Clinical Chemistry and Laboratory Medicine (CCLM)

Creutzfeldt-Jakob disease (CJD) is a rapidly progressive, fatal neurodegenerative disorder caused by the misfolding of the normal prion protein (PrPC) into its pathogenic form (PrPSc). Therefore, effective clinical management and infection control require diagnostic assays that combine sub-attomole sensitivity with a turnaround time compatible with routine practice. While real-time quaking-induced conversion (RT-QuIC) offers excellent analytical performance, its protracted reaction time and dependence on plate readers that integrate high-speed shaking with continuous fluorescence detection restrict its widespread implementation. Endpoint QuIC (EP-QuIC) is an established alternative approach that utilizes vigorous shaking in a ThermoMixer(R) C and relies on a single endpoint fluorescence readout. In this study, we introduced minor modifications to assay conditions to enhance the speed, robustness, and reproducibility of EP-QuIC. The refined assay detected prion seeding activity in sporadic CJD brain samples within 6 h--and, in some cases, as early as 2 h. Unlike RT-QuIC, problematic recombinant PrP batches that impaired assay reliability in RT-QuIC--exhibiting delayed kinetics and spontaneous ThT fluorescence in unseeded reactions--did not produce such effects in EP-QuIC. Instead, they yielded strong, specific fluorescence signals that clearly identified sCJD samples, demonstrating improved batch tolerance and diagnostic efficiency of this approach. When applied to CSF, the EP-QuIC assay showed 100% sensitivity and specificity, highlighting its potential as a rapid, reliable diagnostic tool for prion diseases.

Neurologic autoantibody cell-based assay images are difficult to interpret when fluorescence is weak, focal or dominated by background signal. We developed a visual artificial intelligence overlay that transfers detected cell boundaries from the structural channel to the antibody-staining image and evaluated standalone model concordance and its association with reader classifications. The analysis used 2 763 records for model development and 499 held-out records spanning 14 antibody targets. Four readers assessed the held-out records first without assistance and then with the boundary overlay and model category available. The selected model agreed with the reader-derived three-class reference in 85.2% of records, but recall was lowest for weak-positive or equivocal records. Reader agreement increased in the assisted session, and almost all revised ratings moved towards the model category. The combined display may support consistent visual review while also creating a risk of model-directed anchoring; its role as a second-read aid requires independent validation.

Tissue-based assays provide broad screening for neural autoantibodies but remain vulnerable to technical and reader-dependent variability. We evaluated an integrated workflow combining automated 3,3'-diaminobenzidine tissue-based assay processing, digital whole-slide imaging, and interpretable deep learning. Model development used 2,357 retrospective serum and cerebrospinal fluid images referred from more than 80 institutions and processed in a centralized laboratory, with reference labels established by expert-consensus tissue-based assay interpretation. A prospective validation cohort included 200 images from 200 unique patients (154 positive and 46 negative; 100 serum and 100 cerebrospinal fluid). The same images were evaluated by two expert and two novice readers in a fixed-order two-round study, with unaided interpretation followed by artificial-intelligence-assisted interpretation after a washout of at least two weeks. The standalone binary classifier achieved an area under the receiver operating characteristic curve of 0.922, accuracy of 86.5%, sensitivity of 85.7%, and specificity of 89.1% in prospective validation. Pooled reader accuracy was 90.4% in the unaided round and 97.4% in the artificial-intelligence-assisted round (McNemar P < 0.001), with specificity of 82.6% and 99.5%, respectively. Novice-reader accuracy was 83.3% unaided and 96.5% with artificial-intelligence assistance (P < 0.001), approaching unaided expert accuracy of 97.5%. Among tissue-based-assay-positive cases, reader accuracy for cytoplasmic/nuclear antigen-associated versus neural surface antigen-associated staining-pattern subclassification was 88.8% unaided and 94.0% with artificial-intelligence assistance (P < 0.001). These findings support further multicentre, cross-platform evaluation of artificial-intelligence-assisted neural autoantibody tissue interpretation.

