Frontal cortex pyramidal neuron expression profiles differentiate the prodromal stage from progressive degeneration across the Alzheimer's disease spectrum

Amanda Labuza; Melissa J Alldred; Harshitha Pidikiti; Michael H Malek‐Ahmadi; Sang Han Lee; Adriana Heguy; Paul D Coleman; Souparna Chakrabarty; Gabriela Chiosis; Elliott J Mufson; Stephen D Ginsberg

doi:10.1002/alz.70395

. 2025 Jul 25;21(7):e70395. doi: 10.1002/alz.70395

Frontal cortex pyramidal neuron expression profiles differentiate the prodromal stage from progressive degeneration across the Alzheimer's disease spectrum

Amanda Labuza ^1,², Melissa J Alldred ^1,², Harshitha Pidikiti ¹, Michael H Malek‐Ahmadi ^3,⁴, Sang Han Lee ^1,², Adriana Heguy ^5,⁶, Paul D Coleman ⁷, Souparna Chakrabarty ⁸, Gabriela Chiosis ⁸, Elliott J Mufson ⁹, Stephen D Ginsberg ^1,^2,^10,^11,^✉

PMCID: PMC12290489 PMID: 40709510

Abstract

INTRODUCTION

Underlying causes of Alzheimer's disease (AD) remain unknown, making it imperative to identify molecular mechanisms driving the pathobiology of AD onset and progression.

METHODS

Laser capture microdissection was used to isolate layer III pyramidal neurons from post mortem human prefrontal cortex (Brodmann area 9). Single population RNA sequencing was conducted using tissue from subjects with no cognitive impairment (NCI), mild cognitive impairment (MCI), and AD. Differentially expressed genes (DEGs) were compared across groups.

RESULTS

DEGs increased from prodromal (MCI vs. NCI) to progression (AD vs. MCI) to frank AD (AD vs. NCI). The majority of DEGs and pathways shared between prodromal and progression exhibited a change in the direction of dysregulation unlike pathways between progression and frank AD.

DISCUSSION

Candidate genes and pathways were identified that demarcate early‐stage AD onset from AD progression, providing a roadmap to study cortical cellular vulnerability and key targets for intervention at early stages of AD.

Highlights

Pyramidal neuron differentially expressed genes (DEGs) are directionally divergent between prodromal, progression, and frank Alzheimer's disease (AD).
Pyramidal neuron DEGs are directionally convergent between progression and frank AD.
Dysfunctional bioenergetic pathways increased dysregulation as the AD spectrum progressed.
Immune response pathways were more dysregulated in frank AD than prodromal stages.
DEGs, = biological pathways, and interactomes demarcate specific stages across the AD spectrum.

Keywords: Alzheimer's disease spectrum, bioinformatics, frontal cortex, gene expression, mild cognitive impairment, pyramidal neurons, RNA sequencing, selective vulnerability

1. BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects an estimated 6.9 million Americans ¹ and progresses from no cognitive impairment (NCI), to mild cognitive impairment (MCI), to AD over many years. ² , ³ While the hallmark of AD pathology is defined by the presence of amyloid beta plaques and neurofibrillary tangles (NFTs) ⁴ , ⁵ along with synaptic loss and neuroinflammation, ⁶ , ⁷ , ⁸ , ⁹ it is still not understood how they contribute to the pathobiology of AD. There is an increasing need to study the molecular and cellular changes occurring across the AD spectrum, especially within vulnerable neurons and circuits that underlie memory and executive function as the disease shifts from initial onset to later progressive stages.

Although medial temporal lobe structures (e.g., entorhinal cortex and hippocampus) are considered some of the earliest regions that degenerate in AD, ¹⁰ , ¹¹ there are other key interconnected cortical regions and circuits that are damaged early in the disease process that warrant cell type–specific evaluation for mechanistic insight and therapeutic discovery. Specifically, the prefrontal cortex, especially Brodmann area 9 (BA9), a hub of the default mode network, is recognized as being severely impacted in AD, including during prodromal stages, and progressively worsens as the disease severity increases. ¹² This is important, as default mode network dysfunction is recognized as a core feature underlying executive function defects across the AD spectrum. ¹³ , ¹⁴ , ¹⁵ Excitatory pyramidal neurons residing in the prefrontal cortex, specifically corticocortical projection neurons in layer III of BA9, degenerate in AD, bear NFTs, lose synaptic connectivity, and are in close proximity to amyloid deposits. ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ BA9 layer III neurodegeneration results in a neuronal cortical disconnection syndrome that correlates with cognitive decline in AD subjects compared to age‐matched NCI controls. ²⁰ , ²¹ , ²² , ²³ , ²⁴ Although RNA sequencing (RNA‐seq) analysis of bulk BA9 tissue revealed common pathological pathways involved in AD, ²⁵ , ²⁶ , ²⁷ , ²⁸ conventional bulk tissue RNA‐seq masks complex alterations occurring across cell types. By contrast, isolating vulnerable cell types better informs on the mechanisms of cellular neurodegeneration. ²⁹ , ³⁰ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ , ³⁶

