Biology subjects

Nitta, H.

Publications and source records attributed to Nitta, H..

3 recordsLinked to original sources

bioRxiv2025

Strain diversity drives heterogeneous responses to tuberculosis combination therapy

BackgroundStrain diversity in Mycobacterium tuberculosis (Mtb) underlies distinct clinical presentations and outcomes, but the range of drug susceptibility phenotypes among clinical isolates is poorly understood. We aimed to identify drug response patterns in phylogenetically diverse clinical isolates to combination treatment. MethodsOut of 641 drug-sensitive clinical isolates, we selected 13 strains that capture local and global phylogenetic diversity and included Erdman ATCC-35801 as a reference. We selected ten antibiotics with diverse mechanisms of action to study phenotypic responses to combination therapy. We treated each strain with 10 single drugs, 45 drug pairs, and 20 three-way combinations in standard and cholesterol-rich media. To compare combination treatment responses across strains and conditions that have varying doubling times, we computed normalized growth rate inhibition metrics (GRmax). FindingsMtb clinical strains displayed a broad range of drug response phenotypes across the 65 drug combinations and two metabolic conditions tested. The most effective drug pairs (based on potency and synergy) varied both by strain and metabolic condition. Within our 14-strain panel, strains that were less sensitive to single drugs were also less sensitive to combination treatment, with very few exceptions. For all drug combinations tested, the magnitude of GRmax variation across all strains was driven primarily by variation among genetically related strains, rather than between genetically distant strain groups. InterpretationPreclinical studies should reflect the diversity of Mtb clinical strains; our data suggest that selecting strains based on the range of drug response phenotypes displayed, rather than by genetic diversity alone, may better account for the effects of strain variation. Our findings also support the understanding that constituent drug pairs of high-order combinations target metabolically heterogeneous Mtb. Selection of these pairs should likely involve multiple factors including the infecting strain, metabolic niche, and drug response metrics. FundingGates Foundation INV-027276; NIH P01AI143575&1F32AI174653; Wellcome 206724/Z/17/Z

microbiology↗

bioRxiv2023

Large-scale annotated dataset for cochlear hair cell detection and classification

Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90,000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

neuroscience↗

bioRxiv2023

SKOOTS: Skeleton oriented object segmentation for mitochondria

Segmenting individual instances of mitochondria from imaging datasets can provide rich quantitative information, but is prohibitively time-consuming when done manually, prompting interest in the development of automated algorithms using deep neural networks. Existing solutions for various segmentation tasks are optimized for either: high-resolution three-dimensional imaging, relying on well-defined object boundaries (e.g., whole neuron segmentation in volumetric electron microscopy datasets); or low-resolution two-dimensional imaging, boundary-invariant but poorly suited to large 3D objects (e.g., whole-cell segmentation of light microscopy images). Mitochondria in whole-cell 3D electron microscopy datasets often lie in the middle ground--large, yet with ambiguous borders, challenging current segmentation tools. To address this, we developed skeleton-oriented object segmentation (SKOOTS)--a novel approach that efficiently segments large, densely packed mitochondria. SKOOTS accurately and efficiently segments mitochondria in previously difficult contexts and can also be applied to segment other objects in 3D light microscopy datasets. This approach bridges a critical gap between existing segmentation approaches, improving the utility of automated analysis of three-dimensional biomedical imaging data. We demonstrate the utility of SKOOTS by applying it to segment over 15,000 cochlear hair cell mitochondria across experimental conditions in under 2 hours on a consumer-grade PC, enabling downstream morphological analysis that revealed subtle structural changes following aminoglycoside exposure--differences not detectable using analysis approaches currently used in the field.

neuroscience↗