Putting a finishing touch on GEC's

More than a decade ago genetically encoded calcium indicators (GECIs) entered the stage as new promising tools to image calcium dynamics and neuronal activity in living tissues and designated cell types in vivo. From a variety of initial designs two have emerged as promising prototypes for further optimization: FRET (Forster Resonance Energy Transfer)based sensors and single fluorophore sensors of the GCaMP family. Recent efforts in structural analysis, engineering and screening have broken important performance thresholds in the latest generation for both classes. While these improvements have made GECIs a powerful means to perform physiology in living animals, a number of other aspects of sensor function deserve attention. These aspects include indicator linearity, toxicity and slow response kinetics. Furthermore creating high performance sensors with optically more favorable emission in red or infrared wavelengths as well as new stably or conditionally GECI-expressing animal lines are on the wish list. When the remaining issues are solved, imaging of GECIs will finally have crossed the last milestone, evolving from an initial promise into a fully matured technology

Spaced training enhances memory and prefrontal ensemble stability in mice

It is commonly acknowledged that memory is substantially improved when learning is distributed over time, an effect called the "spacing effect". So far it has not been studied how spaced learning affects the neuronal ensembles presumably underlying memory. In the present study, we investigate whether trial spacing increases the stability or size of neuronal ensembles. Mice were trained in the "everyday memory"task, an appetitive, naturalistic, delayed matching-to-place task. Spacing trials by 60 min produced more robust memories than training with shorter or longer intervals. c-Fos labeling and chemogenetic inactivation established the involvement of the dorsomedial prefrontal cortex (dmPFC) in successful memory storage. In vivo calcium imaging of excitatory dmPFC neurons revealed that longer trial spacing increased the similarity of the population activity pattern on subsequent encoding trials and upon retrieval. Conversely, trial spacing did not affect the size of the total neuronal ensemble or the size of subpopulations dedicated to specific task-related behaviors and events. Thus, spaced learning promotes reactivation of prefrontal neuronal ensembles processing episodic-like memories

In vivo imaging reveals reduced activity of neuronal circuits in a mouse tauopathy model

Pathological alterations of tau protein play a significant role in the emergence and progression of neurodegenerative disorders. Tauopathies are characterized by detachment of the tau protein from neuronal microtubules, and its subsequent aberrant hyperphosphorylation, aggregation and cellular distribution. The exact nature of tau protein species causing neuronal malfunction and degeneration is still unknown. In the present study, we used mice transgenic for human tau with the frontotemporal dementia with parkinsonism-associated P301S mutation. These mice are prone to develop fibrillar tau inclusions, especially in the spinal cord and brainstem. At the same time, cortical neurons are not as strongly affected by fibrillar tau forms, but rather by soluble tau forms. We took advantage of the possibility to induce formation of neurofibrillary tangles in a subset of these cortical neurons by local injection of preformed synthetic tau fibrils. By using chronic in vivo two-photon calcium imaging in awake mice, we were able for the first time to follow the activity of individual tangle-bearing neurons and compare it to the activity of tangle-free neurons over the disease course. Our results revealed strong reduction of calcium transient frequency in layer 2/3 cortical neurons of P301S mice, independent of neurofibrillary tangle presence. These results clearly point to the impairing role of soluble, mutated tau protein species present in the majority of the neurons investigated in this study

PhenoScore: AI-based phenomics to quantify rare disease and genetic variation

While both molecular and phenotypic data are essential when interpreting genetic variants, prediction scores (CADD, PolyPhen, and SIFT) have focused on molecular details to evaluate pathogenicity — omitting phenotypic features. To unlock the full potential of phenotypic data, we developed PhenoScore: an open source, artificial intelligence-based phenomics framework. PhenoScore combines facial recognition technology with Human Phenotype Ontology (HPO) data analysis to quantify phenotypic similarity at both the level of individual patients as well as of cohorts. We prove PhenoScore’s ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 25 out of 26 investigated genetic syndromes against clinical features observed in individuals with other neurodevelopmental disorders. Moreover, PhenoScore was able to provide objective clinical evidence for two distinct ADNP-related phenotypes, that had already been established functionally, but not yet phenotypically. Hence, PhenoScore will not only be of use to unbiasedly quantify phenotypes to assist genomic variant interpretation at the individual level, such as for reclassifying variants of unknown clinical significance, but is also of importance for detailed genotype-phenotype studies

Long-term dynamics of aberrant neuronal activity in awake Alzheimer's disease transgenic mice

Alzheimer's disease (AD) is associated with aberrant neuronal activity, which is believed to critically determine disease symptoms. How these activity alterations emerge, how stable they are over time, and whether cellular activity dynamics are affected by the amyloid plaque pathology remains incompletely understood. We here repeatedly recorded the activity from identified neurons in cortex of awake APPPS1 transgenic mice over four weeks during the early phase of plaque deposition using in vivo two-photon calcium imaging. We found that aberrant activity during this stage largely persisted over the observation time. Novel highly active neurons slowly emerged from former intermediately active neurons. Furthermore, activity fluctuations were independent of plaque proximity, but aberrant activity was more likely to persist close to plaques. These results support the notion that neuronal network pathology observed in models of cerebral amyloidosis is the consequence of persistent single cell aberrant neuronal activity, a finding of potential diagnostic and therapeutic relevance for AD

Hippocampus Leads Ventral Striatum in Replay of Place-Reward Information

While the brain is asleep, the hippocampus plays first fiddle in the orchestra of memory; spatial information is reactivated in the hippocampus shortly in advance of emotional memory traces in the vental striatum

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.PhenoScore is an open-source machine-learning tool that combines facial image recognition with Human Phenotype Ontology for genetic syndrome identification without genomic data, with applications to subgroup analysis and variants of unknown significance classification.Genetics of disease, diagnosis and treatmen