarios. In this article, we compare and evaluate the performance of four different kernels (sigmoid, linear, radial, polynomial) of SVM. The results of the experiments on three standard scRNA-seq datasets indicate that SVM with linear and SVM with sigmoid kernel classify the cells more accurately (approx. 99 %) where SVM linear kernel method has remarkably fast computation time and we also evaluate the results using some single cell specific evaluation matrices F-1 score, MCC, AUC value. Additionally, it sheds light on the potential use of kernels of SVM to give underlying information of single-cell RNA-Seq data more effectively.

MCount: An automated colony counting tool for high-throughput microbiology

Accurate colony counting is crucial for assessing microbial growth in high-throughput workflows. However, existing automated counting solutions struggle with the issue of merged colonies, a common occurrence in high-throughput plating. To overcome this limitation, we propose MCount, the only known solution that incorporates both contour information and regional algorithms for colony counting. By optimizing the pairing of contours with regional candidate circles, MCount can accurately infer the number of merged colonies. We evaluate MCount on a precisely labeled Escherichia coli dataset of 960 images (15,847 segments) and achieve an average error rate of 3.99%, significantly outperforming existing published solutions such as NICE (16.54%), AutoCellSeg (33.54%), and OpenCFU (50.31%). MCount is user-friendly as it only requires two hyperparameters. To further facilitate deployment in scenarios with limited labeled data, we propose statistical methods for selecting the hyperparameters using few labeled or even unlabeled data points, all of which guarantee consistently low error rates. MCount presents a promising solution for accurate and efficient colony counting in application workflows requiring high throughput, particularly in cases with merged colonies.

An augmented GSNMF model for complete deconvolution of bulk RNA-seq data

Performing complete deconvolution analysis for bulk RNA-seq data to obtain both cell type specific gene expression profiles (GEP) and relative cell abundances is a challenging task. One of the fundamental models used, the nonnegative matrix factorization (NMF), is mathematically ill-posed. Although several complete deconvolution methods have been developed, and their estimates compared to ground truth for some datasets appear promising, a comprehensive understanding of how to circumvent the ill-posedness and improve solution accuracy is lacking. In this paper, we first investigated the necessary requirements for a given dataset to satisfy the solvability conditions in NMF theory. Even with solvability conditions, the "unique" solutions of NMF are subject to a rescaling matrix. Therefore, we provide estimates of the converged local minima and the possible rescaling matrix, based on informative initial conditions. Using these strategies, we developed a new pipeline of pseudo-bulk tissue data augmented, geometric structure guided NMF model (GSNMF+). In our approach, pseudo-bulk tissue data was generated, by statistical distribution simulated pseudo cellular compositions and single-cell RNA-seq (scRNA-seq) data, and then mixed with the original dataset. The constituent matrices of the hybrid dataset then satisfy the weak solvability conditions of NMF. Furthermore, an estimated rescaling matrix was used to adjust the minimizer of the NMF, which was expected to reduce mean square root errors of solutions. Our algorithms are tested on several realistic bulk-tissue datasets and showed significant improvements in scenarios with singular cellular compositions.

Combining single-cell ATAC and RNA sequencing for supervised cell annotation

MOTIVATION

Single-cell analysis offers insights into cellular heterogeneity and individual cell function. Cell type annotation is the first and critical step for performing such an analysis. Current methods mostly utilize single-cell RNA sequencing data. Several studies demonstrated improved unsupervised annotation when combining RNA with single-cell ATAC sequencing, but improvements in supervised methods have not been explored.

RESULTS

Single-cell 10x genomics multiome datasets containing paired ATAC and RNA from human peripheral blood mononuclear cells (PBMC) and neuronal cells with Alzheimer's Disease were used for supervised annotation. Using linear and nonlinear dimensionality reduction methods and random forest, support vector machine and logistic regression classification models, we demonstrate the improvement in supervised annotation and prediction confidence in PBMC data when using a combination of RNA seq and ATAC-seq data. No such improvement was observed when annotating neuronal cells. Specifically, F1 scores were improved when using scVI embeddings to annotate PBMC sub-types. CD4 T effector memory cells showed the largest improvement in F1 score.

Identification and monitoring of cell heterogeneity from plasmid recombination during limonene production

Detecting alterations in plasmid structures is often performed using conventional molecular biology. However, these methods are laborious and time-consuming for studying the conditions inducing these mutations, which prevent real-time access to cell heterogeneity during bioproduction. In this work, we propose combining both flow cytometry and fluorescence-activated cell sorting, integrated with mechanistic modelling to study conditions that lead to plasmid recombination using a limonene-producing microbial system as a case study. A gene encoding GFP was introduced downstream of the key enzymes involved in limonene biosynthesis to enable real-time kinetics monitoring and the identification of cell heterogeneity according to microscopic and flow cytometric analyses. Three different plasmid configurations (one correct and two incorrect) were identified through cell sorting based on subpopulations expressing different levels of GFP at 10 and 50 µM IPTG. Higher limonene production (530 mg/L) and lower subpopulation proportion carrying the incorrect plasmid (12%) were observed for 10 µM IPTG compared to 50 µM IPTG (96 mg/L limonene and more than 70% of cell population carrying the incorrect plasmid, respectively) in 100 mL production culture. We also managed to derive exploratory hypotheses regarding the plasmid recombination region using the model and successfully validated them experimentally. Additionally, the results also showed that limonene production was proportional to GFP fluorescence intensity. This correlation could serve as an alternative to using biosensors for a high-throughput screening process. The developed method enables rapid identification of plasmid recombination at single-cell level and correlates the heterogeneity with bioproduction performance. KEY POINTS: • Strategy to study plasmid recombination during bioproduction. • Different plasmid structures can be identified and monitored by flow cytometry. • Mathematical modelling suggests specific alterations in plasmid structures.

