@article{zhang_xiao_johnson_cai_horowitz_mennicke_coffey_haider_threadgill_eliscu_et al._2023, title={Bulk and mosaic deletions of Egfr reveal regionally defined gliogenesis in the developing mouse forebrain}, volume={26}, ISSN={["2589-0042"]}, DOI={10.1016/j.isci.2023.106242}, abstractNote={The epidermal growth factor receptor (EGFR) plays a role in cell proliferation and differentiation during healthy development and tumor growth; however, its requirement for brain development remains unclear. Here we used a conditional mouse allele for Egfr to examine its contributions to perinatal forebrain development at the tissue level. Subtractive bulk ventral and dorsal forebrain deletions of Egfr uncovered significant and permanent decreases in oligodendrogenesis and myelination in the cortex and corpus callosum. Additionally, an increase in astrogenesis or reactive astrocytes in effected regions was evident in response to cortical scarring. Sparse deletion using mosaic analysis with double markers (MADM) surprisingly revealed a regional requirement for EGFR in rostrodorsal, but not ventrocaudal glial lineages including both astrocytes and oligodendrocytes. The EGFR-independent ventral glial progenitors may compensate for the missing EGFR-dependent dorsal glia in the bulk Egfr-deleted forebrain, potentially exposing a regenerative population of gliogenic progenitors in the mouse forebrain.}, number={3}, journal={ISCIENCE}, author={Zhang, Xuying and Xiao, Guanxi and Johnson, Caroline and Cai, Yuheng and Horowitz, Zachary K. and Mennicke, Christine and Coffey, Robert and Haider, Mansoor and Threadgill, David and Eliscu, Rebecca and et al.}, year={2023}, month={Mar} }
 @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{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 corrects the article on p. 5214 in vol. 12, PMID: 34513252.].}, 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{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{cai_zhang_kovalsky_ghashghaei_greenbaum_2021, title={Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet}, volume={16}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0257426}, abstractNote={The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.}, number={9}, journal={PLOS ONE}, author={Cai, Yuheng and Zhang, Xuying and Kovalsky, Shahar Z. and Ghashghaei, H. Troy and Greenbaum, Alon}, year={2021}, month={Sep} }
 @article{muthusamy_brumm_zhang_carmichael_ghashghaei_2018, title={Foxj1 expressing ependymal cells do not contribute new cells to sites of injury or stroke in the mouse forebrain}, volume={8}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-018-19913-x}, abstractNote={Abstract}, journal={SCIENTIFIC REPORTS}, author={Muthusamy, Nagendran and Brumm, Andrew and Zhang, Xuying and Carmichael, S. Thomas and Ghashghaei, H. Troy}, year={2018}, month={Jan} }
 @article{muthusamy_zhang_johnson_yadav_ghashghaei_2017, title={Developmentally defined forebrain circuits regulate appetitive and aversive olfactory learning}, volume={20}, ISSN={["1546-1726"]}, DOI={10.1038/nn.4452}, abstractNote={Postnatal and adult neurogenesis are region- and modality-specific, but the significance of developmentally distinct neuronal populations remains unclear. We demonstrate that chemogenetic inactivation of a subset of forebrain and olfactory neurons generated at birth disrupts responses to an aversive odor. In contrast, novel appetitive odor learning is sensitive to inactivation of adult-born neurons, revealing that developmentally defined sets of neurons may differentially participate in hedonic aspects of sensory learning.}, number={1}, journal={NATURE NEUROSCIENCE}, author={Muthusamy, Nagendran and Zhang, Xuying and Johnson, Caroline A. and Yadav, Prem N. and Ghashghaei, H. Troy}, year={2017}, month={Jan}, pages={20–23} }
 @article{hammad_schmidt_zhang_bray_frohlich_ghashghaei_2015, title={Transplantation of GABAergic Interneurons into the Neonatal Primary Visual Cortex Reduces Absence Seizures in Stargazer Mice}, volume={25}, ISSN={["1460-2199"]}, DOI={10.1093/cercor/bhu094}, abstractNote={Epilepsies are debilitating neurological disorders characterized by repeated episodes of pathological seizure activity. Absence epilepsy (AE) is a poorly understood type of seizure with an estimated 30% of affected patients failing to respond to antiepileptic drugs. Thus, novel therapies are needed for the treatment of AE. A promising cell-based therapeutic strategy is centered on transplantation of embryonic neural stem cells from the medial ganglionic eminence (MGE), which give rise to gamma-aminobutyric acidergic (GABAergic) interneurons during embyronic development. Here, we used the Stargazer (Stg) mouse model of AE to map affected loci using c-Fos immunohistochemistry, which revealed intense seizure-induce activity in visual and somatosensory cortices. We report that transplantation of MGE cells into the primary visual cortex (V1) of Stg mice significantly reduces AE episodes and lowers mortality. Electrophysiological analysis in acute cortical slices of visual cortex demonstrated that Stg V1 neurons exhibit more pronounced increases in activity in response to a potassium-mediated excitability challenge than wildtypes (WT). The defective network activity in V1 was significantly altered following WT MGE transplantation, associating it with behavioral rescue of seizures in Stgs. Taken together, these findings present MGE grafting in the V1 as a possible clinical approach in the treatment of AE.}, number={9}, journal={CEREBRAL CORTEX}, author={Hammad, Mohamed and Schmidt, Stephen L. and Zhang, Xuying and Bray, Ryan and Frohlich, Flavio and Ghashghaei, H. Troy}, year={2015}, month={Sep}, pages={2970–2979} }