Pub Date : 2026-02-06DOI: 10.1186/s12967-025-07619-4
Eric de Sousa, Joana R Lérias, Carolina M Gorgulho, Miguel Chaves-Ferreira, Vitaly Balan, Wenjing Pan, Miranda Byrne-Steele, Zhe Wang, Jian Han, Margarida Gama-Carvalho, Markus Maeurer
<p><strong>Background: </strong>Deep 'bulk' T-cell receptor (TCR) sequencing is a comprehensive approach to gauge the TCR repertoire in clinical specimens to address spatio-temporal differences in TCR compositions. Clonal T-cell expansion in the course of anti-cancer directed cellular immune responses can be antigen-driven, either by commonly shared or mutant tumor-associated antigens (TAAs), by viral targets, or reflect 'bystander activation' of T-cell clones. Different analytic tools and platforms are available to describe the molecular texture of the TCR composition. We report here on an open-access platform 'TCRcloud' that enables to address the unmet need to visualize TCR diversity in cellular immune response, e.g. to checkpoint blockade therapies, termed 'clonal replacement'. We took advantage of a publicly available dataset that linked TCR composition analysis with clinically relevant responses to immune checkpoint inhibitor (ICI) treatment and visualized the TCR changes using the TCRcloud platform described in this report. In order to test 'real world data', we visualized TCRs and B-cell receptors (BCRs) in blood and matching tumor tissue from 3 patients with pancreatic cancer.</p><p><strong>Results: </strong>TCRcloud, is a computational tool to screen the 'TCR data warehouse' for biologically and clinically relevant patterns, i.e. the CDR3 length, number of unique CDR3 transcripts, TCR convergence, different indices gauging the TCR composition in biological samples, i.e. the D50 Index, Gini Coefficient, Shannon Index, Gini-Simpson Index, Chao1 index, as well as the changes in amino acid usage at each position of the TCR and BCR CDR3. TCRcloud is a free open-source software distributed under the MIT license and available from https://github.com/eriicdesousa/TCRcloud or via the Python Package Index (PyPI). TCRcloud is compatible with both TCR and BCR molecular datasets if these fulfill Adaptive Immune Receptor Repertoire (AIRR) community standards. The analysis of a public TCR database allowed us to select a subject to demonstrate detailed molecular changes in the CDR3 TCR datasets which have been associated with relevant clinical responses in patients with basal cell cancer or squamous cell carcinoma receiving checkpoint inhibitor treatment (Yost et al. 10.1038/s41591-019-0522-3). Analysis of real world immune receptor sequencing data obtained from tissue from patients with cancer allowed us to demonstrate the different dynamics in the TCR and BCR in blood and corresponding tumor from of 3 patients with pancreatic cancer.</p><p><strong>Conclusion: </strong>TCRcloud enables to i) intuitively visualize molecular TCR compositions, ii) combine different TCR repertoire measurements within a single radar plot to capture biologically relevant TCR indices in a single image iii) visualize the usage of the V-genes and iv) visualize the frequency of amino acids in the CDR3. This easy to use tool enables to intuitively visualize changes in bulk TCR a
{"title":"TCRcloud: a global visualization tool for T-cell and B-cell receptor transcripts.","authors":"Eric de Sousa, Joana R Lérias, Carolina M Gorgulho, Miguel Chaves-Ferreira, Vitaly Balan, Wenjing Pan, Miranda Byrne-Steele, Zhe Wang, Jian Han, Margarida Gama-Carvalho, Markus Maeurer","doi":"10.1186/s12967-025-07619-4","DOIUrl":"https://doi.org/10.1186/s12967-025-07619-4","url":null,"abstract":"<p><strong>Background: </strong>Deep 'bulk' T-cell receptor (TCR) sequencing is a comprehensive approach to gauge the TCR repertoire in clinical specimens to address spatio-temporal differences in TCR compositions. Clonal T-cell expansion in the course of anti-cancer directed cellular immune responses can be antigen-driven, either by commonly shared or mutant tumor-associated antigens (TAAs), by viral targets, or reflect 'bystander activation' of T-cell clones. Different analytic tools and platforms are available to describe the molecular texture of the TCR composition. We report here on an open-access platform 'TCRcloud' that enables to address the unmet need to visualize TCR diversity in cellular immune response, e.g. to checkpoint blockade therapies, termed 'clonal replacement'. We took advantage of a publicly available dataset that linked TCR composition analysis with clinically relevant responses to immune checkpoint inhibitor (ICI) treatment and visualized the TCR changes using the TCRcloud platform described in this report. In order to test 'real world data', we visualized TCRs and B-cell receptors (BCRs) in blood and matching tumor tissue from 3 patients with pancreatic cancer.</p><p><strong>Results: </strong>TCRcloud, is a computational tool to screen the 'TCR data warehouse' for biologically and clinically relevant patterns, i.e. the CDR3 length, number of unique CDR3 transcripts, TCR convergence, different indices gauging the TCR composition in biological samples, i.e. the D50 Index, Gini Coefficient, Shannon Index, Gini-Simpson Index, Chao1 index, as well as the changes in amino acid usage at each position of the TCR and BCR CDR3. TCRcloud is a free open-source software distributed under the MIT license and available from https://github.com/eriicdesousa/TCRcloud or via the Python Package Index (PyPI). TCRcloud is compatible with both TCR and BCR molecular datasets if these fulfill Adaptive Immune Receptor Repertoire (AIRR) community standards. The analysis of a public TCR database allowed us to select a subject to demonstrate detailed molecular changes in the CDR3 TCR datasets which have been associated with relevant clinical responses in patients with basal cell cancer or squamous cell carcinoma receiving checkpoint inhibitor treatment (Yost et al. 10.1038/s41591-019-0522-3). Analysis of real world immune receptor sequencing data obtained from tissue from patients with cancer allowed us to demonstrate the different dynamics in the TCR and BCR in blood and corresponding tumor from of 3 patients with pancreatic cancer.</p><p><strong>Conclusion: </strong>TCRcloud enables to i) intuitively visualize molecular TCR compositions, ii) combine different TCR repertoire measurements within a single radar plot to capture biologically relevant TCR indices in a single image iii) visualize the usage of the V-genes and iv) visualize the frequency of amino acids in the CDR3. This easy to use tool enables to intuitively visualize changes in bulk TCR a","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146132207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: This study systematically investigated phenotypes causally associated with Alzheimer's disease (AD) across the phenome and validated the findings at cognitive and neuroimaging levels using real-world clinical data.
Methods: We performed phenome-wide Mendelian Randomization (MR) analyses on genetic proxies for over 860 disease phenotypes to identify traits causally associated with AD. Lipid metabolism-related phenotypes identified through MR were further examined in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to assess associations with AD risk, brain structure, and cognition.
Results: MR analyses revealed a significant causal association between lipid metabolism, particularly low-density lipoprotein cholesterol (LDL-C), and the risk of AD (OR: 1.05, 95% CI: 1.03-1.07). In ADNI, higher LDL-C indicators correlated with increased AD risk, reduced hippocampal and entorhinal volumes, and poorer cognitive performance. Notably, elevated cholesterol-to-total lipid ratios in small LDL particles were negatively associated with the entorhinal-hippocampal complex. Among cognitively normal individuals, higher LDL-C indicators were associated with smaller hippocampus-amygdala transition area (HATA) and CA3 head volumes. In those with mild cognitive impairment, higher LDL-C was associated with reduced entorhinal surface area.
Conclusions: Our findings suggest that disrupted LDL-C metabolism may play a causal role in the development and progression of AD.