Background Blood biomarkers are increasingly used to support the diagnosis and monitoring of neurodegenerative diseases. However, their interpretation is complicated by physiological determinants, including age, sex, body-mass index, and renal function, and by differences in absolute concentrations between analytical methods. We aimed to develop population-based reference equations allowing individualized interpretation of the main blood biomarkers used in neurology. Methods In this cross-sectional study, we analysed plasma samples from cognitively unimpaired participants selected from the French CONSTANCES and Three-City population-based cohorts. Generalized additive models for location, scale, and shape were used to model neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated tau 181 (p-tau181), amyloid-{beta}40, amyloid-{beta}42, and their ratios according to age, sex, body-mass index, and renal function. The resulting equations provided individualized expected concentrations, percentiles, and Z-scores. Previously established disease-specific concentrations were converted into Z-score. Cross-calibration equations were developed for NfL measurements across analytical methods and sample matrices. Findings The final reference populations comprised 5123 participants for amyloid biomarkers and p-tau181 and 5122 for NfL and GFAP; median age was 52.3 years and half were women. Between ages 40 and 80 years, expected NfL and GFAP concentrations increased by an average of 2.6% and 2.2% per year, respectively. Renal function, body-mass index, and sex had additional biomarker-specific effects. Application of the equations to clinical cohorts preserved distinct disease-associated profiles: NfL Z-scores were increased across disorders characterised by neuroaxonal injury, whereas p-tau181 and GFAP showed its greatest increase in Alzheimer disease. NfL cross-calibration equations showed excellent agreement between methods and matrices, with intraclass correlation coefficients greater than 0.90. Interpretation This population-based multibiomarker framework enables blood biomarker concentrations to be interpreted relative to individuals with similar physiological characteristics. Publicly available equations, reference curves, and standardized Z-scores could improve individualized interpretation and comparability across biomarkers, laboratories, and clinical populations.

BackgroundThe Philadelphia negative myeloproliferative neoplasms (MPN), including essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF), are characterized by myeloid cell proliferation, thrombosis and inflammation. Suspicion of MPN arises from increased blood count in one or more lineages; however, knowledge on the MPN plasma proteome including biomarkers measurable in blood, are lacking. Comparing the plasma proteome of MPN patients to subjects with elevated blood counts but without MPN diagnosis, may provide an increased understanding of the MPN disease biology as well as diagnostic biomarkers measurable in blood. Patients and methodsWe performed plasma proteome profiling in 87 patients referred to the Department of Hematology due to elevated blood counts. Of these, 55 were diagnosed with MPN and 32 did not fulfill MPN diagnostic criteria and thus constituted the non-MPN control group. ResultsThe frequency of thrombosis was equal between the groups. We found 189 differentially expressed proteins between MPN and non-MPN, enriched for Hemostasis and Platelet activation proteins. Using Lasso multiple regression, we identified SORT1, GP1BA, PSPN, MMP1 and BAG6 separating MPN from non-MPN individuals, and TFRC and SEMA7A specific for MPN subtype PV. Interestingly, TFRC alone had a diagnostic accuracy for identifying PV of 89.3%, and when combined with serum erythropoietin it increased to 99.3%. Only two proteins, IL-6 and GH1, were increased in JAK2 mutant MPN compared to JAK2 wildtype MPN. The same trend was seen for JAK2 mutant ET compared to JAK2 wildtype ET, indicating that IL-6 is induced by JAK STAT activation. However, IL-6 levels did not differ between MPN and non-MPN patients. Discussion/conclusionIn conclusion, hemostasis and platelet activation are inherent to MPN disease whereas little difference was found in proinflammatory cytokines between MPN and non-MPN groups. We demonstrate novel potential blood biomarkers for MPN and MPN subtypes, in particular TFRC for identifying PV patients.

