October Selected literature from CNS journals
Cerebrospinal fluid proteomics identifies early changes in autosomal dominant Alzheimer's disease
Proteomic analysis of serum extracellular vesicles reveals diagnostic signatures and therapeutic targets in breast cancer
3 International Journal of Molecular Sciences
Proteomic profiling of extracellular vesicles derived from corneal epithelial and stromal cells
The mouse aging proteome atlas
Beyond-genomic influences on the biology of the human plasma proteome
Single-cell proteomics characterizes the IFN-γ response and the coexistence of cell states through enhanced feature matching
Single-cell CAR T atlas reveals Type-2 function in 8-year leukemia remission
8 Alzheimer's Research & Therapy
Multi-omics analysis reveals the key factors behind Alzheimer's disease severity
9 Journal of Translational Medicine
Multi-omics analysis reveals the prognostic value of the IGF2BP3/SPHK1 signaling pathway in cancer stem cells of muscle-invasive bladder cancer and its ability to predict response to immunotherapy
Cerebrospinal fluid proteomics identifies early changes in autosomal dominant Alzheimer's disease
CSF proteomics identifies early changes in autosomal dominant Alzheimer’s disease
A team led by Yuanyuan Shen from the Department of Psychiatry at Washington University in the United States analyzed proteins in cerebrospinal fluid (CSF) to identify early biomarkers of autosomal dominant Alzheimer's disease (ADAD), with the aim of monitoring disease progression and informing treatment strategies. This high-throughput proteomic study measured CSF proteins in 286 mutation carriers (MCs) and 177 non-carriers (NCs). A multi-level regression model was developed to distinguish proteins with divergent pseudo-trajectories between the two groups. The findings were validated in an independent ADAD dataset as well as in a sporadic AD dataset, and machine learning techniques were used to develop and validate a predictive model. The study identified 137 proteins with different trajectories between MCs and NCs, eight of which changed before conventional AD biomarkers. These proteins fall into three stages: an early stage (stress response, glutamate metabolism, neuronal mitochondrial injury), a middle stage (neuronal death, apoptosis) and a late presymptomatic stage (microglial changes, cell communication). The predictive model revealed a panel of six proteins that distinguishes MCs from NCs more effectively than conventional biomarkers.
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Proteomic analysis of serum extracellular vesicles reveals diagnostic signatures and therapeutic targets in breast cancer
Proteomic Profiling of Serum Extracellular Vesicles Identifies Diagnostic Signatures and Therapeutic Targets in Breast Cancer
Analysis of extracellular vesicles (EVs) is a promising non-invasive liquid biopsy approach for the detection, prognosis and treatment monitoring of breast cancer. Realizing the potential of this strategy requires a comprehensive understanding of the characteristics and proteomic composition of breast cancer-specific EVs from human samples. A team led by Hongxia Wang, Chen Ding and Wei Zhu at Fudan University used mass spectrometry-based data-independent acquisition (DIA) proteomics to analyze serum EVs from breast cancer patients (n = 126) and healthy donors (n = 70), and validated the findings in five independent cohorts. Examination of the EV proteome enabled the construction of a specific EV protein classifier for diagnosing breast cancer and for distinguishing patients with metastatic disease. Notably, TALDO1 was identified as an EV biomarker of distant metastasis in breast cancer. In vitro and in vivo analyses confirmed the role of TALDO1 in stimulating breast cancer invasion and metastasis. Finally, through high-throughput molecular docking and virtual screening, the authors identified AO-022, a potent allosteric inhibitor of TALDO1, from a library of 271,380 small molecules; the inhibitor suppressed breast cancer migration in vitro and tumor progression in vivo. The study illuminates the proteomic alterations in the serum EVs of breast cancer patients and provides guidance for improving the diagnosis, monitoring and treatment of breast cancer.
