A synaptic molecular dependency network in knockdown of autism- and schizophrenia-associated genes revealed by multiplexed imaging
Reuven Falkovich,
Eric W. Danielson,
Karen Perez de Arce,
Eike-C. Wamhoff,
Juliana Strother,
Anna P. Lapteva,
Morgan Sheng,
Jeffrey R. Cottrell,
Mark Bathe
Affiliations
Reuven Falkovich
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Eric W. Danielson
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Karen Perez de Arce
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Eike-C. Wamhoff
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Juliana Strother
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Anna P. Lapteva
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Morgan Sheng
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
Jeffrey R. Cottrell
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Mark Bathe
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Medical School Initiative for RNA Medicine, Harvard University, Cambridge, MA, USA; Corresponding author
Summary: The complex functions of neuronal synapses depend on their tightly interconnected protein network, and their dysregulation is implicated in the pathogenesis of autism spectrum disorders and schizophrenia. However, it remains unclear how synaptic molecular networks are altered biochemically in these disorders. Here, we apply multiplexed imaging to probe the effects of RNAi knockdown of 16 autism- and schizophrenia-associated genes on the simultaneous joint distribution of 10 synaptic proteins, observing several protein composition phenotypes associated with these risk genes. We apply Bayesian network analysis to infer hierarchical dependencies among eight excitatory synaptic proteins, yielding predictive relationships that can only be accessed with single-synapse, multiprotein measurements performed simultaneously in situ. Finally, we find that central features of the network are affected similarly across several distinct gene knockdowns. These results offer insight into the convergent molecular etiology of these widespread disorders and provide a general framework to probe subcellular molecular networks.
