RESUMO
Colorectal cancer (CRC) is the third most common fatal cancer worldwide, accounting for ≈10% of cancer-related mortality. Metabolic shift occurs from the very early stage during the development of CRC, which is of significant etiological and diagnostic importance toward precision medicine. Here, an advanced molecular tool to characterize the metabolic alterations in CRC, based on metal-organic framework (MOF) hybrids is reported. Consuming only 500 nL of plasma without any sample pretreatment, MOF hybrids yield direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds. A diagnostic prediction model by a machine learning algorithm is constructed, to discriminate CRC patients from normal controls with an average area under the curve of 0.947 for the discovery cohort and 0.912 for the independent validation cohort. In addition, CRC-specific metabolic signature consisting of 34 potential biomarkers, based on the aforementioned diagnostic model is identified. The results advance the design of nanomaterial-based platforms for metabolic analysis and establish a new liquid biopsy tool for CRC screening compatible with the current clinical workflow in practice.
Assuntos
Neoplasias Colorretais/diagnóstico , Metabolômica/métodos , Estruturas Metalorgânicas , Algoritmos , Biomarcadores Tumorais/sangue , Estudos de Coortes , Detecção Precoce de Câncer/métodos , Humanos , Aprendizado de Máquina , Espectrometria de Massas/métodosRESUMO
Low molecular weight compounds play an important role in encoding the current physiological state of an individual. Laser desorption/ionization mass spectrometry (LDI MS) offers high sensitivity with low cost for molecular detection, but it is not able to cover small molecules due to the drawbacks of the conventional matrix. Advanced materials are better alternatives, showing little background interference and high LDI efficiency. Herein, we first classify the current materials with a summary of compositions and structures. Matrix preparation protocols are then reviewed, to enhance the selectivity and reproducibility of MS data better. Finally, we highlight the biomedical applications of material-assisted LDI MS, at the tissue, bio-fluid, and cellular levels. We foresee that the advanced materials will bring far-reaching implications in LDI MS towards real-case applications, especially in clinical settings.
Assuntos
Substâncias Macromoleculares/análise , Animais , Técnicas Biossensoriais , Compostos Inorgânicos de Carbono/química , Humanos , Limite de Detecção , Metais/química , Técnicas Analíticas Microfluídicas , Peso Molecular , Polímeros/química , Reprodutibilidade dos Testes , Compostos de Silício/química , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por MatrizRESUMO
Given the existence of cell heterogeneity, single cell analysis is undergoing a rapid expansion for life science and precision medicine. Recent numerous innovations in analytical platforms and instruments have re-energized the field and led to the emergence of single cell omics with high sensitivity, throughput and multiplexity. The omics knowledge builds the bridge between underlying molecular changes and cell behavior, and facilitates a deeper understanding of disease development processes. Here, the authors highlight important achievements of single cell omics mainly including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, and discuss the biomedical applications of single cell omics in stem cells differentiation, immune cells function, nerve cells development and activity, and circulating tumor cells based cancer research.