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CUBIC-X for whole-brain expansion and hyperhydrative RI matching a, Transmission images of CUBIC-X1-treated whole brains after additional treatment with CUBIC-X1, ScaleA2 and imidazole–antipyrine cocktails. Chemical cocktails of 5% imidazole and 45–65% antipyrine provided almost fully transparent brains without shrinkage. RIs of each chemical are shown in blue. Scale bars, 2 mm. b, Transmission curves of the whole-brain samples shown in a. Light transmittance around the visible region (380–780 nm) was measured (n = 3). c, Time course of swelling ratio of delipidated whole brains after chemical treatment (n = 3). Brain swelling ratios were evaluated against original brain size. For handling purposes, we chose 5% imidazole + 55% antipyrine cocktail (CUBIC-X2). d, Overview of a whole-brain expansion and hyperhydrative RI matching protocol (CUBIC-X protocol) with staining of nuclei. The duration of CUBIC-1 delipidation can be modified to purpose. Scale bars, 2 mm. e–g, CUBIC-X was compatible with fluorescent protein imaging of various reporter mouse brains. Thy1-YFP, PLP-YFP and Mlc1-YFP mouse brains were cleared and expanded with CUBIC-X protocol and imaged with LSFM (10×, NA = 0.6). Volume-rendered images are shown. Scale bars, 100 μm. h, Confocal image (25×, NA = 1.0) of Thy1-YFP mouse brain. Inset: magnified view showing spines (indicated by arrows). Scale bars, 30 μm (main) and 5 μm (inset). i, An LSFM image of an Mlc1-YFP astrocyte, from g. Maximum intensity projection was applied over the 30-μm-thick volume. Scale bar, 40 μm. Experiments were repeated once (f), twice (e,g,i) or three times (h) with independent brains. Representative images are shown. For e–i, a post-CUBIC-X brain was used as a baseline. All values are mean ± s.d.
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A three-dimensional single-cell-resolution mammalian brain atlas will accelerate systems-level identification and analysis of cellular circuits underlying various brain functions. However, its construction requires efficient subcellular-resolution imaging throughout the entire brain. To address this challenge, we developed a fluorescent-protein-com...
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... Although expansion microscopy was only recently established as a new imaging modality, several protocols have now been developed for expanding cells and tissues from a variety of organisms including bacteria, parasites, insects, and vertebrates (Arsenijevic et al., 2023;Atchou et al., 2023;Bai et al., 2023;Bandeira et al., 2023;Campbell et al., 2021;Chang et al., 2017;Chang et al., 2024;Chen et al., 2015;Cheng et al., 2023;Ching et al., 2022;Chozinski et al., 2016;Chozinski et al., 2018;Damstra et al., 2022;Damstra et al., 2023;Dos Santos Pacheco & Soldati-Favre, 2021;Fan et al., 2021;Gallagher & Zhao, 2021;Gambarotto et al., 2019;Gambarotto et al., 2021;Gaudreau-Lapierre et al., 2021;Jurriens et al., 2021;Klimas et al., 2023;Kong & Loncarek, 2021;Kraft et al., 2023;Ku et al., 2016;Kunz et al., 2019;Kunz et al., 2021;Liffner et al., 2023;Lin et al., 2022;Louvel et al., 2023;Mäntylä et al., 2023;Middelhauve et al., 2023;Moye et al., 2023;Murakami et al., 2018;Park et al., 2019;Park et al., 2021;Parveen et al., 2023;Perelsman et al., 2022;Pernal et al., 2020;Pesce et al., 2019;Ponjavi c et al., 2021;Rodriguez-Gatica et al., 2022;Sahabandu et al., 2019;Siegerist et al., 2022;Steib et al., 2022;Tillberg et al., 2016;Tillberg & Chen, 2019;Unnersjö-Jess et al., 2018;Unnersjö-Jess et al., 2021;Wainman, 2021;Wang et al., 2018;Wassie et al., 2019;Wen et al., 2023;Wilmerding et al., 2023;Woo et al., 2020;Yu et al., 2022;Zhao et al., 2017;Zhu et al., 2021;Zhuang & Shi, 2024). ...