High-throughput single-cell sorting by stimulated Raman-activated cell ejection

Raman-activated cell sorting isolates single cells in a nondestructive and label-free manner, but its throughput is limited by small spontaneous Raman scattering cross section. Coherent Raman scattering integrated with microfluidics enables high-throughput cell analysis, but faces challenges with small cells (<3 μm) and tissue sections. Here, we report stimulated Raman-activated cell ejection (S-RACE) that enables high-throughput single-cell sorting by integrating stimulated Raman imaging, in situ image decomposition, and laser-induced cell ejection. S-RACE allows ejection of live bacteria or fungi guided by their Raman signatures. Furthermore, S-RACE successfully sorted lipid-rich Rhodotorula glutinis cells from a cell mixture with a throughput of ~13 cells per second, and the sorting results were confirmed by downstream quantitative polymerase chain reaction. Beyond single cells, S-RACE shows high compatibility with tissue sections. Incorporating a closed-loop feedback control circuit further enables real-time SRS imaging-identification-ejection. In summary, S-RACE opens exciting opportunities for diverse single-cell sorting applications.

Methods for studying microbial acid stress responses: from molecules to populations

The study of how micro-organisms detect and respond to different stresses has a long history of producing fundamental biological insights while being simultaneously of significance in many applied microbiological fields including infection, food and drink manufacture, and industrial and environmental biotechnology. This is well-illustrated by the large body of work on acid stress. Numerous different methods have been used to understand the impacts of low pH on growth and survival of micro-organisms, ranging from studies of single cells to large and heterogeneous populations, from the molecular or biophysical to the computational, and from well-understood model organisms to poorly defined and complex microbial consortia. Much is to be gained from an increased general awareness of these methods, and so the present review looks at examples of the different methods that have been used to study acid resistance, acid tolerance, and acid stress responses, and the insights they can lead to, as well as some of the problems involved in using them. We hope this will be of interest both within and well beyond the acid stress research community.

Growth Phase Contribution in Dictating Drug Transport and Subcellular Accumulation inside Escherichia coli

Depending upon nutrient availability, bacteria transit to multiple growth phases. The transition from the active to nongrowing phase results in reduced drug efficacy and, in some cases, even multidrug resistance. However, due to multiple alterations in the cell envelope, probing the drug permeation kinetics during growth phases becomes perplexing, especially across the Gram-negative bacteria's complex dual membrane envelope. To advance the understanding of drug permeation during the life cycle of Gram-negative bacteria, we sought to address two underlying objectives: (a) how changes are occurring inside the bacterial envelope during growth and (b) how the drug permeation and accumulation vary across both the membranes and in subcellular compartments during growth. Both objectives are met with the help of nonlinear optical technique second-harmonic generation spectroscopy (SHG). Specifically, using SHG, we probed the transport kinetics and accumulation of a quaternary ammonium compound (QAC), malachite green, inside Escherichia coli in various growth phases. Further insight about another QAC molecule, propidium iodide, is accomplished using fluorescence microscopy. Results indicate that actively growing cells have faster drug transport and higher cytoplasmic accumulation than slow- or nongrowing cells. In this regard, the rpoS gene plays a crucial role in limiting drug transport across the saturation phase cultures. Moreover, within a particular growth phase, membrane permeability undergoes gradual changes much before the subsequent growth phase commences. These outcomes signify the importance of reporting the growth phase and rate in drug efficacy studies.

The Biology and Biochemistry of Kynurenic Acid, a Potential Nutraceutical with Multiple Biological Effects

Kynurenic acid (KYNA) is an antioxidant degradation product of tryptophan that has been shown to have a variety of cytoprotective, neuroprotective and neuronal signalling properties. However, mammalian transporters and receptors display micromolar binding constants; these are consistent with its typically micromolar tissue concentrations but far above its serum/plasma concentration (normally tens of nanomolar), suggesting large gaps in our knowledge of its transport and mechanisms of action, in that the main influx transporters characterized to date are equilibrative, not concentrative. In addition, it is a substrate of a known anion efflux pump (ABCC4), whose in vivo activity is largely unknown. Exogeneous addition of L-tryptophan or L-kynurenine leads to the production of KYNA but also to that of many other co-metabolites (including some such as 3-hydroxy-L-kynurenine and quinolinic acid that may be toxic). With the exception of chestnut honey, KYNA exists at relatively low levels in natural foodstuffs. However, its bioavailability is reasonable, and as the terminal element of an irreversible reaction of most tryptophan degradation pathways, it might be added exogenously without disturbing upstream metabolism significantly. Many examples, which we review, show that it has valuable bioactivity. Given the above, we review its potential utility as a nutraceutical, finding it significantly worthy of further study and development.

Evidence for the Role of the Mitochondrial ABC Transporter MDL1 in the Uptake of Clozapine and Related Molecules into the Yeast Saccharomyces cerevisiae

Clozapine is an antipsychotic drug whose accumulation in white cells can sometimes prove toxic; understanding the transporters and alleles responsible is thus highly desirable. We used a strategy in which a yeast (Saccharomyces cerevisiae) CRISPR-Cas9 knock-out library was exposed to cytotoxic concentrations of clozapine to determine those transporters whose absence made it more resistant; we also recognised the structural similarity of the fluorescent dye safranin O (also known as safranin T) to clozapine, allowing it to be used as a surrogate marker. Strains lacking the mitochondrial ABC transporter MDL1 (encoded by YLR188W) showed substantial resistance to clozapine. MDL1 overexpression also conferred extra sensitivity to clozapine and admitted a massive increase in the cellular and mitochondrial uptake of safranin O, as determined using flow cytometry and microscopically. Yeast lacking mitochondria showed no such unusual accumulation. Mitochondrial MDL1 is thus the main means of accumulation of clozapine in S. cerevisiae. The closest human homologue of S. cerevisiae MDL1 is ABCB10.

Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony's unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. KEY POINTS: • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes.

Single Cell mass spectrometry: Towards quantification of small molecules in individual cells

Studying cell heterogeneity can provide a deeper understanding of biological activities, but appropriate studies cannot be performed using traditional bulk analysis methods. The development of diverse single cell bioanalysis methods is in urgent need and of great significance. Mass spectrometry (MS) has been recognized as a powerful technique for bioanalysis for its high sensitivity, wide applicability, label-free detection, and capability for quantitative analysis. In this review, the general development of single cell mass spectrometry (SCMS) field is covered. First, multiple existing SCMS techniques are described and compared. Next, the development of SCMS field is discussed in a chronological order. Last, the latest quantification studies on small molecules using SCMS have been described in detail.

Determination of Ploidy Levels and Nuclear DNA Content in Cryptococcus neoformans by Flow Cytometry: Drawbacks with Variability

Flow cytometry is commonly employed for ploidy determination and cell cycle analysis in cryptococci. The cells are subjected to fixation and staining with DNA-binding fluorescent dyes, most commonly with propidium iodide (PI), before undergoing flow cytometric analysis. In ploidy determination, cell populations are classified according to variations in DNA content, as evidenced by the fluorescence intensity of stained cells. As reported in Saccharomyces cerevisiae, we found drawbacks with PI staining that confounded the accurate analysis of ploidy by flow cytometry when the size of the cryptococci changed significantly. However, the shift in the fluorescence intensity, unrelated to ploidy changes in cells with increased size, could be accurately interpreted by applying the ImageStream system. SYTOX Green or SYBR Green I, reported to enable DNA analysis with a higher accuracy than PI in S. cerevisiae, were nonspecific for nuclear DNA staining in cryptococci. Until dyes or methods capable of reducing the variability inherent in the drastic changes in cell size or shape become available, PI appears to remain the most reliable method for cell cycle or ploidy analysis in Cryptococcus.

The strain-dependent cytostatic activity of Lactococcus lactis on CRC cell lines is mediated through the release of arginine deiminase

BACKGROUND

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers, posing a serious public health challenge that necessitates the development of new therapeutics, therapies, and prevention methods. Among the various therapeutic approaches, interventions involving lactic acid bacteria (LAB) as probiotics and postbiotics have emerged as promising candidates for treating and preventing CRC. While human-isolated LAB strains are considered highly favorable, those sourced from environmental reservoirs such as dairy and fermented foods are also being recognized as potential sources for future therapeutics.

RESULTS

In this study, we present a novel and therapeutically promising strain, Lactococcus lactis ssp. lactis Lc4, isolated from dairy sources. Lc4 demonstrated the ability to release the cytostatic agent - arginine deiminase (ADI) - into the post-cultivation supernatant when cultured under conditions mimicking the human gut environment. Released arginine deiminase was able to significantly reduce the growth of HT-29 and HCT116 cells due to the depletion of arginine, which led to decreased levels of c-Myc, reduced phosphorylation of p70-S6 kinase, and cell cycle arrest. The ADI release and cytostatic properties were strain-dependent, as was evident from comparison to other L. lactis ssp. lactis strains.

CONCLUSION

For the first time, we unveil the anti-proliferative properties of the L. lactis cell-free supernatant (CFS), which are independent of bacteriocins or other small molecules. We demonstrate that ADI, derived from a dairy-Generally Recognized As Safe (GRAS) strain of L. lactis, exhibits anti-proliferative activity on cell lines with different levels of argininosuccinate synthetase 1 (ASS1) expression. A unique feature of the Lc4 strain is also its capability to release ADI into the extracellular space. Taken together, we showcase L. lactis ADI and the Lc4 strain as promising, potential therapeutic agents with broad applicability.

Spectral analysis and sorting of microbial organisms using a spectral sorter

This chapter discusses the problems related to the application of conventional flow cytometers to microbiology. To address some of those limitations, the concept of spectral flow cytometry is introduced and the advantages over conventional flow cytometry for bacterial sorting are presented. We demonstrate by using ThermoFisher's Bigfoot spectral sorter where the spectral signatures of different stains for staining bacteria are demonstrated with an example of performing unmixing on spectral datasets. In addition to the Bigfoot's spectral analysis, the special biosafety features of this instrument are discussed. Utilizing these biosafety features, the sorting and patterning at the single cell level is optimized using non-pathogenic bacteria. Finally, the chapter is concluded by presenting a novel, label free, non-destructive, and rapid phenotypic method called Elastic Light Scattering (ELS) technology for identification of the patterned bacterial cells based on their unique colony scatter patterns.

Bioaerosols in the atmosphere: A comprehensive review on detection methods, concentration and influencing factors

In the past few decades, especially since the outbreak of the coronavirus disease (COVID-19), the effects of atmospheric bioaerosols on human health, the environment, and climate have received great attention. To evaluate the impacts of bioaerosols quantitatively, it is crucial to determine the types of bioaerosols in the atmosphere and their spatial-temporal distribution. We provide a concise summary of the online and offline observation strategies employed by the global research community to sample and analyze atmospheric bioaerosols. In addition, the quantitative distribution of bioaerosols is described by considering the atmospheric bioaerosols concentrations at various time scales (daily and seasonal changes, for example), under various weather, and different underlying surfaces. Finally, a comprehensive summary of the reasons for the spatiotemporal distribution of bioaerosols is discussed, including differences in emission sources, the impact process of meteorological factors and environmental factors. This review of information on the latest research progress contributes to the emergence of further observation strategies that determine the quantitative dynamics of public health and ecological effects of bioaerosols.

