Graphene Field-Effect Transistors In article 2201945, Matthew B. Coppock, Brett Goldsmith, Kiana Aran, and co-workers develop a single multiomics test for detection of respiratory diseases using scalable graphene-based transistors. The embrace of complex, real-time multiomics data, available via graphene-based transistors - converted into human understandable information with the aid of bioinformatics. This can be conceived of as the internet of biology.

The SARS‐CoV‐2 pandemic caused a public health crisis throughout the world and highlighted the need for rapid and sensitive testing as a countermeasure. A sensitive and specific biosensor platform is developed for the detection of antigen and RNA of SARS‐CoV‐2, and its variant (B1.1.529). The demonstrated biosensor platform combines unique protein catalyzed capture bioreceptors (PCCs) for antigen capture and a chimeric (RNA‐DNA) probe for RNA detection using LwaCas13a collateral cleavage activity atop graphene field effect transistors (gFETs). The reported biosensor is able to differentiate unprocessed 10 4 pfu m −1 samples of SARS‐CoV‐2 from Influenza and Rhinovirus. The limit of detection (LOD) calculated for SARS‐CoV‐2 antigen is 10 3 in buffer and 10 4 PFU mL −1 in 10% saliva, while LOD of ≈65 a m calculated for viral RNA isolate without amplification. To provide a high reliability of detection, the role of internal and external factors with respect to gate voltage is further analyzed by Principal Component Analysis (PCA). Based on PCA analysis, the authors are able to classify the samples as pathogen positive or negative ( Y > 0: Positive for pathogen, Y < 0: Negative for pathogen). The reported platform can be quickly adapted for multi‐omics and multiplexed diagnosis of continuously evolving biothreats and global pandemics.

Single-cell responses to different cues are crucial for identifying the mechanisms underlying biological processes to facilitate disease detection and therapeutic development. The migration of single cells due to various stimuli is a key way in which single cells respond to changes in the microenvironment. In this review, we first discuss different single-cell migration stimuli and recent advances in the research of combined cues for finding the dominant stimulus. Then, we examine recent technologies for studying single-cell signaling and migration, their advantages and limitations, and the applications of recent single-cell technologies in multi-omics. In addition, we discuss the application of machine-learning techniques in data acquisition from molecular and cellular data. Finally, we review the latest efforts of commercialization, remaining challenges, and future perspectives for technologies to study single-cell responses to multi-cues.