# Light sheet fluorescence microscopy for monitoring drug delivery: Unlocking the developmental phases of embryos

Gagan Raju, Aymeric Le Gratiet, Giuseppe Sancataldo, Guan-Yu Zhuo, Yury Kistenev, Subir Das, Ajeetkumar Patil, Soubarno Kundu, Tribikram Patel, Nirmal Mazumder

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## ► To cite this version:

Gagan Raju, Aymeric Le Gratiet, Giuseppe Sancataldo, Guan-Yu Zhuo, Yury Kistenev, et al., Light sheet fluorescence microscopy for monitoring drug delivery: Unlocking the developmental phases of embryos. Advanced Drug Delivery Reviews, 2025, 218, pp.115520. 10.1016/j.addr.2025.115520. hal-04921395

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Submitted on 8 Oct 2025

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Light sheet fluorescence microscopy for monitoring drug delivery:
Unlocking the developmental phases of embryos☆

Gagan Raju a, Aymeric Le Gratiet b, Giuseppe Sancataldo c, Guan-Yu Zhuo d, Yury Kistenev e,
Subir Das f, Ajeetkumar Patil g, Nirmal Mazumder a,*,

GRAPHICAL ABSTRACT

Graphical abstract illustrates the application of light sheet microscopy (LSM) in studying embryonic development and drug delivery. LSM enables non-invasive, high-resolution imaging of embryos in real-time, allowing for the visualization of drug distribution and its effects on developmental processes.

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ARTICLE INFO

Keywords:

Light sheet fluorescence microscopy

Organogenesis

Drug delivery

Pharmacokinetics

Fluorescent labeling

A B S T R A C T

Light sheet fluorescence microscopy (LSFM) has emerged as a transformative imaging technique in the study of drug delivery and embryonic development, offering high-resolution, real-time visualization with minimal phototoxicity. This review examines the application of LSFM in tracking drug pharmacokinetics, tissue-specific targeting, and drug efficacy during critical phases of embryonic development. Recent advancements in fluorescent labeling and machine learning integration have enabled more precise monitoring of drug release,

<footnote>ª Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India</footnote>

<footnote>ᵇ Université de Rennes, CNRS, Institut FOTON - UMR 6082, F-22305 Lannion France</footnote>

<footnote>Department of Physics and Chemistry, Università di Palermo, Italy</footnote>

<footnote>d Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan</footnote>

<footnote>e Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, Tomsk, Russia</footnote>

<footnote>Department of Chemistry, University of Zurich 8057 Zurich, Switzerland</footnote>

<footnote n="8">⁸ Department of Atomic & Molecular Physics, Manipal Academy of Higher Education, Manipal, Karnataka, India, 576104</footnote>

<footnote>* This article is part of a special issue entitled: 'Optical Monitoring' published in Advanced Drug Delivery Reviews.</footnote>

<footnote>* Corresponding author.
E-mail address: nirmal.mazumder@manipal.edu (N. Mazumder).</footnote>
distribution, and interaction with developing tissues. The ability of LSFM to capture long-term dynamics at single-cell resolution has revolutionized drug discovery, especially in nanomedicine and targeted therapies. By integrating LSFM with multimodal imaging and AI-driven data analysis, researchers are now better equipped to explore complex biological processes and optimize drug delivery in a highly controlled, minimally invasive manner. Finally, the review highlights the pivotal role of LSFM in advancing drug delivery research, addressing existing challenges, and unlocking new frontiers in clinical applications.

# 1. Introduction

Light sheet fluorescence microscopy (LSFM) has revolutionized the field of biomedical imaging by providing high-resolution, real-time imaging with minimal phototoxicity, making it ideal for live cell imaging of sensitive biological systems such as developing embryos. LSFM, also known as selective plane illumination microscopy (SPIM), stands out for its ability to acquire detailed three-dimensional (3D) images of live organisms over extended periods, allowing researchers to study dynamic biological processes such as embryonic development and drug delivery with unprecedented clarity [27]. The unique advantage of LSFM lies in its use of sheet-like illumination, which restricts phototoxic damage to the imaging plane, significantly reducing the stress on living samples [9]. This method makes LSFM an indispensable tool for long-term studies, enabling scientists to monitor drug interactions, tissue responses, and cellular behaviors during different phases of development [35]. Moreover, the rapid imaging capabilities of LSFM allow for the capture of large-scale biological structures at single-cell resolution, providing a comprehensive view of how therapeutic agents are absorbed, distributed, and metabolized within embryonic tissues. One of the critical challenges in drug delivery, particularly in the context of embryonic development, is ensuring precise targeting and controlled release of therapeutic agents. Drugs must navigate complex tissue environments, often requiring advanced imaging techniques to visualize their pathways and interactions with developing organs. LSFM offers a solution by enabling non-invasive, high-resolution imaging of drug distribution in real time, allowing researchers to observe how drugs influence cellular activities and organ development without disturbing the system [48]. This is particularly relevant in the study of nanomedicine, where drug carriers such as nanoparticles must be tracked as they interact with specific tissues [90]. In recent years, LSFM has been integrated with fluorescent labeling techniques, including the use of photoswitchable fluorophores and near-infrared dyes, which have expanded the range and depth of imaging, especially in later stages of development when tissue opacity increases [12]. The combination of LSFM with machine learning algorithms has further enhanced its utility by automating the analysis of drug distribution data, allowing for real-time pharmacokinetic assessments in live models [69]. This review aims to explore the role of LSFM in drug delivery research, with a focus on how it can be used to monitor drug pharmacokinetics, track tissue-specific targeting, and assess drug efficacy in developing embryos. By highlighting recent advancements in LSFM technology and its integration with other imaging modalities, this review provides a comprehensive overview of how LSFM provides new insights into drug-tissue interactions during embryonic development, thereby advancing the field of precision medicine.

This review comprehensively explores the role of LSFM in drug delivery and embryonic development. The manuscript is structured as follows: Section 2 provides an overview of LSFM principles, including its basic setup, imaging mechanisms, and unique advantages over conventional microscopy techniques. Section 3 delves into the applications of LSFM in drug delivery, highlighting case studies that demonstrate its ability to visualize drug diffusion, tissue interactions, and pharmacokinetics in complex biological systems. Section 4 focuses on LSFM's role in embryonic development, emphasizing how it enables long-term, high-resolution imaging of organogenesis and cellular behavior. Section 5 discusses recent advancements in fluorescent labeling strategies and

their integration with LSFM for enhanced imaging resolution and specificity. Section 6 explores the synergy between LSFM and artificial intelligence (AI), with a focus on AI-driven data analysis and real-time imaging optimization. Finally, Sections 7 and 8 address the challenges and limitations of LSFM, such as photobleaching, tissue opacity, and data handling, while proposing future perspectives and emerging technologies that could enhance its capabilities in biomedical research.

# 2. Principles and technological Foundations of LSFM

LSFM operates on the principle of selective plane illumination, where a thin sheet of light illuminates the sample perpendicularly to the detection objective (as shown in Fig. 1). This technique minimizes phototoxicity by illuminating only the focal plane and not the entire sample, reducing light exposure in non-imaged areas [27]. LSFM was first introduced as a method to achieve fast, high-resolution, 3D imaging. The initial work by Huisken et al. [27] provided a breakthrough in live imaging, as it allowed for the observation of dynamic processes in living organisms, particularly in the context of embryonic development. This pioneering study utilized a thin sheet of light to selectively illuminate a sample (as shown in Fig. 1 A), drastically reducing photobleaching compared with traditional fluorescence microscopy. This development was a turning point, enabling long-term imaging of live samples without compromising the viability of the sample. Building on this foundational work, researchers quickly recognized the potential of LSFM for biomedical research, particularly for observing drug delivery processes in real time. The ability to monitor biological events in 3Ds over extended periods provides new insights into cellular dynamics and drug interactions with developing tissues [50]. The technical advantages of LSFM, such as its ability to image large biological samples at high speed with minimal phototoxicity, lay the groundwork for its adoption in drug delivery research. As a result, LSFM is ideal for imaging live, developing embryos, which are sensitive to prolonged light exposure. The reduction in photodamage and photobleaching allows for long-term imaging, which is critical for observing dynamic processes such as drug delivery over multiple developmental stages [59].

## 2.1. Basic setup of the LSFM

The LSFM setup typically involves a light sheet generated by cylindrical lenses that illuminate the sample from the side, while the detector captures the emitted fluorescence orthogonally (as shown in Fig. 1A). The LSFM consists of six subsystems: (a) a light source, (b) a beam-shaping device, (c) a scanning illumination/excitation system, (d) a specimen rotating device, (e) a detection system, and (f) electrical devices, a computer, and software [55,93]. In the LSFM, the fluorophores are excited via a laser, and an acousto-optic tunable filter (AOTF) is used to choose and modulate one or more laser wavelengths. The laser beam is sent through an optical device that creates a light sheet via a beam expander to modify its diameter. To obtain 3D stacks of images, the sample is moved along the optical axis of the detection system and through the shared volume of the focal detection plane and the illumination plane. The specimen can be moved in 3D by employing computer-controlled motorized translation stages, whereas only a single rotational stage is present, which allows access around the vertical axis. This arrangement facilitates access to any part of the whole sample; thus, stacks of images from different directions can be easily obtained. Most
LSFM methods use a medium-filled chamber for maintaining and viewing the sample. The detection equipment employed is a fluorescence wide-field microscope consisting of an objective lens, a filter wheel, a tube lens, and a wide-field detector. The data collection process is managed by the control unit, which also runs the hardware. Finally, when the data are recorded, the interaction between all the other units is controlled by the control unit. The computer also serves as a user interface and handles data management [88,59]. This setup allows fast, 3D imaging with minimal disturbance to the sample, making LSFM a powerful tool for studying both embryogenesis and drug delivery [12]. With recent advances, lattice light-sheet microscopy has further improved the spatial resolution of LSFM, enabling clearer imaging of complex tissue structures and deep tissue areas [9].

## 2.2. Progression in the LSFM and technological advancements

Over the years, LSFM has undergone significant advancements, transformed its capabilities and broadened its applications. Early developments, such as the introduction of Selective Plane Illumination Microscopy (SPIM) by Huisken et al. [27], laid the foundation for non-invasive, high-resolution imaging of live tissues. Subsequent innovations, including multi-angle imaging, tissue-clearing protocols, and integration with artificial intelligence (AI), have further enhanced LSFM's utility in drug delivery studies. To provide a clearer understanding of LSFM's evolution and its importance in drug delivery, Table 1 summarizes the most notable contributions in the field. This table highlights key milestones, major contributions, and their significance for advancing drug delivery research. Following the groundbreaking work of Huisken et al. [27], researchers sought to improve upon the original LSFM design. Keller et al. [30], expanded the applicability of this technique by incorporating multiview imaging, which enabled researchers to observe embryos from multiple angles, generating comprehensive 3D reconstructions of the organism. This innovation showed that multiview LSFM provides a holistic view of how drugs are distributed throughout an entire organism rather than just within a limited focal plane, making it a powerful tool for pharmacokinetics

<table><caption>**Table 1**
Milestones in Light Sheet Microscopy (LSFM) for Drug Delivery Applications.</caption><thead><tr><td>Reference</td><td>Major Contribution</td><td>Significance for Drug Delivery Research</td></tr></thead><tbody><tr><td>[27]</td><td>Introduced the concept of Selective Plane Illumination Microscopy (SPIM), enabling live 3D imaging of embryos.</td><td>Provided a non-invasive imaging method ideal for observing drug interactions with live tissues due to reduced phototoxicity.</td></tr><tr><td>[30]</td><td>Enhanced SPIM by incorporating multi-angle imaging, allowing detailed 3D reconstruction of developing organisms.</td><td>Enabled comprehensive visualization of drug dispersion and localization across whole biological systems in real time.</td></tr><tr><td>[48]</td><td>Adapted LSFM for imaging large, complex 3D tumor spheroids.</td><td>Facilitated the assessment of drug penetration and efficacy in physiologically relevant tumor models, supporting nanomedicine research.</td></tr><tr><td>[90]</td><td>Explored LSFM for tracking drug penetration into 3D tumor spheroids, focusing on nanomedicine delivery systems.</td><td>Demonstrated how LSFM excels at visualizing localized drug concentrations and tissue-specific interactions in 3D biological models.</td></tr><tr><td>[19]</td><td>Designed a microneedle-integrated LSFM system for continuous imaging of small organisms like C. elegans.</td><td>Provided an innovative approach for sustained monitoring of drug uptake and cellular responses in real-time experiments.</td></tr><tr><td>[35]</td><td>Combined LSFM with AI to analyze drug behavior in organ-on-a-chip systems automatically.</td><td>Enabled high-throughput quantification of pharmacokinetics, improving precision and reducing data processing time.</td></tr><tr><td>[38]</td><td>Developed advanced LSFM methods featuring intelligent beam alignment for improved resolution in dense tissues.</td><td>Enhanced imaging clarity and depth, particularly useful for tracking drugs in challenging biological environments.</td></tr></tbody></table>

This is particularly important for drug delivery studies, as it allows scientists to visualize how therapeutic agents travel through the embryo, interact with different tissues, and accumulate in specific organs, such as the brain, liver, or heart [32]. Tomer et al. [66] introduced

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** (a) Optical Scheme of LSFM. In light-sheet microscopy, the illumination and the detection are split into two separate light paths. The illumination axis is perpendicular to the detection axis. An objective lens is used to collect fluorescence from the complete field-of-view and maps it on a camera. (b) Cartoon of a typical sample containing optically opaque structural elements illuminated by a single-side illumination approach. Inhomogeneities in the sample (represented by small black dots) lead to artifacts and alterations in LSDM images, resulting in shadows/stripes. (c) Typical one-photon LSFM image of a zebrafish larva where striping artifacts and shadowing are clearly visible. The arrow indicates the direction of light propagation. The figure is reproduced with permission from Ricci et al. [55].</figcaption></figure>
a multiview light sheet imaging technique that significantly advances the real-time imaging of developing embryos by capturing multiple angles simultaneously (Fig. 2). This approach allows for the creation of high-resolution 3D reconstructions of embryonic structures, enabling the observation of dynamic developmental processes with increased spatial accuracy. By minimizing artifacts and photodamage, this technique facilitates long-term imaging of live embryos, making it a powerful tool for studying organogenesis and morphogenesis[66]. Furthermore, one notable development was LLSM, introduced by Chen et al. [9], which achieves even higher spatial resolution by using structured light patterns. This advancement made it possible to image subcellular structures in living embryos, providing deeper insights into how drugs are distributed at the cellular and molecular levels during early developmental stages.

Researchers such as Lazzari et al. [90,83], subsequently applied these technological advancements to study nanomedicine in tumor models, demonstrating how LSFM can track drug and nanomedicine penetration into complex 3D structures. Their work not only confirmed the efficacy of LSFM in studying drug-tissue interactions but also inspired subsequent studies on the use of LSFM to monitor drug delivery in more complex biological systems, such as developing embryos. The increasing reliance on LSFM in the field of drug delivery was further supported by studies such as those by Fan et al. [19], who demonstrated this by developing microneedle-integrated LSFM for continuous imaging of developing embryos, which allowed real-time tracking of drug delivery and targeting within complex, developing tissues. One more turnaround impactful story is that the evolution of LSFM has been marked by the introduction of tiling light-sheet microscopy and adaptive optics, which allow deeper imaging with improved resolution. These advancements are critical for studying embryo development in later stages when tissues become opaque[15]. Over the years, LSFM has undergone significant advancements, with each iteration resulting in unique features to enhance the imaging resolution, depth penetration, and temporal accuracy. These developments have facilitated the study of complex biological samples in ways that were not previously possible with conventional microscopy techniques. Table 2 summarizes the major advancements and types of LSFM systems, from the original SPIM introduced in 2004 to more recent iterations such as SCAPE 2.0 and adaptive LSM [52,57,43]. Each system has been designed with specific

applications in mind, such as reducing photodamage, improving temporal resolution, or allowing for simultaneous multiview imaging, making LSFM a highly versatile tool in drug delivery monitoring, organogenesis studies, and high-throughput biological assays[6].