Application and outlook of electrochemical technology in single-cell analysis

Cellular heterogeneity, especially in some important diseased cells like tumor cells, acts as an invisible driver for disease development like cancer progression in the tumor ecosystem, contributing to differences in the macroscopic and microscopic detection of disease lesions like tumors. Traditional analysis techniques choose group information masked by the mean as the analysis sample, making it difficult to achieve precise diagnosis and target treatment, on which could be shed light via the single-cell level determination/bioanalysis. Hence, in this article we have reviewed the special characteristic differences among various kinds of typical single-cell bioanalysis strategies and electrochemical techniques, and then focused on the recent advance and special bio-applications of electrochemiluminescence and micro-nano electrochemical sensing mediated in single-cell bioimaging & bioanalysis. Especially, we have summarized the relevant research exploration of the possibility to establish the in-situ single-cell electrochemical methods to detect cell heterogeneity through determination of specific biomolecules and bioimaging of some important biological processes. Eventually, this review has explored some important advances of electrochemical single-cell detection techniques for the real-time cellular bioimaging and diagnostics of some disease lesions like tumors. It raises the possibility to provide the specific in-situ platform to exploit the versatile, sensitive, and high-resolution electrochemical single-cell analysis for the promising biomedical applications like rapid tracing of some disease lesions or in vivo bioimaging for precise cancer theranostics.

Recent developments in ionization techniques for single-cell mass spectrometry

The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry.

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.

Exosomes in Cancer Progression and Therapy Resistance: Molecular Insights and Therapeutic Opportunities

The development of therapy resistance still represents a major hurdle in treating cancers, leading to impaired treatment success and increased patient morbidity. The establishment of minimally invasive liquid biopsies is a promising approach to improving the early diagnosis, as well as therapy monitoring, of solid tumors. Because of their manifold functions in the tumor microenvironment, tumor-associated small extracellular vesicles, referred to as exosomes, have become a subject of intense research. Besides their important roles in cancer progression, metastasis, and the immune response, it has been proposed that exosomes also contribute to the acquisition and transfer of therapy resistance, mainly by delivering functional proteins and RNAs, as well as facilitating the export of active drugs or functioning as extracellular decoys. Extensive research has focused on understanding the molecular mechanisms underlying the occurrence of resistance and translating these into strategies for early detection. With this review, we want to provide an overview of the current knowledge about the (patho-)biology of exosomes, as well as state-of-the-art methods of isolation and analysis. Furthermore, we highlight the role of exosomes in tumorigenesis and cancer treatment, where they can function as therapeutic agents, biomarkers, and/or targets. By focusing on their roles in therapy resistance, we will reveal new paths of exploiting exosomes for cancer diagnosis and treatment.

Convergence of flow cytometry and bacteriology. Current and future applications: a focus on food and clinical microbiology

Since its development in the 1960s, flow cytometry (FCM) was quickly revealed a powerful tool to analyse cell populations in medical studies, yet, for many years, was almost exclusively used to analyse eukaryotic cells. Instrument and methodological limitations to distinguish genuine bacterial signals from the background, among other limitations, have hampered FCM applications in bacteriology. In recent years, thanks to the continuous development of FCM instruments and methods with a higher discriminatory capacity to detect low-size particles, FCM has emerged as an appealing technique to advance the study of microbes, with important applications in research, clinical and industrial settings. The capacity to rapidly enumerate and classify individual bacterial cells based on viability facilitates the monitoring of bacterial presence in foodstuffs or clinical samples, reducing the time needed to detect contamination or infectious processes. Besides, FCM has stood out as a valuable tool to advance the study of complex microbial communities, or microbiomes, that are very relevant in the context of human health, as well as to understand the interaction of bacterial and host cells. This review highlights current developments in, and future applications of, FCM in bacteriology, with a focus on those related to food and clinical microbiology.

Accelerating discovery: A novel flow cytometric method for detecting fibrin(ogen) amyloid microclots using long COVID as a model

Long COVID has become a significant global health and economic burden, yet there are currently no established methods or diagnostic tools to identify which patients might benefit from specific treatments. One of the major pathophysiological factors contributing to Long COVID is the presence of hypercoagulability; this results in insoluble amyloid microclots that are resistant to fibrinolysis. Our previous research using fluorescence microscopy has demonstrated a significant amyloid microclot load in Long COVID patients. However, this approach lacked the elements of statistical robustness, objectivity, and rapid throughput. In the current study, we have used imaging flow cytometry for the first time to show a significantly increased concentration and size of these microclots. We identified notable variations in size and fluorescence between microclots in Long COVID and those of controls even using a 20× objective. By combining cell imaging and the high-event-rate and full-sample analysis nature of a conventional flow cytometer, imaging flow cytometry can eliminate erroneous results and increase accuracy in gating and analysis beyond what pure quantitative measurements from conventional flow cytometry can provide. Although imaging flow cytometry was used in our study, our results suggest that the signals indicating the presence of microclots should be easily detectable using a conventional flow cytometer. Flow cytometry is a more widely available technique than fluorescence microscopy and has been used in pathology laboratories for decades, rendering it a potentially more suitable and accessible method for detecting microclots in individuals suffering from Long COVID or conditions with similar pathology, such as myalgic encephalomyelitis.

High Sensitivity Extended Nano-Coulter Counter for Detection of Viral Particles and Extracellular Vesicles

We present a chip-based extended nano-Coulter counter (XnCC) that can detect nanoparticles affinity-selected from biological samples with low concentration limit-of-detection that surpasses existing resistive pulse sensors by 2-3 orders of magnitude. The XnCC was engineered to contain 5 in-plane pores each with an effective diameter of 350 nm placed in parallel and can provide high detection efficiency for single particles translocating both hydrodynamically and electrokinetically through these pores. The XnCC was fabricated in cyclic olefin polymer (COP) via nanoinjection molding to allow for high-scale production. The concentration limit-of-detection of the XnCC was 5.5 × 103 particles/mL, which was a 1,100-fold improvement compared to a single in-plane pore device. The application examples of the XnCC included counting affinity selected SARS-CoV-2 viral particles from saliva samples using an aptamer and pillared microchip; the selection/XnCC assay could distinguish the COVID-19(+) saliva samples from those that were COVID-19(-). In the second example, ovarian cancer extracellular vesicles (EVs) were affinity selected using a pillared chip modified with a MUC16 monoclonal antibody. The affinity selection chip coupled with the XnCC was successful in discriminating between patients with high grade serous ovarian cancer and healthy donors using blood plasma as the input sample.