Ultrastructure expansion microscopy (U‐ExM) involves the physical magnification of specimens embedded in hydrogels, which allows for super‐resolution imaging of subcellular structures using a conventional diffraction‐limited microscope. Methods for expansion microscopy exist for several organisms, organs, and cell types, and used to analyze cellular organelles and substructures in nanoscale resolution. Here, we describe a simple step‐by‐step U‐ExM protocol for the expansion, immunostaining, imaging, and analysis of cytoskeletal and organellar structures in kidney tissue. We detail the critical modified steps to optimize isotropic kidney tissue expansion, and preservation of the renal cell structures of interest. We demonstrate the utility of the approach using several markers of renal cell types, centrioles, cilia, the extracellular matrix, and other cytoskeletal elements. Finally, we show that the approach works well on mouse and human kidney samples that were preserved using different fixation and embedding conditions. Overall, this protocol provides a simple and cost‐effective approach to analyze both preclinical and clinical renal samples in high detail, using conventional lab supplies and standard widefield or confocal microscopy.
... Furthermore, overcoming the challenges of 3D imaging posed by the light scattering can be achieved through the utilization of optical tissue clearing (OTC) methods [131,234,235]. OTC methods, such as Benzoic Acid Benzyl Benzoate (BABB) [236], Three-dimensional Imaging Solvent-Cleared Organs (DISCO) [237,238], Clear, Unobstructed Brain/ Body Imaging Cocktails and Computational analysis (CUBIC) [239], See Deep Brain (SeeDB) and Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging/Immunostaining/in situ-hybridization-compatible Tissue hYdrogel (CLARITY) [240] and fructose, urea, and glycerol (FUnGI3) [132], reduce the scattering of light by homogenizing the refractive index of tissues, enabling clear observation throughout the sample thickness [131]. Of note, ExM can simultaneously achieve sample-clearing while expands it [227]. ...
... Importantly, it eliminates the need for access to costly super-resolution microscopes to achieve sub diffraction-resolution fluorescence imaging of cellular structures. Although expansion microscopy was only recently established as a new imaging modality, several protocols have now been developed for expanding cells and tissues from a variety of organisms including bacteria, parasites, insects and vertebrates (Atchou et al., 2023;Bai et al., 2023;Bandeira et al., 2023;Campbell et al., 2021;Chang et al., 2024;Chang et al., 2017;Chen et al., 2015;Cheng et al., 2023;Ching et al., 2022;Chozinski et al., 2016;Chozinski et al., 2018;Damstra et al., 2022;Damstra et al., 2023;Dos Santos Pacheco & Soldati-Favre, 2021;Fan et al., 2021;Gallagher & Zhao, 2021;Gambarotto et al., 2021;Gambarotto et al., 2019;Gaudreau-Lapierre et al., 2021;Jurriens et al., 2021;Klimas et al., 2023;Kraft et al., 2023;Ku et al., 2016;Kunz et al., 2019;Kunz et al., 2021;Liffner et al., 2023;Lin et al., 2022;Louvel et al., 2023;Mäntylä et al., 2023;Middelhauve et al., 2023;Moye et al., 2023;Murakami et al., 2018;Park et al., 2019;Park et al., 2021;Parveen et al., 2023;Perelsman et al., 2022;Pernal et al., 2020;Pesce et al., 2019;Ponjavić et al., 2021;Rodriguez-Gatica et al., 2022;Sahabandu et al., 2019;Siegerist et al., 2022;Steib et al., 2022;Tillberg & Chen, 2019;Tillberg et al., 2016;Unnersjö-2016). MAP utilizes different crosslinking chemistry and, most importantly, involves protein denaturation with heat and detergent instead of enzymatic digestion. ...
