Neuropathology and Applied Neurobiology

Pompe disease (glycogen storage disease type II) is an autosomal recessive lysosomal storage disorder characterized by progressive glycogen accumulation within lysosomes. It leads to their enlargement, autophagosome build-up and defective autophagic flux. Among the pathophysiological features, mitochondrial abnormalities have long been regarded as secondary consequences of lysosomal dysfunction. Typically, they have been described in electron microscopy, revealing paracrystalline inclusions, cristae lost, swollen mitochondria, and glycogen-filled structures. However, the spatial organization and interplay between mitochondria and lysosomes in skeletal muscle remain poorly understood, as does the progression of these alterations with respect to muscle metabolic profile. Here, we present a novel approach combining super-resolution imaging with a deep learning- based image analysis workflow to quantitatively assess mitochondrial and lysosomal remodeling as well as their interactions in skeletal muscle of the main murine model of the Pompe disease. Organelles were analyzed at two specific stages of the disease, according to muscle type, fiber type and subcellular location of the mitochondria. We show that the overall structure of the mitochondrial network is affected as early as the pre-symptomatic stage (1 month), while changes in mitochondrial density are more restricted at this stage and become more widespread as disease progresses (4 months). Importantly, these pathophysiological modifications are highly dependent on the muscle, fiber type and subcellular location. Alongside a rapid and widespread increase in lysosomal size, and a subsequent shift toward tighter lysosomal clustering at the later stage, we observe a progressive, region-specific increase in mitochondria-lysosome interactions that is most pronounced in the intermyofibrillar region. Our findings establish that this original imaging approach provides a relevant and powerful framework for quantitatively analyzing interactions between organelles within skeletal muscle fibers, thus offering new opportunities to explore the subcellular changes underlying disease progression. As such, it represents an interesting tool for monitoring pathophysiology and evaluating the effectiveness of therapeutic interventions.

Background: Neurofilament light chain (NfL) gained FDA recognition in amyotrophic lateral sclerosis (ALS) through SIMOA-based validation, where baseline serum NfL predicts ALSFRS-R slope and survival, and through the 2023 tofersen approval for SOD1-ALS. The commercial Roche Elecsys electrochemiluminescence immunoassay (ECLIA) reads 6- to 8-fold lower than SIMOA, and its clinical utility in ALS is uncharacterized. We assessed whether ECLIA NfL retains this correlation in routine care and whether GFAP or S-100B helps. Methods: Retrospective analysis of 58 chart-confirmed ALS patients at Georgetown University Hospital (2022-2026), biomarkers on the LabCorp Roche Elecsys ECLIA. The NfL-ALSFRS-R correlation was assessed where both measures fell within matching windows; serial NfL, in patients with repeat draws. Results: First-per-patient NfL median was 7.06 pg/mL (IQR 4.06-17.30; CV 99%). Among 31 patients with matched NfL and ALSFRS-R decline rates, Spearman r = 0.704; within 90 days (n = 17), r = 0.809 (both p < 0.0001). Fast progressors (n = 8) had mean NfL 17.10 pg/mL versus 4.64 in slow progressors (n = 21), a 3.7-fold separation. Serial NfL captured rising trajectories and stable low values. GFAP rose within patients but tracked neither progression rate, disease stage, nor motor-neuron predominance; S-100B added no value. Conclusions: Commercial ECLIA brings NfL into routine ALS care; its prognostic correlation with progression rate survives real-world fragmentation. The actionable unit is the longitudinal trajectory, not the single value, read against platform-specific reference ranges and clinical context (genotype, onset, stage). GFAP and S-100B add little. Keywords: amyotrophic lateral sclerosis, neurofilament light chain, biomarkers, implementation science, ECLIA, GFAP, monitoring, tofersen, real-world data

In the pathologically and clinically heterogeneous neurodegenerative disorder amyotrophic lateral sclerosis (ALS), objective biochemical predictors of survival are essential to handle complexity in clinical trials, enrich clinical decision-making and interrogate the biology of disease progression. In this longitudinal study, we performed high-depth proximity extension assay proteomics using 1,095 samples of serum (N=851) and CSF (N=244) from 426 people with ALS, with orthogonal replication in an external cohort of 349 people with ALS. Age- and sex-adjusted Cox analysis identified 57 proteins in serum, including neurofilament light chain (NEFL) and peripherin, as well as five proteins in CSF, including tropomyosin 3 (TPM3) that were associated with survival (FDR-adjusted p[&ge;]0.05). Penalised Cox regression identified a panel of 9 serum proteins - including NEFL, peripherin, TNF receptor superfamily member 27 (EDA2R) and calcitonin - that reflect the extent of disease as well as the progression rate, improving survival prediction compared with models using clinical parameters and NEFL. Joint modelling identified associations between the longitudinal trajectories of serum EDA2R and calcitonin with survival, highlighting their potential role in measuring disease progression. This work indicates the utility of multiple proteins reflecting diverse biological pathways in refining survival stratification and highlights systemic factors in ALS progression.

