Illuminating the Activated Brain: Emerging Activity-Dependent Tools to Capture and Control Functional Neural Circuits

Abstract

Immediate-early genes (IEGs) have long been used to visualize neural activations induced by sensory and behavioral stimuli. Recent advances in imaging techniques have made it possible to use endogenous IEG signals to visualize and discriminate neural ensembles activated by multiple stimuli, and to map whole-brain-scale neural activation at single-neuron resolution. In addition, a collection of IEG-dependent molecular tools has been developed that can be used to complement the labeling of endogenous IEG genes and, especially, to manipulate activated neural ensembles in order to reveal the circuits and mechanisms underlying different behaviors. Here, we review these techniques and tools in terms of their utility in studying functional neural circuits. In addition, we provide an experimental strategy to measure the signal-to-noise ratio of IEG-dependent molecular tools, for evaluating their suitability for investigating relevant circuits and behaviors.

Keywords: Activity-dependent tools; Arc; Emotion; Immediate-early gene; Neural ensembles; c-fos.

Fig. 1

Principle of dual-epoch mapping techniques such as I-FISH and TAI-FISH. These methods use Zif268 or c-fos mRNA and protein signals to differentially label neural ensembles induced by two different stimuli. In TAI-FISH, Xiu et al. [19, 20] used tyramide to improve the signal-to-noise ratio of IHC and achieved better temporal separation between c-fos mRNA and protein signals than I-FISH.

Fig. 2

Experimental procedure to quantify the robustness and specificity of IEG-dependent tools. A Two identical stimuli are applied several days apart (based on the literature [66, 85, 88] and our own tests, the exact time interval between the two stimuli may depend on the tools, brain regions, and behavioral paradigms used) to induce signals in the IEG-dependent tool and endogenous IEG, respectively. B Signals of an IEG-dependent tool are divided into two parts: false positives (neurons only labeled by the IEG-dependent tool, a); and true positives (neurons labeled by both the IEG-dependent tool and endogenous IEG staining, b). The third category, false negative signals (c) are neurons only labeled by endogenous IEG staining. We quantify the robustness of an IEG-dependent tool as b/(b+c), and its specificity by b/(a+b). Examples of low robustness (C) and low specificity (D) are shown. A given IEG-dependent tool needs to have both high robustness and specificity to have a high signal-to-noise ratio.

Fig. 3

Three “two-tiered” IEG-dependent tools (A: Tet-OFF; B: creER; C: CANE) and their respective mechanisms for temporal control. ChR2-EYFP is the reporter-effector for all three tools [64, 67, 88]. In CANE (C), AAV-DIO-ChR2-EYFP is co-injected with CANE-LV-cre.