Neuropathology and Applied Neurobiology

Diagnosing Amyotrophic Lateral Sclerosis (ALS) remains challenging due to its inherent heterogeneity. Cytoplasmic aggregation of TDP-43, observed in approximately 95% of ALS cases, has emerged as a key pathological hallmark. In this observational study, we investigated the feasibility of training deep learning models to classify TDP-43 pro-teinopathic samples versus healthy controls, with a particular focus on understanding how dataset limitations affect model performance. The dataset comprised super-resolution immunofluorescence images in which cytoplasmic and nuclear TDP-43 deposits were quantified using red and pink pixel counts. We formulated three classification tasks: TDP-43 pathology (binary), TDP-43 pathology grades (multiclass), and ALS diagnosis (binary). Initial deep learning experiments yielded inconclusive results, prompting dataset curation and the removal of problematic samples. Subsequent statistical analyses using t-tests, ANOVA, and hierarchical clustering revealed significant differences between healthy and pathological samples in terms of pixel distributions, total protein levels, and TDP-43 compart-mentalisation. These findings suggest that classification based on TDP-43 proteinopathy provides a more reliable framework for deep learning compared to ALS diagnosis, underscoring the importance of data quality and task strati-fication in model performance.

BackgroundCurrent clinical neuropathology practice utilizing DNA methylation information to support diagnosis of central nervous system (CNS) tumors could benefit from increased interpretability and cost reductions. MethodsWe identified and characterized limited sets of genomic regions (i.e. features) that can be used for accurate classification of CNS tumors based on DNA methylation data. The features were selected using a hybrid strategy combining filtering and Elastic Net Logistic Regression (ENLR). A Support Vector Machine (SVM)-based classifier was trained using select 1003 informative features and an established cohort of 60 diagnostic tumor classes comprising 82 tumor DNA methylation classes and 9 control classes. Validation was performed using external microarray and targeted DNA methylation sequencing cohorts. ResultsInformative regions were enriched in enhancers and associated with genes involved in neural development and morphogenesis. In the microarray validation cohort of 1993 samples representing 76 DNA methylation classes, overall accuracy of our SVM classifier was 0.96, when using 1003 features and after the differences to the molecular neuropathology classifier were evaluated based on reported final tumor diagnosis and diagnostic relevance. Its performance remained similar (overall accuracy 0.95-0.96) when the number of features was further decreased, down to 163. An accuracy of 0.94 was detected in the in-house targeted sequencing cohort of 17 cases. ConclusionsThe classification of CNS tumors is feasible and accurate based on a very limited set of genomic regions, which facilitate further method development and the interpretation of classification results, likely benefiting CNS tumor diagnostics worldwide. HighlightsO_LIHybrid feature selection identifies 1,003 CpGs strongly linked to CNS tumors C_LIO_LISVM model achieves 0.96 accuracy with confidence and top-3 predictions C_LIO_LIRobust across sequencing and microarray platforms for clinical use C_LIO_LIReliable even when reduced to 163 CpG features, lowering cost and complexity C_LI

Sporadic cases of Amyotrophic Lateral Sclerosis (sALS) represent the most common form of motor neuron disease. sALS is characterised by pathological cytoplasmic inclusions of TDP-43, so-called reactive astrocyte pathology, and motor neuron degeneration. Early-stage alterations in certain subpopulations of synapses between neurons are thought to be a key driver of the early pathological mechanisms of ALS. However, we do not have a clear understanding of which types of synapses are impacted in ALS. Identifying vulnerable synapses affected in sALS models may provide insights into the key sites of disease pathogenesis. In this study we have performed quantitative high-resolution microscopy to survey different synapse subtypes, including excitatory (glutamatergic), inhibitory (glycinergic) and modulatory (cholinergic C-Bouton) synapses, in the spinal cord of a mouse model of sALS showing inducible TDP-43 pathology (TDP43{Delta}NLS) restricted to neurons. We have identified changes in cholinergic synapses and a subpopulation of excitatory synapses. Mice display robust neuronal TDP-43 pathology and evidence of TDP-43 changes at cholinergic C-boutons. We also observe no evidence of astrocytic pathology nor changes in the fraction of synapses that are contacted by astrocytes, demonstrating that synapse pathology is driven by cell-autonomous (neuronal) mechanisms. Overall, our findings highlight the selective vulnerability of distinct synapse populations in ALS.