RNA‐seq provides an affordable and unbiased assessment of disease mechanism(s) by providing an expanded and rigorous quantitative gene expression profile, encompassing protein coding and non‐coding RNAs (ncRNAs), compared to quantitative reverse transcription polymerase chain reaction or microarray analysis within a given population. ³⁷ , ³⁸ , ³⁹ , ⁴⁰ , ⁴¹ To this end, independent research groups have used single‐cell sequencing techniques including regional brain tissue dissections, deconvolution methods, and bioinformatics to identify post‐hoc cell types. ⁴² , ⁴³ , ⁴⁴ , ⁴⁵ , ⁴⁶ , ⁴⁷ , ⁴⁸ These techniques do not preserve the spatial pattern of individual cell types. ⁴⁹ , ⁵⁰ In contrast, laser capture microdissection (LCM) allows for the isolation of spatially characterized individual cells during collection, providing single population specificity. ³⁴ , ³⁵ , ³⁶ Combining isolated individual cells from post mortem brain samples using LCM combined with advances in RNA‐seq enables an opportunity to study the AD spectrum with high precision and specificity. ⁴¹ , ⁵¹ , ⁵² , ⁵³ , ⁵⁴ , ⁵⁵

In the present study, we use LCM to microisolate the cell soma of BA9 layer III pyramidal neurons from post mortem human brain tissue as described previously ⁵⁶ obtained from subjects across the AD spectrum. Specifically, single population RNA‐seq of layer III pyramidal neurons from each cohort were compared and contrasted for differentially expressed genes (DEGs) via bioinformatic inquiry followed by an integrated mechanistic assessment using several pathway analysis tools. We hypothesize alterations in DEGs and specific pathways in layer III pyramidal neurons, including those underlying synaptic signaling and bioenergetic fitness, will reveal targetable markers that underlie cognitive decline and inform on the changes that occur across the progression of disease.

2. METHODS

2.1. Case materials

Brain tissues obtained post mortem from cases (n = 20) with diagnosis of NCI (n = 8, 2F/6M), MCI (n = 5, 4F/1M), and AD (n = 7, 4F/3M) from the University of Miami Brain Endowment Bank (UMBEB) as part of the National Institutes of Health (NIH) NeuroBioBank were examined. A diagnosis was made post mortem by a combination of neuropathological criteria and ante mortem clinical information obtained from medical records, specifically the Modified Telephone Interview for Cognitive Status. ⁵⁷ , ⁵⁸ , ⁵⁹ Average age was 72.2 ± 9.33 (standard error of the mean) years, and post mortem interval (PMI) was 18.8 ± 9.7 h (Table S1 in supporting information).

2.2. Tissue preparation

Frozen, unfixed 20 µm thick tissue sections from BA9 tissue blocks were cut on a cryostat at −25°C (CM1860 UV) in the coronal plane and mounted on polyethylene naphthalate (PEN) membrane slides (Leica). Slides were kept under desiccant at −80°C until used for LCM. Stringent RNase‐free precautions were used, and solutions were made with RNase‐free water (ThermoFisher). PEN membranes were equilibrated to room temperature (RT) under desiccant (−20°C for 10 minutes, 4°C for 10 minutes, RT for 1 minute) prior to Nissl staining to visualize cortical layer III pyramidal neurons. ⁵⁵ Slides were rinsed in cold phosphate buffered saline (PBS, pH 7.4), immersed in a Nissl stain solution (0.18 M sodium acetate, 0.6% acetic acid, 0.1% thionin; Sigma) for 1 minute at RT, then rinsed twice with PBS. Slides were air‐dried for 5 minutes and then immediately used for LCM of pyramidal neurons.

2.3. Laser capture microdissection

An LCM system (LMD7000; Leica) was used to identify and microisolate pyramidal neurons using a 40× objective (PL‐Fluotar NA 0.60). Neurons from BA9 layer III were outlined using the draw/cut tool (Leica LMD version 8.2.1) and collected by gravity into 50 µL of Qiazol extraction solution with a maximum collection time of 1 hour to preserve RNA integrity (Qiagen; Figure S1A‐C in supporting information) as we previously described. ⁵⁵ Captured cells were counted and frozen in Qiazol at –80°C until 600 to 800 cells per region were isolated. An average of nine tissue sections per subject were required to collect sufficient numbers of Nissl‐stained layer III pyramidal neurons.

RESEARCH IN CONTEXT

Systematic review: The authors reviewed existing peer‐reviewed literature using traditional (e.g., PubMed) sources, meeting abstracts, and presentations. Relevant publications on the importance of single population and single cell sequencing in the context of neurodegenerative disorders, including the Alzheimer's disease (AD) spectrum are appropriately cited.
Interpretation: The results of this study identify key genes and biological pathways that can be used as biomarkers to delineate early‐ or late‐stage AD dementia as well as identify potential targets for pharmacotherapeutic intervention.
Future directions: Any of the differentially expressed genes between all comparisons (e.g., prodromal vs. progression, prodromal vs. frank AD, and progression vs. frank AD) are subject to future mechanistic and translational investigation to identify feasibility as a biomarker or therapeutic target. Furthermore, we show that the direction of gene expression levels (e.g., downregulation vs. upregulation or vice versa) changes between early‐ and late‐stage disease, informing future studies.

