Exploratory Single-Cell RNA Sequencing of Peripheral Blood Identifies Candidate Signatures Associated with Alzheimer’s Disease

Saed Sayad; Mark Hiatt; Dhruvajyoti Roy

doi:10.20944/preprints202604.1781.v1

Submitted:

23 April 2026

Posted:

24 April 2026

You are already at the latest version

Abstract

Background. Alzheimer's disease (AD) is a progressive neurodegenerative disorder with a complex etiology, often diagnosed late in its course. Early detection of AD biomarkers could aid in timely intervention and management. Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to unravel cellular heterogeneity and identify molecular signatures associated with disease states. Here, we employ scRNA-seq on peripheral blood samples to investigate potential predictive biomarkers for AD. Methods. We analyzed the publicly available scRNA-seq dataset GSE181279, comprising peripheral blood cells from three individuals with Alzheimer’s disease and two healthy controls. Single-cell RNA sequencing was performed on these samples to profile the transcriptomic landscape of individual cells. Bioinformatics analyses were employed to identify differentially expressed genes and cellular subtypes associated with AD pathology. Machine learning algorithms were utilized to develop predictive models based on gene expression patterns, aiming to discriminate between AD patients and healthy controls. Results. Our scRNA-seq (GSE181279) analysis revealed distinct gene expression profiles and cellular subtypes in peripheral blood samples from AD patients compared to healthy controls. We identified several dysregulated genes and cell populations associated with AD pathology, including immune cell activation and neuroinflammatory processes. Differential-expression and enrichment analyses identified candidate genes and pathways associated with immune activation, stress-response signaling, and altered cellular homeostasis in AD. In an exploratory leave-one-out analysis, a two-gene model incorporating BTG1 and DUSP1 separated AD from healthy controls within this very small dataset; these findings require validation in larger independent cohorts. Conclusions. This exploratory analysis suggests that peripheral-blood scRNA-seq may help identify candidate biomarkers associated with AD. The identified gene expression signatures and cellular subtypes associated with AD pathology provide valuable insights into the underlying molecular mechanisms of the disease. Furthermore, the development of accurate predictive models based on scRNA-seq data suggests a promising avenue for early diagnosis and intervention in AD. Further validation and prospective studies are warranted to assess the clinical utility and generalizability of these findings in larger cohorts.

Keywords:

Alzheimer's disease

;

single-cell RNA sequencing (scRNA-seq)

;

peripheral blood biomarkers

;

early detection

;

cellular heterogeneity

;

differential gene expression

;

machine learning models

;

immune activation

;

neuroinflammation

;

BTG1 DUSP1

Subject:

Medicine and Pharmacology - Neuroscience and Neurology

Introduction

AD is a progressive neurodegenerative disorder characterized by the deterioration of cognitive function and memory. It is the most common cause of dementia among older adults, affecting millions globally and posing significant challenges to individuals, families, and healthcare systems. The pathological hallmarks of Alzheimer's include the accumulation of amyloid-beta plaques and tau protein tangles in the brain, leading to neuronal loss and brain atrophy. However, the exact mechanisms behind its onset and progression remain incompletely understood. Early diagnosis of Alzheimer's is crucial for managing the disease and potentially slowing its progression. Currently, definitive diagnosis relies heavily on neuroimaging and cerebrospinal fluid analysis, which are often invasive, expensive, and not widely accessible. This has prompted researchers to explore more accessible and minimally invasive biomarkers, especially those detectable in peripheral blood (liquid biopsy biomarkers). Recent advancements in single-cell RNA sequencing (scRNA-seq) have opened new avenues for understanding complex diseases like Alzheimer's at unprecedented resolution [1]. scRNA-seq allows the analysis of gene expression at the individual cell level, providing insights into cellular heterogeneity and the dynamic states of cells. Applying this technology to peripheral blood cells offers a promising approach to uncovering blood-based biomarkers for Alzheimer's disease. This approach may help identify accessible blood-based signatures associated with AD and generate hypotheses for future validation studies.

Data

We obtained the publicly available single-cell RNA-sequencing dataset GSE181279 from the NIH Gene Expression Omnibus/NIH portal (Figure 1). The dataset included five peripheral-blood samples in total: three from individuals with AD and two from healthy controls.

Data Analysis

Integrating the GSE181279 dataset that includes peripheral blood samples from three AD patients and two healthy controls (Figure 2), this study employs a rigorous scRNA-seq pipeline to identify genomic biomarkers for Alzheimer’s disease. Our analytical framework leverages t-tests, Wilcoxon tests, and zero-inflated models to perform differential-expression analyses at the cell level, complemented by pathway and gene-ontology enrichment to map shifts in cellular composition and interaction networks. Moreover, we utilized logistic regression and XGBoost for predictive modeling, using leave-one-out cross-validation to assess performance, ultimately aiming to uncover the molecular drivers and biomarkers that define the AD landscape.