Ultrastructure expansion microscopy (U-ExM) involves the physical magnification of specimens embedded in hydrogels, which allows for super-resolution imaging of subcellular structures using a conventional diffraction-limited microscope. Methods for expansion microscopy exist for several organisms, organs, and cell types, and used to analyze cellular organelles and substructures in nanoscale resolution. Here, we describe a simple step-by-step U-ExM protocol for the expansion, immunostaining, imaging, and analysis of cytoskeletal and organellar structures in kidney tissue. We detail the critical modified steps to optimize isotropic kidney tissue expansion, and preservation of the renal cell structures of interest. We demonstrate the utility of the approach using several markers of renal cell types, centrioles, cilia, the extracellular matrix, and other cytoskeletal elements. Finally, we show that the approach works well on mouse and human kidney samples that were preserved using different fixation and storage conditions. Overall, this protocol provides a simple and cost-effective approach to analyze both pre-clinical and clinical renal samples in high detail, using conventional lab supplies and standard widefield or confocal microscopy.
... To determine N, a, and l for each subsequent region, we turn to estimated biological values and loosely follow their trends. Rodents have approximately 5-10 times more DG granule cells and 2-3 times more CA3 pyramidal cells compared to medial EC layer II principal neurons 14,104,105 . Thus, we choose N DG = 8192 and N CA3 = 2048. ...
The hippocampal subfield CA3 is thought to function as an auto-associative network that stores experiences as memories. Information from these experiences arrives directly from the entorhinal cortex as well as indirectly through the dentate gyrus, which performs sparsification and decorrelation. The computational purpose for these dual input pathways has not been firmly established. We model CA3 as a Hopfield-like network that stores both dense, correlated encodings and sparse, decorrelated encodings. As more memories are stored, the former merge along shared features while the latter remain distinct. We verify our model’s prediction in rat CA3 place cells, which exhibit more distinct tuning during theta phases with sparser activity. Finally, we find that neural networks trained in multitask learning benefit from a loss term that promotes both correlated and decorrelated representations. Thus, the complementary encodings we have found in CA3 can provide broad computational advantages for solving complex tasks.
... Researchers can measure the brain at scale, for example, with electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) techniques [1][2][3]. In addition to EEG and fMRI, tremendous advances in single-cell spatial biology provide information on the physiology of individual cells within tissues [4,5]. There is also a growing appreciation of the importance of spatiotemporal patterns in the brain, from traveling slow waves [6], which are the signature of deep sleep, to waves related to working memory [7], spiral waves during sleep spindles [8], and hemodynamic and cerebrospinal fluid waves that are coupled with electrophysiology [9]. ...
Recent advances in Graphics Processing Unit (GPU) computing have allowed for computational models of whole-brain activity at unprecedented scales. In this work, we use desktop computers to build and simulate a whole-cortex mouse brain model using Hodgkin-Huxley type models for all the most active neurons in the mouse cortex. We compare the model dynamics over different types of connectivity, ranging from uniform random to realistic connectivity derived from experimental data on cell positions and the Allen Brain Atlas. By changing the external drive and coupling strength of neurons in the network, we can produce a wide range of oscillations in the gamma through delta bands. While the global mean-field behaviors of different connectivities share some similarities, an experimentally determined hierarchical connectivity allows for complex, heterogeneous behaviors typically seen in EEG recordings that are not observed in networks with nearest neighbors or uniform coupling. Moreover, our simulations reveal a wide range of spatiotemporal patterns, such as rotational or planar traveling waves, that are observed in experiments. Different traveling waves are observed with different connectivity and coupling strengths on the same connectivity. Our simulations show that many cortical behaviors emerge at scale with the full complexity of the network structure and ionic dynamics. We also provide a computational framework to explore these cortex-wide behaviors further.
