Acta Neuropathologica

ObjectiveBrain branks preserve extensive material relevant to neurodegenerative disease research. As these collections age, tissue becomes archival, raising the question of whether long-term fixed and stored human brain tissue remains suitable for contemporary immunohistochemical analyses. Materials and MethodsForty-one autopsy brains collected between 1946 to 1980 were examined. For each case, midbrain and hippocampus were available both as original paraffin-embedded blocks and as tissue stored long term in fixative. New paraffin blocks were prepared from the long-term fixated tissue. Sections from original and newly prepared blocks were immunohistochemically stained for -synuclein, hyperphosphorylated tau and amyloid-{beta}. Immunoreactivity was assessed using semi-quantitative scoring. ResultsOriginal blocks consistently showed good staining intensity and morphological preservation for each protein pathology. Newly prepared blocks showed slightly lower semi-quantitative scores for Lewy-related pathology, without statistically significant differences, except for astrocytic -synuclein in the substantia nigra in cases from the 1960s. Tau pathology displayed modestly reduced labelling, particularly of the neuropil threads and neurofibrillary tangles, most evident in cases from the 1950s. Amyloid-{beta}-positive senile plaques showed similar or slightly higher scores in newly prepared blocks, with no significant differences across regions. ConclusionHuman brain tissue preserved as paraffin-embedded blocks or stored in fixative for up to 78 years remains suitable for immunohistochemical analyses. Adequate-to-good detection of aggregated of -synuclein, hyperphosphorylated tau and amyloid-{beta} is achievable, indicating preserved pathological hallmarks of Lewy Body Disease and Alzheimers Disease in archival tissue.

Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically heterogeneous, yet the full significance of their histologic diversity is unclear. This is in large part because many features are not readily quantifiable by traditional observer-based light microscopy. Molecular testing improves prognostic stratification, but is not universally accessible. We therefore sought to determine whether an artificial intelligence (AI)-trained program could predict specific genomic and epigenomic patterns in meningiomas, and whether it could extract more prognostic information out of standard hematoxylin and eosin (H&E) histopathology than the current WHO classification. To do this, we developed Morphologic Set Enrichment (MSE), an interpretable computational pathology framework that quantifies statistical enrichment of morphologic patterns, cells, and tissue architecture from H&E whole-slide images. The MSE meningioma histology program was able to accurately predict DNA methylation subtypes and concurrent chromosome 1p/22q losses, in the process identifying specific morphologic patterns associated with key genomic and epigenomic alterations. It also added prognostic value independent of standard clinical and pathological variables. These results demonstrate that AI-based quantitative morphologic profiling can capture clinically and biologically relevant information that redefines risk stratification for meningiomas, incorporating histological information not included in existing grading schemes.

T-cell lymphomas are often histologically indistinguishable from benign T-cell infiltrates. Clonality testing is frequently required for diagnosis. It lacks the spatial context and is slow and expensive, relying on complex, multiplexed PCR reactions, interpreted by experienced scientists or pathologists. We previously published details of a pair of highly specific monoclonal antibodies against the two alternatively used, but very similar, T-cell receptor {beta} constant regions, TCR{beta}1 and TCR{beta}2. We demonstrated the feasibility of immunohistochemical detection of TCR{beta}1 and TCR{beta}2 in formalin-fixed, paraffin-embedded (FFPE) tissue as a novel diagnostic strategy for T-cell lymphomas. Here we validate an improved pairing of TCR{beta}1/2 rabbit monoclonal antibodies, and demonstrate their utility for single and double immunostaining, including with a chimeric mouse anti-TCR{beta}2 antibody. Finally, we show that this staining is amenable to automated cell counting, permitting accurate calculation of the TCR{beta}2:TCR{beta}1 ratio.

