Advances in High-Resolution Photoacoustic Imaging Techniques for Cellular Visualization

Advances in High-Resolution Photoacoustic Imaging Techniques for Cellular Visualization | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 7 May 2025 V1 Latest version Share on Advances in High-Resolution Photoacoustic Imaging Techniques for Cellular Visualization Authors : Hyunjun Kye , Moon Sung Kang , Dongyoung Jo , Byullee Park , Hee Jeong Jang , Jeesu Kim , and Dong-Wook Han 0000-0001-8314-1981 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174660679.99333245/v1 Published VIEW Version of record Peer review timeline 415 views 155 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Photoacoustic imaging is an advanced biomedical imaging technique that combines optical and ultrasound imaging to provide functional molecular information about biological tissues in vivo. In recent years, it has gained increasing attention across various biomedical applications, with notable expansion into high-resolution microscopy for cellular imaging. This review highlights recent advancements in high-resolution photoacoustic imaging techniques aimed at visualizing cellular structures. By reviewing key system configurations and their resulting images, we offer insights into current progress and discuss future directions for high-resolution photoacoustic imaging in cellular applications. Advances in High-Resolution Photoacoustic Imaging Techniques for Cellular Visualization Hyunjun Kye 1,3,§ , Moon Sung Kang 2,§ , Dongyoung Jo 1,§ , Byullee Park 3 , Hee Jeong Jang 2 , Jeesu Kim 1,* , and Dong-Wook Han 1,2,* 1 Department of Cogno-Mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea 2 Institute of Nano-Bio Convergence, Pusan National University, Busan 46241, Republic of Korea 3 Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea § These authors contributed equally to this work Corresponding authors Dong-Wook Han: [email protected] Jeesu Kim: [email protected] Abstract (250 words) Photoacoustic imaging is an advanced biomedical imaging technique that combines optical and ultrasound imaging to provide functional molecular information about biological tissues in vivo . In recent years, it has gained increasing attention across various biomedical applications, with notable expansion into high-resolution microscopy for cellular imaging. This review highlights recent advancements in high-resolution photoacoustic imaging techniques aimed at visualizing cellular structures. By reviewing key system configurations and their resulting images, we offer insights into current progress and discuss future directions for high-resolution photoacoustic imaging in cellular applications. Keywords Photoacoustic imaging, High-resolution imaging, Cellular imaging, Microscopy 1. Introduction Photoacoustic imaging (PAI) is an advanced biomedical modality that visualizes the optical absorption properties of biological tissues with acoustic resolution [1]. By harnessing its unique signal generation mechanism, PAI offers functional molecular information, making it a powerful tool in biomedical research. Since its first successful application in the functional imaging of small animals in vivo [2], PAI has been widely applied across various biomedical fields, including drug delivery monitoring [3], therapy assessment [4], and contrast-enhanced imaging [5]. The foundation of PAI lies in the photoacoustic (PA) effect, discovered by Alexander Graham Bell in 1880 (Fig. 1) [6]. The PA effect describes the generation of acoustic waves following the absorption of electromagnetic energy, which occurs through the following sequence: (1) a short laser pulse, typically a few nanoseconds in duration, illuminates the target tissue; (2) optical absorbers within the tissue absorb the incident light; (3) the absorbed energy is converted into heat, leading to rapid thermoelastic expansion; (4) the tissue subsequently relaxes back to its original state; and (5) the cyclic expansion and contraction generate acoustic waves. These waves are detected by ultrasound (US) transducers and reconstructed into images that map the optical absorption characteristics of the tissue. Although optical imaging techniques such as fluorescence imaging [7] also visualize molecular information, PAI achieves significantly greater imaging depths owing to the much lower scattering of acoustic waves in soft tissues [8]. Figure 1. Schematic of PA imaging. PA, photoacoustic; US, ultrasound; TR, transducer. The generation of PA waves is closely linked to the optical absorption characteristics of biological tissues and strongly depends on the wavelength of the incident light [9]. With appropriate wavelength selection, PAI can specifically target a range of intrinsic chromophores, including hemoglobin [10], melanin [11], lipids [12], cytochromes [13], and cell nuclei [14], as well as exogenous contrast agents used for contrast-enhanced imaging [15]. One of the major advantages of PAI over other biomedical imaging modalities is its scalability, achieved by coupling optical and acoustic focal zones to enable imaging across multiple spatial scales [16]. However, a fundamental trade-off exists between imaging depth and spatial resolution [17]. To image deeper tissues, the system must be configured to detect PA waves that travel longer distances