AmpliSeq Transcriptome of Laser Captured Neurons from Alzheimer Brain: Comparison of Single Cell Versus Neuron Pools

Bibliometric and visual analysis of single-cell multiomics in neurodegenerative disease arrest studies

Background

Neurodegenerative diseases are progressive disorders that severely diminish the quality of life of patients. However, research on neurodegenerative diseases needs to be refined and deepened. Single-cell polyomics is a technique for obtaining transcriptomic, proteomic, and other information from a single cell. In recent years, the heat of single-cell multiomics as an emerging research tool for brain science has gradually increased. Therefore, the aim of this study was to analyze the current status and trends of studies related to the application of single-cell multiomics in neurodegenerative diseases through bibliometrics.

Result

A total of 596 publications were included in the bibliometric analysis. Between 2015 and 2022, the number of publications increased annually, with the total number of citations increasing significantly, exhibiting the fastest rate of growth between 2019 and 2022. The country/region collaboration map shows that the United States has the most publications and cumulative citations, and that China and the United States have the most collaborations. The institutions that produced the greatest number of articles were Harvard Medical School, Skupin, Alexander, and Wiendl. Among the authors, Heinz had the highest output. Mathys, H accumulated the most citations and was the authoritative author in the field. The journal Nature Communications has published the most literature in this field. A keyword analysis reveals that neurodegenerative diseases and lesions (e.g., Alzheimer's disease, amyloid beta) are the core and foundation of the field. Conversely, single-cell multiomics related research (e.g., single-cell RNA sequencing, bioinformatics) and brain nerve cells (e.g., microglia, astrocytes, neural stem cells) are the hot frontiers of this specialty. Among the references, the article "Single-cell transcriptomic analysis of Alzheimer's disease" is the most frequently cited (1,146 citations), and the article "Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq" was the most cited article in the field.

Conclusion

The objective of this study is to employ bibliometric methods to visualize studies related to single-cell multiomics in neurodegenerative diseases. This will enable us to summarize the current state of research and to reveal key trends and emerging hotspots in the field.

Reduced PIN1 expression in neocortical and limbic brain regions in female Alzheimer's patients correlates with cognitive and neuropathological phenotypes

Women have a higher incidence of Alzheimer's disease (AD), even after adjusting for increased longevity. Thus, there is an urgent need to identify genes that underpin sex-associated risk of AD. PIN1 is a key regulator of the tau phosphorylation signaling pathway; however, potential differences in PIN1 expression, in males and females, are still unknown. We analyzed brain transcriptomic datasets focusing on sex differences in PIN1 mRNA levels in an aging and AD cohort, which revealed reduced PIN1 levels primarily within females. We validated this observation in an independent dataset (ROS/MAP), which also revealed that PIN1 is negatively correlated with multiregional neurofibrillary tangle density and global cognitive function in females only. Additional analysis revealed a decrease in PIN1 in subjects with mild cognitive impairment (MCI) compared with aged individuals, again driven predominantly by female subjects. Histochemical analysis of PIN1 in AD and control male and female neocortex revealed an overall decrease in axonal PIN1 protein levels in females. These findings emphasize the importance of considering sex differences in AD research.

High-quality single-cell transcriptomics from ovarian histological sections during folliculogenesis

High-quality, straightforward single-cell RNA sequencing (RNA-seq) with spatial resolution remains challenging. Here, we developed DRaqL (direct RNA recovery and quenching for laser capture microdissection), an experimental approach for efficient cell lysis of tissue sections, directly applicable to cDNA amplification. Single-cell RNA-seq combined with DRaqL allowed transcriptomic profiling from alcohol-fixed sections with efficiency comparable with that of profiling from freshly dissociated cells, together with effective exon-exon junction profiling. The combination of DRaqL with protease treatment enabled robust and efficient single-cell transcriptome analysis from formalin-fixed tissue sections. Applying this method to mouse ovarian sections, we were able to predict the transcriptome of oocytes by their size and identified an anomaly in the size-transcriptome relationship relevant to growth retardation of oocytes, in addition to detecting oocyte-specific splice isoforms. Furthermore, we identified differentially expressed genes in granulosa cells in association with their proximity to the oocytes, suggesting distinct epigenetic regulations and cell-cycle activities governing the germ-soma relationship. Thus, DRaqL is a versatile, efficient approach for high-quality single-cell RNA-seq from tissue sections, thereby revealing histological heterogeneity in folliculogenic transcriptome.

