A fingerprint-based polymeric sensing platform for comprehensive quality assessment of complex culture media in cell manufacturing

IF 7.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Shunsuke Tomita, Kumi Morikawa, Naoshi Kojima, Sayaka Ishihara, Hiroyuki Kusada, Hideyuki Tamaki, Ryoji Kurita
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引用次数: 0

Abstract

The rapid advancement of cell manufacturing across biotechnology, regenerative medicine, and cellular agriculture is driving a growing demand for simple and reliable analytical tools to ensure the consistent quality of complex culture media, whether derived from natural or synthetic sources. Here, we present a hypothesis-free, data-driven polymeric sensing platform that employs an array of synthetic polymer probes to generate fluorescence-response fingerprints, enabling the statistical detection of subtle compositional differences in complex biological mixtures. This approach, based on charged block-copolymers conjugated with aggregation-induced-emission (AIE) fluorophores, successfully distinguishes 16 animal sera and identifies differences in serum origin, lot, and storage conditions through rapid and simple fluorescence measurements. Unexpectedly, the resulting response fingerprints also encode phylogenetically informative signals among animal species. Furthermore, the platform detects quality variations in serum-free supplements for stem-cell culture and naturally derived supplements used for microbial culture, including subtle compositional changes undetectable by standard cell-culture assays. As this fingerprint-based strategy does not require prior assumptions about which specific components are important, it can be flexibly adapted to a diverse array of supplement types and quality control needs. Overall, this versatile sensing platform provides a robust and reproducible framework for proactive quality assessment in cell manufacturing, supporting the reliable production of cell-based products.
基于指纹图谱的细胞制造中复杂培养基质量综合评价聚合物传感平台
细胞制造在生物技术、再生医学和细胞农业领域的快速发展,推动了对简单可靠的分析工具的需求不断增长,以确保复杂培养基的质量一致,无论是来自天然还是合成来源。在这里,我们提出了一个无假设、数据驱动的聚合物传感平台,该平台采用一系列合成聚合物探针来产生荧光响应指纹,从而能够统计检测复杂生物混合物中细微的成分差异。该方法基于带电荷的嵌段共聚物与聚集诱导发射(AIE)荧光团共轭,通过快速和简单的荧光测量,成功区分了16种动物血清,并识别了血清来源、批次和储存条件的差异。出乎意料的是,由此产生的反应指纹也编码动物物种之间的系统发育信息信号。此外,该平台检测用于干细胞培养的无血清补充剂和用于微生物培养的天然衍生补充剂的质量变化,包括标准细胞培养试验无法检测到的细微成分变化。由于这种基于指纹的策略不需要事先假设哪些特定成分是重要的,它可以灵活地适应各种补充类型和质量控制需求。总的来说,这个多功能传感平台为电池制造中的主动质量评估提供了一个强大且可重复的框架,支持基于细胞的产品的可靠生产。
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来源期刊
Chemical Science
Chemical Science CHEMISTRY, MULTIDISCIPLINARY-
CiteScore
14.40
自引率
4.80%
发文量
1352
审稿时长
2.1 months
期刊介绍: Chemical Science is a journal that encompasses various disciplines within the chemical sciences. Its scope includes publishing ground-breaking research with significant implications for its respective field, as well as appealing to a wider audience in related areas. To be considered for publication, articles must showcase innovative and original advances in their field of study and be presented in a manner that is understandable to scientists from diverse backgrounds. However, the journal generally does not publish highly specialized research.
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