Characterization of gene expression profiling of mouse tissues obtained during the postmortem interval

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

Single-Cell RNA Sequencing in Parkinson's Disease

Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) technologies have enhanced the understanding of the molecular pathogenesis of neurodegenerative disorders, including Parkinson's disease (PD). Nonetheless, their application in PD has been limited due mainly to the technical challenges resulting from the scarcity of postmortem brain tissue and low quality associated with RNA degradation. Despite such challenges, recent advances in animals and human in vitro models that recapitulate features of PD along with sequencing assays have fueled studies aiming to obtain an unbiased and global view of cellular composition and phenotype of PD at the single-cell resolution. Here, we reviewed recent sc/snRNA-seq efforts that have successfully characterized diverse cell-type populations and identified cell type-specific disease associations in PD. We also examined how these studies have employed computational and analytical tools to analyze and interpret the rich information derived from sc/snRNA-seq. Finally, we highlighted important limitations and emerging technologies for addressing key technical challenges currently limiting the integration of new findings into clinical practice.

Selective time-dependent changes in activity and cell-specific gene expression in human postmortem brain

As a means to understand human neuropsychiatric disorders from human brain samples, we compared the transcription patterns and histological features of postmortem brain to fresh human neocortex isolated immediately following surgical removal. Compared to a number of neuropsychiatric disease-associated postmortem transcriptomes, the fresh human brain transcriptome had an entirely unique transcriptional pattern. To understand this difference, we measured genome-wide transcription as a function of time after fresh tissue removal to mimic the postmortem interval. Within a few hours, a selective reduction in the number of neuronal activity-dependent transcripts occurred with relative preservation of housekeeping genes commonly used as a reference for RNA normalization. Gene clustering indicated a rapid reduction in neuronal gene expression with a reciprocal time-dependent increase in astroglial and microglial gene expression that continued to increase for at least 24 h after tissue resection. Predicted transcriptional changes were confirmed histologically on the same tissue demonstrating that while neurons were degenerating, glial cells underwent an outgrowth of their processes. The rapid loss of neuronal genes and reciprocal expression of glial genes highlights highly dynamic transcriptional and cellular changes that occur during the postmortem interval. Understanding these time-dependent changes in gene expression in post mortem brain samples is critical for the interpretation of research studies on human brain disorders.

The effects of postmortem delay on mouse and human microglia gene expression

Microglia are specialized macrophages of the central nervous system (CNS) and first to react to pathogens or injury. Over the last decade, transcriptional profiling of microglia significantly contributed to our understanding of their functions. In the case of human CNS samples, either potential CNS pathology in the case of surgery samples, or a postmortem delay (PMD) due to the time needed for tissue access and collection, are potential factors that affect gene expression profiles. To determine the effect of PMD on the microglia transcriptome, we first analyzed mouse microglia, where genotype, antemortem conditions and PMD can be controlled. Microglia were isolated from mice after different PMDs (0, 4, 6, 12, and 24 hr) using fluorescence‐activated cell sorting (FACS). The number of viable microglia significantly decreased with increasing PMD, but even after a 12 hr PMD, high‐quality RNA could be obtained. PMD had very limited effect on mouse microglia gene expression, only 50 genes were differentially expressed between different PMDs. These genes were related to mitochondrial, ribosomal, and protein binding functions. In human microglia transcriptomes we previously generated, 31 of the 50 PMD‐associated mouse genes had human homologs, and their relative expression was also affected by PMD. This study provides a set of genes that shows relative expression changes in relation to PMD, both in mouse and human microglia. Although the gene expression changes detected are subtle, these genes need to be accounted for when analyzing microglia transcriptomes generated from samples with variable PMDs.

Quantitative analysis of noncoding RNA from paired fresh and formalin-fixed paraffin-embedded brain tissues

Formalin-fixed paraffin-embedded (FFPE) tissues are commonly used both clinically and in forensic pathology. Recently, noncoding RNA (ncRNA) has attracted interest among molecular medical researchers. However, it remains unclear whether newly identified ncRNAs, such as long noncoding RNA (lncRNA) and circular RNA (circRNA), remain stable for downstream molecular analysis in FFPE tissues. Here, we assessed the feasibility of using autoptic FFPE brain tissues from eight individuals to perform quantitative molecular analyses. Selected RNA targets (9 mRNAs and 15 ncRNAs) with different amplicon lengths were studied by RT-qPCR in paired fresh and FFPE specimens. For RNA quality assessment, RNA purity and yield were comparable between the two sample cohorts; however, the RNA integrity number decreased significantly during FFPE sampling. Amplification efficiency also displayed certain variability related with amplicon length and RNA species. We found molecular evidence that short amplicons of mRNA, lncRNA, and circRNA were amplified more efficiently than long amplicons. With the assistance of RefFinder, 5S, SNORD48, miR-103a, and miR-125b were selected as reference genes given their high stability. After normalization, we found that short amplicon markers (e.g., ACTB mRNA and MALAT1 lncRNA) exhibited high consistency of quantification in paired fresh/FFPE samples. In particular, circRNAs (XPO1, HIPK3, and TMEM56) presented relatively consistent and stable expression profiles in FFPE tissues compared with their corresponding linear transcripts. Additionally, we evaluated the influence of prolonged storage time on the amplification of gene transcripts and found that short amplicons still work effectively in archived FFPE biospecimens. In conclusion, our findings demonstrate the possibility of performing accurate quantitative analysis of ncRNAs using short amplicons and standardized RT-qPCR assays in autopsy-derived FFPE samples.

