Spatial proteomics in three-dimensional intact specimens.

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.

Cellular and Molecular Probing of Intact Human Organs.

Optical tissue transparency permits scalable cellular and molecular investigation of complex tissues in 3D. Adult human organs are particularly challenging to render transparent because of the accumulation of dense and sturdy molecules in decades-aged tissues. To overcome these challenges, we developed SHANEL, a method based on a new tissue permeabilization approach to clear and label stiff human organs. We used SHANEL to render the intact adult human brain and kidney transparent and perform 3D histology with antibodies and dyes in centimeters-depth. Thereby, we revealed structural details of the intact human eye, human thyroid, human kidney, and transgenic pig pancreas at the cellular resolution. Furthermore, we developed a deep learning pipeline to analyze millions of cells in cleared human brain tissues within hours with standard lab computers. Overall, SHANEL is a robust and unbiased technology to chart the cellular and molecular architecture of large intact mammalian organs.

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.

Neuroimmune cardiovascular interfaces control atherosclerosis.

Atherosclerotic plaques develop in the inner intimal layer of arteries and can cause heart attacks and strokes1. As plaques lack innervation, the effects of neuronal control on atherosclerosis remain unclear. However, the immune system responds to plaques by forming leukocyte infiltrates in the outer connective tissue coat of arteries (the adventitia)2-6. Here, because the peripheral nervous system uses the adventitia as its principal conduit to reach distant targets7-9, we postulated that the peripheral nervous system may directly interact with diseased arteries. Unexpectedly, widespread neuroimmune cardiovascular interfaces (NICIs) arose in mouse and human atherosclerosis-diseased adventitia segments showed expanded axon networks, including growth cones at axon endings near immune cells and media smooth muscle cells. Mouse NICIs established a structural artery-brain circuit (ABC): abdominal adventitia nociceptive afferents10-14 entered the central nervous system through spinal cord T6-T13 dorsal root ganglia and were traced to higher brain regions, including the parabrachial and central amygdala neurons; and sympathetic efferent neurons projected from medullary and hypothalamic neurons to the adventitia through spinal intermediolateral neurons and both coeliac and sympathetic chain ganglia. Moreover, ABC peripheral nervous system components were activated: splenic sympathetic and coeliac vagus nerve activities increased in parallel to disease progression, whereas coeliac ganglionectomy led to the disintegration of adventitial NICIs, reduced disease progression and enhanced plaque stability. Thus, the peripheral nervous system uses NICIs to assemble a structural ABC, and therapeutic intervention in the ABC attenuates atherosclerosis.

Virtual reality-empowered deep-learning analysis of brain cells.

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.

Publisher Correction: Tissue clearing and its applications in neuroscience.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Tissue clearing and its applications in neuroscience.

State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.

Machine learning analysis of whole mouse brain vasculature.

Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain.

A guidebook for DISCO tissue clearing.

Histological analysis of biological tissues by mechanical sectioning is significantly time-consuming and error-prone due to loss of important information during sample slicing. In the recent years, the development of tissue clearing methods overcame several of these limitations and allowed exploring intact biological specimens by rendering tissues transparent and subsequently imaging them by laser scanning fluorescence microscopy. In this review, we provide a guide for scientists who would like to perform a clearing protocol from scratch without any prior knowledge, with an emphasis on DISCO clearing protocols, which have been widely used not only due to their robustness, but also owing to their relatively straightforward application. We discuss diverse tissue-clearing options and propose solutions for several possible pitfalls. Moreover, after surveying more than 30 researchers that employ tissue clearing techniques in their laboratories, we compiled the most frequently encountered issues and propose solutions. Overall, this review offers an informative and detailed guide through the growing literature of tissue clearing and can help with finding the easiest way for hands-on implementation.

Contrasting the Water Quality and Bacterial Community Patterns in Shallow and Deep Lakes: Manyas vs. Iznik.