Single-Cell Proteomics with Spatial Attributes: Tools and Techniques

Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.

Cellcano: supervised cell type identification for single cell ATAC-seq data

Computational cell type identification is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. Here we develop Cellcano, a computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method alleviates the distributional shift between reference and target data and improves the prediction performance. After systematically benchmarking Cellcano on 50 well-designed celltyping tasks from various datasets, we show that Cellcano is accurate, robust, and computationally efficient. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/ .

Analysis of Salmonella lineage-specific traits upon cell sorting

Microbial cell individuality is receiving increasing interest in the scientific community. Individual cells within clonal populations exhibit noticeable phenotypic heterogeneity. The advent of fluorescent protein technology and advances in single-cell analysis has revealed phenotypic cell variant in bacterial populations. This heterogeneity is evident in a wide range of phenotypes, for example, individual cells display variable degrees of gene expression and survival under selective conditions and stresses, and can exhibit differing propensities to host interactions. Last few years, numerous cell sorting approaches have been employed for resolving the properties of bacterial subpopulations. This review provides an overview of applications of cell sorting to analyze Salmonella lineage-specific traits, including bacterial evolution studies, gene expression analysis, response to diverse cellular stresses and characterization of diverse bacterial phenotypic variants.

Linking antigen specific T-cell dynamics in a microfluidic chip to single cell transcription patterns

A new non-invasive screening profile has been realized that can aid in determining T-cell activation state at single-cell level. Production of activated T-cells with good specificity and stable proliferation is greatly beneficial for advancing adoptive immunotherapy as innate immunological cells are not effective in recognizing and eliminating cancer as expected. The screening method is realized by relating intracellular Ca²⁺ intensity and motility of T-cells interacting with APC (Antigen Presenting Cells) in a microfluidic chip. The system is tested using APC pulsed with OVA_257-264 peptide and its modified affinities (N4, Q4, T4 and V4), and the T-cells from OT-1 mice. In addition, single cell RNA sequencing reveals the activation states of the cells and the clusters from the derived profiles can be indicative of the T-cell activation state. The presented system here can be versatile for a comprehensive application to proceed with T-cell-based immunotherapy and screen the antigen-specific T-cells with excellent efficiency and high proliferation.

Electric-Inducive Microbial Interactions in a Thermophilic Anaerobic Digester Revealed by High-Throughput Sequencing of Micron-Scale Single Flocs

Although conductive materials have been shown to improve efficiency in anaerobic digestion (AD) by modifying microbial interactions, the interacting network under thermophilic conditions has not been examined. To identify the true taxon-taxon associations within thermophilic anaerobic digestion (TAD) microbiome and reveal the influence of carbon cloth (CC) addition, we sampled micron-scale single flocs (40-70 μm) randomly isolated from lab-scale thermophilic digesters. Results revealed that CC addition not only significantly boosted methane yield by 25.3% but also increased the spatial heterogeneity of the community in the sludge medium. After CC addition, an evident translocation of Pseudomonas from the medium to the biofilm was observed, showing their remarkable capacity for biofilm formation. Additionally, Clostridium and Thermotogaceae tightly aggregated and steadily co-occurred in the medium and biofilm of the TAD microbiome, which might be associated with their unique extracellular sugar metabolizing style. Finally, CC induced syntrophic interaction between Syntrophomonas and denitrifiers of Rhodocyclaceae. The upregulated respiration-associated electron transferring genes (Cyst-c, complex III) on the cellular membranes of these collaborating partners indicated a potential coupling of the denitrification pathway with syntrophic acetate oxidation via direct interspecies electron transfer (DIET). These findings provide an insight into how conductive materials promote thermophilic digestion performance and open the path for improved community monitoring of biotreatment systems.

Cell Sorting-Directed Selection of Bacterial Cells in Bigger Sizes Analyzed by Imaging Flow Cytometry during Experimental Evolution

Cell morphology is an essential and phenotypic trait that can be easily tracked during adaptation and evolution to environmental changes. Thanks to the rapid development of quantitative analytical techniques for large populations of cells based on their optical properties, morphology can be easily determined and tracked during experimental evolution. Furthermore, the directed evolution of new culturable morphological phenotypes can find use in synthetic biology to refine fermentation processes. It remains unknown whether and how fast we can obtain a stable mutant with distinct morphologies using fluorescence-activated cell sorting (FACS)-directed experimental evolution. Taking advantage of FACS and imaging flow cytometry (IFC), we direct the experimental evolution of the E. coli population undergoing continuous passage of sorted cells with specific optical properties. After ten rounds of sorting and culturing, a lineage with large cells resulting from incomplete closure of the division ring was obtained. Genome sequencing highlighted a stop-gain mutation in amiC, leading to a dysfunctional AmiC division protein. The combination of FACS-based selection with IFC analysis to track the evolution of the bacteria population in real-time holds promise to rapidly select and culture new morphologies and association tendencies with many potential applications.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

Identifying Bacterial Lineages in Salmonella by Flow Cytometry

Advances in technologies that permit high-resolution analysis of events in single cells have revealed that phenotypic heterogeneity is a widespread phenomenon in bacteria. Flow cytometry has the potential to describe the distribution of cellular properties within a population of bacterial cells and has yielded invaluable information about the ability of isogenic cells to diversify into phenotypic subpopulations. This review will discuss several single-cell approaches that have recently been applied to define phenotypic heterogeneity in populations of Salmonella enterica.

Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta

BACKGROUND

Mural cells in ascending aortic aneurysms undergo phenotypic changes that promote extracellular matrix destruction and structural weakening. To explore this biology, we analyzed the transcriptional features of thoracic aortic tissue.

METHODS

Single-nuclear RNA sequencing was performed on 13 samples from human donors, 6 with thoracic aortic aneurysm, and 7 without aneurysm. Individual transcriptomes were then clustered based on transcriptional profiles. Clusters were used for between-disease differential gene expression analyses, subcluster analysis, and analyzed for intersection with genetic aortic trait data.

RESULTS

We sequenced 71 689 nuclei from human thoracic aortas and identified 14 clusters, aligning with 11 cell types, predominantly vascular smooth muscle cells (VSMCs) consistent with aortic histology. With unbiased methodology, we found 7 vascular smooth muscle cell and 6 fibroblast subclusters. Differentially expressed genes analysis revealed a vascular smooth muscle cell group accounting for the majority of differential gene expression. Fibroblast populations in aneurysm exhibit distinct behavior with almost complete disappearance of quiescent fibroblasts. Differentially expressed genes were used to prioritize genes at aortic diameter and distensibility genome-wide association study loci highlighting the genes JUN, LTBP4 (latent transforming growth factor beta-binding protein 1), and IL34 (interleukin 34) in fibroblasts, ENTPD1, PDLIM5 (PDZ and LIM domain 5), ACTN4 (alpha-actinin-4), and GLRX in vascular smooth muscle cells, as well as LRP1 in macrophage populations.

CONCLUSIONS

Using nuclear RNA sequencing, we describe the cellular diversity of healthy and aneurysmal human ascending aorta. Sporadic aortic aneurysm is characterized by differential gene expression within known cellular classes rather than by the appearance of novel cellular forms. Single-nuclear RNA sequencing of aortic tissue can be used to prioritize genes at aortic trait loci.

GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA

Complete deconvolution analysis for bulk RNA-seq data is important and helpful to distinguish whether the differences of disease-associated GEPs (gene expression profiles) in tissues of patients and normal controls are due to changes in cellular composition of tissue samples, or due to GEPs changes in specific cells. One of the major techniques to perform complete deconvolution is nonnegative matrix factorization (NMF), which also has a wide-range of applications in the machine learning community. However, the NMF is a well-known strongly ill-posed problem, so a direct application of NMF to RNA-seq data will suffer severe difficulties in the interpretability of solutions. In this paper, we develop an NMF-based mathematical model and corresponding computational algorithms to improve the solution identifiability of deconvoluting bulk RNA-seq data. In our approach, we combine the biological concept of marker genes with the solvability conditions of the NMF theories, and develop a geometric structures guided optimization model. In this strategy, the geometric structure of bulk tissue data is first explored by the spectral clustering technique. Then, the identified information of marker genes is integrated as solvability constraints, while the overall correlation graph is used as manifold regularization. Both synthetic and biological data are used to validate the proposed model and algorithms, from which solution interpretability and accuracy are significantly improved.

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.

Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification

A bottleneck for microbial community experiments with many samples and/or replicates is the fast quantification of individual taxon abundances, which is commonly achieved through sequencing marker genes such as the 16S rRNA gene. Here, we propose a new approach for high-throughput and high-quality enumeration of human gut bacteria in a defined community, combining flow cytometry and supervised classification to identify and quantify species mixed in silico and in defined communities in vitro. We identified species in a 5-species in silico community with an F1 score of 71%. In addition, we demonstrate in vitro that our method performs equally well or better than 16S rRNA gene sequencing in two-species cocultures and agrees with 16S rRNA gene sequencing data on the most abundant species in a four-species community. We found that shape and size differences alone are insufficient to distinguish species, and that it is thus necessary to exploit the multivariate nature of flow cytometry data. Finally, we observed that variability of flow cytometry data across replicates differs between gut bacterial species. In conclusion, the performance of supervised classification of gut species in flow cytometry data is species-dependent, but is for some combinations accurate enough to serve as a faster alternative to 16S rRNA gene sequencing.

Living in a transient world: ICP-MS reinvented via time-resolved analysis for monitoring single events

After 40 years of development, inductively coupled plasma-mass spectrometry (ICP-MS) can hardly be considered as a novel technique anymore. ICP-MS has become the reference when it comes to multi-element bulk analysis at (ultra)trace levels, as well as to isotope ratio determination for metal(loid)s. However, over the last decade, this technique has managed to uncover an entirely new application field, providing information in a variety of contexts related to the individual analysis of single entities (e.g., nanoparticles, cells, or micro/nanoplastics), thus addressing new societal challenges. And this profound expansion of its application range becomes even more remarkable when considering that it has been made possible in an a priori simple way: by providing faster data acquisition and developing the corresponding theoretical substrate to relate the time-resolved signals thus obtained with the elemental composition of the target entities. This review presents the underlying concepts behind single event-ICP-MS, which are needed to fully understand its potential, highlighting key areas of application (e.g., single particle-ICP-MS or single cell-ICP-MS) as well as of future development (e.g., micro/nanoplastics).