from deep-seated tissue to the surface. This typically involves using marginally focused high-power laser illumination and low-frequency US transducers (typically 2–10 MHz), which allow imaging depths of 1–10 cm but at the cost of reduced spatial resolution (on the order of several hundred micrometers). PA computed tomography (PACT) utilizes such configurations to facilitate deep-tissue imaging [18]. PACT systems often employ various US transducer array geometries to enable rapid image acquisition, making them particularly suited for human applications [19]. Consequently, PACT has been integrated with conventional US imaging (USI) in clinical studies [20]. In contrast, PA microscopy (PAM) is designed to achieve high-resolution imaging at relatively shallow depths [9]. PAM systems tightly focus the optical and/or acoustic zones at the micrometer scale, enabling spatial resolutions of a few micrometers. Single-element, high-frequency US transducers (typically 20–50 MHz) are employed to detect high-frequency acoustic waves, allowing for the detailed visualization of small structures [21]. However, due to the strong attenuation of high-frequency acoustic waves in biological tissues, the imaging depth of PAM is limited to a few millimeters. Consequently, PAM has been widely used for high-resolution imaging of microvasculature in the ear, eye, and brain of small animals such as mice [22-24]. Recently, considerable efforts have been made to advance PAM systems for cellular-level imaging [25]. Cellular imaging is crucial for life science research, as it enables the investigation of key biological processes such as apoptosis, drug delivery, and cellular responses to therapeutic interventions [26, 27]. Achieving high-resolution PA imaging at the cellular scale requires optimization of several system components, including laser delivery mechanisms, scanning strategies, and imaging parameters. This review provides a comprehensive overview of recent advancements in PAM systems aimed at cellular visualization. We examine different PAM configurations and data acquisition techniques developed to achieve high-resolution imaging. Furthermore, we summarize representative applications that utilize various optical wavelengths and discuss the feasibility of PAM for cellular imaging. By presenting the latest progress in high-resolution PA imaging, this review aims to offer insights into current challenges and outline future directions for advancing PAI toward cellular-level investigations. 2. Scanning methods for high-resolution photoacoustic microscopy Optical-resolution PAM (OR-PAM) is a widely adopted configuration in PAI for achieving high-resolution imaging. In OR-PAM, the laser light is tightly focused onto a small spot, creating an optical focal zone significantly smaller than the acoustic focal zone [28]. PA waves are generated within this confined optical focus, enabling micrometer-scale spatial resolution. To acquire PA signals across the imaging region, OR-PAM typically employs a raster-scanning mechanism: a focused laser beam is scanned point-by-point over the sample in a predefined rectangular pattern. Each laser pulse induces localized PA waves, which are detected by a single-element US transducer and processed to reconstruct an image. Two primary scanning techniques are commonly implemented in OR-PAM: mechanical scanning and optical scanning (Fig. 2). In mechanical scanning, either the imaging probe or the sample is physically translated in a stepwise manner to move the laser focus across the imaging region. This approach utilizes piezoelectric or linear actuators for precise motion control while keeping the laser beam stationary. The stability of the optical path enables the acquisition of high-resolution images, making mechanical scanning particularly suitable for fixed samples, such as histological tissue sections. However, the reliance on physical motion limits the imaging speed, rendering this approach less ideal for real-time imaging. Additionally, rapid mechanical movements may introduce vibrations, potentially degrading image quality. Consequently, mechanical scanning is less practical for in vivo imaging where real-time monitoring is required. In contrast, optical scanning employs a pair of mirrors to rapidly steer the laser beam across the imaging region, eliminating the need for mechanical movement. This method minimizes mechanical stress on the sample and enhances system stability, making it particularly advantageous for real-time in vivo imaging of dynamic biological processes, such as hemodynamic studies. However, optical scanning can introduce distortions in the optical path, which may degrade image quality. To address this issue, precise calibration is essential for accurate image reconstruction. Additionally, the field of view (FOV) is inherently limited by the deflection range of the mirrors, often necessitating mosaic reconstruction techniques to cover larger areas. To overcome the limitations of individual scanning strategies, hybrid scanning approaches have been developed, integrating both mechanical and optical scanning methods. In these systems, a coarse mechanical stage enables large-area sample translation, while a fine optical-scanning mechanism performs high-speed imaging within a smaller region of interest (ROI). This hybrid strategy enhances both imaging speed and resolution, expanding the applicability of OR-PAM to a broader range of biomedical studies. The following sections provide an overview of representative OR-PAM systems, with particular emphasis on their applications in cellular imaging. Figure 2. Schematic of scanning mechanisms for high-resolution photoacoustic imaging. 