bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.07.08.602186

MJF-14 proximity ligation assay detects early non-inclusion alpha-synuclein pathology with enhanced specificity and sensitivity

Jensen, N. M.·Fu, Y.·Betzer, C.·Li, H.·Elfarrash, S.·Shaib, A. H.·Krah, D.·Vitic, Z.·Reimer, L.·Gram, H.·Buchman, V.·Denham, M.·Rizzoli, S. O.·Halliday, G. M.·Jensen, P. H.
View original source ↗Export metadata ↓

Abstract

Lewy pathology, consisting of Lewy bodies and Lewy neurites, is the pathological hallmark of synucle-inopathies such as Parkinsons disease and dementia with Lewy bodies, but it is generally thought to represent late-stage pathological changes. In contrast, -synuclein oligomers are regarded as early-stage pathology, likely involved in disease progression and cellular toxicity. Oligomers, however, are not de-tected by standard immunohistochemistry but require specific detection techniques such as the proxim-ity ligation assay (PLA). Here, we describe the MJF-14 PLA, a new PLA towards aggregated -synuclein with unprecedented specificity, attained by the utilization of aggregate conformation-specific -synu-clein antibody MJFR-14-6-4-2 (hereafter MJF-14). Signal in the assay directly correlates with -synuclein aggregation in SH-SY5Y cells, as treatment with aggregation inhibitor ASI1D significantly lowers PLA sig-nal. In human cortical neurons, MJF-14 PLA detects pre-formed fibril-induced aggregation, especially prominent when using stealth PFFs invisible to the MJF-14 antibody. Co-labelling of MJF-14 PLA and pS129--synuclein immunofluorescence in post-mortem dementia with Lewy bodies cases showed that while the MJF-14 PLA reveals extensive non-inclusion pathology, it is not sensitive towards Lewy bodies. In Parkinsons disease brain, direct comparison of PLA and IHC with the MJF-14 antibody, combined with machine learning-based quantification, showed striking -synuclein pathology preceding the formation of conventional Lewy pathology. The majority of the PLA-revealed non-inclusion pathology was found in the neuropil, including some clearly located in the presynaptic terminals. With this work, we introduce an improved -synuclein aggregate PLA to uncover abundant non-inclusion pathology, which deserves future validation with multiple brain bank resources and in different synucleinopathies.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jensen, N. M., Fu, Y., Betzer, C., Li, H., Elfarrash, S., Shaib, A. H., Krah, D., Vitic, Z., Reimer, L., Gram, H., Buchman, V., Denham, M., Rizzoli, S. O., Halliday, G. M., Jensen, P. H.. 2024-07-08. MJF-14 proximity ligation assay detects early non-inclusion alpha-synuclein pathology with enhanced specificity and sensitivity. https://doi.org/10.1101/2024.07.08.602186

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↗