Differential Gene Expression

In the gene expression analysis, a striking pattern emerged showing significant alterations in both upregulated and downregulated genes. Among the most downregulated genes (Table 1), it has been suggested that GSTM1 null genotype is a risk factor for AD in Italian patients [2]. It has been speculated that TYROBP plays a key role in the microglial sensome and the emergence of the disease-associated microglia (DAM) phenotype and also may play a key role in the loss of markers of synaptic integrity [3]. One study highlighted KLF6 and KLF7 overexpression increased neurite growth by 35% and 60%, respectively [4]. CFAP20 encodes a highly conserved protein involved in the post-translational modification of Tubulin subunits of microtubules. Such modifications might be essential for microtubule function and stability in ciliated cells, such as sperm, and in neurons [5]. YPEL5 is a member of the YPEL gene family that is highly conserved in the eukaryotic species and apparently involved in a certain cell division-related function. YPEL5 protein has been shown to be involved in the cell cycle progression. Human YPEL5 has been reported to play a pro-apoptotic role and is also involved in DNA damage induced apoptosis [6]. TSC22D3 encodes a putative anti-inflammatory transcription factor and has been shown to express high mRNA levels in brain-infiltrating CD8+ T cells in AD mice [7]. Shwachman-Diamond syndrome is a rare recessive genetic disease caused by mutations in SBDS gene, at chromosome 7q11. Phenotypically, the syndrome is characterized by exocrine pancreatic insufficiency, bone marrow dysfunction, skeletal dysplasia, and variable cognitive impairments [8]. Diseases associated with ARHGEF10 include Slowed Nerve Conduction Velocity, Autosomal Dominant and Axonal Neuropathy [9].

Conversely, the upregulated genes revealed a pronounced activation in pathways associated with cell proliferation and immune responses. BTG1 belongs to a family of antiproliferative genes and is highly expressed in adult neurogenic niches [10]. In human cortex, DUSP1 protein expression correlates with tau phosphorylation, synaptic defects and cognitive decline in subjects diagnosed with AD [11]. NK cells from patients with AD displayed upregulation of DUSP1 and DUSP2 that are regulators of the ERK signaling pathway and the RNA-binding protein ZFP36L2 that is related to immunosuppression as well as TBX21 involved in NK cell maturation [12]. It has been suggested that YB-1 interaction with β-amyloid prevents formation of filaments that are responsible for neurotoxicity and neuronal death [13]. Upregulation of ATP5F1D might demonstrate dysfunction of the mitochondrial ATP synthase in AD [14]. The heightened expression of RPS2 and NOP53 underscores an escalation in ribosome biogenesis and protein synthesis [15]. A variant of HLA-C was found to be associated with AD [16].

Pathway Analysis

The pathway analysis (Table 2) has uncovered significant shifts in cellular activities, evidenced by the top ten downregulated and upregulated pathways. Among the downregulated pathways, there is a notable decline in the Biosynthesis of DHA-derived sulfido conjugates and Biosynthesis of maresin conjugates in tissue regeneration (MCTR), consistent with altered pathways related to anti-inflammatory and tissue repair processes [17]. Additionally, the pathway Reversible hydration of carbon dioxide is linked to carbonic anhydrase biology, which is a potential target against neurovascular unit dysfunction in AD [18]. Studies in AD mouse models have shown that EGFR inhibitors can attenuate amyloid-beta (Aβ) pathology and improve cognitive function [19]. The decreased activity in Defective B3GALTL causing Peters-plus syndrome (PpS) and O-glycosylation of TSR domain-containing proteins pathways may reflect altered glycosylation-related pathway enrichment, which could affect protein stability and function. In neurons, the number of glycosylation sites on neuronal and synaptic proteins is altered in AD brains, leading to aberrant neuronal adhesion and synaptic transmission [20]. One study identified that NOXA (PMAIP1) was a potential candidate gene that may affect mitochondrial function in hippocampal neuronal cells [21].

In contrast, upregulated pathways highlight enhanced regulatory and proliferative mechanisms. The FOXO-mediated transcription of cell cycle genes and FOXO-mediated transcription pathways are prominently upregulated, indicating a robust activation of transcriptional responses critical for cell cycle control, apoptosis, and stress resistance [22]. The aberrant MAPK pathway may facilitate the development of amyloid-beta (Aβ) and Tau pathology, oxidative stress, neuroinflammation, and brain cell death [23]. Additionally, NOTCH3 mutations trigger changes in vascular smooth muscle cells and the overall cerebral microvasculature, which may play a role in increased amyloid accumulation [24]. The colocalization of the presenilins with kinetochores on the nucleoplasmic surface of the inner nuclear membrane, together with other results, suggests that they may play a role in chromosome organization and segregation, perhaps as kinetochore binding proteins/receptors [25]. Upregulation of Mitotic Spindle Checkpoint pathways and EML4 and NUDC in mitotic spindle formation pathways indicates a strong emphasis on mitotic machinery. It has been postulated that there should be alterations in the mitotic spindle apparatus or in mitosis-related proteins in Alzheimer’s disease cells that could lead to chromosome mis-segregation [26].