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by death of upper and lower motor neurons, usually presented with clinical heterogeneity. Fluid biomarker development remains dominated by neurofilament light chain (NEFL), a marker of neuroaxonal injury. NEFL is however unspecific to ALS and its phenotypes and there is currently a lack of biomarkers that capture ALS heterogeneity such as onset site and ALS-frontotemporal spectrum disorder (ALS-FTSD). Therefore, we investigated whether plasma proteomics could reveal pathway-level signatures that stratify and explain ALS heterogeneity. Methods: We profiled ~5,400 plasma proteins (Olink Explore HT) in 299 patients with ALS and 50 age- and sex comparable healthy controls. We used two complementary analytic frameworks: (i) differential protein abundance analysis to identify altered proteins in ALS and across clinical subgroups, and (ii) weighted gene correlation network analysis (WGCNA) to identify coordinated protein modules and relate them to ALS diagnosis and to ALS-specific clinical traits (site of onset, ALS-FTSD, ALS functional rating scale-revised (ALSFRS-R) score, and plasma NEFL). Results: Differential abundance analysis identified 56 proteins altered in ALS versus controls, of which 40 were increased. WGCNA identified 11 co-expression modules, with ALS samples having the strongest correlation to a protein module (n=51) highly enriched for muscle-related proteins. Out of the 40 proteins that had increased expression levels, 29 overlapped with the muscle-enriched protein module, indicating that muscle related proteins are the dominant circulating proteomic signature in ALS. This signal extended to clinical stratification: spinal-onset patients showed a strong positive association with the muscle-module. Further, differential abundance analysis of spinal- versus bulbar-onset ALS identified changes that mapped predominantly to the same module, supporting a molecular signature of onset phenotype. In contrast, cognitive status (ALS-FTSD) mapped to distinct modules enriched for extracellular matrix/cell-adhesion pathways, consistent with a separable biological axis of disease heterogeneity. Although multiple modules correlated with NEFL, trait-specific signatures were not fully explained by neuroaxonal injury. Notably, the muscle-enriched module increased with higher NEFL and lower ALSFRS-R, supporting its interpretation as a severity-linked, muscle-involvement proxy. Conclusions: Large-scale plasma proteomics reveals that heterogeneity in ALS reflects underlying biological structures. We identified a dominant muscle-associated protein network that distinguished ALS patients from controls and correlated with disease onset phenotype and severity, alongside distinct protein networks linked to ALS-FTSD. By integrating differential protein abundance with network-based analysis, we defined pathway-level biomarker signatures that extend beyond NEFL, enabling biologically informed patient stratification and improved therapeutic monitoring.

Myasthenia gravis (MG) is an autoimmune disease caused by pathogenic autoantibodies against proteins at the neuromuscular junction (NMJ). The diagnosis and clinical management of MG patients largely relies on the detection of antigen-specific autoantibodies targeting acetylcholine receptor (AChR) or muscle-specific kinase (MuSK). Yet a subset of patients remains seronegative for known MG autoantibodies, highlighting a critical need for alternative approaches to identify pathogenic NMJ antibodies. We established a new human in vitro model of the NMJ based on primary human muscle cells that recapitulates key features of the NMJ: differentiation to myotubes, expression of key NMJ proteins and formation of postsynaptic AChR clusters in response to agrin stimulation. The model allows new insights into myogenesis and genetic muscle diseases, and the new muscle cell-based assay (CBA) detected autoantibodies in sera from patients with AChR- and MuSK-positive MG with 96.43% sensitivity and 100% specificity, while healthy control sera showed no reactivity. Incubation with patient sera significantly reduced AChR clustering compared to controls, demonstrating functional pathogenic effects. Thus, we established a physiologically relevant human NMJ model that enables detection and functional characterization of neuromuscular autoantibodies. This novel approach addresses a key limitation of current antigen-specific diagnostics and provides a method for improved detection and characterization of MG antibodies, independent of antigen specificity. One Sentence SummaryWe established a postsynaptic human in vitro neuromuscular junction model to assess binding and pathogenicity of MG autoantibodies. Key messagesO_ST_ABSWhat is already known on this topic?C_ST_ABSCurrent diagnosis of myasthenia gravis (MG) relies largely on the detection of antigen-specific autoantibodies against AChR and MuSK, leaving a clinically relevant subset of patients seronegative. What are the new findings?We established a physiologically relevant human in vitro neuromuscular junction model based on primary human muscle cells and developed a novel muscle cell-based assay (CBA) for the detection of neuromuscular autoantibodies. How might this impact on clinical practice or future developments?The CBA detected autoantibodies in patients with AChR- or MuSK-positive MG with high sensitivity and specificity and demonstrated their functional pathogenic effects on AChR clustering. This antigen-independent approach may improve the detection and functional characterization of MG autoantibodies, particularly in patients who are seronegative in current diagnostic assays. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/743478v1_ufig1.gif" ALT="Figure 1000"> View larger version (38K): org.highwire.dtl.DTLVardef@18ed154org.highwire.dtl.DTLVardef@151036corg.highwire.dtl.DTLVardef@1b7ab34org.highwire.dtl.DTLVardef@1490fe9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Amyotrophic lateral sclerosis (ALS) is a fatal, rapidly progressive neurodegenerative disease of motor neurons for which therapeutics are limited. Improved biomarkers are imperative to improve patient care and therapeutic development. Here, we employed 35-plex isobaric tandem mass tag labeling based on isobutyl-proline reporter group (TMTpro) to perform unbiased proteomic analysis of cerebrospinal fluid (CSF) and plasma from control (n= 28, n= 31) and sporadic ALS (sALS) (n= 39, n= 41), from the Target ALS Global Natural History Study (TALS GNHS). We identified 2,875 proteins in CSF and 1,118 proteins in plasma and identified known and novel differentially expressed proteins (DEPs) between controls and sALS, some of which were orthogonally validated using immunoassay. Comparison of TMTpro-MS and Olink proximity extension assay proteomics revealed common and non-overlapping differentially expressed proteins illustrating strengths unique to each platform. This initial cross-sectional proteomic study of biofluids from the TALS GNHS, with unrestricted availability of study results to the research community, highlights the potential of this resource as a potent platform for ALS biomarker discovery.

Tau aggregation into intracellular neurofibrillary tangles (NFTs) is one of the major hallmarks of Alzheimers disease (AD). Based on neuropathological studies, NFTs have been classified into pre-tangles, mature tangles, and ghost tangles, however the ultrastructural transitions between these stages remain poorly understood. Here, we used correlative light and electron microscopy (CLEM) to structurally characterize tau tangle maturity states in post-mortem human AD brain tissue. Pre-tangles showed no consistent fibrillar ultrastructure. Mature tangles contained densely packed, highly aligned paired helical filaments (PHF) and straight filaments (SF), often organized in spatially distinct bundles within the neuronal soma. Ghost tangles lacked cellular organelles and were composed predominantly of thin fibrils compartmentalized by membranous structures, with fibril morphology differing between compartmentalized and non-compartmentalized regions. Electron tomography and fibril segmentation demonstrated that these fibrils were significantly thinner than PHFs and SFs while immunogold labeling using the 2E9 tau marker confirmed the presence of tau within both mature and ghost tangle fibrils. GFAP-positive astrocytic processes infiltrated fibril-rich compartments within ghost tangles, linking astrocytic engagement with the emergence of this distinct ultrastructural organization. Together, our findings show that ghost-tangles contain a structurally distinct population of tau fibrils, suggesting that tau aggregates undergo astrocytic-mediated structural remodeling at late stages of pathology.

Background and Objectives To investigate the associations of blood-based tau biomarkers with clinical, electrophysiologic and prognostic measures in amyotrophic lateral sclerosis (ALS), and to determine whether they reflect distinct disease-related processes. Methods We studied 119 patients with ALS from a longitudinal observational cohort. Plasma and serum p-tau181, p-tau217, p-tau231, brain-derived tau (BD-tau), NfL and GFAP were measured using Lumipulse and Simoa assays. Associations with demographic variables, disease severity (ALSFRS-R and slow vital capacity), lower motor neuron (LMN), muscle involvement (creatine kinase [CK] and high-sensitivity cardiac troponin T [hs-cTnT]), disease progression and survival were assessed using multivariable models. Results Tau-related biomarkers, specifically p-tau217 and BD-tau, were associated with greater cross-sectional disease severity, reflected by lower ALSFRS-R scores. Plasma and serum p-tau181, p-tau217, p-tau231, and BD-tau were associated with higher CK and hs-cTnT, whereas p-tau181 and p-tau231 were also associated with greater LMN involvement. In contrast, NfL and GFAP were not associated with muscle or LMN involvement. Across analytical platforms, plasma and serum NfL were associated with faster ALSFRS-R decline and shorter survival. NfL was the only biomarker independently associated with both disease progression and survival. Discussion Blood biomarkers capture distinct dimensions of ALS. Tau-related biomarkers are associated with cross-sectional disease severity, LMN involvement and muscle injury, whereas NfL primarily reflects disease progression and survival. These findings support the complementary use of tau-related biomarkers and NfL for ALS phenotypic characterization and prognosis assessment.

Background Despite WHO grade and IDH status, significant survival differences remain in diffuse gliomas. We hypothesized that a brain-aging transcriptomic signature, reflecting neuroinflammation, myeloid infiltration, and synaptic loss, would independently predict survival and allow for molecular reclassification. Methods A neurodegeneration score was derived via PCA of brain MRI volumes from 1,057 OASIS-3 subjects and projected onto 888 TCGA-LGG/GBM (discovery) and 693 CGGA gliomas (validation). A 14-gene signature of glial/myeloid (GFAP, AQP4, TYROBP, TREM2, C1QA, CD68, ITGAM) and neuronal (SYP, DLG4, GRIN1, GRIA1, SNAP25, SYN1, RBFOX3) genes were computed. Elastic-net Cox regression identified a 3-gene panel (C1QA, CD68, GRIA1). Kaplan-Meier, multivariate Cox, decision curve, and single-cell RNA-seq analyses were performed. Results High brain-aging scores predicted poorer overall survival (p < 0.0001) and remained an independent prognostic factor after adjusting for WHO grade and IDH status (z = 4.72, p < 0.001); chronological age was non-significant (p = 0.231). In IDH-mutant gliomas, significance was confirmed in both cohorts (TCGA p = 0.027; CGGA p < 0.0001). Bidirectional reclassification showed high-risk Grade 2 tumors with Grade 3-like survival (p = 0.00089), and indolent Grade 3 tumors resembling Grade 2 by Ki-67. Single-cell RNA-seq confirmed macrophage localization of signature genes; DCA demonstrated net benefit over grade alone at 5-30% probability thresholds. Conclusions A brain-aging transcriptomic signature independently predicts glioma survival beyond WHO grade and IDH status, validated in an independent Chinese cohort, with clinical utility for identifying high-risk Grade 2 and sparing over-treatment of indolent Grade 3 tumors.

BackgroundSensitive biomarkers that objectively stage Huntington disease (HD) are needed to improve participant stratification and facilitate the enrichment of clinical trials with biologically and clinically homogeneous populations. The HDClarity study, an international longitudinal biofluid collection initiative for HD, provides a unique resource for large-scale proteomic profiling of matched CSF and serum samples spanning the disease spectrum. Here, we leveraged baseline proteomic data from HDClarity to characterize protein signatures associated with HD stage and clinical severity, compare measurements across analytical platforms and biofluid compartments, and identify candidate multi-protein panels for disease staging. MethodsBaseline proteomic data generated using Olink Explore ([~]3,000 proteins) and SomaScan v4.1 ([~]7,000 proteins) were analyzed in matched CSF and serum samples from 315 HD gene-expansion carriers and 92 non-HD controls. A total of 2,119 proteins overlapped between Olink and SomaScan, enabling assessment of cross-platform concordance, while CSF-serum relationships were evaluated using all available protein measurements within each assay. Covariate-adjusted linear regression models were used to assess disease stage-associated differences in protein abundance, while partial correlation analyses evaluated relationships between protein abundance, clinical severity in HD gene-expansion carriers, and estimated years to disease onset in premanifest participants. A nested machine-learning pipeline incorporating univariate feature ranking, penalized regression-based feature selection, and repeated cross- validation was used to derive compact multi-protein classifiers for HD staging. ResultsCross-platform and CSF-serum correlations were highly protein-dependent, with some analytes showing strong concordance and others exhibiting weak or inverse relationships. These findings highlight substantial heterogeneity in biomarker behaviour across analytical platforms and biofluids. Adjusted models identified both known HD-associated markers (NEFL, GFAP, CHI3L1) and less well-characterized proteins in CSF and serum whose baseline abundance differed across HD-Integrated Staging System (HD-ISS) and clinical stages. Partial correlation analyses revealed additional candidate biomarkers associated with clinical severity and estimated time to disease onset. Machine-learning models derived compact CSF and serum protein panels that accurately classified participants across HD-ISS stages 0 and 1, as well as the transition from premanifest to early manifest disease. ConclusionsThis study provides the first large-scale orthogonal comparison of matched CSF and serum proteomes in HDClarity, establishing robust baseline proteomic signatures across the HD continuum. Our findings demonstrate the importance of considering both analytical platform and biofluid when interpreting protein biomarkers and identify compact protein panels with potential utility for objective disease staging, patient stratification, and clinical trial enrichment in HD. Trial RegistrationNot applicable. One Sentence SummaryCaron et al. analyzed matched baseline CSF and serum proteomic data from the HDClarity study generated using two orthogonal proteomic platforms, identifying reproducible multi-protein panels capable of staging and stratifying Huntington disease.

