Kalinka, A. T. | Search bioRxiv

bioRxiv2020

Male sex hormones increase excitatory neuron production in developing human neocortex

The presence of male-female brain differences has long been a controversial topic. Yet simply negating the existence of biological differences has detrimental consequences for all sexes and genders, particularly for the development of accurate diagnostic tools, effective drugs and understanding of disease. The most well-established morphological difference is size, with males having on average a larger brain than females; yet a mechanistic understanding of how this difference arises remains to be elucidated. Here, we use brain organoids to test the roles of sex chromosomes and sex steroids during development. While we show no observable differences between XX and XY brain organoids, sex steroids, namely androgens, increase proliferation of cortical neural progenitors. Transcriptomic analysis reveals effects on chromatin remodelling and HDAC activity, both of which are also implicated in the male-biased conditions autism spectrum disorder and schizophrenia. Finally, we show that higher numbers of progenitors result specifically in increased upper-layer excitatory neurons. These findings uncover a hitherto unknown role for male sex hormones in regulating excitatory neuron number within the human neocortex and represent a first step towards understanding the origin of human sex-related brain differences.

developmental biology↗

bioRxiv2020

Improving the sensitivity of differential-expression analyses for under-powered RNA-seq experiments

High-throughput studies, in which thousands of hypothesis tests are conducted simultaneously, can be under-powered when effect sizes are small and there are few replicates. Here, I describe an approach to estimate the FDR for a given experiment such that the ground truth is known. A decision boundary between true and false positive calls can then be learned from the data itself along the axes of fold change and expression level. By excluding hits that fall into the false positive space, the FDR of any given method can be controlled providing a means to employ less conservative methods for detecting differential expression without incurring the usual loss of precision. I show that coupling this approach with a feature-selection method - an elastic-net logistic regression - can increase sensitivity 10-fold above what is achievable with the prevailing methods of the day. An R package implementing these methods is available at https://github.com/alextkalinka/delboy.

bioinformatics↗