A chemogenetic approach for dopamine imaging with tunable sensitivity.

Genetically-encoded dopamine (DA) sensors enable high-resolution imaging of DA release, but their ability to detect a wide range of extracellular DA levels, especially tonic versus phasic DA release, is limited by their intrinsic affinity. Here we show that a human-selective dopamine receptor positive allosteric modulator (PAM) can be used to boost sensor affinity on-demand. The PAM enhances DA detection sensitivity across experimental preparations (in vitro, ex vivo and in vivo) via one-photon or two-photon imaging. In vivo photometry-based detection of optogenetically-evoked DA release revealed that DETQ administration produces a stable 31 minutes window of potentiation without effects on animal behavior. The use of the PAM revealed region-specific and metabolic state-dependent differences in tonic DA levels and enhanced single-trial detection of behavior-evoked phasic DA release in cortex and striatum. Our chemogenetic strategy can potently and flexibly tune DA imaging sensitivity and reveal multi-modal (tonic/phasic) DA signaling across preparations and imaging approaches.

Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information.

Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.

Sensitive multicolor indicators for monitoring norepinephrine in vivo.

Genetically encoded indicators engineered from G-protein-coupled receptors are important tools that enable high-resolution in vivo neuromodulator imaging. Here, we introduce a family of sensitive multicolor norepinephrine (NE) indicators, which includes nLightG (green) and nLightR (red). These tools report endogenous NE release in vitro, ex vivo and in vivo with improved sensitivity, ligand selectivity and kinetics, as well as a distinct pharmacological profile compared with previous state-of-the-art GRABNE indicators. Using in vivo multisite fiber photometry recordings of nLightG, we could simultaneously monitor optogenetically evoked NE release in the mouse locus coeruleus and hippocampus. Two-photon imaging of nLightG revealed locomotion and reward-related NE transients in the dorsal CA1 area of the hippocampus. Thus, the sensitive NE indicators introduced here represent an important addition to the current repertoire of indicators and provide the means for a thorough investigation of the NE system.

ASTRA: a deep learning algorithm for fast semantic segmentation of large-scale astrocytic networks.

Changes in the intracellular calcium concentration are a fundamental fingerprint of astrocytes, the main type of glial cell. Astrocyte calcium signals can be measured with two-photon microscopy, occur in anatomically restricted subcellular regions, and are coordinated across astrocytic networks. However, current analytical tools to identify the astrocytic subcellular regions where calcium signals occur are time-consuming and extensively rely on user-defined parameters. These limitations limit reproducibility and prevent scalability to large datasets and fields-of-view. Here, we present Astrocytic calcium Spatio-Temporal Rapid Analysis (ASTRA), a novel software combining deep learning with image feature engineering for fast and fully automated semantic segmentation of two-photon calcium imaging recordings of astrocytes. We applied ASTRA to several two-photon microscopy datasets and found that ASTRA performed rapid detection and segmentation of astrocytic cell somata and processes with performance close to that of human experts, outperformed state-of-the-art algorithms for the analysis of astrocytic and neuronal calcium data, and generalized across indicators and acquisition parameters. We also applied ASTRA to the first report of two-photon mesoscopic imaging of hundreds of astrocytes in awake mice, documenting large-scale redundant and synergistic interactions in extended astrocytic networks. ASTRA is a powerful tool enabling closed-loop and large-scale reproducible investigation of astrocytic morphology and function.

Complementary encoding of spatial information in hippocampal astrocytes.

Calcium dynamics into astrocytes influence the activity of nearby neuronal structures. However, because previous reports show that astrocytic calcium signals largely mirror neighboring neuronal activity, current information coding models neglect astrocytes. Using simultaneous two-photon calcium imaging of astrocytes and neurons in the hippocampus of mice navigating a virtual environment, we demonstrate that astrocytic calcium signals encode (i.e., statistically reflect) spatial information that could not be explained by visual cue information. Calcium events carrying spatial information occurred in topographically organized astrocytic subregions. Importantly, astrocytes encoded spatial information that was complementary and synergistic to that carried by neurons, improving spatial position decoding when astrocytic signals were considered alongside neuronal ones. These results suggest that the complementary place dependence of localized astrocytic calcium signals may regulate clusters of nearby synapses, enabling dynamic, context-dependent variations in population coding within brain circuits.

A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging.