Amyotrophic lateral sclerosis (ALS) is a neurogenerative disease resulting from progressive degeneration of motor neurons leading to systemic consequences. Despite being the most common motor neuron disease, with increasing global prevalence, limited treatment options exist. Emerging evidence from genetic studies and pathology analyses implicates RNA dysregulation in ALS pathogenesis, however, deep, comprehensive RNA sequencing studies have not been carried out. Here, we analysed >240 ALS and control whole blood transcriptome samples. Cross-sectional (Ncases=121, Ncontrols=53) and longitudinal (Nobservations=103) cohorts supported complementary expression analyses of disease mechanisms across disease stages. Both short (N=241) and long-read (N=16) technologies were utilised to discover splicing changes. Total RNA was extracted from PAXgene whole blood RNA tubes before libraries (Illumina Stranded Total RNA RiboZero Plus) were prepared and sequenced ([~]50M PE reads per sample). Long-read sequencing was performed using the Mas-Seq protocol with Kinnex full-length RNA prep kit and sequenced (PacBio Revio platform, 10M reads per sample) for full-length transcripts. Case-control cohort analyses identified 50 significantly differentially expressed genes, with pathway analyses implicating RNA processing and immune system regulation. Findings were corroborated using existing ALS RNAseq datasets from blood (correlation >0.4), iPSC-MN and post-mortem tissues. Alternative splicing (AS) analyses (LeafCutter) identified 62 clusters. Within-case analyses involved ALS cases with multiple (2-4) visits, detected 144 genes associated with disability progression over time. The long-read sequencing (Ncases=8, Ncontrols=8) provided novel discovery insights, in particular in the HLA region. This comprehensive blood-based transcriptomic dataset reveals both known and novel disease mechanisms in ALS, offering valuable insights that could inform future research and therapeutic development. The results of this study may inform and refine the prioritization of candidate genes and loci in future ALS research.

BackgroundAmyotrophic lateral sclerosis (ALS) displays considerable clinical, genetic and molecular heterogeneity. Machine learning approaches have shown potential to disentangle complex disease landscapes and they have been utilised for patient stratification in ALS. However, lack of independent validation in different populations and in pre-mortem tissue samples have greatly limited their use in clinical and research settings. We overcame such issues by performing a large-scale study of over 600 post-mortem brain and blood samples of people with ALS from four independent datasets from the UK, Italy, the Netherlands and the US. MethodsHierarchical clustering was performed on the 5000 most variably expressed autosomal genes identified from post-mortem motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N=112). The molecular architectures of each cluster were investigated with gene enrichment, network and cell composition analysis. Methylation and genetic data were also used to assess if other omics measures differed between individuals. Validation of these clusters was achieved by applying linear discriminant analysis models based on the KCL BrainBank to the TargetALS US motor cortex (N=93), as well as Italian (N=15) and Dutch (N=397) blood expression datasets. Phenotype analysis was also performed to assess cluster-specific differences in clinical outcomes. ResultsWe identified three molecular phenotypes, which reflect the proposed major mechanisms of ALS pathogenesis: synaptic and neuropeptide signalling, excitotoxicity and oxidative stress, and neuroinflammation. Known ALS risk genes were identified among the informative genes of each cluster, suggesting potential for genetic profiling of the molecular phenotypes. Cell types which are known to be associated with specific molecular phenotypes were found in higher proportions in those clusters. These molecular phenotypes were validated in independent motor cortex and blood datasets. Phenotype analysis identified distinct cluster-related outcomes associated with progression, survival and age of death. We developed a public webserver (https://alsgeclustering.er.kcl.ac.uk) that allows users to stratify samples with our model by uploading their expression data. ConclusionsWe have identified three molecular phenotypes, driven by different cell types, which reflect the proposed major mechanisms of ALS pathogenesis. Our results support the hypothesis of biological heterogeneity in ALS where different mechanisms underly ALS pathogenesis in a subgroup of patients that can be identified by a specific expression signature. These molecular phenotypes show potential for stratification of clinical trials, the development of biomarkers and personalised treatment approaches.