Gene Ontology Analysis

Table 3 shows the downregulated and upregulated gene ontology cell components (CC), biological processes (BP) and molecular functions (MF). The downregulated processes predominantly reflect a decline in cellular maintenance and detoxification activities. Protein kinase C (PKC) has been classified as one of the cognitive kinases controlling memory and learning. By regulating several signaling pathways involved in amyloid and tau pathologies, it also plays an inhibitory role in AD pathophysiology [27]. Abnormalities in cellular junctions and junctional components contribute to impaired neuronal signaling and increased cerebrovascular permeability, which are closely associated with AD advancement [28]. Metabolic processes are also impacted, as indicated by the downregulation of nitrobenzene metabolic process and detoxification of nitrogen compound, highlighting a diminished capacity to handle toxic substances and byproducts [29].

On the other hand, the upregulated processes illustrate a marked enhancement in developmental and regulatory activities. Key among these is the positive regulation of endothelial cell differentiation and positive regulation of myoblast differentiation, which indicate robust promotion of vascular and muscle cell development. The positive regulation of ubiquitin-protein transferase activity suggests increased protein modification activities, which are vital for cellular stress responses and protein quality control [30]. Furthermore, clonal hematopoiesis of indeterminate potential (CHIP), a condition that changes the DNA of some blood cells and increases the risk for blood cancers and cardiovascular disease, may reduce a person’s risk of developing Alzheimer's disease [31].

Exploratory Classification of AD Using Peripheral-Blood Single-cell Transcriptomics

Peripheral blood single-cell transcriptomic data may provide a minimally invasive source of candidate biomarkers for AD. In this exploratory analysis of GSE181279 (three AD samples and two healthy controls), a two-gene model based on BTG1 and DUSP1 (Figure 3 and Figure 4) achieved complete separation of the groups under leave-one-out cross-validation using logistic regression and XGBoost. However, given the extremely small sample size and lack of external validation, these findings should be considered hypothesis-generating rather than evidence of robust predictive performance.

Discussion

We analyzed publicly available peripheral-blood scRNA-seq data to explore candidate transcriptomic signatures associated with AD. The findings underscore significant alterations in gene expression profiles between AD patients and healthy controls, highlighting the intricate molecular landscape of the disease. Notably, the differential expression of genes such as GSTM1, TYROBP, and KLF6, which are implicated in cellular stress responses, immune function, and neuronal integrity, provides crucial insights into the pathophysiology of AD. These genes, alongside others like CFAP20 and TSC22D3, point to disruptions in neuronal structure and immune regulation, suggesting their potential as biomarkers for early AD detection.

Furthermore, the pathway analysis reveals profound shifts in cellular activities, with downregulated pathways such as the Biosynthesis of DHA-derived sulfido conjugates and Reversible hydration of carbon dioxide suggesting diminished anti-inflammatory responses and metabolic dysregulation in AD. Conversely, upregulated pathways like FOXO-mediated transcription and Mitotic Spindle Checkpoint highlight enhanced cellular stress responses and alterations in cell cycle control, which are critical in the context of neurodegeneration and AD progression. These findings align with the hypothesis that AD pathogenesis involves a complex interplay of impaired cellular maintenance, metabolic dysfunction, and heightened stress responses.

The gene ontology analysis further complements these observations by showing decreased cellular maintenance activities, such as cell-cell junction maintenance and membrane raft organization, which are essential for maintaining neuronal and synaptic integrity. This decline mirrors the structural and functional deterioration seen in AD. In contrast, upregulated processes related to endothelial cell differentiation and ubiquitin-protein transferase activity reflect an adaptive increase in cellular repair and stress response mechanisms, potentially as a compensatory effort to counteract AD pathology.

A noteworthy exploratory finding was that a two-gene model using BTG1 and DUSP1 separated AD from healthy controls within this small dataset. The model’s ability to identify AD with 100% accuracy by analyzing the expression of just two genes, BTG1 and DUSP1, underscores the potential of single-cell transcriptomics in improving AD diagnostics. Although encouraging, this observation is preliminary and may reflect overfitting given the limited number of samples.

This study has several important limitations. It is a secondary analysis of a public dataset with only five individuals, which substantially limits statistical power and generalizability. The large number of cells does not substitute for the small number of independent patient samples. In addition, the absence of an external validation cohort and the exploratory use of leave-one-out cross-validation create a substantial risk of overfitting. Finally, peripheral-blood transcriptomic changes may reflect non-specific systemic or inflammatory processes rather than AD-specific biology.