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Proteomic profiling of extracellular vesicles derived from corneal epithelial and stromal cells
Proteomic Characterization of Corneal Epithelial and Stromal Cell-Derived Extracellular VesiclesCommunication between the different layers of the cornea (the epithelial and stromal layers) is a complex and critical part of corneal healing. It has been reported that after corneal injury, bidirectional interactions between epithelial cells and the stroma via the vesicular secretome — extracellular vesicles (EVs) — accelerate wound closure. Specific protein markers of EVs derived from human corneal epithelial cells (HCE), keratocytes (HCKs), fibroblasts (HCFs) and myofibroblasts (HCMs), however, remain poorly understood. A team led by Vincent Yeung at the Schepens Eye Research Institute of Harvard Medical School in Boston used proteomics to analyze EVs derived from corneal epithelial and stromal cells and to explore their potential roles in corneal physiology and pathology. The study found that all EVs were enriched in CD81, and that expression of ITGAV and FN1 was increased in HCM-EVs compared with HCE-EVs and HCF-EVs. All EVs were devoid of GM130 and showed minimal differences in biophysical properties (particle concentration, median particle size and zeta potential). At the proteome level, the team found that proteins associated with fibrotic pathways (such as COL6A1, COL6A2, MMP1, MMP2, TIMP1 and TIMP2) were more enriched in HCM-EVs than in HCE-EVs, HCK-EVs and HCF-EVs. Interestingly, HCE-EVs expressed proteins linked to the EIF-2 signaling pathway — stress-induced signaling that regulates mRNA translation — such as RPS21, RALB, EIF3H and RALA, whereas expression of these proteins was lower in HCK-EVs, HCF-EVs and HCM-EVs. The team isolated EVs from the conditioned media of HCE, HCK, HCF and HCM cells and characterized their biophysical and protein composition by Western blotting, nanoparticle tracking analysis and proteomics. The study shows that EVs from the corneal epithelial and stromal layers have distinct molecular compositions and may provide new protein markers for distinguishing between HCE-EVs, HCK-EVs, HCF-EVs and HCM-EVs.
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The mouse aging proteome atlas
An atlas of the aging mouse proteome reveals the features of age-related post-transcriptional dysregulation
Aging is the greatest risk factor for most chronic diseases, and understanding its underlying mechanisms is essential for preventing and treating age-related disease. The first step in probing the mechanisms of aging is a comprehensive and quantitative assessment of age-related molecular changes. Previous research has focused largely on elucidating transcriptomic changes during aging, yet some changes in mRNA levels that are not accompanied by changes in protein abundance may be inconsequential. Age-related proteomic changes themselves, meanwhile, remain underexplored. Although proteomic studies have revealed age-related changes in mouse tissues such as brain, kidney, liver and lung, the experimental material used was not taken from aged mice, so the aging proteome still holds many unanswered questions. A team led by Naoko Ohtani at the Graduate School of Medicine, Osaka City University, performed quantitative proteomic analysis of whole-tissue lysates and low-solubility protein-enriched fractions from major tissues of mice aged 6, 15, 24 and 30 months, and integrated these data with transcriptomic data collected from the same mouse tissue samples to construct a "mouse aging proteome atlas." It comprehensively describes the principal features of the proteome in aging mice and provides an integrated perspective for future aging research.
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Beyond-genomic influences on the biology of the human plasma proteome
Mapping biological influences on the human plasma proteome beyond the genome
Current broad-capture proteomics platforms can already measure thousands of plasma proteins at once, but most of these proteins are not actively secreted and their origins are unknown. A team led by Julia Carrasco-Zanini at the Precision Healthcare University Research Institute, Queen Mary University of London, integrated genomics with deep phenotypic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in around 8,000 healthy individuals. The team created a data-driven biological impact atlas that maps the biological influences acting on the human plasma proteome and shows how proteins can be grouped according to their principal explanatory factors. For more than one-third (N = 1,575) of protein targets, combined genetic and non-genetic factors explained 10% to 77% of plasma variance (median 19.88%, interquartile range 14.01% to 31.09%), independently of technical factors (median 2.48%, interquartile range 0.78% to 6.41%). Combined with genetically anchored causal inference methods, the atlas highlights potential causal relationships between modifiable risk factors and plasma proteins. For example, across hundreds of protein–disease associations, COL6A3 may mediate the link between declining kidney function and cardiovascular disease. The study provides an atlas of the biological and technical influences on the human plasma proteome to help interpret the findings of proteomic research.
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Single-cell proteomics characterizes the IFN-γ response and the coexistence of cell states through enhanced feature matching
Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states
Proteomic analysis by data-independent acquisition (DIA) has become a powerful way to achieve deep proteome coverage and is gaining traction in label-free single-cell quantification. The optimal experimental design for DIA single-cell proteomics, however, has not been fully explored, and the performance metrics of the subsequent data analysis tools remain to be assessed. A team led by Karl K. Krull at the Stem Cells and Cancer Proteomics Division of the German Cancer Research Center (DKFZ) therefore formally and comprehensively evaluated a DIA data processing strategy that uses a so-called "Matching Enhancer" (ME) co-analyzed with low-input samples to improve sensitivity, proteome coverage and data completeness. The team assessed the matching specificity of DIA-ME with a dual-proteome model and demonstrated that, when using DIA-NN software, false positives and false transfers are kept low while quantitative accuracy is preserved. The study applied DIA-ME to investigate the single-cell proteomic response of U-2OS cells to IFN-γ and reproduced the time-resolved induction of IFN-γ response proteins seen in bulk material. In addition, the team discovered co-expression and anti-correlation patterns of protein expression within the same cell, indicating the existence of mutually exclusive protein modules and the coexistence of different cell states. Overall, the data show that DIA-ME is a powerful, scalable and easy-to-implement strategy for single-cell proteomics.
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Single-cell CAR T atlas reveals Type-2 function in 8-year leukemia remission
Single-cell CAR T atlas reveals type 2 function in 8-year leukaemia remission
Although chimeric antigen receptor (CAR) T cell therapy achieves high response rates in acute lymphoblastic leukemia (ALL), roughly 50% of patients relapse within the first year. Against this backdrop, a team led by Zhiliang Bai in the Department of Biomedical Engineering at Yale University set out to study the molecular determinants of exceptionally long CAR T cell persistence, generating single-cell multi-omics atlases of 695,819 pre-infusion CAR T cells from 82 pediatric ALL patients enrolled in the first two CAR T ALL clinical trials along with six healthy donors, collected at baseline or after CAR-specific stimulation. The study found that elevated Type-2 function in CAR T cell infusion products was significantly associated with patients maintaining B cell aplasia for an average of 8.4 years. Through analysis of ligand–receptor interactions, the researchers found that Type-2 cells sustain the homeostasis of the overall population by modulating dysfunctional subsets, and that adding IL-4 during antigen-specific activation reduces CAR T cell dysfunction and enhances fitness at both the transcriptomic and epigenomic levels. Serial proteomic analysis of post-treatment serum showed higher levels of circulating Type-2 cytokines in patients who remained relapse-free at five or eight years. In a mouse leukemia model, CAR T cell products with higher Type-2 function showed better expansion and antitumor activity, particularly after re-stimulation with tumor cells. CAR T cell products manufactured with IL-4 added during production, or pre-treated with IL-4 before infusion, restored the antitumor efficacy of CAR T cells with low Type-2 function. These results provide insight into the mediators of durable CAR T cell responses and suggest a potential therapeutic strategy for sustaining long-term remission by enhancing the Type-2 function of CAR T cells.
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Multi-omics analysis reveals the key factors behind Alzheimer's disease severity
Multi-omics analysis reveals the key factors involved in the severity of the Alzheimer’s diseaseA team led by Meng Lingqi at the Science for Life Laboratory, KTH Royal Institute of Technology, Sweden, carried out a multi-omics study of 87 Alzheimer's disease (AD) patients to uncover the key factors influencing disease progression. For the first time, the study integrated protein and metabolite data from patient blood samples with gut and salivary microbiome information, in order to fully dissect the complex interactions between host and microbes and their specific impact on AD. By comparing patients at different clinical stages, the researchers identified a series of significant differences, including changes in the concentrations of specific proteins and metabolites in blood and shifts in the structure of gut and salivary microbial communities. The study reports that biomarkers such as SKAP1, NEFL, homovanillate and Paraprevotella clara are significantly associated with AD severity. The team also applied advanced machine learning algorithms to build a predictive model from these multi-omics data for assessing the trajectory of AD, and the model predicts disease progression with reasonable accuracy. The study is of great significance for deepening our understanding of AD pathology and for providing a scientific basis for future diagnostic and therapeutic strategies.
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Multi-omics analysis reveals the prognostic value of the IGF2BP3/SPHK1 signaling pathway in cancer stem cells of muscle-invasive bladder cancer and its ability to predict response to immunotherapy
Multi-omics analysis unveils the predictive value of IGF2BP3/SPHK1 signaling in cancer stem cells for prognosis and immunotherapeutic response in muscle-invasive bladder cancer
A team led by Yaobang Wang at the Institute of Urology and Nephrology, the First Affiliated Hospital of Guangxi Medical University, used single-cell mass spectrometry to identify a specific CD274+ALDH+ cancer stem-like cell population in patients with muscle-invasive bladder cancer (MIBC). These cells were associated with an immunosuppressive state and lower survival rates. Further work showed that high IGF2BP3 expression in these cells is closely linked to poor clinical outcomes in MIBC patients. To understand more deeply how IGF2BP3 regulates SPHK1 expression, the team applied MeRIP-seq and bulk RNA-seq, revealing a complex regulatory mechanism between IGF2BP3 and SPHK1. Using single-nucleus RNA sequencing and single-cell RNA sequencing, the researchers mapped the molecular profile of bladder cancer cells co-expressing IGF2BP3 and SPHK1. In addition, spatial transcriptomics and encoder analysis allowed them to describe in detail the spatial distribution patterns and interactions between IGF2BP3+SPHK1+ bladder cancer cells and immune cells within the tumor microenvironment. The results suggest that the IGF2BP3/SPHK1 signaling pathway may play an important role in prognostic assessment and in the response to immunotherapy in MIBC patients.
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