Multi-omics approaches to decipher the interactions of nanoparticles and biological systems
Related Articles
Optical sorting: past, present and future
Optical sorting combines optical tweezers with diverse techniques, including optical spectrum, artificial intelligence (AI) and immunoassay, to endow unprecedented capabilities in particle sorting. In comparison to other methods such as microfluidics, acoustics and electrophoresis, optical sorting offers appreciable advantages in nanoscale precision, high resolution, non-invasiveness, and is becoming increasingly indispensable in fields of biophysics, chemistry, and materials science. This review aims to offer a comprehensive overview of the history, development, and perspectives of various optical sorting techniques, categorised as passive and active sorting methods. To begin, we elucidate the fundamental physics and attributes of both conventional and exotic optical forces. We then explore sorting capabilities of active optical sorting, which fuses optical tweezers with a diversity of techniques, including Raman spectroscopy and machine learning. Afterwards, we reveal the essential roles played by deterministic light fields, configured with lens systems or metasurfaces, in the passive sorting of particles based on their varying sizes and shapes, sorting resolutions and speeds. We conclude with our vision of the most promising and futuristic directions, including AI-facilitated ultrafast and bio-morphology-selective sorting. It can be envisioned that optical sorting will inevitably become a revolutionary tool in scientific research and practical biomedical applications.
A machine learning approach to leveraging electronic health records for enhanced omics analysis
Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing multimodal machine learning methods. Using two independent datasets, we showed that COMET improved the predictive modelling performance and biological discovery compared with the analysis of omics data with traditional methods. By incorporating electronic health record data into omics analyses, COMET enables more precise patient classifications, beyond the simplistic binary reduction to cases and controls. This framework can be broadly applied to the analysis of multimodal omics studies and reveals more powerful biological insights from limited cohort sizes.
Lung microbial-host interface through the lens of multi-omics
In recent years, our understanding of the microbial world within us has been revolutionized by the use of culture-independent techniques. The use of multi-omic approaches can now not only comprehensively characterize the microbial environment but also evaluate its functional aspects and its relationship with the host immune response. Advances in bioinformatics have enabled high throughput and in-depth analyses of transcripts, proteins and metabolites and enormously expanded our understanding of the role of the human microbiome in different conditions. Such investigations of the lower airways have specific challenges but as the field develops, new approaches will be facilitated. In this review, we focus on how integrative multi-omics can advance our understanding of the microbial environment and its effects on the host immune tone in the lungs.
Revealing the mechanism of cold metal transfer
Cold metal transfer (CMT) is a pioneering feeding system widely used in wire-arc additive manufacturing (WAAM) and welding. However, process optimisation remains challenging. Although CMT has been extensively applied in various industrial sectors, its underlying mechanism is poorly understood because of the complex physics of the interactions between the wire and molten material and the wire’s highly dynamic motion. To elucidate the complexity and features of CMT, we explore the dynamic behaviour and anatomy of molten materials during wire motions (withdrawal and dipping cycles) using high-speed photography at a timescale of microseconds. We reveal a crucial driving force in the melt pool and the frequent ejection of streams or particles during CMT. This study contributes to WAAM and welding by presenting the influential features of ultra-high-dynamics CMT and facilitating the progression of process optimisation.
Integrated proteogenomic characterization of ampullary adenocarcinoma
Ampullary adenocarcinoma (AMPAC) is a rare and heterogeneous malignancy. Here we performed a comprehensive proteogenomic analysis of 198 samples from Chinese AMPAC patients and duodenum patients. Genomic data illustrate that 4q loss causes fatty acid accumulation and cell proliferation. Proteomic analysis has revealed three distinct clusters (C-FAM, C-AD, C-CC), among which the most aggressive cluster, C-AD, is associated with the poorest prognosis and is characterized by focal adhesion. Immune clustering identifies three immune clusters and reveals that immune cluster M1 (macrophage infiltration cluster) and M3 (DC cell infiltration cluster), which exhibit a higher immune score compared to cluster M2 (CD4+ T-cell infiltration cluster), are associated with a poor prognosis due to the potential secretion of IL-6 by tumor cells and its consequential influence. This study provides a comprehensive proteogenomic analysis for seeking for better understanding and potential treatment of AMPAC.
Responses