Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model.

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.

PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD. METHODS: A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes. RESULTS: Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%). CONCLUSION: We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.

Optimization of micelle-encapsulated extremely small sized iron oxide nanoparticles as a T1 contrast imaging agent: biodistribution and safety profile.

BACKGROUND: Iron oxide nanoparticles (IONPs) have been cleared by the Food and Drug Administration (FDA) for various clinical applications, such as tumor-targeted imaging, hyperthermia therapy, drug delivery, and live-cell tracking. However, the application of IONPs as T1 contrast agents has been restricted due to their high r2 values and r2/r1 ratios, which limit their effectiveness in T1 contrast enhancement. Notably, IONPs with diameters smaller than 5 nm, referred to as extremely small-sized IONPs (ESIONs), have demonstrated potential in overcoming these limitations. To advance the clinical application of ESIONs as T1 contrast agents, we have refined a scale-up process for micelle encapsulation aimed at improving the hydrophilization of ESIONs, and have carried out comprehensive in vivo biodistribution and preclinical toxicity assessments. RESULTS: The optimization of the scale-up micelle-encapsulation process, specifically employing Tween60 at a concentration of 10% v/v, resulted in ESIONs that were uniformly hydrophilized, with an average size of 9.35 nm and a high purification yield. Stability tests showed that these ESIONs maintained consistent size over extended storage periods and dispersed effectively in blood and serum-mimicking environments. Relaxivity measurements indicated an r1 value of 3.43 mM- 1s- 1 and a favorable r2/r1 ratio of 5.36, suggesting their potential as T1 contrast agents. Biodistribution studies revealed that the ESIONs had extended circulation times in the bloodstream and were primarily cleared via the hepatobiliary route, with negligible renal excretion. We monitored blood clearance and organ distribution using positron emission tomography and magnetic resonance imaging (MRI). Additionally, MRI signal variations in a dose-dependent manner highlighted different behaviors at varying ESIONs concentrations, implying that optimal dosages might be specific to the intended imaging application. Preclinical safety evaluations indicated that ESIONs were tolerable in rats at doses up to 25 mg/kg. CONCLUSIONS: This study effectively optimized a scale-up process for the micelle encapsulation of ESIONs, leading to the production of hydrophilic ESIONs at gram-scale levels. These optimized ESIONs showcased properties conducive to T1 contrast imaging, such as elevated r1 relaxivity and a reduced r2/r1 ratio. Biodistribution study underscored their prolonged bloodstream presence and efficient clearance through the liver and bile, without significant renal involvement. The preclinical toxicity tests affirmed the safety of the ESIONs, supporting their potential use as T1 contrast agent with versatile clinical application.

CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data.

Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more stable and higher accuracy with short execution time compared to other computational methods to predict the spatial location of excitatory neurons. CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.

Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.

Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene expression patterns by deep learning of tissue images (SPADE), to identify important genes associated with morphological contexts by combining spatial transcriptomic data with coregistered images. SPADE incorporates deep learning-derived image patterns with spatially resolved gene expression data to extract morphological context markers. Morphological features that correspond to spatial maps of the transcriptome were extracted by image patches surrounding each spot and were subsequently represented by image latent features. The molecular profiles correlated with the image latent features were identified. The extracted genes could be further analyzed to discover functional terms and exploited to extract clusters maintaining morphological contexts. We apply our approach to spatial transcriptomic data from different tissues, platforms and types of images to demonstrate an unbiased method that is capable of obtaining image-integrated gene expression trends.

Human neural stem cell-derived extracellular vesicles protect against Parkinson's disease pathologies.

BACKGROUND: Neural stem cells (NSCs) have the ability to generate a variety of functional neural cell types and have a high potential for neuronal cell regeneration and recovery. Thus, they been recognized as the best source of cell therapy for neurodegenerative diseases, such as Parkinson's disease (PD). Owing to the possibility of paracrine effect-based therapeutic mechanisms and easier clinical accessibility, extracellular vesicles (EVs), which possess very similar bio-functional components from their cellular origin, have emerged as potential alternatives in regenerative medicine. MATERIAL AND METHODS: EVs were isolated from human fibroblast (HFF) and human NSC (F3 cells). The supernatant of the cells was concentrated by a tangential flow filtration (TFF) system. Then, the final EVs were isolated using a total EV isolation kit. RESULTS: In this study, we demonstrate the potential protective effect of human NSC-derived EVs, showing the prevention of PD pathologies in 6-hydroxydopamine (6-OHDA)-induced in vitro and in vivo mouse models. Human NSC and F3 cell (F3)-derived EVs reduced the intracellular reactive oxygen species (ROS) and associated apoptotic pathways. In addition, F3-derived EVs induced downregulation of pro-inflammatory factors and significantly decreased 6-OHDA-induced dopaminergic neuronal loss in vivo. F3 specific microRNAs (miRNAs) such as hsa-mir-182-5p, hsa-mir-183-5p, hsa-mir-9, and hsa-let-7, which are involved in cell differentiation, neurotrophic function, and immune modulation, were found in F3-derived EVs. CONCLUSIONS: We report that human NSC-derived EVs show an effective neuroprotective property in an in vitro transwell system and in a PD model. The EVs clearly decreased ROS and pro-inflammatory cytokines. Taken together, these results indicate that NSC-derived EVs could potentially help prevent the neuropathology and progression of PD.

High-quantum yield alloy-typed core/shell CdSeZnS/ZnS quantum dots for bio-applications.