2.4. RNA isolation and library preparation

RNA from 600 to 800 pyramidal neurons was purified using miRNeasy Micro kit (Qiagen) according to manufacturer's specifications with the DNase digest performed twice sequentially. ⁵⁵ RNA quality control was assessed (RNA 6000 pico kit, Agilent) with Dv200 values ranging from 35 to 63. The SMARTer Stranded Total RNA‐Seq Kit v3 Pico Input Mammalian (Takara Bio) was used with 8 µL of input RNA. Samples were quantified (2100 HS DNA kit; Agilent) with a quality control minimum of 2 nM required for downstream RNA‐seq analysis. ⁵⁵ Samples were pooled in equimolar concentrations and assayed on the NovaSeq 6000 using the S1 100 Cycle Flow Cell v1.5 (Illumina) at the New York University Grossman School of Medicine Genome Technology Center (NYUGSOM GTC).

2.5. Statistics and bioinformatics

FastQC v0.11.9 ⁶⁰ was used to perform quality control on raw reads after creating FastQ files. Although Trimmomatic 0.39 ⁶¹ was used for read trimming, this step was not performed if there was no adapter contamination. STAR Aligner (2.7.10a) ⁶² was used to index and align sequence reads to the human reference genome (Gencode GRCh38). Post‐alignment quality control was performed with RSeQC (v4.0.0). ⁶³ Picard (2.27.1; “Picard Toolkit,” 2019) and RSEM (1.3.3) ⁶⁴ were used for the quantification of the aligned reads. Results obtained from RSEM were used for Differential Gene Expression Using R (R‐v4.2.0/RStudio v1+554). In R, a count matrix was generated from the results obtained from RSEM. Genes with > 0.1 counts per million (CPM) were retained for downstream analyses, an approach that eliminates poorly expressed genes. This method does not interfere with statistical analyses and reduces the likelihood of a false positive detection of differential gene expression at the limit of resolution. ⁵⁶ Resulting counts were normalized using the TMM method implemented via edgeR. ⁵⁷ An additional filtering step was performed to remove 10% of lowly expressed genes across all the samples. Differential expression analysis was performed on the filtered normalized counts using the DREAM pipeline, ⁶⁵ which is built onto the limma‐voom platform ⁶⁶ available in the VariancePartition package. ⁶⁷ Along with diagnosis, age, and sex, other covariates included in the analysis were RNA concentration, intergenic percentage, intronic percentage, usable base percentage, correct strand reads percentage, and mRNA base percentage. ⁵⁵ Except for age, sex, diagnosis, and RNA concentration, the other covariates were derived from the quantification of aligned reads using Picard. DEGs were extracted using topTable function in the limma package. ⁶⁸

Pathway analyses included Ingenuity Pathway Analysis (IPA; Qiagen), Kyoto Encyclopedia of Genes and Genomes (KEGG), ⁶⁹ and Gene Ontology (GO) using DEGs (P < 0.05) obtained from layer III pyramidal neurons as described previously. ⁴¹ , ⁵⁵ For analysis via Reactome (REAC) total gene assessment was used, irrespective of P value, with genes sorted by fold change (FC) of > 1 or < 1 in each pairwise comparison performed using gProfiler (version e111_eg58_p18_30541362) with parameters specifically tailored to ensure precise identification of relevant pathways. ⁷⁰ The organism was set to Homo sapiens (hsapiens), and the query included 7321 genes. Pathway sources were restricted in REAC to focus on well‐characterized biological pathways. Only annotated genes were included in the analysis, with the false discovery rate method applied to control for multiple comparisons and a significance threshold for pathways set at 0.05. Additionally, results were highlighted to identify pathways that reached this significance threshold. Pathways showing either consistent upregulation or downregulation across AD stages were selected for further investigation. Results were visualized in Cytoscape using the pathway map template generated as previously described. ⁷⁰ , ⁷¹

3. RESULTS

3.1. Isolation specifically of excitatory pyramidal neurons from layer III frontal cortex

The somatodendritic compartment of post mortem human BA9 pyramidal neurons visualized by Nissl stain from layer III were microisolated via LCM (Figure S1A–C) for downstream gene and pathway‐based analyses via single population RNA‐seq. Multidimensional Scaling (MDS) on pairwise distances revealed separation of each clinical pathological diagnosis (Figure S1D). Using a comprehensive list of cell type–specific markers, ⁷² we confirmed the overwhelming majority of cell markers expressed in LCM captured BA9 neurons are excitatory (P < 0.001) compared to inhibitory neurons, oligodendrocytes, oligodendrocyte precursor cells, microglia, endothelial cells, and T cells (Table S2 in supporting information). Comparing excitatory neuronal and astrocytic markers, there is a trend difference in NCI (P = 0.057) and a significant difference (P < 0.004) in MCI (Table S2). No significant differences were found between excitatory neuronal markers and astrocytic markers in AD cases, possibly due to the identification of immune response elements expressed within neuronal populations in AD neurons. ⁷³

3.2. Identification of DEGs across the AD spectrum

Using the DREAM pipeline ⁶⁵ , ⁶⁷ to interrogate sequences derived from LCM captured layer III BA9 neurons, each diagnostic group was compared and DEGs were identified (P < 0.05). Comparing MCI to NCI, which we term the prodromal assessment, 1306 DEGs were identified, with 910 DEGs upregulated and 396 DEGs downregulated (Figure 1A, B). Comparing AD to MCI, herein termed the progression assessment, 2505 DEGs were identified, with 1008 DEGs upregulated and 1497 DEGs downregulated (Figure 1C, D). Comparing AD to NCI, termed the frank AD assessment, 3259 DEGs were identified, with 1694 DEGs upregulated and 1565 DEGs downregulated (Figure 1E, F).

