Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique with sensitivity down to the single molecule level that provides fine molecular fingerprints, allowing for direct identification of target analytes.

DNA hydrogels, which take advantage of the unique properties of functional DNA motifs, such as specific molecular recognition, programmable and high‐precision assembly, multifunctionality, and excellent biocompatibility, have attracted increasing research interest in the past two decades in diverse fields, especially in biosensing and biomedical applications. The responsiveness of smart DNA hydrogels to external stimuli by changing their swelling volume, crosslinking density, and optical or mechanical properties has facilitated the development of DNA‐hydrogel‐based in vitro biosensing systems and actuators. Furthermore, reducing the sizes of DNA hydrogels to the micro‐ and nanoscale leads to better responsiveness and delivery capacity, thereby making them excellent candidates for rapid detection, in vivo real‐time sensing, and drug release applications. Here, the recent progress in the development of smart DNA hydrogels and DNA microgels for biosensing and biomedical applications is summarized, and the current challenges as well as future prospects are also discussed.

A novel stimuli-responsive hydrogel system with liposomes serving as both noncovalent crosslinkers and functional small molecules carriers for controlled-release is developed. Liposomes can crosslink polyacrylamide copolymers functionalized with cholesterol-modified DNA motifs to yield a DNA hydrogel system, due to the hydrophobic interaction between cholesteryl groups and the lipid bilayer of liposomes. Functional information encoded DNA motifs on the polymer backbones endow the hydrogel with programmable smart responsive properties. In a model system, the hydrogel exhibits stimuli-responsive gel-to-sol transformation triggered by the opening of DNA motifs upon the presence of a restriction endonuclease enzyme, EcoR I, or temperature change, realizing the controlled-release of liposomes which are highly efficient carriers of active small molecules payloads. Two active molecules, 1,1-dioctadecyl-3,3,3,3-tetramethylindodicarbocyanine perchlorate (DiIC18(5)) and calcein, are chosen as the hydrophobic and hydrophilic model payloads, respectively, to address the feasibility of the releasing strategy. Moreover, the hydrogel exhibits injectable property as well as self-recovery behaviors.

With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.