A single cell brain atlas in human Alzheimer’s disease

Grubman, A.; Chew, G.; Ouyang, J. F.; Sun, G.; Choo, X. Y.; McLean, C.; Simmons, R.; Buckberry, S.; Vargas-Landin, D.; Pflueger, J.; Lister, R.; Rackham, O. J. L.; Petretto, E.; Polo, J. M.

doi:10.1101/628347

bioRxiv · 10.1101/628347

A single cell brain atlas in human Alzheimer’s disease

Grubman, A.·Chew, G.·Ouyang, J. F.·Sun, G.·Choo, X. Y.·McLean, C.·Simmons, R.·Buckberry, S.·Vargas-Landin, D.·Pflueger, J.·Lister, R.·Rackham, O. J. L.·Petretto, E.·Polo, J. M.

View original source ↗Export metadata ↓

Abstract

Alzheimers disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimers patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.\n\nHighlightsO_LIWe generated the first human single cell transcriptome in AD patient brains\nC_LIO_LIOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)\nC_LIO_LIOur analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categories\nC_LIO_LIOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clusters\nC_LIO_LIWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clusters\nC_LIO_LIFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.\nC_LI\n\nData resource web interface:http://adsn.ddnetbio.com

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Grubman, A., Chew, G., Ouyang, J. F., Sun, G., Choo, X. Y., McLean, C., Simmons, R., Buckberry, S., Vargas-Landin, D., Pflueger, J., Lister, R., Rackham, O. J. L., Petretto, E., Polo, J. M.. 2019-05-14. A single cell brain atlas in human Alzheimer’s disease. https://doi.org/10.1101/628347

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Circuit and cell-type mechanisms of the spectrolaminar motif of cortical oscillations

Oscillations are a key feature of cortical activity, coordinating spike timing across areas, layers, and cell types. Recent work suggests these rhythms follow a stereotyped organization across cortical depth, the spectrolaminar (SL) motif, with higher-frequency gamma prominent in superficial layers and lower-frequency alpha/beta in deep layers, yet the mechanisms generating it remain poorly understood. Using a bio-realistic model of mouse primary visual cortex, we reproduced the SL motif and found it preserved across diverse visual stimuli. Systematic cell-type- and layer-resolved perturbations showed that the alpha/beta component was intrinsic to every layer, whereas the 50-80 Hz gamma component was predominantly generated within layer 4 (L4) and propagated network-wide through inter-laminar connections. Pvalb interneurons were the principal regulators of gamma, Sst cells shaped low-frequency power, and Vip cells controlled oscillation magnitude. The temporal properties of L4 Pvalb-to-excitatory synapses were especially influential in generating the SL motif. These models and simulation results thus offer a plausible description of circuit mechanisms underlying cortical oscillations and the SL motif and are publicly available to facilitate further studies.

neuroscience↗

bioRxiv2026

Personalized Prediction of Differences in fMRI Connectivity Estimates Across Preprocessing Pipelines

Longitudinal functional MRI of the human brain is valuable for tracking changes in functional connectivity (FC) associated with disease progression or treatment response. Even so, measured FC values depend on preprocessing, and nuisance regression and temporal filtering affect individuals differently. We examined whether we could use earlier scans to predict how preprocessing would affect a later scan. In 69 ADNI (Alzheimers Disease Neuroimaging Initiative) participants scanned three times, we measured how nuisance regression and filtering, alone and in sequence, changed the measured FC. A ridge regression model combined these responses with baseline FC from the third scan to predict the effect of reversing the processing order. Just four coefficients were shared across 79,800 connections. In leave-one-participant-out testing, mean squared error was 0.012, compared with 0.017 for predicting the same average response for everyone. It outperformed the best of seven alternative predictions chosen for each participant (0.013; normalized gain 0.069, 95% bootstrap interval 0.006-0.134), with lower error in 44 of 69 participants. About half the benefit of using each persons earlier responses, rather than the average across participants, came from which connections changed. This advantage persisted after accounting for acquisition variables. The ADNI-trained model reduced mean error in ten Midnight Scan Club participants without refitting. These results support using personal preprocessing history to predict changes in connectivity estimates on newly acquired scans.

neuroscience↗

bioRxiv2026

Temporal Structure in Resting-State fMRI under Marginal and Cross-Spectral Surrogate Controls

Resting-state functional MRI is commonly analyzed using correlations between regional signals and their frequency spectra, which characterize average connectivity and spectral properties but may miss other aspects of temporal organization. Measures of temporal complexity offer complementary information about the recurrence of signal patterns, how fluctuations vary across timescales, and signal roughness. These properties may reveal disease-related alterations in brain activity, offering opportunities for biomarker discovery and insights into mechanisms of neurological and psychiatric disorders. Here we examined sample entropy (SampEn), multifractal de-trended fluctuation analysis (MFDFA), and Higuchi curve shapes in 732 scans from 270 ADNI participants, including cognitively normal individuals, people with mild cognitive impairment, and people with dementia. Using the seven Yeo resting-state networks (Yeo-7), we compared recordings with randomized signals preserving either each networks amplitude distribution and approximate spectrum (IAAFT), or the complete cross-spectrum across networks. If these properties fully explained temporal complexity, real recordings should resemble their randomized references. We therefore counted how often each real recording was the strongest outlier among itself and 20 references. This occurred in 21.7%, 12.7%, and 44.2% of 5,124 network-visits for SampEn, MFDFA, and Higuchi, versus 5.5%, 5.1%, and 6.3% in surrogate-only controls. Cross-spectrum-preserving references yielded 39.8%, 17.5%, and 49.8%. Simulations showed that changes in sampling or signal processing, including abrupt changes in signal amplitude, can produce similar differences without changing the underlying dynamics. The measures showed repeatability across visits but did not improve retrospective prediction of changes in Clinical Dementia Rating Sum of Boxes (CDR-SB) scores. These findings identify temporal features not reproduced by the tested spectral controls and provide a basis for evaluating their potential as disease biomarkers.

neuroscience↗