A synaptic molecular dependency network in knockdown of autism- and schizophrenia-associated genes revealed by multiplexed imaging

Abstract

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.

Keywords: Bayesian network inference; CP: Genomics; CP: Neuroscience; RNAi; autism; genetic screen; molecular network; multiplexed imaging; schizophrenia; siRNA; synapse; synaptic protein.

Figure 1:. Genes and targets.

A) Schematic summarizing approximate synaptic context of the 10 imaged targets. B) Venn diagram of gene knockdowns. C) Representative images of the same neuronal culture in different imaging rounds, showing colocalized puncta of each protein. Bottom right – automatically identified and segmented excitatory (red) and inhibitory (blue) synapses. Scale bars are 5μm.

Figure 2:. Synapse effects of siRNA knockdowns.

A) Representative images from medium-sized dendrites across 4 channels. Bottom left scale bar is 5um. B) Excitatory (Syn+, vGlut+, vGAT−) to inhibitory (Syn+, vGlut−, vGAT+) count ratios. C) Percent of GluR2 negative synapses from all excitatory synapses. D) Estimates of total dendrite proliferation from MAP2 staining, normalized to NonT. B-D blue lines indicate mean and SEM of NonT measurements. E) Log fold change (relative to NonT) of mean levels of each protein in excitatory and inhibitory synapses. Coloring of genes in B-E: red, only ASD, blue, only SCZ, purple, both. F-I) Validation experiments with chronic treatments (DIV 6–19) measured by conventional IF. Mean synaptic protein levels or other measurements depicted as Log₂-fold change from untreated control (for chemical treatment experiments) or NonT siRNA (for RNAi) treated wells. F) Treatment with bpV(pic), a PTEN inhibitor. G) Treatment with Harmine, a Dyrk1a inhibitor. H) SHANK and mGluR1/5 after knockdown of Shank3. I) PSD95, NR1, and density of NR1-positive synapses after short and chronic NMDAR blockade with D-APV, or chronic RNAi knockdown of Grin2a. Bars are mean ± s.e.m. across wells. *p<0.05, **p<0.01, ***p<0.001, two-sided t-test.

Figure 3:. Synaptic multiprotein distributions.

A–C) Composition-defined synaptic subtypes. A) UMAP projection of scaled synaptic measurements. B) Row-normalized (across all clusters) mean levels of each specific protein in specific clusters identified in A. C) Log fold-change of excitatory cluster populations under each treatment. Right: overall composition of the synapse population by cluster. D) Direct correlations between proteins in excitatory synapses. E) Correlations in each pair, controlling for all other 6 proteins.

Figure 4:. Bayesian network inference on simulated networks.

A) Simulated Bayesian network. B) Reconstructed network. C,D) Calculated edge strengths of the edges in (B) versus defined interaction coefficients in (C). C – edge strengths calculated by parent-controlled correlations. F – total (uncorrected) correlations. E) Simulated non-Bayesian network with cycles (CED and BDGH). F) Reconstructed Bayesian network. Cycles cannot be represented but the overall structure and relative edge strengths are preserved. Arrowhead sizes represent predefined interaction parameters αXY in A,E and inferred edge strengths in B,F.

Figure 5:. Bayesian network of 8 excitatory synaptic proteins.

A) The inferred network. Presynaptic proteins in blue, postsynaptic in green. Red arrows indicate substructure probed in B and C. B) Log fold-change in F-actin, PSD95 and SHANK3 after treatment with two actin polymerization perturbations, total and controlled for each protein. C) Log fold-change in PSD95 and Shank3 after treatment with 3 siRNAs: nontargeting, against Dlg4 (PSD95) and against Shank3. Bars are mean ± s.e.m. across wells.

Figure 6:. Network-informed analysis of the genetic screen.

A) ‘Direct’ effects of each treatment on each protein separately. Like 2F but controlled for the parent nodes of each protein. B) Effect of each treatment on the strength of each network edge. C) Network from 4A, each edge colored by the average change in strength across treatments. All colors are log₂-fold-change relative to nontargeting siRNA control.