Chronic Wasting Disease (CWD) is a fatal transmissible spongiform encephalopathy (TSE) that is confined to cervids (deer, moose, elk, and reindeer) but shares key properties with human neurodegenerative conditions such as Alzheimers, Parkinsons, Huntingtons disease and frontal-temporal dementia. CWD and other TSEs are caused by the misfolded prion protein (PrP). Although the identification of diagnostic and prognostic biomarkers at all stages of disease progression is becoming exceedingly critical as CWD continues to increase in prevalence, accurate antemortem testing techniques are extremely limited. This study made use of cervidized transgenic mice (mice carrying the cervid PrP) that recapitulate CWD in various disease stages and investigated the utility of neurological biomarkers and neurobehavioral manifestations for CWD detection. Neurofilament light chain (NFL), glial fibrillary acidic protein (GFAP), and total Tau (t-Tau) were assessed under the hypothesis that combined biomarker signatures might more reliably reflect CWD-related neurodegeneration and disease progression. Analyses at 90, 132, 174, and 230 days post-CWD inoculation show distinct biomarker elevation, with all three biomarkers significantly elevated in the CWD animals by 132 days post-inoculation. To our knowledge, this is the first demonstration that these three plasma biomarkers are useful not only for detecting CWD, but also for identifying it at early antemortem stages of disease. Novel phenotypes were also revealed by comprehensive phenotypic profiling, including rigid tail elevation, increased grip strength, and impaired coordination, to lend further support to plasma biomarker data indicating neurologic impairment associated with brain pathology. Ultimately, the goal is to improve antemortem, non-invasive CWD detection methods to enable earlier detection and assist with disease management.

Cerebrovascular pathology is increasingly implicated in neurodegenerative diseases, yet its pathomechanistic contribution remains poorly defined. Building on prior evidence of dysregulated iron and oxygen homeostasis in early-affected brain regions of progressive supranuclear palsy (PSP), we hypothesized that brain microvascular alterations may play an etiological role in select neurodegenerative proteinopathies. First, we conducted a systematic neuropathological evaluation of 178 brains from the University Health Network Neurodegenerative Brain Collection, including Alzheimers disease-related neuropathologic change (ADNC; n=30), Lewy body disease with high or intermediate ADNC (n=38) and low ADNC (n=16), multiple system atrophy (MSA; n=14), PSP (n=39), frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP; n=10), and controls (n=31). Arteriolosclerosis, microinfarction, and calcification were assessed in the basal ganglia and frontal cortex. Iron burden was correlated by quantification of Perls staining in MSA and PSP, where vessel pathology was most severe. Single-nucleus RNA-sequencing (snRNA-seq) of frontal cortex tissue from control (n=5) and PSP (n=8) cases with varying arteriolosclerosis severity was performed to characterize the vascular transcriptome, with validation against an independent snRNA-seq evaluation of PSP (n= 11), Picks disease (n=9), AD (n=10), and control (n=10) brains. Histological analysis revealed disease-specific involvement of microvascular pathology in neurodegenerative diseases, identifying PSP to demonstrate most prominent and widespread vessel wall thickening across regions examined. Regression analysis using demographic, APOE and MAPT genetic risk status, and neuropathological features of cases corroborated the distinct association with PSP pathology. Elevated iron load in early affected regions of MSA and PSP brains correlated with greater vessel wall thickening, suggesting a possible pathomechanistic relationship between the two disease physiologies. snRNA-seq analysis of vascular transcriptome identified robust upregulation of heat shock proteins and hypoxia-related genes in PSP endothelial cells and pericytes across both datasets. Importantly, we found the proteotoxic signature to be strongly associated with higher vessel scores in PSP cases, linking microvascular morphology to endothelial dysfunction. Our comprehensive neuropathological evaluation coupled with correlative snRNA-seq analysis establish PSP-specific arteriolar thickening associated with endothelial proteotoxic state as a candidate pathogenic mechanism. The cerebral arteriolar unit represents a compelling therapeutic target for disease modification in PSP.

