ABSTRACT
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.
ABSTRACT
Blood-based proteomic analysis is a routine practice for detecting the biomarkers of human disease. The results obtained from blood alone cannot fully reflect the alterations of nerve cells, including neurons and glia cells, in Alzheimer's disease (AD) brains. Therefore, the present study aimed to investigate novel potential AD biomarker candidates, through an integrated multi-omics approach in AD. We propose a comprehensive strategy to identify high-confidence candidate biomarkers by integrating multi-omics data from AD, including single-nuclei RNA sequencing (snRNA-seq) datasets of the prefrontal and entorhinal cortices, as wells as serum proteomic datasets. We first quantified a total of 124,658 nuclei, 8 cell types, and 3701 differentially expressed genes (DEGs) from snRNA-seq dataset of 30 human cortices, as well as 1291 differentially expressed proteins (DEPs) from serum proteomic dataset of 11 individuals. Then, ten DEGs/DEPs (NEBL, CHSY3, STMN2, MARCKS, VIM, FGD4, EPB41L2, PLEKHG1, PTPRZ1, and PPP1R14A) were identified by integration analysis of snRNA-seq and proteomics data. Finally, four novel candidate biomarkers (NEBL, EPB41L2, FGD4, and MARCKS) for AD further stood out, according to bioinformatics analysis, and they were verified by enzyme-linked immunosorbent assay (ELISA) verification. These candidate biomarkers are related to the regulation process of the actin cytoskeleton, which is involved in the regulation of synaptic loss in the AD brain tissue. Collectively, this study identified novel cell type-related biomarkers for AD by integrating multi-omics datasets from brains and serum. Our findings provided new targets for the clinical treatment and prognosis of AD.
ABSTRACT
Objective To evaluate the diagnostic evaluation of isotropic scanning and Lung Care soft- ware in solitary pulmonary nodules,and to improve the diagnostic accuracy.Methods 52 patients suffered from SPN were included in our study.Two experts in CT analyzed the films.First,they read the axial images and made diagnosis.Then isotropic scanning and lung care software approaches were used on 16 spiral CT and another analysis were made again.The results were compared with pathological diagnosis respectively. Results Spiculated sign,lobulated sign,vessel convergence were found more on isotropic scanning approach, that had significant difference with axial images analysis(P