... We propose a method to reconstruct the active neurons and map their connections digitally. Several current tools can help us quantify and reconstruct 3D brain structures with individual neuron positions mapped into a common brain coordinate framework, such as the Allen Brain Atlas Common Coordinate Framework (ABA-CCF) [19] or the CUBIC atlas [20]. Recently, investigators have shown that the whole mouse brain can be scanned and the position of each neuron's cell body determined [20]. ...
... Several current tools can help us quantify and reconstruct 3D brain structures with individual neuron positions mapped into a common brain coordinate framework, such as the Allen Brain Atlas Common Coordinate Framework (ABA-CCF) [19] or the CUBIC atlas [20]. Recently, investigators have shown that the whole mouse brain can be scanned and the position of each neuron's cell body determined [20]. Here, we apply a similar machine-learning . ...
... With neuronal images properly stitched, we now apply machine learning techniques to identify neuron bodies and their positions. We use a two-step convolutional cell detection algorithm implemented for the CUBIC Atlas [20] to identify both the position and the strength of the c-Fos expression level (see Methods). After identifying all neurons, we align our data to the ABA-CCFv3 to use the incorporated connectivity information (Figs 1d-e). ...
Tremendous effort has focused on determining the physical connectivity within the mouse brain. However, the strength of connections within the brain constantly changes throughout the 24-hour day. Here, we combine experimental and computational methods to determine an “active connectivity” of the physical connections between the most active neurons. Brain cells of freely behaving mice are genetically marked with the activity- dependent TRAP2 system, imaged, digitized, and their connectivity is inferred from the latest brain atlases. We apply our methods to determine the most active networks in the early light and early dark hours of the day, two periods with distinct differences in sleep, wake, and feeding behavior. Increased signaling is seen through the visceral and agranular insular (AI) regions in the early day as peripheral stimuli are integrated. On the other hand, there is an increase in the activity of the retrosplenial cortex (RSP) and the anterior cingulate cortex (ACC) during the early night, when more sustained attention is required. Our framework carves a window to the three-dimensional networks of active connections in the mouse brain that underlie spontaneous behaviors or responses to environmental changes, thus providing the basis for direct computer simulations and analysis of such networks in the future.
... Other versions of CUBIC, for example, AbScale, can be implemented with brain-wide immunohistochem istry to image amyloid-β plaques formation, a milestone pathological feature of Alzheimer's disease [51,52]. In order to expand the size of tissue samples, a derivate of the hydrophilic method, CUBIC-X (X is X-fold expansion), was designed to use an imidazole and antipyrine as hyperhydrating reagents [53]. With this method, the adult mouse brain can be expanded 10-fold in volume for whole-brain cell profiling and creating a single-cell resolution 3D mouse brain atlas, which allows mapping gene expression, cell differentiation, and communication [53]. ...
... In order to expand the size of tissue samples, a derivate of the hydrophilic method, CUBIC-X (X is X-fold expansion), was designed to use an imidazole and antipyrine as hyperhydrating reagents [53]. With this method, the adult mouse brain can be expanded 10-fold in volume for whole-brain cell profiling and creating a single-cell resolution 3D mouse brain atlas, which allows mapping gene expression, cell differentiation, and communication [53]. ...
... Hydrophilic methods using water-soluble reagents cannot achieve complete transparency like hydrophobic methods but have better biocompatibility and biosafety with brain tissues. Representative hydrophilic tissue-clearing approaches include FocusClear [61], Scale [62], ScaleS [63], See Deep Brain (SeeDB) [64], See Deep Brain 2 (SeeDB2) [65], rapid clearing method based on Triethanolamine and Formamide (RTF) [66], FRUIT [67], Urea-Based Amino-sugar Mixture (UbasM) [68], Clear, Unobstructed Brain/Body Imaging Cocktails and Computational analysis (CUBIC) [69][70][71][72] and CUBIC-X [73,74]. Based on the principle of securing biomolecules in situ through covalent crosslinking, hydrogelbased methods convert tissue into synthetic gels using polyacrylamide or into reinforced tissue gels using polyepoxide, followed by delipidation and refractive index matching. ...
