Automated Approaches to High-Throughput Respiratory Studies
Authors List
Savannah Lusk1, C. Ward1, A. Chang2, D. Patel, B. Ruiz2, M. Garcia Acosta3, A. Twitchell-Heyne1, S. Fattig1, G. Allen1,2, R. Ray1
1Baylor College of Medicine, 2Rice University, 3U. of Houston, Houston Tx, United States
Increasingly, automation in the lab is being adopted to improve consistency, accuracy, and productivity. Respiratory studies present a unique barrier to automation due to the nature of physiological data recording and interpretation. We present two solutions for respiratory studies:
1) an automated, robotic closed-loop system for neonate pneumotachography, and 2) a front-toend whole-body plethysmography/pneumotachography waveform analysis software suite.
Facemask pneumotachography is a common, precise experimental approach for measuring respiratory outcomes in neonate rodents (P7-8), particularly in autoresuscitation experiments that inform upon Sudden Infant Death Syndrome where failure of autoresuscitation is hypothesized to be a common endpoint. However, current approaches rely on direct, real-time waveform inspection by an observer and their highly variable reaction times to execute responsive changes in assay gas exposures. Our automated, closed-loop robotic system for neonate cardio-respiratory measurements, Looper, represents a novel and significant improvement on typical observer-based interpretation of live data to initiate challenge and recovery gas applications to uncover nuanced features in neonate cardiorespiratory physiology. We demonstrate our system by measuring neonate autoresuscitation in a series of 8 distinct, genetically engineered mouse models (>200 animals) designed to concurrently inhibit and/or excite the noradrenergic and serotonergic systems and define interactions between these two systems in protective neonate reflexes. Both the noradrenergic and serotonergic systems have been heavily implicated in SIDS pathology.
Our results show that these two systems have unique individual roles as well as complex combinatorial roles in mediating the neonate autoresuscitation reflex. Whole body plethysmography is used to study respiration in adult rodent models. Turn-key commercial and custom systems are both used to gather multiple data streams including respiratory waveforms and O2 and CO2 measurements to reasonably estimate tidal volume and metabolic
parameters. These experiments typically produce large amounts of data that are time-consuming and difficult to organize and analyze. Commonly in the field, the investigator may use cumbersome hand annotation subject to observer bias and/ or multiple softwares to manage raw data, calculated
data, statistical analysis, and graphing. Alternatively, commercial software is limited in functionality, prohibitively expensive, and often bundled with turn-key systems that limit options for bespoke respiratory measurement systems. To address these deficiencies, we developed a frontto-end software application, Breathe Easy, for processing raw respiratory recordings and associated metadata (experimental design, age, weight, etc.) into operative respiratory outcomes, publication-worthy graphs, and robust statistical analyses. The open-source program offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. We demonstrate the utility of Breath Easy by analyzing a two-year longitudinal study (1TB cumulative data) on a mouse Alzheimer’s disease model to determine if APP forebrain pathology can drive respiratory homeostatic disruptions. Our work indicates that while forebrain dysfunction in epilepsy, stroke, and brain injury can greatly affect breathing, in our model, APPdriven forebrain pathology did not perturb breathing or chemosensory reflexes.
Funding: National Institutes of Health: R01HL130249, NIHR01HL161142, RF1AG054160 UM1HG006348, R01DK114356; NSF NeuroNex-1707400;
McNair Medical Institute.
Savannah Lusk1, C. Ward1, A. Chang2, D. Patel, B. Ruiz2, M. Garcia Acosta3, A. Twitchell-Heyne1, S. Fattig1, G. Allen1,2, R. Ray1
1Baylor College of Medicine, 2Rice University, 3U. of Houston, Houston Tx, United States
Increasingly, automation in the lab is being adopted to improve consistency, accuracy, and productivity. Respiratory studies present a unique barrier to automation due to the nature of physiological data recording and interpretation. We present two solutions for respiratory studies:
1) an automated, robotic closed-loop system for neonate pneumotachography, and 2) a front-toend whole-body plethysmography/pneumotachography waveform analysis software suite.
Facemask pneumotachography is a common, precise experimental approach for measuring respiratory outcomes in neonate rodents (P7-8), particularly in autoresuscitation experiments that inform upon Sudden Infant Death Syndrome where failure of autoresuscitation is hypothesized to be a common endpoint. However, current approaches rely on direct, real-time waveform inspection by an observer and their highly variable reaction times to execute responsive changes in assay gas exposures. Our automated, closed-loop robotic system for neonate cardio-respiratory measurements, Looper, represents a novel and significant improvement on typical observer-based interpretation of live data to initiate challenge and recovery gas applications to uncover nuanced features in neonate cardiorespiratory physiology. We demonstrate our system by measuring neonate autoresuscitation in a series of 8 distinct, genetically engineered mouse models (>200 animals) designed to concurrently inhibit and/or excite the noradrenergic and serotonergic systems and define interactions between these two systems in protective neonate reflexes. Both the noradrenergic and serotonergic systems have been heavily implicated in SIDS pathology.
Our results show that these two systems have unique individual roles as well as complex combinatorial roles in mediating the neonate autoresuscitation reflex. Whole body plethysmography is used to study respiration in adult rodent models. Turn-key commercial and custom systems are both used to gather multiple data streams including respiratory waveforms and O2 and CO2 measurements to reasonably estimate tidal volume and metabolic
parameters. These experiments typically produce large amounts of data that are time-consuming and difficult to organize and analyze. Commonly in the field, the investigator may use cumbersome hand annotation subject to observer bias and/ or multiple softwares to manage raw data, calculated
data, statistical analysis, and graphing. Alternatively, commercial software is limited in functionality, prohibitively expensive, and often bundled with turn-key systems that limit options for bespoke respiratory measurement systems. To address these deficiencies, we developed a frontto-end software application, Breathe Easy, for processing raw respiratory recordings and associated metadata (experimental design, age, weight, etc.) into operative respiratory outcomes, publication-worthy graphs, and robust statistical analyses. The open-source program offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. We demonstrate the utility of Breath Easy by analyzing a two-year longitudinal study (1TB cumulative data) on a mouse Alzheimer’s disease model to determine if APP forebrain pathology can drive respiratory homeostatic disruptions. Our work indicates that while forebrain dysfunction in epilepsy, stroke, and brain injury can greatly affect breathing, in our model, APPdriven forebrain pathology did not perturb breathing or chemosensory reflexes.
Funding: National Institutes of Health: R01HL130249, NIHR01HL161142, RF1AG054160 UM1HG006348, R01DK114356; NSF NeuroNex-1707400;
McNair Medical Institute.
