Protection of tissue physicochemical properties using polyfunctional crosslinkers

Park, Young Gyun; Sohn, Chang Ho; Chen, Ritchie; McCue, Margaret; Yun, Dae Hee; Drummond, Gabrielle T.; Ku, Taeyun; Evans, Nicholas B.; Oak, Hayeon Caitlyn; Trieu, Wendy; Choi, Heejin; Jin, Xin; Lilascharoen, Varoth; Wang, Ji; Truttmann, Matthias C.; Qi, Helena W.; Ploegh, Hidde L.; Golub, Todd R.; Chen, Shih‐Chi; Frosch, Matthew P.; Kulik, Heather J.; Lim, Byung Kook; Chung, Kwanghun

doi:10.1038/nbt.4281

Cited by 295 publications

(313 citation statements)

References 71 publications

(98 reference statements)

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“…To assess whether connectionally-distinct BLA.am, BLA.al, and BLA.ac projection neurons were morphologically different, representative cells in each domain were labeled via a G-deleted rabies injection in the dorsomedial CPc (Movie 1), ventral CPc (Movie 2), and in the medial ACB (Movie 3, Movie 4, Movie 5), respectively ( Figure 1D). The SHIELD clearing protocol for thick sections was followed 49,50 and sections were imaged using an Andor Dragonfly high speed spinning disk confocal microscope. Manual reconstruction and quantitative analysis of neurons was performed using Aivia ( Figure 8D) and quantitative analysis was performed using Fiji and L-Measure.…”

Section: Blaa Neuron Morphologymentioning

confidence: 99%

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Connectivity characterization of the mouse basolateral amygdalar complex

Hintiryan

Bowman

Johnson

et al. 2019

Preprint

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The basolateral amygdalar complex (BLA) is implicated in behavioral processing ranging from fear acquisition to addiction. Newer methods like optogenetics have enabled the association of circuitspecific functionality to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. In this work, we applied computational analysis techniques to the results of our circuittracing experiments to create a foundational, comprehensive, multiscale connectivity atlas of the mouse BLA. The analyses identified three domains within the classically defined anterior BLA (BLAa) that house target-specific projection neurons with distinguishable cell body and dendritic morphologies. Further, we identify brain-wide targets of projection neurons located in the three BLAa domains as well as in the posterior BLA (BLAp), ventral BLA (BLAv), lateral (LA), and posterior basomedial (BMAp) nuclei. Projection neurons that provide input to each nucleus are also identifed. Functional characterization of some projection-defined BLA neurons were demonstrated via optogenetic and recording experiments. Hypotheses relating function to connection-defined BLA cell types are proposed. rACAd, Anterior cingulate cortical area, dorsal part, rostral region cACAd, Anterior cingulate cortical area, dorsal part, caudal region ACAv, Anterior cingulate cortical area, ventral part rACAv, Anterior cingulate cortical area, ventral part, rostral region cACAv, Anterior cingulate cortical area, ventral part, caudal region ACB, Nucleus accumbens AD, Anterodorsal nucleus of the thalamus ADP, Anterodorsal preoptic nucleus of the hypothalamus AHN, Anterior hypothalamic nucleus AI, Agranular insular cortical area AId, Agranular insular cortical area, dorsal part AIv, Agranular insular cortical area, ventral part AIv, Agranular insular cortical area, posterior part AM, Anteromedial nucleus of the thalamus AMd, Anteromedial nucleus of the thalamus, dorsal part AMv, Anteromedial nucleus of the thalamus, ventral part AON, Anterior olfactory nucleus AONm, Anterior olfactory nucleus, medial part AONpv, Anterior olfactory nucleus, posteroventral part ARH, Arcuate hypothalamic nucleus AUD, Auditory cortical area AUDd, Dorsal auditory cortical area AUDp, Primary auditory cortical area AUDv, Ventral auditory cortical area AV, Anteroventral nucleus of the thalamus AVP, Anteroventral preoptic nucleus AVPV, Anteroventral periventricular nucleus bic, Brachium of the inferior colliculus BLA, Basolateral amygdalar complex BLAa, Basolateral amygdalar nucleus, anterior part BLA.am, Basolateral amydalar nucleus, anterior part, medial domain BLA.al, Basolateral amydalar nucleus, anterior part, lateral domain BLA.ac, Basolateral amydalar nucleus, anterior part, caudal domain BLAp, Basolateral amygdalar nucleus, posterior part BLAv, Basolateral amygdalar nucleus, ventral part BMA, Basomedial amygdalar nucleus BMAa, Basomedial amygdalar nucleus, anterior part BMAp, Basomedial...

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Section: Blaa Neuron Morphologymentioning

confidence: 99%

“…One week was allowed for tracer transport following injections, after which the animals were perfused. A 3D tissue processing workflow was followed for implementation of the SHIELD clearing protocol 49,50 . See…”

Section: Workflow For Assessment Of Neuronal Morphologymentioning

confidence: 99%

Connectivity characterization of the mouse basolateral amygdalar complex

Hintiryan

Bowman

Johnson

et al. 2019

Preprint

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“…The overall image processing pipeline described in this protocol is designed to calculate fluorescence summary statistics from whole-mouse brain images acquired with LSFM on a perregion basis. Our pipeline has been used to quantify mRuby2 and EGFP fluorescence of virally labeled neurons and presynaptic terminals in SHIELD-processed mouse brain hemispheres 6 . Thus, the overall pipeline may be applied in systems neuroscience to quantify fluorescent reporters in cleared samples from mouse models.…”

