A diffusion MRI-based spatiotemporal continuum of the embryonic mouse brain for probing gene–neuroanatomy connections

Abstract

The embryonic mouse brain undergoes drastic changes in establishing basic anatomical compartments and laying out major axonal connections of the developing brain. Correlating anatomical changes with gene-expression patterns is an essential step toward understanding the mechanisms regulating brain development. Traditionally, this is done in a cross-sectional manner, but the dynamic nature of development calls for probing gene–neuroanatomy interactions in a combined spatiotemporal domain. Here, we present a four-dimensional (4D) spatiotemporal continuum of the embryonic mouse brain from E10.5 to E15.5 reconstructed from diffusion magnetic resonance microscopy (dMRM) data. This study achieved unprecedented high-definition dMRM at 30- to 35-µm isotropic resolution, and together with computational neuroanatomy techniques, we revealed both morphological and microscopic changes in the developing brain. We transformed selected gene-expression data to this continuum and correlated them with the dMRM-based neuroanatomical changes in embryonic brains. Within the continuum, we identified distinct developmental modes comprising regional clusters that shared developmental trajectories and similar gene-expression profiles. Our results demonstrate how this 4D continuum can be used to examine spatiotemporal gene–neuroanatomical interactions by connecting upstream genetic events with anatomical changes that emerge later in development. This approach would be useful for large-scale analysis of the cooperative roles of key genes in shaping the developing brain.

Developmental Mouse Brain Common Coordinate Framework

“…Advances in tissue clearing and high resolution microscopy have been critical to increase our ability to examine individual cell types in the whole mouse brain at single cell resolution 18,46,48,49 . MRI has been used to examine macroscopic changes in brain morphology, connectivity, and function 25,[50][51][52][53][54][55] . Furthermore, recently developed spatial transcriptomic approaches have begun to unravel the gene expression landscape in the adult and developing mouse brain 8,10,11,40,[56][57][58][59] .…”

“…Moreover, the selected seven developmental ages, though comprehensive, may be too sparse to fully capture the rapid cellular changes occurring in early brain development. Cross-age registration and interpolation can generate mappings between template ages that characterize morphological changes and examine aligned datasets over the temporal axis 25 . We also expect that ongoing discoveries will necessitate further segmentation and updates to DevCCF annotations.…”

“…Cross-age registration and interpolation can generate mappings between template ages that characterize morphological changes and examine aligned datasets over the temporal axis 25 . We also expect that ongoing discoveries will necessitate further segmentation and updates to DevCCF annotations.…”

“…However, we lack high resolution 3D mouse brain atlases that account for developmental anatomy as well as ontologically consistent segmentations that can be used from fetal to adult brains. Developing brains undergo rapid shape and volume changes with cell proliferation and migration, often guided by regionally distinct gene expression [22][23][24][25][26][27] . Previously, the Allen Developing Mouse Brain Atlas (ADMBA) was created using a series of 2D histological sections with genoarchitecture guided segmentations based on the prosomeric model 24,28 .…”

Towards reliable reconstruction of the mouse brain thalamocortical connectivity using diffusion MRI

“…In contrast, diffusion magnetic resonance imaging (dMRI) tractography (Jbabdi et al, 2015; Mori and van Zijl, 2002) permits non-invasive mapping of multiple pathways in the entire brain. Combining advanced dMRI tractography and computational tools further allows non-invasive exploration of structural connectivity in rodent brains and alterations due to genetic modifications or pathological conditions (Arefin et al, 2021; Arefin et al, 2017; Calabrese et al, 2015; Degiorgis et al, 2022; Mechling et al, 2016; Moldrich et al, 2010; Ren et al, 2007; Wang et al, 2020a; Wang et al, 2006; White et al, 2020; Wu et al, 2022; Yee et al, 2018). However, more wide-spread applications of dMRI tractography in connectome analysis has been hindered by its relatively low sensitivity and specificity, especially in gray matter (GM) structures, (Aydogan et al, 2018; Calamante, 2019; Schilling et al, 2019; Thomas et al, 2014) due to the lack of direct links between dMRI signals and the underlying cellular structures (e.g.…”