3. Histopathological photoacoustic imaging with ultraviolet wavelengths One of the major applications of OR-PAM has been histopathological imaging of sectioned tissues. Using ultraviolet (UV) light, which is selectively absorbed by nucleic acids such as RNA and DNA in cell nuclei, PAI can produce histology-like images comparable to those obtained through conventional optical microscopy with hematoxylin and eosin (H&E) staining. Notably, unlike traditional staining methods, PAI enables the acquisition of multilayered images without requiring any staining procedures. Consequently, substantial efforts have been devoted to developing OR-PAM systems based on UV illumination (UV-PAM) for the label-free cellular analysis of tissue specimens (Table 1). Table 1. Summary of UV-PAM for label-free cellular imaging of tissues. PAM, photoacoustic microscopy; UV, ultraviolet; \(\lambda\), wavelength; PRF, pulse repetition frequency; \(E_{p}\), pulse energy;\(f_{c}\), center frequency; \(A_{\text{scan}}\), scanning area;\(t_{\text{scan}}\), scanning time; \(\delta\), spatial resolution; MEMS, micro-electromechanical systems. \(\lambda\) PRF [kHz] \(E_{p}\) Type \(f_{c}\) Type \(A_{\text{scan}}\) \(t_{\text{scan}}\) \(\delta\) Target Ref 266 10 35 Ring 50 Mechanical 0.25×0.25 ~3 * 0.70 Mouse small intestine tissue [29] 266 10 35 Unfocused 50 Mechanical 5×5 ~100 0.33 Human breast cancer tissue [30] 266 5 10 Customized (unfocused) 45 Mechanical 3×3 ~32 32.4 Glioma xenografted mouse brain tissue [31] 266 10 19 Unfocused 20 MEMS mirror + Mechanimal 10×10 4 1.2 Human liver cancer tissue [32] 266 20 55 Transparent ~28 Mechanical 1×1 30 0.47 Mouse brain tissue [33] 266 10 35 Ring 42 Mechanical 1×1 11 40 Human bone tissue [34] 266 55 - Unfocused 25 Galvano mirror + Mechanimal 5×5 15 - Mouse brain tissue [35] 266 20 19 Unfocused 20 MEMS mirror + Mechanimal 0.7×1 ~0.5 1.2 Human liver cancer tissue [36] * Calculated from values provided in literature Figure 3. Optical-resolution PAM using ultra-violet light for label-free histopathological imaging of tissue. (a) Schematic of the PAM system utilizing a ring-shaped US TR. (b) PA and H&E images of mouse small intestine tissue. (c) Schematic of the PAM system using a focused US TR. (d) PA and H&E images of mouse brain tissue . The yellow lines highlighting the brain glioma region. (e) Schematic of the PAM system with optical scanning via a MEMS mirror. (f) PA and H&E images of human colon tissue, distinguishing normal and cancerous regions. (g) Schematic of the PAM system using a TUT. (h) Comparison of PA images of the mouse cerebellum obtained using the conventional OUC and the proposed TUT, alongside the corresponding H&E image. Key features are numerically labeled: (1) cerebellum layer; (2) Purkinje cell layer; and (3) granular layer. PA, photoacoustic; US, ultrasound; H&E, hematoxylin and eosin; PAM, photoacoustic microscopy; TR, transducer; CL, condenser lens; OL, objective lens; PH, pinhole; TH, tissue holder; BS, beam splitter; BE, beam expander; NDF, neutral density filter; PD, photodiode; AMP, amplifier; DAQ, data acquisition module; MEMS, microelectromechanical systems; OUC, opto-ultrasound combiner; TUT, transparent ultrasound transducer; NA, numerical aperture. The images are adapted with permission from [29, 31-33]. Among the initial UV-PAM configurations, Yao et al. demonstrated label-free PAI of RNA and DNA in cell nuclei [29]. Their system utilized an Nd:YLF laser with a wavelength of 266 nm, a pulse width of beam was delivered through the center of a ring-shaped focused US transducer with a center frequency of 50 MHz and a focal length of 7 mm (Fig. 3a). The imaging probe was immersed in a water tank, sealed with a polyethylene membrane at the bottom. The target specimens were positioned on a two-axis motorized translational stage, with a minimal scan step size of 0.31 μm. This setup produced high-resolution images with a spatial resolution of approximately 700 nm. Label-free PA images of sectioned small intestine tissues from mice were compared with corresponding H&E-stained optical images (Fig. 3b). The results demonstrated that the PA imaging system effectively visualized cell nuclei with structural features comparable to those seen in conventional histology, highlighting the feasibility of UV-PAM for label-free histopathological imaging. The same group later refined their approach to achieve imaging of unprocessed human breast tissue [30]. Unlike the previous configuration, this system delivered the laser beam from below the sample and detected PA waves from the opposite side using a US transducer. A three-axis motorized translational stage was used for mechanical scanning to acquire volumetric data. They obtained label-free PA images of unprocessed human breast tissue with a spatial resolution of approximately 330 nm. These high-resolution images allowed visualization of key cellular features, such as nuclear size and packing density, which hold potential