Frontiers of Model Animals for Neuroscience:Two Prosperous Aging Model Animals forPromoting Neuroscience Research

A model animal showing spontaneous onset is a useful tool for investigating the mechanism of disease. Here, I would like to introduce two aging model animals expected to be useful for neuroscience research: the senescence-accelerated mouse (SAM) and the klotho mouse. The SAM was developed as a mouse showing a senescence-related phenotype such as a short lifespan or rapid advancement of senescence. In particular, SAMP8 and SAMP10 show age-related impairment of learning and memory. SAMP8 has spontaneous spongy degeneration in the brain stem and spinal cord with aging, and immunohistochemical studies reveal excess protein expression of amyloid precursor protein and amyloid β in the brain, indicating that SAMP8 is a model for Alzheimer’s disease. SAMP10 also shows age-related impairment of learning and memory, but it does not seem to correspond to Alzheimer’s disease because senile plaques primarily composed of amyloid β or neurofibrillary tangles primarily composed of phosphorylated tau were not observed. However, severe atrophy in the frontal cortex, entorhinal cortex, amygdala, and nucleus accumbens can be seen in this strain in an age-dependent manner, indicating that SAMP10 is a model for normal aging. The klotho mouse shows a phenotype, regulated by only one gene named α-klotho, similar to human progeria. The α-klotho gene is mainly expressed in the kidney and brain, and oxidative stress is involved in the deterioration of cognitive function of the klotho mouse. These animal models are potentially useful for neuroscience research now and in the near future.     

The Effect of Calf Gender on Milk Production in Seasonal Calving Cows and Its Impact on Genetic Evaluations

Gender of the calf whose birth initiates lactation could influence whole lactation milk yield of the dam due to hormonal influences on mammary gland development, or through calf gender effects on gestation length. Fetal gender could influence late lactation yields because cows become pregnant at peak lactation. The effects of calf gender sequences in parities 1–3 were assessed by separately fitting animal models to datasets from New Zealand comprising 274 000 Holstein Friesian and 85 000 Jersey cows, decreasing to 12 000 and 4 000 cows by parity 3. The lactation initiated by the birth of a female rather than a male calf was associated with a 0.33–1.1% (p≤0.05) higher milk yield. Female calf gender had carryover effects associated with higher milk yield in second lactations for Holstein Friesians (0.24%; p = 0.01) and third lactations for Jerseys (1.1%; p = 0.01). Cows giving birth to bull calves have 2 day longer gestations, which reduces lactation length in seasonal calving herds. Adding a covariate for lactation length to the animal model eroded some of these calf gender effects, such that calving a female led to higher milk yield only for second lactation Holstein Friesians (1.6%; p = 0.002). The interval centering method generates lower estimates of whole lactation yield when Wood’s lactation curves are shifted to the right by 2 days for male calves and this explained the higher yield in female calves when differences in lactation length were considered. Correlations of estimated breeding values between models including or excluding calf gender sequence were 1.00 for bulls or cows. Calf gender primarily influences milk yield through increased gestation length of male calves, and bias associated with the interval centering method used to estimate whole lactation milk yields. Including information on calf gender is unlikely to have an effect on selection response in New Zealand dairy cattle.     

Climate-Based Models for Pulsed Resources Improve Predictability of Consumer Population Dynamics: Outbreaks of House Mice in Forest Ecosystems

Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts) are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT) for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer–resource dynamics to predict invasive house mouse (Mus musculus) outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year’s advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer–resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.     

Flat-floored Air-lifted Platform: A New Method for Combining Behavior with Microscopy or Electrophysiology on Awake Freely Moving Rodents

It is widely acknowledged that the use of general anesthetics can undermine the relevance of electrophysiological or microscopical data obtained from a living animal’s brain. Moreover, the lengthy recovery from anesthesia limits the frequency of repeated recording/imaging episodes in longitudinal studies. Hence, new methods that would allow stable recordings from non-anesthetized behaving mice are expected to advance the fields of cellular and cognitive neurosciences. Existing solutions range from mere physical restraint to more sophisticated approaches, such as linear and spherical treadmills used in combination with computer-generated virtual reality. Here, a novel method is described where a head-fixed mouse can move around an air-lifted mobile homecage and explore its environment under stress-free conditions. This method allows researchers to perform behavioral tests (e.g., learning, habituation or novel object recognition) simultaneously with two-photon microscopic imaging and/or patch-clamp recordings, all combined in a single experiment. This video-article describes the use of the awake animal head fixation device (mobile homecage), demonstrates the procedures of animal habituation, and exemplifies a number of possible applications of the method.     