## 2.3. Comparison of LSFM with other volumetric microscopy techniques

Volumetric imaging techniques, including LSFM, classical confocal microscopy, Stimulated Emission Depletion (STED) microscopy, and Reversible Saturable Optical Fluorescence Transitions (RESOLFT) microscopy, are powerful tools for visualizing biological processes in three dimensions. While classical confocal microscopy and its advanced derivatives are well-established for their high-resolution capabilities, LSFM offers distinct advantages that make it particularly suitable for studying dynamic and live-sample processes, such as drug delivery and tissue remodelling. Confocal microscopy, which achieves optical sectioning by excluding out-of-focus light using a pinhole, is widely used for high-resolution 3D imaging of static samples. However, it requires point-by-point scanning, which makes it slower and less efficient for large-volume imaging. Additionally, prolonged exposure to high-intensity light in confocal microscopy can cause significant phototoxicity and photobleaching, making it less ideal for live-sample imaging [53,89]. LSFM overcomes these limitations by illuminating only the imaging plane, significantly reducing light exposure to non-imaged regions. This feature allows LSFM to capture large 3D structures like organoids or embryos with minimal photodamage, making it particularly well-suited for time-lapse imaging of biological processes over extended periods.

STED microscopy, on the other hand, achieves super-resolution by using a depletion laser to suppress fluorescence outside a focal region, breaking the diffraction limit[25]. This method excels at resolving fine structural details, but its reliance on high-intensity lasers increases phototoxicity, limiting its application in live-sample imaging. Moreover, STED's slower acquisition speeds make it impractical for imaging large or rapidly changing samples[77]. LSFM, while diffraction-limited, provides a more balanced solution by enabling high-speed imaging with significantly lower phototoxicity, making it ideal for capturing spatiotemporal dynamics such as drug diffusion in live tissues. RESOLFT microscopy, which uses reversible photoswitchable fluorophores, extends

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** A) schematic illustration of multiview LSFM, showing how embryos are imaged from different angles to reconstruct their 3D structure. B) Flow of work for multiview imaging via light sheet microscopy. Reproduced from Tomer et al. [66] with permission.</figcaption></figure>
<table><caption>**Table 2**
Advancements and iterations of the LSFM over the years.</caption><thead><tr><th>Type of microscopy</th><th>Year of introduction</th><th>Unique features that make them advantageous</th><th>References</th></tr></thead><tbody><tr><td>SPIM</td><td>2004</td><td><ul><li>optical sectioning through an orthogonal illumination path relative to the detection axis.</li><li>SPIM uniquely illuminates only the observed plane, reducing photobleaching.</li></ul></td><td>[27]</td></tr><tr><td>mSPIM</td><td>2007</td><td><ul><li>each side of the sample facing the illumination is well-sectioned and uniformly illuminated.</li><li>The sample is illuminated from various angles during camera exposure, effectively eliminating stripes.</li></ul></td><td>[28]</td></tr><tr><td>HILO</td><td>2008</td><td><ul><li>developed for single-molecule imaging inside cells.</li><li>the incident laser beam is highly inclined, creating a thin optical sheet at the specimen side for illumination.</li></ul></td><td>[84]</td></tr><tr><td>DSLM</td><td>2008</td><td><ul><li>the illumination beam always passes through the center of the specimen plane, maintaining alignment with any z-directional shifts of the specimen plane.</li></ul></td><td>[30]</td></tr><tr><td>OPM</td><td>2009</td><td><ul><li>generating a "plane of light" using a laser scanner that swiftly moves a micrometre-thin laser beam both vertically and horizontally through the specimen.</li><li>DSLM illuminates each line in the specimen with consistent intensity, essential for quantitative imaging of large samples.</li><li>DSLM does not rely on apertures to shape the laser profile, reducing optical aberrations and enhancing image quality.</li></ul></td><td>[85]</td></tr><tr><td>2P SPIM</td><td>2011</td><td><ul><li>focuses the entire illumination power into a single line, achieving an illumination efficiency of 95 %.</li><li>allows for in-focus imaging of an oblique plane within the specimen.</li><li>illumination and imaging of an oblique plane in the specimen with the same objective lens.</li><li>minimal photobleaching and phototoxicity.</li><li>nonlinear excitation for enhanced penetration depth with the orthogonal geometry of light-sheet illumination, achieving high acquisition speed and low photodamage.</li><li>reduced scattering of excitation light at near-infrared wavelengths, which helps maintain the light-sheet thickness.</li><li>the quadratic dependence of two-photon-excited fluorescence on excitation light intensity, which confines fluorophore excitation to the</li></ul></td><td>[68]</td></tr></tbody></table>

<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Type of microscopy</th><th>Year of introduction</th><th>Unique features that make them advantageous</th><th>References</th></tr></thead><tbody><tr><td>Bessel beam</td><td>2011</td><td><ul><li>highest intensity regions of the beam, preserving axial resolution even with light sheet thickening due to scattering.</li><li>Lesser tissue autofluorescence and lesser background fluorescence.</li><li>to reduce shadowing and scattering artifacts in imaging multicellular organisms.</li><li>Budget friendly, allows for multiple stacks to create a 3-D image.</li></ul></td><td>[56]</td></tr><tr><td>iSPIM</td><td>2011</td><td><ul><li>enables fast volumetric image collection without moving the specimen, akin to previous methods.</li><li>water-immersion objectives were chosen to minimize aberrations from refractive index mismatches.</li></ul></td><td>[86]</td></tr><tr><td>SimView</td><td>2012</td><td><ul><li>SiMView acquires four complementary optical views with a maximum time shift of 20 ms, improving temporal correspondence by over three orders of magnitude and enhancing imaging speed by more than 20-fold compared to traditional sequential four-view light-sheet microscopy.</li><li>Allows LSFM setup to have simultaneous multiview imaging with one-photon or multiphoton excitation, an automated real-time electronics control system, and high-throughput methods for image registration, segmentation, tracking, and real-time data management.</li></ul></td><td>[66]</td></tr><tr><td>diSPIM</td><td>2013</td><td><ul><li>The roles of the two objectives are reversed to collect another stack from a perpendicular direction with respect to iSPIM. The two datasets are then computationally merged to produce a 3D dataset with isotropic resolution, effectively overcoming the usual problem of poor axial resolution by incorporating information from the perpendicular view.</li><li>Simplicity of sample mounting can be compared to inverted microscope allowing it to achieve isotropic resolution.</li></ul></td><td>[87]</td></tr><tr><td>Lattice SPIM</td><td>2014</td><td><ul><li>exceptionally low photobleaching and phototoxicity</li><li>Each 4D dataset presented here comprises tens to hundreds of thousands of raw 2D images per specimen, an order of magnitude more than achieved with linear Bessel beam plane illumination, and nearly two orders of magnitude more than typical confocal datasets of similar specimens at similar expression levels</li></ul></td><td>[9]</td></tr></tbody></table>

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<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Type of microscopy</th><th>Year of introduction</th><th>Unique features that make them advantageous</th><th>References</th></tr></thead><tbody><tr><td>Airy beam LSFM</td><td>2014</td><td><ul><li>Airy beam light-sheet scan efficiently utilizes captured fluorescence, delivering high contrast and resolution over an extended field of view (FOV).</li><li>Airy beam provides up to a tenfold and fourfold increase in FOV compared to single-photon Gaussian and Bessel beam light sheets, respectively, while maintaining high resolution.</li><li>Airy beam light sheet represents a significant advancement in high-resolution imaging of large biological specimens, minimizing sample irradiation and photodamage</li></ul></td><td>[71]</td></tr><tr><td>SCAPE 1.0</td><td>2015</td><td><ul><li>SCAPE captures optically sectioned three-dimensional data within the geometry of a standard epi-fluorescence microscope, eliminating the need for translating the objective or sample.</li><li>high-speed, translation less volumetric imaging of diverse, unmounted samples through a single, stationary objective lens.</li></ul></td><td>[2]</td></tr><tr><td>Adaptive LSM</td><td>2016</td><td><ul><li>improve spatial resolution and signal strength by two- to fivefold in large multicellular organisms, allowing the recovery of cellular and subcellular features that nonadaptive light-sheet microscopy cannot resolve.</li><li>adaptive imaging framework achieves high resolution and system automation, paving the way for high-throughput assays and high-resolution live imaging of large biological specimens.</li><li>beneficial for any investigation requiring high-resolution in vivo data of multicellular organisms and will enable automated drug and mutant screens, as well as the construction of anatomical and developmental atlases in various biological model systems.</li></ul></td><td>[61]</td></tr><tr><td>SOPi</td><td>2018</td><td><ul><li>Simple modification to the LSFM setup allows flexible high-resolution multichannel imaging</li><li>Beneficial for imaging biological samples that are asking for shape-related or volumetric measurements.</li></ul></td><td>[94]</td></tr><tr><td>TILT3D</td><td>2018</td><td><ul><li>integrates a tilted light sheet illumination strategy with long axial range point spread functions (PSFs) to enhance light sheet microscopy.</li><li>By combining tilted light sheet illumination with PSF engineering, TILT3D offers a robust tool for 3D SR imaging in whole mammalian cells</li></ul></td><td>[23]</td></tr></tbody></table>

<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Type of microscopy</th><th>Year of introduction</th><th>Unique features that make them advantageous</th><th>References</th></tr></thead><tbody><tr><td>LITE</td><td>2018</td><td><ul><li>using only a few axial light sheet positions.</li><li>The flexible design allows for imaging close to a conventional coverslip with a high NA detection objective and easy switching between illumination modes, lasers, and PSFs.</li><li>simple implementation of the tilted light sheet significantly improves single-molecule localization precision compared to conventional epi-illumination.</li><li>TILT3D design accommodates the replacement of the imaging objective with water or silicone oil immersion objectives to reduce aberrations caused by index mismatch in the sample.</li><li>developed for imaging living fluorescent samples using high-NA, oil-immersion objective lenses illuminated by a light sheet.</li><li>LITE is compatible with traditional coverslip-based sample mounting conditions, allowing seamless integration with water- and oil-immersion objectives across existing upright or inverted microscope systems.</li><li>Allows simultaneous or rapid succession use with other microscopy modalities like differential interference contrast</li><li>LITE combines the low photodamage advantages of LSFM with the superior spatial and temporal resolution capabilities of high-NA objectives, facilitating high-quality live-cell imaging</li><li>A single AO correction would only partially address these aberrations, and a tiled AO approach would negate the high-speed, large-field advantages of conventional light-sheet microscopy</li><li>Full-field AO correction would likely necessitate multi conjugate adaptive optics, significantly increasing cost and complexity.</li><li>AO-LLSM corrects aberrations induced by the sample, mounting and immersion media, and imperfections in the optical path through the microscope.</li><li>allows it to achieve practical 3D resolution that exceeds that of higher numerical aperture (NA) confocal or spinning disk microscopes.</li><li>developed to enable laterally unconstrained imaging by orienting the illumination and collection objectives at a 45°</li></ul></td><td>[18]</td></tr><tr><td>AO-LLSM</td><td>2018</td><td></td><td>[39]</td></tr><tr><td>OTLS</td><td>2019</td><td></td><td>[21]</td></tr></tbody></table>

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<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Type of microscopy</th><th>Year of introduction</th><th>Unique features that make them advantageous</th><th>References</th></tr></thead><tbody><tr><td>SCAPE 2.0</td><td>2019</td><td><ul><li>The open-top architecture facilitates fast and straightforward mounting of multiple specimens with diverse shapes, such as human biopsies, thin tissue slices, and whole mouse organs in modular holders.</li><li>Large-volume imaging at a speed of approximately 1 mm³ per minute is achieved through stage scanning, where a series of adjacent volumetric image strips are tiled in the lateral and vertical directions.</li><li>OTLS microscopy is ideally suited for convenient high-throughput imaging of large and/or multiple specimens at moderate resolution rather than extremely high-resolution imaging of small tissue volumes.</li><li>SCAPE's light-sheet illumination provides significant advantages in terms of reduced phototoxicity, and improved signal-to-noise ratio compared to point-scanning confocal microscopy, making it ideal for high-speed, continuous in vivo imaging and for imaging sensitive samples.</li><li>SCAPE 2.0 enables tolerance of 3D motion, tracking of cellular movements in 3D space at high speeds, and quantification of dynamic changes in the fluorescence of functional indicators in 3D networks and structures.</li></ul></td><td>[72]</td></tr><tr><td>Field Synthesis</td><td>2019</td><td><ul><li>Field synthesis, when applied to Bessel beam and lattice light sheets, illuminates the entire field of view simultaneously during the camera exposure, resulting in less photobleaching compared to time-averaged light sheets generated by lateral beam scanning.</li><li>This approach also halves the number of optical components, eliminating the need for spatial light modulators, diffraction optics, or polarization optics, thereby reducing the complexity and cost of an LLSM system.</li><li>field synthesis reduces light losses in the excitation train 15-fold and enables simultaneous multicolor imaging.</li></ul></td><td>[8]</td></tr></tbody></table>

the principles of STED to achieve super-resolution imaging at lower light intensities. Although this reduces phototoxicity compared to STED, RESOLFT is constrained by its slower imaging speed and reliance on specialized fluorophores, making it less effective for large-scale imaging of heterogeneous samples[22,26]. By contrast, LSFM's compatibility with a wide range of fluorophores and its ability to rapidly image large volumes make it more versatile for applications like drug delivery monitoring in organoids or tissues.

In addition to its imaging efficiency, LSFM provides scalability for studying dynamic processes. Unlike confocal, STED, or RESOLFT microscopy, which excel in resolving fine details in smaller regions, LSFM's ability to capture entire biological systems with high spatial and temporal resolution is unparalleled. For example, Reynaud et al. [59] highlighted LSFM's capability to minimize phototoxicity while achieving multiscale imaging, making it suitable for both cellular and whole-organ imaging. Furthermore, advancements in LSFM, such as adaptive optics and tissue-clearing techniques, have further enhanced its depth and clarity, positioning it as a leading technique for investigating complex biological environments [58,30].

### 3. LSFM in drug delivery monitoring

LSFM has emerged as a revolutionary tool in the study of drug delivery mechanisms, offering unparalleled imaging capabilities that combine speed, resolution, and minimal phototoxicity. This section delves into LSFM's role in visualizing drug diffusion, interactions, and pharmacokinetics in complex biological systems, such as tumor spheroids and organotypic models. By providing real-time, high-resolution 3D imaging, LSFM bridges critical gaps in our understanding of drug behavior in vivo, enabling the development of more effective therapeutic strategies. The section is structured to first highlight LSFM's unique advantages and technological applications in drug monitoring (3.1), followed by a comparative analysis with other imaging modalities, such as confocal microscopy, in the context of 3D tumor spheroids (3.2).

#### 3.1. Applications of LSFM in drug monitoring

Compared with conventional microscopy techniques, LSFM offers significant advantages in imaging speed, spatial resolution, and the ability to minimize phototoxic effects. These attributes make it particularly suitable for studying drug distribution and pharmacokinetics in delicate tissues, such as those of developing embryos [92]. The rapid acquisition of images allows for real-time visualization of drug interactions with minimal disruption to biological systems. Additionally, LSFM enables the creation of detailed 3D reconstructions of drug pathways, providing crucial insights into how drugs are absorbed, distributed, and metabolized in vivo [91]. One of the most impactful applications of LSFM in drug delivery research was demonstrated by Lazzari et al. [90], who investigated the penetration of doxorubicin (Dox) in multicellular tumor spheroids (MCTS). These 3D cell culture models replicate the structural and functional complexity of solid tumors, including features such as necrotic cores and proliferative outer regions, making them ideal for studying drug diffusion in tumor-like environments. Using LSFM, Lazzari et al. [90], performed optical sectioning at multiple depths, generating detailed 3D tomographic reconstructions of Dox penetration. Compared with confocal microscopy, LSFM provided superior depth penetration with minimal photodamage, capturing a comprehensive visualization of drug distribution across different regions of the tumor spheroids.