... Figure 5 shows several typical whole-brain optical imaging results. Brain-wide cell analyses involve mapping the distributions of genetically defined cell types [73,74,134,157], cell molecular features [58,75,158], and cells expressing immediate-early genes (IEGs) [132,133,158]. These datasets allow the precise dissection of the cellular composition of different brain regions and an understanding of the principles of mammalian brain organization [159]. ...
The mammalian brain is a highly complex network that consists of millions to billions of densely-interconnected neurons. Precise dissection of neural circuits at the mesoscopic level can provide important structural information for understanding the brain. Optical approaches can achieve submicron lateral resolution and achieve “optical sectioning” by a variety of means, which has the natural advantage of allowing the observation of neural circuits at the mesoscopic level. Automated whole-brain optical imaging methods based on tissue clearing or histological sectioning surpass the limitation of optical imaging depth in biological tissues and can provide delicate structural information in a large volume of tissues. Combined with various fluorescent labeling techniques, whole-brain optical imaging methods have shown great potential in the brain-wide quantitative profiling of cells, circuits, and blood vessels. In this review, we summarize the principles and implementations of various whole-brain optical imaging methods and provide some concepts regarding their future development.
... Segmentation-based methods are incredibly efficient tools for rapid quantification across many brain regions and subjects, especially when combined with 3-dimensional digital atlases to supply the region delineations 72,73 : reasonably powerful computers are capable of performing quantification of cell populations across entire brains. [31][32][33]52,55,[74][75][76][77] Thus, segmentation-based methods should be the method of choice for studies with a broad scope. ...
... An important opportunity arising from the recent progress in using segmentation-based methods [31][32][33]52,[74][75][76][77] is increased re-usability through sharing of all generations of data related to cell counting. For large datasets, e.g., from the whole brain, data should preferably be shared according to the FAIR principles 92 in a machine-readable format in a public repository. ...
Quantifying how the cellular composition of brain regions vary across development, aging, sex, and disease, is crucial in experimental neuroscience, and the accuracy of different counting methods is continuously debated. Due to the tedious nature of most counting procedures, studies are often restricted to one or a few brain regions. Recently, there have been considerable methodological advances in combining semi-automated feature extraction with brain atlases for cell quantification. Such methods hold great promise for scaling up cell-counting efforts. However, little focus has been paid to how these methods should be implemented and reported to support reproducibility. Here, we provide an overview of practices for conducting and reporting cell counting in mouse and rat brains, showing that critical details for interpretation are typically lacking. We go on to discuss how novel methods may increase efficiency and reproducibility of cell counting studies. Lastly, we provide practical recommendations for researchers planning cell counting.
... On the one hand, a large number of tools have been designed for small imaging datasets and lack the computational structure to scale properly to the TB-sized images. 14,63-70 On the other hand, quantification algorithms validated on large-scale datasets are either based on standard image filters, 14,66 are designed to work with simple nuclear staining, or need an additional background channel. 69 Overall, the innovations introduced in this work enable routine and scalable analyses that were not possible using previously reported methodology. ...
Fear responses are functionally adaptive behaviors that are strengthened as memories. Indeed, detailed knowledge of the neural circuitry modulating fear memory could be the turning point for the comprehension of this emotion and its pathological states. A comprehensive understanding of the circuits mediating memory encoding, consolidation, and retrieval presents the fundamental technological challenge of analyzing activity in the entire brain with single-neuron resolution. In this context, we develop the brain-wide neuron quantification toolkit (BRANT) for mapping whole-brain neuronal activation at micron-scale resolution, combining tissue clearing, high-resolution light-sheet microscopy, and automated image analysis. The robustness and scalability of this method allow us to quantify the evolution of activity patterns across multiple phases of memory in mice. This approach highlights a strong sexual dimorphism in recruited circuits, which has no counterpart in the behavior. The methodology presented here paves the way for a comprehensive characterization of the evolution of fear memory.