BACKGROUND: Quantum dots (QDs) have been used as fluorophores in various imaging fields owing to their strong fluorescent intensity, high quantum yield (QY), and narrow emission bandwidth. However, the application of QDs to bio-imaging is limited because the QY of QDs decreases substantially during the surface modification step for bio-application. RESULTS: In this study, we fabricated alloy-typed core/shell CdSeZnS/ZnS quantum dots (alloy QDs) that showed higher quantum yield and stability during the surface modification for hydrophilization compared with conventional CdSe/CdS/ZnS multilayer quantum dots (MQDs). The structure of the alloy QDs was confirmed using time-of-flight medium-energy ion scattering spectroscopy. The alloy QDs exhibited strong fluorescence and a high QY of 98.0%. After hydrophilic surface modification, the alloy QDs exhibited a QY of 84.7%, which is 1.5 times higher than that of MQDs. The QY was 77.8% after the alloy QDs were conjugated with folic acid (FA). Alloy QDs and MQDs, after conjugation with FA, were successfully used for targeting human KB cells. The alloy QDs exhibited a stronger fluorescence signal than MQD; these signals were retained in the popliteal lymph node area for 24 h. CONCLUSION: The alloy QDs maintained a higher QY in hydrophilization for biological applications than MQDs. And also, alloy QDs showed the potential as nanoprobes for highly sensitive bioimaging analysis.

Hippocampal glucose uptake as a surrogate of metabolic change of microglia in Alzheimer's disease.

BACKGROUND: Dynamically altered microglia play an important role in the progression of Alzheimer's disease (AD). Here, we found a close association of the metabolic reconfiguration of microglia with increased hippocampal glucose uptake on [18F]fluorodeoxyglucose (FDG) PET. METHODS: We used an AD animal model, 5xFAD, to analyze hippocampal glucose metabolism using both animal FDG PET and ex vivo FDG uptake test. Cells of the hippocampus were isolated to perform single-cell RNA-sequencing (scRNA-seq). The molecular features of cells associated with glucose metabolism were analyzed at a single-cell level. In order to apply our findings to human brain imaging study, brain FDG PET data obtained from the Alzheimer's Disease Neuroimaging Initiative were analyzed. FDG uptake in the hippocampus was compared according to the diagnosis, AD, mild cognitive impairment, and controls. The correlation analysis between hippocampal FDG uptake and soluble TREM2 in cerebrospinal fluid was performed. RESULTS: In the animal study, 8- and 12-month-old 5xFAD mice showed higher FDG uptake in the hippocampus than wild-type mice. Cellular FDG uptake tests showed that FDG activity in hippocampal microglia was increased in the AD model, while FDG activity in non-microglial cells of the hippocampus was not different between the AD model and wild-type. scRNA-seq data showed that changes in glucose metabolism signatures including glucose transporters, glycolysis and oxidative phosphorylation, mainly occurred in microglia. A subset of microglia with higher glucose transporters with defective glycolysis and oxidative phosphorylation was increased according to disease progression. In the human imaging study, we found a positive association between soluble TREM2 and hippocampal FDG uptake. FDG uptake in the hippocampus at the baseline scan predicted mild cognitive impairment conversion to AD. CONCLUSIONS: We identified the reconfiguration of microglial glucose metabolism in the hippocampus of AD, which could be evaluated by FDG PET as a feasible surrogate imaging biomarker for microglia-mediated inflammation.

Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning-based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading. METHODS: A total of 121 clinical routines [18F]Florbetaben PET images were collected for the randomized blind-reader study. The amyloid PET images were visually interpreted by three experts independently blind to other information. The readers qualitatively interpreted images without quantification at the first reading session. After more than 2-week interval, the readers additionally interpreted images with the quantification results provided by the deep learning system. The qualitative assessment was based on a 3-point BAPL score (1: no amyloid load, 2: minor amyloid load, and 3: significant amyloid load). The confidence score for each session was evaluated by a 3-point score (0: ambiguous, 1: probably, and 2: definite to decide). RESULTS: Inter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27 ± 0.078 for visual reading-only session vs. 1.66 ± 0.63 for a visual reading session with the deep learning system). CONCLUSION: Our results highlight the impact of deep learning-based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading.

Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports.

PURPOSE: Basal/acetazolamide brain perfusion single-photon emission computed tomography (SPECT) has been used to evaluate functional hemodynamics in patients with carotid artery stenosis. We aimed to develop a deep learning model as a support system for interpreting brain perfusion SPECT leveraging unstructured text reports. METHODS: In total, 7345 basal/acetazolamide brain perfusion SPECT images and their text reports were retrospectively collected. A long short-term memory (LSTM) network was trained using 500 randomly selected text reports to predict manually labeled structured information, including abnormalities of basal perfusion and vascular reserve for each vascular territory. Using this trained LSTM model, we extracted structured information from the remaining 6845 text reports to develop a deep learning model for interpreting SPECT images. The model was based on a 3D convolutional neural network (CNN), and the performance was tested on the other 500 cases by measuring the area under the receiver-operating characteristic curve (AUC). We then applied the model to patients who underwent revascularization (n = 33) to compare the estimated output of the CNN model for pre- and post-revascularization SPECT and clinical outcomes. RESULTS: The AUC of the LSTM model for extracting structured labels was 1.00 for basal perfusion and 0.99 for vascular reserve for all 9 brain regions. The AUC of the CNN model designed to identify abnormal perfusion was 0.83 for basal perfusion and 0.89 for vascular reserve. The output of the CNN model was significantly improved according to the revascularization in the target vascular territory, and its changes in brain territories were concordant with clinical outcomes. CONCLUSION: We developed a deep learning model to support the interpretation of brain perfusion SPECT by converting unstructured text reports into structured labels. This model can be used as a support system not only to identify perfusion abnormalities but also to provide quantitative scores of abnormalities, particularly for patients who require revascularization.