Pluripotent stem cell-derived models of neurological diseases reveal early transcriptional heterogeneity

Bibliometric analysis of research on neurodegenerative diseases and single-cell RNA sequencing: Opportunities and challenges

Neurodegeneration, characterized by the progressive deterioration in neuronal structure or function, presents an elusive mechanism. The use of single-cell RNA sequencing (scRNA-seq) technology in the clinic is becoming increasingly prevalent in recent decades. This technology offers unparalleled cell-level insights into neurodegenerative diseases, establishing itself as a potent tool for elucidating these diseases underlying mechanisms. Here, we made a deep investigation for scRNA-seq research in neurodegenerative diseases using bibliometric analysis from 2009 to 2022. We observed a robust upward trajectory in the number of publications on this subject. The United States stood out as the principal contributor to this expanding field. Specifically, the University of California System exhibited notable research prowess in this field. Alzheimer disease and Parkinson disease were the diseases most frequently investigated. Key research hotspots include the creation of a molecular brain atlas and identification of vulnerable neuronal subpopulations and potential therapeutic targets at the transcriptomic level.

Integrated analysis of plasma proteome and cortex single-cell transcriptome reveals the novel biomarkers during cortical aging

Background

With the increase of age, multiple physiological functions of people begin gradually degenerating. Regardless of natural aging or pathological aging, the decline in cognitive function is one of the most obvious features in the process of brain aging. Brain aging is a key factor for several neuropsychiatric disorders and for most neurodegenerative diseases characterized by onset typically occurring late in life and with worsening of symptoms over time. Therefore, the early prevention and intervention of aging progression are particularly important. Since there is no unified conclusion about the plasma diagnostic biomarkers of brain aging, this paper innovatively employed the combined multi-omics analysis to delineate the plasma markers of brain aging.

Methods

In order to search for specific aging markers in plasma during cerebral cortex aging, we used multi-omics analysis to screen out differential genes/proteins by integrating two prefrontal cortex (PFC) single-nucleus transcriptome sequencing (snRNA-seq) datasets and one plasma proteome sequencing datasets. Then plasma samples were collected from 20 young people and 20 elder people to verify the selected differential genes/proteins with ELISA assay.

Results

We first integrated snRNA-seq data of the post-mortem human PFC and generated profiles of 65,064 nuclei from 14 subjects across adult (44-58 years), early-aging (69-79 years), and late-aging (85-94 years) stages. Seven major cell types were classified based on established markers, including oligodendrocyte, excitatory neurons, oligodendrocyte progenitor cells, astrocytes, microglia, inhibitory neurons, and endotheliocytes. A total of 93 cell-specific genes were identified to be significantly associated with age. Afterward, plasma proteomics data from 2,925 plasma proteins across 4,263 young adults to nonagenarians (18-95 years old) were combined with the outcomes from snRNA-seq data to obtain 12 differential genes/proteins (GPC5, CA10, DGKB, ST6GALNAC5, DSCAM, IL1RAPL2, TMEM132C, VCAN, APOE, PYH1R, CNTN2, SPOCK3). Finally, we verified the 12 differential genes by ELISA and found that the expression trends of five biomarkers (DSCAM, CNTN2, IL1RAPL2, CA10, GPC5) were correlated with brain aging.

Conclusion

Five differentially expressed proteins (DSCAM, CNTN2, IL1RAPL2, CA10, GPC5) can be considered as one of the screening indicators of brain aging, and provide a scientific basis for clinical diagnosis and intervention.

Robust joint clustering of multi-omics single-cell data via multi-modal high-order neighborhood Laplacian matrix optimization

MOTIVATION

Simultaneous profiling of multi-omics single-cell data represents exciting technological advancements for understanding cellular states and heterogeneity. Cellular indexing of transcriptomes and epitopes by sequencing allowed for parallel quantification of cell-surface protein expression and transcriptome profiling in the same cells; methylome and transcriptome sequencing from single cells allows for analysis of transcriptomic and epigenomic profiling in the same individual cells. However, effective integration method for mining the heterogeneity of cells over the noisy, sparse, and complex multi-modal data is in growing need.

RESULTS

In this article, we propose a multi-modal high-order neighborhood Laplacian matrix optimization framework for integrating the multi-omics single-cell data: scHoML. Hierarchical clustering method was presented for analyzing the optimal embedding representation and identifying cell clusters in a robust manner. This novel method by integrating high-order and multi-modal Laplacian matrices would robustly represent the complex data structures and allow for systematic analysis at the multi-omics single-cell level, thus promoting further biological discoveries.

AVAILABILITY AND IMPLEMENTATION

Matlab code is available at https://github.com/jianghruc/scHoML.

Non-coding RNAs associated with Prader-Willi syndrome regulate transcription of neurodevelopmental genes in human induced pluripotent stem cells

Mutations and aberrant gene expression during cellular differentiation lead to neurodevelopmental disorders, such as Prader-Willi syndrome (PWS), which results from the deletion of an imprinted locus on paternally inherited chromosome 15. We analyzed chromatin-associated RNA in human induced pluripotent cells (iPSCs) upon depletion of hybrid small nucleolar long non-coding RNAs (sno-lncRNAs) and 5' snoRNA capped and polyadenylated long non-coding RNAs (SPA-lncRNAs) transcribed from the locus deleted in PWS. We found that rapid ablation of these lncRNAs affects transcription of specific gene classes. Downregulated genes contribute to neurodevelopment and neuronal maintenance, while upregulated genes are predominantly involved in the negative regulation of cellular metabolism and apoptotic processes. Our data reveal the importance of SPA-lncRNAs and sno-lncRNAs in controlling gene expression in iPSCs and provide a platform for synthetic experimental approaches in PWS studies. We conclude that ncRNAs transcribed from the PWS locus are critical regulators of a transcriptional signature, which is important for neuronal differentiation and development.