International Journal of Surgical Pathology, Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 21, 2024
Language: Английский
Frontiers in Cellular and Infection Microbiology, Journal Year: 2025, Volume and Issue: 14
Published: Jan. 20, 2025
Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust targeted immune response. Recent advancements in bioinformatics artificial intelligence (AI) have significantly enhanced the design, prediction, optimization of mRNA vaccines. This paper reviews technologies that streamline vaccine development, from genomic sequencing lipid nanoparticle (LNP) formulation. We discuss how accurate predictions neoantigen structures guide sequences effectively target cells. Furthermore, we examine AI-driven approaches optimize mRNA-LNP formulations, enhancing delivery stability. These technological innovations not only improve but also enhance pharmacokinetics pharmacodynamics, offering promising avenues personalized immunotherapy.
Language: Английский
Citations
5
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: May 10, 2024
Abstract Large-scale single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have transformed biomedical research into a data-driven field, enabling the creation of comprehensive data atlases. These methodologies facilitate detailed understanding biology pathophysiology, aiding in discovery new therapeutic targets. However, complexity sheer volume from these technologies present analytical challenges, particularly robust cell typing, integration complex relationships cells. To address we developed CELLama (Cell Embedding Leverage Language Model Abilities), framework that leverage language model to transform ’sentences’ encapsulate gene expressions metadata, universal cellular embedding for various analysis. CELLama, serving as foundation model, supports flexible applications ranging typing analysis contexts, independently manual reference selection or intricate dataset-specific workflows. Our results demonstrate has significant potential determining types across multi-tissue atlases their interactions unraveling tissue dynamics.
Language: Английский
Citations
5
Cancers, Journal Year: 2024, Volume and Issue: 16(9), P. 1615 - 1615
Published: April 23, 2024
While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis treatment, their limitation in preserving critical spatial information has been a notable drawback. This context is essential for understanding cellular interactions tissue dynamics. Multiplex digital profiling (MDSP) technologies overcome this by enabling the simultaneous analysis of transcriptome proteome data within intact architecture tissues. In breast research, MDSP emerged as promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, discovering drug targets. review highlights potential revolutionize clinical applications, ranging from risk assessment diagnostics prognostics, patient monitoring, customization treatment strategies, including trial guidance. We discuss major techniques, applications integration practice, addressing both current limitations. Emphasizing strategic use stratification women with benign disease, we also highlight its transformative reshaping landscape research treatment.
Language: Английский
Citations
4
Toxicologic Pathology, Journal Year: 2025, Volume and Issue: unknown
Published: March 22, 2025
Spatial transcriptomics (ST) is revolutionizing our understanding of the central nervous system (CNS) by providing spatially resolved gene expression data. This mini review explores impact ST on CNS research, particularly in neurodegenerative diseases like Alzheimer’s, Parkinson’s, multiple sclerosis, and amyotrophic lateral sclerosis. We describe two foundational methods: sequencing-based imaging-based. Key studies are reviewed highlighting power data sets to map transcriptomes disease-specific histomorphology, elucidate molecular mechanisms regional cellular vulnerability, integrate single-cell with tissue mapping, reveal receptor-ligand interactions. Despite current challenges interpretation resolution limits, holds promise for identifying novel drug targets, evaluating their therapeutic potential, bridging gaps between animal models human advance development CNS-targeting compounds.
Language: Английский
Citations
0
OBM Geriatrics, Journal Year: 2025, Volume and Issue: 09(02), P. 1 - 32
Published: April 14, 2025
Cellular senescence (aging) is a physiological process that plays role in tissue remodeling, wound healing, and embryogenesis. Prolonged of cells can have detrimental effects trigger secretory phenotype (SASP, senescence-associated phenotype), degenerative disorders, cancer, age-related diseases. Suitable biomarkers range different laboratory methods are used to investigate these complex relationships vitro vivo. Since universal biomarker for cell has not yet been identified, numerous identify senescent cell. The detection quantification their SASP provide the basis targeted treatment patient. In parallel, single-cell analysis also required quantitative assessment therapy result. Depending on facilities performing analysis, wide available. this review, we general overview accessible techniques such as immunohistochemistry using microscopy automated flow cytometry introduce new possibilities by modern like mass spectrometry or genetic method single cells. focus here use routine laboratories. classical with enzyme immunoassays, measurement products (IL-6, IL-8), part work. This review discusses ideas visualisation clinical patient data gerontology. An outlook potential future optimization improve rejuvenate status patients cellular organ-specific level discussed.
