A new anesthesia protocol enabling longitudinal lung function measurements in neonatal rabbits by micro-CT

Micro-CT imaging is an emerging technology with many applications in small animals, e.g. the study of pulmonary diseases, although clear guidelines and critical mass of evidence are still missing in the preclinical literature. The neonatal rabbit is a valuable model for studying pulmonary development. However, the longitudinal monitoring of lung function by micro-CT can be challenging. Distinctive datasets corresponding to the end-inspiration and end-expiration phases need to be generated and analyzed to derive lung functional parameters. The quality of CT scans and the reliability of parameters obtained remains highly dependent on the anesthesia protocol used. Three different anesthetic protocols were tested. The combination of dexmedetomidine 0.25 mg/kg injected intraperitoneally followed by 1% isoflurane was found to facilitate CT imaging at 4 and 11 days after birth. Contrarily, isoflurane and ketamine plus xylazine were found unsuitable, and thus not investigated further. Total lung volumes significantly increased at day 11 compared to baseline in both respiratory phases, while lung tissue remained constant. As expected, functional residual capacity, air/tissue ratio and minute ventilation were significantly increased at day 11 in each animal. Those parameters were correlated with inspiratory capacity, compliance, elastance and resistance of both respiratory system and tissue component, as measured by flexiVent. Lung development was also evaluated by histomorphometric analyses. In conclusion, we have identified a safe and suitable anesthesia protocol for micro-CT imaging in neonatal rabbits. Moreover, the possibility to longitudinally measure lung function in the same subject dramatically reduced the intra-experimental variability.

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal. We compare our method to previous approaches on a large-scale dataset of two-photon imaging recordings of retinal bipolar cell responses to visual stimuli. This dataset provides a unique benchmark as it contains biological signal from well-defined cell types that is obscured by large inter-experimental variability. In a supervised setting, we compare the generalization performance of cell type classifiers across experiments, which we validate with anatomical cell type distributions from electron microscopy data. In an unsupervised setting, we remove inter-experimental variability from data which can then be fed into arbitrary downstream analyses. In both settings, we find that our method achieves the best trade-off between removing inter-experimental variability and preserving biological signal. Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience.

The Inflammatory Bowel Disease Transcriptome and Metatranscriptome Meta-Analysis (IBD TaMMA) framework

Inflammatory bowel disease (IBD) is a class of chronic disorders whose etiogenesis is still unknown. Despite the high number of IBD-related omics studies, the RNA-sequencing data produced results that are hard to compare because of the experimental variability and different data analysis approaches. We here introduce the IBD Transcriptome and Metatranscriptome Meta-Analysis (TaMMA) framework, a comprehensive survey of publicly available IBD RNA-sequencing datasets. IBD TaMMA is an open-source platform where scientists can explore simultaneously the freely available IBD-associated transcriptomics and microbial profiles thanks to its interactive interface, resulting in a useful tool to the IBD community.

Statistical Approach to Incorporating Experimental Variability into a Mathematical Model of the Voltage-Gated Na+ Channel and Human Atrial Action Potential

The voltage-gated Na+ channel Nav1.5 is critical for normal cardiac myocyte excitability. Mathematical models have been widely used to study Nav1.5 function and link to a range of cardiac arrhythmias. There is growing appreciation for the importance of incorporating physiological heterogeneity observed even in a healthy population into mathematical models of the cardiac action potential. Here, we apply methods from Bayesian statistics to capture the variability in experimental measurements on human atrial Nav1.5 across experimental protocols and labs. This variability was used to define a physiological distribution for model parameters in a novel model formulation of Nav1.5, which was then incorporated into an existing human atrial action potential model. Model validation was performed by comparing the simulated distribution of action potential upstroke velocity measurements to experimental measurements from several different sources. Going forward, we hope to apply this approach to other major atrial ion channels to create a comprehensive model of the human atrial AP. We anticipate that such a model will be useful for understanding excitability at the population level, including variable drug response and penetrance of variants linked to inherited cardiac arrhythmia syndromes.

Normalized unitary synaptic signaling of the hippocampus and entorhinal cortex

Limited experimental yield, heterogeneous recordings conditions, and ambiguous neuronal identification have so far prevented the systematic characterization of synaptic signals for all connections of any neural system. Introducing a novel strategy to overcome these challenges, we report the first comprehensive synaptic quantification among all known neuron types of the hippocampal-entorhinal network. First, we reconstructed > 2,600 synaptic traces from ~ 1,200 publications into a unified model of synaptic dynamics. We then trained a deep learning architecture with the resulting parameters, each annotated with detailed metadata. The model learned to predict the synaptic properties of all 3,120 circuit connections in arbitrary conditions with accuracy approaching the intrinsic experimental variability. Analysis of normalized data revealed that synaptic signals are controlled by few latent variables associated with specific molecular markers and interrelating conductance, kinetics, and short-term plasticity. We freely release the tools and full dataset of unitary synaptic values in 32 covariate settings via Hippocampome.org.