Background: Traumatic brain injury (TBI) increases long-term risk for cognitive decline and neurodegeneration, motivating objective blood biomarkers that can be measured repeatedly over time. However, reliance on venipuncture limits longitudinal monitoring, particularly outside clinic settings. Objective: We conducted a pilot feasibility study comparing fingerstick capillary blood collected as dried plasma spots (DPS) with matched venous EDTA plasma for quantification of glial fibrillary acidic protein (GFAP) and neurofilament light (NfL). Methods: Paired samples were collected prospectively across matched time points spanning hours to decades post-injury and assayed using high-sensitivity immunoassays. Results: Absolute protein recovery from DPS was lower and more variable than venous plasma, but paired GFAP and NfL measurements remained well-correlated. We further evaluated the GFAP/NfL ratio as an internal, sample-based normalization strategy and observed improved concordance between capillary DPS and venous plasma compared with either analyte alone. Conclusions: These pilot findings support the analytical feasibility of capillary DPS sampling for GFAP and NfL measurement in TBI and identify the GFAP/NfL ratio as an exploratory internal-normalization approach. Formal analytical and clinical validation, including outcome linkage and prospective evaluation, will be required before capillary DPS can be considered for clinical monitoring applications.

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.

Background: Rapid and accurate measurement of procalcitonin (PCT) is useful for diagnosing bacterial infections and guiding antibiotic therapy, yet current laboratory-based immunoassays require centralised infrastructure, delaying clinical decision-making and limiting access in low-resource settings. We developed a battery-free, smartphone-connected electrochemical lateral flow assay with linked analytics for the detection of PCT (ELLA-PCT) enabling quantitative testing at the point of care without conventional laboratory instrumentation. Methods: We designed a competitive electrochemical lateral flow assay using gold nanoparticles co-functionalised with a PCT-specific DNA aptamer and ferrocene hexanethiol as a redox reporter. A miniaturised near-field communication (NFC) potentiostat embedded within a disposable cassette enabled wireless electroanalytical measurements using a smartphone. Analytical performance was assessed in buffer and serum, including limit of detection (LOD), linearity, specificity, and stability. Clinical evaluation was performed on 27 serum samples from nine adults undergoing antibiotic treatment for suspected bacterial infection, with results compared against the reference Time-Resolved Amplified Cryptate Emission (TRACE) assay. Findings: ELLA-PCT achieved an LOD of 46 pg/mL and a linear detection range of 0.5-100 ng/mL. The ELLA-PCT assays remained stable for three months under ambient storage conditions, and cross-reactivity with calcitonin, C-reactive protein, and interleukin-6 remained below clinically relevant thresholds. Clinical results showed strong correlation with TRACE (R2 = 0.979, p < 0.0001), with a mean bias of 0.05 ng/mL and narrow 95% limits of agreement (-0.69 to 0.89 ng/mL). Importantly, ELLA-PCT delivered results in only 30 minutes without requiring external power or clinical laboratory instrumentation. In comparison to TRACE, the total turnaround time was reduced by at least 50%, which typically requires at least one hour from sample collection to results at a hospital setting. Interpretation: ELLA-PCT is an antibody-free, smartphone-connected test that provides laboratory-grade quantitative PCT measurement using only an NFC-enabled electroanalytical sensor and a mobile phone. The platform has a strong potential to decentralise diagnosis of infectious diseases, support antibiotic stewardship, and enable remote, real-time monitoring in outpatient and resource-limited settings. Larger studies are needed to evaluate the use of whole-blood samples and the integration of the platform into digital clinical workflows.

