A nasal chemosensation-dependent critical window for somatosensory development.

Nasal chemosensation is considered the evolutionarily oldest mammalian sense and, together with somatosensation, is crucial for neonatal well-being before auditory and visual pathways start engaging the brain. Using anatomical and functional approaches in mice, we reveal that odor-driven activity propagates to a large part of the cortex during the first postnatal week and enhances whisker-evoked activation of primary whisker somatosensory cortex (wS1). This effect disappears in adult animals, in line with the loss of excitatory connectivity from olfactory cortex to wS1. By performing neonatal odor deprivation, followed by electrophysiological and behavioral work in adult animals, we identify a key transient regulation of nasal chemosensory information necessary for the development of wS1 sensory-driven dynamics and somatosensation. Our work uncovers a cross-modal critical window for nasal chemosensation-dependent somatosensory functional maturation.

Homosynaptic plasticity induction causes heterosynaptic changes at the unstimulated neighbors in an induction pattern and location-specific manner.

Dendritic spines are highly dynamic structures whose structural and functional fluctuations depend on multiple factors. Changes in synaptic strength are not limited to synapses directly involved in specific activity patterns. Unstimulated clusters of neighboring spines in and around the site of stimulation can also undergo alterations in strength. Usually, when plasticity is induced at single dendritic spines with glutamate uncaging, neighboring spines do not show any significant structural fluctuations. Here, using two-photon imaging and glutamate uncaging at single dendritic spines of hippocampal pyramidal neurons, we show that structural modifications at unstimulated neighboring spines occur and are a function of the temporal pattern of the plasticity-inducing stimulus. Further, the relative location of the unstimulated neighbors within the local dendritic segment correlates with the extent of heterosynaptic plasticity that is observed. These findings indicate that naturalistic patterns of activity at single spines can shape plasticity at nearby clusters of synapses, and may play a role in priming local inputs for further modifications.

The temporal pattern of synaptic activation determines the longevity of structural plasticity at dendritic spines.

Learning is thought to involve physiological and structural changes at individual synapses. Synaptic plasticity has predominantly been studied using regular stimulation patterns, but neuronal activity in the brain normally follows a Poisson distribution. We used two-photon imaging and glutamate uncaging to investigate the structural plasticity of single dendritic spines using naturalistic activation patterns sampled from a Poisson distribution. We showed that naturalistic activation patterns elicit structural plasticity that is both NMDAR and protein synthesis-dependent. Furthermore, we uncovered that the longevity of structural plasticity is dependent on the temporal structure of the naturalistic pattern. Finally, we found that during the delivery of the naturalistic activity, spines underwent rapid structural growth that predicted the longevity of plasticity. This was not observed with regularly spaced activity. These data reveal that different temporal organizations of the same number of synaptic stimulations can produce rather distinct short and long-lasting structural plasticity.

Sparse postnatal labeling and quantification of superficial cortical cell synapses in the mouse neocortex.

We present a strategy for measuring the density of presynaptic boutons of superficial neuronal cells in the mouse neocortex. First, we show how to sparsely label individual postmitotic cells by neonatal pial-surface electroporation. Then, we present a custom-made code that allows quantification of the density of presynaptic boutons along axonal processes. Although we applied this strategy to somatostatin-positive cells, a major population of cortical interneurons, the same approach can be adjusted to target other types of neuronal cells. For complete details on the use and execution of this protocol, please refer to Gesuita et al. (2022).1.

Microglia contribute to the postnatal development of cortical somatostatin-positive inhibitory cells and to whisker-evoked cortical activity.

Microglia play a key role in shaping the formation and refinement of the excitatory network of the brain. However, less is known about whether and how they organize the development of distinct inhibitory networks. We find that microglia are essential for the proper development of somatostatin-positive (SST+) cell synapses during the second postnatal week. We further identify a pair of molecules that act antagonistically to one another in the organization of SST+ cell axonal elaboration. Whereas CX3CL1 acts to suppress axonal growth and complexity, CXCL12 promotes it. Assessing the functional importance of microglia in the development of cortical activity, we find that a whisker stimulation paradigm that drives SST+ cell activation leads to reduced cortical spiking in brains depleted of microglia. Collectively, our data demonstrate an important role of microglia in regulating the development of SST+ cell output early in life.

