NAD(P)H binding configurations revealed by time-resolved fluorescence and two-photon absorption

Different Roles of Dioxin-Catabolic Plasmids in Growth, Biofilm Formation, and Metabolism of Rhodococcus sp. Strain p52

Microorganisms harbor catabolic plasmids to tackle refractory organic pollutants, which is crucial for bioremediation and ecosystem health. Understanding the impacts of plasmids on hosts provides insights into the behavior and adaptation of degrading bacteria in the environment. Here, we examined alterations in the physiological properties and gene expression profiles of Rhodococcus sp. strain p52 after losing two conjugative dioxin-catabolic megaplasmids (pDF01 and pDF02). The growth of strain p52 accelerated after pDF01 loss, while it decelerated after pDF02 loss. During dibenzofuran degradation, the expression levels of dibenzofuran catabolic genes on pDF01 were higher compared to those on pDF02; accordingly, pDF01 loss markedly slowed dibenzofuran degradation. It was suggested that pDF01 is more beneficial to strain p52 under dibenzofuran exposure. Moreover, plasmid loss decreased biofilm formation, especially after pDF02 loss. Transcriptome profiling revealed different pathways enriched in upregulated and downregulated genes after pDF01 and pDF02 loss, indicating different adaptation mechanisms. Based on the transcriptional activity variation, pDF01 played roles in transcription and anabolic processes, while pDF02 profoundly influenced energy production and cellular defense. This study enhances our knowledge of the impacts of degradative plasmids on native hosts and the adaptation mechanisms of hosts, contributing to the application of plasmid-mediated bioremediation in contaminated environments.

Damage-evoked signals in cochlear neurons and supporting cells

In addition to hearing loss, damage to the cochlea can lead to gain of function pathologies such as hyperacusis. It has been proposed that painful hyperacusis, noxacusis, may be carried to the central nervous system by type II cochlear afferents, sparse, unmyelinated neurons that share morphological and neurochemical traits with nociceptive C-fibers of the somatic nervous system. Also like in skin, damage elicits spreading calcium waves within cochlear epithelia. These are mediated by extracellular ATP combined with IP3-driven release from intracellular calcium stores. Type II afferents are excited by ATP released from damaged epithelia. Thus, the genesis and propagation of epithelial calcium waves is central to cochlear pathology, and presumably hyperacusis. Damage-evoked signals in type II afferents and epithelial cells have been recorded in cochlear explants or semi-intact otic capsules. These efforts have included intracellular electrical recording, use of fluorescent calcium indicators, and visualization of an activity-dependent, intrinsic fluorescent signal. Of relevance to hyperacusis, prior noise-induced hearing loss leads to the generation of prolonged and repetitive activity in type II neurons and surrounding epithelia.

Restoration of metabolic functional metrics from label-free, two-photon human tissue images using multiscale deep-learning-based denoising algorithms

Significance

Label-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these images severely complicate the extraction of biologically useful information.

Aim

We aim to employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-signal-to-noise ratio (SNR), TPEF images.

Approach

TPEF images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used to assess the impact of various denoising models, preprocessing methods, and data on metrics of image quality and the recovery of six metrics of metabolic function from the images relative to ground truth images.

Results

Optimized recovery of the redox ratio and mitochondrial organization is achieved using a novel algorithm based on deep denoising in the wavelet transform domain. This algorithm also leads to significant improvements in peak-SNR (PSNR) and structural similarity index measure (SSIM) for all images. Interestingly, other models yield even higher PSNR and SSIM improvements, but they are not optimal for recovery of metabolic function metrics.

Conclusions

Denoising algorithms can recover diagnostically useful information from low SNR label-free TPEF images and will be useful for the clinical translation of such imaging.

Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding

Organoid models have been used to address important questions in developmental and cancer biology, tissue repair, advanced modelling of disease and therapies, among other bioengineering applications. Such 3D microenvironmental models can investigate the regulation of cell metabolism, and provide key insights into the mechanisms at the basis of cell growth, differentiation, communication, interactions with the environment and cell death. Their accessibility and complexity, based on 3D spatial and temporal heterogeneity, make organoids suitable for the application of novel, dynamic imaging microscopy methods, such as fluorescence lifetime imaging microscopy (FLIM) and related decay time-assessing readouts. Several biomarkers and assays have been proposed to study cell metabolism by FLIM in various organoid models. Herein, we present an expert-opinion discussion on the principles of FLIM and PLIM, instrumentation and data collection and analysis protocols, and general and emerging biosensor-based approaches, to highlight the pioneering work being performed in this field.

Restoration of metabolic functional metrics from label-free, two-photon cervical tissue images using multiscale deep-learning-based denoising algorithms

Label-free, two-photon imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, this modality suffers from low signal arising from limitations imposed by the maximum permissible dose of illumination and the need for rapid image acquisition to avoid motion artifacts. Recently, deep learning methods have been developed to facilitate the extraction of quantitative information from such images. Here, we employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-SNR, two-photon images. Two-photon excited fluorescence (TPEF) images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used. We assess the impact of the specific denoising model, loss function, data transformation, and training dataset on established metrics of image restoration when comparing denoised single frame images with corresponding six frame averages, considered as the ground truth. We further assess the restoration accuracy of six metrics of metabolic function from the denoised images relative to ground truth images. Using a novel algorithm based on deep denoising in the wavelet transform domain, we demonstrate optimal recovery of metabolic function metrics. Our results highlight the promise of denoising algorithms to recover diagnostically useful information from low SNR label-free two-photon images and their potential importance in the clinical translation of such imaging.