Pedigree-Free Estimates of Heritability in the Wild: Promising Prospects for Selfing Populations

Estimating the genetic variance available for traits informs us about a population’s ability to evolve in response to novel selective challenges. In selfing species, theory predicts a loss of genetic diversity that could lead to an evolutionary dead-end, but empirical support remains scarce. Genetic variability in a trait is estimated by correlating the phenotypic resemblance with the proportion of the genome that two relatives share identical by descent (‘realized relatedness’). The latter is traditionally predicted from pedigrees (Φ_A: expected value) but can also be estimated using molecular markers (average number of alleles shared). Nevertheless, evolutionary biologists, unlike animal breeders, remain cautious about using marker-based relatedness coefficients to study complex phenotypic traits in populations. In this paper, we review published results comparing five different pedigree-free methods and use simulations to test individual-based models (hereafter called animal models) using marker-based relatedness coefficients, with a special focus on the influence of mating systems. Our literature review confirms that Ritland’s regression method is unreliable, but suggests that animal models with marker-based estimates of relatedness and genomic selection are promising and that more testing is required. Our simulations show that using molecular markers instead of pedigrees in animal models seriously worsens the estimation of heritability in outcrossing populations, unless a very large number of loci is available. In selfing populations the results are less biased. More generally, populations with high identity disequilibrium (consanguineous or bottlenecked populations) could be propitious for using marker-based animal models, but are also more likely to deviate from the standard assumptions of quantitative genetics models (non-additive variance).     

Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain

We have developed an open software platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules’ local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly’s entire brain by integration of their independently developed models of its constituent processing units. We demonstrate the power of Neurokernel’s model integration by combining independently developed models of the retina and lamina neuropils in the fly’s visual system and by demonstrating their neuroinformation processing capability. We also illustrate Neurokernel’s ability to take advantage of direct GPU-to-GPU data transfers with benchmarks that demonstrate scaling of Neurokernel’s communication performance both over the number of interface ports exposed by an emulation’s constituent modules and the total number of modules comprised by an emulation.     

Comprehensive mouse microbiota genome catalog reveals major difference to its human counterpart

Mouse is the most used model for studying the impact of microbiota on its host, but the repertoire of species from the mouse gut microbiome remains largely unknown. Accordingly, the similarity between human and mouse microbiomes at a low taxonomic level is not clear. We construct a comprehensive mouse microbiota genome (CMMG) catalog by assembling all currently available mouse gut metagenomes and combining them with published reference and metagenome-assembled genomes. The 41’798 genomes cluster into 1’573 species, of which 78.1% are uncultured, and we discovered 226 new genera, seven new families, and one new order. CMMG enables an unprecedented coverage of the mouse gut microbiome exceeding 86%, increases the mapping rate over four-fold, and allows functional microbiota analyses of human and mouse linking them to the driver species. Comparing CMMG to microbiota from the unified human gastrointestinal genomes shows an overlap of 62% at the genus but only 10% at the species level, demonstrating that human and mouse gut microbiota are largely distinct. CMMG contains the most comprehensive collection of consistently functionally annotated species of the mouse and human microbiome to date, setting the ground for analysis of new and reanalysis of existing datasets at an unprecedented depth.     

Multi-Environment Model Estimation for Motility Analysis of Caenorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans'' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans'' segmentation and ‘skeletonizing’ across a wide range of motility assays.     

Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease

Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression.     

