Mapping the perturbome network of cellular perturbations

Caldera, Michael; Müller, Felix; Kaltenbrunner, Isabel; Licciardello, Marco P.; Lardeau, Charles-Hugues; Kubicek, Stefan; Menche, Jörg

doi:10.1038/s41467-019-13058-9

“…In addition to linking connectivity and coding in single-neuron perturbations, our theory outlines how multiple-cell perturbations can be used to study functional properties of neuronal networks, by mapping higher-order interactions between influencers. A similar mathematical approach has been used recently to analyze the interaction of drugs and their resulting changes in cell morphologies, in order to shed light on the link between drug combinations and treatment of diseases ( 39 ). Targeted multiple-neuron perturbations of functionally identified neurons have in fact been used recently to shed light on the dynamics of persistent activity and short-term memory in mice ( 40 ).…”

Section: Discussionmentioning

confidence: 99%

Theory of neuronal perturbome in cortical networks

Sadeh

¹

,

Clopath

²

2020

Proc. Natl. Acad. Sci. U.S.A.

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To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modeling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory–inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images, and this was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding and paves the road to map the perturbome of neuronal networks in future studies.

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“…In addition to linking connectivity and coding in single-neuron perturbations, our theory outlines how multiple-cell perturbations can be used to study functional properties of neuronal networks, by mapping higher-order interactions between influencers. A similar mathematical approach has been recruited recently to analyze the interaction of drugs and their resulting changes in cell morphologies, in order to shed light on the link between drug combinations and treatment of diseases (Caldera et al, 2019). Targeted multiple-neuron perturbations of functionally identified neurons have in fact been used recently to shed light on the dynamics of persistent activity and short-term memory in mice (Daie, Svoboda and Druckmann, 2019).…”

Section: Discussionmentioning

confidence: 99%

Theory of Neuronal Perturbome: Linking Connectivity to Coding via Perturbations

Sadeh

¹

,

Clopath

²

2020

Preprint

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To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modelling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition-dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory-inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images in turn, which was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding, and paves the road to map the perturbome of neuronal networks in future studies. amplification and suppression). We found that to obtain feature-specific suppression, strong and functionally-specific subnetworks of E and I were necessary. That is, both E and I neurons with similar receptive fields (RFs) should be connected together more strongly than their non-similar counterparts, which was consistent with recent results in visual cortex (Znamenskiy et al., 2018).Our modelling results shed light on the above mentioned controversy by showing that featurespecific amplification and suppression could both exist in the cortex, depending on the regime of functional similarity between the influencers and the influencees. Our model suggests specific predictions on how to observe this in the cortex. Computational modelling also helped us to formulate further predictions that experiments could not directly assess, for instance regarding the temporal evolution of functional influence. We linked the result of single-neuron perturbations to sensory processing, by studying how our model networks in different regimes encode and decode natural images. More generally, we show that our theory can be extended to study multiple-cell perturbations to map the perturbome of neuronal networks in future. Results Single-Neuron Perturbations in Large-scale Networks of Visual CortexWe studied the effect of single-neuron perturbations on functional properties of neurons in largescale network models of visual cortex (Figure 1A). Individual excitatory and inhibitory neurons were modelled ...

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“…Deep learning-based approaches have been developed 25 , 83 that are inherently well suited for tackling this problem, as they can produce MOA-specific, reweighted image-based profiles, thereby deconvoluting the complex phenotypes arising from polypharmacology 44 . We are only beginning to scratch the surface of image-based profiling of single perturbations, but testing pairs of reagents has begun for limited sets of small molecules 131 and genetic perturbations 132 . Larger such datasets should lead to a better understanding of how to identify and deconvolute polypharmacology in profiles.…”

Section: Identifying the Moamentioning

confidence: 99%

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Chandrasekaran

¹

,

Ceulemans

²

,

Boyd

³

et al. 2020

Nat Rev Drug Discov

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Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug’s activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

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Mapping the perturbome network of cellular perturbations

Cited by 45 publications

References 62 publications

Theory of neuronal perturbome in cortical networks

Theory of neuronal perturbome in cortical networks

Theory of Neuronal Perturbome: Linking Connectivity to Coding via Perturbations

Image-based profiling for drug discovery: due for a machine-learning upgrade?

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