Twenty percent of familial amyotrophic lateral sclerosis (fALS) cases are linked to mutations in the Superoxide Dismutase 1 (SOD1) gene and accumulation of misfolded SOD1 aggregates. SOD1 misfolding from the broader ALS population without SOD1 mutations is less clear. Here, we report SOD1 seeding activity in antemortem cerebrospinal fluid (CSF) from ALS participants with and without SOD1 mutations during ALS progression. Antemortem CSF from controls, SOD1-ALS, and sporadic ALS (sALS) patients was subjected to SOD1 seed amplification real-time quaking induced conversion (RT-QuIC) assays. SOD1-ALS CSF exhibited shorter lag phase and increased ThioflavinT (ThT) fluorescence amplitude compared to healthy controls and those with spinal muscular atrophy. CSF from sALS participants, who had no mutations in SOD1 or nine other ALS risk genes, also displayed SOD1 seeding activity, indicating wild-type SOD1 is aggregate-prone in the broader ALS population. Longitudinal CSF data indicated that SOD1 seeding activity correlates with ALS progression via the ALS Functional Rating Scale Revised (ALSFRS-R) slope decline and CSF neurofilament light. Our sALS CSF cohort primarily comprised of participants less than 2 years from symptom onset, suggesting that SOD1 seeding activity is an early biomarker that may enable inclusion in clinical trials. With the FDA-approval of tofersen (Qalsody), a SOD1-lowering antisense oligonucleotide, new SOD1 diagnostic, prognostic and pharmacodynamic biomarkers may enable SOD1-targeting strategies that could benefit the broader ALS population.

Amyotrophic lateral sclerosis - frontotemporal dementia spectrum disorder (ALS/FTD) is a complex neurodegenerative disease; up to 10% of cases are familial, usually arising from single dominant mutations in >30 causative genes. Transgenic mouse models that overexpress human ALS/FTD causative genes have been the preferred organism for in vivo modelling. However, while conferring human protein biochemistry, these overexpression models are not ideal for dosage-sensitive proteins such as TDP-43 or FUS. We have created three next-generation genomically humanised knock-in mouse models for ALS/FTD research, by replacing the entire mouse coding region of Sod1, Tardbp (TDP-43) and Fus, with their human orthologues to preserve human protein biochemistry, with exons and introns intact to enable future modelling of coding or non-coding mutations and variants and to preserve human splice variants. In generating these mice, we have established a new-standard of quality control: we demonstrate the utility of indirect capture for enrichment of a region of interest followed by Oxford Nanopore sequencing for robustly characterising large knock-in alleles. This approach confirmed that targeting occurred at the correct locus and to map homologous recombination events. Furthermore, extensive expression data from the three lines shows that homozygous humanised animals only express human protein, at endogenous levels. Characterisation of humanised FUS animals showed that they are phenotypically normal compared to wildtype littermates throughout their lifespan. These humanised mouse strains are critically needed for preclinical assessment of interventions, such as antisense oligonucleotides (ASOs), to modulate expression levels in patients, and will serve as templates for the addition of human ALS/FTD mutations to dissect disease pathomechanisms.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterised by motor neuron deterioration. Genetic factors play a significant role in all cases, with 15 genome-wide significant study (GWAS) risk loci identified to date. Follow-up of these loci is a powerful strategy for research translation, as drug targets supported by genetic evidence are more likely to succeed in clinical development. Here, we focus on the RPSA-MOBP locus on chromosome 3 (lead SNP, rs631312, OR = 1.08 95% CI: 1.06-1.10, p = 3.3 x 10-{superscript 1}{superscript 2}). We employ integrative in silico analyses to prioritise candidate genes, combining multiple omics-based approaches, including Functional Mapping and Annotation (FUMA), Polygenic Priority Scoring (PoPS), Transcriptome-Wide Association across/within tissues (TWAS), gene-based test (mBAT-combo), chromatin interaction mapping (H-MAGMA), and Summary data Mendelian Randomisation (SMR), with GWAS data (Ncases = 29,612, Ncontrols = 122,656). Both RPSA and MOBP were prioritised as candidate genes in multiple analyses. In-vivo expression analyses in ALS blood or iPSC-motor neurons were unremarkable for these genes but also other-relevant ALS genes. RPSA, highly conserved in zebrafish (92% homology), was selected for functional modelling, noting previously generated Mobp-ko mice show minimal phenotypic changes. CRISPR/Cas9-induced rpsa loss-of-function (LOF) in zebrafish triggers progressive and severe phenotypes mimicking pathology observed in SMN- and TDP43-deficient zebrafish, two key proteins/genes associated with diseases of the motor neurons. RPSA-deficient animals exhibit marked motor neuron axon pathology, progressive loss of motor function and rapid decline culminating with premature death at around 7 days- post-fertilisation. These phenotypes were notably similar to those observed in SMN and TDP-43 zebrafish models, together with prominent cardiovascular abnormalities. This study identifies RPSA as a critical gene for motor neuron health, with implications for ALS pathogenesis. The RPSA/MOBP locus is also associated with other neurodegenerative diseases including frontotemporal dementia/FTD, corticobasal degeneration/CBD and progressive supranuclear palsy/PSP, highlighting its potential as a therapeutic target for multiple conditions.