On-demand deterministic release of particles and cells using stretchable microfluidics

Microfluidic technologies have been widely used for single-cell studies as they provide facile, cost-effective, and high-throughput evaluations of single cells with great accuracy. Capturing single cells has been investigated extensively using various microfluidic techniques. Furthermore, cell retrieval is crucial for the subsequent study of cells in applications such as drug screening. However, there are no robust methods for the facile release of the captured cells. Therefore, we developed a stretchable microfluidic cell trapper for easy on-demand release of cells in a deterministic manner. The stretchable microdevice consists of several U-shaped microstructures to capture single cells. The gap at the bottom edge of the microstructure broadens when the device is stretched along its width. By tuning the horizontal elongation of the device, ample space is provided to release particle/cell sizes of interest. The performance of the stretchable microdevice was evaluated using particles and cells. A deterministic release of particles was demonstrated using a mixture of 15 μm and 20 μm particles. The retrieval of the 15 μm particles and the 20 μm particles was achieved with elongation lengths of 1 mm and 5 mm, respectively. Two different cell lines, T47D breast cancer cells and J774A.1 macrophages, were employed to characterise the cell release capability of the device. The proposed stretchable micro cell trapper provided a deterministic recovery of the captured cells by adjusting the elongation length of the device. We believe that this stretchable microfluidic platform can provide an alternative method to facilely release trapped cells for subsequent evaluation.

Nanomaterial-Based Immunocapture Platforms for the Recognition, Isolation, and Detection of Circulating Tumor Cells

Circulating tumor cells (CTCs) are a type of cancer cells that circulate in the peripheral blood after breaking away from solid tumors and are essential for the establishment of distant metastasis. Up to 90% of cancer-related deaths are caused by metastatic cancer. As a new type of liquid biopsy, detecting and analyzing CTCs will provide insightful information for cancer diagnosis, especially the in-time disease status, which would avoid some flaws and limitations of invasive tissue biopsy. However, due to the extremely low levels of CTCs among a large number of hematologic cells, choosing immunocapture platforms for CTC detection and isolation will achieve good performance with high purity, selectivity, and viability. These properties are directly associated with precise downstream analysis of CTC profiling. Recently, inspired by the nanoscale interactions of cells in the tissue microenvironment, platforms based on nanomaterials have been widely explored to efficiently enrich and sensitively detect CTCs. In this review, various immunocapture platforms based on different nanomaterials for efficient isolation and sensitive detection of CTCs are outlined and discussed. First, the design principles of immunoaffinity nanomaterials are introduced in detail. Second, the immunocapture and release of platforms based on nanomaterials ranging from nanoparticles, nanostructured substrates, and immunoaffinity microfluidic chips are summarized. Third, recent advances in single-cell release and analysis of CTCs are introduced. Finally, some perspectives and challenges are provided in future trends of CTC studies.

Assessing Viability and Stress Tolerance of Probiotics—A Review

The interest in probiotics has increased rapidly the latest years together with the global market for probiotic products. Consequently, establishing reliable microbiological methods for assuring the presence of a certain number of viable microorganisms in probiotic products has become increasingly important. To assure adequate numbers of viable cells, authorities are enquiring for information on viability rates within a certain shelf-life in colony forming units (CFU). This information is obtained from plate count enumeration, a method that enables detection of bacterial cells based on their ability to replicate. Although performing plate count enumeration is one manner of assessing viability, cells can still be viable without possessing the ability to replicate. Thus, to properly assess probiotic viability, further analysis of a broader group of characteristics using several types of methods is proposed. In addition to viability, it is crucial to identify how well the cells in a probiotic product can survive in the gastrointestinal tract (GIT) and thus be able to mediate the desired health benefit while passing through the human body. A broad spectrum of different assay designs for assessing probiotic gastric tolerance have been used in research and quality control. However, the absence of any consensus on how to assess these qualities makes it difficult to compare between laboratories and to translate the results into in vivo tolerance. This review presents and discusses the complexity of assuring that a probiotic is suitable for beneficial consumption. It summarizes the information that can be subtracted from the currently available methods for assessment of viability and stress tolerance of a probiotic, hereby altogether defined as “activity.” Strengths and limitations of the different methods are presented together with favorable method combinations. Finally, the importance of choosing a set of analyses that reveals the necessary aspects of probiotic activity for a certain product or application is emphasized.

Microfluidic Compartmentalization Platforms for Single Cell Analysis

Many cellular analytical technologies measure only the average response from a cell population with an assumption that a clonal population is homogenous. The ensemble measurement often masks the difference among individual cells that can lead to misinterpretation. The advent of microfluidic technology has revolutionized single-cell analysis through precise manipulation of liquid and compartmentalizing single cells in small volumes (pico- to nano-liter). Due to its advantages from miniaturization, microfluidic systems offer an array of capabilities to study genomics, transcriptomics, and proteomics of a large number of individual cells. In this regard, microfluidic systems have emerged as a powerful technology to uncover cellular heterogeneity and expand the depth and breadth of single-cell analysis. This review will focus on recent developments of three microfluidic compartmentalization platforms (microvalve, microwell, and microdroplets) that target single-cell analysis spanning from proteomics to genomics. We also compare and contrast these three microfluidic platforms and discuss their respective advantages and disadvantages in single-cell analysis.

Development of Lipidoid Nanoparticles for siRNA Delivery to Neural Cells

Lipidoid nanoparticles (LNPs) are the delivery platform in Onpattro, the first FDA-approved siRNA drug. LNPs are also the carriers in the Pfizer-BioNTech and Moderna COVID-19 mRNA vaccines. While these applications have demonstrated that LNPs effectively deliver nucleic acids to hepatic and muscle cells, it is unclear if LNPs could be used for delivery of siRNA to neural cells, which are notoriously challenging delivery targets. Therefore, the purpose of this study was to determine if LNPs could efficiently deliver siRNA to neurons. Because of their potential delivery utility in either applications for the central nervous system and the peripheral nervous system, we used both cortical neurons and sensory neurons. We prepared siRNA-LNPs using C12-200, a benchmark ionizable cationic lipidoid along with helper lipids. We demonstrated using dynamic light scattering that the inclusion of both siRNA and PEG-lipid provided a stabilizing effect to the LNP particle diameters and polydispersity indices by minimizing aggregation. We found that siRNA-LNPs were safely tolerated by primary dorsal root ganglion neurons. Flow cytometry analysis revealed that Cy5 siRNA delivered via LNPs into rat primary cortical neurons showed uptake levels similar to Lipofectamine RNAiMAX-the gold standard commercial transfection agent. However, LNPs demonstrated a superior safety profile, whereas the Lipofectamine-mediated uptake was concomitant with significant toxicity. Fluorescence microscopy demonstrated a time-dependent increase in the uptake of LNP-delivered Cy5 siRNA in a human cortical neuron cell line. Overall, our results suggest that LNPs are a viable platform that can be optimized for delivery of therapeutic siRNAs to neural cells.

