Biology subjects

Shi, S.-H.

Publications and source records attributed to Shi, S.-H..

2 recordsLinked to original sources

bioRxiv2020

Distinct progenitor behavior underlying neocortical gliogenesis related to tumorigenesis

Radial glial progenitors (RGPs) are responsible for producing the vast majority of neurons and glia in the neocortex. While RGP behavior and progressive generation of neocortical neurons have been delineated, the exact process of neocortical gliogenesis remains elusive. Here, we report the precise progenitor cell behavior and gliogenesis program at single-cell resolution in the mouse neocortex. RGPs transition from neurogenesis to gliogenesis progressively, producing astrocytes, oligodendrocytes, or both in well-defined propensities of 60%:15%:25%, respectively, via fate-restricted "intermediate" precursor cells. While the total number of precursor cells generated by individual RGPs appears stochastic, the output of individual precursor cells exhibit clear patterns in number and subtype, and form discrete local subclusters. Clonal loss of tumor suppressor Neurofibromatosis type 1 leads to excessive production of glia selectively, especially oligodendrocyte precursor cells. These results delineate the cellular program of neocortical gliogenesis quantitatively and suggest the cellular and lineage origin of primary brain tumor.

developmental biology↗

bioRxiv2020

Inferring Maps of Cellular Structures from MRI Signals using Deep Learning

1H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each pixel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.

bioengineering↗