Background Whole-genome sequencing (WGS) has improved the diagnosis of rare genetic disorders, yet interpretation of non-coding variants that affect splicing remains challenging. In silico predictions alone are insufficient, and short-read RNA sequencing may fail to capture complex or low-abundance splicing events. Targeted amplicon-based long-read RNA sequencing (Amp-LRS) offers a cost-effective approach for functional validation of candidate splice-altering variants. Methods We applied Amp-LRS to five patients with neurological disorders (central nervous system, peripheral nervous system, or muscle) harbouring candidate non-coding variants predicted to alter splicing. RNA was extracted from fibroblasts or peripheral blood, and full-length transcript amplicons were sequenced using Oxford Nanopore Technologies. Nonsense-mediated decay (NMD) inhibition was performed on fibroblast cultures using cycloheximide. Results Amp-LRS validated all five candidate variants, including intronic and UTR variants in POLR3A, OPA1, PYROXD1, GDAP1, and SPG11. Aberrant splicing events included exon skipping, intron retention, cryptic splice site activation, and pseudoexon inclusion, often resulting in frameshifts and premature termination codons. For POLR3A and OPA1, multiple abnormal isoforms arose from single variants, highlighting the complexity of splicing disruption. Some pathogenic effects were detectable only in a minority of reads and variably enriched by NMD inhibition, consistent with being hypomorphic. The approach was successfully applied using accessible tissues and enabled multiplexed sequencing at low per-sample cost. Conclusions Amp-LRS is a sensitive, versatile, and cost-effective method for functional assessment of non-coding splice-altering variants identified by WGS. By enabling full-length transcript analysis from accessible tissues, this approach improves interpretation of variants of uncertain significance and could enhance molecular diagnosis in rare neurological diseases.

BackgroundPATZ1 fusion-positive central nervous system (CNS) tumors frequently harbor MN1::PATZ1 fusions as driver mutations, provisionally classified as a rare DNA methylation class of low-grade neuroepithelial tumors. Radiographically, they resemble pilocytic astrocytomas with tumor and cystic components, but their supratentorial cortex location and higher recurrence rates are distinguishing features. An intermediate clinical course, despite focal high-grade histopathology, underscores the need for longitudinal molecular and immune analyses to refine classification and standard therapy. Case SummaryA female pediatric patient presented with neurological symptoms, including headache and right upper extremity weakness. MRI revealed a large cystic lesion in the left frontal lobe, leading to a differential diagnosis of low-grade glioma and ependymoma. Genomic analysis identified an MN1::PATZ1 fusion. The tumor recurred after gross total resection prompting a second resection. Transcriptomic and histopathologic assessments identified multiglial lineage, and high-grade features closely related to adult glioblastoma alongside pro-inflammatory activity in the primary tumor. The recurrent tumor showed reduced malignancy, and oligodendroglioma-like features. Increased MHC gene expression, immune checkpoint receptors (PDCD1, CTLA4, TIGIT,TIM3), T cell regulators (CXCR6), and elevated macrophage frequency, coupled with reduced PD-L1 in the recurrent tumor, suggest a complex anti-tumor immune response constrained by T cell dysregulation. This case, along with two other MN1::PATZ1 fusion-positive tumors, identifies a distinct transcriptomic subtype separate from circumscribed astrocytic glioma, highlighting upregulation of growth factor receptor pathways, like PI3K/AKT, and immune dysfunction linked to recurrence. ConclusionLongitudinal multi-omics analyses of recurrent MN1::PATZ1 fusion-positive CNS tumors revealed tumor maturation, immune dysfunction, and potential therapeutic targets. Introductory ParagraphPATZ1 fusion-positive central nervous system (CNS) tumors are rare, predominantly pediatric and frequently recurrent neoplasms provisionally classified as neuroepithelial tumors. Their pronounced histopathological and clinical heterogeneity, along with limited immunological characterization complicates their treatment standardization. We report a new case of an MN1::PATZ1 fusion-positive CNS tumor with recurrence, highlighting its radiographic similarities to low-to-intermediate grade pediatric glioma. Longitudinal multi-omics analyses of this case, along with additional MN1::PATZ1 fusion-positive CNS tumors, however, delineates a transcriptome subtype resembling adult high-grade glioma, with activated oncogenic and pro-inflammatory programs. The recurrent tumor exhibits features of decreased malignancy and enhanced glial differentiation, phenotypically shifting towards oligodendroglioma, suggesting tumor maturation. This was accompanied by increased antigen presentation programs, indicating immune engagement, while increased immune checkpoint expression and microglia/macrophage frequency indicate T cell exhaustion and immunomodulation, respectively. This longitudinal study highlights potential therapeutic strategies targeting both the tumor and its immune environment in MN1::PATZ1 fusion-positive CNS tumors.

