Fluid preservation in brain banking: a review.

Fluid preservation is nearly universally used in brain banking to store fixed tissue specimens for future research applications. However, the effects of long-term immersion on neural circuitry and biomolecules are not well characterized. As a result, there is a need to synthesize studies investigating fluid preservation of brain tissue. We searched PubMed and other databases to identify studies measuring the effects of fluid preservation in nervous system tissue. We categorized studies based on the fluid preservative used: formaldehyde solutions, buffer solutions, alcohol solutions, storage after tissue clearing, and cryoprotectant solutions. We identified 91 studies containing 197 independent observations of the effects of long-term storage on cellular morphology. Most studies did not report any significant alterations due to long-term storage. When present, the most frequent alteration was decreased antigenicity, commonly attributed to progressive crosslinking by aldehydes that renders biomolecules increasingly inaccessible over time. To build a mechanistic understanding, we discuss biochemical aspects of long-term fluid preservation. A subset of lipids appears to be chemical altered or extracted over time due to incomplete retention in the crosslinked gel. Alternative storage fluids mitigate the problem of antigen masking but have not been extensively characterized and may have other downsides. We also compare fluid preservation to cryopreservation, paraffin embedding, and resin embedding. Overall, existing evidence suggests that fluid preservation provides maintenance of neural architecture for decades, including precise structural details. However, to avoid the well-established problem of overfixation caused by storage in high concentration formaldehyde solutions, fluid preservation procedures can use an initial fixation step followed by an alternative long-term storage fluid. Further research is warranted on optimizing protocols and characterizing the generalizability of the storage artifacts that have been identified.

Spatial proteomics of hippocampal subfield-specific pathology in Alzheimer's disease and primary age-related tauopathy.

INTRODUCTION: Alzheimer's disease (AD) and primary age-related tauopathy (PART) both harbor 3R/4R hyperphosphorylated-tau (p-tau)-positive neurofibrillary tangles (NFTs) but differ in the spatial p-tau development in the hippocampus. METHODS: Using Nanostring GeoMx Digital Spatial Profiling, we compared protein expression within hippocampal subregions in NFT-bearing and non-NFT-bearing neurons in AD (n = 7) and PART (n = 7) subjects. RESULTS: Proteomic measures of synaptic health were inversely correlated with the subregional p-tau burden in AD and PART, and there were numerous differences in proteins involved in proteostasis, amyloid beta (Aß) processing, inflammation, microglia, oxidative stress, and neuronal/synaptic health between AD and PART and between definite PART and possible PART. DISCUSSION: These results suggest subfield-specific proteome differences that may explain some of the differences in Aß and p-tau distribution and apparent pathogenicity. In addition, hippocampal neurons in possible PART may have more in common with AD than with definite PART, highlighting the importance of Aß in the pathologic process. HIGHLIGHTS: Synaptic health is inversely correlated with local p-tau burden. The proteome of NFT- and non-NFT-bearing neurons is influenced by the presence of Aß in the hippocampus. Neurons in possible PART cases share more proteomic similarities with neurons in ADNC than they do with neurons in definite PART cases.

Histopathologic brain age estimation via multiple instance learning.

Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.

Postmortem changes in brain cell structure: a review.

Brain cell structure is a key determinant of neural function that is frequently altered in neurobiological disorders. Following the global loss of blood flow to the brain that initiates the postmortem interval (PMI), cells rapidly become depleted of energy and begin to decompose. To ensure that our methods for studying the brain using autopsy tissue are robust and reproducible, there is a critical need to delineate the expected changes in brain cell morphometry during the PMI. We searched multiple databases to identify studies measuring the effects of PMI on the morphometry (i.e. external dimensions) of brain cells. We screened 2119 abstracts, 361 full texts, and included 172 studies. Mechanistically, fluid shifts causing cell volume alterations and vacuolization are an early event in the PMI, while the loss of the ability to visualize cell membranes altogether is a later event. Decomposition rates are highly heterogenous and depend on the methods for visualization, the structural feature of interest, and modifying variables such as the storage temperature or the species. Geometrically, deformations of cell membranes are common early events that initiate within minutes. On the other hand, topological relationships between cellular features appear to remain intact for more extended periods. Taken together, there is an uncertain period of time, usually ranging from several hours to several days, over which cell membrane structure is progressively lost. This review may be helpful for investigators studying human postmortem brain tissue, wherein the PMI is an unavoidable aspect of the research.

