Distributed X chromosome inactivation in brain circuitry is associated with X-linked disease penetrance of behavior.

The precise anatomical degree of brain X chromosome inactivation (XCI) that is sufficient to alter X-linked disorders in females is unclear. Here, we quantify whole-brain XCI at single-cell resolution to discover a prevalent activation ratio of maternal to paternal X at 60:40 across all divisions of the adult brain. This modest, non-random XCI influences X-linked disease penetrance: maternal transmission of the fragile X mental retardation 1 (Fmr1)-knockout (KO) allele confers 55% of total brain cells with mutant X-active, which is sufficient for behavioral penetrance, while 40% produced from paternal transmission is tolerated. Local XCI mosaicism within affected maternal Fmr1-KO mice further specifies sensorimotor versus social anxiety phenotypes depending on which distinct brain circuitry is most affected, with only a 50%-55% mutant X-active threshold determining penetrance. Thus, our results define a model of X-linked disease penetrance in females whereby distributed XCI among single cells populating brain circuitries can regulate the behavioral penetrance of an X-linked mutation.

Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments.

The Allen Mouse Brain Connectivity Atlas consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild-type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class-specific whole-brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class-specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method that we previously used to fill in spatial gaps to also fill in gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method, we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type- and structure-specific connectivities. We also show that the wild-type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model.