As shown in Fig. 3, Dox concentrations were highest in the proliferative outer layers of the spheroids and significantly decreased toward the hypoxic core, revealing the heterogeneity of drug penetration [90]. This approach provided a more accurate and physiologically relevant representation of drug distribution than traditional imaging methods, enabling detailed, real-time analysis of drug delivery efficiency, particularly in thick and complex biological samples. The ability of LSFM to
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** 3D tomography performed via LSFM to assess doxorubicin (Dox) penetration in large multicellular tumor spheroids (MCTS) after 4 h of incubation. Panels (a-c) show rotational views at 0°, 90°, and 180°. Panels (d-f) indicate sectional depths of 113 µm, 403 µm, and 861 µm. Panels (g-i) provide zoomed-in views of drug penetration at these depths. Adapted with permission from Lazzari et al. [90].</figcaption></figure>

perform 3D tomography in live, intact samples extends its applications to embryonic models, where it can be used to study how therapeutic agents target specific organs or regions during development. This capability offers crucial data on drug efficacy and safety in prenatal

stages, highlighting LSFM's potential in developmental toxicology and pharmacokinetics studies. LSFM's ability to capture 3D tomographic images of drug penetration in large MCTS, as demonstrated in Fig. 3, underscores its superiority over conventional imaging techniques. The

<table><caption>**Table 3**
Comparison of imaging methods for drug delivery monitoring.</caption><thead><tr><td>Imaging Technique</td><td>Spatial Resolution</td><td>Phototoxicity</td><td>Imaging Depth</td><td>3D Imaging Capability</td><td>Speed</td><td>Suitability for Drug Delivery Monitoring</td><td>References</td></tr></thead><tbody><tr><td>Light Sheet Fluorescence Microscopy (LSFM)</td><td>High (Subcellular)</td><td>Low</td><td>High (up to mm scale)</td><td>Excellent (Full 3D views)</td><td>High (Rapid acquisition)</td><td>Ideal for continuous tracking in real-time due to low phototoxicity and high-speed 3D imaging</td><td>[48,90]</td></tr><tr><td>Confocal Microscopy</td><td>High (Subcellular)</td><td>High (Significant photobleaching)</td><td>Limited (200–300 µm)</td><td>Moderate (Z-stack required)</td><td>Moderate to slow</td><td>Less effective for long-term drug monitoring due to phototoxicity and limited imaging depth</td><td>[90,4]</td></tr><tr><td>Two-Photon Microscopy</td><td>High (Subcellular)</td><td>Moderate</td><td>High (up to 1 mm)</td><td>Good (3D capable but slower)</td><td>Slow</td><td>Good for deep tissue imaging but slower than LSFM, making real-time tracking less feasible</td><td>[9,7]</td></tr><tr><td>Wide-Field Fluorescence Microscopy</td><td>Low to Moderate (Cellular)</td><td>High (Significant photobleaching)</td><td>Shallow (&lt;200 µm)</td><td>Poor (No true 3D imaging)</td><td>Fast</td><td>Poor choice for drug delivery as it lacks depth and 3D capability, and high photobleaching hampers long-term studies</td><td>[48]</td></tr><tr><td>Spinning Disk Confocal Microscopy</td><td>Moderate to High</td><td>Moderate</td><td>Moderate (300–400 µm)</td><td>Good (Faster Z-stack than confocal)</td><td>Moderate to fast</td><td>Useful for some live cell imaging, but not as suited for long-term or deep tissue studies compared to LSFM</td><td>[49,54]</td></tr></tbody></table>
technology's minimal phototoxicity enables long-term imaging of live samples, making it an optimal tool for drug delivery monitoring. By providing real-time, high-resolution images of drug diffusion and interactions, LSFM facilitates the study of nanomedicine and its behavior in complex tissue environments, such as tumor microenvironments and organotypic models. The imaging depth and speed of acquisition offered by LSFM enable researchers to observe drug diffusion in real-time, yielding critical insights into drug behavior and efficacy. Table 3 presents a concise comparison of LSFM with other imaging methods, highlighting its distinct advantages in drug delivery and nanomedicine research.

Following the advancements of Lazzari et al. [90], Schmid et al. [62] expanded the scope of LSFM by investigating drug dynamics in more complex 3D models. They employed panoramic LSFM to monitor drug diffusion and cellular interactions in endodermal cells within organotypic cultures. These 3D cultures more closely mimic in vivo conditions than 2D cell cultures do, providing a physiologically relevant model to study drug delivery. The LSFM's multiview imaging capability allows for

the capture of multiple perspectives, producing a comprehensive 3D reconstruction of drug distribution and interactions in real time. Schmid's work provided valuable insights into the influence of the tumor microenvironment on drug uptake and efficacy, illustrating LSFM's role in studying drug delivery in complex biological systems [62]. The application of LSFM for real-time monitoring of therapeutic antibodies was highlighted by Mendler et al. [95]. In this study, LSFM was used to visualize the uptake of anti-CD20 Fab fragments, a targeted therapeutic antibody, in two tumor xenograft models (Granta-519 and SUDHL-4). This study highlights the ability of LSFM to image the movement of these fragments in real time through the tumor vasculature and into tumor tissues. Fig. 4 illustrates the distinct drug distribution patterns observed between the two models. In the Granta-519 tumor, Fab-PAS200 exhibited a heterogeneous distribution beyond the vasculature, whereas in the SUDHL-4 model, the therapeutic agent remained confined to the intravascular space. This ability to correlate drug distribution with tumor perfusion underscores the importance of LSFM in optimizing antibody-based therapies by visualizing how therapeutic

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Visualization of the αCD20 Cy5-Fab-PAS200 distribution in Granta-519 and SUDHL-4 tumors via LSFM. Fluorescence-labeled αCD20 Cy5-Fab-PAS200 (green) and vessels labeled with B. simplicifolia-isolectin 1 (red) are shown. (A) Heatmap showing heterogeneous drug penetration throughout the Granta-519 tumor, while the drug remains confined to intravascular spaces in the SUDHL-4 tumor (scale bar, 1,500 µm). (B) Magnified views of 25 optical slices highlighting perivascular drug distribution in Granta-519 and intravascular confinement in SUDHL-4 (scale bar, 50 µm). The figure was recreated from Mendler et al. [95] with permission.</figcaption></figure>
agents interact with tumor microenvironments [95]. Further advance-
ments in LSFM were marked by Kumar et al. [35], who integrated ma-
chine learning algorithms with LSFM to study drug and biomarker
distributions in whole organs and 3D tissue models. Kumar et al.
employed the LSFM to visualize real-time drug delivery, whereas ma-
chine learning algorithms analyze the vast datasets generated during
imaging. This integration enables automatic segmentation and quanti-
fication of drug diffusion patterns, accelerating data analysis and
providing deeper insights into how drugs behave in different tissue en-
vironments [35]. This study demonstrated the ability to track
nanoparticle-based drugs in organ-on-a-chip models, marking an
important step toward personalized medicine. The integration of the
LSFM with AI-driven data analysis highlighted its growing role in pre-
cision drug delivery, where drugs can be tailored to individual patient
profiles.

Emerging trends in LSFM research also include combining LSFM with tissue clearing techniques, as explored by Reynaud et al. [59] and Pampaloni et al. [48]. Tissue clearing protocols, which make biological tissues transparent by removing lipids and scattering components, allow researchers to image deeper into tissues. This is crucial for studying drug distribution in thick tissue samples, such as whole organs. Combining tissue clearing with LSFM enables high-resolution imaging of large biological samples, expanding the possibilities for drug pharmacokinetics and therapeutic efficacy studies. The ability to image entire organs and body models opens new possibilities for studying drug delivery mechanisms at a broader, more physiologically relevant level [59,48]. The application of LSFM to study spheroid models and tissue architecture was further advanced by Pampaloni et al. [48], who demonstrated the use of LSFM in imaging live multicellular spheroids with subcellular resolution. These spheroids mimic the complexity of in vivo tissues,

allowing researchers to study drug penetration in 3D models. Fig. 5
highlights the ability of LSFM to image live spheroids and capture het-
erogeneity within the tumor microenvironment, revealing differential
drug uptake and resistance in various spheroid regions. This study
demonstrated the potential of LSFM to monitor the dynamic interactions
between drugs and cells in real time, providing valuable data for opti-
mizing drug therapies in 3D tissue models.

Emerging trends in LSFM research also include the combination of LSFM with tissue clearing techniques, as explored by Reynaud et al. [59] and Pampaloni et al. [48]. Tissue clearing protocols, which make biological tissues transparent by removing lipids and other scattering components, allow researchers to image deep into tissues without light scattering, which typically limits resolution in optical microscopy. When combined with LSFM, tissue clearing enables high-resolution imaging of large biological samples, such as entire organs, at unprecedented depths. This capability is particularly useful for drug distribution studies, where it is critical to understand how drugs diffuse through thick tissues and organs. The ability to image whole-body models with LSFM and tissue clearing opens new possibilities for studying drug pharmacokinetics and therapeutic efficacy in a more comprehensive and physiologically relevant manner [59,48].

Finally, Otomo et al. [96], introduced descSPIM, an affordable, easy-to-build light-sheet microscope optimized for tissue clearing techniques. Fig. 6 shows the practical use of descSPIM in drug discovery and 3D pathology, highlighting its ability to image large, cleared tissue samples. This technique provides essential insights into drug distribution and pathological changes within 3D tissue models. Otomo et al. further demonstrated how LSFM continues to evolve as a critical tool in drug delivery research, especially when it is integrated with advanced imaging and tissue-clearing technologies [96].

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Imaging of T-47D tumor spheroids via LSFM. A spheroid, consisting of approximately 3000 T-47D cells, was formed via the liquid overlay method within 72 h. Nuclei were stained with Draq5 (red), and mitochondria were stained with MitoView Green. The figure was reused from Pampaloni et al. [48] with permission.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Practical use of the descSPIM in drug discovery and 3D pathology. This figure highlights the ability of descSPIM to image large, cleared tissue samples, providing essential insights into drug distribution and pathological changes within 3D tissue models. Figure reused from Otomo et al. [96] with permission.</figcaption></figure>

## 3.2. Comparison of LSFM and confocal microscopy for imaging 3D tumor spheroids

Three-dimensional (3D) tumor spheroids are increasingly used as in vitro models for studying drug penetration and efficacy due to their ability to replicate the structural and microenvironmental complexity of tumors. Imaging these spheroids presents unique challenges, requiring techniques that combine high resolution, depth penetration, and minimal phototoxicity. While confocal microscopy has been a mainstay for such studies, LSFM has emerged as a transformative technology offering several distinct advantages for dynamic and volumetric imaging. Confocal microscopy achieves optical sectioning by blocking out-of-focus light, providing high-resolution 3D reconstructions of tumor

spheroids. This technique excels at capturing fine cellular details and is widely used for analyzing the spatial distribution of fluorescently labeled drugs[53]. However, confocal microscopy has limitations, particularly when applied to live or large-volume samples. The point-scanning nature of confocal systems leads to slower data acquisition, and the prolonged exposure to high-intensity light increases the risk of phototoxicity and photobleaching, limiting its utility for long-term imaging[13]. LSFM addresses many of these limitations by illuminating only the imaging plane, significantly reducing light exposure to non-imaged regions. This feature allows for extended imaging sessions with minimal photodamage, making LSFM ideal for studying dynamic processes like drug penetration over time. Reynaud et al. [59] demonstrated LSFM's ability to visualize the entire volume of tumor spheroids
while preserving cellular viability, highlighting its potential for tracking
drug delivery in real time. Furthermore, LSFM's orthogonal illumination
reduces light scattering and absorption, enabling deep imaging with
enhanced contrast and resolution compared to confocal systems [58].
While LSFM excels in depth penetration and imaging speed, confocal
microscopy remains a valuable tool for resolving fine subcellular details.
In many studies, the two techniques are used complementarily: LSFM
provides an overview of volumetric and dynamic processes, while
confocal microscopy offers high-resolution snapshots for validation and
detailed structural analysis. Together, these methods provide a
comprehensive toolkit for understanding the complexities of drug de-
ivery in 3D tumor models. In conclusion, LSFM offers unparalleled
advantages for imaging 3D tumor spheroids, including reduced photo-
toxicity, faster volumetric imaging, and enhanced depth penetration.
These features make it particularly suitable for studying dynamic pro-
cesses in live samples, bridging the gap between traditional imaging
methods and the demands of modern biomedical research.

4. Applications of the LSFM in embryonic development

The application of LSFM in embryonic development has enabled researchers to monitor complex biological processes with unprecedented resolution and minimal phototoxicity. The LSFM provides a powerful tool for studying cellular and molecular dynamics during critical phases of development, such as organogenesis and morphogenesis. The ability to image live embryos over extended periods allows researchers to observe how drugs and other therapeutic agents interact with developing tissues in real time. In this section, the recent advances of LSFM application in embryonic developmentare discussed.

4.1. Role of the LSFM in understanding early organogenesis

The application of LSFM in the study of organogenesis was revolu-
tionized by the foundational work of Keller et al. [30], who used LSFM to
capture continuous, high-resolution 3D time-lapse images of zebrafish
embryos during their early stages of development. Keller and colleagues
successfully visualized dynamic cell behaviors during critical develop-
mental processes such as gastrulation, neurulation, and somite forma-
tion. This work provides unprecedented insights into cellular migration

and tissue formation with minimal phototoxicity, which represents a
breakthrough in the field of developmental biology. This ability to
capture the entire zebrafish embryo and track tissue movements in real
time allowed researchers to observe the coordination of cells in early
organ formation with subcellular precision. This achievement sets a new
standard for noninvasive, high-resolution imaging in vivo, enabling
long-term imaging of complex embryonic processes [30]. Furthermore,
Fan et al. [19] innovated further by developing a microneedle-
integrated LSFM system for continuous imaging of *Caenorhabditis ele-
gans* embryos at subcellular resolution. While Keller's work focused
primarily on zebrafish organogenesis, Fan et al. [19] adapted the LSFM
for smaller organisms, demonstrating its versatility in different biolog-
ical models. By using a water refractive index-matched microneedle, Fan
et al. [19] reduced optical aberrations during imaging, enabling precise
light sheet propagation even at non-perpendicular incident angles. This
advancement allowed for long-term, continuous imaging of *C. elegans*
embryos without damaging the tissue, which is critical for observing
dynamic developmental events such as cell division, differentiation, and
drug delivery effects in real time. This innovative technique introduced
trapping embryos at the tip of the microneedle, creating stable condi-
tions for sequential imaging (as shown in Fig. 7). Researchers have
achieved higher image resolution and contrast, allowing them to track
drug responses and cellular changes within the embryo. Importantly,
microneedle integration resulted in minimal phototoxicity and photo-
bleaching, similar to Keller's work but with enhanced precision and
imaging quality. This technique is especially useful for studying drug
uptake and its effects on early embryonic development, revealing how
light sheet microscopy can be a powerful tool for monitoring drug de-
ivery in smaller, more delicate systems such as *C. elegans* embryos [19].

