Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown
Published: Jan. 1, 2024
Language: Английский
Saudi Journal for Health Sciences, Journal Year: 2025, Volume and Issue: 14(1), P. 30 - 41
Published: Jan. 1, 2025
Inherited cardiomyopathies are a diverse group of heart muscle diseases caused by genetic mutations that result in structural and functional abnormalities the myocardium. Understanding genotype–phenotype correlations these conditions is vital for personalized cardiac medicine, enabling targeted therapeutic strategies predictive diagnostics. This review explores major types inherited cardiomyopathies–hypertrophic cardiomyopathy, dilated arrhythmogenic restrictive cardiomyopathy–and provides detailed insights into how different manifest as clinical features. The integration multi-omics approaches advanced disease modeling techniques has enhanced our ability to dissect correlations. also discusses implications findings including tailored strategies, diagnostics, future research directions. JOURNAL/sjfhs/04.03/01772839-202501000-00004/figure1/v/2025-04-19T121403Z/r/image-tiff
Language: Английский
Citations
0
Published: April 22, 2025
Abstract Spatial multi-omics data offer a powerful framework for integrating diverse molecular profiles while maintaining the spatial organization of cells. However, inherent variations in quality and noise levels across different modalities pose significant challenges to accurate integration analyses. In this paper, we introduce CANDIES, which leverages conditional diffusion model contrastive learning effectively denoise integrates data. With our innovative algorithm designs, CANDIES not only enhances data, but also yields unified comprehensive joint representation, thereby empowering many downstream analysis. We conduct extensive evaluations on synthetic real datasets, including CITE-seq from human skin biopsy tissue, MISAR-seq mouse brain, ATAC-RNA-seq embryo 10× visium lymph nodes. shows superior performance various tasks, denoising, domain identification, spatiotemporal trajectories reconstruction, association mapping complex traits. particular, show that representations can be integrated with rich resources genome-wide studies (GWASs), allowing domains linked traits, yielding spatially resolved interpretation traits their relevant tissues.
Language: Английский
Citations
0
Oncogene, Journal Year: 2024, Volume and Issue: 43(37), P. 2751 - 2767
Published: Aug. 9, 2024
Language: Английский
Citations
3
Science Bulletin, Journal Year: 2024, Volume and Issue: 69(10), P. 1380 - 1385
Published: March 21, 2024
Language: Английский
Citations
1
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: May 21, 2024
Abstract Myocardial infarction (MI) continues to be a leading cause of death worldwide. Even though it is well-established that the complex interplay between different cell types determines overall healing response after MI, precise changes in tissue architecture are still poorly understood. Here we generated an integrative cellular map acute phase murine MI using combination imaging-based transcriptomics (Molecular Cartography) and antibody-based highly multiplexed imaging (Sequential Immunofluorescence), which enabled us evaluate cell-type compositions at subcellular resolution over time. One striking finding these analyses was identification novel mode leukocyte accumulation infarcted heart via endocardium - inner layer heart. To investigate underlying mechanisms driving this previously unknown infiltration route, performed unbiased spatial proteomic analysis Deep Visual Proteomics (DVP). When comparing endocardial cells homeostatic hearts hearts, DVP identified von Willebrand Factor (vWF) as upregulated mediator inflammation 24 hours post-MI. further explore immune mediating capabilities vWF its effect on repair, functional blocking during MI. This resulted reduced amount by CCR2 + monocytes worse cardiac function Our study provides first with subsequently discovers route infiltration. Furthermore, critical agent for
Language: Английский
Citations
1
Transplantation, Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 6, 2024
The Banff classification is regularly updated to integrate recent advances in the characterization of kidney allograft rejection, gathering novel diagnostic, prognostic, and theragnostic data into a diagnostic pathogenesis-based framework. Despite ongoing research on noninvasive biomarkers remains, date, biopsy-centered, primarily relying semiquantitative histological scoring system that overall lacks reproducibility granularity. Besides, ability histopathological injuries transcriptomics analyses from bulk tissue accurately infer pathogenesis rejection questioned. This review discusses findings past, current, emerging innovative tools have potential enhance samples. First, digitalization pathological workflows rise deep learning should yield more reproducible quantitative results routine slides. Additionally, histomorphometric features could be discovered with an genuine clinical implementation perspective. Second, multiplex immunohistochemistry enables in-depth situ phenotyping cells formalin-fixed samples, which can decipher heterogeneity immune infiltrate during rejection. Third, gradually integrated classification, its specific context use currently under extensive consideration. Finally, single-cell spatial paraffin-embedded samples are techniques capable producing up genome-wide unprecedented precision levels. Combining all these approaches gives us hope for will address current blind spots system.
