Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model.

Alzheimer's disease (AD) is broadly characterized by neurodegeneration, pathology accumulation, and cognitive decline. There is considerable variation in the progression of clinical symptoms and pathology in humans, highlighting the importance of genetic diversity in the study of AD. To address this, we analyze cell composition and amyloid-beta deposition of 6- and 14-month-old AD-BXD mouse brains. We utilize the analytical QUINT workflow- a suite of software designed to support atlas-based quantification, which we expand to deliver a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. In applying the expanded QUINT workflow, we quantify near-global age-related increases in microglia, astrocytes, and amyloid-beta, and we identify strain-specific regional variation in neuron load. To understand how individual differences in cell composition affect the interpretation of bulk gene expression in AD, we combine hippocampal immunohistochemistry analyses with bulk RNA-sequencing data. This approach allows us to categorize genes whose expression changes in response to AD in a cell and/or pathology load-dependent manner. Ultimately, our study demonstrates the use of the QUINT workflow to standardize the quantification of immunohistochemistry data in diverse mice, - providing valuable insights into regional variation in cellular load and amyloid deposition in the AD-BXD model.

The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases.

Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model.

Alzheimer's disease (AD) is characterized by neurodegeneration, pathology accumulation, and progressive cognitive decline. There is significant variation in age at onset and severity of symptoms highlighting the importance of genetic diversity in the study of AD. To address this, we analyzed cell and pathology composition of 6- and 14-month-old AD-BXD mouse brains using the semi-automated workflow (QUINT); which we expanded to allow for nonlinear refinement of brain atlas-registration, and quality control assessment of atlas-registration and brain section integrity. Near global age-related increases in microglia, astrocyte, and amyloid-beta accumulation were measured, while regional variation in neuron load existed among strains. Furthermore, hippocampal immunohistochemistry analyses were combined with bulk RNA-sequencing results to demonstrate the relationship between cell composition and gene expression. Overall, the additional functionality of the QUINT workflow delivers a highly effective method for registering and quantifying cell and pathology changes in diverse disease models.

A neuroscientist's guide to using murine brain atlases for efficient analysis and transparent reporting.

Brain atlases are widely used in neuroscience as resources for conducting experimental studies, and for integrating, analyzing, and reporting data from animal models. A variety of atlases are available, and it may be challenging to find the optimal atlas for a given purpose and to perform efficient atlas-based data analyses. Comparing findings reported using different atlases is also not trivial, and represents a barrier to reproducible science. With this perspective article, we provide a guide to how mouse and rat brain atlases can be used for analyzing and reporting data in accordance with the FAIR principles that advocate for data to be findable, accessible, interoperable, and re-usable. We first introduce how atlases can be interpreted and used for navigating to brain locations, before discussing how they can be used for different analytic purposes, including spatial registration and data visualization. We provide guidance on how neuroscientists can compare data mapped to different atlases and ensure transparent reporting of findings. Finally, we summarize key considerations when choosing an atlas and give an outlook on the relevance of increased uptake of atlas-based tools and workflows for FAIR data sharing.

Aversive Learning of Colored Lights in Walking Honeybees.

The honeybee has been established as an important model organism in studies on visual learning. So far the emphasis has been on appetitive conditioning, simulating floral discrimination, and homing behavior, where bees perform exceptionally well in visual discrimination tasks. However, bees in the wild also face dangers, and recent findings suggest that what is learned about visual percepts is highly context dependent. A stimulus that follows an unpleasant period, is associated with the feeling of relief- or safety in humans and animals, thus acquiring a positive meaning. Whether this is also the case in honeybees is still an open question. Here, we conditioned bees aversively in a walking arena where each half was illuminated by light of a specific wavelength and intensity, one of which was combined with electric shocks. In this paradigm, the bees' preferences to the different lights were modified through nine conditioning trials, forming robust escape, and avoidance behaviors. Strikingly, we found that while 465 nm (human blue) and 590 nm (human yellow) lights both could acquire negative valences (inducing avoidance response), 525 nm (human green) light could not. This indicates that green light holds an innate meaning of safety which is difficult to overrule even through intensive aversive conditioning. The bees had slight initial preferences to green over the blue and the yellow lights, which could be compensated by adjusting light intensity. However, this initial bias played a minor role while the chromatic properties were the most salient characteristics of the light stimuli during aversive conditioning. Moreover, bees could learn the light signaling safety, revealing the existence of a relief component in aversive operant conditioning, similar to what has been observed in other animals.

Different Roles for Honey Bee Mushroom Bodies and Central Complex in Visual Learning of Colored Lights in an Aversive Conditioning Assay.

The honey bee is an excellent visual learner, but we know little about how and why it performs so well, or how visual information is learned by the bee brain. Here we examined the different roles of two key integrative regions of the brain in visual learning: the mushroom bodies and the central complex. We tested bees' learning performance in a new assay of color learning that used electric shock as punishment. In this assay a light field was paired with electric shock. The other half of the conditioning chamber was illuminated with light of a different wavelength and not paired with shocks. The unrestrained bee could run away from the light stimulus and thereby associate one wavelength with punishment, and the other with safety. We compared learning performance of bees in which either the central complex or mushroom bodies had been transiently inactivated by microinjection of the reversible anesthetic procaine. Control bees learned to escape the shock-paired light field and to spend more time in the safe light field after a few trials. When ventral lobe neurons of the mushroom bodies were silenced, bees were no longer able to associate one light field with shock. By contrast, silencing of one collar region of the mushroom body calyx did not alter behavior in the learning assay in comparison to control treatment. Bees with silenced central complex neurons did not leave the shock-paired light field in the middle trials of training, even after a few seconds of being shocked. We discussed how mushroom bodies and the central complex both contribute to aversive visual learning with an operant component.