A single-cell atlas of ABCA7 loss-of-function reveals lipid disruptions, mitochondrial dysfunction and DNA damage in neurons

von Maydell, D.; Wright, S.; Bonner, J. M.; Pao, J.; Menchaca, G. S.; Welch, G.; Boix, C. A.; Mathys, H.; Leclerc, G.; Leary, N.; Samaan, G.; Kellis, M.; Tsai, L.-H.

doi:10.1101/2023.09.05.556135

bioRxiv · 10.1101/2023.09.05.556135

A single-cell atlas of ABCA7 loss-of-function reveals lipid disruptions, mitochondrial dysfunction and DNA damage in neurons

von Maydell, D.·Wright, S.·Bonner, J. M.·Pao, J.·Menchaca, G. S.·Welch, G.·Boix, C. A.·Mathys, H.·Leclerc, G.·Leary, N.·Samaan, G.·Kellis, M.·Tsai, L.-H.

View original source ↗Export metadata ↓

Abstract

Loss-of-function (LoF) variants in the lipid transporter ABCA7 significantly increase Alzheimers disease risk (odds ratio {approx} 2), yet the underlying pathogenic mechanisms and specific neural cell types affected remain unclear. To investigate this, we generated a single-nucleus RNA sequencing atlas of 36 human postmortem prefrontal cortex samples, including 12 carriers of ABCA7 LoF variants and 24 matched non-carriers. ABCA7 LoF variants were associated with transcriptional changes across all major neural cell types. Excitatory neurons, which expressed the highest levels of ABCA7, showed significant alterations in oxidative phosphorylation, lipid metabolism, DNA damage responses, and synaptic signaling pathways. ABCA7 LoF-associated transcriptional changes in neurons were similarly perturbed in carriers of the common AD missense variant ABCA7 p.Ala1527Gly (n = 240 controls, 135 carriers) predicted by molecular dynamic simulations to disrupt ABCA7 structure -, indicating that findings from our study may extend to large portions of the at-risk population. Human induced pluripotent stem cell (iPSC)-derived neurons carrying ABCA7 LoF variants closely recapitulated the transcriptional changes observed in human postmortem neurons. Biochemical experiments further demonstrated that ABCA7 LoF disrupts mitochondrial membrane potential via regulated uncoupling, increases oxidative stress, and alters phospholipid homeostasis in neurons, notably elevating saturated phosphatidylcholine levels. Supplementation with CDP-choline to enhance de novo phosphatidylcholine synthesis effectively reversed these transcriptional changes, restored mitochondrial uncoupling, and reduced oxidative stress. Additionally, CDP-choline normalized amyloid-{beta} secretion and alleviated neuronal hyperexcitability in ABCA7 LoF neurons. This study provides a detailed transcriptomic profile of ABCA7 LoF-induced changes and highlights phosphatidylcholine metabolism as a key driver in ABCA7-induced risk. Our findings suggest a promising therapeutic approach that may benefit a large proportion of individuals at increased risk for Alzheimers disease.

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

von Maydell, D., Wright, S., Bonner, J. M., Pao, J., Menchaca, G. S., Welch, G., Boix, C. A., Mathys, H., Leclerc, G., Leary, N., Samaan, G., Kellis, M., Tsai, L.-H.. 2023-09-06. A single-cell atlas of ABCA7 loss-of-function reveals lipid disruptions, mitochondrial dysfunction and DNA damage in neurons. https://doi.org/10.1101/2023.09.05.556135

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