Neocortical Microdissection at Columnar and Laminar Resolution for Molecular Interrogation

Experience-dependent regulation of dopaminergic signaling in the somatosensory cortex

Dopamine critically influences reward processing, sensory perception, and motor control. Yet, the modulation of dopaminergic signaling by sensory experiences is not fully delineated. Here, by manipulating sensory experience using bilateral single-row whisker deprivation, we demonstrated that gene transcription in the dopaminergic signaling pathway (DSP) undergoes experience-dependent plasticity in both granular and supragranular layers of the primary somatosensory (barrel) cortex (S1). Sensory experience and deprivation compete for the regulation of DSP transcription across neighboring cortical columns, and sensory deprivation-induced changes in DSP are topographically constrained. These changes in DSP extend beyond cortical map plasticity and influence neuronal information processing. Pharmacological regulation of D2 receptors, a key component of DSP, revealed that D2 receptor activation suppresses excitatory neuronal excitability, hyperpolarizes the action potential threshold, and reduces the instantaneous firing rate. These findings suggest that the dopaminergic drive originating from midbrain dopaminergic neurons, targeting the sensory cortex, is subject to experience-dependent regulation and might create a regulatory feedback loop for modulating sensory processing. Finally, using topological gene network analysis and mutual information, we identify the molecular hubs of experience-dependent plasticity of DSP. These findings provide new insights into the mechanisms by which sensory experience shapes dopaminergic signaling in the brain and might help unravel the sensory deficits observed after dopamine depletion.

Nonlinear super-resolution signal processing allows intracellular tracking of calcium dynamics

Objective.Traditional quantification of fluorescence signals, such asΔF/F, relies on ratiometric measures that necessitate a baseline for comparison, limiting their applicability in dynamic analyses. Our goal here is to develop a baseline-independent method for analyzing fluorescence data that fully exploits temporal dynamics to introduce a novel approach for dynamical super-resolution analysis, including in subcellular resolution.Approach.We introduce ARES (Autoregressive RESiduals), a novel method that leverages the temporal aspect of fluorescence signals. By focusing on the quantification of residuals following linear autoregression, ARES obviates the need for a predefined baseline, enabling a more nuanced analysis of signal dynamics.Main result.We delineate the foundational attributes of ARES, illustrating its capability to enhance both spatial and temporal resolution of calcium fluorescence activity beyond the conventional ratiometric measure (ΔF/F). Additionally, we demonstrate ARES's utility in elucidating intracellular calcium dynamics through the detailed observation of calcium wave propagation within a dendrite.Significance.ARES stands out as a robust and precise tool for the quantification of fluorescence signals, adept at analyzing both spontaneous and evoked calcium dynamics. Its ability to facilitate the subcellular localization of calcium signals and the spatiotemporal tracking of calcium dynamics-where traditional ratiometric measures falter-underscores its potential to revolutionize baseline-independent analyses in the field.

The tuning of tuning: How adaptation influences single cell information transfer

Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. To effectively transfer information about the stimulus to the next processing level, a neuron needs to be able to adapt its working range to the properties of the stimulus. Here, we focus on the intrinsic neural properties that influence information transfer in cortical neurons and how tightly their properties need to be tuned to the stimulus statistics for them to be effective. We start by measuring the intrinsic information encoding properties of putative excitatory and inhibitory neurons in L2/3 of the mouse barrel cortex. Excitatory neurons show high thresholds and strong adaptation, making them fire sparsely and resulting in a strong compression of information, whereas inhibitory neurons that favour fast spiking transfer more information. Next, we turn to computational modelling and ask how two properties influence information transfer: 1) spike-frequency adaptation and 2) the shape of the IV-curve. We find that a subthreshold (but not threshold) adaptation, the 'h-current', and a properly tuned leak conductance can increase the information transfer of a neuron, whereas threshold adaptation can increase its working range. Finally, we verify the effect of the IV-curve slope in our experimental recordings and show that excitatory neurons form a more heterogeneous population than inhibitory neurons. These relationships between intrinsic neural features and neural coding that had not been quantified before will aid computational, theoretical and systems neuroscientists in understanding how neuronal populations can alter their coding properties, such as through the impact of neuromodulators. Why the variability of intrinsic properties of excitatory neurons is larger than that of inhibitory ones is an exciting question, for which future research is needed.

Cortical Representation of Touch in Silico

With its six layers and ~ 12,000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents'. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex's granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in silico network replicates the known properties of touch representations and whisker deprivation-induced changes in synaptic strength induced in vivo. Simulations show that the history of the membrane potential acts as a spatial filter that determines the presynaptic population of neurons contributing to a post-synaptic action potential; this spatial filtering might be critical for synaptic integration of top-down and bottom-up information.

A databank for intracellular electrophysiological mapping of the adult somatosensory cortex

Background

Neurons in the supragranular layers of the somatosensory cortex integrate sensory (bottom-up) and cognitive/perceptual (top-down) information as they orchestrate communication across cortical columns. It has been inferred, based on intracellular recordings from juvenile animals, that supragranular neurons are electrically mature by the fourth postnatal week. However, the dynamics of the neuronal integration in adulthood is largely unknown. Electrophysiological characterization of the active properties of these neurons throughout adulthood will help to address the biophysical and computational principles of the neuronal integration.

Findings

Here, we provide a database of whole-cell intracellular recordings from 315 neurons located in the supragranular layers (L2/3) of the primary somatosensory cortex in adult mice (9–45 weeks old) from both sexes (females, N = 195; males, N = 120). Data include 361 somatic current-clamp (CC) and 476 voltage-clamp (VC) experiments, recorded using a step-and-hold protocol (CC, N = 257; VC, N = 46), frozen noise injections (CC, N = 104) and triangular voltage sweeps (VC, 10 (N = 132), 50 (N = 146) and 100 ms (N = 152)), from regular spiking (N = 169) and fast-spiking neurons (N = 66).

Conclusions

The data can be used to systematically study the properties of somatic integration and the principles of action potential generation across sexes and across electrically characterized neuronal classes in adulthood. Understanding the principles of the somatic transformation of postsynaptic potentials into action potentials will shed light onto the computational principles of intracellular information transfer in single neurons and information processing in neuronal networks, helping to recreate neuronal functions in artificial systems.