Background & AimsThe increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective methods of histopathological assessment. Conventional histological approaches are time-consuming and rely on interpreters experience. Therefore, the results obtained may suffer from high variability and only offer coarse categorisation. In this study, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features for MASH/MASLD assessment. MethodsSegmentation was applied to H&E sections from 45 mice and 44 humans with MASH/MASLD. The method, which we named qHisto (quantitative histology), utilises the nnU-Net framework and quantifies key histological components of the MASH score, including macro- and microvesicular steatosis, fibrosis, inflammation, hepatocellular ballooning and glycogenated nuclei. Additionally, we characterized the tissue using novel features that are inaccessible through manual histology, such as the distribution of fat droplet sizes, aspect ratio of nuclei and heatmaps. ResultsqHisto parameters showed strong positive correlations with conventional histology scores (fat area R=0.91, inflammation density R=0.7, ballooning density R=0.49) and also with quantitative magnetic resonance imaging (fat area vs. hepatic fat fraction R=0.87). Our novel scores showed that deformation of nuclei is driven by large fat droplets rather than the overall amount of fat. ConclusionsA key advantage of our method is spatially resolved, precise histological quantification. These features provide a finely resolved assessment of disease severity than conventional categorical scoring. By automating time-consuming and repetitive readouts, qHisto improves standardisation and reproducibility of MASH/MASLD feature quantification and provides scalable, slide-wide readouts that can support histopathologists and enhance clinical assessment and therapeutic development. Impact and ImplicationsThe proposed method provides an objective, automatic tool for comprehensive, histological liver analysis of MASH/MASLD, which can be extended to other diseases and organs. By offering classic and novel quantitative parameters and scores, our method could support histologists in their daily routines and provide researchers with further insight into steatotic liver diseases.

Protocadherin-12 (PCDH12), a cell-adhesion protein belonging to the non-clustered protocadherin family, plays a crucial role in the establishment and regulation of neuronal connections and communication. Bi-allelic loss-of-function (LoF) variants in the PCDH12 gene have been associated with several neurodevelopmental disorders (NDDs) such as diencephalic-mesencephalic junction dysplasia (DMJD) syndrome, cerebral palsy, and cerebellar ataxia, often accompanied by ocular abnormalities. However, genotypes exhibit variable expressivity. Affected individuals sharing the same PCDH12 variant presenting differing phenotypic severities have posed major challenges towards identification of the underlying pathogenic mechanisms. Here, we report three affected individuals from two families, each harbouring non-truncating pathogenic missense variants in PCDH12. The patients are compound heterozygous, with each individual carrying one extracellular [c.1742T>G (p.Val581Gly) and c.1861_2del/insCA (p.Ile621His)] and one intracellular variant [c.3370C>T (p.Arg1124Cys) and c.3445G>A (p.Asp1149Asn] on each allele. The children present with a range of phenotypes similar to those associated with LoF variants. One child exhibited microcephaly and seizures, while the two siblings displayed developmental delays and severe behavioral disorders. All three children experienced some degree of visual impairment. The missense variants provided new insights into the neurodevelopmental consequences of compromised PCDH12 function by distinguishing the specific consequences associated with dysfunction in the extracellular versus intracellular domains of PCDH12. All identified missense variants are predicted to be deleterious and destabilizing. The expression of PCDH12 in HEK293T and HeLa cells demonstrated that PCDH12 is expressed effectively, regardless of the presence of missense variants. However, the extracellular variants p.Val581Gly and p.Ile621His compromised the stability of PCDH12's homophilic adhesion. Additionally, we found evidence of an interaction between PCDH12 and the extracellular domain of the epilepsy-associated PCDH19 protein. PCDH12 extracellular missense variants also negatively impact this interaction. Our study provides evidence that PCDH12 mediates both homophilic and heterophilic interactions. Our findings also highlight the importance of stable PCDH12-mediated adhesion, emphasizing the need to further study the functional consequences of PCDH12 missense variants on brain and visual system development.