Comparison of bias and resolvability in single-cell and single-transcript methods

Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark different methods for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods.

Impact of the H275Y and I223V Mutations in the Neuraminidase of the 2009 Pandemic Influenza Virus In Vitro and Evaluating Experimental Reproducibility

The 2009 pandemic H1N1 (H1N1pdm09) influenza virus is naturally susceptible to neuraminidase (NA) inhibitors, but mutations in the NA protein can cause oseltamivir resistance. The H275Y and I223V amino acid substitutions in the NA of the H1N1pdm09 influenza strain have been separately observed in patients exhibiting oseltamivir-resistance. Here, we apply mathematical modelling techniques to compare the fitness of the wild-type H1N1pdm09 strain relative to each of these two mutants. We find that both the H275Y and I223V mutations in the H1N1pdm09 background significantly lengthen the duration of the eclipse phase (by 2.5 h and 3.6 h, respectively), consistent with these NA mutations delaying the release of viral progeny from newly infected cells. Cells infected by H1N1pdm09 virus carrying the I223V mutation display a disadvantageous, shorter infectious lifespan (17 h shorter) than those infected with the wild-type or MUT-H275Y strains. In terms of compensating traits, the H275Y mutation in the H1N1pdm09 background results in increased virus infectiousness, as we reported previously, whereas the I223V exhibits none, leaving it overall less fit than both its wild-type counterpart and the MUT-H275Y strain. Using computer simulated competition experiments, we determine that in the presence of oseltamivir at doses even below standard therapy, both the MUT-H275Y and MUT-I223V dominate their wild-type counterpart in all aspects, and the MUT-H275Y outcompetes the MUT-I223V. The H275Y mutation should therefore be more commonly observed than the I223V mutation in circulating H1N1pdm09 strains, assuming both mutations have a similar impact or no significant impact on between-host transmission. We also show that mathematical modelling offers a relatively inexpensive and reliable means to quantify inter-experimental variability and assess the reproducibility of results.

Impact of the H275Y and I223V Mutations in the Neuraminidase of the 2009 Pandemic Influenza Virus In Vitro and Evaluating Experimental Reproducibility

The 2009 pandemic H1N1 (H1N1pdm09) influenza virus is naturally susceptible to neuraminidase (NA) inhibitors, but mutations in the NA protein can cause oseltamivir resistance. The H275Y and I223V amino acid substitutions in the NA of the H1N1pdm09 influenza strain have been separately observed in patients exhibiting oseltamivir-resistance. Here, we apply mathematical modelling techniques to compare the fitness of the wild-type H1N1pdm09 strain relative to each of these two mutants. We find that both the H275Y and I223V mutations in the H1N1pdm09 background significantly lengthen the duration of the eclipse phase (by 2.5 h and 3.6 h, respectively), consistent with these NA mutations delaying the release of viral progeny from newly infected cells. Cells infected by H1N1pdm09 virus carrying the I223V mutation display a disadvantageous, shorter infectious lifespan (17 h shorter) than those infected with the wild-type or MUT-H275Y strains. In terms of compensating traits, the H275Y mutation in the H1N1pdm09 background results in increased virus infectiousness, as we reported previously, whereas the I223V exhibits none, leaving it overall less fit than both its wild-type counterpart and the MUT-H275Y strain. Using computer simulated competition experiments, we determine that in the presence of oseltamivir at doses even below standard therapy, both the MUT-H275Y and MUT-I223V dominate their wild-type counterpart in all aspects, and the MUT-H275Y outcompetes the MUT-I223V. The H275Y mutation should therefore be more commonly observed than the I223V mutation in circulating H1N1pdm09 strains, assuming both mutations have a similar impact or no significant impact on between-host transmission. We also show that mathematical modelling offers a relatively inexpensive and reliable means to quantify inter-experimental variability and assess the reproducibility of results.

Inflammatory Bowel Disease Transcriptome and Metatranscriptome Meta-Analysis (IBD TaMMA) framework

Inflammatory bowel disease (IBD) is a class of chronic inflammatory gut disorders whose aetiogenesis is still unknown. Despite the high number of omics studies, the RNA sequencing (RNA-Seq) data produced for a better IBD pathogenesis understanding cannot be compared because of the experimental variability and different data analysis approaches. To overcome this hurdle, we here introduce the open-source IBD Transcriptome and Metatranscriptome Meta-Analysis (TaMMA) framework, a comprehensive survey of publicly available IBD RNA-Seq datasets. IBD TaMMA will expedite the profiling of the IBD-associated transcriptome and metatranscriptome, holding out the strong promise of being of high impact for the IBD community.