A New Weighted Degree Centrality Measure: The Application in an Animal Disease Epidemic

In recent years researchers have investigated a growing number of weighted heterogeneous networks, where connections are not merely binary entities, but are proportional to the intensity or capacity of the connections among the various elements. Different degree centrality measures have been proposed for this kind of networks. In this work we propose weighted degree and strength centrality measures (WDC and WSC). Using a reducing factor we correct classical centrality measures (CD) to account for tie weights distribution. The bigger the departure from equal weights distribution, the greater the reduction. These measures are applied to a real network of Italian livestock movements as an example. A simulation model has been developed to predict disease spread into Italian regions according to animal movements and animal population density. Model’s results, expressed as infected regions and number of times a region gets infected, were related to weighted and classical degree centrality measures. WDC and WSC were shown to be more efficient in predicting node’s risk and vulnerability. The proposed measures and their application in an animal network could be used to support surveillance and infection control strategy plans.     

Explaining Bacterial Dispersion on Leaf Surfaces with an Individual-Based Model (PHYLLOSIM)

We developed the individual-based model PHYLLOSIM to explain observed variation in the size of bacterial clusters on plant leaf surfaces (the phyllosphere). Specifically, we tested how different ‘waterscapes’ impacted the diffusion of nutrients from the leaf interior to the surface and the growth of individual bacteria on these nutrients. In the ‘null’ model or more complex ‘patchy’ models, the surface was covered with a continuous water film or with water drops of equal or different volumes, respectively. While these models predicted the growth of individual bacterial immigrants into clusters of variable sizes, they were unable to reproduce experimentally derived, previously published patterns of dispersion which were characterized by a much larger variation in cluster sizes and a disproportionate occurrence of clusters consisting of only one or two bacteria. The fit of model predictions to experimental data was about equally poor (<5%) regardless of whether the water films were continuous or patchy. Only by allowing individual bacteria to detach from developing clusters and re-attach elsewhere to start a new cluster, did PHYLLOSIM come much closer to reproducing experimental observations. The goodness of fit including detachment increased to about 70–80% for all waterscapes. Predictions of this ‘detachment’ model were further supported by the visualization and quantification of bacterial detachment and attachment events at an agarose-water interface. Thus, both model and experiment suggest that detachment of bacterial cells from clusters is an important mechanism underlying bacterial exploration of the phyllosphere.     

Consumption of non–cow’s milk beverages and serum vitamin D levels in early childhood

Background:

Vitamin D fortification of non–cow’s milk beverages is voluntary in North America. The effect of consuming non–cow’s milk beverages on serum 25-hydroxyvitamin D levels in children is unclear. We studied the association between non–cow’s milk consumption and 25-hydroxyvitamin D levels in healthy preschool-aged children. We also explored whether cow’s milk consumption modified this association and analyzed the association between daily non–cow’s milk and cow’s milk consumption.

Methods:

In this cross-sectional study, we recruited children 1–6 years of age attending routinely scheduled well-child visits. Survey responses, and anthropometric and laboratory measurements were collected. The association between non–cow’s milk consumption and 25-hydroxyvitamin D levels was tested using multiple linear regression and logistic regression. Cow’s milk consumption was explored as an effect modifier using an interaction term. The association between daily intake of non–cow’s milk and cow’s milk was explored using multiple linear regression.

Results:

A total of 2831 children were included. The interaction between non–cow’s milk and cow’s milk consumption was statistically significant (p = 0.03). Drinking non–cow’s milk beverages was associated with a 4.2-nmol/L decrease in 25-hydroxyvitamin D level per 250-mL cup consumed among children who also drank cow’s milk (p = 0.008). Children who drank only non–cow’s milk were at higher risk of having a 25-hydroxyvitamin D level below 50 nmol/L than children who drank only cow’s milk (odds ratio 2.7, 95% confidence interval 1.6 to 4.7).

Interpretation:

Consumption of non–cow’s milk beverages was associated with decreased serum 25-hydroxyvitamin D levels in early childhood. This association was modified by cow’s milk consumption, which suggests a trade-off between consumption of cow’s milk fortified with higher levels of vitamin D and non–cow’s milk with lower vitamin D content.Goat’s milk and plant-based milk alternatives made from soy, rice, almonds, coconut, hemp, flax or oats (herein called “non–cow’s milk”) are increasingly available on supermarket shelves. Many consumers may be switching from cow’s milk to these beverages.^1–3 Parents may choose non–cow’s milk beverages for their children because of perceived health benefits. However, it is unclear whether they offer health advantages over cow’s milk or, alternatively, whether they increase the risk of nutritional inadequacy.In the United States and Canada, cow’s milk products are required to contain about 40 IU of vitamin D per 100 mL, making it the major dietary source of vitamin D for children.^4–8 The only other food source with mandatory vitamin D fortification in Canada is margarine, which is required to contain 53 IU per 10 mL (10 g).⁵ Fortification of non–cow’s milk beverages with vitamin D is also possible, but it is voluntary in both countries. Furthermore, there is little regulation on the vitamin D content even if such beverages are fortified.^5,6,9We conducted a study to test the association between total daily consumption of non–cow’s milk and serum 25-hydroxyvitamin D levels in a population of healthy urban preschool-aged children attending routinely scheduled well-child visits. We hypothesized that vitamin D stores would be lower in children who consume non–cow’s milk. The secondary objectives were to explore how consumption of cow’s milk might modify this association and to study the association between daily intake of non–cow’s milk and cow’s milk.     

Investigating the Effects of Antipsychotics and Schizotypy on the N400 Using Event-Related Potentials and Semantic Categorization

Within the field of cognitive neuroscience, functional magnetic resonance imaging (fMRI) is a popular method of visualizing brain function. This is in part because of its excellent spatial resolution, which allows researchers to identify brain areas associated with specific cognitive processes. However, in the quest to localize brain functions, it is relevant to note that many cognitive, sensory, and motor processes have temporal distinctions that are imperative to capture, an aspect that is left unfulfilled by fMRI’s suboptimal temporal resolution. To better understand cognitive processes, it is thus advantageous to utilize event-related potential (ERP) recording as a method of gathering information about the brain. Some of its advantages include its fantastic temporal resolution, which gives researchers the ability to follow the activity of the brain down to the millisecond. It also directly indexes both excitatory and inhibitory post-synaptic potentials by which most brain computations are performed. This sits in contrast to fMRI, which captures an index of metabolic activity. Further, the non-invasive ERP method does not require a contrast condition: raw ERPs can be examined for just one experimental condition, a distinction from fMRI where control conditions must be subtracted from the experimental condition, leading to uncertainty in associating observations with experimental or contrast conditions. While it is limited by its poor spatial and subcortical activity resolution, ERP recordings’ utility, relative cost-effectiveness, and associated advantages offer strong rationale for its use in cognitive neuroscience to track rapid temporal changes in neural activity. In an effort to foster increase in its use as a research imaging method, and to ensure proper and accurate data collection, the present article will outline – in the framework of a paradigm using semantic categorization to examine the effects of antipsychotics and schizotypy on the N400 – the procedure and key aspects associated with ERP data acquisition.     

Nocturnal lighting in animal research should be replicable and reflect relevant ecological conditions

In nature, light is a key driver of animal behaviour and physiology. When studying captive or laboratory animals, researchers usually expose animals to a period of darkness, to mimic night. However, ‘darkness’ is often poorly quantified and its importance is generally underappreciated in animal research. Even small differences in nocturnal light conditions can influence biology. When light levels during the dark phase are not reported accurately, experiments can be impossible to replicate and compare. Furthermore, when nocturnal light levels are unrealistically dark or bright, the research is less ecologically relevant. Such issues are exacerbated by huge differences in the sensitivity of different light meters, which are not always described in study methods. We argue that nocturnal light levels need to be reported clearly and precisely, particularly in studies of animals housed indoors (e.g. ‘<0.03 lux’ rather than ‘0 lux’ or ‘dark’), and that these light levels should reflect conditions that the animal would experience in a natural context.     