Background: Quantitative genome wide association studies (GWAS) primarily rely on additive linear models that compare average phenotypic differences between genotype groups. While effective for detecting common variants of moderate effect in large sample sizes, such approaches inherently reduce high resolution phenotypic data to summary statistics (group averages), potentially limiting the detection of subtle genotype phenotype relationships. Genomic Informational Field Theory (GIFT) is a recently developed methodology that preserves the fine-grained informational structure of quantitative traits by analysing ranked phenotypic configurations rather than relying solely on mean differences. Methods: We applied GIFT to genetic and neuropathological data from the Brains for Dementia Research cohort, a well characterised dataset of 563 individuals, and compared its performance with conventional GWAS. Principal component analysis (PCA) derived matrix was used to derive independent quantitative traits linked to from Alzheimer disease (AD) neuropathology measures (CERAD, Thal, Braak staging), with and without inclusion of age at death. Principal component analyses were performed using GWAS and GIFT frameworks on the same filtered genotype dataset. Results: Both GWAS and GIFT identified genome-wide significant associations (pvalue<0.000001) within the APOE locus (NECTIN2/TOMM40/APOE/APOC1), demonstrating concordance with established AD genetic variants. However, GIFT detected additional significant 19 SNPs beyond those identified by GWAS. Variants associated with AD pathology implicated genes involved in amyloid processing, neuronal apoptosis, synaptic function, neuroinflammation, and metabolic regulation. Notably, GIFT identified 29 loci associated with age at death related variation that were not detected by GWAS, highlighting genes linked to lipophagy, mitochondrial quality control, sphingolipid metabolism, frailty, and aging-related processes. Conclusions: GIFT recapitulates canonical GWAS findings while uncovering additional biologically relevant associations. By preserving the fine-grained structure of phenotypic data distributions and detecting non random genotype segregation across ranked trait values, GIFT enables the identification of associations that remained undetected by traditional average based GWAS approaches. These results demonstrate that rethinking analytical representation, rather than solely increasing sample size, can expand discovery potential of genetic association studies, offering a transparent and complementary framework for quantitative genomics in deeply phenotyped datasets.

Lysosomal dysfunction is central to Parkinsons disease pathogenesis, with GBA1 as the strongest established genetic risk factor. Numerous other genes involved in lysosomal sphingolipid, glycosphingolipid and ceramide metabolism have been proposed as contributors to Parkinsons disease, underscoring the need for comprehensive genetic analyses across these pathways. We analysed rare variants (minor allele frequency < 0.01) across 36 lysosomal genes (excluding GBA1) in 8,267 individuals with Parkinsons disease and 68,208 controls, including a subset of 793 early-onset Parkinsons disease ([&le;]50 years) cases. Targeted sequencing was performed in four cohorts at McGill University (3,456 Parkinsons disease patients and 2,664 controls) and results were combined with whole-genome sequencing data from the UK Biobank (2,848 cases, 62,451 controls), and from the Accelerating Medicines Partnership - Parkinsons Disease (1,963 cases, 3,093 controls). We analysed the association of rare variants in these genes with Parkinsons disease using Sequence Kernel Association Test-Optimal (SKAT-O) across variant classes (all rare variants, nonsynonymous, loss-of-function and predicted damaging variants with a Combined Annotation Dependent Depletion (CADD) score >20), with meta-analysis across cohorts. We additionally performed per-domain analyses for variants in gene segments encoding functional domains. False discovery rate correction was applied. Meta-analysis identified a significant association between rare variants in ST3GAL3 and Parkinsons disease (Pfdr=0.04). Several additional lysosomal genes showed nominal associations (P<0.05), including HGSNAT, ASAH1, CTSD, HEXA, ST3GAL4 and SGPP1. Domain-based analyses identified a strong enrichment of nonsynonymous variants within the beta-acetyl-hexosaminidase-like domain of HEXA (P = 8.0 x 10), although this signal did not survive correction for multiple testing (Pfdr=0.154). In early-onset Parkinsons disease, domain-based analyses revealed significant associations in NAGLU (Pfdr=7.3x10) and ST3GAL5 (Pfdr=0.03). Together, these results provide genetic evidence that rare variants across multiple lysosomal pathways, particularly those related to sialylation, ganglioside metabolism, ceramide biology, and lysosomal proteolysis, may contribute to Parkinsons disease susceptibility beyond GBA1, highlighting biologically coherent pathways for future replication and functional investigation.

