Multiomic single-cell profiling identifies critical regulators of postnatal brain
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Dopaminergic modulation and dosage effects on brain state dynamics and working memory component processes in Parkinson’s disease
Parkinson’s disease (PD) is primarily diagnosed through its characteristic motor deficits, yet it also encompasses progressive cognitive impairments that profoundly affect quality of life. While dopaminergic medications are routinely prescribed to manage motor symptoms in PD, their influence extends to cognitive functions as well. Here we investigate how dopaminergic medication influences aberrant brain circuit dynamics associated with encoding, maintenance and retrieval working memory (WM) task-phases processes. PD participants, both on and off dopaminergic medication, and healthy controls, performed a Sternberg WM task during fMRI scanning. We employ a Bayesian state-space computational model to delineate brain state dynamics related to different task phases. Importantly, a within-subject design allows us to examine individual differences in the effects of dopaminergic medication on brain circuit dynamics and task performance. We find that dopaminergic medication alters connectivity within prefrontal-basal ganglia-thalamic circuits, with changes correlating with enhanced task performance. Dopaminergic medication also restores engagement of task-phase-specific brain states, enhancing task performance. Critically, we identify an “inverted-U-shaped” relationship between medication dosage, brain state dynamics, and task performance. Our study provides valuable insights into the dynamic neural mechanisms underlying individual differences in dopamine treatment response in PD, paving the way for more personalized therapeutic strategies.
Label-free live cell recognition and tracking for biological discoveries and translational applications
Label-free, live cell recognition (i.e. instance segmentation) and tracking using computer vision-aided recognition can be a powerful tool that rapidly generates multi-modal readouts of cell populations at single cell resolution. However, this technology remains hindered by the lack of accurate, universal algorithms. This review presents related biological and computer vision concepts to bridge these disciplines, paving the way for broad applications in cell-based diagnostics, drug discovery, and biomanufacturing.
Baicalein inhibits cell proliferation and induces apoptosis in brain glioma cells by downregulating the LGR4-EGFR pathway
Patients diagnosed with brain glioma have a poor prognosis and limited therapeutic options. LGR4 is overexpressed in brain glioma and involved in the tumorigenesis of many tumors. Baicalein (BAI) is a kind of flavonoid that has exhibited anti-tumor effects in various tumors. Nevertheless, the functions and associations of BAI and LGR4 in brain glioma remain unclear. In this study, Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases were used to perform expression and survival analysis of LGR4 in brain glioma patients. Subsequently, the significance of LGR4-EGFR in brain glioma cells (HS683 and KNS89) and brain glioma animal models was explored by RNA interference and subcutaneous transplantation. Additionally, brain glioma cells were treated with BAI to explore the roles and mechanisms of BAI in brain glioma. The results showed that LGR4 was highly expressed in brain glioma and was related to a poor prognosis. LGR4 knockdown repressed the proliferation and EGFR phosphorylation but induced apoptosis in brain glioma cells. However, these effects were reversed by EGFR overexpression and CBL knockdown. In contrast, both in vitro and in vivo experiments revealed that LGR4 overexpression facilitated brain glioma cell malignant behavior and promoted tumor development, but these effects were rescued by BAI and an EGFR inhibitor. Furthermore, si-LGR4 accelerated EGFR protein degradation, while oe-LGR4 exhibited the opposite effect. Without affecting normal cellular viability, BAI inhibited malignant behavior, interacted with LGR4, and blocked the LGR4-EGFR pathway for brain glioma cells. In conclusion, our data suggested that BAI inhibited brain glioma cell proliferation and induced apoptosis by downregulating the LGR4-EGFR pathway, which provides a novel strategy and potential therapeutic targets to treat brain glioma.
Sensory input, sex and function shape hypothalamic cell type development
Mammalian behaviour and physiology undergo major changes in early life. Young animals rely on conspecifics to meet their needs and start showing nutritional independence and sex-specific social interactions at weaning and puberty, respectively. How neuronal populations regulating homeostatic functions and social behaviours develop during these transitions remains unclear. We used paired transcriptomic and chromatin accessibility profiling to examine the developmental trajectories of neuronal populations in the hypothalamic preoptic region, where cell types with key roles in physiological and behavioural control have been identified1,2,3,4,5,6. These data show a marked diversity of developmental trajectories shaped by the sex of the animal, and the location and behavioural or physiological function of the corresponding cell types. We identify key stages of preoptic development, including early diversification, perinatal emergence of sex differences, postnatal maturation and refinement of signalling networks, and nonlinear transcriptional changes accelerating at the time of weaning and puberty. We assessed preoptic development in various sensory mutants and find a major role for vomeronasal sensing in the timing of preoptic cell type maturation. These results provide new insights into the development of neurons controlling homeostatic functions and social behaviours and lay ground for examining the dynamics of these functions in early life.
Self-reports map the landscape of task states derived from brain imaging
Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.
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