@article{cai_zhang_li_ghashghaei_greenbaum_2023, title={COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains}, volume={3}, ISSN={["2667-2375"]}, DOI={10.1016/j.crmeth.2023.100454}, abstractNote={Tissue clearing renders entire organs transparent to accelerate whole-tissue imaging; for example, with light-sheet fluorescence microscopy. Yet, challenges remain in analyzing the large resulting 3D datasets that consist of terabytes of images and information on millions of labeled cells. Previous work has established pipelines for automated analysis of tissue-cleared mouse brains, but the focus there was on single-color channels and/or detection of nuclear localized signals in relatively low-resolution images. Here, we present an automated workflow (COMBINe, Cell detectiOn in Mouse BraIN) to map sparsely labeled neurons and astrocytes in genetically distinct mouse forebrains using mosaic analysis with double markers (MADM). COMBINe blends modules from multiple pipelines with RetinaNet at its core. We quantitatively analyzed the regional and subregional effects of MADM-based deletion of the epidermal growth factor receptor (EGFR) on neuronal and astrocyte populations in the mouse forebrain.}, number={4}, journal={CELL REPORTS METHODS}, author={Cai, Yuheng and Zhang, Xuying and Li, Chen and Ghashghaei, H. Troy and Greenbaum, Alon}, year={2023}, month={Apr} }
 @article{rai_li_ghashghaei_greenbaum_2023, title={Deep learning-based adaptive optics for light sheet fluorescence microscopy}, volume={14}, ISSN={["2156-7085"]}, DOI={10.1364/BOE.488995}, abstractNote={Light sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that is often used to image intact tissue-cleared specimens with cellular or subcellular resolution. Like other optical imaging systems, LSFM suffers from sample-induced optical aberrations that decrement imaging quality. Optical aberrations become more severe when imaging a few millimeters deep into tissue-cleared specimens, complicating subsequent analyses. Adaptive optics are commonly used to correct sample-induced aberrations using a deformable mirror. However, routinely used sensorless adaptive optics techniques are slow, as they require multiple images of the same region of interest to iteratively estimate the aberrations. In addition to the fading of fluorescent signal, this is a major limitation as thousands of images are required to image a single intact organ even without adaptive optics. Thus, a fast and accurate aberration estimation method is needed. Here, we used deep-learning techniques to estimate sample-induced aberrations from only two images of the same region of interest in cleared tissues. We show that the application of correction using a deformable mirror greatly improves image quality. We also introduce a sampling technique that requires a minimum number of images to train the network. Two conceptually different network architectures are compared; one that shares convolutional features and another that estimates each aberration independently. Overall, we have presented an efficient way to correct aberrations in LSFM and to improve image quality.}, number={6}, journal={BIOMEDICAL OPTICS EXPRESS}, author={Rai, Mani Ratnam and Li, Chen and Ghashghaei, H. Troy and Greenbaum, Alon}, year={2023}, month={Jun}, pages={2905–2919} }
 @article{moatti_cai_li_popowski_cheng_ligler_greenbaum_2023, title={Tissue clearing and three-dimensional imaging of the whole cochlea and vestibular system from multiple large-animal models}, volume={4}, ISSN={["2666-1667"]}, DOI={10.1016/j.xpro.2023.102220}, abstractNote={The inner ear of humans and large animals is embedded in a thick and dense bone that makes dissection challenging. Here, we present a protocol that enables three-dimensional (3D) characterization of intact inner ears from large-animal models. We describe steps for decalcifying bone, using solvents to remove color and lipids, and imaging tissues in 3D using confocal and light sheet microscopy. We then detail a pipeline to count hair cells in antibody-stained and 3D imaged cochleae using open-source software. For complete details on the use and execution of this protocol, please refer to (Moatti et al., 2022).1.}, number={2}, journal={STAR PROTOCOLS}, author={Moatti, Adele and Cai, Yuheng and Li, Chen and Popowski, Kristen D. and Cheng, Ke and Ligler, Frances S. and Greenbaum, Alon}, year={2023}, month={Jun} }