Background: Alpha-mannosidosis (AM) is a debilitating lysosomal storage disorder (LSD) caused by insufficient activity of alpha-mannosidase, an enzyme responsible for cleavage of alpha-linked mannose residues from N-glycan-type oligosaccharides during their degradation. As a result, specific oligosaccharides accumulate in tissues and are also detectable in body fluids. AM is a progressive multi-organ disease for which hematopoietic stem cell transplantation (HSCT) and enzyme replacement therapy (ERT) are therapeutic options, however, treatment results depend considerably on early diagnosis. Therefore, inclusion of AM into newborn screening panels would significantly contribute to improving therapeutic outcome. Methods: A rapid and fully quantitative method for the specific AM oligosaccharide biomarker Man2GlcNAc in dried blood spots (DBS) using ultra-performance liquid chromatography coupled to tandem-mass spectrometry was developed. DBS from 420 controls, 27 AM patients (including five newborn screening samples), and 35 patients with other LSDs were analyzed. Results: A distinct separation in biomarker concentrations was observed between unaffected individuals and untreated AM patients (0.1 - 0.8 vs 3.2 - 11.3 mol Man2GlcNAc/L, p<0.0001). Furthermore, Man2GlcNAc concentrations significantly differed between untreated patients and those on ERT (n = 17, p<0.0001) or after HSCT (n = 15, p<0.0001), and between both treatment groups (p<0.0001). Moreover, we demonstrated that this method is appropriate for monitoring treatment efficacy by analysis of samples from individual patients before and after initiation of therapy. We retrospectively analyzed residual samples of dried blood spots (DBS) from routine newborn screening that had been stored from patients with later-confirmed AM, which resulted in clear separation from the concentrations observed in a newborn screening control cohort (7.7 - 11.3 vs 0.1 - 0.8 mol Man2GlcNAc/L, p=0.0001). Conclusion: We present a simple and rapid method for quantitative analysis of the specific AM oligosaccharide biomarker Man2GlcNAc, which is remarkably stable compared to enzymatic assays. Thus, the method is useful for selective screening in suspected cases as well as for monitoring the response to different forms of treatment. Furthermore, Man2GlcNAc has the potential to serve as a useful biomarker for newborn screening offering the prospect of earlier treatment initiation, improved therapeutic outcome and higher quality of life for AM patients.

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.

Importance: Phosphorylated tau at threonine 217 (p-tau217) and brain-derived tau (BD-tau) are increasingly used as blood biomarkers of Alzheimer's disease (AD) and related neurological disorders. However, the distributions and biological correlates of elevated plasma p-tau217 and BD-tau in plasma from individuals eligible for blood donation remain unclear. Objective: To estimate the proportions of plasma p-tau217 and BD-tau exceeding exploratory study-specific thresholds, and examine associated inflammatory and transcriptomic features. Design: Cross-sectional study. Setting: Hospital blood bank. Participants: 257 de-identified donor samples were obtained between September 2025 and June 2026. Plasma samples from 30 population reference participants and 20 AD patients were used to derive study-specific thresholds. Complementary transcriptomic analyses were performed in 29 fresh whole blood samples. Main Outcomes and Measures: The primary outcome was the proportion of donor samples exceeding the study-specific ROC-derived Youden threshold. The proportion exceeding the prespecified published p-tau217 threshold was the secondary outcome. Other measures included plasma concentrations of BD-tau, tumor necrosis factor alpha (TNF-alpha), and interleukin 6 (IL-6); correlations among plasma p-tau217, BD-tau and inflammatory cytokines; and associated transcriptomic alterations. Results: Plasma p-tau217 concentrations exceeded the study-specific ROC-derived Youden threshold in 53 of 256 donor samples (21%; 95% CI, 16%-26%) and the published threshold, used for a secondary comparison, in 99 of 256 samples (39%; 95% CI, 33%-45%). Plasma BD-tau concentrations exceeded the study-specific threshold in 89 of 257 samples (35%; 95% CI, 29%-41%). Plasma p-tau217 concentrations were positively associated with plasma TNF-alpha (Kendall's tau = 0.339; P < .001) and IL-6 (Kendall's tau = 0.310; P < .001) concentrations. Exploratory transcriptomic analyses identified gene expression patterns associated with p-tau217 concentrations. Measurement using a second p-tau217 antibody showed the similar findings. Conclusions and Relevance: Plasma p-tau217 and BD-tau concentrations exceeded exploratory study-specific thresholds in a subset of blood-donor samples, and higher p-tau217 concentrations were associated with inflammatory biomarkers and exploratory transcriptomic differences. These findings raise the possibility of elevated circulating tau species in some blood donors, but their clinical significance requires confirmation. Whether elevated circulating p-tau217 in donated blood has biological consequences for transfusion recipients is unknown, but deserves investigation.

