Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA.
J Physiol. 2023 Nov;601(21):4767-4806. doi: 10.1113/JP284363. Epub 2023 Oct 3.
Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic and degenerative diseases converging on autonomic control failure. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. We present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds and defining experimental variables. Breathe Easy was validated against manual selection by experts, which represents the current standard in the field. We demonstrate Breathe Easy's utility by examining a 2-year longitudinal study of an Alzheimer's disease mouse model to assess contributions of forebrain pathology in disordered breathing. Whole body plethysmography has become an important experimental outcome measure for a variety of diseases with primary and secondary respiratory indications. Respiratory dysfunction, while not an initial symptom in many of these disorders, often drives disability or death in patient outcomes. Breathe Easy provides an open-source respiratory analysis tool for all respiratory datasets and represents a necessary improvement upon current analytical methods in the field. KEY POINTS: Respiratory dysfunction is a common endpoint for disability and mortality in many disorders throughout life. Whole body plethysmography in rodents represents a high face-value method for measuring respiratory outcomes in rodent models of these diseases and disorders. Analysis of key respiratory variables remains hindered by manual annotation and analysis that leads to low throughput results that often exclude a majority of the recorded data. Here we present a software suite, Breathe Easy, that automates the process of data selection from raw recordings derived from plethysmography experiments and the analysis of these data into operative outcomes and publication-worthy graphs with statistics. We validate Breathe Easy with a terabyte-scale Alzheimer's dataset that examines the effects of forebrain pathology on respiratory function over 2 years of degeneration.
全面准确地分析呼吸和代谢数据对于模拟先天性、致病性和退行性疾病导致的自主控制失败至关重要。缺乏高通量分析呼吸数据集的工具仍然是一个主要挑战。我们提出了 Breathe Easy，这是一种新颖的开源管道，用于将原始记录和相关元数据处理为可操作的结果、有出版价值的图形以及强大的统计分析，包括 QQ 图和残差图，用于假设查询和数据转换。该管道使用易于使用的图形用户界面上传数据文件、设置波形特征阈值和定义实验变量。Breathe Easy 通过专家手动选择进行了验证，这是该领域目前的标准。我们通过检查阿尔茨海默病小鼠模型的为期 2 年的纵向研究来评估前脑病理学对呼吸障碍的贡献，展示了 Breathe Easy 的实用性。全身 plethysmography 已成为具有原发性和继发性呼吸指征的各种疾病的重要实验结果衡量标准。呼吸功能障碍虽然在许多这些疾病中不是初始症状，但在患者结局中往往会导致残疾或死亡。Breathe Easy 为所有呼吸数据集提供了开源呼吸分析工具，代表了该领域当前分析方法的必要改进。要点：呼吸功能障碍是许多疾病在整个生命过程中导致残疾和死亡的常见终点。在这些疾病和障碍的啮齿动物模型中，全身 plethysmography 代表了一种高面值的测量呼吸结果的方法。关键呼吸变量的分析仍然受到手动注释和分析的阻碍，这导致低通量结果，这些结果往往排除了大部分记录的数据。在这里，我们提出了一个软件套件 Breathe Easy，它可以自动从 plethysmography 实验中衍生的原始记录中选择数据，并将这些数据分析为可操作的结果和有出版价值的图形，同时还提供统计信息。我们使用一个包含 TB 规模的阿尔茨海默病数据集来验证 Breathe Easy，该数据集在 2 年的退化过程中检查了前脑病理学对呼吸功能的影响。