The cerebrospinal fluid (CSF) proteome offers a direct readout of central nervous system (CNS) biology but its genetic architecture remains incompletely defined. We conducted the largest single-site CSF genome-wide association study (GWAS) to date, analysing 7,092 SomaScan proteins in 1,259 individuals. Using a covariate-adjusted model including proteomic PCs and disease status, we identified 1,971 genome-wide significant pQTLs (954 cis, 971 trans), 1,409 of which replicated in an independent CSF dataset. We discovered 264 previously unreported loci, replicated 511 associations, refined 80 known loci, and 265 proxy-based associations. Using a previously published reproducibility framework, we show that robust discovery concentrates in reliable measurements, underscoring the importance of rigorous quality control. Enrichment analyses revealed immune/complement and extracellular matrix biology. Mendelian randomization prioritised causal proteins: PILRA, TREM2, IL34, CR2, SHARPIN and ERBB1 (Alzheimers disease); BST1 and GPNMB (Parkinsons disease); STX6 (Creutzfeldt Jacobs disease); and ATXN3 and B4GALNT1 (Amyotrophic lateral sclerosis), providing a scalable framework for orthogonal target validation in neurodegeneration. HighlightsCSF A{beta}42 and p-tau, CSF total protein and Qalb are major contributors to the proteomic variance and may act as potential confounders. Most pQTLs were found in proteins classified as "reproducible" based on our CSF proteomic score. The most stringently adjusted GWAS model maximized pQTL discovery among the highest-reproducibility proteins (score 1 and A). We identified 264 novel CSF pQTLs that were not described in previous analyses, replicated 511 CSF pQTLs, 80 map refinements and 265 proxy SNPs, predominantly involved in immune-related, inflammatory, and extracellular matrix mechanisms. We found 281 CSF pQTLs that were also systemic modulators of plasma protein levels that were enriched in immune-related and extracellular matrix mechanisms. We identified and validated several causal proteins associated with AD and other neurodegenerative disorders.

Clinical progression is strongly linked to grey matter atrophy in multiple sclerosis (MS), detectable early on MRI and progressing non-randomly across the brain. However, the mechanisms driving its spatio-temporal progression and individual variability remain unclear. Using MRIs from 2,187 participants, alongside normative data, we systematically investigated network-based mechanisms underlying MS-related atrophy. Regional atrophy colocalised with functional cortical hubs, supporting the nodal stress hypothesis, and propagated along anatomical and functional connections, consistent with transneuronal degeneration. Lesional disconnection and transcriptomic vulnerability played marginal roles. Patient- and subgroup-level analyses revealed that network-based mechanisms are specifically linked to MS-related neurodegeneration and may operate differently in distinct subtypes or disease phases. Atrophy patterns were anchored to the connectivity profiles of disease epicentres involving the visual, sensorimotor, and temporal cortices, and the hippocampi and thalami. Network-based measures enhanced the prediction of future atrophy progression in individual with MS, providing a mechanistic framework to understand neurodegeneration in MS.

Spinal muscular atrophy (SMA) is a severe neuromuscular disorder caused by reduced expression of the survival motor neuron (SMN) protein. In addition to affecting motor neuron survival, SMN deficiency impacts multisystem physiology and neurotransmission. Dopaminergic dysfunction has been reported in mouse models of SMA, leading to postural and locomotor impairments that improve upon treatment with L-DOPA and benserazide. However, whether altered dopamine metabolism contributes to clinical symptoms in SMA patients remains unclear. To investigate this issue, we conducted a real-world observational study involving pediatric patients with SMA1, SMA2, and SMA3. We performed a longitudinal measurement of the main dopamine-related catabolites - 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA) - in cerebrospinal fluid (CSF) samples collected at baseline and after five intrathecal doses of Nusinersen, an SMN-enhancing therapy. No significant differences were observed in CSF DOPAC and HVA levels across SMA types or following treatment, and no association emerged with SMN2 copy number. In contrast, lower baseline DOPAC levels were detected in SMA1 patients requiring gastrostomy and tracheostomy, and were associated with reduced improvement on the CHOP-INTEND scale. These findings suggest that reduced central dopaminergic turnover reflects disease progression in SMA1 and is associated with more severe clinical impairment and limited functional recovery.

