Inference of a causal synaptic molecular network from multiplexed imaging and its perturbations in autism and schizophrenia

Reuven Falkovich ¹ Eric Danielson ¹ Karen Perez de-Arce ² Wamhoff Eike-Christian ¹ Strother Juliana ¹ Anna Lapteva ¹ Morgan Sheng ^1,2,3 Jeffrey Cottrell ² Mark Bathe ^1,2,4

¹Biological Engineering, Massachusetts Institute of Technology, USA
²Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, USA
³Picower Institute for Learning and Memory, Massachusetts Institute of Technology, USA
⁴Harvard Medical School Initiative for RNA Medicine, Harvard University, USA

The complex functions of neuronal synapses in the central nervous system depend on their tightly interacting, compartmentalized molecular network of hundreds of proteins spanning the pre- and post-synaptic sites. Due to the high interconnectivity of any molecular subsystem in the synapse, a whole-network view is needed to understand the biochemical processes and synapse state diversity underlying different forms of plasticity and metaplasticity and, importantly, their dysfunction in cognitive disorders like autism and schizophrenia.

I describe the use of PRISM – a quantitative, high-throughput, single-synapse multiplexed imaging technique – in combination with Bayesian network inference, to derive a graph of causal conditional dependencies among proteins in the excitatory synapse. The resulting model is predictive, in that it yields new hypotheses of downstream effects of perturbing individual nodes, which can be directly tested. Applying this analysis to previously obtained multiplexed data of RNAi knockdowns of 16 schizophrenia- and autism-associated genes, we show that central features of the network are similarly perturbed across all genetic knockdowns, despite having very different targets and divergent effects on individual synaptic proteins. This offers insight into the convergent molecular etiology of these debilitating, hereditary and highly polygenic disorders.

Thus, the combination of PRISM imaging with Bayesian network inference, which can also be integrated with live imaging data and dynamic network inference, offers a novel data modality and hypothesis-generating tool for understanding complex protein networks in situ in cells, organelles, and subcellular structures, as well as their responses to chemical or genetic perturbations.