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

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

Iva xanthiifolia leaf extract reduced the diversity of indigenous plant rhizosphere bacteria.

BACKGROUND: Iva xanthiifolia, native to North America, is now widely distributed in northeastern China and has become a vicious invasive plant. This article aims to probe the role of leaf extract in the invasion of I. xanthiifolia. METHODS: We collected the rhizosphere soil of Amaranthus tricolor and Setaria viridis in the invasive zone, the noninvasive zone and the noninvasive zone treated with extract from I. xanthiifolia leaf, and obtained I. xanthiifolia rhizosphere soil in the invasive zone. All wild plants were identified by Xu Yongqing. I. xanthiifolia (collection number: RQSB04100), A. tricolor (collection number: 831,030) and S. viridis (collection number: CF-0002-034) are all included in Chinese Virtual Herbarium ( https://www.cvh.ac.cn/index.php ). The soil bacterial diversity was analyzed based on the Illumina HiSeq sequencing platform. Subsequently, taxonomic analysis and Faprotax functional prediction were performed. RESULTS: The results showed that the leaf extract significantly reduced the diversity of indigenous plant rhizosphere bacteria. A. tricolor and S. viridis rhizobacterial phylum and genus abundances were significantly reduced under the influence of I. xanthiifolia or its leaf extract. The results of functional prediction showed that bacterial abundance changes induced by leaf extracts could potentially hinder nutrient cycling in native plants and increased bacterial abundance in the A. tricolor rhizosphere related to aromatic compound degradation. In addition, the greatest number of sensitive Operational Taxonomic Units (OTUs) appeared in the rhizosphere when S. viridis was in response to the invasion of I. xanthiifolia. It can be seen that A. tricolor and S. viridis have different mechanisms in response to the invasion of I. xanthiifolia. CONCLUSION: I. xanthiifolia leaves material has potential role in invasion by altering indigenous plant rhizosphere bacteria.

Engineered hypermutation adapts cyanobacterial photosynthesis to combined high light and high temperature stress.

Photosynthesis can be impaired by combined high light and high temperature (HLHT) stress. Obtaining HLHT tolerant photoautotrophs is laborious and time-consuming, and in most cases the underlying molecular mechanisms remain unclear. Here, we increase the mutation rates of cyanobacterium Synechococcus elongatus PCC 7942 by three orders of magnitude through combinatory perturbations of the genetic fidelity machinery and cultivation environment. Utilizing the hypermutation system, we isolate Synechococcus mutants with improved HLHT tolerance and identify genome mutations contributing to the adaptation process. A specific mutation located in the upstream non-coding region of the gene encoding a shikimate kinase results in enhanced expression of this gene. Overexpression of the shikimate kinase encoding gene in both Synechococcus and Synechocystis leads to improved HLHT tolerance. Transcriptome analysis indicates that the mutation remodels the photosynthetic chain and metabolism network in Synechococcus. Thus, mutations identified by the hypermutation system are useful for engineering cyanobacteria with improved HLHT tolerance.

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

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

Single-cell rapid identification, in situ viability and vitality profiling, and genome-based source-tracking for probiotics products.

Rapid expansion of the probiotics industry demands fast, sensitive, comprehensive, and low-cost strategies for quality assessment. Here, we introduce a culture-free, one-cell-resolution, phenome-genome-combined strategy called Single-Cell Identification, Viability and Vitality tests, and Source-tracking (SCIVVS). For each cell directly extracted from the product, the fingerprint region of D2O-probed single-cell Raman spectrum (SCRS) enables species-level identification with 93% accuracy, based on a reference SCRS database from 21 statutory probiotic species, whereas the C-D band accurately quantifies viability, metabolic vitality plus their intercellular heterogeneity. For source-tracking, single-cell Raman-activated Cell Sorting and Sequencing can proceed, producing indexed, precisely one-cell-based genome assemblies that can reach ~99.40% genome-wide coverage. Finally, we validated an integrated SCIVVS workflow with automated SCRS acquisition where the whole process except sequencing takes just 5 h. As it is >20-fold faster, >10-time cheaper, vitality-revealing, heterogeneity-resolving, and automation-prone, SCIVVS is a new technological and data framework for quality assessment of live-cell products.

