COMBINe: Automated Detection and Classification of Neurons and Astrocytes in Tissue Cleared Mouse Brains

Cai, Yuheng; Zhang, Xuying; Li, Chen; Ghashghaei, H. Troy; Greenbaum, Alon

doi:10.1101/2022.12.06.519291

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…Do ventral gliogenic populations migrate into the cortex to compensate for loss of the EGFR-dependent gliogenic progenitors in Nes:F/F cortices, and if so, when and how? We have begun to address some of these questions by implementing automated and unbiased technologies into analysis of the forebrain 77 . Future studies addressing these questions will enhance our understanding of the important developmental process of gliogenesis and its regulation of forebrain homeostasis and function.…”

Section: Discussionmentioning

confidence: 99%

Bulk and Mosaic Deletions ofEgfrReveal Regionally Defined Gliogenesis in the Developing Mouse Forebrain

Zhang

Xiao

Johnson

et al. 2022

Preprint

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The Epidermal growth factor receptor (EGFR) plays a role in cell proliferation and differentiation in healthy development and in tumors, 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 deletions of Egfr revealed that ventral but not dorsal telencephalic EGFR expression is critical for forebrain homeostasis and the presence of transiently EGFR-dependent glial lineages including robust depletion of oligodendrocytes that last the mouse lifetime. Sparse deletion using Mosaic Analysis with Double Markers (MADM) surprisingly reveals a regional requirement for EGFR in dorsal, but not ventral glial lineages including both astrocytes and oligodendrocytes. These findings lead to a model in which EGFR-independent ventral glial progenitors compensate for the absence of EGFR-dependent dorsal glia in the forebrain, thus masking its precise role in the bulk model.

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Section: Discussionmentioning

confidence: 99%

Bulk and Mosaic Deletions ofEgfrReveal Regionally Defined Gliogenesis in the Developing Mouse Forebrain

Zhang

Xiao

Johnson

et al. 2022

Preprint

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“…Two mouse brains [46] and four prairie vole brains were subjected to pretreatment involving methanol, immunolabeling, and clearing [47]. Postnatal day 30 mouse brain samples were stained using chicken anti-GFP (with Alexa Fluor 647 as the secondary antibody) and rabbit anti-RFP (with Cy3 as the secondary antibody).…”

Section: Methodsmentioning

confidence: 99%

Enhancing Light-Sheet Fluorescence Microscopy Illumination Beams through Deep Design Optimization

Li,

Rai,

Cai

et al. 2023

Preprint

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Light sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times for imaging of tissue-cleared specimen. This allows for high-resolution 3D imaging of large tissue volumes. Inherently to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, with the notion that the illumination beam only illuminates a thin section that is being imaged. Therefore, substantial efforts are dedicated to identifying slender, non-diffracting beam profiles that can yield uniform and high-contrast images. An ongoing debate concerns the employment of the most optimal illumination beam; Gaussian, Bessel, Airy patterns and/or others. Comparisons among different beam profiles is challenging as their optimization objective is often different. Given that our large imaging datasets (∼0.5TB images per sample) is already analyzed using deep learning models, we envisioned a different approach to this problem by hypothesizing that we can tailor the illumination beam to boost the deep learning models performance. We achieve this by integrating the physical LSFM illumination model after passing through a variable phase mask into the training of a cell detection network. Here we report that the joint optimization continuously updates the phase mask, improving the image quality for better cell detection. Our method’s efficacy is demonstrated through both simulations and experiments, revealing substantial enhancements in imaging quality compared to traditional Gaussian light sheet. We offer valuable insights for designing microscopy systems through a computational approach that exhibits significant potential for advancing optics design that relies on deep learning models for analysis of imaging datasets.

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COMBINe: Automated Detection and Classification of Neurons and Astrocytes in Tissue Cleared Mouse Brains

Cited by 2 publications

References 44 publications

Bulk and Mosaic Deletions ofEgfrReveal Regionally Defined Gliogenesis in the Developing Mouse Forebrain

Bulk and Mosaic Deletions ofEgfrReveal Regionally Defined Gliogenesis in the Developing Mouse Forebrain

Enhancing Light-Sheet Fluorescence Microscopy Illumination Beams through Deep Design Optimization

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