Distribution of DEGs in layer III pyramidal neurons across the AD spectrum. DEGs were identified between MCI and NCI subjects, labeled as prodromal (A, B), AD and MCI labeled as progression (C, D), and AD and NCI labeled as frank AD (E, F). Histograms (A, C, E) show the distribution of DEGs by logFC. The majority of DEGs cluster in < 2 logFC, with fewer genes showing greater logFC. Purple indicates DEGs that were upregulated, and green indicates DEGs that were downregulated. Volcano plots (B, D, F) compare the logFC to the P value (shaded to reflect P < 0.05 or P < 0.01) of each DEG. Select DEGs are highlighted (green downregulated; purple upregulated). *ATP1A2*, sodium/potassium‐transporting ATPase subunit alpha‐2; AD, Alzheimer's disease; *ADAMTS2*, a disintegrin and metalloproteinase with thrombospondin motifs 2; *AQP4*, aquaporin 4; DEG, differentially expressed gene; *DUSP6*, dual‐specificity phosphatase 6; *ELK1*, ETS like‐1 protein Elk‐1; *GFAP*, glial fibrillary acidic protein; *LGALS3*, galectin‐3; logFC, log fold change; *LRRC37A*, leucine rich repeat containing 37A; *LRRTM4*, leucine rich repeat transmembrane neuronal 4; MCI, mild cognitive impairment; NCI, no cognitive impairment; *NKAIN4* sodium/potassium transporting ATPase interacting 4; *NPTX2*, neuronal pentraxin 2; *PDE6B*, rod cGMP phosphodiesterase subunit beta; *PPEF1*, serine/threonine‐protein phosphatase with EF‐hand domain 1; *SLC7A2*, solute carrier family 7 member 2; *VGF*, VGF nerve growth factor inducible; *ZNF502*, zinc finger protein 502.

Highly significant DEGs include genes/encoded proteins known to be dysregulated in AD including leucine‐rich repeats (LRR), ⁷⁴ , ⁷⁵ galectin 3 (LGALS3), ⁷⁶ ETS transcription factor (ELK1), ⁷⁷ , ⁷⁸ protein phosphatase with EF‐hand domain 1 (PPEF1), ⁷⁹ and glial fibrillary acidic protein (GFAP; ⁷³ , ⁸⁰ Figure 1B, D, F). RNA U5A small nuclear 1 (RNU5A‐1) goes from significantly downregulated in prodromal to significantly upregulated in progression, which we observe with several DEGs. In contrast, the majority of DEGs between progression and frank AD comparisons are directionally convergent, including upregulation of nuclear paraspeckle assembly transcript 1 (NEAT1), and downregulation of chromogranin B (CHGB).

Comparison of DEGs obtained from prodromal, progression, and frank AD were evaluated to determine directionality and overlap during disease progression. A total of 196 DEGs are dysregulated across all comparisons of clinical groups (Figure 2). The majority of these DEGs are directionally convergent (i.e., upregulated [or downregulated] between prodromal, progression, and/or frank AD), with 95 DEGs convergently upregulated and 40 DEGs convergently downregulated during disease progression (Figure 2A, B). Prodromal and progression comparisons were frequently observed to have directionally divergent DEGs (e.g., DEGs change direction from upregulation to downregulation or vice versa across cohorts). Directionally divergent DEGs between progression and frank AD are also directionally divergent between prodromal and progression, indicating unique gene expression alterations in prodromal compared to progression and frank AD. There are statistically more DEGs directionally divergent between prodromal and progression than between prodromal and frank AD (P < 0.001) or progression and frank AD (P < 0.01; Figure S2 in supporting information). A total of 500 from the 634 total shared DEGs between prodromal and progression are directionally divergent, compared to just 29 of the 1077 total shared DEGs between progression and frank AD (Figure 2A), further revealing the significant difference between prodromal and the two later stages.

Comparison of shared DEGs between prodromal, progression, and frank AD. A, Venn diagram depicts overlap of DEGs between prodromal (orange), progression (red), and frank AD (brown). Total DEGs in each category are shown. The number of DEGs shared between each condition are broken down into the number of convergent DEGs and the number of divergent DEGs. A total of 196 DEGs were found to be shared between all three comparisons. B, Many DEGs were divergent across the AD spectrum. While 95 DEGs were upregulated in each comparison and 40 were downregulated in all comparisons, the remaining 61 show divergence between prodromal and progression. C–E, Pie charts identify RNA sequenced to specific type of RNA. Sequenced RNA was principally protein coding 88.3% in prodromal (C), 88.7% in progression (D), 93.0% in frank AD (E). AD, Alzheimer's disease; DEG, differentially expressed gene.

3.3. IPA pathway analysis of DEGs across the AD spectrum

IPA was used to examine canonical pathways using DEGs identified across the AD spectrum. We curated a list of relevant pathways from the top 20 canonical pathways, which were compared among prodromal, progression, and frank AD (Figure 3). Key findings include the observation that the synaptogenesis signaling pathway is pervasively downregulated in all three stages (prodromal, progression, and frank AD) in BA9 layer III pyramidal neurons. In contrast, mitochondrial dysfunction is downregulated in early disease (prodromal) but upregulated (directionally divergent) in each of the later stages (progression and frank AD). Notably, actin cytoskeleton signaling is downregulated in prodromal, upregulated in progression, and downregulated in frank AD. DEGs that are directionally divergent within key pathways include mitochondrial dysfunction (130 total; 72 in prodromal, 95 in progression, and 108 in frank AD), actin cytoskeleton signaling (96 total; 63 in prodromal, 58 in progression, 73 in frank AD), and oxidative phosphorylation (58 total; 44 in progression, 52 in frank AD) that are statistically greater than the synaptogenesis signaling pathway (136 total; 79 in progression, and 98 in frank AD).