Alzheimers disease and related dementias (ADRD)1 and Parkinsons disease and related disorders (PDRD)2 have substantial genetic contributions, yet the role of rare damaging coding variants remains incompletely characterized at population scale3-6. We performed gene-based burden testing of rare loss-of-function and deleterious missense variants using whole-genome sequencing data from large population biobanks combined with disease-specific sequencing cohorts, leveraging proxy phenotypes to maximize statistical power for late-onset neurodegenerative diseases7. We confirmed rare variant burden in established ADRD genes (ABCA7, PSEN1, ADAM10, ATP8B4, GRN, SORL1, TREM2, SHARPIN) and PDRD genes (GBA1, LRRK2). We additionally identified novel associations in ADRD (IMPA2, PMM2, SYNE1, CHRNA4, FCGR1A) and PDRD (ANKRD27, CCL7, USP19, SKP1, KANSL3). The strongest signal was observed for ANKRD27, where damaging variants clustered within domains mediating interactions with Rab GTPases and retromer components. Our results demonstrate the power of population-scale sequencing combined with proxy phenotypes to identify rare coding risk genes for neurodegenerative diseases.

Developmental and epileptic encephalopathies (DEEs) are a group of severe childhood-onset neurological disorders, often caused by rare genetic variants affecting brain development and excitability. Despite advances in genomic sequencing, a substantial proportion of DEE cases remain unsolved. Here, we identify THAP12 as a novel disease-causing gene associated with autosomal recessive DEE. Whole-genome sequencing in two siblings who presented with infantile spasms and progressed to Lennox-Gastaut syndrome revealed compound heterozygous variants in THAP12, leading to a reduction in protein abundance, consistent with a loss-of-function mechanism. To confirm this mechanism in vivo, we generated mouse models carrying either of the two patient-specific alleles. Both homozygous and compound heterozygous animals exhibited embryonic lethality, confirming the essential and dosage-sensitive role of Thap12 during early development. Zebrafish loss-of-function models recapitulated major aspects of the human phenotype, including microcephaly, brain hypoplasia, abnormal neuronal activity, and increased seizure sensitivity. Transcriptomic profiling of larval zebrafish brains revealed dysregulation of cell cycle and apoptotic pathways, in line with increased cell death and reduced proliferation observed in mutant embryos. Notably, overexpression of wild-type human THAP12 mRNA rescued these in vivo phenotypes, while the patient-derived variants allele failed to do so. Altogether, our findings demonstrate that THAP12 is essential for early brain development and neuronal survival, and that biallelic loss-of-function variants in this gene underlie a previously unrecognized etiology of autosomal recessive DEE. These results provide a mechanistic framework linking, for the first time, THAP12 dysfunction to neurodevelopmental pathology and open new avenues for diagnosis in undiagnosed DEE cases.

While 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is an established biomarker in amyotrophic lateral sclerosis (ALS), the metabolic correlates of motor neuron disease motor variants remain poorly defined. This is why we investigated patterns of cerebral glucose metabolism across the spectrum of motor neuron disorders (MND), including progressive muscular atrophy (PMA), primary lateral sclerosis (PLS) and amyotrophic lateral sclerosis (ALS). We retrospectively included 18 PMA, 25 PLS and 43 matched non-hereditary ALS patients according to most recent diagnostic criteria. FDG-PET imaging revealed similar widespread hypometabolism in PMA, as in ALS, whereas PLS showed a more focal motor cortical pattern of hypometabolism. Despite clinical differences between MND subtypes, PMA and ALS showed similar FDG-PET metabolic patterns, whereas PLS exhibited a more restricted cortical signature in this retrospective study.