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

Background: Blood culture-negative infective endocarditis (BCNIE) poses a significant diagnostic challenge. This study evaluated a multimodal diagnostic algorithm combining serological and molecular methods at the Argentine National Reference Laboratory. Methods: A prospective analysis was conducted on 53 consecutive patients with suspected BCNIE referred between January 2019 and December 2024. The diagnostic workflow included indirect immunofluorescence for Bartonella spp. and Coxiella burnetii, species-specific PCR for Bartonella spp. and Tropheryma whipplei, and broad-range 16S rRNA PCR with Sanger sequencing on available blood and valvular tissue specimens. Results: An etiological diagnosis was established in 17 of 53 patients (32.1%). Bartonella spp. was the predominant pathogen (47.1%; 8/17), followed by T. whipplei (35.3%; 6/17) and Streptococcus spp. (17.6%; 3/17). All Bartonella cases were initially detected via serology, with molecular confirmation achieved exclusively through valvular tissue analysis. Conclusions: Implementing a standardized multimodal diagnostic algorithm significantly enhances etiological yields in BCNIE. The findings emphasize the complementary value of frontline serology and targeted molecular testing, highlighting that simultaneous submission of serum, blood, and valvular tissue is essential for optimal diagnosis.

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.

Huntington's disease is caused by a CAG repeat tract expansion in the huntingtin gene, resulting in production of pathogenic N-terminal huntingtin protein fragments associated with disease pathology. Despite their central role, detection of these fragments has relied on a limited antibody repertoire with reproducibility concerns. Here, we describe the generation and characterization of recombinant rabbit monoclonal antibodies targeting two reciprocal neoepitopes flanking the huntingtin exon 1/exon 2 junction corresponding to amino acids P90 and K91. The P90 antibodies (clones 1B12, 11G2) demonstrate fragment-length-selective recognition of the C-terminal HTTexon1 P90 neoepitope with no detectable binding to full length huntingtin. A side-by-side comparison of the widely used monoclonal antibody MW8 from two different sources revealed measurable lot-to-lot drift in its fragment selectivity, whereas the recombinant P90 antibodies, expressed from a defined, sequenced clone, maintained consistent specificity, addressing this source-dependent variability. Whereas P90-positive fragments can arise through alternative splicing of the HTT1a transcript, generation of the reciprocal K91 N-terminal HTTexon2 neoepitope would require site-specific proteolytic cleavage, a mechanism that has not yet been directly tested for lack of a suitable reagent. The K91 antibody (clone 7G10) binds the N-terminal K91 neoepitope with high affinity and specificity over full length huntingtin and provides, for the first time, a tool capable of directly interrogating whether such cleavage occurs. Neoepitope specificity of these antibodies was orthogonally confirmed by protease digestion (Lys-N and Arg-C) coupled with intact mass spectrometry. As an additional outcome of the immunization and selection strategy, we discovered human-mouse cross-reactive antibodies (clones 27F5, 31C10) targeting the proline-rich domain of huntingtin that will facilitate mouse-human translational studies. All antibodies are recombinant, ensuring long-term reproducibility, and are being made available, along with their sequences, to the research community.

Background: Plasma phosphorylated tau at threonine 217 (p-Tau217) is a leading blood-based biomarker for Alzheimer's disease (AD). Robust analytical characterization on high-throughput platforms is essential for research use and clinical translation. Objective: To evaluate the analytical performance of an automated plasma p-Tau217 immunoassay and characterize its discrimination of PET-defined amyloid status. Methods: We performed analytical validation of the Access Research Use Only (RUO) plasma p-Tau217 immunoassay on the Beckman Coulter DxI 9000 Access Immunoassay Analyzer and evaluated biomarker discrimination of PET-defined amyloid pathology in a subset of the Bio-Hermes-001 cohort spanning the symptomatic cognitive continuum (mild cognitive impairment or mild AD dementia; cognitively unimpaired participants excluded; n = 449). Analytical precision, sensitivity, linearity, specificity, interference, and sample stability were assessed per Clinical and Laboratory Standards Institute guidelines. Discrimination of PET-defined amyloid status was evaluated using receiver operating characteristic curve and indeterminate zone analyses. Results: The assay demonstrated high precision (within-laboratory CV </=7.1%), excellent sensitivity (limit of detection 0.018-0.021 pg/mL), linearity across the analytical measuring range (R-squared > 0.99), strong epitope specificity (</=1.0% cross-reactivity with other tau phosphoisoforms), and minimal interference from over 60 endogenous and exogenous substances. In 449 research participants plasma p-Tau217 showed strong discrimination between amyloid-positive and amyloid-negative groups (AUC 0.881; 95% CI 0.846-0.915). Application of indeterminate zones systematically improved classification metrics at the cost of fewer definitive classifications. Conclusions: These findings support the Access p-Tau217 (RUO) assay as a robust, high-throughput assay for plasma biomarker-based discrimination of PET-defined amyloid pathology in AD applications.