for the development of automated malignancy detection algorithms. Song et al. further explored the clinical potential of UV-PAM by developing a system to delineate cancerous regions (Fig. 3c) [31]. Their setup used a 266-nm nanosecond-pulsed laser directed at the bottom of the specimen to generate PA signals. A 45-MHz water-immersed US transducer detected the resulting PA waves from the opposite side. The system was mounted on a two-axis translational stage, with a scanning step size of 50 nm. Label-free PA images of human glioma xenografted mouse brain tissues were obtained and compared with H&E-stained images (Fig. 3d). Quantitative evaluation demonstrated that the system could accurately identify tumor regions based on nuclear size and tumor area, highlighting its potential as an intraoperative tool for assessing cancer margins. While motorized scanning-based UV-PAM systems offer high-quality imaging, their slow imaging speed remains a challenge. To address this, Baik et al. introduced an optical-scanning method for high-speed UV-PAM (Fig. 3e) [32]. This system used a waterproof one-axis microelectromechanical systems (MEMS) scanner for rapid scanning, complemented by a two-axis linear translational stage to extend the imaging range to 24×24 mm². The system employed 266 nm laser pulses with a pulse width of 0.8 ns at a pulse repetition frequency of 10 kHz. The generated PA waves were detected by a 20 MHz unfocused US transducer positioned on the same side as the laser source. An opto-ultrasound beam combiner (OUC) was used for coaxial alignment of the optical and acoustic beam paths, improving the signal-to-noise ratio (SNR) to Using this approach, the researchers successfully obtained H&E-compatible PA images of colon and liver specimens from cancer patients (Fig. 3f). These images delineated the boundary between normal and cancerous regions, demonstrating significant potential for intraoperative histopathology with minimal tissue preparation. In a subsequent study, Kim et al. optimized the coaxial alignment by developing a transparent US transducer with 61.1% transparency at 266 nm (Fig. 3g) [33]. Compared to previous ring-shaped transducers or OAC designs, the transparent transducer improved the numerical aperture (NA) to 0.38. High-resolution imaging of cancerous animal tissues was achieved, with a spatial resolution of approximately 470 nm (Fig. 3h). The PA images showed a high correlation with conventional H&E-stained images, reinforcing the potential of UV-PAM for rapid and accurate identification of cancerous cells. Figure 4. Deep learning models for generating virtually stained PA images that mimic H&E-stained optical images. (a) Schematic illustration of the transformation from PAM to H&E images using the CycleGAN architecture. Two generators (\(G\) and \(F\)) convert original images into virtual images, while two discriminators (\(D_{X}\) and\(D_{Y}\)) differentiate between original and virtual images. The generators also convert the virtual images back to recovered versions. (b) Virtually stained PA images and corresponding H&E images of mouse brain tissue. (c) Deep learning framework for automated histological image analysis, encompassing virtual staining, segmentation, and classification. (d) Segmentation of cell nuclei and a 3D scatter plot of extracted features from virtually stained PA images and corresponding H&E images, using human liver tissue. PA, photoacoustic; PAM, photoacoustic microscopy; H&E, hematoxylin and eosin. The images are adapted with permission from [35, 36]. Despite advancements in label-free UV-PAM, H&E-stained optical microscopy remains the gold standard for histopathological examination. To facilitate the clinical translation of UV-PAM, researchers have explored deep learning-based virtual staining techniques to mimic H&E-stained images. Cao et al. demonstrated an unsupervised generative adversarial network (GAN) for virtual staining [34]. Their system used a 42-MHz US transducer and a 266-nm pulsed laser to acquire high-resolution PA images of bone tissue, which were then processed to generate pseudocolor histological images resembling traditional H&E staining. Similarly, Kang et al. demonstrated the feasibility of virtual staining by employing a cycle-consistent GAN (CycleGAN) to translate PA images into H&E-equivalent images (Fig. 4a) [35]. In their model, two generators ( \(G\) and \(F\) in Fig. 4a) competitively generate virtual H&E and PA images, while fake discriminators ( \(D_{X}\) and \(D_{Y}\) in Fig. 4a) assess their authenticity. They trained the four deep learning models (two generators and two discriminators) to minimize four types of loss functions: adversarial loss ( \(\mathcal{L}_{A}\) in Fig. 4a), cycle-consistency loss ( \(\mathcal{L}_{\text{CC}}\) in Fig. 4a), identity loss, and structural similarity index measure loss ( \(\mathcal{L}_{\text{SSIM}}\) in Fig. 4a). These results confirmed the effectiveness of the technique in producing histologically accurate images (Fig. 4b). Expanding on these approaches, Yoon et al. introduced a series of deep learning models for virtual staining, feature segmentation, and classification (Fig. 4c) [36]. Using PA images of human liver tissue, their model successfully generated H&E-equivalent images and quantified cancer-related parameters such as cell area, cell count, and inter-nuclear distance (Fig. 4d). These studies highlight the potential of deep learning techniques to enhance the diagnostic capabilities of UV-PAM, enabling pathologists to identify cancerous features without the need for labor-intensive and time-consuming tissue preparation. 