Artificial intelligence-derived neurofibrillary tangle burden is associated with antemortem cognitive impairment.

Tauopathies are a category of neurodegenerative diseases characterized by the presence of abnormal tau protein-containing neurofibrillary tangles (NFTs). NFTs are universally observed in aging, occurring with or without the concomitant accumulation of amyloid-beta peptide (Aß) in plaques that typifies Alzheimer disease (AD), the most common tauopathy. Primary age-related tauopathy (PART) is an Aß-independent process that affects the medial temporal lobe in both cognitively normal and impaired subjects. Determinants of symptomology in subjects with PART are poorly understood and require clinicopathologic correlation; however, classical approaches to staging tau pathology have limited quantitative reproducibility. As such, there is a critical need for unbiased methods to quantitatively analyze tau pathology on the histological level. Artificial intelligence (AI)-based convolutional neural networks (CNNs) generate highly accurate and precise computer vision assessments of digitized pathology slides, yielding novel histology metrics at scale. Here, we performed a retrospective autopsy study of a large cohort (n = 706) of human post-mortem brain tissues from normal and cognitively impaired elderly individuals with mild or no Aß plaques (average age of death of 83.1 yr, range 55-110). We utilized a CNN trained to segment NFTs on hippocampus sections immunohistochemically stained with antisera recognizing abnormal hyperphosphorylated tau (p-tau), which yielded metrics of regional NFT counts, NFT positive pixel density, as well as a novel graph-theory based metric measuring the spatial distribution of NFTs. We found that several AI-derived NFT metrics significantly predicted the presence of cognitive impairment in both the hippocampus proper and entorhinal cortex (p < 0.0001). When controlling for age, AI-derived NFT counts still significantly predicted the presence of cognitive impairment (p = 0.04 in the entorhinal cortex; p = 0.04 overall). In contrast, Braak stage did not predict cognitive impairment in either age-adjusted or unadjusted models. These findings support the hypothesis that NFT burden correlates with cognitive impairment in PART. Furthermore, our analysis strongly suggests that AI-derived metrics of tau pathology provide a powerful tool that can deepen our understanding of the role of neurofibrillary degeneration in cognitive impairment.

Interpretable deep learning of myelin histopathology in age-related cognitive impairment.

Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (Aß) plays a major role in Alzheimer's type age-related cognitive impairment, in addition to other etiopathologies such as Aß-independent hyperphosphorylated tau, cerebrovascular disease, and myelin damage, which also warrant further investigation. Classical methods, even in the setting of the gold standard of postmortem brain assessment, involve semi-quantitative ordinal staging systems that often correlate poorly with clinical outcomes, due to imperfect cognitive measurements and preconceived notions regarding the neuropathologic features that should be chosen for study. Improved approaches are needed to identify histopathological changes correlated with cognition in an unbiased way. We used a weakly supervised multiple instance learning algorithm on whole slide images of human brain autopsy tissue sections from a group of elderly donors to predict the presence or absence of cognitive impairment (n = 367 with cognitive impairment, n = 349 without). Attention analysis allowed us to pinpoint the underlying subregional architecture and cellular features that the models used for the prediction in both brain regions studied, the medial temporal lobe and frontal cortex. Despite noisy labels of cognition, our trained models were able to predict the presence of cognitive impairment with a modest accuracy that was significantly greater than chance. Attention-based interpretation studies of the features most associated with cognitive impairment in the top performing models suggest that they identified myelin pallor in the white matter. Our results demonstrate a scalable platform with interpretable deep learning to identify unexpected aspects of pathology in cognitive impairment that can be translated to the study of other neurobiological disorders.

Ex situ perfusion fixation for brain banking: a technical report.