An integrated transcriptomic analysis of autism spectrum disorder

Autism spectrum disorder (ASD) is not a single disease but a set of disorders. To find clues of ASD pathogenesis in transcriptomic data, we performed an integrated transcriptomic analysis of ASD. After screening based on several standards in Gene Expression Omnibus (GEO) database, we obtained 11 series of transcriptomic data of different human tissues of ASD patients and healthy controls. Multidimensional scaling analysis revealed that datasets from the same tissue had bigger similarity than from different tissues. Functional enrichment analysis demonstrated that differential expressed genes were significantly enriched in inflammation/immune response, mitochondrion-related function and oxidative phosphorylation. Interestingly, genes enriched in inflammation/immune response were up-regulated in the brain tissues and down-regulated in the blood. In addition, drug prediction provided several compounds which might reverse gene expression profiles of ASD patients. And we also replicated the methods and criteria of transcriptomic analysis with datasets of ASD animal models and healthy controls, the results from animal models consolidated the results of transcriptomic analysis of ASD human tissues. In general, the results of our study may provide researchers a new sight of understanding the etiology of ASD and clinicians the possibilities of developing medical therapies.

Using Postmortem hippocampi tissue can interfere with differential gene expression analysis of the epileptogenic process

Neuropathological studies often use autopsy brain tissue as controls to evaluate changes in protein or RNA levels in several diseases. In mesial temporal lobe epilepsy (MTLE), several genes are up or down regulated throughout the epileptogenic and chronic stages of the disease. Given that postmortem changes in several gene transcripts could impact the detection of changes in case-control studies, we evaluated the effect of using autopsy specimens with different postmortem intervals (PMI) on differential gene expression of the Pilocarpine (PILO)induced Status Epilepticus (SE) of MTLE. For this, we selected six genes (Gfap, Ppia, Gad65, Gad67, Npy, and Tnf-α) whose expression patterns in the hippocampus of PILO-injected rats are well known. Initially, we compared hippocampal expression of naïve rats whose hippocampi were harvested immediately after death (0h-PMI) with those harvested at 6h postmortem interval (6h-PMI): Npy and Ppia transcripts increased and Tnf-α transcripts decreased in the 6h-PMI group (p<0.05). We then investigated if these PMI-related changes in gene expression have the potential to adulterate or mask RT-qPCR results obtained with PILO-injected rats euthanized at acute or chronic phases. In the acute group, Npy transcript was significantly higher when compared with 0h-PMI rats, whereas Ppia transcript was lower than 6h-PMI group. When we used epileptic rats (chronic group), the RT-qPCR results showed higher Tnf-α only when compared to 6h-PMI group. In conclusion, our study demonstrates that PMI influences gene transcription and can mask changes in gene transcription seen during epileptogenesis in the PILO-SE model. Thus, to avoid erroneous conclusions, we strongly recommend that researchers account for changes in postmortem gene expression in their experimental design.

Systematic analysis of gene expression patterns associated with postmortem interval in human tissues

Postmortem mRNA degradation is considered to be the major concern in gene expression research utilizing human postmortem tissues. A key factor in this process is the postmortem interval (PMI), which is defined as the interval between death and sample collection. However, global patterns of postmortem mRNA degradation at individual gene levels across diverse human tissues remain largely unknown. In this study, we performed a systematic analysis of alteration of gene expression associated with PMI in human tissues. From the Genotype-Tissue Expression (GTEx) database, we evaluated gene expression levels of 2,016 high-quality postmortem samples from 316 donors of European descent, with PMI ranging from 1 to 27 hours. We found that PMI-related mRNA degradation is tissue-specific, gene-specific, and even genotype-dependent, thus drawing a more comprehensive picture of PMI-associated gene expression across diverse human tissues. Additionally, we also identified 266 differentially variable (DV) genes, such as DEFB4B and IFNG, whose expression is significantly dispersed between short PMI (S-PMI) and long PMI (L-PMI) groups. In summary, our analyses provide a comprehensive profile of PMI-associated gene expression, which will help interpret gene expression patterns in the evaluation of postmortem tissues.

Cell Death-Associated Ribosomal RNA Cleavage in Postmortem Tissues and Its Forensic Applications

Estimation of postmortem interval (PMI) is a key issue in the field of forensic pathology. With the availability of quantitative analysis of RNA levels in postmortem tissues, several studies have assessed the postmortem degradation of constitutively expressed RNA species to estimate PMI. However, conventional RNA quantification as well as biochemical and physiological changes employed thus far have limitations related to standardization or normalization. The present study focuses on an interesting feature of the subdomains of certain RNA species, in which they are site-specifically cleaved during apoptotic cell death. We found that the D8 divergent domain of ribosomal RNA (rRNA) bearing cell death-related cleavage sites was rapidly removed during postmortem RNA degradation. In contrast to the fragile domain, the 5' terminal region of 28S rRNA was remarkably stable during the postmortem period. Importantly, the differences in the degradation rates between the two domains in mammalian 28S rRNA were highly proportional to increasing PMI with a significant linear correlation observed in mice as well as human autopsy tissues. In conclusion, we demonstrate that comparison of the degradation rates between domains of a single RNA species provides quantitative information on postmortem degradation states, which can be applied for the estimation of PMI.