 @article{li_rai_ghashghaei_greenbaum_2022, title={Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning}, volume={13}, ISSN={["2156-7085"]}, DOI={10.1364/BOE.447392}, abstractNote={Light-sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that provides optical sectioning with reduced photodamage. LSFM is routinely used in life sciences for live cell imaging and for capturing large volumes of cleared tissues. LSFM has a unique configuration, in which the illumination and detection paths are separated and perpendicular to each other. As such, the image quality, especially at high resolution, largely depends on the degree of overlap between the detection focal plane and the illuminating beam. However, spatial heterogeneity within the sample, curved specimen boundaries, and mismatch of refractive index between tissues and immersion media can refract the well-aligned illumination beam. This refraction can cause extensive blur and non-uniform image quality over the imaged field-of-view. To address these issues, we tested a deep learning-based approach to estimate the angular error of the illumination beam relative to the detection focal plane. The illumination beam was then corrected using a pair of galvo scanners, and the correction significantly improved the image quality across the entire field-of-view. The angular estimation was based on calculating the defocus level on a pixel level within the image using two defocused images. Overall, our study provides a framework that can correct the angle of the light-sheet and improve the overall image quality in high-resolution LSFM 3D image acquisition.}, number={2}, journal={BIOMEDICAL OPTICS EXPRESS}, author={Li, Chen and Rai, Mani Ratnam and Ghashghaei, H. Troy and Greenbaum, Alon}, year={2022}, month={Feb}, pages={888–901} }
 @article{moatti_li_sivadanam_cai_ranta_piedrahita_cheng_ligler_greenbaum_2022, title={Ontogeny of cellular organization and LGR5 expression in porcine cochlea revealed using tissue clearing and 3D imaging}, volume={25}, ISSN={["2589-0042"]}, DOI={10.1016/j.isci.2022.104695}, abstractNote={Over 11% of the world's population experience hearing loss. Although there are promising studies to restore hearing in rodent models, the size, ontogeny, genetics, and frequency range of hearing of most rodents' cochlea do not match that of humans. The porcine cochlea can bridge this gap as it shares many anatomical, physiological, and genetic similarities with its human counterpart. Here, we provide a detailed methodology to process and image the porcine cochlea in 3D using tissue clearing and light-sheet microscopy. The resulting 3D images can be employed to compare cochleae across different ages and conditions, investigate the ontogeny of cochlear cytoarchitecture, and produce quantitative expression maps of LGR5, a marker of cochlear progenitors in mice. These data reveal that hair cell organization, inner ear morphology, cellular cartography in the organ of Corti, and spatiotemporal expression of LGR5 are dynamic over developmental stages in a pattern not previously documented.}, number={8}, journal={ISCIENCE}, author={Moatti, Adele and Li, Chen and Sivadanam, Sasank and Cai, Yuheng and Ranta, James and Piedrahita, Jorge A. and Cheng, Alan G. and Ligler, Frances S. and Greenbaum, Alon}, year={2022}, month={Aug} }
 @article{rai_li_greenbaum_2022, title={Quantitative analysis of illumination and detection corrections in adaptive light sheet fluorescence microscopy}, volume={13}, ISSN={["2156-7085"]}, DOI={10.1364/BOE.454561}, abstractNote={Light-sheet fluorescence microscopy (LSFM) is a high-speed, high-resolution and minimally phototoxic technique for 3D imaging of in vivo and in vitro specimens. LSFM exhibits optical sectioning and when combined with tissue clearing techniques, it facilitates imaging of centimeter scale specimens with micrometer resolution. Although LSFM is ubiquitous, it still faces two main challenges that effect image quality especially when imaging large volumes with high-resolution. First, the light-sheet illumination plane and detection lens focal plane need to be coplanar, however sample-induced aberrations can violate this requirement and degrade image quality. Second, introduction of sample-induced optical aberrations in the detection path. These challenges intensify when imaging whole organisms or structurally complex specimens like cochleae and bones that exhibit many transitions from soft to hard tissue or when imaging deep (> 2 mm). To resolve these challenges, various illumination and aberration correction methods have been developed, yet no adaptive correction in both the illumination and the detection path have been applied to improve LSFM imaging. Here, we bridge this gap, by implementing the two correction techniques on a custom built adaptive LSFM. The illumination beam angular properties are controlled by two galvanometer scanners, while a deformable mirror is positioned in the detection path to correct for aberrations. By imaging whole porcine cochlea, we compare and contrast these correction methods and their influence on the image quality. This knowledge will greatly contribute to the field of adaptive LSFM, and imaging of large volumes of tissue cleared specimens.}, number={5}, journal={BIOMEDICAL OPTICS EXPRESS}, author={Rai, Mani Ratnam and Li, Chen and Greenbaum, Alon}, year={2022}, month={May}, pages={2960–2974} }