ObjectiveAmyotrophic lateral sclerosis (ALS) is a devastating neuromuscular disease with limited therapeutic options. Diagnostic and surrogate endpoint biomarkers are needed for early disease detection, clinical trial design, and personalized medicine. MethodsWe tested the predictive power of a large set of primary skin fibroblast (n=443) from sporadic and familial ALS patients and healthy controls. We measured morphometric features of endoplasmic reticulum, mitochondria, and lysosomes by imaging with vital dyes. We also analysed immunofluorescence images of ALS-linked proteins, including TDP-43 and stress granule components. We studied fibroblasts under basal conditions and under metabolic (galactose medium), oxidative (arsenite), and heat stress conditions. We then employed machine learning (ML) techniques on the dataset to develop biomarkers. ResultsStress perturbations caused robust changes in the measured features, such as organellar morphology, stress granule formation, and TDP-43 mislocalization. ML approaches were able to predict the perturbation with near perfect performance (ROC-AUC > 0.99). However, when trying to predict disease state or disease groups (e.g., sporadic, or familial ALS), the performance of the ML algorithm was more modest (ROC-AUC Control vs ALS = 0.63). We also detected modest but significant scores when predicting clinical features, such as age of onset (ROC-AUC late vs early = 0.60). ConclusionsOur findings indicate that the ML morphometry we developed can accurately predict if human fibroblasts are under stress, but the differences between ALS and controls, while statistically significant, are small and pose a challenge for the development of biomarkers for clinical use by these approaches.

The application of multiplex immunofluorescence to human post-mortem tissue would drive observational studies of selective vulnerability in neurodegenerative proteinopathies. Efficient elution of antibodies is critical for flexibility of antibody combinations and the ability to utilize a sample in multiple rounds of immunostaining. Here, we test two elution strategies for antibodies relevant to the study of selective vulnerability in neurodegenerative diseases in post-mortem human samples from both long-fixed and short-fixed tissue. Both 2-Mercaptoethanol/SDS-based and Urea/SDS/Glycine-based elution strategies work well with the antibodies selected, confirming observations from previous studies with other antibody types.

