Scientists reveal how different brain cells contribute to the progression of Alzheimer’s, unlocking new insights for developing personalized treatments and improving the accuracy of diagnosis across all disease stages.
Study: Integrated multimodal cell atlas of Alzheimer’s disease. Image credits: illustrissima / Shutterstock
This is evident from a recent study published in the journal Nature Neuroscience, researchers combined single-nucleus RNA sequencing (snRNA-seq), spatial genomics, single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq), multiomics and pre-existing reference atlases to identify the molecular and cellular changes in the middle of the time to evaluate gyrus (MTG) across the spectrum of Alzheimer’s disease (AD) progression.
They further used quantitative neuropathology in combination with a machine learning model to come up with a patient-specific pseudoprogression score (CPS), a continuous metric that ranks donors along a neuropathological continuum.
Study results revealed the presence of two different major disease phases (early/slow and late/exponential), each with unique cell physiology.
A small subset of donors in the study also showed a third ‘terminal’ stage of disease, characterized by more severe pathology.
In particular, the article provides a framework for integrating previously confusing lines of evidence, allowing cross-validation of observations of Alzheimer’s disease across studies, thereby increasing the robustness and consistency of research findings in seemingly disjointed studies.
Background
Alzheimer’s disease (AD) is a neurological disorder characterized by the progressive accumulation of amyloid beta (Aβ) plaques and hyperphosphorylated Tau (pTau) in the brain, brainstem, and limbic systems. This results in shrinking of brain cells, loss of neural connections, and even cell degradation and death, leading to loss of memory and routine functional abilities.
AD is a global public health risk, currently estimated to impact more than 55 million patients and their families. Alarmingly, epidemiological projections expect the prevalence of AD to increase to 78 million in 2030 and 139 million in 2050, making it the fastest growing neurological disease in the world today.
AD is the main risk association with dementia, further discounting neuroscientific research aimed at determining the risk factors, pathophysiological mechanisms and severity of the disease.
Cell morphology and physiological changes during AD progression have been extensively characterized, resulting in the formulation of ‘aggregate scores’ (e.g. Braak, CERAD, Thal and ADNC) to describe the severity of AD.
Unfortunately, conventional studies routinely describe site-specific changes in the brain but fail to specify the vulnerable and disease-associated cell type-specific changes that accompany AD progression.
Recent advances in spatial and single-cell genomics technologies, together with the widespread adoption of multiomics analyses, have given rise to ‘brain cell atlases’: detailed, high-resolution, brain-wide cell physiological references of cellular properties in genomics, transcriptomics and patch-sequencing data approaches .
These provide a standardized knowledge base of AD and dementia-associated brain cell types, significantly increasing our understanding of the processes underlying the risk and severity of AD.
In particular, these approaches make it possible to map cellular changes in highly curated brain reference atlases, improving our understanding of which cell types are most vulnerable in the early and late stages of the disease.
About the study
The current study combines high-resolution single-nucleus and multiomics approaches with quantitative neuropathology-inspired temporal disease modeling, highlighting the numerous unique cell types and the changes they experience as AD progresses. It targets the middle temporal gyrus (MTG), the brain region associated with semantic retrieval and language processing.
It highlights the cell characteristics and location and the morphological or gene expression changes that occur during different stages of AD.
Because AD stages are poorly defined (dependent on conventional aggregate scores), the current study uses quantitative neuropathology in combination with immunohistochemistry (IHC) and Bayesian inference models to identify discrete AD progression stages.
Study data were obtained from 84 postmortem donors (51 women, ages – 65 to 102) from two independent studies (University of Washington ADRC and Kaiser Permanente Washington Health Research Institute ACT Study) consolidated into the UW BioRepository and Integrated Neuropathology (BRaIN) dataset.
Participants were screened for those undergoing a “precision rapid procedure” – a standardized methodology for optimized tissue collection and preservation – while excluding those with a history of confounding degenerative conditions.
Experimental procedures included immunohistochemistry (IHC; single and duplex) for quantitative neuropathological analyses. IHC outcomes were used within a Bayesian inference framework to calculate a new ‘continuous pseudoprogression score (CPS)’, an objective measure in which study participants were ordered along a neuropathological continuum of AD progression.
Single-nucleus cell isolations were obtained from MTG cortical areas using cryo-dissections, flow cytometry, and snRNA-seq libraries constructed from these nuclei. Genomic measurements were compared to reference atlases using platforms such as ChromA, providing unprecedented resolution on cell type-specific vulnerabilities. Spatial transcriptomics (MERSCOPE platform) and patch-seq data (publicly available) completed the dataset, allowing validation of the CPS and estimation of the electrophysiological characteristics of different cell types at different AD stages.
Findings of the study
The current study updates the BRAIN Initiative Cell Census Network (BICCN) with new MTG-specific cell and disease stage information.
The quantitative neuropathologically derived CPS metric calculated here demonstrated the presence of two typical epochs (early/slow progression and late/exponential progression) epochs in AD progression. A rare subset of older patients further demonstrated a third ‘terminal’ era.
“In the early era, donors had sparse Aβ plaques (although they were increasing in size) and pTau plaques+ tangle-bearing neurons, accompanied by early increases in inflammatory or reactive microglial and astrocytic states and associated gene expression changes in relevant inflammatory and plaque-induced genes. In the later era, there is an exponential increase in Aβ and pTau pathology, a continued increase in inflammatory microglia and astrocyte states, and a decrease in the expression of both the OPC differentiation program and oligodendrocyte expression of myelin-associated proteins (formerly characterized using quantitative PCR).”
The study identified multiple vulnerable cell types, including excitatory neurons in layer 2/3 (L2/3 IT), somatostatin (Sst) inhibitory neurons and oligodendrocytes. These cells showed early vulnerability, while other types such as Pvalb+ interneurons deteriorated later in disease progression.
Extensive, previously hidden cascades of cell type-specific activation and excitation were observed, suggesting that microglial activation during early (mildly severe) AD stages causes losses of astrocytes, oligodendrocytes and corticocortical (L2/3 IT) neurons, which in turn contributes to cognitive dysfunction.
Furthermore, spatial transcriptomics confirmed the correlation between specific vulnerable cell populations and AD severity, especially in supragranular layers of the cortex.
These observations were strongest among participants who showed the most extreme cognitive decline later in life, suggesting a biological underpinning. CPS further analyzes identified neuronal and non-neuronal subtypes at increased risk for AD and dementia (n=58).
Most importantly, the current work provides a platform and methodologies that enable integration, direct comparisons, and standard annotations (cell states and types), increasing the consistency and robustness of future AD research.
Conclusions
The current study uses multiple advanced neuropathological techniques, including single-cell nuclear genomics, multiomics and quantitative neuropathological analyses, to reveal changes in individual MTG cell types at different stages of AD progression. It identifies distinct eras of AD progression and the physiological changes associated with these stages.
The study also highlights specific vulnerable neuronal subtypes, such as Sst and L2/3 IT neurons, and their critical role in cognitive decline associated with AD. It unravels genetic, demographic and behavioral risk associations, potentially exacerbating the severity of AD.
Most importantly, the current work provides a database and standardization suggestions to improve future AD research.