Synchrotron-based X-ray fluorescence microscopy reveals accumulation of polymyxins in single human alveolar epithelial cells.

Intravenous administration of the last-line polymyxins results in poor drug exposure in the lungs and potential nephrotoxicity; while inhalation therapy offers better pharmacokinetics/pharmacodynamics for pulmonary infections by delivering the antibiotic to the infection site directly. However, polymyxin inhalation therapy has not been optimized and adverse effects can occur. This study aimed to quantitatively determine the intracellular accumulation and distribution of polymyxins in single human alveolar epithelial A549 cells. Cells were treated with an iodine-labeled polymyxin probe FADDI-096 (5.0 and 10.0 µM) for 1, 4, and 24 h. Concentrations of FADDI-096 in single A549 cells were determined by synchrotron-based X-ray fluorescence microscopy. Concentration- and time-dependent accumulation of FADDI-096 within A549 cells was observed. The intracellular concentrations (mean ± SEM, n ≥ 189) of FADDI-096 were 1.58 ± 0.11, 2.25 ± 0.10, and 2.46 ± 0.07 mM following 1, 4 and 24 h of treatment at 10 µM, respectively. The corresponding intracellular concentrations following the treatment at 5 µM were 0.05 ± 0.01, 0.24 ± 0.04, and 0.25 ± 0.02 mM (n ≥ 189). FADDI-096 was mainly localized throughout the cytoplasm and nuclear region over 24 h. The intracellular zinc concentration increased in a concentration- and time-dependent manner. This is the first study to quantitatively map the accumulation of polymyxins in human alveolar epithelial cells and provides crucial insights for deciphering the mechanisms of their pulmonary toxicity. Importantly, our results may shed light on the optimization of inhaled polymyxins in patients and the development of new-generation safer polymyxins.

Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis.

Methane flux (FCH4 ) measurements using the eddy covariance technique have increased over the past decade. FCH4 measurements commonly include data gaps, as is the case with CO2 and energy fluxes. However, gap-filling FCH4 data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap-filling method for other fluxes, to FCH4 datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH4 . However, there is still no consensus regarding FCH4 gap-filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH4 datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH4 and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH4 datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA-derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap-filling uncertainty is much larger than measurement uncertainty in accumulated CH4 budget. Therefore, the approach used for FCH4 gap filling can have important implications for characterizing annual ecosystem-scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes.

Controlled graphene encapsulation: a nanoscale shield for characterising single bacterial cells in liquid.

High-resolution single-cell imaging in their native or near-native state has received considerable interest for decades. In this research, we present an innovative approach that can be employed to study both morphological and nano-mechanical properties of hydrated single bacterial cells. The proposed strategy is to encapsulate wet cells with monolayer graphene with a newly developed water membrane approach, followed by imaging with both electron microscopy (EM) and atomic force microscopy (AFM). A computational framework was developed to provide additional insights, with the detailed nanoindentation process on graphene modelled based on the finite element method. The model was first validated by calibration with polymer materials of known properties, and the contribution of graphene was then studied and corrected to determine the actual moduli of the encapsulated hydrated sample. Application of the proposed approach was performed on hydrated bacterial cells (Klebsiella pneumoniae) to correlate the structural and mechanical information. EM and energy-dispersive x-ray spectroscopy imaging confirmed that the cells in their near-native stage can be studied inside the miniaturised environment enabled with graphene encapsulation. The actual moduli of the encapsulated hydrated cells were determined based on the developed computational model in parallel, with results comparable with those acquired with wet AFM. It is expected that the successful establishment of controlled graphene encapsulation offers a new route for probing liquid/live cells with scanning probe microscopy, as well as correlative imaging of hydrated samples for both biological and material sciences.

Automated patterning and probing with multiple nanoscale tools for single-cell analysis.

The nano-manipulation approach that combines Focused Ion Beam (FIB) milling and various imaging and probing techniques enables researchers to investigate the cellular structures in three dimensions. Such fusion approach, however, requires extensive effort on locating and examining randomly-distributed targets due to limited Field of View (FOV) when high magnification is desired. In the present study, we present the development that automates 'pattern and probe' particularly for single-cell analysis, achieved by computer aided tools including feature recognition and geometric planning algorithms. Scheduling of serial FOVs for imaging and probing of multiple cells was considered as a rectangle covering problem, and optimal or near-optimal solutions were obtained with the heuristics developed. FIB milling was then employed automatically followed by downstream analysis using Atomic Force Microscopy (AFM) to probe the cellular interior. Our strategy was applied to examine bacterial cells (Klebsiella pneumoniae) and achieved high efficiency with limited human interference. The developed algorithms can be easily adapted and integrated with different imaging platforms towards high-throughput imaging analysis of single cells.