TDP-43 proteinopathy is a neuropathological hallmark of nearly all amyotrophic lateral sclerosis (ALS) and approximately half of frontotemporal dementia (FTD) cases. Nuclear loss of TDP-43 leads to widespread RNA misprocessing, such as the inclusion of cryptic exons that are no longer repressed by TDP-43. Notably, in-frame cryptic exons encode novel cryptic peptides that can be detected in biofluids, including that found in the HDGFL2 transcript. Here, we quantified HDGFL2 cryptic peptide and neurofilament light chain (NfL) in paired cerebrospinal fluid (CSF) and plasma samples from ALS and FTD patients. Cryptic HDGFL2 peptide was detected in the CSF of ALS patients, whereas no significant differences were observed between genetic and behavioral FTD subgroups. In contrast, NfL levels were elevated in both ALS and FTD, although this biomarker does not reflect TDP-43 pathology. Notably, NfL:HDGFL2 cryptic peptide ratio outperformed either marker alone in discriminating ALS and FTD cases from controls, achieving high specificity. Moreover, this ratio correlated with disease progression in ALS, suggesting added prognostic value. Collectively, our findings support the NfL:HDGFL2 cryptic peptide ratio as a promising fluid biomarker that integrates neurodegeneration with TDP-43 dysfunction, potentially improving diagnostic accuracy, disease stratification, and longitudinal monitoring in TDP-43-associated neurodegenerative disorders.

Histopathological analysis of tissue sections is an invaluable resource in neurodegeneration research. Importantly, cell-to-cell variation in both the presence and severity of a given phenotype is however a key limitation of this approach, reducing the signal to noise ratio and leaving unresolved the potential of single-cell scoring for a given disease attribute. Here, we developed an image processing pipeline for automated identification and profiling of motor neurons (MNs) in amyotrophic lateral sclerosis (ALS) pathological tissue sections. This approach enabled unbiased analysis of hundreds of cells, from which hundreds of features were readily extracted. Next by testing different machine learning methods, we automated the identification of phenotypically distinct MN subpopulations in VCP- and SOD1-mutant transgenic mice, revealing common aberrant phenotypes in cellular shape. Additionally we established scoring metrics to rank cells and tissue samples for both disease probability and severity. Finally, by adapting this methodology to human post-mortem tissue analysis, we validated our core finding that morphological descriptors strongly discriminate ALS from control healthy tissue at the single cell level. In summary, we show that combining automated image processing with machine learning methods substantially improves the speed and reliability of identifying phenotypically diverse MN populations. Determining disease presence, severity and unbiased phenotypes at single cell resolution might prove transformational in our understanding of ALS and neurodegenerative diseases more broadly.

ObjectiveTo test antibody response and formation of immune-complexes to neurofilaments and dipeptide-repeats, the translational products of the mutated C9orf72 gene, as potential biomarkers for clinical stratification of amyotrophic lateral sclerosis (ALS). MethodsUsing neurofilament protein isoforms plasma expression as reference, antibodies and immune-complexes against neurofilament-light, medium and heavy chain and poly-(GP)-GR dipeptide-repeats were tested in blood from 105 fast and slow progressing ALS patients, 26 C9orf72 mutation carriers (C9+ve) ALS patients and 77 healthy controls (HC) using single-molecule and immune-capture assays. Longitudinal antibody/immune-complex responses were measured in serial blood samples from 37 (including 11 C9+ve) patients. ResultsAntibodies and immune-complex reactivity was higher in ALS patients than HC, particularly in C9+ve ALS patients, and modestly correlated with total neurofilament protein isoforms (r:0.24 p=0.002; r:0.18 p=0.02 respectively). Neurofilament-light immune-complexes and neurofilament-heavy antibodies had the best diagnostic performances distinguishing ALS subtypes from HC (AUC=0.68 p<0.01; AUC=0.68 p<0.001 respectively). Neurofilament-light immune-complexes (AUC=0.69 p<0.01) and poly-(GP) dipeptide-repeats antibodies (AUC=0.71 p<0.001) separated C9+ve from C9-ve patients. Multivariate mortality hazard ratio and Kaplan-Meier analyses showed low neurofilament-heavy antibody levels associated with increased survival. Longitudinal analysis identified raising levels of antibodies against neurofilaments in fast progressing ALS and of neurofilament-light immune-complexes in C9+ve patients. InterpretationC9+ve and fast progressing ALS patients have a distinct neurofilament and dipeptide-repeat immuno-phenotype, with increasing levels of blood neurofilament-light immune-complexes and neurofilament antibodies with disease progression. The study of the expression of these biomarkers in the natural history of ALS may shed light on disease initiation and progression and provide novel pharmacodynamic biomarkers in emerging C9orf72 gene silencing therapies.

