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Developments in imaging tools are making it possible to record activity from both large neuronal populations and subcellular components in freely moving animals. Although these developments are enabling relationships between brain activity and complex behaviors to be explored, many challenges need to be overcome before the potential of the freely moving animal can be fully utilized.
The development of systems combining rapid volumetric imaging with three-dimensional tracking has enabled the measurement of brain-wide dynamics in freely behaving animals such as worms, flies, and fish. These advances provide an exciting opportunity to understand the organization of neural circuits in the context of voluntary and natural behaviors. In this Comment, we highlight recent progress in this burgeoning area of research.
One major challenge in neuroscience is to uncover how defined neural circuits in the brain encode, store, modify, and retrieve information. Meeting this challenge comprehensively requires tools capable of recording and manipulating the activity of intact neural networks in naturally behaving animals. Head-mounted miniature microscopes are emerging as a key tool to address this challenge. Here we discuss recent work leading to the miniaturization of neural imaging tools, the current state of the art in this field, and the importance and necessity of open-source options. We finish with a discussion on what the future may hold for miniature microscopy.
A machine learning model predicts the genotype of CRISPR–Cas9 gene editing products, thereby enabling precise, template-free correction of disease-associated mutations.
Miniaturized, head-mounted fluorescent microscopes give researchers a clear view of neuronal activity as animals freely explore and interact with their surroundings.
LEAP is a deep-learning-based approach for the analysis of animal pose. LEAP’s graphical user interface facilitates training of the deep network. The authors illustrate the method by analyzing Drosophila and mouse behavior.
A transcriptional analysis of kidney organoids reveals batch effects as the key drivers of variation, mainly through differences in maturity, and provides a list of highly variable genes and a method for estimating differentiation stage for improved disease modeling.
kBET informs attempts at single-cell RNA-seq data integration by quantifying batch effects and determining how well batch regression and normalization approaches remove technical variation while preserving biological variability.
Expansion microscopy allows super-resolution images of diverse samples to be acquired on conventional microscopes, thus democratizing super-resolution imaging. This Perspective reviews available methods and provides practical guidance for users.
A compressed sensing approach enables the identification of key neurons involved in a particular behavior with few measurements, using genetic tools with limited specificity. The approach is demonstrated in the C. elegans interneuron circuitry.
A protocol adapted to xeno- and feeder-free conditions is shown to generate reliable and consistent cortical brain organoids across differentiations and source stem cell lines, making it suitable for disease modeling and other applications.
DART-seq alters droplet sequencing in a simple and flexible way to simultaneously profile the transcriptome and multiplexed targeted RNAs, such as viral transcripts and immunoglobulin chains, in single cells.
A user-friendly ImageJ plugin enables the application and training of U-Nets for deep-learning-based image segmentation, detection and classification tasks with minimal labeling requirements.