In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true for large field-of-view (FOV) imaging. Here, we present CITE-On (Cell Identification and Trace Extraction Online), a convolutional neural network-based algorithm for fast automatic cell identification, segmentation, identity tracking, and trace extraction in two-photon calcium imaging data. CITE-On processes thousands of cells online, including during mesoscopic two-photon imaging, and extracts functional measurements from most neurons in the FOV. Applied to publicly available datasets, the offline version of CITE-On achieves performance similar to that of state-of-the-art methods for offline analysis. Moreover, CITE-On generalizes across calcium indicators, brain regions, and acquisition parameters in anesthetized and awake head-fixed mice. CITE-On represents a powerful tool to speed up image analysis and facilitate closed-loop approaches, for example in combined all-optical imaging and manipulation experiments.

Extracellular matrix alterations in the ketamine model of schizophrenia.

The neural extracellular matrix (ECM) plays an important role in regulation of perisomatic GABAergic inhibition and synaptic plasticity in the hippocampus and cortex. Decreased labeling of perineuronal nets, a form of ECM predominantly associated with parvalbumin-expressing interneurons in the brain, has been observed in post-mortem studies of schizophrenia patients, specifically, in brain areas such as prefrontal cortex, entorhinal cortex, and amygdala. Moreover, glial ECM in the form of dandelion clock-like structures was reported to be altered in schizophrenia patients. Here, we verified whether similar abnormalities in neural ECM can be reproduced in a rat model of schizophrenia, in which animals received sub-chronic administration of ketamine to reproduce the aspects of disease related to disrupted signaling through N-methyl-D-aspartate receptors. Our study focused on two schizophrenia-related brain areas, namely the medial prefrontal cortex (mPFC) and hippocampus. Semi-quantitative immunohistochemistry was performed to evaluate investigate ECM expression using Wisteria floribunda agglutinin (WFA) and CS56 antibody, both labeling distinct chondroitin sulfate epitopes enriched in perineuronal nets and glial ECM, respectively. Our analysis revealed that ketamine-treated rats exhibit reduced number of WFA-labeled perineuronal nets, and a decreased intensity of parvalbumin fluorescence in mPFC interneurons somata. Moreover, we found an increased expression of CS56 immunoreactive form of ECM. Importantly, the loss of perineuronal nets was revealed in the mPFC, and was not detected in the hippocampus, suggesting regional specificity of ECM alterations. These data open an avenue for further investigations of functional importance of ECM abnormalities in schizophrenia as well as for search of treatments for their compensation.

Rapid generation of functional dopaminergic neurons from human induced pluripotent stem cells through a single-step procedure using cell lineage transcription factors.

Current protocols for in vitro differentiation of human induced pluripotent stem cells (hiPSCs) to generate dopamine (DA) neurons are laborious and time-expensive. In order to accelerate the overall process, we have established a fast protocol by expressing the developmental transcription factors ASCL1, NURR1, and LMX1A. With this method, we were able to generate mature and functional dopaminergic neurons in as few as 21 days, skipping all the intermediate steps for inducting and selecting embryoid bodies and rosette-neural precursors. Strikingly, the resulting neuronal conversion process was very proficient, with an overall efficiency that was more than 93% of all the coinfected cells. hiPSC-derived DA neurons expressed all the critical molecular markers of the DA molecular machinery and exhibited sophisticated functional features including spontaneous electrical activity and dopamine release. This one-step protocol holds important implications for in vitro disease modeling and is particularly amenable for exploitation in high-throughput screening protocols.

Role of Btg2 in the progression of a PDGF-induced oligodendroglioma model.

Tumor progression is a key aspect in oncology. Not even the overexpression of a powerful oncogenic stimulus such as platelet derived growth factor-B (PDGF-B) is sufficient per se to confer full malignancy to cells. In previous studies we showed that neural progenitors overexpressing PDGF-B need to undergo progression to acquire the capability to give rise to secondary tumor following transplant. By comparing the expression profile of PDGF-expressing cells before and after progression, we found that progressed tumors consistently downregulate the expression of the antiproliferative gene Btg2. We therefore tested whether the downregulation of Btg2 is sufficient and necessary for glioma progression with loss and gain of function experiments. Our results show that downregulation of Btg2 is not sufficient but is necessary for tumor progression since the re-introduction of Btg2 in fully progressed tumors dramatically impairs their gliomagenic potential. These results suggest an important role of Btg2 in glioma progression. Accordingly with this view, the analysis of public datasets of human gliomas showed that reduced level of Btg2 expression correlates with a significantly worse prognosis.