A significant challenge in ALS research is the heterogeneity of the disease. Even mutations within the same gene can lead to different disease prognosis. For instance, in silico protein modeling predicts distinct properties for distinct SOD1 mutations. With this in mind, in this study, we generated and characterized 3D iPSC-derived MN spheroids carrying homozygous knock-in SOD1 mutations (D90A and G93A), as well as a double mutation (D90A/G93A), to evaluate potential synergistic effects. An isogenic control line with the same genetic background was used for phenotypic comparisons with the knock-in variants. Mutant SOD1 MN spheroids exhibited multiple ALS-related phenotypes including altered SOD1 expression, reduced cell viability, downregulation of neurofilament (NF) subunit expression, hypoactivity, and altered burst activity. Our results highlight the advantages of using 3D MN spheroids as a disease model and stress the importance of considering phenotype variability at the genetic level in ALS.

BackgroundAmyotrophic lateral sclerosis (ALS) is a uniformly fatal neurodegenerative disease characterized by progressive cortical and spinal motor neuron loss, with most patients surviving only 2-5 years post-diagnosis. While approximately 10% of cases are familial (fALS), the remaining 90% are sporadic (sALS) with unknown genetic drivers. Importantly, clinical presentations are heterogeneous in both sporadic and familial ALS, underscoring the complexity of the disease. A pathological hallmark of ALS is the mislocalization of RNA-binding protein TDP-43 from the nucleus to the cytoplasm. This mislocalization produces both loss of function consequences, such as widespread RNA processing and splicing defects, as well as potential toxic gain of function effects associated with cytoplasmic aggregation. ResultsIn this study, we used RT-PCR data from induced pluripotent stem cell-derived motor neurons derived from 180 sALS and C9orf72 fALS patients from the Answer ALS collection to identify biological subgroups based on TDP-43 loss-of-function signatures. Spectral embedding revealed four distinct molecular clusters, including one subgroup genetically similar to controls and another with the most dysregulated mRNA expression, suggesting differing disease severity. Linear mixed models were then used to assess the longitudinal trajectory of over 90 clinical measures, and the between-cluster interaction effects were evaluated. Conclusions36 clinical outcomes showed significant differences across clusters, supporting the presence of biologically and clinically distinct ALS subtypes based on the TDP-43 associated pathogenic cascade. These findings demonstrate a critical role of RNA profiling in uncovering biologically meaningful subtypes of ALS, potentially allowing for more precise prognostic tools and the development of future personalized therapeutic approaches.

BackgroundChitotriosidase (Chit-1) and chitinase-3-like protein 1 (CHI3L1) protein levels are increased in the cerebrospinal fluid (CSF) of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimers disease (AD). Few studies have examined the spatial expression of chitinase expressing cells with respect to neuropathologic hallmarks of disease. MethodsRNA-sequencing was used to examine Chit-1 and CHI3L1 gene expression in the spinal cord and motor cortex. Immunohistochemistry was used to characterize the distribution of Chit-1 and CHI3L1 expressing cells in ALS, C9-ALS, FTLD, AD, and non-neurologic disease controls. Immunofluorescence confocal microscopy was used to correlate distribution of Chit-1 and CHI3L1 expressing cells to TDP pathology. ResultsChit-1 gene expression was increased in the spinal cord, and CHI3L1 expression was increased in both the spinal cord and motor cortex of sALS and C9-ALS patients when compared to controls. Highest levels of Chit-1+ glia were in cortical regions that contain hallmark neuropathology for each neurodegenerative disease. CHI3L1+ glia were only significantly increased in sALS. Neither Chit-1+ nor CHI3L1+ glia were in close proximity to pTDP containing neurons in the motor cortex gray matter; however, there was a significant co-localization of glial pTDP with Chit-1 and CHI3L1 in the motor cortex white matter. ConclusionsChit-1 and CHI3L1 expressing cells were most abundant in the white matter of cortical regions affected by each neurodegenerative disease and the spinal cord. Chit-1 or CHI3L1 expressing cells in the white matter also contained phosphorylated TDP-43. We also observed correlations between levels of Chit-1 or CHI3L1 expressing cells in the white matter to disease duration. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABSPrior studies identified elevated levels of Chit-1 and CHI3L1 proteins in the CSF of various neurodegenerative conditions, though few studies examined levels of Chit-1 and CHI3L1 expressing cells both spatially and in relation to disease pathology. What this study addsWe performed an extensive spatial characterization of Chit-1 and CHI3L1 protein levels across multiple regions and neurodegenerative conditions. This study also correlates Chit-1 and CHI3L1 expression to TDP pathology and other clinical parameters of disease duration. How this study might affect research, practice or policyOur findings indicate that the majority of Chit-1 and CHI3L1 expressing glia are located in the cortical subpial layer and the white matter, suggesting a role for chitinases in modulating neuroinflammatory mechanisms or reparative/regenerative responses in the white matter of ALS and other neurodegenerative diseases. This study suggests new therapeutic opportunities for targeting chitinase expressing cells in neurodegenerative diseases.

BackgroundAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by abnormal protein aggregates in motor neurons. Present and earlier proteomic studies to characterize peptides in cerebrospinal fluid (CSF) associated with motoneuron pathology did not target the low molecular weight proteins and peptides. We generated the hypothesis that specific changes in CSF peptides or low molecular weight proteins are significantly changed in ALS, and that these changes may support deciphering molecular pathophysiology and even guide approaches towards therapeutic interventions. MethodsCerebrospinal fluid (CSF) from 50 ALS patients and 50 non-ALS controls was collected, centrifuged immediately after collection, aliquoted into polypropylene test tubes, frozen within 30-40 min after the puncture and stored at -80{degrees}C until use. Peptides were sequenced using capillary electrophoresis or liquid chromatography / mass spectrometry (CE-MS/MS or LC-MS/MS) analyses. FindingsIn cerebrospinal CSF from 50 patients and 50 non-ALS controls 33 peptides were found, of which 14 could be sequenced using a non-lytic single pot proteomic detection method, CE/MS. ALS deregulated peptides vs. controls included Integral membrane protein 2B, Neurosecretory protein VGF, Osteopontin, Neuroendocrine protein 7B2 (Secretogranin-V), EGF-containing fibulin-like extracellular matrix protein 1, Xylosyltransferase 1 XT-1, Chromogranin-A, Superoxide dismutase SOD-1, Secretogranin-1 (Chromogranin B), NR2F2 Nuclear Receptor Subfamily 2 Group F Member 2 and Collagen alpha-1(VII) chain. InterpretationMost striking deregulations in CSF from ALS patients were found in VGF, Osteopontin, SOD-1 and EFEMP1 peptides. No associations of disease severity, duration and region of onset with sequenced peptides were found. Declarations of InterestS. Petri received honoraria as speaker/consultant from Biogen GmbH, Roche, Novartis, Teva, Cytokinetics Inc., Desitin, Italfarmaco, Ferrer, Amylyx, and Zambon; and grants from DGM e.V, Federal Ministry of Education and Research, German Israeli Foundation for Scientific Research and Development, EU Joint Program for Neurodegenerative Disease Research. J. Beige received funding from GSK and German Federal Ministries of Research and Health. FundingThere was no funding to the presented investigation Ethical ApprovalThis study was approved by the ethics committee of Hannover Medical School. Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki. Key words: ALS, CSF, proteomics, biomarker, peptidomics, peptide deregulation

