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.

The tree shrew brain has garnered considerable attention due to its remarkable similarities to human brain. However, the cellular composition and genetic signatures of tree shrew hippocampus across postnatal life remain poorly characterized. Here, we establish the first single-nucleus transcriptomic atlas of tree shrew hippocampus spanning postnatal life, detailing the dynamics and diversity of the neurogenic lineage, oligodendrocytes, microglia, and endothelial cells. Notably, cross-species transcriptomic comparison among humans, macaques, tree shrews, and mice reveals that the tree shrew transcriptome resembles that of macaques, making it a promising model for simulating human neurological diseases. More interestingly, we identified a unique class of tree shrew-specific neural stem cells and established SOX6, ADAMTS19, and MAP2 as their markers. Furthermore, aberrant gene expression and cellular dysfunction in the tree shrew hippocampus are linked to neuroinflammation and cognitive impairment during tree shrew aging. Our study provides extensive resources on cell composition and transcriptomic profiles, serving as a foundation for future research on neurodevelopmental and neurological disorders in tree shrews.

Previous studies have largely overlooked cellular differential alterations across differentially affected brain regions in both disease mechanisms and therapeutic development of Alzheimer’s disease (AD). This study aimed to compare the differential cellular and transcriptional changes in the prefrontal cortex (PFC) and entorhinal cortex (EC) of AD patients through an integrated single-cell transcriptomic analysis. We integrated three single-cell RNA sequencing (scRNA-seq) datasets comprising PFC and EC samples from AD patients and age-matched healthy controls. A total of 124,658 nuclei and 31 cell clusters were obtained and classified into eight major cell types, with EC exhibiting much more pronounced transcriptional alterations than PFC. Through network analysis, we pinpointed hub regulatory genes that form interconnected networks driving AD pathogenesis, findings validated by RT-qPCR showing more pronounced expression changes in EC versus PFC of AD mice. Moreover, dysregulation of the LINC01099-associated regulatory networks in the PFC and EC, showing correlation with AD progression, may present new therapeutic targets for AD. Together, these results suggest that effective AD biomarkers and therapeutic strategies may require simultaneous, precise targeting of specific cell populations across multiple brain regions.

Treatment-resistant depression (TRD) is a refractory form of major depressive disorder (MDD) for which conventional antidepressant strategies are often insufficient. Transcranial magnetic stimulation (TMS) has demonstrated clinical efficacy in TRD, but its biological mechanisms remain incompletely understood. This narrative review synthesizes current evidence across transcriptomic, epigenomic, proteomic, metabolomic, and connectomic levels and is complemented by exploratory secondary analyses of publicly available omics datasets. Transcriptomic and spatial transcriptomic studies suggest that TMS modulates cell type- and region-specific transcriptional programs related to synaptic plasticity and neuronal development. Epigenetic evidence suggests potential roles for DNA methylation and lactylation-related regulation, whereas proteomic and metabolomic studies indicate changes in synaptic proteins, neurotransmitter systems, lipid metabolism, and peripheral metabolites. Connectomic studies further show that TMS can alter functional and structural connectivity involving prefrontal and large-scale brain networks associated with treatment response. Together, current evidence indicates biological changes across multiple levels, although direct causal links between molecular changes and circuit remodeling remain largely unproven. Future longitudinal multi-omics studies in patients with TRD may help identify biomarkers, clarify mechanisms, and improve individualized TMS treatment.

The neurotransmitter system (NTS) composed of neurotransmitter receptors and transporters is associated with a variety of diseases and disorders. The identification of disease‐associated NTS features is important for understanding disease mechanisms and for prioritizing potential therapeutic targets. The differences in the composition of NTS in aging and different diseases are not well understood. Here, we show how to integrate multi‐omics data at scale, including single‐cell and spatial transcriptomic, to identify disease‐associated NTS. By integrating, annotating, and analyzing 368 publicly available single‐nucleus RNA sequencing (snRNA‐seq) datasets from 17 cohort studies, we created a snRNA‐seq dataset for the prefrontal cortex (PFC) of the human brain. The resulting dataset included > 1 million cells covering healthy individuals throughout the life course after birth and eight common neuropsychiatric disorders. We comprehensively describe the cellular heterogeneity of NTS in aging and disease, and resolve the relationship between age or sex and disease‐associated NTS. A module composed of NTS and their regulatory genes was constructed and showed disease‐associated discriminatory signal, with supportive validation from protein‐level and external datasets. In order to screen NTS networks that can simulate disease brain tissue, we conducted a comparative analysis of five disease‐related cerebral organoids and found partial similarity between patient‐derived organoids and parental brain tissues. Our work provides a multi‐disease cell atlas based on large‐scale snRNA‐seq data and analyses the disease and sex heterogeneity of NTS, which provides new insights into the mechanisms of multiple CNS diseases and sheds light on disease precision therapy against NTS.

Central nervous system (CNS) aging is a major risk factor for many disorders, including cerebrovascular disease, neurodegeneration, and amyotrophic lateral sclerosis, yet the cellular pathways driving its progression across CNS regions remain poorly defined. Here we present a single-nucleus transcriptomic atlas spanning 7 human CNS regions, comprising ∼1.0 million nuclei from 235 postmortem samples derived from 200 neurologically and psychiatrically normal donors aged 19-101 years. Across regions, we delineate both shared and region-specific features of CNS aging, integrating analyses of transcriptional noise, programmed cell-death signatures, disease associations, metabolic reprogramming, and transcriptomic remodeling. We identify cross-regional vulnerability of astroglia, oligodendrocytes, and excitatory and inhibitory neurons, and show that microglial and astrocytic activation represents a broadly conserved aging response across the CNS, highlighting potential targets for intervention. Finally, we present within-dataset proof-of-concept predictive modeling use cases based on aging-associated gene signatures, providing a resource for within-atlas prioritization and hypothesis generation of candidate biomarkers of CNS aging. Together, this work offers a region-by-region map of the aging human CNS and informs the selection of specific cell types and/or regions for future anti-aging strategies.