Elucidating distinct molecular signatures of Lewy body dementias

Dementia with Lewy bodies and Parkinson's disease dementia are common neurodegenerative diseases that share similar neuropathological profiles and spectra of clinical symptoms but are primarily differentiated by the order in which symptoms manifest. The question of whether a distinct molecular pathological profile could distinguish these disorders is yet to be answered. However, in recent years, studies have begun to investigate genomic, epigenomic, transcriptomic and proteomic differences that may differentiate these disorders, providing novel insights in to disease etiology. In this review, we present an overview of the clinical and pathological hallmarks of Lewy body dementias before summarizing relevant research into genetic, epigenetic, transcriptional and protein signatures in these diseases, with a particular interest in those resolving "omic" level changes. We conclude by suggesting future research directions to address current gaps and questions present within the field.

Non-invasive evaluation of subjective sensitive skin by transcriptomics using mRNA in skin surface lipids

Sensitive skin is a condition characterized by hypersensitivity to environmental stimuli, and its pathophysiology has not been fully elucidated. Questionnaires based on subjective symptoms, intervention tests, and measuring devices are used to diagnose sensitive skin; however, objective evaluation methods, including biomarkers, remain to be established. This study aimed to investigate the molecular profiles of self-reported sensitive skin, understand its pathophysiology and explore its biomarkers. Here, we analysed RNAs in skin surface lipids (SSL-RNAs), which can be obtained non-invasively by wiping the skin surface with an oil-blotting film, to compare the transcriptome profiles between questionnaire-based "sensitive" (n = 11) and "non-sensitive" (n = 10) skin participants. Exactly 417 differentially expressed genes in SSL-RNAs from individuals with sensitive skin were identified, of which C-C motif chemokine ligand 17 and interferon-γ pathways were elevated, while 50 olfactory receptor (OR) genes were downregulated. The expression of the detectable 101 OR genes was lower in individuals with sensitive skin compared to that in those with non-sensitive skin and was particularly associated with the subjective sensitivity among skin conditions. The receiver operating characteristic (ROC) curve demonstrated that the mean expression levels of OR genes in SSL-RNAs could discriminate subjective skin sensitivity with an area under the ROC curve of 0.836. SSL-RNA profiles suggest a mild inflammatory state in sensitive skin, and overall OR gene expression could be a potential indicator for sensitive skin.

Estimating and Correcting for Off-Target Cellular Contamination in Brain Cell Type Specific RNA-Seq Data

Transcriptionally profiling minor cellular populations remains an ongoing challenge in molecular genomics. Single-cell RNA sequencing has provided valuable insights into a number of hypotheses, but practical and analytical challenges have limited its widespread adoption. A similar approach, which we term single-cell type RNA sequencing (sctRNA-seq), involves the enrichment and sequencing of a pool of cells, yielding cell type-level resolution transcriptomes. While this approach offers benefits in terms of mRNA sampling from targeted cell types, it is potentially affected by off-target contamination from surrounding cell types. Here, we leveraged single-cell sequencing datasets to apply a computational approach for estimating and controlling the amount of off-target cell type contamination in sctRNA-seq datasets. In datasets obtained using a number of technologies for cell purification, we found that most sctRNA-seq datasets tended to show some amount of off-target mRNA contamination from surrounding cells. However, using covariates for cellular contamination in downstream differential expression analyses increased the quality of our models for differential expression analysis in case/control comparisons and typically resulted in the discovery of more differentially expressed genes. In general, our method provides a flexible approach for detecting and controlling off-target cell type contamination in sctRNA-seq datasets.