Stereotaxic Infusion of Oligomeric Amyloid-beta into the Mouse Hippocampus

Alzheimer’s disease is a neurodegenerative disease affecting the aging population. A key neuropathological feature of the disease is the over-production of amyloid-beta and the deposition of amyloid-beta plaques in brain regions of the afflicted individuals. Throughout the years scientists have generated numerous Alzheimer’s disease mouse models that attempt to replicate the amyloid-beta pathology. Unfortunately, the mouse models only selectively mimic the disease features. Neuronal death, a prominent effect in the brains of Alzheimer’s disease patients, is noticeably lacking in these mice. Hence, we and others have employed a method of directly infusing soluble oligomeric species of amyloid-beta - forms of amyloid-beta that have been proven to be most toxic to neurons - stereotaxically into the brain. In this report we utilize male C57BL/6J mice to document this surgical technique of increasing amyloid-beta levels in a select brain region. The infusion target is the dentate gyrus of the hippocampus because this brain structure, along with the basal forebrain that is connected by the cholinergic circuit, represents one of the areas of degeneration in the disease. The results of elevating amyloid-beta in the dentate gyrus via stereotaxic infusion reveal increases in neuron loss in the dentate gyrus within 1 week, while there is a concomitant increase in cell death and cholinergic neuron loss in the vertical limb of the diagonal band of Broca of the basal forebrain. These effects are observed up to 2 weeks. Our data suggests that the current amyloid-beta infusion model provides an alternative mouse model to address region specific neuron death in a short-term basis. The advantage of this model is that amyloid-beta can be elevated in a spatial and temporal manner.     

From simple to detailed models for cell polarization

Many mathematical models have been proposed for the process of cell polarization. Some of these are ‘functional models’ that capture a class of dynamical behaviour, whereas others are derived from features of signalling molecules. Some mechanistic models are detailed, and therefore complex, whereas others are simplified. Each type contributes to our understanding of cell polarization. However, the huge variety at different levels of detail makes comparisons challenging. Here, we provide examples of both elementary and more detailed models for polarization. We also display how a recent mathematical method, local perturbation analysis, can provide an appropriate tool for such comparisons. This technique simplifies and speeds up the model development process by revealing the effect of model extensions, parameter variations and in silico manipulations such as knock-out or over-expression of key molecules. Finally, simulations in both one dimension and two dimensions, and particularly in deforming two-dimensional ‘cells’, can highlight behaviour not captured by traditional simulation methods.     

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large‐scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS‐telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat‐use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat‐use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat‐use studies.     

Scaling laws of ambush predator ‘waiting’ behaviour are tuned to a common ecology

The decisions animals make about how long to wait between activities can determine the success of diverse behaviours such as foraging, group formation or risk avoidance. Remarkably, for diverse animal species, including humans, spontaneous patterns of waiting times show random ‘burstiness’ that appears scale-invariant across a broad set of scales. However, a general theory linking this phenomenon across the animal kingdom currently lacks an ecological basis. Here, we demonstrate from tracking the activities of 15 sympatric predator species (cephalopods, sharks, skates and teleosts) under natural and controlled conditions that bursty waiting times are an intrinsic spontaneous behaviour well approximated by heavy-tailed (power-law) models over data ranges up to four orders of magnitude. Scaling exponents quantifying ratios of frequent short to rare very long waits are species-specific, being determined by traits such as foraging mode (active versus ambush predation), body size and prey preference. A stochastic–deterministic decision model reproduced the empirical waiting time scaling and species-specific exponents, indicating that apparently complex scaling can emerge from simple decisions. Results indicate temporal power-law scaling is a behavioural ‘rule of thumb’ that is tuned to species’ ecological traits, implying a common pattern may have naturally evolved that optimizes move–wait decisions in less predictable natural environments.     

Isolation and Culture of Adult Mouse Cardiomyocytes for Cell Signaling and in vitro Cardiac Hypertrophy

Technological advances have made genetically modified mice, including transgenic and gene knockout mice, an essential tool in many research fields. Adult cardiomyocytes are widely accepted as a good model for cardiac cellular physiology and pathophysiology, as well as for pharmaceutical intervention. Genetically modified mice preclude the need for complicated cardiomyocyte infection processes to generate the desired genotype, which are inefficient due to cardiomyocytes’ terminal differentiation. Isolation and culture of high quantity and quality functional cardiomyocytes will dramatically benefit cardiovascular research and provide an important tool for cell signaling transduction research and drug development. Here, we describe a well-established method for isolation of adult mouse cardiomyocytes that can be implemented with little training. The mouse heart is excised and cannulated to an isolated heart system, then perfused with a calcium-free and high potassium buffer followed by type II collagenase digestion in Langendorff retrograde perfusion mode. This protocol yields a consistent result for the collection of functional adult mouse cardiomyocytes from a variety of genetically modified mice.