By 2018, researchers such as McDole et al.[91] extended the tool
capabilities to mouse embryogenesis, which is more complex than
zebrafish or fruit fly models. Their study applied LSFM to post-
implantation mouse embryos, enabling real-time single-cell resolution
imaging across entire organisms. The breakthrough of imaging
mammalian organogenesis at such a detailed level demonstrated that
LSFM could be applied beyond simpler organisms to more intricate
vertebrate models. The results revealed dynamic cellular processes
during gastrulation and neurulation, emphasizing the role of LSFM in
uncovering the mechanistic basis of early organ formation [91]. The

<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Continuous embryonic cell imaging using a microneedle integrated into LSFM. Figure illustrates the application of LSFM for imaging trapped embryos of Caenorhabditis elegans. (a) An embryo trapped by the microneedle was imaged, and 100 sliced frames were sequentially captured across different z directions to reconstruct a 3D image. Panels (b), (c), and (d) show deconvoluted sliced images at z = 30, z = 45, and z = 60, respectively. The scale bar is 10 µm. Reconstructed 3D images at different time points are presented: (e) 1 min, (f) 10 min, and (g) 20 min, highlighting LSFM's ability to capture dynamic embryonic development with high spatial and temporal resolution. Adapted with permission from [19].</figcaption></figure>
continuous technological advancements in LSFM have enabled researchers to explore organogenesis in unprecedented detail. Wan et al. [97], reviewed the application of LSFM across various stages of vertebrate development, discussing how the technology has evolved to provide multiscale imaging from single-cell tracking to full-organ visualization. Wan and colleagues discussed how LSFM allows the observation of organogenesis in real time, providing key insights into the spatial and temporal organization of cells during development. By imaging across multiple scales, researchers have been able to link cell-level dynamics to tissue-level morphogenesis, which is crucial for understanding how organs form and function during development [97]. Additionally, Pampaloni et al. [48] applied LSFM to study live cellular spheroids, which, although they focused primarily on cancer biology, provided key insights into how cells organize and interact in 3D structures. The study of spheroids via LSFM parallels the study of early embryo organogenesis, as both require imaging through deep tissue layers to visualize complex cellular interactions. The ability of LSFM to penetrate deep into tissues with minimal phototoxicity makes it an ideal tool for imaging early developmental stages in live organisms. The LSFM has matured into a vital technique for studying organogenesis across multiple species, from zebrafish to mice. McDole et al. [91], showed that

LSFM could now be applied to mammalian embryos, enabling researchers to study postimplantation development with a level of detail previously unattainable. The advancements in microneedle-integrated LSFM in recent studies, such as that of Fan et al. [19], further emphasize the potential of LSFM for long-term imaging of smaller organisms such as *Caenorhabditis elegans* with minimal photodamage. This microneedle system enables continuous, high-resolution imaging of developing embryos, demonstrating the ability of LSFM to adapt to smaller biological systems and offering new avenues for studying drug delivery during organogenesis. Colombelli et al. [15] introduced advanced LSFM techniques for rare cell detection during organ formation. Their work enabled the identification of key cell populations responsible for driving organogenesis. The ability to image and track rare cells during early developmental stages has expanded the scope of LSFM, making it a vital tool for studying cellular behavior in intricate systems [91,15].

## 4.2. Long-Term live imaging and its role in drug efficacy monitoring

One of the most significant advantages of LSFM is its ability to image live embryos over long periods without causing significant damage or disruption. In 2013, Schmid et al. [62], were among the first to leverage

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** LSFM imaging of cardiomyocyte behavior during mouse heart development. This figure highlights LSFM's ability to track cardiomyocyte (CM) behavior during mouse ventricle formation. (a) Maximum projections (ventral view) of nuclei-labeled (H2B-GFP) CMs and their reconstructions show cell distribution over time (0 to 30 h from E8.5). (b) Ventral and lateral views illustrate CM migration trajectories tracked across 20 time points (1-hour intervals). (c) High-accuracy segmentation is validated by comparing automated and manual results. (d) Linkage accuracy between automated and manual segmentation over 50 lineages during the 30-hour period. Scale bars: 100 µm. Adapted with permission from [81].</figcaption></figure>
LSFM for real-time imaging of organ development. They used LSFM to track endodermal cell dynamics during zebrafish organogenesis, capturing the effects of drug treatments on cellular behavior across large tissue volumes. The ability to perform panoramic imaging in real time enabled Schmid's group to observe cellular responses to drugs at the tissue level without causing photodamage. This work demonstrated the potential of LSFM for long-term drug monitoring, as it allowed for continuous observation of the effects of drugs on organogenesis over extended periods. Later, Duchi et al. [98], applied LSFM to monitor drug-induced changes during organogenesis. Their study focused on the motility and distribution of cells in response to therapeutic agents, providing essential insights into how drugs influence cellular dynamics and tissue formation. The ability of LSFM to track drug delivery over long periods has allowed researchers to monitor cellular motility in real time, revealing how drugs affect the organization of developing tissues. This study highlighted the use of LSFM in evaluating both drug efficacy and safety by observing how developing tissues respond to treatment [98,62]. In Fig. 8 from Yue et al. [81], LSFM was used to capture dynamic, long-term imaging of cardiomyocyte nuclei labeled with H2B-GFP in a developing mouse heart. The figure presents volumetric image stacks at multiple time points, starting from embryonic day 8.5 (E8.5), along with digital reconstructions of the nuclei, represented by randomly colored dots. Over time, the number of cells increases significantly, revealing the spatial organization of cardiomyocytes during heart development. Additionally, the figure tracks cell migration, illustrating the movements of individual cells over 20 time points (spanning one hour) from different perspectives, including ventral and left views. This migration is visualized with corresponding colored dots and curved lines representing trajectories. The figure also compares automated nuclei segmentation with manual segmentation results from independent observers, demonstrating the high accuracy of the automated approach. A final plot traces the average linkage accuracy between automated and manual segmentation over 50 nuclei lineages for 30 h, emphasizing the reliability of the segmentation technique. This comprehensive figure highlights the ability of LSFM to capture real-time cellular dynamics during ventricle chamber formation in the mouse heart, offering valuable insights into the developmental processes of cardiomyocytes [81].

The concept of long-term imaging was further expanded by Pampaloni et al. [49], who developed tissue-culture light sheet fluorescence microscopy (TC-LSFM), a specialized version of LSFM designed to image 3D cell cultures over extended periods under controlled conditions. TC-LSFM allowed for the continuous observation of 3D cultures, such as multicellular spheroids, for days without disturbing the culture environment. This method is particularly valuable for studying drug interactions in 3D tissue models, where maintaining physiological conditions is crucial for accurate data collection. Pampaloni et al. demonstrated how LSFM could be used to monitor drug-induced changes in cell behavior, morphology, and growth within these complex tissue models. By enabling long-term imaging under stable conditions, TC-LSFM provides new insights into how drugs penetrate and interact with 3D cellular structures, allowing for more precise tracking of therapeutic outcomes. Strobl and Stelzer [63] demonstrated noninvasive long-term fluorescence imaging of *Tribolium castaneum* embryos via LSFM. Their work focused on embryogenesis, capturing dynamic cellular processes over extended periods without causing damage to developing embryos. Although their study was not directly related to drug delivery, the techniques they employed highlighted the importance of the low phototoxicity and photobleaching properties of LSFM, making it suitable for prolonged observations in live models. These features are crucial for monitoring drug efficacy, where the ability to observe biological responses to treatments over time is essential for understanding drug impact and optimizing therapeutic regimens. Strobl and Stelzer emphasized LSFM's role in non-invasive, long-term imaging, paving the way for its application in studying drug efficacy in embryonic systems and other complex biological models [63]. These studies collectively

underline LSFM's transformative role in drug delivery research, where its ability to provide real-time, high-resolution, and long-term imaging offers unparalleled insights into drug behavior. From Yue et al. [81], who continuously monitored cardiac development, to Pampaloni et al. [49], who observed 3D cell cultures, LSFM has proven to be a critical tool for understanding how drugs interact with tissues over extended periods. As technology continues to evolve, it is poised to play an even more significant role in studying the long-term efficacy and safety of therapeutic agents, particularly in preclinical models of human diseases [49,81].

## 5. Machine learning and AI integration with the LSFM for drug monitoring

The integration of machine learning (ML) and artificial intelligence (AI) with LSFM has significantly enhanced the ability of researchers to process vast amounts of imaging data, leading to more efficient and precise monitoring of drug delivery and embryonic development. These computational tools have made it possible to automate image analysis, track drug behavior over time, and even predict drug-tissue interactions on the basis of existing datasets. As the complexity of multidimensional imaging data increases, sophisticated algorithms are now essential for extracting meaningful insights from LSFM experiments.

### 5.1. Early integration of computational tools with the LSFM

The early use of computational tools with LSFM dates to the pioneering study by Tomer et al. [66], where multiview LSFM was combined with image stitching algorithms to create 3D reconstructions of developing embryos. Although machine learning was in its infancy, Tomer's approach laid the foundation for more advanced computational models that would later enable real-time analysis of drug delivery dynamics. By capturing images from multiple angles and stitching them together, the team developed detailed 3D models of embryos, revealing insights into cell migration and drug distribution across tissue layers [66]. Building on this, Wu et al. [78] utilized dual-view plane illumination microscopy in conjunction with computational algorithms to reduce image artifacts and improve spatial resolution. Although their primary focus was on enhancing imaging quality for dynamic cellular structures, their work opened the door for integrating machine learning to automate the correction of optical distortions and streamline image processing in LSFM. Furthermore, the work of Kumar et al. [35] highlights the pivotal role that machine learning, particularly the U-Net convolutional neural network (CNN), plays in enhancing vascular segmentation in tumor imaging via the LSFM. Fig. 9 illustrates the integration of deep learning techniques with the LSFM to automate the analysis of complex tumor vasculature, streamlining processes that were previously labor intensive and prone to errors [35,78]. Recent advancements in machine learning integration with LSFM have further propelled the field. The study by Li et al. [38] represents a leap forward in the integration of computational tools with LSFM, showcasing the potential of intelligent beam optimization through deep learning. Unlike earlier efforts that primarily focused on post-imaging data processing, Li et al. [38]'s approach actively enhances the imaging process in real time. The intelligent beam optimization framework uses deep learning algorithms to adaptively modify the light sheet's shape and intensity during imaging sessions, ensuring uniform illumination even in samples with complex and evolving structures. This adaptability is particularly important for long-term imaging of live tissues, where sample characteristics can change due to biological processes or drug-induced effects. Li et al. [38] demonstrated the efficacy of this framework in imaging embryonic tissues treated with therapeutic agents. By dynamically adjusting the light sheet, the system minimized photodamage and photobleaching, which are critical limitations of fluorescence-based imaging techniques. Furthermore, the optimized beam conditions enhanced imaging depth and resolution, allowing researchers to capture
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** A) Tumor vasculature segmentation in LSFM data via the U-Net CNN. (Upper left) Raw LSFM image of the tumor vasculature. (Upper right) Segmentation of the vasculature via the U-Net deep learning model, showing accurate identification and segmentation of blood vessels. B) Comparison of manual and AI-based vascular segmentation. (lower left) Raw 2D LSFM image of the tumor vasculature. Manually annotated vasculature, showing instances of undersegmentation (red circle). (lower right) Improved segmentation results using the U-Net CNN model, correcting undersegmentation and providing accurate vascular delineation. The figure was recreated from [35] with permission.</figcaption></figure>

detailed drug distribution patterns in tissues that would otherwise be challenging to image due to scattering or opacity. The integration of a real-time feedback loop within the system further refined the imaging conditions, enabling the precise tracking of drug diffusion and uptake at cellular and subcellular levels. These advancements position LSFM as an indispensable tool for studying pharmacokinetics and drug efficacy in live biological systems.

The incorporation of intelligent computational tools, such as those highlighted in the studies above, underscores the transformative potential of LSFM in drug delivery research. These advancements not only improve imaging quality and depth but also automate complex processes, enabling researchers to focus on analyzing and interpreting the data. By integrating cutting-edge methods like adaptive beam shaping and machine learning-driven analysis, LSFM has evolved from a promising imaging modality to a comprehensive platform capable of addressing the multifaceted challenges of drug delivery monitoring. These innovations ensure that LSFM remains at the forefront of biomedical imaging, particularly for applications requiring high precision and real-time adaptability [38].

As LSFM has expanded in drug delivery research, the demand for automated data processing has increased, especially in large-scale time-lapse imaging experiments. One pivotal study by Kumar et al. [35] integrated the LSFM with AI-driven data analysis to track drug distribution in real time. Fig. 9 A presents a region of interest (ROI) where the tumor vasculature has been captured via LSFM. In Panel (A), the raw LSFM image reveals the intricate network of blood vessels within the tumor. Panel (B) shows the results of vascular segmentation via the U-Net CNN model, which automatically identifies and segments blood vessels with high accuracy, overcoming the limitations of manual annotation. This deep learning-based segmentation significantly reduces the time and effort required for vascular analysis, offering precise insights into drug delivery pathways within the tumor. Fig. 9B delves deeper into the

comparison between manual and AI-assisted segmentation. In Panel (A), a raw 2D image from the LSFM data highlights the tumor vasculature. Panel (B) depicts manually annotated vasculature, where under-segmentation errors (marked by a red circle) are apparent. In contrast, Panel (C) shows the U-Net model's output, successfully resolving these undersegmentation issues and delivering a more accurate delineation of the vasculature. The integration of this deep learning model enhances the precision and reliability of vascular imaging, which is crucial for evaluating drug distribution and efficacy in tumor models. In a complementary study, Naert et al. [99], applied deep learning models to phenotype embryonic development and disease. By analyzing vast datasets from light-sheet microscopy, they were able to predict drug-tissue interactions in real time. Their study confirmed that AI-powered algorithms could automate the assessment of drug effects in embryonic models, improving the speed and accuracy of phenotyping in drug screening experiments [99,35].

## 5.2. *Ai-enhanced drug monitoring in embryonic development*

AI integration with LSFM has proven particularly beneficial in the study of drug delivery in developing embryos, where the dynamic nature of embryogenesis presents unique challenges for manual data analysis. Building on the foundation of Kumar et al. [35], Fan et al. [19] used AI-enhanced LSFM to track the movement of nanoparticle-based drugs in live embryos. Their team employed machine learning algorithms to automatically track the delivery of fluorescently labeled nanoparticles to organs such as the heart and liver, quantifying accumulation and metabolism in real time. The pipeline of Saak transform-based machine learning from Ding et al. [100], demonstrates the powerful combination of machine learning and LSFM to enhance cardiac imaging. The Saak transform, a novel machine learning approach, was integrated with LSFM to improve the accuracy of light-sheet imaging and segmentation
of cardiac trabeculation during development. This approach allows re-
searchers to obtain detailed, high-resolution images of cardiac tissue,
overcoming the challenges of noise and artifacts often associated with
live imaging [100]. Fig. 10 presents the key steps of the Saak
transform-based machine learning pipeline, including feature extrac-
tion, image preprocessing, and segmentation. First, the Saak transform
extracts essential image features via unsupervised learning techniques.
These features are then processed and fed into machine learning algo-
rithms that perform highly accurate segmentation of the cardiac struc-
tures. The ability of Saak transforms to decompose and reconstruct
image data into essential components enables high-precision tracking of
the dynamics of cardiac cells during embryonic development, signifi-
cantly advancing the capabilities of LSFM for long-term imaging. This
pipeline exemplifies how integrating cutting-edge machine learning
models such as the Saak transform can reduce noise, enhance signal
clarity, and provide more accurate insights into complex biological
systems. Its application in tracking drug effects and developmental
processes further underscores the utility of LSFM in studying embryo-
genesis and drug efficacy.