{"title":"A phenome-wide hunt for risk factors of Alzheimer's disease: from metabolic clues to neuroimaging evidence.","authors":"Dongming Liu, Ancha Baranova, Wenxi Sun, Hongbao Cao, Bing Zhang, Fuquan Zhang","doi":"10.1186/s12967-026-07756-4","DOIUrl":"https://doi.org/10.1186/s12967-026-07756-4","url":null,"abstract":"<p><strong>Background: </strong>This study systematically investigated phenotypes causally associated with Alzheimer's disease (AD) across the phenome and validated the findings at cognitive and neuroimaging levels using real-world clinical data.</p><p><strong>Methods: </strong>We performed phenome-wide Mendelian Randomization (MR) analyses on genetic proxies for over 860 disease phenotypes to identify traits causally associated with AD. Lipid metabolism-related phenotypes identified through MR were further examined in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to assess associations with AD risk, brain structure, and cognition.</p><p><strong>Results: </strong>MR analyses revealed a significant causal association between lipid metabolism, particularly low-density lipoprotein cholesterol (LDL-C), and the risk of AD (OR: 1.05, 95% CI: 1.03-1.07). In ADNI, higher LDL-C indicators correlated with increased AD risk, reduced hippocampal and entorhinal volumes, and poorer cognitive performance. Notably, elevated cholesterol-to-total lipid ratios in small LDL particles were negatively associated with the entorhinal-hippocampal complex. Among cognitively normal individuals, higher LDL-C indicators were associated with smaller hippocampus-amygdala transition area (HATA) and CA3 head volumes. In those with mild cognitive impairment, higher LDL-C was associated with reduced entorhinal surface area.</p><p><strong>Conclusions: </strong>Our findings suggest that disrupted LDL-C metabolism may play a causal role in the development and progression of AD.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146125435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-04DOI: 10.1186/s12967-025-07567-z
Fatima Aldali, Li Tang, Yujie Yang, Yunjie Huang, Yajie Li, Chunchu Deng, Hong Chen
<p><strong>Background: </strong>Peripheral nerve injuries (PNIs) remain a major clinical and socioeconomic challenge, frequently resulting in motor weakness, sensory loss, and chronic neuropathic pain that cause long-term disability and restrict daily function. Functional recovery is limited by slow axonal regrowth, Wallerian degeneration, interstitial fibrosis, and progressive denervation-induced muscle atrophy. Although microsurgical epineurial repair and autologous nerve grafting are standard treatments, clinical outcomes remain inconsistent, especially in long-gap or delayed repairs. These limitations underscore the need for more effective regenerative strategies that address both the structural and biological barriers to nerve recovery.</p><p><strong>Main body: </strong>Contemporary research on PNIs focuses on four interconnected domains: structural reconstruction, biological acceleration, functional remodelling, and anatomical restoration. Advanced nerve-guidance conduits offer biomimetic, aligned pathways that reduce axonal misdirection and complement microsuture or autograft repair. Biological approaches, including localized delivery of neurotrophic factors, mesenchymal stem cells, induced-pluripotent stem cell derivatives, and their exosomes, enhance Schwann cell reprogramming, angiogenesis, and pro-regenerative immune polarization while reducing risks associated with live cell transplantation. Non-invasive biophysical stimulation modalities, such as electrical stimulation, magnetic fields, photobiomodulation, low-intensity pulsed ultrasound, and piezoelectric scaffolds, further promote axonal growth and neurotrophic signaling. Emerging integrated strategies that combine stem cell-derived exosomes with physical cues demonstrate synergistic regeneration in preclinical models, representing promising avenues for treating critical-sized nerve gaps. Multi-omics technologies, including transcriptomics, proteomics, metabolomics, and spatial profiling, have deepened mechanistic understanding of Schwann cell plasticity, axon-glia communication, and injury-induced inflammatory dynamics. However, clinical translation remains constrained by heterogeneity in study design, biomaterial manufacturing, regulatory requirements, and the lack of validated biomarkers for monitoring nerve regeneration. Overcoming these obstacles will require coordinated efforts across surgery, biomaterials engineering, stem cell biology, pharmacology, neuromodulation, and rehabilitation medicine.</p><p><strong>Conclusions: </strong>Recent progress in biomaterial conduits, cell-free biologics, and biophysical stimulation is transforming PNI treatment and providing options that surpass conventional microsurgical repair. Continued advancement will require reliable biomarkers, standardized production and evaluation methods, and well-designed randomized controlled trials. Coordinated collaboration across research, clinical practice, industry, and regulatory agencies is essential t