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

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

Single-Cell Identification, Drug Susceptibility Test, and Whole-genome Sequencing of Helicobacter pylori Directly from Gastric Biopsy by Clinical Antimicrobial Susceptibility Test Ramanometry.

BACKGROUND: The battle against Helicobacter pylori (H. pylori) infections demands fast, reliable, and sensitive methods for pathogen identification (ID), antimicrobial susceptibility tests (ASTs) based on metabolic response, and genome-wide mutation profiling that reveals resistance mechanisms. METHODS: Here we introduce Clinical Antimicrobial Susceptibility Test Ramanometry for H. pylori (CAST-R-HP), and its validation with clinical samples. This method performs rapid ID, metabolism inhibition-based AST, and high-quality whole-genome sequencing for cells of targeted resistance phenotype, all at precisely 1-cell resolution and directly from biopsy samples. RESULTS: In CAST-R-HP, automated acquisition and machine learning of single-cell Raman spectra (SCRS) enable distinguishing individual H. pylori cells directly from a biopsy sample, with 98.5 ± 0.27% accuracy in ID. Moreover, by adding a 48- to72-h D2O feeding and drug exposure step prior to SCRS acquisition, CAST-R-HP reports AST for levofloxacin and clarithromycin with 100% accuracy, based on metabolic inhibition level. Furthermore, CAST-R-HP supports rapid sorting, low-bias DNA amplification, and full genome sequencing of single H. pylori cells with the SCRS defined, targeted drug-susceptibility phenotype, via Raman-activated gravity-driven cell encapsulation and sequencing. The genome-wide mutation map (maximum 99.70% coverage), at precisely 1-cell resolution, not only elucidates the drug-susceptibility phenotypes but also unveils their underlying molecular mechanisms. CONCLUSION: The culture independency, shorter turnaround time, high resolution, and comprehensive information output suggest that CAST-R-HP is a powerful tool for diagnosing and treating H. pylori infections.

Assessing Efficacy of Clinical Disinfectants for Pathogenic Fungi by Single-Cell Raman Microspectroscopy.

Disinfectants are crucial for root canal therapy (RCT), as metabolism of canal-inhabiting microbes can cause refractory infections. To develop effective yet patient- and environment-friendly disinfectant formulations, we quantitatively assessed the metabolism-inhibiting effects of intracanal disinfectants via D2O-probed Single-Cell Raman Spectra (SCRS), using Candida albicans (C. albicans) as a pathogen model. For chlorhexidine gluconate (CHX), sodium hypochlorite (NaClO), and hydrogen peroxide (H2O2), at their MIC of 4, 168, and 60 µg/ml, respectively, despite the complete growth halt, metabolic activity of individual fungal cells was reduced on average by 0.4%, 93.9%, and 94.1% at 8 h, revealing a "nongrowing but metabolically active" (NGMA) state that may underlie potential refractory infections, particularly for CHX. In contrast, at their Metabolic Activity-based Minimum Inhibitory Concentrations (MIC-MA) of 8, 336, and 120 µg/ml, respectively, metabolic activity of all cells was completely halted throughout 8 h exposure. Moreover, combined use of NaClO+H2O2 (combination at 0.5× MIC-MA each) outperforms solo uses of CHX, NaClO, H2O2, or other binary combinations. Furthermore, dynamics of SCRS revealed distinct fungicidal mechanisms of CHX, NaClO, H2O2, and their pairwise combinations. MIC-MA is advantageous in critically assessing antifungal efficacy, and NaClO+H2O2 can potentially serve as a more efficient disinfectant formula for fungal pathogens.

Accurate PSF determination in x ray image restoration.