Pre-infusion cerebrospinal fluid (CSF) proteomics may enable risk stratification for immune effector cell-associated neurotoxicity syndrome (ICANS) after chimeric antigen receptor T-cell therapy, but disease-specific baseline variation may influence biomarker interpretation. We compared pre-infusion CSF proteomic profiles from 28 patients with diffuse large B-cell lymphoma (DLBCL) and 9 with multiple myeloma (MM). Although principal component analysis showed substantial overlap, orthoPLS-DA identified significant disease-associated discrimination supported by permutation testing. Proteins contributing to this separation were enriched for plasma cell-related, extracellular, and metabolic signatures. ICANS occurred in 7 of 28 DLBCL patients but in none of the 9 MM patients. MM cases aligned with the ICANS-negative group in binary analysis while remaining distinct from both DLBCL subgroups in three-group analysis. These findings indicate that pre-infusion CSF proteomics captures disease-specific molecular structure that should be considered when developing and interpreting biomarkers of CAR-T-associated neurotoxicity.

Clear cell renal cell carcinoma (ccRCC) remains the most lethal urological malignancy, with high metastatic rates, both at initial diagnosis and during disease progression, contributing to poor survival outcomes. Current diagnostic and prognostic approaches rely primarily on histopathology, limiting early detection of localized disease and relevant intervention for metastatic patients. Here, we performed the most extensive to-date mass spectrometry-based discovery profiling of longitudinal plasma samples collected across multiple clinical follow-up points spanning up to five years post-diagnosis., to characterize the circulating plasma proteome and identify biomarkers for localized and metastatic disease. Network analysis identified protein modules enriched in pathways involved in matrix remodeling and metabolic deregulation, perpetuating the ccRCC phenotype. A five-protein signature, comprising PRL, THBS1, ANGPT1, IGFBP1, and SRGN, demonstrated high diagnostic performance for localized ccRCC. Notably, PRL appeared as a promising stand-alone biomarker (AUC = 0.812), with independent validation confirming its utility as a diagnostic biomarker. Importantly, a six-protein signature (AMBP, C1S, C2, IGFBP3, RASGRP2, TFRC) stringently distinguished metastatic from high-grade non-metastatic ccRCC cases. Further validation of these signatures could inform clinical decision-making, enabling early detection of metastasis and minimal residual disease and real-time longitudinal monitoring for ccRCC patients. Statement of SignificanceThis study presents the most comprehensive longitudinal plasma proteomic dataset for ccRCC to date, defining robust circulating protein biomarker signatures for both localized and metastatic disease, and establishing a proteomic landscape for minimally invasive, real-time monitoring and improved clinical management of ccRCC patients. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/742031v1_ufig1.gif" ALT="Figure 1000"> View larger version (35K): org.highwire.dtl.DTLVardef@16a0a0aorg.highwire.dtl.DTLVardef@b950b1org.highwire.dtl.DTLVardef@60ca2dorg.highwire.dtl.DTLVardef@7980c3_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: The Boston Criteria v2.0 represent the gold standard for diagnosing Cerebral Amyloid Angiopathy (CAA), but their application is currently precluded in mixed small vessel disease (SVD), where deep and lobar hemorrhages coexist. The aims of this study are: (i) to determine which cerebrospinal fluid (CSF) biomarker (A{beta}42, A{beta}40, A{beta}42/40 ratio) is the best candidate to support the CAA diagnosis; (ii) to define a data-driven cut-off, and (iii) to explore if a biomarker-integrated classification significantly improves the phenotypical concordance with the suspected predominant SVD (CAA vs. arteriosclerosis). Methods: We analyzed data from a retrospective multicenter cohort of patients with suspected CAA, defined as probable CAA (Boston criteria v2.0) but allowing deep hemorrhagic lesions, and with available CSF biomarkers. We visually quantified MRI-visible SVD markers (e.g., cerebral microbleeds [CMB], cortical superficial siderosis [cSS], lacunes) and their association with MRI-visible SVD features. We employed a Gaussian Mixture Model (GMM) to identify a data-driven threshold for amyloid positivity (A+). Then, we compared the prevalence of MRI-visible manifestations of SVD between subgroups applying different frameworks, namely the current MRI-based classification (probable CAA vs. mixed SVD) and a CSF biomarker-integrated classification (A+ vs. A-). Results: We enrolled 121 patients (age: 72 [66-77] years; 60% probable CAA, 40% mixed SVD with suspected CAA). The CSF A{beta}42/40 ratio showed a bimodal distribution and consistent associations with all CAA-specific radiological features. The CSF biomarker-integrated reclassification, particularly using the GMM cut-off, significantly improved the distinction between subgroups regarding CAA- and arteriosclerosis-related MRI features (e.g., cSS presence: probable CAA vs. mixed SVD: aOR=2.84 [95%CI 1.27-6.39], p=0.011; A+ vs. A-: aOR=12.68 [95%CI 4.31-37.32], p<0.001; deep lacunes presence: probable CAA vs. mixed SVD: aOR=0.20 [95%CI 0.08-0.50], p<0.001; A+ vs. A-: aOR=0.04 [95%CI 0.01-0.11], p<0.001). Notably, patients classified as A+ never demonstrated more than four deep CMBs. Discussion: A CSF biomarker-integrated classification may improve the classification of CAA compared with the current MRI-based framework. These findings are cohort-specific and would benefit from further validation, especially with a neuropathological reference. Still, these results support a future transition toward an integrated biological-radiological framework, which may refine in vivo CAA diagnosis, particularly in mixed SVD.

Abstract Background: The stem cell factor receptor or c-Kit is a type III receptor tyrosine kinase, activated by its ligand Stem cell factor (SCF). Up on activation, c-kit induces signaling pathways that regulates blood cell proliferation, survival, differentiation, and migration. Several studies reported that c-Kit/SCF signaling, contributes to the development and progression of acute myeloid leukemia (AML) in patients. However, the downstream proteins regulated by c-kit activation and their clinical significance in AML remain poorly explored. Methods: Human Acute megakaryoblastic leukemia (Mo7e) cells, were-stimulated with SCF and global protein expression were profiled using two-dimensional gel electrophoresis coupled with MALDI-TOF and LC-MS/MS. Differentially expressed proteins were functionally characterized and validated using patient data from the TCGA-LAML and matched normal data from GTEx, GEO datasets, and quantitative RT-PCR. Their diagnostic and prognostic significance was assessed using ROC, Cox regression, LASSO, Kaplan Meier survival analyses, and a prognostic nomogram model. Results: Proteomic profiling identified 14 differentially expressed proteins in SCF-stimulated Mo7e cells, which are predicted to involved in cytoskeletal organization, protein folding, metabolism, vesicular trafficking, and translational regulation. Transcriptomic analysis of the TCGA-LAML cohort revealed significant dysregulation of CFL1, CCT8, HSP90B1, MDH2, EIF5A, GSN, and TPI1. Integrated ROC, Cox regression, and LASSO analyses identified CFL1, CCT8, and GSN as the most robust prognostic biomarkers associated with poor overall survival in LAML patients. Their expression patterns were validated in independent GEO datasets and by qRT-PCR in SCF stimulated Mo7e cells. Finally, a three-gene nomogram model was developed and validated to predict the overall survival probability of AML patients at 1-, 3-, and 5-year time points. Conclusions: This study identifies CFL1, CCT8, and GSN as key downstream effectors of c-Kit signaling as prognostic biomarkers for AML. These findings provide mechanistic insights into c-Kit-driven leukemogenesis and establish a clinically relevant three-gene signature for AML risk stratification and potential therapeutic targeting.