Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA DOI Creative Commons
Qinglong Zhang,

Liping Kang,

Haoran Yang

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 26(1)

Опубликована: Ноя. 22, 2024

Abstract Alternative polyadenylation (APA) is an important driver of transcriptome diversity that generates messenger RNA isoforms with distinct 3′ ends. The rapid development single-cell and spatial transcriptomic technologies opened up new opportunities for exploring APA data to discover hidden cell subpopulations invisible in conventional gene expression analysis. However, gene-level analysis tools are not fully applicable data, commonly used unsupervised dimensionality reduction methods often disregard experimentally derived annotations such as type identities. Here, we proposed a supervised analytical framework termed spvAPA, specifically from both transcriptomics data. First, iterative imputation method based on weighted nearest neighbor was designed recover missing signatures, by integrating modalities. Second, feature selection sparse partial least squares discriminant devised identify features distinguishing types or morphologies. Additionally, spvAPA improves the visualization high-dimensional discovering novel subtypes, which considers dual modalities APA. Evaluations across nine datasets demonstrate effectiveness applicability spvAPA. available at https://github.com/BMILAB/spvAPA.

Язык: Английский

Multiomics analysis unveils the cellular ecosystem with clinical relevance in aldosterone-producing adenomas with KCNJ5 mutations DOI Creative Commons
Maki Yokomoto‐Umakoshi, Masamichi Fujita, Hironobu Umakoshi

и другие.

Proceedings of the National Academy of Sciences, Год журнала: 2025, Номер 122(9)

Опубликована: Фев. 26, 2025

Aldosterone-producing adenomas (APA), a major endocrine tumor and leading subtype of primary aldosteronism, cause secondary hypertension with high cardiometabolic risks. Despite potentially producing multiple steroid hormones, detailed cellular mechanisms in APA remain insufficiently studied. Our multiomics analysis focusing on KCNJ5 mutations, which represent the most common genetic form, revealed marked heterogeneity. Tumor cell reprogramming initiated from stress-responsive cells to aldosterone-producing or cortisol-producing cells, latter progressing proliferative stromal-like cells. These subtypes showed spatial segregation, exhibited genomic intratumor Among nonparenchymal lipid-associated macrophages, were abundant APA, might promote progression suggesting their role microenvironment. Intratumor cortisol synthesis was correlated increased blood levels, associated development vertebral fractures, hallmark osteoporosis. This study unveils complex ecosystem clinical relevance providing insights into biology that could inform future approaches.

Язык: Английский

Процитировано

0
Single‐Cell Atlas Reveals Tumorigenic Profiles and Immune Dynamics of Adrenal Incidentalomas DOI Creative Commons
Meng Wang, Guangmin Zheng, Xiaoyong Hu

и другие.

Advanced Science, Год журнала: 2025, Номер unknown

Опубликована: Апрель 7, 2025

Abstract Adrenal incidentalomas (AIs) are commonly detected endocrine lesions, identified during imaging for unrelated conditions. These lesions exhibit considerable heterogeneity and diverse clinical outcomes. This study employed single‐cell RNA sequencing to investigate tumorigenic characteristics of AIs, including non‐functional adrenocortical adenomas, Conn's syndrome, pheochromocytomas. Through integrating public datasets, 302 696 cells analyzed. Three cell subtypes gene expression patterns linked tumorigenesis. Clusterin emerges as a potential biomarker adenomas. Adrenocortical tumor show dysregulated hormone secretion transcription factor steroidogenic 1 (SF1) is significantly upregulated, distinguishing cortical from medullary tumors. In pheochromocytomas, MYCN proto‐oncogene (MYCN)‐positive cluster correlates with poorer survival. Immune microenvironment analysis reveals specific immune roles in progression. Specifically, myeloid may regulate benign tumors, while lymphoid cells, such CD8‐positive (CD8+) T appear promote activation infiltration malignant Overall, this enhances the understanding adrenal adenoma heterogeneity, revealing crucial transcriptional profiles, interactions, clinically relevant candidate biomarkers.

Язык: Английский

Процитировано

0
Investigation of Human Aging at the Single-Cell Level DOI
Yunjin Li, Qixia Wang,

Yuan Xuan

и другие.

Ageing Research Reviews, Год журнала: 2024, Номер 101, С. 102530 - 102530

Опубликована: Окт. 11, 2024

Язык: Английский

Процитировано

2
Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA DOI Creative Commons
Qinglong Zhang,

Liping Kang,

Haoran Yang

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 26(1)

Опубликована: Ноя. 22, 2024

Abstract Alternative polyadenylation (APA) is an important driver of transcriptome diversity that generates messenger RNA isoforms with distinct 3′ ends. The rapid development single-cell and spatial transcriptomic technologies opened up new opportunities for exploring APA data to discover hidden cell subpopulations invisible in conventional gene expression analysis. However, gene-level analysis tools are not fully applicable data, commonly used unsupervised dimensionality reduction methods often disregard experimentally derived annotations such as type identities. Here, we proposed a supervised analytical framework termed spvAPA, specifically from both transcriptomics data. First, iterative imputation method based on weighted nearest neighbor was designed recover missing signatures, by integrating modalities. Second, feature selection sparse partial least squares discriminant devised identify features distinguishing types or morphologies. Additionally, spvAPA improves the visualization high-dimensional discovering novel subtypes, which considers dual modalities APA. Evaluations across nine datasets demonstrate effectiveness applicability spvAPA. available at https://github.com/BMILAB/spvAPA.

Язык: Английский

Процитировано

0