Toward the next-generation phyto-nanomedicines: cell-derived nanovesicles (CDNs) for natural product delivery

Phytochemicals are plant-derived bioactive compounds, which have been widely used for therapeutic purposes. Due to the poor water-solubility, low bioavailability and non-specific targeting characteristic, diverse classes of nanocarriers are utilized for encapsulation and delivery of bio-effective agents. Cell-derived nanovesicles (CDNs), known for exosomes or extracellular vesicles (EVs), are biological nanoparticles with multiple functions. Compared to the artificial counterpart, CDNs hold great potential in drug delivery given the higher stability, superior biocompatibility and the lager capability of encapsulating bioactive molecules. Here, we provide a bench-to-bedside review of CDNs-based nanoplatform, including the bio-origin, preparation, characterization and functionalization. Beyond that, the focus is laid on the therapeutic effect of CDNs-mediated drug delivery for natural products. The state-of-art development as well as some pre-clinical applications of using CDNs for disease treatment is also summarized. It is highly expected that the continuing development of CDNs-based delivery systems will further promote the clinical utilization and translation of phyto-nanomedicines.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Membrane Transporters Involved in the Antimicrobial Activities of Pyrithione in Escherichia coli

Pyrithione (2-mercaptopyridine-N-oxide) is a metal binding modified pyridine, the antibacterial activity of which was described over 60 years ago. The formulation of zinc-pyrithione is commonly used in the topical treatment of certain dermatological conditions. However, the characterisation of the cellular uptake of pyrithione has not been elucidated, although an unsubstantiated assumption has persisted that pyrithione and/or its metal complexes undergo a passive diffusion through cell membranes. Here, we have profiled specific membrane transporters from an unbiased interrogation of 532 E. coli strains of knockouts of genes encoding membrane proteins from the Keio collection. Two membrane transporters, FepC and MetQ, seemed involved in the uptake of pyrithione and its cognate metal complexes with copper, iron, and zinc. Additionally, the phenotypes displayed by CopA and ZntA knockouts suggested that these two metal effluxers drive the extrusion from the bacterial cell of potentially toxic levels of copper, and perhaps zinc, which hyperaccumulate as a function of pyrithione. The involvement of these distinct membrane transporters contributes to the understanding of the mechanisms of action of pyrithione specifically and highlights, more generally, the important role that membrane transporters play in facilitating the uptake of drugs, including metal-drug compounds.

Flow cytometry method for absolute counting and single-cell phenotyping of mycobacteria

Detection and accurate quantitation of viable Mycobacterium tuberculosis is fundamental to understanding mycobacterial pathogenicity, tuberculosis (TB) disease progression and outcomes; TB transmission; drug action, efficacy and drug resistance. Despite this importance, methods for determining numbers of viable bacilli are limited in accuracy and precision owing to inherent characteristics of mycobacterial cell biology—including the tendency to clump, and “differential” culturability—and technical challenges consequent on handling an infectious pathogen under biosafe conditions. We developed an absolute counting method for mycobacteria in liquid cultures using a bench-top flow cytometer, and the low-cost fluorescent dyes Calcein-AM (CA) and SYBR-gold (SG). During exponential growth CA + cell counts are highly correlated with CFU counts and can be used as a real-time alternative to simplify the accurate standardisation of inocula for experiments. In contrast to CFU counting, this method can detect and enumerate cell aggregates in samples, which we show are a potential source of variance and bias when using established methods. We show that CFUs comprise a sub-population of intact, metabolically active mycobacterial cells in liquid cultures, with CFU-proportion varying by growth conditions. A pharmacodynamic application of the flow cytometry method, exploring kinetics of fluorescent probe defined subpopulations compared to CFU is demonstrated. Flow cytometry derived Mycobacterium bovis bacillus Calmette-Guérin (BCG) time-kill curves differ for rifampicin and kanamycin versus isoniazid and ethambutol, as do the relative dynamics of discrete morphologically-distinct subpopulations of bacilli revealed by this high-throughput single-cell technique.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction

BACKGROUND

Cell type identification is one of the most important questions in single-cell RNA sequencing (scRNA-seq) data analysis. With the accumulation of public scRNA-seq data, supervised cell type identification methods have gained increasing popularity due to better accuracy, robustness, and computational performance. Despite all the advantages, the performance of the supervised methods relies heavily on several key factors: feature selection, prediction method, and, most importantly, choice of the reference dataset.

RESULTS

In this work, we perform extensive real data analyses to systematically evaluate these strategies in supervised cell identification. We first benchmark nine classifiers along with six feature selection strategies and investigate the impact of reference data size and number of cell types in cell type prediction. Next, we focus on how discrepancies between reference and target datasets and how data preprocessing such as imputation and batch effect correction affect prediction performance. We also investigate the strategies of pooling and purifying reference data.

CONCLUSIONS

Based on our analysis results, we provide guidelines for using supervised cell typing methods. We suggest combining all individuals from available datasets to construct the reference dataset and use multi-layer perceptron (MLP) as the classifier, along with F-test as the feature selection method. All the code used for our analysis is available on GitHub ( https://github.com/marvinquiet/RefConstruction_supervisedCelltyping ).