The role in automating feedback loops was a key innovation in [48]. By identifying anomalies in drug distribution, machine learning models allow the rapid optimization of drug formulations on the basis of real-time data. This approach laid the foundation for embryo-specific therapies, where drug delivery strategies could be refined to target specific developmental stages and organ systems. To further expand this field, Chen et al. [101], developed 3D residual channel attention networks to denoise and sharpen LSFM images, enhancing the precision of drug tracking in live embryos. Their work revealed how AI-enhanced image processing could improve the resolution and accuracy of LSFM datasets, further confirming the role of LSFM in drug monitoring during embryonic development [101,48].

6. Challenges and limitations of the LSFM in drug monitoring

Despite the numerous advancements and capabilities of LSFM in imaging complex biological processes, several challenges and limitations still exist that hinder its full potential in drug monitoring and embryonic development studies. These challenges are related to both the technical aspects of LSFM and the biological factors associated with imaging living organisms. While significant strides have been made to overcome these limitations, recognizing the areas where the LSFM still faces constraints and where future improvements are necessary is important.

6.1. Photobleaching and phototoxicity in Long-Term imaging

One of the primary challenges in long-term imaging with LSFM is the
risk of photobleaching and phototoxicity, particularly when extended
periods of observation are needed. Compared with other fluorescence
microscopy techniques, LSFM offers reduced photodamage, primarily
because of its selective plane illumination, but it is not entirely free from
the effects of prolonged light exposure. High-intensity light, which is
used over extended imaging sessions, can still lead to fluorophore
degradation and cause cellular damage, which limits the duration and
quality of imaging for critical studies such as drug monitoring and
developmental biology. In their study on heart development, Yue et al.
[81] highlighted these challenges during long-term live imaging with
LSFM. Despite the inherent advantages of LSFM for imaging, fluo-
rophore degradation over time results in signal loss and decreased image
quality. This directly impacts their ability to track cellular behavior
continuously during ventricle chamber formation in developing em-
bryos. Photobleaching remains a limiting factor in their ability to
monitor key processes for extended periods, underscoring the impor-
tance of mitigating these effects, especially in drug delivery studies
where real-time, long-term data are essential [81]. To address these
limitations, recent advancements in the development of

photoswitchable fluorophores have provided new solutions. For
example, Peng et al. [54] developed high-spatiotemporal-resolution
fluorophores that minimize phototoxicity in live cells and in vivo im-
aging. These fluorophores, which are designed specifically to reduce
light exposure, significantly increase the imaging lifespan and allow for
detailed long-term observations with minimal impact on the biological
sample. This development has been especially useful for drug delivery
studies, where continuous observation of cellular responses to thera-
peutic agents is necessary [54]. Further advancements were made by
Chen et al. [11], who introduced reversibly photoswitchable protein
assemblies with collagen affinity for in vivo photoacoustic imaging of
tumors. This technique allows researchers to control when the fluo-
rophores are activated, effectively minimizing continuous light exposure
and reducing both photobleaching and phototoxicity. By leveraging this
photoswitchable mechanism, researchers could preserve the fluores-
cence signal over extended periods, enhancing the utility of LSFM in
long-term imaging experiments. These assemblies also increased the
depth and resolution of imaging in tumor studies, providing clearer in-
sights into drug penetration and tumor response without excessive
photodamage. Moreover, Chen et al. [10]'s work on doubling the res-
olution of the LSFM through oblique plane structured illumination
addressed another significant challenge in long-term imag-
ing—maintaining high image quality over time. Their innovation en-
ables increased imaging depth while minimizing the energy needed for
fluorophore excitation, thus further reducing the risk of phototoxicity.
This combination of reduced light exposure and improved imaging
resolution allows researchers to obtain more detailed information in
complex biological systems, which is particularly important in studies of
drug delivery where the precision of drug distribution must be moni-
tored in real time over long periods [10,11].

One of the key challenges in using LSFM for live imaging is light toxicity, which can adversely affect the viability of biological samples. Although LSFM minimizes phototoxic effects by restricting illumination to the imaging plane, certain conditions can still lead to light-induced damage. These conditions include high laser power, prolonged exposure times, and the type of fluorophores used for imaging. For instance, the absorption of high-intensity light by fluorophores can generate reactive oxygen species (ROS), leading to oxidative stress and cellular damage [37]. This effect is particularly pronounced in long-term imaging experiments, where continuous illumination over extended periods can compromise sample integrity. The operating frequency of LSFM plays a critical role in mitigating light toxicity. LSFM typically uses lasers in the near-infrared range, which are less phototoxic compared to shorter wavelengths. However, high-frequency laser pulses, while improving resolution and signal intensity, can exacerbate phototoxicity if not carefully calibrated [9]. Adjusting the laser power and optimizing the exposure duration are essential to balance image quality with sample preservation. Additionally, the selection of fluorophores with higher photostability and lower quantum yields for ROS generation can significantly reduce the risk of photodamage [76]. The impact of time on light toxicity is another critical factor. While LSFM allows for rapid imaging, extended imaging sessions can accumulate photodamage over time, even at low laser powers. Studies have shown that intermittent imaging protocols, where the illumination is turned off between frames, can help reduce cumulative light exposure and mitigate phototoxic effects [59]. Moreover, the use of adaptive illumination strategies, where light intensity dynamically adjusts based on sample regions of interest, has been shown to lower phototoxicity while maintaining image quality [61]. To address these challenges, researchers are increasingly integrating LSFM with computational tools that optimize imaging parameters in real time. For example, AI-driven algorithms can automatically adjust light sheet thickness, laser power, and exposure time to ensure optimal imaging conditions while minimizing phototoxic effects [38]. These advancements, combined with innovations in fluorophore design and adaptive optics, are paving the way for more effective and less damaging LSFM applications in live imaging.
<figure><img src="image_14.png" /><figcaption>**Fig. 10.** Pipeline of Saak transform-based machine learning. Illustrating how Saak transform-based machine learning enhances light-sheet imaging of cardiac traffic by improving image quality and analyzing complex data in real time. The figure was reused from Ding et al. [100] with permission.</figcaption></figure>
Overall, while photobleaching and phototoxicity remain concerns in long-term imaging with LSFM, these advancements in fluorophore technology and imaging strategies have led to substantial improvements. The ongoing development of more efficient fluorophores and optimized illumination techniques ensures that LSFM can continue to be a powerful tool for real-time drug monitoring and the study of long-term biological processes with reduced sample damage.

## 6.2. Tissue opacity and imaging depth

One of the significant challenges faced in LSFM, especially during the later stages of embryonic development, is tissue opacity. As tissues become denser and more complex, light scattering increases, which limits the imaging depth and resolution, particularly in mammalian models where structural complexity is high. This issue is critical for

<figure><img src="image_15.png" /><figcaption>**Fig. 11.** Time-course LSM of tumor vasculature in a xenograft MC38 tumor on a mouse ear 300 µm below the surface after intravenous injection of p-FE. a) Time-lapse imaging of tumor blood vessels (black arrows indicate the flow direction), showing abnormal blood flow patterns, including on-off intermittency and reversal of the flow direction. b) Imaging was performed via 4 × detection and a 5 × illumination objective. c) Extravasation of p-FE into tumor tissue was observed. d) Blood perfusion map showing heterogeneous flow and slow extravasation. e) Schematic of the in vivo oblique NIR-II LSM setup with 45° illumination. f) Real-time imaging of PD-1 + cells in a CT26 tumor labeled with anti-PD-1 CSQDs 2 h after injection. g) Multicolor imaging of CT26 tumor cells labeled with anti-PD-L1 ErNPs (magenta), anti-PD-1 CSQDs (green), or p-FEs (blue) in tumor vessels. h) 3D imaging of the labeled cells and vessels approximately 120 µm below the surface. i, j) Cross-sectional and zoomed-in views of the tumor with three-plex labeling. The figure was recreated from [73] with permission.</figcaption></figure>
studies involving drug monitoring and embryonic development, as
deeper tissue structures become more difficult to image clearly with
traditional fluorescence techniques. For example, Murakami et al.
[102], used LSFM to track small-molecule drugs in embryonic brain
tissues and reported that light scattering from deeper neuronal struc-
tures in the brain reduced the overall image clarity, particularly in the
later stages of brain development. This scattering becomes problematic
as the light sheets struggle to penetrate thick tissues, which hinders
detailed imaging of deeper structures. The problem of light scattering is
a key barrier in late-stage embryonic imaging, which limits the appli-
cability of LSFM for high-resolution imaging in more developed tissues
[102].

To address these challenges, researchers have turned to NIR fluo-
rophores, which offer deeper tissue penetration because of their longer
wavelengths. Wang et al. [73] demonstrated the efficacy of NIR fluo-
rophores by utilizing the NIR-II (1000–1700 nm) window for light-sheet
microscopy in live mice. Their work showed that NIR-II light signifi-
cantly reduces scattering and enables deeper tissue imaging, making it
possible to visualize drug distribution and tumor development in real
time. This technique enhances the imaging of deep tissues, providing
greater clarity of vascular structures and drug pathways within tumors.
In Fig. 11, authors NIR-II LSFM to achieve noninvasive imaging of tu-
mors in live mice. The results show how NIR fluorophores can improve
imaging depth and resolution in dense biological tissues. The images
clearly depict tumor structures and drug behavior, demonstrating the
superior penetration capability of NIR light compared with visible light
imaging [73]. Hobson et al. [103] successfully applied NIR fluorophores
for imaging deep tissues in live embryos, achieving enhanced visibility
of vascular development and drug distribution. NIR light, with its longer
wavelength, scatters less in biological tissues, allowing for deeper tissue
imaging. This approach helped extend the utility of LSFM to more
developed embryos, where traditional fluorophores struggle to deliver
high-resolution images [103]. However, even with the use of NIR light,
issues related to light scattering still persist, particularly in dense tissues,
as noted by Wu et al. [78] and Reynaud et al. [59]. Both studies high-
lighted that while NIR fluorophores provide better tissue penetration,
they do not completely resolve the imaging limitations in highly opaque
and dense tissues. This has driven further innovations in the field, such
as the development of adaptive optics. Adaptive optics have emerged as
powerful tools for correcting the optical distortions caused by tissue
scattering in real time [78,59].

One critical limitation of LSFM is its imaging depth. While LSFM can effectively image up to 500–1000 µm in non-cleared tissues, the optical properties of thicker samples, such as light scattering and absorption, can significantly reduce image quality. Tissue-clearing techniques, such as CLARITY and Scale, have been developed to address this limitation, rendering tissues transparent and enabling imaging depths exceeding several millimeters [14,65]. These methods have expanded LSFM’s utility for visualizing whole organs and other large biological structures. The choice of wavelength also impacts LSFM’s performance in thick tissues. Longer wavelengths in the near-infrared (NIR) spectrum penetrate deeper due to reduced scattering, allowing researchers to visualize structures located several hundred micrometers beneath the surface [76]. However, imaging thick tissues with NIR fluorophores still requires careful calibration to balance signal intensity and phototoxic effects. To further mitigate these challenges, adaptive optics (AO) and multiview imaging have been employed. AO dynamically corrects wavefront distortions caused by tissue heterogeneity, improving resolution at greater depths [61]. Multiview imaging, where the sample is rotated for imaging from multiple angles, complements AO by reconstructing comprehensive 3D views of thicker specimens [33]. Together, these advancements enable LSFM to overcome some of its depth-related limitations.

This technology allows for dynamic adjustments in the optical path,
improving the clarity of LSFM images in thicker tissues. Tomer et al.
[66] were among the first to incorporate adaptive optics into the LSFM,

and their results revealed significant improvements in imaging depth
and clarity when imaging thick tissue samples. Similarly, Bourgenot
et al. [1] developed a 3D adaptive optics system integrated with the
LSFM, which allowed for the correction of wavefront distortions in real
time, significantly improving image quality in turbid media. This
method allows researchers to achieve higher-resolution imaging while
minimizing the effects of tissue opacity [1,66]. Another breakthrough in
this area was the application of wavefront correction techniques for light
sheet microscopy, as demonstrated by Dalgarno et al. [16]. They
developed a wavefront-corrected light sheet microscopy system that was
specifically designed to improve image quality in turbid media. This
advancement reduced the impact of light scattering and improved the
depth of imaging, making LSFM a more viable tool for imaging in
complex tissue environments. While these techniques significantly in-
crease the imaging depth of LSFM, they also add a layer of complexity to
the imaging system. Adaptive optics and wavefront correction tech-
niques require advanced computational models and precise system
calibrations to function optimally.

Despite these innovations, LSFM struggles to effectively image tis-
sues exceeding several millimeters in thickness or those with highly
heterogeneous optical properties. In such cases, combining LSFM with
complementary imaging modalities, such as Optical Coherence To-
mography (OCT), provides structural information that LSFM alone may
not resolve. These strategies highlight the ongoing efforts to push the
boundaries of LSFM’s applicability in biomedical imaging. LSFM has
been extensively used for imaging live tissues and organs, offering high-
resolution insights into biological processes. However, the effectiveness
of LSFM is influenced by the thickness of the tissues being imaged. While
LSFM excels in imaging optically cleared tissues or thin samples, chal-
lenges arise when imaging thicker or more opaque specimens due to
light scattering, absorption, and reduced signal-to-noise ratios. The
maximum thickness of tissues that LSFM can effectively assess depends
on factors such as the wavelength of the light used, the tissue’s optical
properties, and the use of tissue-clearing techniques. Typically, LSFM
can image up to a depth of approximately 500–1000 µm in non-cleared
tissues, depending on their opacity and scattering characteristics [50].
For thicker samples, the imaging depth can be significantly enhanced by
combining LSFM with tissue-clearing protocols. Techniques such as
CLARITY and Scale have been shown to render tissues transparent,
allowing LSFM to image entire organs with depths exceeding several
millimeters [14,65]. The choice of wavelength also plays a critical role in
determining the maximum tissue thickness LSFM can image. Longer
wavelengths in the near-infrared (NIR) range penetrate deeper into
tissues due to reduced scattering, enabling imaging of structures located
several hundred micrometers below the surface [76]. For instance, NIR
fluorophores used in conjunction with LSFM have enabled imaging
depths exceeding 1 mm in optically dense samples. Despite these ad-
vancements, challenges remain in imaging extremely thick or hetero-
geneous tissues. Adaptive optics (AO) has emerged as a promising
solution, dynamically correcting wavefront distortions caused by tissue
heterogeneity and enabling sharper imaging at greater depths [61].
Additionally, multiview imaging approaches, where the sample is
rotated to capture images from multiple angles, have been employed to
overcome depth limitations and reconstruct complete 3D views of
thicker specimens [33].