Conclusion

In summary, this exploratory analysis of a public peripheral-blood scRNA-seq dataset identified candidate genes, pathways, and gene-ontology signatures associated with AD. A preliminary two-gene classifier based on BTG1 and DUSP1 separated AD from healthy controls within this dataset, but the findings are limited by the very small sample size and lack of external validation. Larger, prospectively characterized cohorts will be needed to determine whether these signals have true diagnostic value.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. GSE181279 comprises 13,506 normal control (“NC”) single cells and 21,861 AD single cells. Gene annotations were obtained from GeneCards, pathway annotations from Reactome, and gene ontology data from QuickGO.

Figure 2. GSE181279 comprises five samples including three AD patients and two normal controls (“NC”). The y-axis shows the number of cells for each sample.

Figure 3. BTG1 boxplots for the two groups of normal controls (“NC”) and Alzheimer’s disease (“AD”).

Figure 4. DUSP1 boxplots for the two groups of normal controls (“NC”) and Alzheimer’s disease (“AD”).

Table 1. Top 10 downregulated and upregulated genes through single-cell gene expression analysis.

Downregulated		Upregulated
Gene Symbol	Gene Name	Gene Symbol	Gene Name
GSTM1	Glutathione S-Transferase Mu 1	BTG1	BTG Anti-Proliferation Factor 1
MYADM	Myeloid Associated Diff. Marker	DUSP1	Dual Specificity Phosphatase 1
MTRNR2L2	MT-RNR2 Like 2 (Pseudogene)	PNRC1	Proline Rich Nuclear Receptor Coactivator 1
TYROBP	Transmembrane Immune Signaling Adaptor TYROBP	ZFP36L2	ZFP36 Ring Finger Protein Like 2
KLF6	KLF Transcription Factor 6	RPS2	Ribosomal Protein S2
CFAP20	Cilia And Flagella Associated Protein	YBX1	Y-Box Binding Protein 1
YPEL5	Yippee Like 5	ATP5F1D	ATP Synthase F1 Subunit Delta
TSC22D3	TSC22 Domain Family Member 3	NOP53	NOP53 Ribosome Biogenesis Factor
SBDS	SBDS Ribosome Maturation Factor	HLA-C	Major Histocompatibility Complex, Class I, C
ARHGEF10	Rho Guanine Nucleotide Exchange Factor 10	TRIR	Telomerase RNA Component Interacting RNase

Table 2. Top 10 downregulated and upregulated pathways through single-cell gene enrichment analysis.

Downregulated	Upregulated
Biosynthesis of DHA-derived sulfido conjugates	FOXO-mediated transcription of cell cycle genes
Biosynthesis of maresin conjugates in tissue regeneration (MCTR)	RMTs methylate histone arginines
Reversible hydration of carbon dioxide	RAF-independent MAPK1/3 activation
Inhibition of Signaling by Overexpressed EGFR	FOXO-mediated transcription
Signaling by Overexpressed Wild-Type EGFR in Cancer	PTK6 Regulates Proteins Involved in RNA Processing
Activation, myristolyation of BID and translocation to mitochondria	Noncanonical activation of NOTCH3
Defective B3GALTL causes Peters-plus syndrome (PpS)	Amplification of signal from the kinetochores
O-glycosylation of TSR domain-containing proteins	Amplification of signal from unattached kinetochores via a MAD2
Activation of NOXA and translocation to mitochondria	Mitotic Spindle Checkpoint
Assembly of Viral Components at the Budding Site	EML4 and NUDC in mitotic spindle formation

Table 3. Top 10 downregulated and upregulated GO terms through single-cell gene enrichment analysis.

Downregulated	Upregulated
(BP) negative regulation of protein kinase C signaling	(BP) positive regulation of endothelial cell differentiation
(BP) cell-cell junction maintenance	(BP) positive regulation of fibroblast apoptotic process
(BP) membrane raft organization	(BP) positive regulation of myoblast differentiation
(BP) nitrobenzene metabolic process	(BP) positive regulation of ubiquitin-protein transferase activity
(BP) positive regulation of feeding behavior	(BP) negative regulation of mitotic cell cycle
(BP) negative regulation of activation-induced cell death of T cells	(BP) definitive hemopoiesis
(BP) positive regulation of translational initiation in response to stress	(BP) somatic stem cell division
(BP) positive regulation of endoplasmic reticulum stress-induced eIF2 alpha dephosphorylation	(BP) negative regulation of mitotic cell cycle phase transition
(BP) positive regulation of peptidyl-serine dephosphorylation	(BP) negative regulation of stem cell differentiation
(BP) cellular detoxification of nitrogen compound	(BP) negative regulation of fat cell differentiation

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