Curated list of canonical pathways from IPA analysis across the AD spectrum. IPA generated a list of all canonical pathways significantly differentiated in prodromal, progression, and frank AD. The top 20 pathways with the lowest P values in each comparison were examined and a curated list was formed. The –log(P value) of each pathway was compared and contrasted. IPA provides z scores to predict if pathways were upregulated (purple) or downregulated (green). Gray indicates that direction could not be predicted. Pathways in the top 20 are highlighted in darker colors, while pathways that were significantly differentiated, but not in the top 20 pathways, are shown in paler colors. Note that phagosome maturation and protein ubiquination pathway were not significantly differentiated in prodromal so no –log(P value) is given. AD, Alzheimer's disease; IPA, Ingenuity Pathway Analysis.

Canonical pathways in IPA were compared across the three clinical stages (Figure 4). Similar to observations with DEGs, the most abundant directionally divergent pathways were identified between prodromal and progression (65.22%, 180 pathways). A total of 29 DEGs are directionally divergent between progression and frank AD, although 157 pathways (50%) of shared canonical pathways are directionally divergent, indicating these 29 DEGs likely play key roles in cellular functions in late‐stage disease progression. Between prodromal and frank AD 99 pathways (35.61%) are directionally divergent.

Shared canonical pathways via IPA analysis across the AD spectrum. IPA analysis revealed several pathways were shared between prodromal, progression, and frank AD comparisons. The percentage of shared pathways (left) and total number of shared pathways (right) were broken down into those that were upregulated in both comparisons (purple), downregulated in both comparisons (green), or were divergent between comparisons (blue). Canonical pathways were principally shared between progression and frank AD comparisons. AD, Alzheimer's disease; IPA, Ingenuity Pathway Analysis.

3.4. KEGG pathway analysis of DEGs across the AD spectrum

In parallel with the IPA analysis, KEGG was used to interrogate relevant pathways using DEGs across the AD spectrum. No pathways were identified as significantly differentiated in KEGG in prodromal comparisons. Several neurodegenerative pathways including “Alzheimer disease” and “Pathways of neurodegeneration ‐multiple diseases” were significantly altered in both progression and frank AD (Table 1). A key dysregulated pathway, “Alzheimer disease” is shown for frank AD (Figure 5), which indicates rapidly accelerated fibrosarcoma kinase (Raf), nuclear factor kappa light chain enhancer of activated B cells (NFK‐b), and protein kinase B (Akt), among others, are upregulated while downregulated genes include amyloid precursor protein (APP), N‐methyl‐D‐aspartate receptor (NMDAR), and phospholipase C (PLC). Additionally, “Synaptic vesicle cycle” and “Oxidative phosphorylation” pathways were altered in both progression and frank AD, similar to results seen in IPA. “Endocytosis” is altered in progression (late stage of disease progression), but not in frank AD, suggesting this pathway plays a critical role in the differentiation between MCI and AD.

TABLE 1.

Curated list of pathways via KEGG analysis.

	Progression	Frank AD
Curated KEGG pathways	−log(P value)	−log(P value)
Proteasome	4.19	3.91
Synaptic vesicle cycle	3.94	4.01
Endocytosis	3.28
Ribosome	3.03	6.78
Huntington disease	17.11	13.23
Parkinson disease	13.95	13.20
Prion disease	13.87	13.43
Oxidative phosphorylation	13.84	11.64
Alzheimer disease	12.15	13.33
Amyotrophic lateral sclerosis	11.97	9.16
Pathways of neurodegeneration—multiple diseases	10.68	13.56
Focal adhesion		10.42
Regulation of actin cytoskeleton		10.20
MAPK signaling pathway		7.23
Rap1 signaling pathway		7.19

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Note: KEGG generated a list of all canonical pathways significantly differentiated in progression and frank AD. No pathways were significantly different between MCI and NCI (prodromal) subjects. The top 20 pathways in each comparison were examined, and a curated list was formed. The −log(P value) of each pathway was compared. KEGG does not provide z scores to predict the direction of pathway activation. Pathways that were significantly differentiated but not in the top 20 pathways are highlighted in gray.

Abbreviations: AD, Alzheimer's disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; MCI, mild cognitive impairment; NCI, no cognitive impairment.

KEGG pathway “Alzheimer Disease” identified in frank AD. “Alzheimer Disease” is depicted as a representative color coded KEGG pathway highlighting changes seen in the frank AD comparison (AD vs. NCI). While KEGG does not provide z scores for the whole pathway as IPA does, they do provide graphical representation of each pathway. Each identified encoded protein is colored for the z score on a scale of −3 to 3. Highly upregulated DEGs are shown in darker purple while highly downregulated DEGs are shown in darker green. Encoded proteins colored white were not statistically different between AD and NCI subjects. AD, Alzheimer's disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; IPA, Ingenuity Pathway Analysis; NCI, no cognitive impairment.

