Lin Yang

Unlocking the dynamic inner workings of the brain continues to remain a grand challenge of the 21st century. To this end, functional neuroimaging modalities represent an outstanding approach to better understand the mechanisms of both normal and abnormal brain functions. The ability to image brain function with ever increasing spatial and temporal resolution has made a significant leap over the past several decades. Further delineation of functional networks could lead to improved understanding of brain function in both normal and diseased states. This paper reviews recent advancements and current challenges in dynamic functional neuroimaging techniques, including electrophysiological source imaging, multimodal neuroimaging integrating fMRI with EEG/MEG, and functional connectivity imaging.

Mesenchymal stem cells (MSCs) were regarded as one of the most promising type of seed cells in tissue engineering due to its easy accessibility and multipotent feature of being able to differentiate into adipocyte, osteoblast, cardiomyocytes, and neurons. For years, MSCs have been applied in treating cardiovascular disease, reconstructing kidney injury, and remodeling immune system with remarkable achievements. Basic researches revealed that its clinic effects are not only due to their pluripotent ability but also through their paracrine function that they synthesize and secrete a broad spectrum of growth factors and cytokines. Recent studies show that exosomes is the main paracrine executor of MSCs. The lipid bilayer of exosome maintains its stability and integrity and keeps biological potency of biological substance within it. MSC-derived exosomes were shown to be successful in treating many diseases, including tumor and cardiovascular diseases. However, the exact composition of MSC-derived exosomes is not known yet. In this review, we will discuss the lipid, protein, and microRNA contents within MSC-derived exosomes based on current studies to guide further research and clinical applications of MSC-derived exosomes.

Abstract Mouse cortical radial glial cells (RGCs) are primary neural stem cells that give rise to cortical oligodendrocytes, astrocytes, and olfactory bulb (OB) GABAergic interneurons in late embryogenesis. There are fundamental gaps in understanding how these diverse cell subtypes are generated. Here, by combining single-cell RNA-Seq with intersectional lineage analyses, we show that beginning at around E16.5, neocortical RGCs start to generate ASCL1 + EGFR + apical multipotent intermediate progenitors (MIPCs), which then differentiate into basal MIPCs that express ASCL1, EGFR, OLIG2, and MKI67. These basal MIPCs undergo several rounds of divisions to generate most of the cortical oligodendrocytes and astrocytes and a subpopulation of OB interneurons. Finally, single-cell ATAC-Seq supported our model for the genetic logic underlying the specification and differentiation of cortical glial cells and OB interneurons. Taken together, this work reveals the process of cortical radial glial cell lineage progression and the developmental origins of cortical astrocytes and oligodendrocytes.

Background Developmental disorders (DDs) are early onset disorders affecting 5%–10% of children worldwide. Chromosomal microarray analysis detecting CNVs is currently recommended as the first-tier test for DD diagnosis. However, this analysis omits a high percentage of disease-causing single nucleotide variations (SNVs) that warrant further sequencing. Currently, next-generation sequencing can be used in clinical scenarios detecting CNVs, and the use of exome sequencing in the DD cohort ahead of the microarray test has not been evaluated. Methods Clinical exome sequencing (CES) was performed on 1090 unrelated Chinese DD patients who were classified into five phenotype subgroups. CNVs and SNVs were both detected and analysed based on sequencing data. Results An overall diagnostic rate of 41.38% was achieved with the combinational analysis of CNV and SNV. Over 12.02% of patients were diagnosed based on CNV, which was comparable with the published CMA diagnostic rate, while 0.74% were traditionally elusive cases who had dual diagnosis or apparently homozygous mutations that were clarified. The diagnostic rates among subgroups ranged from 21.82% to 50.32%. The top three recurrent cytobands with diagnostic CNVs were 15q11.2-q13.1, 22q11.21 and 7q11.23. The top three genes with diagnostic SNVs were: MECP2 , SCN1A and SCN2A . Both the diagnostic rate and spectrums of CNVs and SNVs showed differences among the phenotype subgroups. Conclusion With a higher diagnostic rate, more comprehensive observation of variations and lower cost compared with conventional strategies, simultaneous analysis of CNVs and SNVs based on CES showed potential as a new first-tier choice to diagnose DD.