The objectives of this study are to monitor the physicochemical properties of two freshwater lakes with different chemical characteristics and trophic status over a year (2019) and assess the bacterial diversity by a high-throughput sequencing method for a certain time. Carlson Trophic Index analysis revealed that, whereas the deep lake, Iznik Lake, (TSImean = 48.9) has mesotrophic characteristics, the shallow lake Manyas Lake (TSImean = 74.2) was found at a hypertrophic status. The most important parameters controlling water qualities in the lakes were temperature, alkalinity, and phosphate levels. Although the bacterial communities were dominated by the same phyla (Cyanobacteria, Bacteroidetes, Actinomicrobia, Proteobacteria, and Verrucomicrobia) in both lakes, the communities differed distinctly at the lower levels. Whereas Sporichthyaceae in Manyas Lake accounted for 10% of the total reads, the major share of the sequences was assigned to Cyanobacteria Family I (8%) in Iznik Lake. The hypertrophic Manyas Lake had a more diverse bacterial community rather than Iznik Lake and contained higher numbers of unique Operational Taxonomic Units.

3D high resolution generative deep-learning network for fluorescence microscopy imaging.

Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, constrained by the physical principles of optical imaging, volume imaging has to balance imaging resolution and speed. Here, we develop a new, to the best of our knowledge, 3D deep learning network based on a dual generative adversarial network (dual-GAN) framework for recovering high-resolution (HR) volume images from high speed acquired low-resolution (LR) volume images. The proposed method does not require a precise image registration process and meanwhile guarantees the predicted HR volume image faithful to its corresponding LR volume image. The results demonstrated that our method can recover ${20} {\times} /1.0\text-{\rm NA}$20×/1.0-NA volume images from coarsely registered ${5} {\times} /0.16\text-{\rm NA}$5×/0.16-NA volume images collected by light-sheet microscopy. This method would provide great potential in applications which require high resolution volume imaging.

Whole-Brain Analysis of Cells and Circuits by Tissue Clearing and Light-Sheet Microscopy.

In this photo essay, we present a sampling of technologies from laboratories at the forefront of whole-brain clearing and imaging for high-resolution analysis of cell populations and neuronal circuits. The data presented here were provided for the eponymous Mini-Symposium presented at the Society for Neuroscience's 2018 annual meeting.

Shrinkage-mediated imaging of entire organs and organisms using uDISCO.

Recent tissue-clearing approaches have become important alternatives to standard histology approaches. However, light scattering in thick tissues and the size restrictions on samples that can be imaged with standard light-sheet microscopy pose limitations for analyzing large samples such as an entire rodent body. We developed 'ultimate DISCO' (uDISCO) clearing to overcome these limitations in volumetric imaging. uDISCO preserves fluorescent proteins over months and renders intact organs and rodent bodies transparent while reducing their size up to 65%. We used uDISCO to image neuronal connections and vasculature from head to toe over 7 cm and to perform unbiased screening of transplanted stem cells within the entire body of adult mice. uDISCO is compatible with diverse labeling methods and archival human tissue, and it can readily be used in various biomedical applications to study organization of large organ systems throughout entire organisms.

Poly(amidoamine) dendrimer-coated magnetic nanoparticles for the fast purification and selective enrichment of glycopeptides and glycans.

Glycosylated proteins modulate various important functions of organisms. To reveal the functions of glycoproteins, in-depth characterization studies are necessary. Although mass spectrometry is a very efficient tool for glycoproteomic and glycomic studies, efficient sample preparation methods are required prior to analyses. In the study, poly(amidoamine) dendrimer-coated magnetic nanoparticles were presented for the specific enrichment and fast purification of glycopeptides and glycans. The enrichment and purification performance of the developed method was evaluated both at the glycopeptide, and the glycan level using several standard glycoprotein digests and released glycan samples. The poly(amidoamine) dendrimer-coated magnetic nanoparticles not only showed selective affinity (Immunoglobulin G/Bovine Serum Albumin, 1/10 by weight) to glycopeptides and released glycans but also good sensitivity (0.4 ng/µL for Immunoglobulin G) for glycoproteomic and glycomic applications. Thirty-five glycopeptides of Immunoglobulin G were detected after enrichment with poly(amidoamine) dendrimer-coated magnetic nanoparticles. In addition, 55 18 O tagged deamidated glycopeptides belonging to human plasma glycoproteome were confirmed. Finally, fifty 2-aminobenzoic acid, and 30 procainamide-labelled human plasma N-glycans released from human plasma glycoproteins were determined after purifications. The results indicate that the proposed enrichment and purification method using poly(amidoamine) dendrimer-coated magnetic nanoparticles could be simply adjusted to sample preparation methods.

An inhibitory antibody targeting carbonic anhydrase XII abrogates chemoresistance and significantly reduces lung metastases in an orthotopic breast cancer model in vivo.