4. Photoacoustic imaging of organic molecules with infrared wavelengths PAI in the mid-infrared (MIR) spectral region offers unique potential for visualizing organic molecular information within biological samples. This capability arises from the strong optical absorption of MIR light by the vibrational modes of chemical bonds such as C–H, O–H, and N–H [37]. These bonds are abundant in biological molecules, including proteins, lipids, and water, enabling the generation of PA signals when tissues are illuminated by MIR light. Despite its promising potential, conventional MIR-PAM has limitations in terms of reduced lateral resolution due to greater optical diffraction. Additionally, water exhibits strong absorption in the MIR region, resulting in high background signals and reduced imaging contrast. Nevertheless, the ability to noninvasively visualize molecular compositions without the use of contrast agents has valuable potential for characterizing and differentiating biological tissues [38-40]. In this section, we review recent advances in MIR-PAM systems designed for cellular-level biochemical analysis, as summarized in Table 2. Table 2. Summary of MIR-PAM for label-free cellular imaging of tissues. PAM, photoacoustic microscopy; MIR, mid-infrared; \(\lambda\), wavelength; PRF, pulse repetition frequency; \(E_{p}\), pulse energy;\(f_{c}\), center frequency; \(A_{\text{scan}}\), scanning area;\(t_{\text{scan}}\), scanning time; \(\delta\), spatial resolution. \(\lambda\) PRF [kHz] \(E_{p}\) Type \(f_{c}\) Type \(A_{\text{scan}}\) \(t_{\text{scan}}\) \(\delta\) Target Ref 3240, 6050 1 - Focused 25 Mechanical 1×1 120 0.26 Lipids and proteins in fibroblast cell [41] 3400–11000 10 800 * Focused 25 Mechanical 5×5 16 5.3 Lipids and proteins in pancreatic mouse tissues [42] 3506, 6452 100 - Focused 25 Mechanical 5×5 35 5.3 White adipose tissue from mouse [43] 3059, 3531, 5764, 6452, 6826, 8540, 9217, 9497 100 - Focused 20 Mechanical 5×5 16 2.5 Carotid atherosclerosis [44] * Calculated from values provided in literature; § Converted form wavenumber To overcome the spatial resolution limitations of MIR-PAM, Shi et al. introduced a novel localization technique involving additional UV laser illumination [41]. In their system, a pulsed MIR laser selectively elevated the temperature of chromophore-specific regions. A tightly focused, confocal UV pulsed laser (266 nm) was then used to detect the transient temperature rise through PA signal generation. By measuring the PA signals from the UV laser before and after MIR illumination, the researchers calculated the fractional change in PA amplitude, a parameter directly correlated with the MIR absorption characteristics of the sample. Because the focal zone of the UV beam was much smaller than that of the MIR beam, the lateral resolution of the resulting image was determined by the UV light, enabling high-resolution imaging of MIR-absorbing molecules. Using this approach, the researchers successfully acquired multispectral PA images of lipids (at 3,420 nm), proteins (at 6,050 nm), and nucleic acids (at 266 nm) in single fibroblast cells. These results demonstrated the feasibility of label-free, high-resolution molecular differentiation at the cellular level, advancing the utility of MIR-PAM for cellular and biochemical imaging applications. Figure 5. MIR-PAM for WAT imaging. (a) Schematic illustration of MIR-PAM. (b) Label-free identification of crown-like structures (red circles) in WAT extracted from mice. PA, Photoacoustic; MIR, mid-infrared; PAM, photoacoustic microscopy; WAT, white adipose tissue; TR, ultrasound transducer; OL, objective lens; QCL, quantum cascade laser; N 2 , nitrogen; CFM, confocal microscopy; BODIPY, boron dipyrromethene. The images are adapted with permission from [43]. Pleitez et al. also demonstrated the use of MIR-PAM to visualize cellular carbohydrates, lipids, and proteins in cells (Fig. 5a) [42]. They employed a tunable pulsed quantum cascade laser for PA wave generation, covering a spectral range of 3,400–11,000 nm (corresponding to wavenumbers of 2,941–909 cm⁻¹). For high-resolution imaging, a 0.5-NA reflective objective was used to focus the laser beam, and PA waves were detected by a 25-MHz focused US transducer. The performance of the system was validated by comparing the contrast of carbohydrate bonds in the pancreatic mouse tissues. Building on this, Ko et al. extended the application of MIR-PAM by investigating inflammation-related changes in excised white adipose tissue (WAT) from mice [43]. Their system produced label-free PA images of adipocytes, which were compared to conventional boron dipyrromethene (BODIPY)-labeled confocal microscopy (Fig. 5b). The multispectral PA images clearly delineated lipid-rich regions within adipocytes at 3,506 nm (2,852 cm⁻¹), while surrounding proteins and water appeared at 6,452 nm (1,550 cm⁻¹). Notably, the PA images revealed crown-like structures in the WAT (highlighted in red circles in Fig. 5b), characteristic of inflammation. These structures were confirmed by confocal microscopy, demonstrating the capability of MIR-PAM to detect inflammation in adipose tissue without the need for staining or labeling. Expanding on these capabilities, Visscher et al. applied the same MIR-PAM system to analyze atherosclerotic plaques in tissues excised from human patients undergoing carotid endarterectomy [44]. They performed multispectral PA imaging using wavelengths between 3,410–3,610 nm and 5,750–11,110 nm (corresponding to wavenumbers of 2,932–2,770 cm⁻¹ and 1,739–900 cm⁻¹), enabling visualization of lipids, proteins, cholesterol, and carbohydrates in cross-sectioned carotid artery samples. The resulting images revealed necrotic regions consistent with H&E-stained histology and showed a strong correlation with lipid distributions detected by optical microscopy after staining. These findings underscore the potential of MIR-PAM as a powerful, label-free imaging tool for biochemical characterization, particularly in the assessment of vascular pathologies such as lipid accumulation in blood vessels, a key marker for early detection and monitoring of atherosclerosis. 5. Photoacoustic microscopy to achieve deeper imaging depth A key advantage of PAI over pure optical techniques is its superior imaging depth. To further enhance this capability, various strategies have been developed to extend the imaging depth of PAI while maintaining cellular-level detail (Table 3). One such approach is acoustic-resolution PAM (AR-PAM), which sacrifices optical resolution to achieve deep-tissue imaging in the millimeter range [45-47]. Unlike OR-PAM, AR-PAM typically uses high-power lasers with lower pulse repetition rates to deliver light deeper into tissue. In this configuration, the spatial resolution is determined by the focal zone of the US transducer that detects the generated PA waves. Table 3. Summary of PAM for label-free cellular imaging with enhanced imaging detph. PAM, photoacoustic microscopy; \(\lambda\), wavelength; PRF, pulse repetition frequency; \(E_{p}\), pulse energy;\(f_{c}\), center frequency; \(A_{\text{scan}}\), scanning area;\(t_{\text{scan}}\), scanning time; \(\delta\), spatial resolution. \(\lambda\) PRF [kHz] \(E_{p}\) Type \(f_{c}\) Type \(A_{\text{scan}}\) \(t_{\text{scan}}\) \(\delta\) Target Ref 532 0.5 3.6 [μJ/mm 2 ] Focused 50 Mechanical 4×3 4 29.6 Brain organoid [48] 266 5 18 [nJ] Focused 22 Mechanical 3×3 ~27 1.13 Mouse brain section [49] 532 20 15 [mJ/cm 2 ] Focused 19 Mechanical 2.2×2.2 ~40 * 2.3 Brain organoid [50] * Calculated from values provided in literature Figure 6. PA approaches for deeper imaging depth. (a) Schematic illustration of AR-PAM. (b) BFM and PA images of human midbrain-like organoids. (c) Schematic configuration, phase distribution, and normalized light intensity distribution with and without the metalens. (d) PA images of sectioned mouse brain tissue with and without the metalens. (e) PA images of unprocessed thick tissues with and without the metalens. (f) Schematic of PA imaging with extended DOF. (g) PA images showing neuromelanin content in forebrain and midbrain organoids. PA, photoacoustic; AR-PAM, acoustic resolution photoacoustic microscopy; BS, beam splitter; PD, photodetector; AMP, amplifier; PC, personal computer; BFM, bright-field optical microscopy; 3D, three-dimensional; ML, metalens; OL, objective lens; TR, ultrasound transducer; DOF, depth of focus. The images are adapted with permission [48-50]. Englert et al. demonstrated the potential of AR-PAM for deep-tissue imaging by visualizing three-dimensional (3D) structures within human brain organoids [48]. Their system employed a 50-MHz focused transducer and a 532-nm laser (Fig. 6a), achieving a lateral resolution of approximately 30 μm and an axial resolution of 8.6 μm. Using a raster-scanning motor stage, they acquired images over a 4×3 mm² area in just 4 min. The team successfully visualized neuromelanin distribution in midbrain-like organoids at a depth of 1 mm. The neuromelanin content observed in the PA images of six histological slices showed a linear correlation with values obtained from histological analysis (Fig. 6b). These results highlight the potential of AR-PAM for studying neuromelanin-related pathology in brain organoids, with significant implications for Parkinson’s disease research. An alternative technique for achieving deeper high-resolution imaging involves extending the depth of focus (DOF) of the optical beam. In standard OR-PAM, a tightly confined focal zone is used to maximize spatial resolution; however, this also limits the DOF. By engineering a longer DOF, maintaining a high image resolution over a larger depth range becomes possible. Song et al. addressed this limitation by integrating a metalens, which introduces a predesigned phase distribution to the incoming light, effectively extending the DOF from 21 to 290 μm (Fig. 6c) [49]. The previously discussed configuration was utilized to evaluate the effectiveness of metalens [31]. In imaging thin mouse brain sections (7 μm), PA images obtained with the metalens were comparable in quality to those acquired using a conventional setup, confirming its effectiveness (Fig. 6d). More importantly, for thicker, unprocessed brain tissue samples, the metalens significantly improved image quality across a broader depth range, enabling clear visualization of cell nuclei beyond the typical DOF of traditional systems (Fig. 6e). Building on this concept, Barulin et al. recently demonstrated a metalens-based volumetric PAI system for live human brain organoids [50]. Their design extended the DOF to 580 μm (Fig. 6f), achieving a lateral resolution of 2.3 μm and an axial resolution of 90 μm. This approach enabled detailed visualization of neuromelanin within human forebrain and midbrain organoids at depths exceeding 0.5 mm (Fig. 6g). These results highlight the potential of PAI for volumetric imaging in neurodegenerative disease research and drug discovery applications. 