McLean, C. | bioRxiv Science

bioRxiv2018

Regional diversity in the postsynaptic proteome of the mouse brain

The proteome of the postsynaptic terminal of excitatory synapses comprises over one thousand proteins in vertebrate species and plays a central role in behavior and brain disease. The brain is organized into anatomically distinct regions and whether the synapse proteome differs across these regions is poorly understood. Postsynaptic proteomes were isolated from seven forebrain and hindbrain regions in mice and their composition determined using proteomic mass spectrometry. Seventy-four percent of proteins showed differential expression and each region displayed a unique compositional signature. These signatures correlated with the anatomical divisions of the brain and their embryological origins. Biochemical pathways controlling plasticity and disease, protein interaction networks and individual proteins involved with cognition all showed differential regional expression. Combining proteomic and connectomic data shows that interconnected regions have specific proteome signatures. Diversity in synapse proteome composition is key feature of mouse and human brain structure.

neuroscience↗

bioRxiv2017

Improving Phenotypic Measurements in High-Content Imaging Screens

Image-based screening is a powerful technique to reveal how chemical, genetic, and environmental perturbations affect cellular state. Its potential is restricted by the current analysis algorithms that target a small number of cellular phenotypes and rely on expert-engineered image features. Newer algorithms that learn how to represent an image are limited by the small amount of labeled data for ground-truth, a common problem for scientific projects. We demonstrate a sensitive and robust method for distinguishing cellular phenotypes that requires no additional ground-truth data or training. It achieves state-of-the-art performance classifying drugs by similar molecular mechanism, using a Deep Metric Network that has been pre-trained on consumer images and a transformation that improves sensitivity to biological variation. However, our method is not limited to classification into predefined categories. It provides a continuous measure of the similarity between cellular phenotypes that can also detect subtle differences such as from increasing dose. The rich, biologically-meaningful image representation that our method provides can help therapy development by supporting high-throughput investigations, even exploratory ones, with more sophisticated and disease-relevant models.

cell biology↗