Visualization and Quantification of Post-stroke Neural Connectivity and Neuroinflammation Using Serial Two-Photon Tomography in the Whole Mouse Brain

Katherine Poinsatte, Dene Betz, Vanessa O. Torres, Apoorva D. Ajay, Shazia Mirza, Uma M. Selvaraj, Erik J. Plautz, Xiangmei Kong, Sankalp Gokhale, Julian P. Meeks, Denise M.O. Ramirez, Mark P. Goldberg, Ann M. Stowe

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Whole-brain volumetric microscopy techniques such as serial two-photon tomography (STPT) can provide detailed information on the roles of neuroinflammation and neuroplasticity throughout the whole brain post-stroke. STPT automatically generates high-resolution images of coronal sections of the entire mouse brain that can be readily visualized in three dimensions. We developed a pipeline for whole brain image analysis that includes supervised machine learning (pixel-wise random forest models via the “ilastik” software package) followed by registration to a standardized 3-D atlas of the adult mouse brain (Common Coordinate Framework v3.0; Allen Institute for Brain Science). These procedures allow the detection of cellular fluorescent signals throughout the brain in an unbiased manner. To illustrate our imaging techniques and automated image quantification, we examined long-term post-stroke motor circuit connectivity in mice that received a motor cortex photothrombotic stroke. Two weeks post-stroke, mice received intramuscular injections of pseudorabies virus (PRV-152), a trans-synaptic retrograde herpes virus driving expression of green fluorescent protein (GFP), into the affected contralesional forelimb to label neurons in descending tracts to the forelimb musculature. Mice were sacrificed 3 weeks post-stroke. We also quantified sub-acute neuroinflammation in the post-stroke brain in a separate cohort of mice following a 60 min transient middle cerebral artery occlusion (tMCAo). Naive e450+-labeled splenic CD8+ cytotoxic T cells were intravenously injected at 7, 24, 48, and 72 h post-tMCAo. Mice were sacrificed 4 days after stroke. Detailed quantification of post-stroke neural connectivity and neuroinflammation indicates a role for remote brain regions in stroke pathology and recovery. The workflow described herein, incorporating STPT and automated quantification of fluorescently labeled features of interest, provides a framework by which one can objectively evaluate labeled neuronal or lymphocyte populations in healthy and injured brains. The results provide region-specific quantification of neural connectivity and neuroinflammation, which could be a critical tool for investigating mechanisms of not only stroke recovery, but also a wide variety of brain injuries or diseases.

Original languageEnglish
Article number1055
JournalFrontiers in Neuroscience
Volume13
DOIs
StatePublished - Oct 4 2019

Bibliographical note

Funding Information:
The authors acknowledge the Moody Foundation Flow Cytometry Facility at the Children's Research Institute and the Neuro-Models Facility at UT Southwestern. Funding. This study was funded by grants to AS from NIH/NINDS (NS088555), to AS from the Dana Foundation David Mahoney Neuroimaging Program, to VT from the NIH Intergrative Immunology Training Program (5T32AI005284-40) the Haggerty Center for Brain Injury and Repair, and the Texas Institute for Brain Injury and Repair (TIBIR) (UTSW). SG was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award Number KL2TR001103. PRV-152 was generously provided by Dr. Lynn Enquist (Princeton University, Princeton, NJ) through the Center for Neuroanatomy with Neurotropic Viruses (NIH P40 OD010996).

Publisher Copyright:
© Copyright © 2019 Poinsatte, Betz, Torres, Ajay, Mirza, Selvaraj, Plautz, Kong, Gokhale, Meeks, Ramirez, Goldberg and Stowe.

Keywords

  • CD8 T cells
  • corticospinal tract
  • neural connectivity
  • neuroinflammation
  • pseudorabies virus
  • serial two-photon tomography
  • stroke
  • whole brain imaging

ASJC Scopus subject areas

  • Neuroscience (all)

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