Carbonic anhydrase XII (CAXII) is a membrane-tethered ectoenzyme involved in intracellular pH regulation and overexpressed across various types of human cancer. Because CAXII inhibition shows antitumor activity in vitro, it is thought that the enzyme is mandatory for maximum tumor growth, above all under hypoxic conditions. Recently, it has been shown that CAXII is co-expressed along with the P-glycoprotein (P-GP) on many tumor cells and that both proteins physically interact. Of interest, blocking CAXII activity also decreases P-GP activity in cancer cells both in vitro and in vivo. Previously, we have reported on the development of a monoclonal antibody, termed 6A10, which specifically and efficiently blocks human CAXII activity. Here, we demonstrate that 6A10 also indirectly reduces P-GP activity in CAXII/P-GP double-positive chemoresistant cancer cells, resulting in enhanced chemosensitivity as revealed by enhanced accumulation of anthracyclines and increased cell death in vitro. Even more important, we show that mice carrying human triple-negative breast cancer xenografts co-treated with doxorubicin (DOX) and 6A10 show a significantly reduced number of metastases. Collectively, our data provide evidence that the inhibition of CAXII with 6A10 is an attractive way to reduce chemoresistance of cancer cells and to interfere with the metastatic process in a clinical setting.

Evaluation of Jeffamine® Core PAMAM Dendrimers for Simultaneous Removal of Divalent Heavy Metal Ions from Aqueous Solutions by Polymer Assisted Ultrafiltration.

Different generations (G3-G4) of amine-terminated Jeffamine® T-403 core poly(amidoamine) PAMAM dendrimers (JCPDs) were used as new macromolecular heavy metal chelating agent templates in polymer assisted ultrafiltration (PAUF) for the investigation of their removal ability for some of the divalent metal ions: Cu, Co, Ni, Cd, and Zn from aqueous solutions under competitive conditions. The effects of pH and generation size of JCPDs were also investigated. Extent of binding (EOB) data can be appropriately expressed by a tetradentate coordination for JCPDs at pH 9 where the maximum removal of metal ions was observed. At pH 9.0, the affinity of both generations towards heavy metal ions was also observed in the decreasing order of Zn(II) > Co(II) > Ni(II) > Cu(II) > Cd(II). Results revealed that the highest total binding capacity was observed for G3-NH2 (262.79 ± 1.62 mg/g) as a little bit higher than that of G4-NH2 (257.27 ± 2.57 mg/g). EOB studies also proved the active contribution of amide groups to metal binding ability of PAMAMs. Both generations were selective towards Cu(II) ions at lower pH 5 and pH 7. From these results, it was concluded that studied JCPDs have the desired technical properties to be used for the removal of toxic metals from wastewaters.

Interfering with the Chronic Immune Response Rescues Chronic Degeneration After Traumatic Brain Injury.

UNLABELLED: After traumatic brain injury (TBI), neurons surviving the initial insult can undergo chronic (secondary) degeneration via poorly understood mechanisms, resulting in long-term cognitive impairment. Although a neuroinflammatory response is promptly activated after TBI, it is unknown whether it has a significant role in chronic phases of TBI (>1 year after injury). Using a closed-head injury model of TBI in mice, we showed by MRI scans that TBI caused substantial degeneration at the lesion site within a few weeks and these did not expand significantly thereafter. However, chronic alterations in neurons were observed, with reduced dendritic spine density lasting >1 year after injury. In parallel, we found a long-lasting inflammatory response throughout the entire brain. Deletion of one allele of CX3CR1, a chemokine receptor, limited infiltration of peripheral immune cells and largely prevented the chronic degeneration of the injured brain and provided a better functional recovery in female, but not male, mice. Therefore, targeting persistent neuroinflammation presents a new therapeutic option to reduce chronic neurodegeneration. SIGNIFICANCE STATEMENT: Traumatic brain injury (TBI) often causes chronic neurological problems including epilepsy, neuropsychiatric disorders, and dementia through unknown mechanisms. Our study demonstrates that inflammatory cells invading the brain lead to secondary brain damage. Sex-specific amelioration of chronic neuroinflammation rescues the brain degeneration and results in improved motor functions. Therefore, this study pinpoints an effective therapeutic approach to preventing secondary complications after TBI.