Background: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease with no known cure. Disease progression in people living with ALS is heterogeneous, hindering personalized treatment development. The current gold standard for measuring disease progression in ALS, the ALS Functional Rating Scale - Revised (ALSFRS-R), is widely used but based on subjective measurements. Blood-based neurofilament light (NfL) has been studied as a diagnostic and prognostic biomarker but less information exists on its utility as a disease progression biomarker. Methods: We present results from blood draws of 300 participants in the FDA-funded Clinic-Based Multi-Site ALS Natural History and Biofluid study of the ALS Natural History Consortium (NHC). Plasma NfL levels were measured and analyzed against different disease progression metrics based on the ALSFRS-R. Results: NfL levels were found to be correlated with the ALSFRS-R average rate of change (r=-0.53, 95% CI -0.62 to -0.42). This association differed at a cutoff value of 61 pg/mL, with stronger correlations below this cutoff (r=-0.51 vs r=-0.18). Survival differed stratifying by this cutoff value, with participants under the cutoff having higher survival probabilities. The predictive value of NfL when predicting time to death was higher compared with the first ALSFRS-R across different event horizons. A model including both was better when predicting events up to 2 years after diagnosis. Conclusions: These results highlight the utility of NfL as a disease progression biomarker in ALS alongside ALSFRS-R based disease progression metrics. The cutoff value can aid in clinical trial stratification, pragmatic trial planning, and clinical care.

Traumatic brain injury produces widespread axonal damage can be assessed histologically using amyloid precursor protein (APP) immunohistochemistry, which labels injured axonal profiles at cellular resolution [1, 2]. However, quantification of APP pathology remains a major bottleneck: annotation is manual, time-consuming, spatially localized, and variable across raters, limiting scalability and reproducibility. This limitation is particularly important in studies that use histology as a reference for neuroimaging or other tissue-level measurements, where cellular APP pathology must be quantified in a spatial form that can be aligned with imaging abnormalities. Here, we introduce PIGMENT, an annotation-efficient deep-learning framework for automated segmentation and quantification of APP-positive pathology in porcine white matter histology. PIGMENT uses a compact SegFormer-B0 architecture trained on 525 expert-annotated 512 x 512-pixel tiles from four APP-stained sections across three pigs. Because APP-positive profiles are sparse, fragmented, stain-variable, and morphologically diverse, PIGMENT combines limited expert labels with APP-specific augmentation designed to model variation in APP-positive intensity, size, continuity, fragmentation, and local tissue context. We evaluated PIGMENT using an instance-level detection rate that measures whether discrete APP-positive components are localized. Across held-out APP-stained data, PIGMENT achieved a mean instance-level detection rate of 0.86. Across the configurations tested, the highest mean detection rate was achieved by a training set that included sections from different animals, suggesting that annotation diversity may be an important factor under limited-label conditions. By extending limited high-confidence expert annotations into whole-section APP burden maps, PIGMENT provides a scalable framework for characterizing the extent and spatial distribution of traumatic axonal injury. These maps may support future studies that align histological injury burden with imaging-derived measures.

Amyotrophic lateral sclerosis/parkinsonism-dementia complex (ALS/PDC) is a fatal neurodegenerative disorder that was once hyperendemic in the island of Guam (Mariana Islands, US) and a few other Pacific locales. Despite extensive investigations into its origins, the etiology of ALS/PDC remains unclear. ALS/PDC is, at the neuropathology level, characterized by tau-dominant multiple proteinopathy in brain and spinal cord. It was recently reported that Guam ALS/PDC brain extracts exhibit tau seeding activity in fluorescence resonance energy transfer (FRET)-based biosensor cells. To build upon those findings and explore the nature of tau seeds in ALS/PDC in more detail, we used an alanine mutational scanning (Ala scan) approach to determine the seeding profile of tau in nervous tissues of Guam ALS/PDC cases. First, we confirmed the detection of tau seeding activity in ALS/PDC brain samples in tau biosensor cells. Notably, we could also detect potent tau seeding activity in spinal cord. Subsequent Ala scan assays demonstrated that ALS/PDC tau displays an aggregate incorporation pattern that resembles that of chronic traumatic encephalopathy (CTE)-type tau. This result is consistent with recent electron cryo-microscopy studies of tau, which revealed that ALS/PDC tau filaments are predominantly of the CTE-type. The structural characteristics and seeding behavior of ALS/PDC tau, as well as the regional distribution of tau pathology at post-mortem, suggest that ALS/PDC is a CTE-like tauopathy. Significance StatementNeurodegenerative tauopathies are characterized by proteinaceous deposits containing microtubule-associated tau in nervous tissue. Emerging evidence suggests that disease-associated tau proteins adopt abnormal, self-propagating conformations characteristic of prions. Here, we employed alanine mutational scanning (Ala scan) to determine the nature of prion-like tau seeds in ALS/PDC, a mysterious disorder that occurred formerly in high incidence in certain regions in the western Pacific. We show that the Ala scan incorporation profile of ALS/PDC tau is similar to that of abnormal tau proteins in chronic traumatic encephalopathy (CTE). The findings lend support to the idea that ALS/PDC can be classified structurally as a CTE-like tauopathy. This work may have important implications for our understanding of ALS/PDC as well as common neurological disorders beyond the Pacific.