6. Super-resolution photoacoustic imaging OR-PAM has been widely used for high-resolution PAI. However, the spatial resolution is fundamentally constrained by the optical diffraction limit, which restricts the visualization of structures smaller than the optical beam spot. To overcome this limitation, several super-resolution PAI (SR-PAI) techniques have been developed to obtain images beyond the optical diffraction limit (Table 4). Table 4. Summary of super-resolution PA imaging systems. PA, photoacoustic; \(\lambda\), wavelength; PRF, pulse repetition frequency;\(E_{p}\), pulse energy; \(f_{c}\), center frequency;\(A_{\text{scan}}\), scanning area; \(t_{\text{scan}}\), scanning time;\(\delta\), spatial resolution. \(\lambda\) PRF [kHz] \(E_{p}\) Type \(f_{c}\) Type \(A_{\text{scan}}\) \(t_{\text{scan}}\) \(\delta\) Target Ref 532 2.35 100 Focused 40 Mechanical 6.4×6.4 112 - Mitochondria + Melanosome [51] 266 10 25 Focused 50 Mechanical 430×130 35.7 0.64 Mouse brain tissue [52] 266 10 20 Unfocused 20 MEMS mirror + Mechanical 500×500 70 1.2 Mouse brain tissue [53] 780 50 200 Focused 25 Galvano mirror + Mechanical 250×250 6.9 0.94 Melanosome [54] * calculated from the values provided in the literature One of the pioneering approaches for SR-PAI was introduced by Danielli et al. [51]. They utilized nonlinear PA effects, such as optical saturation and thermal nonlinearity, to enhance spatial resolution. In their system, a 532-nm pulsed laser was configured to deliver a train of four pulses, each with progressively increasing energy at each pixel. This pulse sequence induced nonlinear PA responses, from which high-order signal components were extracted to achieve a spatial resolution of 88 nm. Using this technique, subcellular structures in mitochondria and fibroblasts were successfully visualized. Subsequently, the same group introduced a technique based on nonlinear Grüneisen relaxation [52]. This dual-laser system used one laser to locally heat the tissue, thereby increasing the Grüneisen parameter, while a second laser generated the PA signal. This configuration enabled resolution beyond the optical diffraction limit, achieving a lateral resolution of approximately 0.64 μm in mouse brain tissue slices. Although these methods have successfully achieved significant improvements in spatial resolution, the complexity involved in inducing nonlinear effects presents technical challenges. Another innovative direction in SR-PAI is the use of expansion microscopy principles to physically enlarge biological samples, allowing conventional systems to resolve features beyond their typical resolution limits. For instance, Kim et al. demonstrated SR-PAI on expanded mouse brain slices [53]. They used a UV-PAM system, similar to the previously discussed setup, achieving a lateral resolution of 1.2 μm [32]. In their study, mice were transcardially perfused with a fixative solution, and brain tissues were extracted, sectioned into 40-μm slices, incubated in a monomer solution, and embedded between cover glasses (Fig. 7a). After a 1.9-fold physical expansion, the PA images clearly revealed individual nuclei, in contrast to the marginal delineation observed in unprocessed tissues (Fig. 7b). Similarly, Li et al. employed a near-infrared (NIR) PAM system with a 780 nm laser to visualize melanosomes in expanded B16-F10 melanoma cells [54]. They achieved PA imaging of expanded cells at magnification levels of 8×, 27×, and 64× (Fig. 7c). Analysis of the image profiles indicated that the measured sizes of the expanded melanosomes aligned well with the expected values from the literature, which report an average melanosome diameter of approximately 0.5 μm (Fig. 7d). Collectively, these emerging SR-PAI techniques represent powerful tools for subcellular and molecular-level analysis. Figure 7. Super-resolution PA imaging results with expanded tissues. (a) Photograph of mouse brain sections before and after expansion. (b) PA images of original and expanded mouse brain sections. (c) PA images of melanoma cells with varying expansion rates. (d) Profiles of normalized PA amplitude in the enlarged images shown in (b). PA, Photoacoustic. The images are adapted with permission from [53, 54] 7. Discussion and outlook PAI has rapidly evolved into a powerful imaging modality for visualizing biological tissues at cellular and subcellular levels, offering a unique combination of optical contrast and acoustic resolution. This review outlines recent advancements in PAI systems, highlighting their growing roles in both functional and structural biomedical imaging [55]. However, despite these advances, several challenges and opportunities remain. One persistent limitation is the trade-off between imaging depth and spatial resolution. Techniques such as AR-PAM, extended DOF using metalenses, and tissue-clearing-compatible imaging may enable deeper cellular imaging while maintaining spatial resolution. Moreover, increasing the temporal resolution of PAI through faster scanning mechanisms or high-repetition-rate laser sources is critical for capturing dynamic biological processes in vivo [56-60]. A promising direction for future research lies in the integration of artificial intelligence (AI) and deep learning algorithms into the PAI pipeline [61-66]. AI can be applied to improve image reconstruction from sparse or noisy data, enable real-time spectral unmixing, generate virtual histological staining, and extract quantitative cellular biomarkers with high accuracy. These tools can enable automated image interpretation, which is crucial for applications such as intraoperative guidance, pathology triage, and high-throughput screening in drug discovery. Multispectral and functional PAI of cells is emerging as a valuable tool for delineating biomolecular composition and physiological states. By tuning the excitation wavelengths, PAI can selectively visualize various endogenous chromophores without the need for exogenous contrast agents. Future efforts should focus on developing a multispectral laser source design, which is crucial for functional analysis [67, 68]. These functional capabilities open new avenues for investigating metabolism, hypoxia, and cellular heterogeneity. Another emerging area is PAI of bioprinted or micro-patterned cells, which has gained attention in tissue engineering and regenerative medicine [69-71]. As bioprinting technologies enable the spatially controlled deposition of cells, biomaterials, and growth factors, PAI offers a non-invasive platform to monitor the viability, distribution, functional state, and maturation of these engineered tissues in real-time. Future studies should explore tailored contrast mechanisms for tracking specific cell types or scaffold components, as well as integrating PAI systems into bioreactors and organ-on-chip platforms for continuous monitoring. Additionally, the development of non-contact PAI techniques, particularly PA remote sensing (PARS), offers a paradigm shift by eliminating the need for physical coupling media [72-75]. Non-contact systems can reduce mechanical interference between optical illumination and acoustic detection in conventional PAI systems. While current PARS faces limitations in terms of signal sensitivity and penetration depth, its ability to achieve high-resolution images with a non-contact configuration has been gaining attention. Future research will also benefit from the fusion of PAI with complementary imaging modalities, such as fluorescence, optical coherence tomography (OCT), and USI [76-78]. These multimodal imaging setups can provide synergistic structural and molecular information. Furthermore, advancements in miniaturization and fiber-based PAI probes will accelerate the transition of this technology into endoscopic, point-of-care, and intraoperative applications. In summary, continued innovations in PAI system design, functional imaging strategies, artificial intelligence integration, and multimodal compatibility are expected to expand the scope and impact of PAI in biomedical research. As these technologies mature, PAI is poised to become a key tool for fundamental studies in cell biology. Acknowledgment This work was supported by a 2-Year Research Grant of Pusan National University References [1] Kim C, Favazza C, and Wang L V, ” In Vivo Photoacoustic Tomography of Chemicals: High-Resolution Functional and Molecular Optical Imaging at New Depths,” Chem. Rev. 2010, 110(5) 2756-2782.[2] Wang X, Pang Y, Ku G, Xie X, Stoica G, and Wang L V, ”Noninvasive Laser-Induced Photoacoustic Tomography for Structural and Functional In Vivo Imaging of the Brain,” Nat. Biotechnol. 2003, 21(7) 803-806.[3] Park B, Park S, Kim J, and Kim C, ”Listening to Drug Delivery and Responses via Photoacoustic Imaging,” Adv. Drug Deliv. 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Information & Authors Information Version history V1 Version 1 07 May 2025 Peer review timeline Published VIEW Version of Record 1 Sep 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cellular imaging high-resolution imaging microscopy photoacoustic imaging Authors Affiliations Hyunjun Kye Sungkyunkwan University - Suwon Campus View all articles by this author Moon Sung Kang Pusan National University View all articles by this author Dongyoung Jo Pusan National University View all articles by this author Byullee Park Sungkyunkwan University - Suwon Campus View all articles by this author Hee Jeong Jang Pusan National University View all articles by this author Jeesu Kim Pusan National University View all articles by this author Dong-Wook Han 0000-0001-8314-1981 [email protected] Pusan National University View all articles by this author Metrics & Citations Metrics Article Usage 415 views 155 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hyunjun Kye, Moon Sung Kang, Dongyoung Jo, et al. Advances in High-Resolution Photoacoustic Imaging Techniques for Cellular Visualization. Authorea . 07 May 2025. 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