Neurotransmitter identity and electrophysiological phenotype are genetically coupled in midbrain dopaminergic neurons

Abstract

Most neuronal types have a well-identified electrical phenotype. It is now admitted that a same phenotype can be produced using multiple biophysical solutions defined by ion channel expression levels. This argues that systems-level approaches are necessary to understand electrical phenotype genesis and stability. Midbrain dopaminergic (DA) neurons, although quite heterogeneous, exhibit a characteristic electrical phenotype. However, the quantitative genetic principles underlying this conserved phenotype remain unknown. Here we investigated the quantitative relationships between ion channels’ gene expression levels in midbrain DA neurons using single-cell microfluidic qPCR. Using multivariate mutual information analysis to decipher high-dimensional statistical dependences, we unravel co-varying gene modules that link neurotransmitter identity and electrical phenotype. We also identify new segregating gene modules underlying the diversity of this neuronal population. We propose that the newly identified genetic coupling between neurotransmitter identity and ion channels may play a homeostatic role in maintaining the electrophysiological phenotype of midbrain DA neurons.

Elements of qualitative cognition: An information topology perspective

“…As a result illustrated in [134], I k values allows to distinguish subpopulation, much better than G k that moreover do not have a direct homological meaning. In agreement with IIT theory that assigns consciousness according to G k measure [5,150], a conclusion following [147,134], is that genetic expression participate to consciousness, to its slow component on epigenetic regulation timescales. It allows to refund the (semi-)classical definitions of internal energy as a special case for phase space variable and the usual isotherm relation of thermodynamics [151,152,133]:…”

“…The main theorems, definitions and data analysis [147,134,133] establish the following results, here included with comments about their relevance regarding consciousness and neural processing theories.…”

“…We hence propose that such positive modules provide a statistically rooted definition of neural assemblies, generalizing correlation measures to the nonlinear cases [165]. The negative minima of I k , commonly called synergistic interactions [166] or negentropy following Schrodinger [167], identify the variables that most segregate the population, and hence detect clusters corresponding to exclusive differential activity in subpopulations [147,134,133]. This negativity of Free Energy component is discussed in [137] in the perspective of physic, and provides a topological signature of condensation phenomenon corresponding to the clustering of data point.…”

The Poincaré-Shannon Machine: Statistical Physics and Machine Learning Aspects of Information Cohomology

“…The present paper aims to provide a comprehensive introduction and interpretation in terms of statistics, statistical physics and machine learning of the information cohomology theory developed in References [ 1 , 2 ]. It presents the computational aspects of the application of information cohomology to data presented in Reference [ 3 ] and in the associated paper [ 2 ], which consists of an unsupervised classification of cell types or gene modules and provides a generic model for epigenetic co-regulation and differentiation.…”

“…One can remark that the setting of information cohomology is equivalent to the Potts models that generalizes the spin models to arbitrary multivalued variables (see Reference [ 29 ] for review) and considers all possible k -ary statistical interactions in a similar way as the multispin interaction models do ( k -spin interaction models that generalize pairwise and nearest-neighbor models, see Reference [ 30 ] and reference therein). I describe the computational and combinatorial restrictions from the lattice of partitions to the simplicial sub-lattice that allows one to compute in practice the information cohomology on data as in References [ 2 , 3 ] and that defines entropy and information landscapes and their respective paths that provide the discrete informational analog of path integrals. The combinatorics of possible interactions that are computed by the information landscapes is equivalent to the computational “exponential wall” encountered in many particle studies, notably density functional theory (DFT), as exposed by Kohn [ 31 ].…”