Language: Английский
Citations
1
Published: Aug. 9, 2024
Inherited cardiomyopathies are a diverse group of heart muscle diseases caused by genetic mutations that result in structural and functional abnormalities the myocardium. Understanding genotype-phenotype correlations these conditions is vital for personalized cardiac medicine, enabling targeted therapeutic strategies predictive diagnostics. This review explores major types inherited cardiomyopathies—hypertrophic cardiomyopathy (HCM), dilated (DCM), arrhythmogenic right ventricular (ARVC), restrictive (RCM)—and provides detailed insights into how different manifest as clinical features. The integration multi-omics approaches advanced disease modeling techniques has enhanced our ability to dissect correlations. also discusses implications findings including tailored strategies, diagnostics, future research directions.
Language: Английский
Citations
1
PeerJ, Journal Year: 2024, Volume and Issue: 12, P. e17860 - e17860
Published: Sept. 13, 2024
The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand molecular mechanisms governing individual disease progression, precise acquisition biological data, including genome, transcriptome, proteome, metabolome, epigenome, with single-cell resolution spatial information body's context, is essential. This foundational serves as basis for deciphering cellular mechanisms. Although multi-omics technology can provide such epigenome resolution, sample preparation process leads loss information. Spatial technology, however, facilitates characterization tissue samples, while retaining their context. Consequently, these techniques significantly enhance our understanding pathology. Currently, has played a vital role elucidating various processes tumor biology, occurrence, development, metastasis, particularly realms immunity heterogeneity microenvironment. Therefore, this article provides comprehensive overview transcriptomics, proteomics, metabolomics-related technologies application research concerning esophageal cancer, gastric colorectal cancer. objective foster implementation digestive diseases. review will new technical insights biology researchers.
Language: Английский
Citations
1
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 5, 2024
Abstract Spatially resolved transcriptomics has become the method of choice to characterise complexity biomedical tissue samples. Until recently, scientists have been restricted profiling methods with high spatial resolution but for a limited set genes or that can profile transcriptome-wide at low resolution. Through recent developments, there are now which offer subcellular and full transcriptome coverage. However, utilizing gene these new remains elusive due several factors including detection efficiency, computational cost difficulties in delineating cell borders. Here we present Sainsc (Segmentation-free analysis in-situ capture data), combines cell-segmentation free approach efficient data processing nanometer data. generate cell-type maps accurate assignment level, together corresponding scores facilitate interpretation local confidence assignment. We demonstrate its utility accuracy across different tissues methods. Compared other methods, requires lower resources scalable performance, enabling interactive exploration. is compatible common frameworks available as open-source software multiple programming languages.
Language: Английский
Citations
0
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 9, 2024
Abstract A key advantage of single-cell multimodal joint profiling is the modality interplay, which essential for deciphering cell fate. However, while current analytical methods can leverage additive benefits, they fall short to explore synergistic insights profiling, thereby diminishing profiling. Here, we introduce CellMATE, a M ulti-head dversarial T raining-based E arly-integration approach specifically developed CellMATE capture both and benefits inherent in through auto-learning distributions simultaneously represents all features into unified latent space. Through extensive evaluation across diverse scenarios, demonstrated its superiority ensuring utility cross-modal properties, uncovering cellular heterogeneity plasticity, delineating differentiation trajectories. uniquely unlocks full potential elucidate dynamic nature cells during critical processes as differentiation, development diseases. Graphical abstracts
Language: Английский
Citations
0