3.5. GO pathway analysis of DEGs across the AD spectrum

GO pathway analyses ⁸¹ were used to inform on biological processes dysregulated during disease progression. We grouped relevant biological processes and counted the number of pathways changed across prodromal, progression, and frank AD comparisons. Distinct changes are observed between each comparison, showing that early‐ and late‐stage changes are unique disease signposts (Figure 6, Table S3 in supporting information). The signal transduction category, while represented in all three comparisons, exhibits relatively more dysregulated processes in prodromal (24.47%) than in progression (7.86%) and frank AD (16.17%). Processes involving RNA and protein show higher relative dysregulation in progression (13.93% and 32.50%, respectively) compared to prodromal (2.13%, 20.75%) and frank AD (3.75%, 26.04%), while the receptor category has relatively few dysregulated processes in progression (4.64%; Figure 6A–C). Autophagy is observed in progression and frank AD. There is only one pathway involving either exocytosis or endocytosis (termed here as “‐cytosis”) in prodromal, suggesting endocytic and exocytic dysregulation are limited to the later stages of AD progression in layer III pyramidal neurons (Table S3). In addition to having distinct distributions of biological processes, prodromal has 43 unique processes including 11 involved in signaling, 9 involved in protein, and 9 involved in receptors (Figure 6A, D, E). Progression has 72 unique processes including 36 involving RNA and 17 involving protein (Figure 6B, D, E). Frank AD has 232 unique processes including 26 developmental processes, 49 involving protein, and 39 involved in signaling (Figure 6C, D, E). Frank AD has a greater number of DEGs than prodromal and progression, and not surprisingly, more unique processes determined by bioinformatic inquiry. Overall, the top GO categories for prodromal and frank AD are protein, signal transduction, and synaptic, while the progression top three are protein, RNA, and synaptic (Table S3). Together, these findings suggest processes involving formation or breakdown of protein and those involving synaptic function are vital at all stages of disease progression.

Biological processes differentiated by GO analysis across the AD spectrum. GO analysis was used to categorize biological processes differentiated in prodromal (A), progression (B), and frank AD (C). Processes were grouped into those labeled axon, ‐cytosis (exocytosis, endocytosis, and phagocytosis), dendritic, developmental processes, neurotransmitter, protein, receptors, RNA, signal transduction, synaptic, and autophagy. Pie charts (A–C) illustrate the percentage each group comprises. Bar charts show the number (D) and percentage (E) of processes in each group unique to each comparison. Prodromal (orange), progression (red), and frank AD (brown) bars are shown. AD, Alzheimer's disease; GO, Gene Ontology.

Pathways involved in neuronal growth and function, including axonal, dendritic, and synaptic, among others, were further assessed in GO (Table S4 in supporting information). Neuronal function was altered at each stage of disease, but in distinct ways. Prodromal showed alterations in neurotransmitter uptake and neurotransmitter biosynthesis which were not affected in late‐stage AD. Alternatively, frank AD had changes in endocytosis, axon regeneration, dendritic spine development, and GABAergic synaptic transmission that were not altered in MCI (prodromal or progression). Several pathways that were changed in all three stages showed higher significance as disease progressed, including regulation of trans‐synaptic signaling and regulation of synaptic plasticity. This is not reflective of every single pathway, as some select pathways, including regulation of synapse organization display a small P value at progression.

3.6. Reactome pathway analysis of DEGs across the AD spectrum

Thus far the bioinformatic analysis was limited to DEGs with statistical significance of P < 0.05. However, this threshold alone may not provide a comprehensive understanding of how widespread transcriptomic changes across the AD spectrum may impact cellular and molecular functionality. To better capture these global changes and their functional implications, the analysis was expanded to include all DEGs with log fold changes (logFC) > 1 and < 1, regardless of P value. Using these broader DEG sets, pathway enrichment analysis was conducted using gProfiler ⁸² with resulting analysis limited to significant Reactome pathways (P‐adjust < 0.05) to provide a structured overview of functional impacts. The enriched pathways were then mapped onto a previously reported visually intuitive Cytoscape template. ⁷⁰ , ⁸³ This template organizes pathways in a “geographical map” format, where functional relationships are analogous to continents and countries, making it easy to visualize and comprehend the interconnectedness of pathways and the overall functional disruption caused by transcriptomic changes. This approach complements the DEG‐centric analysis by offering a global perspective on transcriptomic shifts and their broader implications in AD pathophysiology.

The pathway maps revealed distinct patterns of transcriptomic dysregulation across the stages of AD, highlighting a shift in the balance of upregulated versus downregulated pathways (Figure 7, Table S5 in supporting information). In prodromal, upregulated transcripts were associated with 273 enriched pathways, while only 95 pathways were linked to downregulated transcripts. However, as the disease progresses, this pattern reverses. In progression, 372 pathways were impacted by downregulated transcripts compared to just 61 by upregulated transcripts. A similar trend is observed in frank AD, with 341 pathways influenced by downregulated transcripts and 105 by upregulated transcripts. This shift suggests a dynamic reorganization of functional pathways as AD advances.