Early diagnosis of Parkinsons disease (PD) remains challenging because motor symptoms appear only after extensive neurodegeneration, and a definitive diagnosis still relies on post-mortem neuropathology. Increasing evidence implicates the enteric nervous system (ENS) in prodromal disease stages, but routine ENS-based diagnosis is limited by the complexity of intestinal tissue organization and the need for specific labeling strategies. Here, we developed a label-free autofluorescence (AF) imaging workflow combined with unbiased morphometric analysis to identify neurodegenerative alterations in fixed human colonic tissue. Using a correlative multiscale imaging approach, we generated a database of almost 800 high-resolution confocal images from myenteric and submucosal plexuses of controls, PD, and Alzheimers disease (AD) patients. Blind evaluation by four expert histologists showed reliable identification of control tissue but lower sensitivity for pathological cases, reflecting the heterogeneous distribution of ENS lesions. Semi-quantitative and morphometric image analyses identified a distinct population of enlarged enteric neurons, termed large neural cells (LNCs), strongly enriched in PD and AD compared with controls. LNCs contained autofluorescent cytoplasmic inclusions and frequently prominent nucleoli, both features largely absent from control tissue independent of aging. Co-localization with the amyloid-binding probe Amytracker (AmyT) demonstrated that AF granules correspond to {beta}-sheet-rich protein aggregates rather than merely age-related lipofuscin granules. Similar alterations were detected in intact three-dimensional (3-D) colonic biopsies, demonstrating the feasibility of volumetric ENS imaging without tissue clearing. Together, our results establish label-free AF imaging as a rapid and clinically compatible strategy for detecting enteric neurodegenerative pathology. This approach provides a framework for the future development of ENS-based biomarkers and supports the use of volumetric intestinal imaging for early diagnosis of neurodegenerative diseases.

The clinical and molecular heterogeneity observed in amyotrophic lateral sclerosis (ALS) presents a challenge for diagnosis, prognosis, and treatment. RNA sequencing of post-mortem brain samples from ALS patients has identified several subtypes with distinct molecular signatures. We sought to evaluate these subtypes across diverse tissues and datasets and assess the feasibility of supervised machine learning models for sample classification. Unsupervised clustering and pathway analysis were performed to confirm the presence of ALS subtypes in motor cortex samples. Three machine learning strategies were then used to create models based on post-mortem motor cortex expression data of 112 people with ALS from the London Neurodegenerative Diseases Brain Bank. These models were subsequently improved through feature selection and evaluated in independent cohorts from motor cortex (n = 257, NYGC ALS Consortium) and blood (n = 96, Macquarie University Neurodegenerative Disease Biobank) samples. Multi-class linear discriminant analysis (LDA) models were then used for subtype classification. Clustering of ALS post-mortem motor cortex samples confirmed the presence of three subtypes: neuroinflammation (ALS-Neu), extracellular matrix organisation and muscle contraction (ALS-OxA), and synaptic and neuropeptide signalling (ALS-SNs). Among all machine learning strategies, random forests produced the most accurate and stable models for binary classification ([~]93% accuracy across the three subtypes). After feature selection, random forest models were able to classify samples from an independent post-mortem motor cortex cohort in their respective subtypes (AUC of [~]0.98 across the three subtypes). When these models were evaluated in blood using LDA, we found consistent clustering patterns, with samples aligning in the same subtype regions of the post-mortem motor cortex samples, with ALS-SNs being the subtype in which samples were classified with the highest confidence (LDA class probability [~]86%). Moreover, classification for this subtype improved when blood samples were collected closer to death. Our findings support the presence of three gene expression-based ALS subtypes in motor cortex samples and the utility of machine learning strategies for subtype classification. We also observed that the subtypes identified in the brain partially match those in the blood, with samples from the late stages of the disease more likely to be correctly predicted into the ALS-SNs cluster. This suggests a longitudinal effect in subtype identification that requires further investigation.

