通过基于染色体构象的核型分析(C-MoKa)对受孕失败和妊娠失败患者的染色体异常进行全面分析。

IF 3.2 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY
Xiao Bao, Yuxia Yang, Wenbin Niu, Yimin Wang, Hao Shi, Yangyun Zou, Yidong Liu, Cheng Wan, Jun Ren, Sijia Lu, Yingpu Sun
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引用次数: 0

摘要

背景介绍染色体异常是受孕失败和妊娠失败的主要原因。虽然传统的细胞遗传学技术(如核型分析)有助于识别结构变异(SV),但在检测复杂重排和隐性结构方面却面临挑战。在这项研究中,我们开发了一种名为基于染色体构象的核型分析(C-MoKa)的新方法,用于全面检测受孕失败和妊娠丢失患者中不同类型的染色体异常:方法:共收集了70份临床样本,这些样本均显示出已知的SVs、镶嵌性非整倍体、拷贝数变异(CNVs)和单亲裂殖(uniparental disomy)等结果。和单亲裂殖症(UPD)的临床样本,并进行了 C-MoKa 分析。我们对不同技术(包括核型分析、CNV-seq和CMA)得出的结果进行了比较和分析:结果:在临床样本中观察和分析到了不同变异的染色体构象模式。我们的 C-MoKa 方法不仅验证了核型分析、CNV-seq 和 CMA 的所有结果,还提供了更详细的结果。它的片段分辨率更高(100 kb)。此外,C-MoKa 在一次检测中解码错综复杂的重排方面表现出更高的灵敏度:我们的研究结果凸显了 C-MoKa 在临床环境中精确揭示 SV、镶嵌性非整倍体、CNV 和 UPD 的潜在作用,这将对进一步的临床决策产生重大影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprehensive analysis of chromosome abnormalities by chromosome conformation based karyotyping (C-MoKa) in patients with conception failure and pregnancy loss.

Background: Chromosome abnormalities are a leading cause of conception failure and pregnancy loss. While traditional cytogenetics technologies like karyotyping have been helpful in identifying structural variations (SVs), they face challenges in detecting complex rearrangements and cryptic structures. In this study, we developed a new method called chromosome conformation based karyotyping (C-MoKa) to comprehensively detect different types of chromosomal abnormalities in patients with conception failure and pregnancy loss.

Methods: A total of 70 clinical samples exhibiting known results of SVs, mosaic aneuploidies, copy number variations (CNVs) and uniparental disomy (UPD) were included in our cohort and underwent C-MoKa analysis. The results obtained from different techniques, including karyotyping, CNV-seq, and CMA were compared and analyzed.

Results: Distinct chromosomal conformation patterns of various variations were observed and analyzed in clinical samples. Our C-MoKa method not only validated all the findings of karyotyping, CNV-seq and CMA, but also provided more detailed results. It demonstrated superior fragment resolution (<500 Kb) and more precise breakpoints (>100 kb). Moreover, C-MoKa showed higher sensitivity in decoding intricate rearrangements in a single test.

Conclusions: Our results highlight the potential utility of C-MoKa in precisely unraveling SVs, mosaic aneuploidies, CNVs, and UPD in clinical settings, which can significantly impact further clinical decision-making.

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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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