The choroid plexus is a key cerebral invasion route for T cells after stroke.

Neuroinflammation contributes substantially to stroke pathophysiology. Cerebral invasion of peripheral leukocytes-particularly T cells-has been shown to be a key event promoting inflammatory tissue damage after stroke. While previous research has focused on the vascular invasion of T cells into the ischemic brain, the choroid plexus (ChP) as an alternative cerebral T-cell invasion route after stroke has not been investigated. We here report specific accumulation of T cells in the peri-infarct cortex and detection of T cells as the predominant population in the ipsilateral ChP in mice as well as in human post-stroke autopsy samples. T-cell migration from the ChP to the peri-infarct cortex was confirmed by in vivo cell tracking of photoactivated T cells. In turn, significantly less T cells invaded the ischemic brain after photothrombotic lesion of the ipsilateral ChP and in a stroke model encompassing ChP ischemia. We detected a gradient of CCR2 ligands as the potential driving force and characterized the neuroanatomical pathway for the intracerebral migration. In summary, our study demonstrates that the ChP is a key invasion route for post-stroke cerebral T-cell invasion and describes a CCR2-ligand gradient between cortex and ChP as the potential driving mechanism for this invasion route.

Synthesis of surface-modified TREN-cored PAMAM dendrimers and their effects on the solubility of sulfamethoxazole (SMZ) as an analog antibiotic drug.

Sulfamethoxazole (SMZ) is a sulfonamide and used widely in the treatment of bacteriostatic and urinary tract infections with trimethoprim as an antibiotic. The problem with SMZ is its poor water solubility, therefore, low bioavailability in clinical applications. In this study, we synthesized new-generation Tris(2-aminoethyl)amine (TREN)-cored amine (NH2), Tris(hydroxymethyl)aminomethane (TRIS), and carboxyl (COOH) terminated different generations T2-T4 poly(amidoamine) PAMAM dendrimers. Synthesized PAMAMs were characterized by 1H NMR, 13C NMR, ATR-FTIR, spectroscopic titrations, and evaluated as potential solubility enhancers and drug carriers of sulfonamides by taking SMZ as a model drug. The effect of concentration, generation, and surface groups of PAMAMs on the solubility of SMZ was also investigated. Results showed that the solubility of SMZ improved significantly with an increasing generation size (T2-T4) and PAMAM dendrimer concentration (0-2 mM). The role of PAMAMs in the solubility enhancement of SMZ was in the order of T4.NH2 > T4.COOH > T3.NH2 > T4.TRIS > T2.NH2 > T3.COOH > T3.TRIS > T2.COOH > T2.TRIS, and in the ranges of 5- to 45-fold with maximum SMZ loading 7 to 61 mole/mole per PAMAM dendrimer molecule. In vitro release studies demonstrated that SMZ-PAMAM dendrimer complexes at the end of 2-h drug release (16-26%) was considerable slower than pure SMZ (38.8%).

The effect of PAMAM dendrimer concentration, generation size and surface functional group on the aqueous solubility of candesartan cilexetil.

This article investigates the aqueous solubility of the poorly soluble drug candesartan cilexetil (CC) in the presence of poly(amidoamine) (PAMAM) dendrimers. The effect of variables such as concentration, generation size (G2-G4), and surface groups (NH2, COOH and TRIS) of PAMAMs on the aqueous solubility of CC was studied. A two-factor factorial (3 × 3) ANOVA design was used to study the effect of generation size and surface functional group of the PAMAMs. The results showed that the aqueous solubility of CC in the presence of carboxyl and TRIS-terminated PAMAMs was higher than those of amine-terminated PAMAMs, and the effect of surface functional group of the PAMAMs on the aqueous solubility of CC was dependent on the generation size (p < 0.05). The sequence of the observed solubility fold enhancement due to PAMAMs was G4.COOH (8378)>G3.COOH (3456)>G4.TRIS (2362)>G2.COOH (1013)>G3.TRIS (749)>G2.TRIS (293)>G4.NH2 (91)>G3.NH2 (50)>G2.NH2 (37). The CC-PAMAM dendrimer inclusion complexes were characterized by UV-Vis, attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and differential thermal analysis (DTA) techniques. Regarding the results of these techniques, improvement in the solubility of CC is expected primarily through the intermolecular hydrogen bonding between the drug and internal tertiary and surface functional groups of the studied PAMAMs.