SEX DIFFERENCES IN ESTRADIOL SIGNALING IN THE ZEBRA FINCH (TAENIOPYGIA GUTATTA) AUDITORY CORTEX

Although several sex differences have been described in brain structure, function, and development, sex as a biological factor is underrepresented in neuroscience studies. In the mammalian brain, there are sex differences in the mechanism of rapid estradiol actions on neuronal physiology. In the songbird, the brain is a major source of estradiol production, and estradiol rapidly modulates auditory responsiveness through dynamic changes and an unknown receptor mechanism. I set out to determine if there are sex differences in rapid estradiol modulation of auditory cortical activity, as has been shown in other systems. I tested this hypothesis through three aims: 1) to determine whether the identity of interneurons in the auditory regions of the brain differs between the sexes,2) test whether acute, endogenous estradiol production is necessary for auditory responsiveness in both sexes and 3) test whether the membrane estrogen receptor GPER1 is necessary and sufficient to shape auditory-evoked activity in both sexes. I found that male and female estrogen-producing and estrogen-sensitive cells did not differ in coexpression with interneuron subtype markers in auditory cortical regions. I also determined that more regions of the male auditory cortex depend on acute, endogenous estrogen production for auditory-induced gene expression than that of females, indicating that males are more sensitive to acute-synthesis of estrogens than females. Finally, I found that narrow-spiking (NS) neurons in the caudomedial nidopallium are more associated with auditory responses than broad-spiking (BS) neurons in males whereas in females these cell types are similar. GPER1 is necessary for the full auditory responsiveness and coding but only in NS neurons of males, indicating an alternative receptor mechanism in females. In this dissertation, I describe a mechanism by which rapid estrogen modulates auditory responsiveness in males, but females have differences in the reliance on brain derived estradiol as well as receptors that mediate estradiol’s actions. This dissertation provides a framework to study sex differences using a mechanistic approach, and highlights the importance of sex as a biological variable in physiological studies even in brain regions with anatomical similarities

Biological Sex, Estradiol and Striatal Medium Spiny Neuron Physiology: A Mini-Review

The caudate-putamen, nucleus accumbens core and shell are important striatal brain regions for premotor, limbic, habit formation, reward, and other critical cognitive functions. Striatal-relevant behaviors such as anxiety, motor coordination, locomotion, and sensitivity to reward, all change with fluctuations of the menstrual cycle in humans and the estrous cycle in rodents. These fluctuations implicate sex steroid hormones, such as 17β-estradiol, as potent neuromodulatory signals for striatal neuron activity. The medium spiny neuron (MSN), the primary neuron subtype of the striatal regions, expresses membrane estrogen receptors and exhibits sex differences both in intrinsic and synaptic electrophysiological properties. In this mini-review, we first describe sex differences in the electrophysiological properties of the MSNs in prepubertal rats. We then discuss specific examples of how the human menstrual and rat estrous cycles induce differences in striatal-relevant behaviors and neural substrate, including how female rat MSN electrophysiology is influenced by the estrous cycle. We then conclude the mini-review by discussing avenues for future investigation, including possible roles of striatal-localized membrane estrogen receptors and estradiol

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic

The Evolutionary Anatomy, Biomechanics, and Diversification Dynamics of Repeatedly Evolved Masseter Muscles in Rodents

Explaining large scale transitions in discrete morphology is one of the most challenging problems in biology, requiring input from anatomy, biomechanics, ecology, paleontology, development, and genomics to appropriately contextualize evolutionary events. Rodents evolved discrete masseter muscle arrangements including novel muscle units that evolve homoplastically in clades of diverse ecological strategies and differing levels of species richness. These derived conditions, sciuromorphy, hystricomorphy, and myomorphy, differ from the primitive condition, protrogomorphy, in the anterior attachment of novel masseter muscles that have been thought represent specializations for gnawing or chewing mechanics. Here I test the hypotheses that these novel muscles function optimally at particular bite points and bias clade histories towards more gnawing or chewing intensive ecologies. I examine the functional ecomorphology of rodent dentition relative to masseter conditions, the biomechanics of individual muscles in different chewing positions, and the detailed anatomy of these muscles in key taxa with extreme cranial modifications, using a dataset comprised of over 200 rodent species. These studies reveal that sciuromorphy and hystricognathy are associated with gnawing and chewing specializations, respectively. In contrast, clades characterized by myomorphy demonstrate limited morphological and biomechanical evolution at lower evolutionary rates despite their high rates of taxonomic diversification and wide range of ecological habits compared to other groups. Strikingly, non-myomorph arid adapted rodents with large auditory bullae and reduced size of the temporalis muscle demonstrate major jaw muscle rearrangements, but arid-adapted myomorphs with temporalis reduction fail to demonstrate similar compensatory adaptations, implying that dual anterior attachments of derived masseter muscles are sufficient to conduct most major functional roles of the masticatory system. These data suggest that at least sciuromorphy and myomorphy conform to the typical hypotheses of muscle function and gnawing specialties, and are consistent with myomorphy serving as a key innovation in rodent dietary and functional evolution. These studies demonstrate that masseter conditions have profound impacted the functional and ecological evolution of rodents and baised clades toward differing histories that favor gnawing, chewing, or both strategies for ingestion. These studies demonstrate that masseter conditions have profoundly impacted the functional and ecological evolution of rodents and baised clades toward differing histories that favor gnawing, chewing, or both strategies for ingestion. Furthermore, this study has shown that detailed examination of masseter anatomy in rodents can still reveal previously undocumented morphologies, and that the functional flexibility of myomorphy significantly affects the dynamics of rodent macroevolution

Deep learning in image-based phenotypic drug discovery

International audienceModern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal ‘hit’ compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD)