Citations

Abstract Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analyses. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein–protein interaction analysis, and major histocompatibility complex (MHC)–peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical post-processing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.

We report a patient who presented clinically with progressive supranuclear palsy (PSP) but was pathologically diagnosed as having primary lateral sclerosis (PLS) with magnetic resonance imaging (MRI) with a quantitative susceptibility mapping (QSM) protocol. A 70‐year‐old man was clinically diagnosed with PSP due to early falls and unresponsiveness to levodopa therapy. Postmortem pathological examination revealed mild loss of Betz cells, gliosis, and transactive response DNA binding protein of 43 kDa (TDP‐43)‐positive inclusions in the motor cortex, leading to the pathological diagnosis of PLS. To explore methods for differentiating PLS from PSP, ante‐mortem QSM images were visually and quantitatively assessed for abnormal increases in magnetic susceptibility in the motor cortex. Prussian blue and Luxol fast blue combined with periodic acid‐Schiff staining were also performed to understand the source of the susceptibility increases. QSM showed clear hyperintense signals in the motor cortex. Magnetic susceptibility in the motor cortex was higher in the PLS patient (Z = 4.7, p < 0.001) compared to normal controls and pathologically diagnosed PSP patients. Pathological examination of the region showed intracortical myelin loss, as well as iron deposition. Underlying pathological processes for the increased magnetic susceptibility include not only iron deposition but also intracortical myelin. Our case suggests that QSM is a potential tool to differentiate PLS from PSP, providing insights for accurate diagnosis and enhancing clinical decision‐making.

BACKGROUND AND OBJECTIVES: Despite standard of care with maximal safe resection and chemoradiation, glioblastoma (GBM) is the most common and aggressive type of primary brain cancer. Surgical resection provides a window of opportunity to locally treat gliomas while the patient is recovering and before initiating concomitant chemoradiation. The objective was to assess the safety and establish the maximum tolerated dose of adipose-derived mesenchymal stem cells (AMSCs) for the treatment of recurrent GBM. Secondary objectives were to assess the toxicity profile and long-term survival outcomes of patients enrolled in the trial. In addition, biospecimens will be collected to explore the local and systemic responses to this therapy. METHODS: We will conduct a phase 1, dose-escalated, nonrandomized, open-label, clinical trial of patients with GBM who are undergoing surgical resection for recurrence. Up to 18 patients will receive intracavitary application of AMSCs encapsulated in fibrin glue during surgical resection. All patients will be followed for up to 5 years for safety and survival data. Adverse events will be recorded using the CTCAE V5.0. EXPECTED OUTCOMES: This study will explore the maximum tolerated dose of AMSCs along with the toxicity profile of this therapy in patients with recurrent GBM. In addition, preliminary long-term survival and progression-free survival outcome analysis will be used to power further randomized studies. Finally, cerebrospinal fluid and blood will be obtained throughout the treatment period to investigate circulating molecular and inflammatory tumoral/stem cell markers and explore the mechanism of action of the therapeutic intervention. DISCUSSION: This prospective translational study will determine the initial safety and toxicity profile of local delivery of AMSCs for recurrent GBM. It will also provide additional survival metrics for future randomized trials.

Abstract INTRODUCTION Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within non‐Hispanic White (NHW) populations. Here we provide an extensive survey of the proteomic landscape of AD across diverse racial/ethnic groups. METHODS Two cortical regions, from multiple centers, were harmonized by uniform neuropathological diagnosis. Among 998 unique donors, 273 donors self‐identified as African American, 229 as Latino American, and 434 as NHW. RESULTS While amyloid precursor protein and the microtubule‐associated protein tau demonstrated higher abundance in AD brains, no significant race‐related differences were observed. Further proteome‐wide and focused analyses (specific amyloid beta [Aβ] species and the tau domains) supported the absence of racial differences in these AD pathologies within the brain proteome. DISCUSSION Our findings indicate that the racial differences in AD risk and clinical presentation are not underpinned by dramatically divergent patterns in the brain proteome, suggesting that other determinants account for these clinical disparities. Highlights We present a large‐scale proteome (∼10,000 proteins) of DLPFC (998) and STG (244) across AD cases. About 50% of samples were from racially and ethnically diverse brain donors. Key AD proteins (amyloid and tau) correlated with CERAD and Braak stages. No significant race‐related differences in amyloid and tau protein levels were observed in AD brains. AD‐associated protein changes showed a strong correlation between the brain proteomes of African American and White individuals. This dataset advances understanding of ethnoracial‐specific AD pathways and potential therapies.