BackgroundDefinitive diagnosis of amyotrophic lateral sclerosis (ALS) is only possible through a postmortem examination. Extracellular vesicles (EVs) have emerged as promising minimally invasive biomarkers for ALS, but studies vary widely in methodology and reproducibility. We conducted a systematic review and meta-analysis to evaluate the diagnostic potential of EV-associated proteins and RNAs in ALS. MethodsFollowing PRISMA guidelines, we searched PubMed and EMBASE from inception to October 15, 2025. Thirty-nine studies met inclusion criteria. Random-effects models were used for continuous outcomes, and diagnostic accuracy was assessed using hierarchical summary ROC and bivariate random-effects models. Publication bias was evaluated using Begg, Egger, and funnel plots. ResultsEV-associated TDP-43 was the most frequently studied protein. Meta-analysis of five studies showed a moderate but non-significant increase in ALS vs. controls (SMD = 1.30) with high heterogeneity (I{superscript 2} = 97.8%). Sixteen studies assessing EV-RNA biomarkers showed minimal overlap and limited independent replication. Diagnostic accuracy meta-analysis across 11 studies yielded moderate performance (AUC = 0.839). No publication bias was found across both meta-analyses. ConclusionsEV biomarkers for ALS show biological promise but are limited by methodological variability and insufficient replication. Standardized protocols, transparent data sharing, and independent validation are needed.

Frontotemporal dementia (FTD) is a neurodegenerative disorder with a strong heritable component. Frontotemporal lobar degeneration (FTLD) refers to the pathological changes seen in FTD, characterised by atrophy of the frontal and temporal lobes and the presence of abnormal protein inclusions. In the case of FTLD with hyperphosphorylated TDP-43 positive inclusions (FTLD-TDP), five pathological subtypes (A, B, C, D, and E) are observed based on the types and distribution of inclusions found in the brain. In all subtypes, there tends to be a large variability in the number of pathological inclusions observed between cases, with limited correlation to clinical manifestations. TDP-43 is an RNA binding protein belonging to the heterogeneous nuclear ribonucleoprotein (hnRNP) family which along with other hnRNPs modulates multiple aspects of RNA processing. HnRNPs other than TDP-43 have been implicated in several neurological diseases, including ALS, FTLD-TDP, FTLD-FUS and Alzheimers disease. Multiple hnRNPs have been found in pathological inclusions in specific subtypes of FTLD-TDP, suggesting potential roles in the disease process. The role of the hnRNP network in FTLD disease pathogenesis, however, has not yet been investigated. This study aimed to comprehensively evaluate the presence and expression of hnRNP proteins in two pathological subtypes of sporadic FTLD-TDP (A and C) as well as the genetic form FTLD-TDP A C9orf72 using immunohistochemistry and gene expression analysis by single-nuclei RNA-sequencing. We found that there was great variability in frequency of TDP-43 pathology across and within FTLD-TDP pathological subtypes. Finally, our findings suggest that distinct global transcriptomic profiles may underlie the different pathological subtypes of FTLD-TDP. The most prominent transcriptomic changes were observed in oligodendrocytes and astrocytes, involving multiple hnRNPs across FTLD subtypes compared to controls. Transcriptomic co-expression analysis further revealed that glial clusters were more strongly associated with RNA processing dysfunction and contribute to disease classification. Together, these findings highlight the involvement of the hnRNP network and glial-specific RNA processing alterations in FTLD-TDP pathophysiology, offering new insight into the molecular distinctions between pathological subtypes and potential targets for future investigation.

Tofersen is the first effective and approved therapy for familial ALS caused by pathogenic variants in the SOD1 gene. Following treatment with tofersen, neurofilaments in patients CSF and serum display a faster response than clinical parameters, underlining their importance as a biomarker for treatment response in clinical trials. This evidence led us to hypothesize that this novel treatment might represent an opportunity to identify additional therapy-responsive biomarkers for ALS. We chose the commercial NUcleic acid Linked Immuno-Sandwich Assay (NULISA), to investigate a predefined panel of 120 neural, glial and inflammatory markers in CSF and serum samples longitudinally collected from SOD1-ALS patients at baseline and three months after tofersen treatment. We identified a set of proteins (beyond pNfH and NfL) whose levels differed between SOD1-ALS and the matched control group and that were responsive to treatment with tofersen, including A{beta}42, NPY and UCHL1. Even though our results warrant validation in larger cohorts and longer follow-up time, they may pave the way for a panel of responsive proteins solidifying biomarker endpoints in clinical trials.