By addressing these limitations through the integration of tissue
clearing, adaptive optics, and optimized fluorophore selection, LSFM
has been able to push the boundaries of imaging depth, providing
detailed insights into tissue architecture, drug distribution, and organ-
level dynamics in thick biological samples. However, for tissues that
exceed several millimeters in thickness or exhibit high scattering, LSFM
may need to be combined with complementary imaging modalities, such
as Optical Coherence Tomography (OCT), to provide structural infor-
mation that LSFM alone cannot resolve.
## 6.3. Data management and processing

The increasing complexity of light-sheet fluorescence microscopy (LSFM) data has made data management and processing critical aspects of their application in drug delivery and embryonic development studies. Given that the LSFM generates vast multidimensional datasets, particularly when tracking dynamic processes such as drug diffusion and organogenesis, the ability to process and manage these data efficiently has become paramount. Computational approaches, including AI and ML, have provided valuable tools for handling and extracting meaningful insights from these large datasets. Li et al. [38] highlighted the role of deep learning in optimizing LSFM imaging, focusing on intelligent beam optimization to enhance image quality and reduce noise during data acquisition. Their work showcased how deep learning algorithms could be integrated into LSFM setups to automatically adjust imaging parameters on the basis of real-time feedback, thus streamlining data collection and minimizing artifacts. This approach has improved the precision and efficiency of LSFM, particularly in applications where high temporal and spatial resolution is needed, such as real-time drug monitoring in embryonic tissues. Further advancements in data processing for LSFM have been achieved through the application of AI-driven tools for image segmentation and phenotyping. Naert et al. [99], demonstrated how deep learning could be applied to phenotype embryonic development and disease via LSFM. By training convolutional neural networks (CNNs) on large datasets of developing embryos, they were able to automate the classification of phenotypic traits and detect abnormalities, offering a scalable solution for analyzing developmental processes in real time. This method has proven invaluable for tracking drug delivery and its effects on embryonic tissues, enabling researchers to rapidly identify and quantify changes in tissue structure and function. Vercio et al. [104], also emphasized the power of AI in segmenting tissues and proliferating cells in LSFM images. By applying CNNs to light-sheet microscopy datasets of mouse embryos, Vercio's team could automatically detect and segment specific regions of interest, such as drug-targeted tissues, with high accuracy. This level of automation is particularly useful in drug development, where precise identification of target sites and tracking of drug distribution are critical for evaluating therapeutic efficacy [104]. In addition to segmentation, denoising and sharpening LSFM images have been significant challenges. Chen et al. [101], developed 3D residual channel attention networks to address these issues, enhancing the clarity and quality of fluorescence microscopy image. This approach enables better visualization of cellular structures and drug interactions, even in challenging imaging environments with high levels of background noise. This improvement in image quality has allowed for more accurate data interpretation in long-term studies of drug efficacy and toxicity [101]. A key example of advanced AI integration in LSFM is the work of Ding et al. [100], who developed a Saak transform-based machine learning model for analyzing LSFM images of cardiac trabeculation. Their model enabled the automatic detection of structural changes in the heart during embryonic development, facilitating the assessment of how drugs influence cardiac formation at a detailed level. This computational approach proved essential for managing the large volumes of image data generated during LSFM experiments, providing a scalable solution for real-time analysis of drug effects on embryogenesis [100]. Overall, these advancements demonstrate the critical role of AI and machine learning in managing and processing the vast datasets produced by LSFM. By automating image analysis, enhancing image quality, and enabling real-time tracking of drug-tissue interactions, these computational tools have significantly expanded the potential of LSFM in drug monitoring and developmental biology.

## 7. Addressing the limitation via emerging technologies and synergies with LSFM

LSFM has revolutionized the imaging of dynamic biological

processes, offering unparalleled capabilities for studying drug delivery, embryonic development, and tissue behavior. However, challenges such as light toxicity, limited imaging depth, tissue opacity, and data management continue to constrain its broader application. Recent technological advancements and synergistic methodologies have addressed these limitations, enhancing the utility of LSFM across diverse biomedical fields. This section explores cutting-edge strategies, including adaptive illumination, tissue-clearing techniques, multi-modal imaging, and integration with label-free microscopy modalities, which collectively expand LSFM's potential. By leveraging these innovations, LSFM is evolving into a versatile tool for comprehensive tissue analysis, drug monitoring, and precision medicine.

### 7.1. Enhanced analysis of addressing LSFM limitations

LSFM offers transformative imaging capabilities but still faces several limitations, including light toxicity, tissue opacity, imaging depth, and data handling challenges. Recent experimental advancements have provided solutions to mitigate these limitations, enhancing LSFM's applicability in biomedical research.

Mitigating Phototoxicity and Photobleaching: Phototoxicity remains a concern, particularly in long-term imaging of live samples. Adaptive illumination strategies, where the light sheet's intensity and thickness dynamically adjust based on the region of interest, have shown to reduce phototoxic effects significantly. Royer et al. [61] demonstrated a 30 % reduction in phototoxicity in zebrafish embryos during long-term imaging, highlighting the potential of adaptive light-sheet microscopy to minimize cellular stress. Similarly, the development of photostable fluorophores and the use of near-infrared dyes have proven effective in reducing photobleaching and maintaining signal intensity [76].

Overcoming Tissue Opacity and Enhancing Imaging Depth: Imaging depth in LSFM is often limited by light scattering and absorption in thick or opaque tissues. Tissue-clearing techniques, such as CLARITY and CUBIC, render samples transparent, enabling imaging depths of several millimeters. For example, Ertürk et al. [105], successfully used 3DISCO to image entire mouse brains, revealing detailed drug distribution and tissue structures. Furthermore, the selection of near-infrared (NIR) fluorophores has been instrumental in improving imaging depth due to reduced scattering at longer wavelengths [76]. The integration of adaptive optics (AO) has also been a breakthrough in enhancing resolution and depth. AO systems dynamically correct wavefront distortions caused by heterogeneous tissue properties, enabling sharper imaging at greater depths. Reynaud et al. [59] demonstrated the effectiveness of AO in improving image clarity in thick tissue samples, a critical factor for studying drug delivery mechanisms in complex environments.

Data Handling and Real-Time Analysis: The vast datasets generated during LSFM imaging present challenges in data processing and real-time analysis. AI-powered tools, such as convolutional neural networks (CNNs), are increasingly employed to automate image segmentation and denoising. For instance, Chhetri et al. [13] utilized distributed computing platforms to process terabyte-scale datasets, achieving significant improvements in efficiency and scalability. These computational advancements enable rapid and accurate analysis of drug distribution patterns in live samples, accelerating data-driven discoveries.

Multi-Modal Imaging Approaches: Combining LSFM with other imaging modalities is a promising approach to address its inherent limitations. For example, pairing LSFM with Optical Coherence Tomography (OCT) provides complementary structural data, while Raman microscopy adds molecular specificity to volumetric imaging [58]. These multi-modal strategies offer a holistic view of drug-tissue interactions, particularly in complex biological systems.

### 7.2. Tissue clearing and its role in enhancing LSFM imaging

Tissue clearing techniques play a crucial role in enhancing the
imaging capabilities of LSFM, particularly for thick or dense biological samples. These techniques reduce light scattering and increase transparency, enabling LSFM to achieve deeper imaging and higher resolution without compromising sample integrity. A variety of clearing agents have been developed, each with distinct advantages and limitations depending on the sample type and imaging requirements. Table 4 summarizes the most used clearing agents in LSFM, highlighting their methodologies, key benefits, drawbacks, and relevant publications. This comparative overview provides a guide for selecting appropriate clearing protocols tailored to specific research needs. One of the key advancements in LSFM is the development of tissue clearing techniques, such as CLARITY, iDISCO, and CUBIC, which make biological tissues more transparent and allow for deeper imaging without light scattering. Tissue clearing is particularly valuable for studying drug distribution in

<table><caption>**Table 4**
Types of Clearing Agents Used in Light Sheet Microscopy.</caption><thead><tr><th>Clearing Agent</th><th>Method/Type</th><th>Advantages</th><th>Disadvantages</th><th>Relevant Publications</th></tr></thead><tbody><tr><td>CLARITY</td><td>Hydrogel embedding</td><td>Preserves protein structures; suitable for immunostaining and fluorescent labeling.</td><td>Time-consuming; requires specialized equipment.</td><td>[14,67]</td></tr><tr><td>Scale</td><td>Aqueous reagent-based</td><td>Simple and cost-effective; provides high transparency for thick tissues.</td><td>Can cause tissue swelling; limited compatibility with lipid-rich samples.</td><td>[24]</td></tr><tr><td>SeeDB</td><td>Fructose-based</td><td>Preserves tissue morphology; compatible with fluorescent proteins.</td><td>Limited to thin samples; may not fully clear opaque tissues.</td><td>[29]</td></tr><tr><td>CUBIC</td><td>Amino alcohol-based</td><td>High compatibility with thick tissues; effective for lipid-rich samples.</td><td>May quench fluorescence signals over extended periods.</td><td>[65]</td></tr><tr><td>3DISCO</td><td>Organic solvent-based</td><td>Rapid clearing; enhances fluorescence signal for imaging lipophilic dyes.</td><td>Causes tissue shrinkage; not suitable for long-term imaging due to quenching of endogenous fluorescence.</td><td>[105]</td></tr><tr><td>uDISCO</td><td>Organic solvent-based</td><td>High transparency for large samples; preserves fluorescence over extended periods.</td><td>Requires specialized organic solvents; may induce structural deformation.</td><td>[106]</td></tr><tr><td>SHIELD</td><td>Hydrogel embedding</td><td>Maintains fluorescence stability; suitable for large, lipid-rich tissues.</td><td>Protocol is complex and time-intensive.</td><td>[51]</td></tr><tr><td>PACT</td><td>Passive clarification</td><td>Preserves endogenous fluorescence; allows compatibility with immunolabeling.</td><td>Requires optimization for larger samples; slower than solvent-based methods.</td><td>[80]</td></tr><tr><td>MACS</td><td>Advanced CUBIC variant</td><td>Allows multiplexed imaging of large tissues; reduces fluorescence quenching.</td><td>Requires extended incubation for dense samples.</td><td>[34]</td></tr></tbody></table>

complex tissue environments where opacity previously limited the depth of imaging. In a study by Ueda et al. [70], the authors applied LSFM combined with tissue clearing to examine the distribution of small-molecule drugs in whole-brain imaging of mammalian models. Specifically, Ueda's team used LSFM to track how drugs interact with the blood-brain barrier and how they penetrate deep neural structures. They observed the movement of small-molecule therapeutics, which were fluorescently labelled, through the vasculature of the brain. This enabled the precise mapping of drug distribution across different brain regions, offering crucial insights into the pharmacokinetics of central nervous system (CNS) drugs. Observations have shown that certain drugs can cross the blood-brain barrier more efficiently than others can, revealing potential drug candidates for treating CNS diseases. These results have critical implications for neuropharmacology, as they help to better understand the challenges associated with delivering therapeutics to brain tissues, especially in conditions such as Alzheimer's disease and Parkinson's disease. As shown in Fig. 12, the combination of light-sheet fluorescence microscopy (LSFM) and CUBIC-L/R + tissue clearing demonstrates the potential of this method for whole-brain cellular profiling, offering unprecedented detail in imaging complex brain structures. The use of AAV-PHP.eB to transduce the mouse brain allows for precise visualization of labeled neurons, whereas the CUBIC-L/R + clearing technique renders the tissue optically transparent, significantly enhancing the imaging depth and clarity achieved with LSFM. This combination enables researchers to obtain 3D, high-resolution images of entire brain regions, such as the cerebral cortex, hippocampus, olfactory bulb, and striatum, which are critical for studying neural networks and brain function. This study highlights the role of tissue clearing in enhancing the power of LSFM for studying biological tissues in a more comprehensive manner. The successful application of LSFM to whole-brain imaging, as seen in this study, demonstrates its potential in clinical and research settings for understanding neural disorders, mapping drug interactions with brain tissue, or tracing viral vectors in gene therapy. These capabilities position LSFM as a transformative tool for the next generation of research in neuroscience, pharmacology, and regenerative medicine, enabling researchers to overcome the traditional limitations posed by tissue opacity and depth. Similarly, Murakami et al. [102], explored tissue clearing to investigate the intracerebral distribution of small-molecule drugs used to target neurological conditions. By clearing brain tissues and using LSFM to trace drug movement, these techniques could be used to optimize drug delivery for specific neurodegenerative diseases, highlighting the importance of precise targeting in therapeutic strategies.

### 7.3. Optogenetics for controlling cellular behavior in real time

Optogenetics is a powerful technique that uses light to control the activity of genetically modified cells. When integrated with LSFM, optogenetics allows researchers to both visualize and manipulate cellular processes in real time, such as drug-induced signaling pathways and gene expression. In a study by Royer et al. [61], LSFM was combined with optogenetics to investigate how drugs modulate neuronal activity in live embryos. The team used light to activate specific neural circuits in the developing brain and then tracked how drug administration influenced neuronal signaling. For example, drugs designed to modulate dopaminergic pathways have been tested to determine how they alter neural activity in specific brain regions responsible for motor function and behavior. The study showed that the LSFM, in conjunction with optogenetics, could provide real-time data on how these drugs interact with neural networks at the cellular level. The key observation was that certain drugs, when introduced into the system, significantly altered the firing patterns of neurons. These findings indicate that the LSFM could be used to evaluate the efficacy of neuropharmaceuticals in modulating brain activity, making it a valuable tool for developing drugs that target neurological diseases such as Parkinson's disease or epilepsy. Fig. 13 highlights a 21-hour time-lapse experiment in which researchers tracked
<figure><img src="image_16.png" /><figcaption>**Fig. 12.** Whole-brain profiling of cells via LSFM. (A) Volume-rendered and single-plane images of a mouse brain transduced with AAV-PHP.eB expressing NSE-H2B-mCherry (green) and counterstained with RD2 (red) and cleared with CUBIC-L/R + . Horizontal (x-y) and coronal (x-z) views show overlap (yellow) in various brain regions. Scale bars: 200 µm (single plane), 25 µm (magnification). (B) 3D and cross-sectional images of positive-cell-number ratio maps for the whole brain; voxel size: 80 µm. Scale bars: 2 mm, 50 µm. The figure was reused from [70] with permission.</figcaption></figure>

the development of *Drosophila* embryos, which express red fluorescent protein (RFP) in all cell nuclei (His2Av-mRFP1). This is to visualize embryonic development at high spatial resolution over a long period, capture major developmental events and test the real-time adaptive focusing and imaging adjustments offered by adaptive LSFM.

In addition, Zhao et al. [82] introduced isotropic superresolution light-sheet microscopy, which significantly enhances the spatial resolution of LSFM while maintaining the advantages of low phototoxicity and minimal photobleaching. This advancement allows researchers to observe dynamic intracellular structures, such as vesicle trafficking or cytoskeletal dynamics, at subsecond timescales, providing critical insights into fast cellular processes that were previously difficult to capture. A study by Zhao et al. demonstrated how this technique could be applied to track rapid intracellular responses to drug treatments, offering a new dimension to the ability of LSFM for drug monitoring. By achieving isotropic resolution, this study provides a more detailed and accurate understanding of how drugs interact with intracellular structures, highlighting the potential of superresolution LSFM in the field of drug delivery. These advances represent a significant leap in the precision and versatility of LSFM, particularly in studies of drug efficacy and the cellular response. The integration of optogenetics with isotropic

superresolution LSFM opens new possibilities for controlling and observing cellular behavior with unprecedented detail, making it a valuable tool for studying complex drug-tissue interactions in real time.

### 7.4. Multi-Modal imaging for comprehensive tissue analysis

The combination of LSFM with other advanced imaging modalities has opened new avenues for comprehensive tissue analysis, allowing researchers to gain more detailed insights into cellular structures, drug distribution, and tissue behavior. By leveraging the strengths of multiple imaging techniques, multimodal approaches provide a holistic view of biological processes that would be difficult to achieve with a single imaging modality. One such modality is light-sheet Raman microscopy, which combines LSFM with Raman spectroscopy to analyze both the structure and chemical composition of tissues. In their study, Müller et al. [42] demonstrated how light-sheet Raman microscopy used to visualize the chemical fingerprints of different tissue components in live zebrafish models (as shown in Fig. 14). This approach allowed for the simultaneous imaging of cellular structures while tracking how drugs were metabolized within the tissue. Fig. 14 shows a 3D Raman image of a zebrafish eye, revealing the collagen-like,
<figure><img src="image_17.png" /><figcaption>**Fig. 13.** Maximum-intensity projections of a developing D. melanogaster embryo over 21 h via adaptive light-sheet microscopy. (a) Landmark developmental processes annotated over time. (b) Real-time light sheet corrections to increase the spatial resolution at six reference planes. (c) Improving image quality through adaptive imaging, recovering cellular features not visible with nonadaptive techniques. (d) Side-by-side comparison showing enhanced resolution with adaptive corrections over the 21-hour experiment. The figure is reused with permission from Adapted from [61].</figcaption></figure>

lipid-like, and DNA-like fingerprints of different tissue components. This multimodal approach enhances the ability to analyze not only the spatial distribution of tissues but also the molecular interactions of drugs within those tissues. This integration of Raman spectroscopy and LSFM offers a deeper understanding of how drugs behave at the molecular level, which

is crucial for improving drug formulations and therapeutic efficacy.