BackgroundGenome-wide association studies (GWAS), with independent replication in large European consortia, have identified a common nonsense variant in IL-34 (Y213X) as a genetic risk factor for late-onset Alzheimers disease (AD). However, the biological consequences of this IL-34 mutation in humans, its prevalence in the population, and the mechanisms by which IL-34-Y213X alters microglial homeostasis, cerebrospinal fluid (CSF) proteomic networks, and amyloid pathology remain poorly understood. MethodsWe combined human genetics, cerebrospinal fluid (CSF) and serum proteomics, large-scale phenome-wide association analyses, and preclinical experimental models to define the impact of human IL-34 deficiency. IL-34 concentrations were first quantified in CSF and serum from deeply phenotyped AD cohorts stratified by the common IL-34-Y213X nonsense variant. IL-34 levels and IL-34-Y213X status were then integrated with unbiased CSF proteomic networks and AD biomarkers. Using complementary mouse models of IL-34 loss in an APP/PS1 transgenic background, we examined the effects of IL-34 deficiency on microglial survival, tiling, and plaque encapsulation. Finally, we performed postmortem analyses of temporal cortex from AD patients carrying IL-34-Y213X to assess microglial density, spatial organization, and plaque-associated responses. FindingsIL-34-Y213X was a strong, dose-dependent loss-of-function allele that reduced IL-34 levels by up to 2.5 standard deviations in CSF and serum and was common in multiple populations. IL-34 deficiency reshaped CSF proteomic networks, downregulating axon guidance and microglial support modules while upregulating inflammatory and extracellular matrix signatures, and showed pleiotropic associations with neurological, inflammatory, and metabolic traits. In APP/PS1 mice, genetic IL-34 deletion selectively depleted homeostatic gray-matter microglia, disrupted microglial tiling, and impaired plaque encapsulation, resulting in altered amyloid structure and enhancing neuritic injury. Concordantly, AD patients homozygous for IL-34-Y213X displayed markedly reduced cortical microglial density and increased microglial spatial dispersion, indicating a breakdown of the microglial network organization in the human brain. InterpretationA common human IL-34 loss-of-function variant creates a naturally occurring model of IL-34 deficiency that links microglial survival, CSF network signatures, and amyloid pathology in both mice and humans. These findings position IL-34/CSF1R signaling as a critical determinant of microglial resilience in AD and highlight IL-34-dependent pathways as potential targets for disease modification.

BackgroundPrognosis and therapeutic management in Parkinsons disease remain challenging due to the diseases heterogeneous progression and symptom presentation and lack of reliable biomarkers to predict individual disease trajectories. ObjectiveTo determine whether baseline blood transcriptomes, analyzed through biologically defined pathway gene sets, contain signatures that distinguish distinct motor and non-motor progression trajectories in Parkinsons disease. MethodsUsing data from the Parkinsons Progression Markers Initiative cohort, we developed a pathway-based computational framework to derive individualized molecular severity scores from baseline blood transcriptomic profiles by integrating pathway-level gene expression with longitudinal clinical data. Severity indices for motor and non-motor features established domain-specific progression trajectories of sporadic Parkinsons disease. Machine learning models were trained to predict patient trajectory membership from baseline transcriptomics. Findings were validated in genetic subcohorts and externally in the Parkinsons Disease Biomarkers Program cohort. ResultsMolecular severity scores were associated with key clinical features. Analysis of score changes revealed two non-motor and two motor progression groups, each characterized by specific gene signatures (20 genes for non-motor; 121 for motor). From baseline transcriptomic data, we accurately predicted an individuals trajectory group (0.87 for motor progression). The framework demonstrated high generalizability across independent and genetic cohorts, producing clinically coherent profiles. ConclusionsOur analysis reveals that baseline blood transcriptomic profiles delineate motor and non-motor progression trajectories in sporadic Parkinsons disease. The results are consistent with prior findings and may contribute to the identification of novel biomarkers, thereby informing and potentially optimizing the design of clinical trials aimed at modifying disease progression.

BackgroundHistologic descriptors such as lymphovascular invasion (LVI), visceral pleural invasion (VPI), spread through air spaces (STAS), and grading system have each been associated with adverse outcomes in lung adenocarcinoma (LUAD). However, with the exception of VPI, these features are not formally incorporated into the TNM staging system. We evaluated the prognostic value and incremental contribution of these histologic descriptors within the framework of the 9th edition TNM staging system. MethodsIn total, 1,745 individuals diagnosed with stage I-III invasive non-mucinous lung adenocarcinoma (NM-LUAD) were included in this study, comprising 1139 French-Canadian patients who underwent surgical resection at IUCPQ-Universite Laval (discovery cohort) and 606 patients from the National Cancer Center Hospital in Tokyo, Japan (validation cohort). The objective of this study was to assess the prognostic contribution of histologic descriptors, including STAS, and LVI, as complements to conventional 9th edition TNM staging. ResultsGrade 3 tumors, LVI, and STAS were identified in 880 (50.4%), 809 (46.4%), and 775 (44.4%) of 1745 cases, respectively. Histologic grade and LVI demonstrated the strongest associations, particularly in early-stage disease, while STAS exhibited a stage-dependent effect, being more impactful in stages II-III. VPI showed less consistent prognostic value. Incorporating these histologic descriptors into TNM staging improved prognostic model performance, with the largest gains driven by histologic grade and LVI, while STAS provided additional, complementary prognostic refinement. ConclusionThese findings demonstrate that key histologic descriptors--including grading system, LVI, and STAS--represent robust and reproducible prognostic parameters. Importantly, these descriptors provide complementary, stage-dependent information that may enhance risk stratification and inform refinement of future TNM staging frameworks, including the forthcoming 10th edition.