AbstractINTRODUCTIONPrimary age‐related tauopathy (PART) is characterized by neurofibrillary tangles and minimal β‐amyloid deposition, diagnosed postmortem. This study investigates 18F‐flortaucipir (FTP) PET imaging for antemortem PART diagnosis.METHODSWe analyzed FTP PET scans from 50 autopsy‐confirmed PART and 13 control subjects. Temporal lobe uptake was assessed both qualitatively and quantitatively. Demographic and clinicopathological characteristics and voxel‐level uptake using SPM12 were compared between FTP‐positive and FTP‐negative cases. Intra‐reader reproducibility was evaluated with Krippendorff's alpha.RESULTSMinimal/mild and moderate FTP uptake was seen in 32% of PART cases and 62% of controls, primarily in the left inferior temporal lobe. No demographic or clinicopathological differences were found between FTP‐positive and FTP‐negative cases. High intra‐reader reproducibility (α = 0.83) was noted.DISCUSSIONFTP PET imaging did not show a specific uptake pattern for PART diagnosis, indicating that in vivo PART identification using FTP PET is challenging. Similar uptake in controls suggests non‐specific uptake in PART.Highlights 18F‐flortaucipir (FTP) PET scans were analyzed for diagnosing PART antemortem. 32% of PART cases had minimal/mild FTP uptake in the left inferior temporal lobe. Similar to PART FTP uptake was found in 62% of control subjects. No specific uptake pattern was found, challenging in vivo PART diagnosis.

Abstract Introduction/Objective Recent advancements in artificial intelligence (AI), particularly with Large Language Models like ChatGPT-4Vision (GPT-4V), have expanded the potential for automated interpretation of medical images. This study evaluates the diagnostic accuracy of GPT-4V in histopathological analysis of neurodegenerative diseases and compares its performance with traditional Convolutional Neural Networks (CNNs). Methods/Case Report We utilized 1515 histopathological images, including hematoxylin and eosin (H&E) staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer’s disease, progressive supranuclear palsy, and corticobasal degeneration. GPT-4V’s performance was assessed through multi- step prompts to assess how textual context influences the image interpretation. In the quantitative analysis, the diagnostic accuracy of GPT-4V and the CNN-based model YOLOv8 was assessed for classifying three tau lesions—astrocytic plaques, neuritic plaques, and tufted astrocytes. The analysis used both zero-shot and few-shot learning methods, where GPT-4V and YOLOv8 were evaluated on their ability to distinguish these lesions using ten hold-out test images and 490 training images per lesion. Results (if a Case Study enter NA) GPT-4V accurately recognized Lewy bodies in all cases, but it showed limited accuracy in identifying neurofibrillary tangles (20%) and Pick bodies (0%) on H&E slides. Notably, GPT-4V often suggested Alzheimer’s disease as a potential diagnosis, relying more on contextual clues, such as the prevalence of certain diseases, rather than direct image analysis. In the tau immunohistochemical analysis, GPT-4V’s diagnoses were strongly influenced by additional information about staining and brain region. However, few-shot learning markedly improved GPT-4V’s diagnostic capabilities from 40% accuracy with zero-shot learning to 90% with few-shot learning, reaching best accuracy with 20-shot learning. This performance compared favorably against YOLOv8, which also achieved 90% accuracy but required 100-shot learning. Conclusion While GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its accuracy. This approach is particularly promising for neuropathology, where acquiring extensive labeled datasets is challenging. The findings highlight the need for ongoing refinement of AI applications in pathology, emphasizing balanced integration of textual and visual data to optimize diagnostic efficacy and reliability.

AbstractINTRODUCTIONUnderstanding the relationship between amyloid beta (Aβ) positron emission tomography (PET) and Aβ cerebrospinal fluid (CSF) biomarkers will define their potential utility in Aβ treatment. Few population‐based or neuropathologic comparisons have been reported.METHODSParticipants 50+ years with Aβ PET and Aβ CSF biomarkers (phosphorylated tau [p‐tau]181/Aβ42, n = 505, and Aβ42/40, n = 54) were included from the Mayo Clinic Study on Aging. From these participants, an autopsy subgroup was identified (n = 47). The relationships of Aβ PET and Aβ CSF biomarkers were assessed cross‐sectionally in all participants and longitudinally in autopsy data.RESULTSCross‐sectionally, more participants were Aβ PET+ versus Aβ CSF− than Aβ PET− versus Aβ CSF+ with an incremental effect when using Aβ PET regions selected for early Aβ deposition. The sensitivity for the first detection of Thal phase ≥ 1 in longitudinal data was higher for Aβ PET (89%) than p‐tau181/Aβ42 (64%).DISCUSSIONAβ PET can detect earlier cortical Aβ deposition than Aβ CSF biomarkers. Aβ PET+ versus Aβ CSF− findings are several‐fold greater using regional Aβ PET analyses and in peri‐threshold‐standardized uptake value ratio participants.Highlights Amyloid beta (Aβ) positron emission tomography (PET) has greater sensitivity for Aβ deposition than Aβ cerebrospinal fluid (CSF) in early Aβ development. A population‐based sample of participants (n = 505) with PET and CSF tests was used. Cortical regions showing early Aβ on Aβ PET were also used in these analyses. Neuropathology was used to validate detection of Aβ by Aβ PET and Aβ CSF biomarkers.