An interactive time series image analysis software for dendritic spines.

Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.

Postmitotic Prox1 Expression Controls the Final Specification of Cortical VIP Interneuron Subtypes.

Throughout development, neuronal identity is controlled by key transcription factors that determine the unique properties of a cell. During embryogenesis, the transcription factor Prox1 regulates VIP-positive cortical interneuron migration, survival, and connectivity. Here, we explore the role of Prox1 as a regulator of genetic programs that guide the final specification of VIP interneuron subtypes in early postnatal life. Synaptic in vitro electrophysiology in male and female mice shows that postnatal Prox1 removal differentially affects the dynamics of excitatory inputs onto VIP bipolar and multipolar subtypes. RNA sequencing reveals that one of the downstream targets of Prox1 is the postsynaptic protein Elfn1, a constitutive regulator of presynaptic release probability. Further genetic, pharmacological, and electrophysiological experiments demonstrate that removing Prox1 reduces Elfn1 function in VIP multipolar but not in bipolar cells. Finally, overexpression experiments and analysis of native Elfn1 mRNA expression reveal that Elfn1 levels are differentially controlled at the post-transcriptional stage. Thus, in addition to activity-dependent processes that contribute to the developmental trajectory of VIP cells, genetic programs engaged by Prox1 control the final differentiation of multipolar and bipolar subtypes.SIGNIFICANCE STATEMENT The transcription factor Prox1 generates functional diversification of cortical VIP interneuron subtypes in early postnatal life, thus expanding the inhibitory repertoire of the cortex.

Developmental divergence of sensory stimulus representation in cortical interneurons.

Vasocative-intestinal-peptide (VIP+) and somatostatin (SST+) interneurons are involved in modulating barrel cortex activity and perception during active whisking. Here we identify a developmental transition point of structural and functional rearrangements onto these interneurons around the start of active sensation at P14. Using in vivo two-photon Ca2+ imaging, we find that before P14, both interneuron types respond stronger to a multi-whisker stimulus, whereas after P14 their responses diverge, with VIP+ cells losing their multi-whisker preference and SST+ neurons enhancing theirs. Additionally, we find that Ca2+ signaling dynamics increase in precision as the cells and network mature. Rabies virus tracings followed by tissue clearing, as well as photostimulation-coupled electrophysiology reveal that SST+ cells receive higher cross-barrel inputs compared to VIP+ neurons at both time points. In addition, whereas prior to P14 both cell types receive direct input from the sensory thalamus, after P14 VIP+ cells show reduced inputs and SST+ cells largely shift to motor-related thalamic nuclei.

Tracking-assisted Detection of Dendritic Spines in Time-Lapse Microscopic Images.

Detecting morphological changes of dendritic spines in time-lapse microscopy images and correlating them with functional properties such as memory and learning, are fundamental and challenging problems in neurobiology research. In this paper, we propose an algorithm for dendritic spine detection in time series. The proposed approach initially performs spine detection at each time point and improves the accuracy by exploiting the information obtained from tracking of individual spines over time. To detect dendritic spines in a time point image we employ an SVM classifier trained by pre-labeled SIFT feature descriptors in combination with a dot enhancement filter. Second, to track the growth or loss of spines, we apply a SIFT-based rigid registration method for the alignment of time-series images. This step takes into account both the structure and the movement of objects, combined with a robust dynamic scheme to link information about spines that disappear and reappear over time. Next, we improve spine detection by employing a probabilistic dynamic programming approach to search for an optimum solution to accurately detect missed spines. Finally, we determine the spine location more precisely by performing a watershed-geodesic active contour model. We quantitatively assess the performance of the proposed spine detection algorithm based on annotations performed by biologists and compare its performance with the results obtained by the noncommercial software NeuronIQ. Experiments show that our approach can accurately detect and quantify spines in 2-photon microscopy time-lapse data and is able to accurately identify spine elimination and formation.

Dendritic spine classification using shape and appearance features based on two-photon microscopy.

BACKGROUND: Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity. NEW METHOD: We propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of oriented gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features. RESULTS: Our shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines. COMPARISON WITH EXISTING METHODS: Our proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem. CONCLUSIONS: Results validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space.