When a source's focal spot is known, an x ray image can be significantly improved by a deconvolution algorithm with the point spread function (PSF). We propose a simple method to measure the PSF for image restoration using x ray speckle imaging. In this method, the PSF is reconstructed with intensity and total variation constraints from a single x ray speckle of an ordinary diffuser. Compared with the traditional time-consuming measurement with a pinhole camera, the speckle imaging method is fast and easy to execute. When the PSF is available, we use a deconvolution algorithm to reconstruct the sample's radiography image, and the result provides more structural details than the original images.

Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST-R.

Antimicrobial susceptibility tests (ASTs) are pivotal in combating multidrug resistant pathogens, yet they can be time-consuming, labor-intensive, and unstable. Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry (CAST-R), based on D2O-probed Raman microspectroscopy. Featuring a liquid robot for sample pretreatment and a machine learning-based control scheme for data acquisition and quality control, the 3-h, automated CAST-R process accelerates AST by >10-fold, processes 96 paralleled antibiotic-exposure reactions, and produces high-quality Raman spectra. The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism-based AST, which shows 99% essential agreement and 93% categorical agreement with the broth microdilution method (BMD) when tested on 100 Acinetobacter baumannii isolates. Further tests on 26 clinically positive blood samples for eight antimicrobials, including tigecycline, meropenem, ceftazidime, ampicillin/sulbactam, oxacillin, clindamycin, vancomycin, and levofloxacin reveal 93% categorical agreement with BMD-based results. The automation, speed, reliability, and general applicability of CAST-R suggest its potential utility for guiding the clinical administration of antimicrobials.

Artificial intelligence-assisted automatic and index-based microbial single-cell sorting system for One-Cell-One-Tube.

Identification, sorting, and sequencing of individual cells directly from in situ samples have great potential for in-depth analysis of the structure and function of microbiomes. In this work, based on an artificial intelligence (AI)-assisted object detection model for cell phenotype screening and a cross-interface contact method for single-cell exporting, we developed an automatic and index-based system called EasySort AUTO, where individual microbial cells are sorted and then packaged in a microdroplet and automatically exported in a precisely indexed, "One-Cell-One-Tube" manner. The target cell is automatically identified based on an AI-assisted object detection model and then mobilized via an optical tweezer for sorting. Then, a cross-interface contact microfluidic printing method that we developed enables the automated transfer of cells from the chip to the tube, which leads to coupling with subsequent single-cell culture or sequencing. The efficiency of the system for single-cell printing is >93%. The throughput of the system for single-cell printing is ~120 cells/h. Moreover, >80% of single cells of both yeast and Escherichia coli are culturable, suggesting the superior preservation of cell viability during sorting. Finally, AI-assisted object detection supports automated sorting of target cells with high accuracy from mixed yeast samples, which was validated by downstream single-cell proliferation assays. The automation, index maintenance, and vitality preservation of EasySort AUTO suggest its excellent application potential for single-cell sorting.

Rational Optimization of Raman-Activated Cell Ejection and Sequencing for Bacteria.

In Raman-activated cell ejection and sequencing (RACE-Seq), success rate and sequence coverage have generally been low for shotgun sequencing of individual post-RACE cells. Here we quantitatively evaluated the influence of cell lysis condition, nucleic acid amplification condition, and parameters of Raman measurement on RACE-Seq performance. Variations in laser energy input during Raman signal acquisition, but not duration of alkaline lysate lysis, temperature, or measurement under dry or aqueous conditions, are vital to the success of multiple displacement amplification (MDA). In fact, laser irradiation is reversely linked to MDA product quality. However, introduction of oils prior to MDA, by mitigating such negative effects of Raman irradiation, elevates genome coverage of post-RACE Escherichia coli cells from <20% to ∼50%, while greatly improving the success rate of RACE-Seq for soil microbiota. Our findings provide a practical solution for enhancing RACE-Seq performance and pinpoint protection of cells from laser irradiation as a priority in method development.