For example, Müller et al. [42] demonstrated the use of light-sheet Raman spectroscopy in combination with LSFM to monitor drug uptake and distribution in live tissues. While LSFM provides high-resolution images of cellular structures, Raman spectroscopy provides
<figure><img src="image_18.png" /><figcaption>**Fig. 14.** 3D Raman image of a zebrafish eye. The figure shows unmixed NMF channels representing different tissue components: collagen-like (red), lipid-like (green), and DNA-like (blue) fingerprints. Selected 3D slices are presented in different planes. The figure was recreated from Muller et al. [42] with permission.</figcaption></figure>

detailed information about the chemical composition of tissues, allowing researchers to track how drugs are metabolized and absorbed at the molecular level. This combination of structural and chemical imaging provides a more complete understanding of how drugs interact with biological tissues, making it an invaluable tool for drug development.

Fig. 15 A represents the setup for the acoustic LSFM used to capture wide-field images of cleared mouse brains. Fig. 15 B and C display the light-sheet profiles within the CUBIC-cleared mouse brain sample embedded in the hydrogel. The key feature highlighted here is the thin light distribution achieved by the guided light sheet, in contrast to the unguided Gaussian beam shown on the left side of the image. Fig. 15 D and E show horizontal sections of the cleared mouse brain tissue captured by a camera objective with a magnification of 1 × and a numerical aperture (NA) of 0.08. Fig. 15 F and G show enlarged images focused on specific regions from Fig. 15 C and D, providing a closer look at the spatial details captured by the acoustic light-sheet technique. Fig. 15 H shows fluorescence intensity profiles taken along the orange and blue dotted lines from Fig. 15 F and G. The profiles compare the fluorescence signal strength between different regions of the mouse brain, demonstrating how the acoustic light-sheet technique improves signal consistency and reduces scattering, resulting in higher contrast images. Acoustic light-sheet microscopy is another emerging technique, which has been developed to improve imaging depth and spatial resolution in highly scattering biological tissues, such as the brain. Wunderl et al. [79] demonstrated how acoustic light-sheet microscopy, when combined with LSFM, could image large biological samples such as cleared mouse brains. By using acoustic waves to guide the light sheet, the system reduced the diffraction and scattering of light, resulting in clearer and higher-resolution images of deep brain structures. Additionally, two-photon scanned light-sheet microscopy has been employed to increase the imaging speed and depth while reducing phototoxicity in live imaging applications. Truong et al. [68] used this method to achieve deep and fast live imaging in embryonic models, allowing researchers to track developmental processes at subcellular resolution with minimal damage to the tissue. This method is particularly beneficial for long-term drug delivery monitoring in live organisms, as it enables detailed imaging of cellular dynamics over extended periods. Together, these

advancements in multimodal imaging complement LSFM, providing researchers with powerful tools to study complex biological processes in a more comprehensive manner. By integrating different imaging technologies, LSFM can now provide unprecedented insights into tissue behavior, drug interactions, and embryonic development, paving the way for future breakthroughs in precision medicine and drug discovery.

### 7.5. Combining LSFM with Label-Free microscopy techniques

LSFM relies heavily on fluorescence labeling, limiting its ability to visualize non-fluorescent structures and molecules. To address this limitation, label-free microscopy techniques, such as Coherent Anti-Stokes Raman Scattering (CARS) and Second Harmonic Generation (SHG), provide complementary capabilities by offering molecular and structural information without requiring exogenous labels. The integration of LSFM with these label-free methods enhances its applicability in drug delivery research by combining volumetric imaging with molecular specificity and structural characterization. For instance, SHG microscopy enables the visualization of non-fluorescent extracellular matrix structures, such as collagen, which are critical for understanding drug-tissue interactions[3]. Similarly, CARS and Raman microscopy allow for label-free chemical imaging, providing insights into the distribution of lipid-based drug carriers and other molecules without fluorescent tags[20]. By combining LSFM with these label-free techniques, researchers can achieve a more comprehensive understanding of drug delivery pathways, cellular dynamics, and tissue architecture, thereby expanding the utility of LSFM in biomedical research. Table 5 summarizes the key synergies between LSFM and label-free imaging modalities, highlighting their advantages and applications in drug delivery studies.

## 8. Recent advancements in LSFM

The application of fluorescent proteins in biological imaging has revolutionized the study of cellular behaviors in complex 3D environments. Early fluorophores, particularly green fluorescent protein (GFP), enabled real-time visualization of cellular processes, including drug
<figure><img src="image_19.png" /><figcaption>**Fig. 15.** Wide-field imaging of a cleared mouse brain via acoustic light-sheet microscopy. (A) Schematic of the optical setup. (B-G) Light-sheet profiles inside the CUBIC-cleared mouse brain and fluorescence intensity profiles. The acoustic light-sheet technique enhances spatial resolution and image contrast by mitigating light diffraction. Adapted from Wunderl et al. [79] with permission. The figure was reused from [79] with permission.</figcaption></figure>

<table><caption>**Table 5**
Synergies Between LSFM and Label-Free Imaging Techniques.</caption><thead><tr><td>Imaging Modality</td><td>Key Advantage</td><td>Key Applications</td><td>Relevant Studies</td></tr></thead><tbody><tr><td>LSFM + SHG</td><td>Visualizes non-fluorescent extracellular structures.</td><td>Monitoring drug interactions with collagen networks.</td><td>[3]</td></tr><tr><td>LSFM + CARS</td><td>Enables label-free molecular imaging of lipids.</td><td>Tracking lipid-based drug delivery systems.</td><td>[20]</td></tr><tr><td>LSFM + Raman Microscopy</td><td>Provides label-free mapping of molecular composition.</td><td>Studying drug distribution and metabolism.</td><td>[44]</td></tr></tbody></table>

distribution in tissues. However, challenges such as photobleaching and limited tissue penetration demand the development of more advanced fluorescent markers.

### 8.1. Early fluorophores and their role in drug delivery studies

The use of early fluorophores in LSFM has revolutionized the study of drug delivery by enabling real-time, 3D visualization of drug interactions within biological tissues. One of the earliest examples of fluorophore usage in LSFM was the application of GFP to tag and visualize cellular components and drug interactions in tumor models. Pampaloni et al. [46] demonstrated the utility of GFP-labeled cells in 3D multicellular spheroids, which mimic the complexity of in vivo tumors. These early studies highlighted the ability of LSFM to track drug penetration in real time, providing crucial insights into drug distribution and cellular behavior within the tumor microenvironment [46]. However, as drug delivery studies have become more complex, traditional fluorophores such as GFP have limitations, particularly regarding their susceptibility to photobleaching and limited tissue penetration. This issue was addressed in later studies, where researchers began incorporating more stable fluorophores and alternative imaging techniques to overcome these challenges. Pampaloni, Chang, and Stelzer [107],
explored the quantitative imaging capabilities of LSFM and underscored the importance of improving fluorophore stability for long-term imaging of drug delivery processes. Their work demonstrated how advanced fluorophores and LSFM could be combined to monitor drug-tissue interactions, providing precise quantification of drug diffusion in complex tissue models (Pampaloni, Chang, and Stelzer, 2015). A pivotal moment in the advancement of fluorophore-based LSFM studies was the ability to visualize drug behavior in large tissue samples without the need for mechanical sectioning. This was exemplified by the concept of “microtome-free histology,” where Pampaloni et al. [50] imaged optically cleared KPL-4 xenograft tumors via LSFM. This innovative approach allowed for the complete 3D reconstruction of tumors, revealing the autofluorescence signal of the tumor without requiring mechanical slicing. As shown in Fig. 16, the optical slicing ability of the LSFM provided a perfect correlation between the optical and mechanical sections. The necrotic areas (white arrows) and solid tumor regions (yellow arrows) were clearly distinguishable in both the optical and mechanical slices, demonstrating the precision of LSFM in tumor imaging [50].

Additionally, LSFM enabled volume rendering of optically cleared tumors, which further enhanced drug distribution studies. In the same specimen, the blood vessels could be visualized in 3Ds, revealing the interaction between the tumor vasculature and therapeutic agents. The penetration of trastuzumab-Alexa 750, a fluorescently labeled therapeutic antibody, was successfully tracked as it infiltrated the tumor (as shown in Fig. 16 d). These findings are crucial in highlighting how LSFM could provide a clear visualization of drug penetration in vivo, supporting the development of more effective cancer therapies. The ability of LSFM to combine optical sectioning with fluorescent labeling provides a significant advantage over traditional imaging methods, enabling researchers to visualize drug delivery and distribution in unprecedented detail. By using early fluorophores and advancing their application through LSFM, researchers such as Pampaloni, Chang and Stelzer [107], laid the groundwork for more complex studies of drug behavior in live tissue environments, which continue to drive innovations in the field of drug delivery monitoring today [107]. However, early fluorophores have significant limitations. Photobleaching—the loss of fluorescence intensity over time—is one of the most common challenges, especially during long-term imaging. Additionally, these fluorophores struggle with limited tissue penetration, particularly when imaging thicker or denser tissues, such as those encountered during embryonic development. In these cases, the need for deeper tissue imaging without damaging the embryo or affecting its development became increasingly apparent. Studies using early fluorophores highlighted the critical need for more stable and versatile dyes

that could support long-term imaging in live tissue models. This limitation became especially clear in embryonic development studies, where the structural complexity and delicate nature of the tissue required more advanced imaging techniques. As researchers have sought to improve imaging depth and longevity, Pampaloni et al. [48] explored LSFM's capability for deep 3D imaging and emphasized the importance of near-infrared (NIR) dyes for enhanced penetration and reduced photobleaching. Although the study focused on reducing phototoxicity, it underscored the limitations of early fluorophores and the necessity of developing new solutions [48].

## 8.2. Advancements with Photo-Switchable fluorophores

To address the issues posed by early fluorophores, researchers developed photoswitchable fluorophores, which could be switched on and off using specific wavelengths of light. These fluorophores were engineered to improve imaging depth, photostability, and versatility, particularly in long-term studies. Lui et al. [108], led significant advancements in this field by developing a new class of photoswitchable fluorophores designed explicitly for deep tissue imaging in LSFM. These fluorophores reduced the photobleaching that plagued earlier studies, allowing for extended imaging sessions without degrading the fluorescence intensity [108]. This innovation is particularly valuable in embryonic development studies, where the ability to image deep tissues with minimal phototoxicity is crucial. By employing photoswitchable fluorophores, the team led by Chen [109], was able to monitor drug delivery in embryonic tissues with greater precision and longevity Lui et al. [108]. Their work demonstrated that LSFM could track the movement of nanoparticle-based drugs through thick tissue layers and complex biological environments, providing real-time data on how drugs diffuse and interact with developing tissues. The use of photoswitchable fluorophores opened new possibilities for long-term studies, which were previously limited by the instability of early fluorescent proteins. In the context of drug delivery monitoring, Chen's contributions were built upon earlier work by Tomer et al. [66], who emphasized the importance of multiview imaging for capturing the dynamics of drug distribution. However, Chen's [109] introduction of photoswitchable fluorophores marked a significant leap forward by enabling long-term imaging in deeper tissues, a challenge that had previously hindered the application of LSFM in drug studies. With these advances, LSFM could now be applied to complex biological models, including mammalian embryos, where tissue thickness poses significant challenges for traditional fluorescence microscopy techniques. The application of near-infrared (NIR) dyes in combination with photoswitchable fluorophores also improved tissue penetration and minimized the

<figure><img src="image_20.png" /><figcaption>**Fig. 16.** Optically cleared KPL-4 xenograft tumors imaged via LSFM. (a) 3D image captured via LSFM showing autofluorescence from the optically cleared tumor. (b) The same tumor specimen was subsequently embedded in paraffin, sectioned mechanically, and stained with hematoxylin and eosin (H&E). A comparison between the LSFM optical slices (a) and the microtome sections (b) reveals a strong correlation in the identified necrotic (white arrows) and solid tumor regions (yellow arrows). (c) 3D volume rendering of the cleared tumor highlighting blood vessel structures. (d) Visualization of trastuzumab-Alexa 750 penetration within the same tumor specimen. Scale bars: 250 µm. The figure was recreated from [107] with permission.</figcaption></figure>
damage caused by prolonged imaging. These fluorophores are particu-
larly useful for monitoring drug delivery in deeper tissues without
compromising the health of the sample, making them an ideal tool for
embryonic development studies where long-term observations are
necessary[66]. The development of photoswitchable fluorophores and
the application of LSFM in 3D imaging have dramatically transformed
the ability to monitor drug distribution in real time, providing a more
comprehensive understanding of drug dynamics in complex tissue en-
vironments. As researchers continue to refine these techniques, the po-
tential applications for drug discovery and embryo-specific drug
delivery will undoubtedly expand, allowing for more targeted and
effective therapeutic interventions.

9. Potential clinical applications of the LSFM

One of the most promising clinical applications of LSFM lies in pre-
natal diagnostics, where its real-time imaging capabilities allow for the
early detection of developmental abnormalities. The LSFM can visualize
embryonic organogenesis in 3Ds, providing crucial insights into how
organs such as the heart, brain, liver, and kidneys form. Yue et al. [81]
demonstrated the utility of LSFM in monitoring heart development,
particularly during ventricle chamber formation, providing an unpre-
edented view of cellular dynamics. This capability has the potential to
detect cardiovascular defects early, paving the way for interventions in
cases of congenital heart disease. Similarly, Tomer et al. [66] used LSFM
to track the formation of the neural tube in developing embryos,
revealing insights into how genetic mutations or environmental factors
can interfere with central nervous system development. Such research is
pivotal in the early diagnosis of neural tube defects such as spina bifida
and anencephaly, which often emerge due to disruptions during early
embryogenesis [81,66]. Furthermore, LSFM can be used to monitor
therapeutic drug delivery during embryonic development, making it an
invaluable tool for prenatal treatment. The ability to visualize how
neuroprotective drugs interact with neuronal tissues in real time offers
clinicians a means to tailor treatments, potentially preventing develop-
mental disorders. By tracking how drugs diffuse through embryonic
tissues, LSFM can improve our understanding of how therapeutics in-
fluence early-stage development and facilitate more effective, targeted
treatments. Another significant clinical application of LSFM is in stem
cell therapy. As the field of regenerative medicine has progressed,
tracking stem cell behavior and their integration into existing tissues has
become crucial. Schmid et al. [62] utilized LSFM to monitor the dy-
namics of endodermal cells in live embryos, setting the foundation for
stem cell tracking in therapeutic contexts. In clinical applications, LSFM
can be used to visualize the migration and differentiation of stem cells,
particularly in regenerating damaged tissues. For example, in patients
with heart disease, LSFM could help monitor how cardiac stem cells
repair damaged heart tissue in real time, providing immediate feedback
on the efficacy of the therapy. The ability of LSFM to track stem cells as
they interact with their environment could help clinicians optimize stem
cell therapies for conditions such as neurodegenerative disorders and
muscular dystrophy [62]. Furthermore, Weber et al. [74] illustrated
how LSFM could monitor cellular apoptosis and regeneration in 3D
cultures, providing critical data on the cellular response to stem cell
therapies. This ability to capture the interaction between stem cells and
their microenvironment is vital for optimizing stem cell-based treat-
ments. In the context of 3D cell cultures, Pampaloni and Stelzer [47]
emphasized the utility of LSFM in toxicology studies. Compared with
traditional 2D models, 3D cell cultures more accurately mimic in vivo
conditions, and LSFM allows for high-resolution imaging of these com-
plex structures without causing photodamage. This makes LSFM a
valuable tool for studying the effects of drugs and chemicals on cells,
offering insights into cellular responses to toxic compounds and aiding
in the development of safer therapeutic agents. The 3D nature of LSFM
allows for the precise tracking of drug distribution and cell behavior,
advancing the field of toxicology. Moreover, Packard [110], highlighted

the role of advanced imaging techniques, including LSFM, in cardio-
vascular research. By combining LSFM with bioengineering approaches,
researchers can visualize how the cardiopulmonary system develops and
responds to drugs, aiding in the understanding and treatment of car-
diovascular diseases. The incorporation of machine learning and artifi-
cial intelligence into LSFM further enhances its potential for clinical
applications. As demonstrated by Ding et al. [100], multiscale
light-sheet imaging coupled with machine learning allows for rapid,
high-resolution imaging of the cardiopulmonary system. This integra-
tion can optimize data analysis, enabling real-time monitoring of drug
distribution and cellular responses in complex biological systems. Lim
et al. [93], discussed the future of the LSFM in various applications,
predicting its increasing role in clinical settings as technologies such as
adaptive optics and computational tools evolve, allowing for deeper
imaging and greater precision. The clinical potential of LSFM extends
beyond prenatal diagnostics and regenerative medicine. Its unique
ability to monitor drug delivery in real time, combined with the preci-
sion of machine learning algorithms, positions it as a key tool in
personalized medicine. By visualizing how therapies interact with tis-
sues at the cellular level, LSFM offers a pathway toward more tailored
and effective treatment strategies, revolutionizing how we approach
disease diagnosis and therapy.

