SNORD113–114 cluster maintains haematopoietic stem cell self-renewal via orchestrating the translation machinery
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Conversion of placental hemogenic endothelial cells to hematopoietic stem and progenitor cells
Hematopoietic stem and progenitor cells (HSPCs) are critical for the treatment of blood diseases in clinic. However, the limited source of HSPCs severely hinders their clinical application. In the embryo, hematopoietic stem cells (HSCs) arise from hemogenic endothelial (HE) cells lining the major arteries in vivo. In this work, by engineering vascular niche endothelial cells (VN-ECs), we generated functional HSPCs in vitro from ECs at various sites, including the aorta-gonad-mesonephros (AGM) region and the placenta. Firstly, we converted mouse embryonic HE cells from the AGM region (aHE) into induced HSPCs (iHSPCs), which have the abilities for multilineage differentiation and self-renewal. Mechanistically, we found that VN-ECs can promote the generation of iHSPCs via secretion of CX3CL1 and IL1A. Next, through VN-EC co-culture, we showed that placental HE (pHE) cells, a type of extra-embryonic HE cells, were successfully converted into iHSPCs (pHE-iHSPCs), which have multilineage differentiation capacity, but exhibit limited self-renewal ability. Furthermore, comparative transcriptome analysis of aHE-iHSPCs and pHE-iHSPCs showed that aHE-iHSPCs highly expressed HSC-specific and self-renewal-related genes. Moreover, experimental validation showed that retinoic acid (RA) treatment promoted the transformation of pHE cells into iHSPCs that have self-renewal ability. Collectively, our results suggested that pHE cells possess the potential to transform into self-renewing iHSPCs through RA treatment, which will facilitate the clinical application of placental endothelial cells in hematopoietic cell generation.
Stem cell transcriptional profiles from mouse subspecies reveal cis-regulatory evolution at translation genes
A key goal of evolutionary genomics is to harness molecular data to draw inferences about selective forces that have acted on genomes. The field progresses in large part through the development of advanced molecular-evolution analysis methods. Here we explored the intersection between classical sequence-based tests for selection and an empirical expression-based approach, using stem cells from Mus musculus subspecies as a model. Using a test of directional, cis-regulatory evolution across genes in pathways, we discovered a unique program of induction of translation genes in stem cells of the Southeast Asian mouse M. m. castaneus relative to its sister taxa. We then mined population-genomic sequences to pursue underlying regulatory mechanisms for this expression divergence, finding robust evidence for alleles unique to M. m. castaneus at the upstream regions of the translation genes. We interpret our data under a model of changes in lineage-specific pressures across Mus musculus in stem cells with high translational capacity. Our findings underscore the rigor of integrating expression and sequence-based methods to generate hypotheses about evolutionary events from long ago.
Integrated proteogenomic characterization of ampullary adenocarcinoma
Ampullary adenocarcinoma (AMPAC) is a rare and heterogeneous malignancy. Here we performed a comprehensive proteogenomic analysis of 198 samples from Chinese AMPAC patients and duodenum patients. Genomic data illustrate that 4q loss causes fatty acid accumulation and cell proliferation. Proteomic analysis has revealed three distinct clusters (C-FAM, C-AD, C-CC), among which the most aggressive cluster, C-AD, is associated with the poorest prognosis and is characterized by focal adhesion. Immune clustering identifies three immune clusters and reveals that immune cluster M1 (macrophage infiltration cluster) and M3 (DC cell infiltration cluster), which exhibit a higher immune score compared to cluster M2 (CD4+ T-cell infiltration cluster), are associated with a poor prognosis due to the potential secretion of IL-6 by tumor cells and its consequential influence. This study provides a comprehensive proteogenomic analysis for seeking for better understanding and potential treatment of AMPAC.
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.
A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity. Validation against state-of-the-art segmentation and detection methods on unseen datasets demonstrated ACE’s high generalizability and performance. Applying ACE in two distinct neurobiological contexts, we discovered subregional effects missed by existing atlas-based analyses and showcase ACE’s ability to reveal localized or laminar neuronal activity brain-wide. Our open-source pipeline enables whole-brain mapping of neuronal ensembles at a high level of precision across a wide range of neuroscientific applications.
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