Rapid Single-Cell Microbiological Analysis: Toward Precision Management of Infections and Dysbiosis

Rapid Phenotypic Antibiotics Susceptibility Analysis by a 3D Printed Prototype

One of the most important public health concerns is the increase in antibiotic-resistant pathogens and corresponding treatment of associated infections. Addressing this challenge requires more efficient use of antibiotics, achievable by the use of evidence-based, effective antibiotics identified by antibiotic susceptibility testing (AST). However, the current standard method of phenotypic AST used for this purpose requires 48 h or more from sample collection to result. Until results are available, broad-spectrum antibiotics are used to avoid delaying treatment. The turnaround time must therefore be shortened in order for the results to be available before the second administration of antibiotics. The phenotypic electrochemical AST method presented here identifies effective antibiotics within 5-10 h after sampling. Spiked serum samples, including polymicrobial samples, with clinically relevant pathogens and respective concentrations commonly found in bloodstream infections (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Pseudomonas aeruginosa) are used. Direct loading of the test with diluted serum eliminates the need for a pre-culture, as required by existing methods. Furthermore, by combining several electrochemical measurement procedures with computational analysis, allowing the method to be used both online and offline, the AST achieves a sensitivity of 94.44% and a specificity of 95.83% considering each replicate individually.

Single-cell pathogen diagnostics for combating antibiotic resistance

Bacterial infections and antimicrobial resistance are a major cause for morbidity and mortality worldwide. Antimicrobial resistance often arises from antimicrobial misuse, where physicians empirically treat suspected bacterial infections with broad-spectrum antibiotics until standard culture-based diagnostic tests can be completed. There has been a tremendous effort to develop rapid diagnostics in support of the transition from empirical treatment of bacterial infections towards a more precise and personalized approach. Single-cell pathogen diagnostics hold particular promise, enabling unprecedented quantitative precision and rapid turnaround times. This Primer provides a guide for assessing, designing, implementing and applying single-cell pathogen diagnostics. First, single-cell pathogen diagnostic platforms are introduced based on three essential capabilities: cell isolation, detection assay and output measurement. Representative results, common analysis methods and key applications are highlighted, with an emphasis on initial screening of bacterial infection, bacterial species identification and antimicrobial susceptibility testing. Finally, the limitations of existing platforms are discussed, with perspectives offered and an outlook towards clinical deployment. This Primer hopes to inspire and propel new platforms that can realize the vision of precise and personalized bacterial infection treatments in the near future.

Combinatorial nanodroplet platform for screening antibiotic combinations

The emergence and spread of multidrug resistant bacterial strains and concomitant dwindling of effective antibiotics pose worldwide healthcare challenges. To address these challenges, advanced engineering tools are developed to personalize antibiotic treatments by speeding up the diagnostics that is critical to prevent antibiotic misuse and overuse and make full use of existing antibiotics. Meanwhile, it is necessary to investigate novel antibiotic strategies. Recently, repurposing mono antibiotics into combinatorial antibiotic therapies has shown great potential for treatment of bacterial infections. However, widespread adoption of drug combinations has been hindered by the complexity of screening techniques and the cost of reagent consumptions in practice. In this study, we developed a combinatorial nanodroplet platform for automated and high-throughput screening of antibiotic combinations while consuming orders of magnitude lower reagents than the standard microtiter-based screening method. In particular, the proposed platform is capable of creating nanoliter droplets with multiple reagents in an automatic manner, tuning concentrations of each component, performing biochemical assays with high flexibility (e.g., temperature and duration), and achieving detection with high sensitivity. A biochemical assay, based on the reduction of resazurin by the metabolism of bacteria, has been characterized and employed to evaluate the combinatorial effects of the antibiotics of interest. In a pilot study, we successfully screened pairwise combinations between 4 antibiotics for a model Escherichia coli strain.

A Rapid Single-Cell Antimicrobial Susceptibility Testing Workflow for Bloodstream Infections

Bloodstream infections are a significant cause of morbidity and mortality worldwide. The rapid initiation of effective antibiotic treatment is critical for patients with bloodstream infections. However, the diagnosis of bloodborne pathogens is largely complicated by the matrix effect of blood and the lengthy blood tube culture procedure. Here we report a culture-free workflow for the rapid isolation and enrichment of bacterial pathogens from whole blood for single-cell antimicrobial susceptibility testing (AST). A dextran sedimentation step reduces the concentration of blood cells by 4 orders of magnitude in 20–30 min while maintaining the effective concentration of bacteria in the sample. Red blood cell depletion facilitates the downstream centrifugation-based enrichment step at a sepsis-relevant bacteria concentration. The workflow is compatible with common antibiotic-resistant bacteria and does not influence the minimum inhibitory concentrations. By applying a microfluidic single-cell trapping device, we demonstrate the workflow for the rapid determination of bacterial infection and antimicrobial susceptibility testing at the single-cell level. The entire workflow from blood to categorical AST result can be completed in less than two hours.

Geospatial Spread of Antimicrobial Resistance, Bacterial and Fungal Threats to Coronavirus Infectious Disease 2019 (COVID-19) Survival, and Point-of-Care Solutions

CONTEXT.—

Point-of-care testing (POCT) is inherently spatial, that is, performed where needed, and intrinsically temporal, because it accelerates decision-making. POCT efficiency and effectiveness have the potential to facilitate antimicrobial resistance (AMR) detection, decrease risks of coinfections for critically ill patients with coronavirus infectious disease 2019 (COVID-19), and improve the cost-effectiveness of health care.

OBJECTIVES.—

To assess AMR identification by using POCT, describe the United States AMR Diagnostic Challenge, and improve global standards of care for infectious diseases.

DATA SOURCES.—

PubMed, World Wide Web, and other sources were searched for papers focusing on AMR and POCT. EndNote X9.1 (Clarivate Analytics) consolidated abstracts, URLs, and PDFs representing approximately 500 articles were assessed for relevance. Panelist insights at Tri•Con 2020 in San Francisco and finalist POC technologies competing for a US $20,000,000 AMR prize are summarized.

CONCLUSIONS.—

Coinfections represent high risks for COVID-19 patients. POCT potentially will help target specific pathogens, refine choices for antimicrobial drugs, and prevent excess morbidity and mortality. POC assays that identify patterns of pathogen resistance can help tell us how infected individuals spread AMR, where geospatial hotspots are located, when delays cause death, and how to deploy preventative resources. Shared AMR data "clouds" could help reduce critical care burden during pandemics and optimize therapeutic options, similar to use of antibiograms in individual hospitals. Multidisciplinary health care personnel should learn the principles and practice of POCT, so they can meet needs with rapid diagnostic testing. The stakes are high. Antimicrobial resistance is projected to cause millions of deaths annually and cumulative financial loses in the trillions by 2050.