Mapping global pathway dysfunctions across the AD spectrum. Global pathway enrichment changes identified by DEGs across the AD spectrum (prodromal, progression, and frank AD) using Reactome pathway enrichment analysis, visualized in Cytoscape. Each circle on the map represents a protein pathway, with pathways highlighted by color‐coded P values reflecting their significance. The identity of representative pathways is indicated by text labels next to the corresponding circles. The left panels show downregulated pathways, and the right panels show upregulated pathways. Dysregulation in the prodromal stage is distinct, with pathways impacted by directionally divergent transcript changes, whereas pathways in the progression and frank AD stages show greater directional convergence. These maps provide a comprehensive, visually intuitive representation of how transcriptomic changes affect global functional pathways across different stages of AD. AD, Alzheimer's disease; DEG, differentially expressed gene.

4. DISCUSSION

We interrogated gene expression alterations (DEGs, biological pathways, and interactomes) across the AD spectrum (NCI, MCI, and AD) using microisolated corticocortical projection layer III pyramidal neurons from human post mortem prefrontal cortex (BA9). The highest number of DEGs were found between NCI and AD (frank AD). The present unbiased findings support our hypothesis that the greatest number of gene changes occur as the disease progresses to later stages. However, DEGs dysregulated in prodromal stage may be of most interest in the context of diagnosis and prevention, as intervening early in the disease process is ideal from a targeted therapeutic standpoint. The 288 DEGs found exclusively in the prodromal stage are presumably the first genes to be affected in the neurodegenerative process in the progression of AD. To reiterate, these DEGs are significantly dysregulated initially, but do not progress with disease, making them candidates for early disease markers. This type of precision analysis across the AD spectrum has only been enabled by single population analysis of vulnerable neurons that contribute to the default mode network and are thought to be key components of executive function.

The relatively low number of transcript changes seen in prodromal compared to later stages of the disease aligns with the proposition that dysregulation in the proteome—beyond expression changes—may be the primary driver of functional disruptions in early AD. ⁷⁰ , ⁸³ , ⁸⁴ , ⁸⁵ , ⁸⁶ Proteomic alterations, such as post‐translational modifications or changes in protein–protein interactions, may precede extensive transcriptomic changes and contribute to the subtle yet significant functional impairments characteristic of the prodromal phase.

A total of 196 DEGs in BA9 layer III pyramidal neurons are shared under all three comparisons (prodromal, progression, and frank AD). We propose these shared DEGs can be candidates for biomarkers of the entire AD spectrum (e.g., pan‐AD markers) as they are altered both in early‐ and late‐stage degeneration. Among key genes upregulated under all three conditions is GFAP. Though traditionally thought of as an astrocyte marker, several independent research groups have detected GFAP upregulation in AD neurons, ⁷³ , ⁸⁰ , ⁸⁷ , ⁸⁸ consistent with the present findings, indicating in a disease state that markers from one cell type can be found in other cell types that does not typically occur under normative conditions. While a benefit of single population RNA‐seq analysis is the DEG and subsequent pathway analysis is derived from defined cell types, we cannot completely rule out potential signals emanating from other nearby cell types. To investigate whether glial contamination occurred, we interrogated expression levels of hundreds of cell type markers (Table S2). We found no correlation with markers for microglia, oligodendrocytes, oligodendrocyte progenitor cells, or endothelial cells. Excitatory neuronal markers are present in NCI, MCI, and AD samples. There are more excitatory neuronal markers than astrocytic markers in NCI (P < 0.057; trend) and MCI (P < 0.05). There are astrocytic markers present specifically in AD, likely reflecting that neuroinflammation is upregulated in AD. ⁷ , ⁸ , ⁹ , ⁸⁹ , ⁹⁰

DEGs shared between frank AD and prodromal or progression were found to be principally directionally convergent. By contrast, DEGs in prodromal and progression are 78.9% directionally divergent. We postulate this is a compensatory mechanism through progression of the AD. We posit DEGs upregulated in prodromal serve as a compensatory mechanism at disease onset but cannot overcome progressive degeneration and downregulate in progression. Conversely, 11 DEGs are first downregulated in prodromal, but as neurons degenerate, they upregulate in progression. Collectively, these findings of directionally divergent DEGs indicate that the transition from MCI to AD is a tipping point in the pathogenesis of AD.

A key finding from this study is that the pathway comparison data across the AD spectrum demonstrates clear differences among prodromal, progression, and frank AD. This is evidenced by the finding that multiple key pathways show each entity is distinct from one another. Changes in oxidative phosphorylation and mitochondrial dysfunction in IPA (Figure 3) and synaptic signaling and endocytosis/exocytosis in GO analysis (Figure 6) are specific to each disease stage. GO pathway analysis indicates the prodromal comparison is separate from both the progression and frank AD comparisons (Figure 6). This suggests the earliest stages of the disease are distinct and identifiable via DEG and pathway analysis of vulnerable pyramidal neurons. Further, both IPA and KEGG analyses reveal key unique pathways change at discrete stages of progression across the AD spectrum (Figure 3 and Table 1). Through KEGG, which contains more disease pathways than IPA, progression and frank AD had several neurodegenerative pathways that were significantly altered. This coincides with the progression of cellular degeneration as well as increased neuropathology. IPA revealed affected pathways including oxidative phosphorylation and mitochondrial dysfunction, supported independently by previous studies in post mortem AD brain and relevant animal models. ⁹¹ , ⁹² , ⁹³ , ⁹⁴ , ⁹⁵ , ⁹⁶ , ⁹⁷ , ⁹⁸ , ⁹⁹ , ¹⁰⁰ , ¹⁰¹

Reactome findings support the dynamic reorganization of functional pathways as AD progresses. During the prodromal stage, the prevalence of upregulated pathways may reflect compensatory mechanisms attempting to counteract early dysfunctions and maintain homeostasis. However, as the disease progresses, the dominance of downregulated pathways likely indicates widespread loss of cellular functionality and impaired regulatory networks (Figure 7 and Table S5 in supporting information). These findings underscore a key aspect of AD pathology: the gradual collapse of cellular systems, transitioning from an initial state of adaptation to one of progressive dysfunction and failure. This trajectory is consistent with the broader understanding of AD as a disease of systems‐level collapse, in which compensatory processes ultimately give way to irreparable deficits.