Perfusion fixation is a well-established technique in animal research to improve preservation quality in the study of many tissues, including the brain. There is a growing interest in using perfusion to fix postmortem human brain tissue to achieve the highest fidelity preservation for downstream high-resolution morphomolecular brain mapping studies. Numerous practical barriers arise when applying perfusion fixation in brain banking settings, including the large mass of the organ, degradation of vascular integrity and patency prior to the start of the procedure, and differing investigator goals sometimes necessitating part of the brain to be frozen. As a result, there is a critical need to establish a perfusion fixation procedure in brain banking that is flexible and scalable. This technical report describes our approach to developing an ex situ perfusion fixation protocol. We discuss the challenges encountered and lessons learned while implementing this procedure. Routine morphological staining and RNA in situ hybridization data show that the perfused brains have well-preserved tissue cytoarchitecture and intact biomolecular signal. However, it remains uncertain whether this procedure leads to improved histology quality compared to immersion fixation. Additionally, ex vivo magnetic resonance imaging (MRI) data suggest that the perfusion fixation protocol may introduce imaging artifacts in the form of air bubbles in the vasculature. We conclude with further research directions to investigate the use of perfusion fixation as a rigorous and reproducible alternative to immersion fixation for the preparation of postmortem human brains.

Perfusion fixation in brain banking: a systematic review.

BACKGROUND: Perfusing fixatives through the cerebrovascular system is the gold standard approach in animals to prepare brain tissue for spatial biomolecular profiling, circuit tracing, and ultrastructural studies such as connectomics. Translating these discoveries to humans requires examination of postmortem autopsy brain tissue. Yet banked brain tissue is routinely prepared using immersion fixation, which is a significant barrier to optimal preservation of tissue architecture. The challenges involved in adopting perfusion fixation in brain banks and the extent to which it improves histology quality are not well defined. METHODOLOGY: We searched four databases to identify studies that have performed perfusion fixation in human brain tissue and screened the references of the eligible studies to identify further studies. From the included studies, we extracted data about the methods that they used, as well as any data comparing perfusion fixation to immersion fixation. The protocol was preregistered at the Open Science Framework: https://osf.io/cv3ys/ . RESULTS: We screened 4489 abstracts, 214 full-text publications, and identified 35 studies that met our inclusion criteria, which collectively reported on the perfusion fixation of 558 human brains. We identified a wide variety of approaches to perfusion fixation, including perfusion fixation of the brain in situ and ex situ, perfusion fixation through different sets of blood vessels, and perfusion fixation with different washout solutions, fixatives, perfusion pressures, and postfixation tissue processing methods. Through a qualitative synthesis of data comparing the outcomes of perfusion and immersion fixation, we found moderate confidence evidence showing that perfusion fixation results in equal or greater subjective histology quality compared to immersion fixation of relatively large volumes of brain tissue, in an equal or shorter amount of time. CONCLUSIONS: This manuscript serves as a resource for investigators interested in building upon the methods and results of previous research in designing their own perfusion fixation studies in human brains or other large animal brains. We also suggest several future research directions, such as comparing the in situ and ex situ approaches to perfusion fixation, studying the efficacy of different washout solutions, and elucidating the types of brain donors in which perfusion fixation is likely to result in higher fixation quality than immersion fixation.

Brain Cell Type Specific Gene Expression and Co-expression Network Architectures.

Elucidating brain cell type specific gene expression patterns is critical towards a better understanding of how cell-cell communications may influence brain functions and dysfunctions. We set out to compare and contrast five human and murine cell type-specific transcriptome-wide RNA expression data sets that were generated within the past several years. We defined three measures of brain cell type-relative expression including specificity, enrichment, and absolute expression and identified corresponding consensus brain cell "signatures," which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identified robust cell-specific gene modules. To facilitate the use of the cell type-specific genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identified a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.

Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease.

BACKGROUND: Oligodendrocytes (OLs) and myelin are critical for normal brain function and have been implicated in neurodegeneration. Several lines of evidence including neuroimaging and neuropathological data suggest that Alzheimer's disease (AD) may be associated with dysmyelination and a breakdown of OL-axon communication. METHODS: In order to understand this phenomenon on a molecular level, we systematically interrogated OL-enriched gene networks constructed from large-scale genomic, transcriptomic and proteomic data obtained from human AD postmortem brain samples. We then validated these networks using gene expression datasets generated from mice with ablation of major gene expression nodes identified in our AD-dysregulated networks. RESULTS: The robust OL gene coexpression networks that we identified were highly enriched for genes associated with AD risk variants, such as BIN1 and demonstrated strong dysregulation in AD. We further corroborated the structure of the corresponding gene causal networks using datasets generated from the brain of mice with ablation of key network drivers, such as UGT8, CNP and PLP1, which were identified from human AD brain data. Further, we found that mice with genetic ablations of Cnp mimicked aspects of myelin and mitochondrial gene expression dysregulation seen in brain samples from patients with AD, including decreased protein expression of BIN1 and GOT2. CONCLUSIONS: This study provides a molecular blueprint of the dysregulation of gene expression networks of OL in AD and identifies key OL- and myelination-related genes and networks that are highly associated with AD.