{"title":"Peripheral nerve repair: innovations and future directions.","authors":"Fatima Aldali, Li Tang, Yujie Yang, Yunjie Huang, Yajie Li, Chunchu Deng, Hong Chen","doi":"10.1186/s12967-025-07567-z","DOIUrl":"https://doi.org/10.1186/s12967-025-07567-z","url":null,"abstract":"<p><strong>Background: </strong>Peripheral nerve injuries (PNIs) remain a major clinical and socioeconomic challenge, frequently resulting in motor weakness, sensory loss, and chronic neuropathic pain that cause long-term disability and restrict daily function. Functional recovery is limited by slow axonal regrowth, Wallerian degeneration, interstitial fibrosis, and progressive denervation-induced muscle atrophy. Although microsurgical epineurial repair and autologous nerve grafting are standard treatments, clinical outcomes remain inconsistent, especially in long-gap or delayed repairs. These limitations underscore the need for more effective regenerative strategies that address both the structural and biological barriers to nerve recovery.</p><p><strong>Main body: </strong>Contemporary research on PNIs focuses on four interconnected domains: structural reconstruction, biological acceleration, functional remodelling, and anatomical restoration. Advanced nerve-guidance conduits offer biomimetic, aligned pathways that reduce axonal misdirection and complement microsuture or autograft repair. Biological approaches, including localized delivery of neurotrophic factors, mesenchymal stem cells, induced-pluripotent stem cell derivatives, and their exosomes, enhance Schwann cell reprogramming, angiogenesis, and pro-regenerative immune polarization while reducing risks associated with live cell transplantation. Non-invasive biophysical stimulation modalities, such as electrical stimulation, magnetic fields, photobiomodulation, low-intensity pulsed ultrasound, and piezoelectric scaffolds, further promote axonal growth and neurotrophic signaling. Emerging integrated strategies that combine stem cell-derived exosomes with physical cues demonstrate synergistic regeneration in preclinical models, representing promising avenues for treating critical-sized nerve gaps. Multi-omics technologies, including transcriptomics, proteomics, metabolomics, and spatial profiling, have deepened mechanistic understanding of Schwann cell plasticity, axon-glia communication, and injury-induced inflammatory dynamics. However, clinical translation remains constrained by heterogeneity in study design, biomaterial manufacturing, regulatory requirements, and the lack of validated biomarkers for monitoring nerve regeneration. Overcoming these obstacles will require coordinated efforts across surgery, biomaterials engineering, stem cell biology, pharmacology, neuromodulation, and rehabilitation medicine.</p><p><strong>Conclusions: </strong>Recent progress in biomaterial conduits, cell-free biologics, and biophysical stimulation is transforming PNI treatment and providing options that surpass conventional microsurgical repair. Continued advancement will require reliable biomarkers, standardized production and evaluation methods, and well-designed randomized controlled trials. Coordinated collaboration across research, clinical practice, industry, and regulatory agencies is essential t","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146113652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-04DOI: 10.1186/s12967-026-07768-0
Zhipeng Liu, Junbin Wang, Xia Wu, Hong Shi, Youming Ding, Xuejian Liu
{"title":"EP300 promotes hepatocellular carcinoma proliferation, migration and in vivo tumorigenicity revealed by integrated experimental and bioinformatic analyses.","authors":"Zhipeng Liu, Junbin Wang, Xia Wu, Hong Shi, Youming Ding, Xuejian Liu","doi":"10.1186/s12967-026-07768-0","DOIUrl":"https://doi.org/10.1186/s12967-026-07768-0","url":null,"abstract":"","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146119254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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