Rapid identification of bacterial isolates using microfluidic adaptive channels and multiplexed fluorescence microscopy.

We demonstrate the rapid capture, enrichment, and identification of bacterial pathogens using Adaptive Channel Bacterial Capture (ACBC) devices. Using controlled tuning of device backpressure in polydimethylsiloxane (PDMS) devices, we enable the controlled formation of capture regions capable of trapping bacteria from low cell density samples with near 100% capture efficiency. The technical demands to prepare such devices are much lower compared to conventional methods for bacterial trapping and can be achieved with simple benchtop fabrication methods. We demonstrate the capture and identification of seven species of bacteria with bacterial concentrations lower than 1000 cells per mL, including common Gram-negative and Gram-positive pathogens such as Escherichia coli and Staphylococcus aureus. We further demonstrate that species identification of the trapped bacteria can be undertaken in the order of one-hour using multiplexed 16S rRNA-FISH with identification accuracies of 70-98% with unsupervised classification methods across 7 species of bacteria. Finally, by using the bacterial capture capabilities of the ACBC chip with an ultra-rapid antimicrobial susceptibility testing method employing fluorescence imaging and convolutional neural network (CNN) classification, we demonstrate that we can use the ACBC chip as an imaging flow cytometer that can predict the antibiotic susceptibility of E. coli cells after identification.

Infection Inspection: using the power of citizen science for image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin.

Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli. Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain's own minimum inhibitory concentration. We used the users' classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.

Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli.

The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current culture-based turnaround times for bacterial identification in clinical samples and antimicrobial susceptibility testing (AST) are typically 18-24 h. We present a novel proof-of-concept methodological advance in susceptibility testing based on the deep-learning of single-cell specific morphological phenotypes directly associated with antimicrobial susceptibility in Escherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells for a lab-reference fully susceptible E. coli strain, across four antibiotics (ciprofloxacin, gentamicin, rifampicin and co-amoxiclav). For ciprofloxacin, we demonstrate our models reveal significant (p < 0.001) differences between bacterial cell populations affected and unaffected by antibiotic treatment, and show that given treatment with a fixed concentration of 10 mg/L over 30 min these phenotypic effects correlate with clinical susceptibility defined by established clinical breakpoints. Deploying our approach on cell populations from six E. coli strains obtained from human bloodstream infections with varying degrees of ciprofloxacin resistance and treated with a range of ciprofloxacin concentrations, we show single-cell phenotyping has the potential to provide equivalent information to growth-based AST assays, but in as little as 30 min.

On the use of Raman spectroscopy to characterize mass-produced graphene nanoplatelets.

Raman spectroscopy is one of the most common methods to characterize graphene-related 2D materials, providing information on a wide range of physical and chemical properties. Because of typical sample inhomogeneity, Raman spectra are acquired from several locations across a sample, and analysis is carried out on the averaged spectrum from all locations. This is then used to characterize the "quality" of the graphene produced, in particular the level of exfoliation for top-down manufactured materials. However, these have generally been developed using samples prepared with careful separation of unexfoliated materials. In this work we assess these metrics when applied to non-ideal samples, where unexfoliated graphite has been deliberately added to the exfoliated material. We demonstrate that previously published metrics, when applied to averaged spectra, do not allow the presence of this unexfoliated material to be reliably detected. Furthermore, when a sufficiently large number of spectra are acquired, it is found that by processing and classifying individual spectra, rather than the averaged spectrum, it is possible to identify the presence of this material in the sample, although quantification of the amount remains approximate. We therefore recommend this approach as a robust methodology for reliable characterization of mass-produced graphene-related 2D materials using confocal Raman spectroscopy.

Deagglomeration of DNA nanomedicine carriers using controlled ultrasonication.

Control over the agglomeration state of manufactured particle systems for drug and oligonucleotide intracellular delivery is paramount to ensure reproducible and scalable therapeutic efficacy. Ultrasonication is a well-established mechanism for the deagglomeration of bulk powders in dispersion. Its use in manufacturing requires strict control of the uniformity and reproducibility of the cavitation field within the sample volume to minimise within-batch and batch-to-batch variability. In this work, we demonstrate the use of a reference cavitating vessel which provides stable and reproducible cavitation fields over litre-scale volumes to assist the controlled deagglomeration of a novel non-viral particle-based plasmid delivery system. The system is the Nuvec delivery platform, comprising polyethylenimine-coated spiky silica particles with diameters of âˆ¼ 200 nm. We evaluated the use of controlled cavitation at different input powers and stages of preparation, for example before and after plasmid loading. Plasmid loading was confirmed by X-ray photoelectron spectroscopy and gel electrophoresis. The latter was also used to assess plasmid integrity and the ability of the particles to protect plasmid from potential degradation caused by the deagglomeration process. We show the utility of laser diffraction and differential centrifugal sedimentation in quantifying the efficacy of product de-agglomeration in the microscale and nanoscale size range respectively. Transmission electron microscopy was used to assess potential damages to the silica particle structure due to the sonication process.

Rapid monitoring of graphene exfoliation using NMR proton relaxation.

Graphene is now being produced on an industrial scale and there is a pressing need for rapid in-line measurements of particle size for Quality Assurance and Quality Control (QA/QC). Standardised characterisation techniques such as electron microscopy and scanning probe microscopy can be time consuming and may require pre-processing steps and/or solvent elimination prior to measurements. Herein, we demonstrate the use of nuclear magnetic resonance (NMR) proton relaxation as a powerful method for monitoring the sonication assisted liquid phase exfoliation of graphene. This technique requires little or no sample preparation and the resulting spin-spin relaxation time showed a strong correlation with particle size, exfoliation yield and specific surface area measurements. As the NMR proton relaxation method is rapid, inexpensive, and can potentially be operated in-line, it shows great promise to become a valuable QA/QC method for graphene production methods in liquid.

Using nuclear magnetic resonance proton relaxation to probe the surface chemistry of carbon 2D materials.

Nanomaterials exhibit a high surface-area-to-mass ratio, making surface properties key to optimising product performance. However, characterising surfaces at the nanoscale is difficult to achieve, especially as nanomaterials are often in liquid dispersions. Herein, we demonstrate the use of nuclear magnetic resonance proton relaxation for rapid characterisation of the surface chemistry of graphitic materials.

Controlled Sonication as a Route to in-situ Graphene Flake Size Control.

Ultrasonication is widely used to exfoliate two dimensional (2D) van der Waals layered materials such as graphene. Its fundamental mechanism, inertial cavitation, is poorly understood and often ignored in ultrasonication strategies resulting in low exfoliation rates, low material yields and wide flake size distributions, making the graphene dispersions produced by ultrasonication less economically viable. Here we report that few-layer graphene yields of up to 18% in three hours can be achieved by optimising inertial cavitation dose during ultrasonication. We demonstrate that inertial cavitation preferentially exfoliates larger flakes and that the graphene exfoliation rate and flake dimensions are strongly correlated with, and therefore can be controlled by, inertial cavitation dose. Furthermore, inertial cavitation is shown to preferentially exfoliate larger graphene flakes which causes the exfoliation rate to decrease as a function of sonication time. This study demonstrates that measurement and control of inertial cavitation is critical in optimising the high yield sonication-assisted aqueous liquid phase exfoliation of size-selected nanomaterials. Future development of this method should lead to the development of high volume flow cell production of 2D van der Waals layered nanomaterials.