Potential limitations of the present study include a lack of ante mortem data available on individuals within the subject cohort and a small sample size. Having extensive preclinical data would provide deeper insights into the correlations among DEGs, dysregulated biological pathways, and cognitive decline. Unfortunately, these data were unavailable, which is a common issue when using post mortem human brain tissue. Additionally, this study is limited to transcriptomic assessments. While changes in gene expression levels are informative, further experimentation is required to validate key changes at the encoded protein and metabolomic levels, which has yet to be operationalized at the single population level for post mortem human brain tissues. ³⁶ Moreover, additional studies are required to identify which hub proteins are drivers of degeneration, such as modeling in relevant animal models and human‐derived cells for mechanistic interactions. As presented in the Results section, we provide evidence for > 200 candidates for biomarkers of early degeneration, but they may not all be easily accessible or verifiable in clinical pathological studies. For example, alterations seen in layer III pyramidal neurons may not be faithfully represented in biofluids used for biomarker studies, such as peripheral blood/plasma and/or cerebrospinal fluid, necessitating further investigations.

In summary, single population profiling of vulnerable layer III BA9 pyramidal neurons across the AD spectrum demonstrates that even with some overlap, NCI, MCI, and AD are independent entities. We identify distinct changes in gene expression and key bioinformatic pathways from prodromal, progression, and frank AD stages. Genes changed only from NCI to MCI (prodromal) are prime candidates for early disease biomarkers, whereas genes changed in all three conditions are candidates for pan‐AD biomarkers. These data can inform future studies of therapeutic targets and highlight the importance of evaluating single populations of vulnerable neurons across the AD spectrum.

CONFLICT OF INTEREST STATEMENT

Declarations of interest: none. Author disclosures are available in the supporting information.

CONSENT STATEMENT

Consent was not necessary.

Supporting information

Supporting Information

ALZ-21-e70395-s003.pdf^{(1.6MB, pdf)}

Supporting Information

ALZ-21-e70395-s002.pdf^{(420.4KB, pdf)}

Supporting Information

ALZ-21-e70395-s001.pdf^{(609.5KB, pdf)}

ACKNOWLEDGMENTS

Human tissue was obtained from the NIH NeuroBioBank. Funding was provided by support from grants AG014449, AG072599, AG074004, AG077103, AG052909, and AG085572 from the National Institutes of Health.

Labuza A, Alldred MJ, Pidikiti H, et al. Frontal cortex pyramidal neuron expression profiles differentiate the prodromal stage from progressive degeneration across the Alzheimer's disease spectrum. Alzheimer's Dement. 2025;21:e70395. 10.1002/alz.70395

DATA AVAILABILITY STATEMENT

The dataset(s) supporting the conclusions of this article are available as follows. Single population RNA‐seq data analyzed within this study are available from GEO (www.ncbi.nlm.nih.gov/geo; GSE285831) or from the corresponding author upon request. LCM images for brain tissue (synapse.org; syn64504132) and RNA/library metadata (syn64504151) are available from the Ginsberg lab portal on www.synapse.org upon request.

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Supporting Information

ALZ-21-e70395-s003.pdf^{(1.6MB, pdf)}

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ALZ-21-e70395-s002.pdf^{(420.4KB, pdf)}

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Data Availability Statement

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PERMALINK

Frontal cortex pyramidal neuron expression profiles differentiate the prodromal stage from progressive degeneration across the Alzheimer's disease spectrum

Amanda Labuza

Melissa J Alldred

Harshitha Pidikiti

Michael H Malek‐Ahmadi

Sang Han Lee

Adriana Heguy

Paul D Coleman

Souparna Chakrabarty

Gabriela Chiosis

Elliott J Mufson

Stephen D Ginsberg

Abstract

INTRODUCTION

METHODS

RESULTS

DISCUSSION

Highlights

1. BACKGROUND

2. METHODS

2.1. Case materials

2.2. Tissue preparation

2.3. Laser capture microdissection

RESEARCH IN CONTEXT

2.4. RNA isolation and library preparation

2.5. Statistics and bioinformatics

3. RESULTS

3.1. Isolation specifically of excitatory pyramidal neurons from layer III frontal cortex

3.2. Identification of DEGs across the AD spectrum

FIGURE 1.

FIGURE 2.

3.3. IPA pathway analysis of DEGs across the AD spectrum

FIGURE 3.

FIGURE 4.

3.4. KEGG pathway analysis of DEGs across the AD spectrum

TABLE 1.

FIGURE 5.

3.5. GO pathway analysis of DEGs across the AD spectrum

FIGURE 6.

3.6. Reactome pathway analysis of DEGs across the AD spectrum

FIGURE 7.

4. DISCUSSION

CONFLICT OF INTEREST STATEMENT

CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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