Language: Английский
Citations
0
Laboratory Investigation, Journal Year: 2025, Volume and Issue: unknown, P. 104190 - 104190
Published: April 1, 2025
Language: Английский
Citations
0
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 11, 2024
ABSTRACT It is of critical importance to our understanding Alzheimer’s disease (AD) pathology determine how key pathological factors are interconnected and implicated in nerve cell death, clinical symptoms, progression. The formation extracellular beta-amyloid (Aβ) plaques the major hallmark AD Aβ has been suggested be a inducer AD, driving pathogenesis. Exactly plaque begins ongoing deposition proceeds initiates subsequent neurotoxic mechanisms not well understood. primary aim research elucidate biochemical processes underlying early brain tissue. We recently introduced chemical imaging paradigm based on mass spectrometry (MSI) metabolic isotope labelling follow stable kinetics (iSILK) vivo track build-up Aβ. Herein, knock-in mouse models ( App NL-F ) that develop gradually metabolically labeled with isotopes. This approach timestamps amyloid during period initial allowing fate aggregating species from before earliest events through maturation tracked. To identify molecular cellular response maturation, we integrated iSILK single transcriptomics performed adjacent tissue sections. enabled changes gene expression tracked as function age (as encoded peptide isotopologue pattern) distinct due chronological or severity. identified correlates negatively patterns associated synaptic 10-month-old animals but persists into 18 months. Finally, hyperspectral confocal microscopy multiomic image structural isomers, revealing positive correlation between maturity. analysis three categories plaques, each impact surrounding microenvironment. Here, older, more compact were most significant synapse loss toxicity. These data show isotope-encoded MS can used delineate toxicity dynamics vivo. Moreover, for first time functional integration dynamic MSI, whole genome-wide spatial at level. offers an unprecedented combination temporal resolution enabling description precipitating modulates synaptotoxic mechanisms.
Language: Английский
Citations
2
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 1, 2024
Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research various domains. However, the high cost of current ST data generation techniques restricts its application large-scale population studies. Consequently, there is a pressing need to maximize use available resources achieve robust statistical power. One fundamental question analysis detect differentially expressed genes (DEGs) among different conditions using data. Such DEG often performed but associated power calculation rarely discussed literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), estimation tool designed support detection with 10X Genomics Visium enables both before any experiments or after preliminary are collected, making it suitable for wide variety analyses We also provide user-friendly, program-free web (https://lanshui.shinyapps.io/PoweREST/), allowing users interactively calculate and visualize study along relevant parameters.
Language: Английский
Citations
1
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 18, 2024
Abstract The rise of spatial transcriptomics (ST) is transforming our understanding tumor heterogeneity by enabling high-resolution, location-specific mapping gene expression across the microenvironment. However, translational potential still limited its high cost, hindering assembly large patient cohorts needed for robust biomarker discovery. Here we present Path2Space , a deep learning model trained on data to predict directly from histopathology slides. Studying breast cancer, was first one ST cancer patients cohort and further independently tested validated two other cohorts. identifies 4,500 genes whose robustly predicted in all three cohorts, markedly more accurate than existing predictors. Second, show that based inferred ST, accurately infers cell-type abundances microenvironment (TME). Thirdly, Applying TCGA identify TME spatially grounded subsets have different survival rates. Fourth, analyzing treated with trastuzumab, HER2 levels predictive response, concordant bystander hypothesis. Finally, developed interpretable predicting trastuzumab response H&E-slides. This model’s performance surpasses prediction models use measured bulk multi-omics data. approach capable delineating an unprecedented scale It heralds upcoming development biomarkers treatment promises transform precision oncology both developing worlds.
Language: Английский
Citations
1
Cancers, Journal Year: 2024, Volume and Issue: 16(17), P. 3100 - 3100
Published: Sept. 6, 2024
Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution throughput have led to an increase scientific interest, notably cancer research. This scoping study reviews the challenges practical applications of ST, summarizing current methods, trends, data analysis techniques for neoplasm We analyzed 41 articles published by end 2023 alongside public repositories. The findings indicate biology is important focus research, with a rising number studies each year. Visium (10x Genomics, Pleasanton, CA, USA) leading platform, SCTransform from Seurat R library preferred method normalization integration. Many incorporate additional types like single-cell sequencing immunohistochemistry. Common include discovering composition function tumor tissues their heterogeneity, characterizing microenvironment, or identifying interactions between cells, including patterns co-occurrence. However, nearly half lacked comprehensive processing protocols, hindering reproducibility. By recommending greater transparency sharing methods adapting caution, this review aims improve reproducibility reliability future
Language: Английский
Citations
0