DGCA: A comprehensive R package for Differential Gene Correlation Analysis.

BACKGROUND: Dissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition. RESULTS: In this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC). CONCLUSIONS: DGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.

Transcriptome analysis identifies Bacillus anthracis genes that respond to CO2 through an AtxA-dependent mechanism.

BACKGROUND: Upon infection of a mammalian host, Bacillus anthracis responds to host cues, and particularly to elevated temperature (37°C) and bicarbonate/CO2 concentrations, with increased expression of virulence factors that include the anthrax toxins and extracellular capsular layer. This response requires the presence of the pXO1 virulence plasmid-encoded pleiotropic regulator AtxA. To better understand the genetic basis of this response, we utilized a controlled in vitro system and Next Generation sequencing to determine and compare RNA expression profiles of the parental strain and an isogenic AtxA-deficient strain in a 2 × 2 factorial design with growth environments containing or lacking carbon dioxide. RESULTS: We found 15 pXO1-encoded genes and 3 chromosomal genes that were strongly regulated by the separate or synergistic actions of AtxA and carbon dioxide. The majority of the regulated genes responded to both AtxA and carbon dioxide rather than to just one of these factors. Interestingly, we identified two previously unrecognized small RNAs that are highly expressed under physiological carbon dioxide concentrations in an AtxA-dependent manner. Expression levels of the two small RNAs were found to be higher than that of any other gene differentially expressed in response to these conditions. Secondary structure and small RNA-mRNA binding predictions for the two small RNAs suggest that they may perform important functions in regulating B. anthracis virulence. CONCLUSIONS: A majority of genes on the virulence plasmid pXO1 that are regulated by the presence of either CO2 or AtxA separately are also regulated synergistically in the presence of both. These results also elucidate novel pXO1-encoded small RNAs that are associated with virulence conditions.

Tumor therapy with a urokinase plasminogen activator-activated anthrax lethal toxin alone and in combination with paclitaxel.

PA-U2, an engineered anthrax protective antigen that is activated by urokinase was combined with wildtype lethal factor in the treatment of Colo205 colon adenocarcinoma in vitro and B16-BL6 mouse melanoma in vitro and in vivo. This therapy was also tested in combination with the small molecule paclitaxel, based on prior reports suggesting synergy between ERK1/2 inhibition and chemotherapeutics. Colo205 was sensitive to PA-U2/LF while B16-BL6 was not. For the combination treatment of B16-BL6, paclitaxel showed a dose response in vitro, but cells remained resistant to PA-U2/LF even in the presence of paclitaxel. In vivo, each therapy slowed tumor progression, and an additive effect between the two was observed. Since LF targets tumor vasculature while paclitaxel is an antimitotic, it is possible the agents were acting against different cells in the stroma, precluding a synergistic effect. The engineered anthrax toxin PA-U2/LF warrants further development and testing, possibly in combination with an antiangiogenesis therapy such as sunitinib or sorafinib.

A further application of the active time model to multiple concurrent variable-interval schedules.

In this experiment we show that the active time model (ATM) accurately predicts probe data from multiple concurrent VI VI schedules. Subjects were trained under a concurrent VI 30-s VI 60-s and a concurrent VI 60-s VI 120-s schedule. Two types of unreinforced probes were then conducted. The first paired the two VI 60-s stimuli. These stimuli, while equivalent in their associated absolute rates of reinforcement, differed in their relative rates of reinforcement. The second probe paired the VI 30-s stimulus with the relatively rich VI 60-s stimulus. In contrast with the first probe, these stimuli differed in their absolute rates of reinforcement, while being similar in their relative rates. During the first set of probes, birds preferred the VI 60-s stimulus trained with the VI 120-s schedule. During the second set of probes, birds were indifferent to the two stimuli. These results are less extreme than others reported in the literature. Nonetheless, we found that ATM accurately fit individual subject data in both sets of probes. In contrast a variant of scalar expectancy theory did not fit the data at either the individual or group level.