 @article{li_moatti_zhang_ghashghaei_greenabum_2021, title={Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy}, volume={12}, ISSN={["2156-7085"]}, url={http://dx.doi.org/10.1364/boe.427099}, DOI={10.1364/BOE.427099}, abstractNote={Light-sheet fluorescence microscopy (LSFM) is a minimally invasive and high throughput imaging technique ideal for capturing large volumes of tissue with sub-cellular resolution. A fundamental requirement for LSFM is a seamless overlap of the light-sheet that excites a selective plane in the specimen, with the focal plane of the objective lens. However, spatial heterogeneity in the refractive index of the specimen often results in violation of this requirement when imaging deep in the tissue. To address this issue, autofocus methods are commonly used to refocus the focal plane of the objective-lens on the light-sheet. Yet, autofocus techniques are slow since they require capturing a stack of images and tend to fail in the presence of spherical aberrations that dominate volume imaging. To address these issues, we present a deep learning-based autofocus framework that can estimate the position of the objective-lens focal plane relative to the light-sheet, based on two defocused images. This approach outperforms or provides comparable results with the best traditional autofocus method on small and large image patches respectively. When the trained network is integrated with a custom-built LSFM, a certainty measure is used to further refine the network’s prediction. The network performance is demonstrated in real-time on cleared genetically labeled mouse forebrain and pig cochleae samples. Our study provides a framework that could improve light-sheet microscopy and its application toward imaging large 3D specimens with high spatial resolution.}, number={8}, journal={BIOMEDICAL OPTICS EXPRESS}, publisher={The Optical Society}, author={Li, Chen and Moatti, Adele and Zhang, Xuying and Ghashghaei, H. Troy and Greenabum, Alon}, year={2021}, month={Aug}, pages={5214–5226} }
 @article{li_moatti_zhang_ghashghaei_greenbaum_2022, title={Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy: publishers note (vol 12, pg 5214, 2021)}, volume={13}, ISSN={["2156-7085"]}, DOI={10.1364/BOE.450829}, abstractNote={This publisher’s note amends the spelling of the fifth
					author’s name in [Biomed. Opt. Express 12,
					5214 (2021)10.1364/BOE.427099].}, number={1}, journal={BIOMEDICAL OPTICS EXPRESS}, author={LI, Chen and Moatti, Adele and Zhang, Xuying and Ghashghaei, H. Troy and Greenbaum, Alon}, year={2022}, month={Jan}, pages={373–373} }
 @article{moatti_cai_li_sattler_edwards_piedrahita_ligler_greenbaum_2020, title={Three-dimensional imaging of intact porcine cochlea using tissue clearing and custom-built light-sheet microscopy}, volume={11}, ISSN={["2156-7085"]}, url={http://dx.doi.org/10.1364/boe.402991}, DOI={10.1364/BOE.402991}, abstractNote={Hearing loss is a prevalent disorder that affects people of all ages. On top of the existing hearing aids and cochlear implants, there is a growing effort to regenerate functional tissues and restore hearing. However, studying and evaluating these regenerative medicine approaches in a big animal model (e.g. pigs) whose anatomy, physiology, and organ size are similar to a human is challenging. In big animal models, the cochlea is bulky, intricate, and veiled in a dense and craggy otic capsule. These facts complicate 3D microscopic analysis that is vital in the cochlea, where structure-function relation is time and again manifested. To allow 3D imaging of an intact cochlea of newborn and juvenile pigs with a volume up to ∼ 250 mm3, we adapted the BoneClear tissue clearing technique, which renders the bone transparent. The transparent cochleae were then imaged with cellular resolution and in a timely fashion, which prevented bubble formation and tissue degradation, using an adaptive custom-built light-sheet fluorescence microscope. The adaptive light-sheet microscope compensated for deflections of the illumination beam by changing the angles of the beam and translating the detection objective while acquiring images. Using this combination of techniques, macroscopic and microscopic properties of the cochlea were extracted, including the density of hair cells, frequency maps, and lower frequency limits. Consequently, the proposed platform could support the growing effort to regenerate cochlear tissues and assist with basic research to advance cures for hearing impairments.}, number={11}, journal={BIOMEDICAL OPTICS EXPRESS}, publisher={The Optical Society}, author={Moatti, Adele and Cai, Yuheng and Li, Chen and Sattler, Tyler and Edwards, Laura and Piedrahita, Jorge and Ligler, Frances S. and Greenbaum, Alon}, year={2020}, month={Nov}, pages={6181–6196} }