Section: Applications Of the Methodsmentioning

confidence: 99%

“…These challenges must be addressed in order to perform the quantitative analyses needed to answer the complex biological questions at hand. SHIELD is a tissue transformation technique that preserves endogenous biomolecules for imaging within intact biological systems 6 . SHIELD retains fluorescent protein signals through the clearing process and is compatible with stochastic electrotransport staining, allowing visualization and quantification of fluorescence signals throughout the entire brain 7 .…”

Section: Introductionmentioning

confidence: 99%

Scalable image processing techniques for quantitative analysis of volumetric biological images from light-sheet microscopy

Swaney

Kamentsky

Evans

et al. 2019

Preprint

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Here we describe an image processing pipeline for quantitative analysis of terabyte-scale volumetric images of SHIELD-processed mouse brains imaged with light-sheet microscopy. The pipeline utilizes open-source packages for destriping, stitching, and atlas alignment that are optimized for parallel processing. The destriping step removes stripe artifacts, corrects uneven illumination, and offers over 100x speed improvements compared to previously reported algorithms. The stitching module builds upon Terastitcher to create a single volumetric image quickly from individual image stacks with parallel processing enabled by default. The atlas alignment module provides an interactive web-based interface that automatically calculates an initial alignment to a reference image which can be manually refined. The atlas alignment module also provides summary statistics of fluorescence for each brain region as well as region segmentations for visualization. The expected runtime of our pipeline on a whole mouse brain hemisphere is 1-2 d depending on the available computational resources and the dataset size.Recently, LSFM images of a whole mouse brain have been used to create a single-cell mouse brain atlas 4 . The pipeline consisted of a heterogeneous mix of MATLAB, Python, and C++ software as well as expensive computer hardware, including a dedicated image processing server equipped with four NVIDIA graphics processing units (GPU). In order to handle individual volumetric images that are larger than the amount of available memory, images were processed slice-by-slice for cell detection and rescaled to a manageable size for atlas alignment. Although computationally impressive, such tools often require a great deal of programming expertise or access to proprietary software. As a result, the current large-scale image processing pipelines may be inaccessible to non-experts, and there is a need for large-scale image processing tools for researchers focused on biological questions rather than computational challenges.The protocols presented here are designed to be easy to setup and applicable to users without much experience in setting up complex development environments. Since some users may only want to use part of our pipeline, the protocols are partitioned into three computational modules: first, our image destriping for removing streaks and performing flat-field correction in raw LSFM images; second, stitching for creating a single 3D image from the individual 2D images; and third,

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“…Based on factors like clearing capability, target biomolecule fixation, ability to perform ex-vivo labeling, fluorescence retention and imaging depth, we chose to explore two methods 1) tissue lipid matrix replacementbased CLARITY (Chung et al 2013) with modifications (Tomer et al 2014;Yang et al 2014;Zheng and Rinaman 2016;Krolewski et al 2018) and 2) non-aqueous solvent-based iDISCO+ (Renier et al 2014). There has been pioneering work done (Sylwestrak et al 2016;Kramer et al 2018;Park et al 2018) but the combination of HCR FISH with clearing techniques still remains relatively unexplored especially with fresh-frozen tissues. Flash-freezing fresh rodent brains provides flexible utilization for studying a wide array of biomolecules (e.g., RNA, protein, metabolite) using multiple methods like in situ hybridization, immunohistochemistry, PCR, western blotting, spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Optimization and quantitative evaluation of fluorescence in situ hybridization chain reaction in clarified fresh-frozen brain tissues

Kumar

et al. 2019

Preprint

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Transcript labelling in intact tissues using in situ hybridization chain reaction has potential to provide vital spatiotemporal information for molecular characterization of heterogeneous neuronal populations. However, it remains relatively unexplored in fresh-frozen brain which provides more flexible utilization than perfused tissue. In the present study, we optimized the combination of in situ hybridization chain reaction in fresh-frozen rodent brains and then evaluated the uniformity of neuronal labelling between two clearing methods, CLARITY and iDISCO+. We found that CLARITY yielded higher signal-to-noise ratios but more limited imaging depth and required longer clearing times, whereas, iDISCO+ resulted in better tissue clearing, greater imaging depth and a more uniform labelling of larger samples. Based on these results, we used iDISCO+-cleared fresh-frozen rodent brains to further validate this combination and map the expression of a few genes of interest pertaining to mood disorders. We then examined the potential of in situ hybridization chain reaction to label transcripts in cleared postmortem human brain tissues. The combination failed to produce adequate mRNA labelling in postmortem human cortical slices but produced visually adequate labeling in the cerebellum tissues. We next, investigated the multiplexing ability of in situ hybridization chain reaction in cleared tissues which revealed inconsistent fluorescence output depending upon the fluorophore conjugated to the hairpins.Finally, we applied our optimized protocol to assess the effect of glucocorticoid receptor overexpression on basal somatostatin expression in the mouse cortex. The constitutive glucocorticoid receptor overexpression resulted in lower number density of somatostatinexpressing neurons compared to wild type. Overall, the combination of in situ hybridization chain reaction with clearing methods especially iDISCO+ may find broad application in the transcript analysis in rodent studies but its limited use in postmortem human tissues can be improved by further optimizations.

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Protection of tissue physicochemical properties using polyfunctional crosslinkers

Cited by 295 publications

References 71 publications

Connectivity characterization of the mouse basolateral amygdalar complex

Connectivity characterization of the mouse basolateral amygdalar complex

Scalable image processing techniques for quantitative analysis of volumetric biological images from light-sheet microscopy

Optimization and quantitative evaluation of fluorescence in situ hybridization chain reaction in clarified fresh-frozen brain tissues

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