1. Introduction

The use of live cells or microorganisms in optics and photonics is rapidly becoming an emerging polarization in biomedical and biotechnology areas in recent years. Single or ensembles of biological cells can behave as natural optical devices and their behavior is exploitable for medical diagnostics purposes. For example, cells can act as biolenses for imaging at nanoscale or optical tweezers for manipulating the matter at nanoscale. Some cells can produce photonics jets or host optical micro-resonators for lasing or even lithography purposes. Liquid medium with red blood cells can also sustain optical propagation and nonlinear effects. This feature issue allows for archival publication of the most recent work in this fascinating interdisciplinary field that is across biology and photonics, which opens new scenarios for biomedical applications.

2. Summary of contributions

After rigorous peer-review, we selected 14 papers to be included in this feature issue. The following summary highlights the scope of excellent work.

2.1 Microscopy

Advances in microscope techniques for imaging of living cells have always been a hot pursuit in biophotonics. De Mercado et al. presented label-free imaging of HeLa cells using astigmatism microscopy [1]. They found that astigmatism caused by the cell nucleus could lead to aberrations up to hundreds of nanometers. For stem-cell imaging, Jiang et al. proposed time-lapse phase-contrast microscopy [2]. They investigated the dynamic structural changes in the actin cytoskeleton during cell migration. Quantitative phase imaging, performed by numerical hologram reconstruction, is another well-recognized technique for investigation of intracellular structures and their dynamic processes. To improve the accuracy and efficiency of hologram reconstruction, Jaferzadeh et al. proposed fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model [3]. Deep-learning-based microscopy is suitable for precise cell assessment and classification. Park et al. reported dynamic full-field optical coherence microscopy combined with machine learning [4]. Their method could be used for cell viability assessment at accuracy of 93.92 ± 0.86%. Liu et al. presented a method for bacteria classification via hyperspectral microscopy coupled with machine learning [5], with the classification accuracy of two bacterial species up to 98.06%.

2.2 Biosensors

Optical techniques have enhanced the speed and resolution of biosensors for developing new detecting methods or useful tool boxes. Hereby, Chen et al. presented a signal-amplified method for detecting Cucurbitacin E in cells via directly spreading cells on a plasmonic interface of a traditional SPR sensor [6]. The invited review by De Tommasi and De Luca provides an overview on the main metallization techniques applied on diatom biosilica and the diatom-based plasmonic devices applied in biochemical sensing, diagnostics and therapeutics [7].

2.3 Trapping and manipulation

Optical trapping and manipulation techniques are natively applicable in biomedical studies. Three papers included in this issue reported new optical methods of trapping and manipulating cell-based micromotors and microlenses. Yuan et al presented the use of optical tweezers to actuate cellular micromotors [8]. By measuring the rotation rate of the micromotors, the ambient viscosity could be detected in real time. Compared with the free-space optical tweezers, fiber-based tweezers have the advantages of flexibility and miniaturization. Jiang et al. used fiber-based tweezers to trap yeast cells and manipulate them to the surface of imaging objects [9]. The trapped yeast cells acted as near-field magnifying lenses that resolved a feature size of 100 nm. To improve the biocompatibility, Chen et al. utilized red blood cells (RBCs) as endogenous microlenses [10]. The focuses of the RBC microlenses were adjusted by changing the shape of the cells with optical forces, making them an adjustable and fully biocompatible optical device for diagnosis of blood disease.

2.4 Optical diagnosis and therapy

With the features of high precision and temporal-spatial resolution, optical diagnosis and therapy have been considered as a preferable technique in both laboratorial and clinical research. Wang et al. carried out a systematic study on cross-polarized diffraction image (p-DI) pairs of 3098 mature RBCs using optical cell models with varied morphology, refractive index, and orientation [11], showing the strong potential of p-DI data for rapid and accurate screening examinations of RBC shapes in routine clinical tests. Xu et al. found that the optimal channels for different diseases are different when an image photoplethysmography technology is used for disease classification and verified this conclusion in the classification model of heart disease and diabetes mellitus [12]. To address the inability for light to penetrate the skin and reach deep lesions, Wu et al. developed a polylactic acid microneedles array as a novel light transmission platform to perform in vitro evaluation regarding the effect of light therapy on skin cancer [13], showing that blue light can be transmitted by the microneedles to skin cells and effectively affect the cell viability. Using the lensing effect of intracellular lipid droplets (LDs), Pirone et al. combined the in-flow tomographic phase microscopy with numerical simulations to develop a fast, label-free, non-destructive, and high-throughput screening approach based on the analysis of the 2D phase maps recorded by a holographic flow cytometer [14], boosting the diagnosis of LD-related pathologies. Chen et al. used lightwaves at 12 wavelengths (400–900 nm) to conduct photo-biomodulation to heal diabetic foot ulcer (DFU) wounds in vitro and in vivo [15]. In a mimic DFU rat model, they found that a combination light strategy had the best healing effect and thus can be applied clinically.