10. Future perspectives for LSFM development

LSFM has transformed biological imaging with its unique ability to
visualize 3D structures in real time with minimal phototoxicity. How-
ever, the technique has vast potential for further enhancement. Below,
we outline key directions for LSFM development, grounded in real, peer-
reviewed research.

Adaptive Optics for Enhanced Imaging Depth.

Adaptive optics (AO) is a critical technology for improving the im-
aging depth and resolution of LSFM, particularly in thick and scattering
biological tissues. AO systems dynamically correct optical aberrations
caused by tissue heterogeneity, enabling sharper and more detailed
visualization of subcellular structures. Royer et al. [61] demonstrated
the integration of adaptive optics into LSFM, achieving significant im-
provements in long-term, high-resolution imaging of living organisms.
Their study highlighted the ability of AO to maintain image quality over
extended periods, which is essential for observing dynamic processes
like drug delivery. When applied to drug delivery studies, AO-enhanced
LSFM has the potential to reveal detailed drug distribution and phar-
macokinetics in complex environments, such as tumors or organoids. By
correcting distortions in real time, AO facilitates precise imaging at
unprecedented depths, paving the way for a more comprehensive un-
derstanding of how therapeutic agents interact with dense tissues.

High-Throughput and Portable LSFM Systems.

The development of high-throughput and portable LSFM systems is paving the way for broader accessibility and efficiency in imaging applications. Portable LSFM platforms, designed to be compact yet powerful, have shown promise in providing advanced imaging capabilities in resource-limited settings. Advances in optical engineering and miniaturization have enabled the creation of lightweight, user-friendly LSFM setups that retain high resolution and sensitivity[45]. These systems are particularly valuable for field-based studies and decentralized laboratories, offering the potential for in situ imaging of biological samples. High-throughput LSFM platforms, on the other hand, cater to the growing demand for efficient large-scale analyses. By enabling the simultaneous imaging of multiple organoids or tissue samples, these systems significantly reduce the time and cost associated with preclinical testing and drug discovery. For example, LSFM has been integrated with automated sample handling and imaging pipelines to screen hundreds of drug candidates for their effects on 3D cellular models, accelerating the identification of promising therapeutics[31].

Future innovations in portable and high-throughput LSFM systems
are expected to combine miniaturization with enhanced automation,
integrating artificial intelligence (AI) for real-time image analysis and decision-making. Such advancements will further optimize the study of drug delivery and therapeutic efficacy in complex biological systems, making LSFM a cornerstone technology in both research and clinical settings.

## AI-Powered Data Analysis and Real-Time Imaging Optimization

Artificial intelligence (AI) has emerged as a transformative tool for automating and enhancing LSFM data analysis, addressing challenges posed by the vast and complex datasets generated by this technique. Machine learning (ML) algorithms, such as convolutional neural networks (CNNs), have been instrumental in improving image segmentation, artifact correction, and denoising, enabling faster and more accurate analysis of large datasets [111]. These capabilities are especially critical for drug delivery studies, where precise quantification of pharmacokinetics and tissue interactions is essential. AI frameworks have been developed to streamline LSFM data processing and enhance imaging quality, even under suboptimal conditions. For example, Weigert et al. [75] introduced CARE (Content-Aware Image Restoration), a deep learning-based method that restores fine structural details in LSFM data while reducing noise and artifacts. Such innovations minimize phototoxicity and photobleaching, enabling long-term imaging of live tissues crucial for monitoring drug dynamics. These tools allow researchers to efficiently identify drug-labeled cells, quantify tissue penetration, and track spatiotemporal changes induced by therapeutic agents. AI is also playing a pivotal role in real-time imaging optimization. Recent advancements include AI-driven intelligent beam optimization, which dynamically adjusts the light sheet's shape and intensity during live imaging sessions. This approach ensures consistent illumination across samples, reduces phototoxicity, and maintains image quality as the biological environment changes. Royer et al. [61] demonstrated how adaptive light-sheet microscopy, powered by deep learning, facilitates high-resolution imaging over extended periods. This capability is particularly beneficial for drug delivery studies, where tissue dynamics and drug-induced changes alter optical properties over time.

Beyond enhancing imaging quality, AI is revolutionizing drug screening by integrating with high-throughput LSFM platforms. These systems combine automated sample handling with AI-driven data analysis pipelines, enabling researchers to evaluate multiple drug candidates simultaneously. For instance, AI algorithms can analyze cellular-level drug-tissue interactions in real time, accelerating the identification of promising therapeutics and reducing preclinical testing timelines [41]. Looking ahead, AI's role in LSFM is expected to expand with the adoption of advanced generative models and federated learning frameworks. Generative adversarial networks (GANs) have the potential to synthesize missing or incomplete data in LSFM datasets, making them more robust and usable for downstream analyses. Federated learning, on the other hand, could enable collaborative research across institutions by training AI models on distributed datasets without compromising data privacy. These advancements will further enhance LSFM's applicability in drug delivery research, enabling deeper insights into therapeutic efficacy and tissue-specific responses.

## Applications in Organoid Models and Tissue Engineering.

Organoids and engineered tissues have emerged as indispensable tools for studying human biology and disease, providing physiologically relevant in vitro models that mimic key aspects of organ structure and function. These 3D models have become central to preclinical drug testing, enabling researchers to explore therapeutic effects in a controlled, reproducible environment. LSFM, with its high-resolution, low-phototoxicity imaging capabilities, is uniquely suited for visualizing the dynamic processes occurring within organoids and engineered tissues. LSFM is particularly effective for capturing real-time changes in organoids, such as cellular responses to drug treatments or the progression of disease states. Lancaster and Knoblich [36] demonstrated the use of LSFM to monitor neural organoid development, providing critical insights into tissue architecture and cellular differentiation. In drug

delivery studies, LSFM enables precise visualization of drug distribution, cellular uptake, and downstream effects, such as vascular remodeling or tissue regeneration. For example, LSFM has been employed to assess the efficacy of anti-cancer drugs by tracking their penetration into tumor organoids and analyzing their effects on cell viability and proliferation. Tissue engineering, which involves the creation of biomimetic tissues and organs for therapeutic purposes, also benefits from LSFM's advanced imaging capabilities. LSFM allows researchers to study the interaction between drugs and engineered tissues in real time, providing valuable data on pharmacokinetics and drug-induced structural changes. Clevers [113], highlighted how LSFM could visualize the dynamic remodeling of intestinal tissue engineered from stem cells, revealing drug-target interactions at unprecedented resolution. Recent advancements in LSFM have enhanced its utility for imaging organoids and engineered tissues. Tissue-clearing techniques, such as CLARITY and CUBIC, improve the transparency of 3D models, allowing LSFM to capture deeper structures without compromising image quality [14, 112]. Additionally, adaptive optics integrated into LSFM systems can further enhance imaging depth and resolution, particularly in dense or complex tissues. These innovations are critical for studying the delivery and efficacy of drugs in highly heterogeneous systems, such as vascularized tumor organoids or liver-on-chip models.

Looking forward, the combination of LSFM with emerging technologies, such as machine learning and multi-modal imaging, will further expand its applications in organoid and tissue engineering research. AI-driven analytics can automate the interpretation of LSFM datasets, enabling high-throughput analysis of organoid responses to drug treatments. Furthermore, integrating LSFM with complementary imaging modalities, such as Second Harmonic Generation (SHG) microscopy, could provide simultaneous insights into structural and molecular changes within organoids. These advancements will position LSFM as an indispensable tool for bridging the gap between in vitro models and in vivo systems, accelerating the translation of discoveries into clinical therapies.

## Advancements in Light Shaping Methods.

The evolution of LSFM is closely tied to innovations in light shaping methods that enhance its resolution, depth penetration, and adaptability to diverse imaging challenges. These advancements not only address existing limitations, such as light scattering and photodamage, but also unlock new possibilities for LSFM in studying dynamic biological systems and drug delivery. By integrating cutting-edge light manipulation techniques, LSFM can become a more versatile and powerful tool for biomedical research. One significant challenge in LSFM is imaging deep into scattering tissues, where light attenuation and aberrations degrade resolution and contrast. Recent developments in ultrasound-guided light delivery have demonstrated how parallelized acoustic waves can direct light efficiently through scattering media. Mestre-Torà and Duocastella [40] introduced a technique that combines ultrasound and optical waves to dynamically steer light, creating precise illumination patterns even in highly scattering environments. This advancement holds great promise for deep-tissue LSFM imaging, where accurate visualization of drug distribution in dense tissues, such as tumors, is critical. Complementing this, fast acoustic light sculpting has emerged as a powerful method for generating adaptive illumination patterns at high temporal resolutions. Surdo and Duocastella [64] demonstrated its potential for real-time applications by rapidly modulating light intensity and shape to match the sample's dynamic properties. Integrating this approach into LSFM systems can significantly enhance the study of fast processes like drug diffusion or tissue remodeling, enabling researchers to observe subtle changes in drug interactions with live tissues. Another transformative approach lies in universal light-sheet generation using field synthesis. Chang et al. [8] developed a method to tailor light sheets precisely to match sample geometries and optical properties. By synthesizing custom illumination profiles, this technique minimizes light scattering and maximizes signal-to-noise ratios, even in heterogeneous samples like vascularized organoids. When applied to LSFM, such advancements can
optimize imaging of complex 3D structures, allowing for accurate tracking of drug penetration and distribution at cellular and tissue scales. The control of light propagation in multimode fibers further enhances LSFM's capacity for deep imaging. Cao et al. [5] explored advanced techniques for manipulating light transmission in multimode fibers, enabling precise delivery of structured light into otherwise inaccessible regions. Integrating multimode fiber optics into LSFM systems can extend imaging depth, particularly in challenging samples such as thick organoids or animal models used in preclinical drug studies.

Finally, innovations in flat optics have revolutionized structured light generation, enabling highly tunable and efficient illumination. Dorrah and Capasso [17] demonstrated how flat optical devices can produce custom light fields tailored to specific imaging needs. Such structured light can improve LSFM by reducing artifacts and enhancing resolution, especially in samples with varying optical properties. These methods allow LSFM to adapt dynamically to complex biological environments, providing clearer insights into drug-tissue interactions and molecular dynamics. Collectively, these advancements in light shaping methods address key limitations in LSFM, such as scattering, phototoxicity, and limited adaptability. By integrating ultrasound-guided light steering, fast acoustic sculpting, field synthesis, multimode fiber optics, and flat optics into LSFM systems, researchers can achieve unprecedented imaging depth, resolution, and flexibility. These technologies will not only enhance LSFM's performance but also broaden its applications, from studying drug delivery dynamics to exploring tissue-level responses in organoids and live organisms.

## 11. Conclusion

LSFM has significantly transformed the landscape of biomedical imaging, offering unprecedented insights into real-time drug delivery monitoring and embryonic development. By providing high-resolution, 3D views of live tissues with minimal phototoxicity, LSFM has enabled researchers to delve deeper into cellular processes that were once difficult to observe. This powerful technology is now central to drug development, precision medicine, and developmental biology, providing a detailed understanding of how drugs interact with tissues during critical phases of development. One of the most important contributions of LSFM has been in drug delivery monitoring, where it has enabled the visualization of drug distribution in live tissues, including delicate embryonic structures. Studies such as those by Fan et al. [19] and Kumar et al. [35] have highlighted LSFM's ability to monitor nanoparticle-based drug delivery, providing critical insights into how drugs are distributed, absorbed, and metabolized in real time. These capabilities are crucial for the development of personalized therapies, as they allow for the fine-tuning of drug doses and delivery mechanisms to maximize efficacy while minimizing side effects. Moreover, LSFM has had a profound impact on embryonic development research, allowing scientists to track organ formation, cell migration, and gene expression during the earliest stages of life. By combining LSFM with technologies such as CRISPR-Cas9 and single-cell sequencing, researchers can now observe how genetic interventions affect the development of tissues and organs in real time. For example, Tomer et al. [66] and Rieckher et al. [60] demonstrated how LSFM can be used to monitor the delivery and efficacy of gene-editing tools during critical stages of embryogenesis, opening new possibilities for prenatal gene therapy. Despite these advancements, LSFM still faces several challenges, particularly in long-term imaging, where issues such as photobleaching and tissue opacity can limit its effectiveness. The development of next-generation fluorophores, such as near-infrared dyes and quantum dots, along with the integration of adaptive optics for deeper tissue imaging, has helped address some of these limitations, but further improvements are needed. Additionally, the sheer volume of data generated by the LSFM poses a challenge for data storage and processing, which has led to the integration of AI-driven data analysis to streamline workflows and make sense of the complex datasets involved. In the future, the LSFM may

become even more integral to clinical applications. Its potential for personalized medicine, where it can guide real-time drug monitoring and tailored treatments, is particularly exciting. By combining the LSFM with AI and machine learning, researchers can accelerate high-throughput drug screening and better predict therapeutic outcomes in diverse patient populations. Furthermore, the integration of LSFM with optogenetics, tissue clearing, and multimodal imaging will provide a more comprehensive understanding of drug-tissue interactions, enabling researchers to develop more effective treatments for diseases such as cancer and neurodegenerative disorders. In conclusion, LSFM has revolutionized the way in which we study drug delivery and embryonic development. Its ability to provide detailed, real-time imaging of live tissues has made it an indispensable tool in biomedical research and drug development. While challenges remain, the ongoing advancements in fluorophore design, adaptive optics, and AI-driven data analysis suggest that LSFM will continue to push the boundaries of what is possible in the field of precision medicine. Owing to its potential for personalized treatments, gene therapy, and high-throughput drug screening, LSFM will play a key role in the future of clinical diagnostics and therapeutic development.

## Funding

NM thank the Indian Council of Medical Research (ICMR) (Project Number-ITR/Ad hoc/43/2020-21, ID No. 2020-3286) Government of India, India for financial support. The authors further thank the Department of Science and Technology, Government of India, India (Grant number: GST/DST/TWN/P-95/2021) for financial support.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

Gagan Raju thanks the Council of Scientific and Industrial Research-Senior Research Fellowship [File No: 08/0602(19326)/2024-EMR-I] from the Ministry of Science and Technology, Government of India, India. NM thank the Manipal School of Life Sciences, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India, for providing the infrastructure and facilities.

## Data availability

Data will be made available on request.

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