Blood-based proteomic profiling is now widely applied in neurodegenerative and neuroinflammatory disease, yet the choice between serum and plasma remains poorly characterised for high-multiplex platforms. Many legacy biobanks hold mainly serum, whereas most current NUcleic-acid-Linked Immuno-Sandwich Assay (NULISA) studies use plasma. We compared the 130-protein NULISAseq central nervous system (CNS) Disease Panel head-to-head in matched serum and plasma collected at the same draw from 62 participants (30 neurodegenerative, 19 demyelinating, 13 healthy controls). Agreement was measured with Spearman correlation (rho), Lin's concordance correlation coefficient (CCC), the intraclass correlation coefficient (ICC) and the mean paired serum-to-plasma difference (dNPQ). Concordance was moderate to high: 123 of 130 proteins reached significance and 18 reached rho >= 0.90, with a median rho of 0.72 (range 0.10-0.988). Proteins fell into three tiers. Cytoskeletal markers (NEFH rho=0.988; NEFL rho=0.947) and glial GFAP (rho=0.949, |dNPQ|<0.5) were interchangeable between matrices. Phosphorylated tau (pTau) species retained excellent rank concordance but carried a systematic plasma-greater-than-serum offset (pTau-181 rho=0.869, dNPQ=+0.67; pTau-217 rho=0.846, dNPQ=+0.64; pTau-231 rho=0.885, dNPQ=+0.89). Platelet-derived analytes (CD40LG rho=0.102, dNPQ=-4.74; BDNF rho=0.223, dNPQ=-2.69) and intracellular synaptic proteins (NRGN, SNAP25, ENO2) diverged markedly. For most clinically relevant neurodegeneration markers, especially cytoskeletal and glial proteins, serum is a valid substitute for plasma; absolute thresholds for phosphorylated tau and amyloid peptides require matrix-specific calibration, and platelet-sensitive analytes cannot be compared across matrices without strictly standardised pre-analytical conditions.

Background: Single cell RNA sequencing provides a wealth of information to explore the complexities of the tumour microenvironment, but crucially the spatial topology of the tumour is lost and studying cellular interactions is limited. Spatial transcriptomics aims to address this however the technique remains cost prohibitive for the generation of data from meaningfully-sized clinical cohorts. In contrast, spatial proteomic profiling with multiplex immunofluorescence, preserves spatial interactions, is relatively cost accessible, and is scalable for large clinical cohorts to address powerful translational questions. Whilst multiplex approaches have advanced in recent years, we note that cancer-associated fibroblasts (CAFs) have been explored in less detail, potentially due to difficulties associated with CAF heterogeneity and the diversity of markers used to define them. Methods: We designed, optimised, and validated a multiplex immunofluorescence panel that combines four frequently used CAF markers; alpha smooth muscle actin (aSMA), fibroblast activation protein (FAP), podoplanin (PDPN) and platelet-derived growth factor receptor alpha (PDGFRa) with CD8 and pan-cytokeratin. Here we share our methodology and the practical considerations taken to inform the final panel design. We also highlight the benefits of robust optimisation experiments.

Amyotrophic lateral sclerosis (ALS) is a motor neuron disease, leading to progressive muscle weakness and motor impairment. Growing evidence indicates that cerebellar Purkinje cells, which play a central role in motor coordination, are also affected in ALS. However, it is unclear whether the molecular events that initiate neurodegeneration in these ALS-relevant motor-controlling neurons are shared or distinct. Here, we used a MATR3 S85C knock-in (KI) mouse model of early-stage ALS with stage-specific motor phenotypes and selective vulnerability of motor neurons and Purkinje cells to decipher the molecular events underlying neurodegeneration in these two neuronal populations. We found that a profound reduction in detectable MATR3 S85C immunoreactivity (hereafter referred to as MATR3 loss) in both motor neurons and Purkinje cells precedes the onset of motor dysfunction and neuropathology, implicating MATR3 loss as the earliest detectable molecular event. Our bulk cerebellar RNA profiling and motor neuron-specific RNA profiling data at the onset of MATR3 loss revealed distinct molecular signatures. In the cerebellum, Ngfr expression emerged in Purkinje cells before the onset of neuronal loss and remained elevated throughout the disease course. This increase was accompanied by activation of the JNK-mediated cell death pathway. In the motor neurons, elevated Fgf21 and integrated stress response (ISR) gene expression were the first to be observed and persisted throughout disease progression, consistent with previous findings in SOD1 mouse models. Our findings provide mechanistic insights into the initiation of neurodegeneration in ALS-relevant motor-controlling neurons and implicate potential neuron type-specific targets for future therapeutics.