BackgroundImmune checkpoint inhibition has transformed cancer therapy; however, many patients fail to respond to single-agent blockade, and combination strategies are often limited by toxicity. Central nervous system tumors exploit multiple immunosuppressive pathways, including the CD200 and PD-1/PD-L1 axis to evade anti-tumor immunity and support tumor aggressiveness. MethodsWe investigated ARL200, a peptide ligand targeting the CD200 activation receptor (CD200AR) using in vitro immune assays, murine syngeneic tumor models, phosphoproteomics, and correlative studies from a first-in-human trial in recurrent glioblastoma. ResultsARL200 exposure activated DAP10/12-dependent signaling and downregulated multiple inhibitory immune checkpoint receptors, including CD200R1, PD-1, and CTLA-4, and checkpoint ligands, CD200 protein and PD-L1, through suppression of the JAK1/3-SHP-STAT-IKK/{beta}-NF{kappa}B pathway. Distinct ARL200 variant peptides elicited unique immune responses. In patients with recurrent glioblastoma, ARL200 treatment was associated with immune activation, reduced inhibitory checkpoint expression, and evidence of antigen-specific memory responses without treatment-related toxicity. ConclusionsTargeting CD200AR enables coordinated modulation of multiple immune checkpoints with a single agent, representing a next-generation immunotherapeutic strategy opening a new pathway for treating aggressive malignancies. Key PointsO_LIARL200 elicits an active immune response for the development of a potent and durable anti-tumor response C_LIO_LIARL200 abolishes the suppressive effects of multiple immune checkpoint blockades C_LIO_LIDifferent ARL200 sequences drive alternative immune responses. C_LI Importance of the StudyTumors exploit multiple immune checkpoint pathways to suppress antitumor immunity, particularly within the immunosuppressive microenvironment of the central nervous system. Current immune checkpoint inhibitors often require combination therapy to achieve clinical efficacy, frequently at the cost of increased toxicity. In this study, we demonstrate that targeting the CD200 activation receptor (CD200AR) with a peptide ligand provides a novel strategy to simultaneously downregulate multiple inhibitory immune checkpoints, including CD200R1, PD-1, PD-L1, and CTLA-4, through a shared intracellular signaling pathway. ARL200 engagement activates DAP10/12-dependent signaling while suppressing the JAK1/3-SHP-STAT-IKK/{beta}-NF{kappa}B axis, thereby overriding tumor-mediated immunosuppression. Importantly, this multi-checkpoint modulation is achieved with a single therapeutic agent and translates to immune activation and clinical responses in patients with recurrent glioblastoma, with minimal treatment-related toxicity. These findings establish CD200AR targeting as a next-generation immunotherapeutic approach with the potential to improve the safety and efficacy of immune-based therapies for aggressive CNS malignancies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/26345679v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@17a5010org.highwire.dtl.DTLVardef@11e67eborg.highwire.dtl.DTLVardef@1387c07org.highwire.dtl.DTLVardef@156d418_HPS_FORMAT_FIGEXP M_FIG C_FIG

Drug-induced liver injury (DILI) is an acute inflammatory liver disease caused not only by prescription and over-the-counter medications but also by health foods and dietary supplements. Typically, DILI patients recover once the causative substance is identified and discontinued. In contrast, autoimmune hepatitis (AIH) results from the immune-mediated destruction of hepatocytes due to a breakdown of self-tolerance mechanisms. Patients presenting with acute-onset AIH often lack characteristic clinical features, such as autoantibodies, and require prompt steroid treatment to prevent progression to liver failure. Liver biopsy currently remains the gold standard to differentiate acute DILI from AIH; however, general pathologists face significant diagnostic challenges due to overlapping histopathological features. This study integrates pathology expertise with deep learning-based artificial intelligence (AI) to differentiate DILI from AIH using histopathological images. Our AI model demonstrates promising classification accuracy (Accuracy 74%, AUC 0.81). This paper presents a detailed pathological analysis alongside AI methods, discusses the current model performance and limitations, and proposes directions for future improvements.