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Background In mammals, cold exposure induces browning of white adipose tissue (WAT) and alters WAT gene expression and lipid metabolism to boost adaptive thermogenesis and maintain body temperature. Understanding the lipidomic and transcriptomic profiles of WAT upon cold exposure provides insights into the adaptive changes associated with this process. Results Here, we applied mass spectrometry and RNA sequencing (RNA-seq) to provide a comprehensive resource for describing the lipidomic or transcriptome profiles in cold-induced inguinal WAT (iWAT). We showed that short-term (3-day) cold exposure induces browning of iWAT, increases energy expenditure, and results in loss of body weight and fat mass. Lipidomic analysis shows that short-term cold exposure leads to dramatic changes of the overall composition of lipid classes WAT. Notably, cold exposure induces significant changes in the acyl-chain composition of triacylglycerols (TAGs), as well as the levels of glycerophospholipids and sphingolipids in iWAT. RNA-seq and qPCR analysis suggests that short-term cold exposure alters the expression of genes and pathways involved in fatty acid elongation, and the synthesis of TAGs, sphingolipids, and glycerophospholipids. Furthermore, the cold-induced lipid dynamics and gene expression pathways in iWAT are contrary to those previously observed in metabolic syndrome, neurodegenerative disorders, and aging, suggesting beneficial effects of cold-induced WAT browning on health and lifespan. Conclusion We described the significant alterations in the composition of glyphospholipids, glycerolipids, and sphingolipids and expression of genes involved in thermogenesis, fatty acid elongation, and fatty acid metabolism during the response of iWAT to short-term cold exposure. We also found that some changes in the levels of specific lipid species happening after cold treatment of iWAT are negatively correlated to metabolic diseases, including obesity and T2D.

The prevailing model of cerebellar learning states that climbing fibers (CFs) are both driven by, and serve to correct, erroneous motor output. However, this model is grounded largely in studies of behaviors that utilize hardwired neural pathways to link sensory input to motor output. To test whether this model applies to more flexible learning regimes that require arbitrary sensorimotor associations, we developed a cerebellar-dependent motor learning task that is compatible with both mesoscale and single-dendrite-resolution calcium imaging in mice. We found that CFs were preferentially driven by and more time-locked to correctly executed movements and other task parameters that predict reward outcome, exhibiting widespread correlated activity in parasagittal processing zones that was governed by these predictions. Together, our data suggest that such CF activity patterns are well-suited to drive learning by providing predictive instructional input that is consistent with an unsigned reinforcement learning signal but does not rely exclusively on motor errors.

Background In addition to its contractile properties and role in movement, skeletal muscle plays an important function in regulating whole-body glucose and lipid metabolism. A central component of such regulation is mitochondria, whose quality and function are essential in maintaining proper metabolic homeostasis, with defects in processes such as autophagy and mitophagy involved in mitochondria quality control impairing skeletal muscle mass and function, and potentially leading to a number of associated diseases. Cold exposure has been reported to markedly induce metabolic remodeling and enhance insulin sensitivity in the whole body by regulating mitochondrial biogenesis. However, changes in lipid metabolism and lipidomic profiles in skeletal muscle in response to cold exposure are unclear. Here, we generated lipidomic or transcriptome profiles of mouse skeletal muscle following cold induction, to dissect the molecular mechanisms regulating lipid metabolism upon acute cold treatment. Results Our results indicated that short-term cold exposure (3 days) can lead to a significant increase in intramuscular fat deposition. Lipidomic analyses revealed that a cold challenge altered the overall lipid composition by increasing the content of triglyceride (TG), lysophosphatidylcholine (LPC), and lysophosphatidylethanolamine (LPE), while decreasing sphingomyelin (SM), validating lipid remodeling during the cold environment. In addition, RNA-seq and qPCR analysis showed that cold exposure promoted the expression of genes related to lipolysis and fatty acid biosynthesis. These marked changes in metabolic effects were associated with mitophagy and muscle signaling pathways, which were accompanied by increased TG deposition and impaired fatty acid oxidation. Mechanistically, HIF-1α signaling was highly activated in response to the cold challenge, which may contribute to intramuscular fat deposition and enhanced mitophagy in a cold environment. Conclusions Overall, our data revealed the adaptive changes of skeletal muscle associated with lipidomic and transcriptomic profiles upon cold exposure. We described the significant alterations in the composition of specific lipid species and expression of genes involved in glucose and fatty acid metabolism. Cold-mediated mitophagy may play a critical role in modulating lipid metabolism in skeletal muscle, which is precisely regulated by HIF-1α signaling.

Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank for clinical history and follow-up data. It is still challenging to achieve single cell/nucleus RNA (sc/snRNA) profile in FFPE tissues. Here, we develop a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers. snRandom-seq shows a minor doublet rate (0.3%), a much higher RNA coverage, and detects more non-coding RNAs and nascent RNAs, compared with state-of-art high-throughput scRNA-seq technologies. snRandom-seq detects a median of >3000 genes per nucleus and identifies 25 typical cell types. Moreover, we apply snRandom-seq on a clinical FFPE human liver cancer specimen and reveal an interesting subpopulation of nuclei with high proliferative activity. Our method provides a powerful snRNA-seq platform for clinical FFPE specimens and promises enormous applications in biomedical research. Formalin-fixed paraffin-embedded (FFPE) tissues constitute a vast and valuable patient material bank, but single nucleus RNAseq using such tissues is challenging. Here the authors develop a droplet-based method called snRandom-seq for high-throughput and sensitive single nucleus RNA-seq of FFPE samples.

Background Ageing is accompanied by sarcopenia and intramuscular fat (IMAT) infiltration. In skeletal muscle, fat infiltration is a common feature in several myopathies and is associated with muscular dysfunction and insulin resistance. However, the cellular origin and lipidomic and transcriptomic changes during fat infiltration in skeletal muscle remain unclear. Methods In the current study, we generated a high IMAT‐infiltrated skeletal muscle model by glycerol (GLY) injection. Single‐cell RNA sequencing and lineage tracing were performed on GLY‐injured skeletal muscle at 5 days post‐injection (DPI) to identify the cell origins and dynamics. Lipidomics and RNA sequencing were performed on IMAT‐infiltrated skeletal muscle at 14 DPI (or 17 DPI for the cold treatment) to analyse alterations of lipid compositions and gene expression levels. Results We identified nine distinct major clusters including myeloid‐derived cells (52.13%), fibroblast/fibro/adipogenic progenitors (FAPs) (23.24%), and skeletal muscle stem cells (2.02%) in GLY‐injured skeletal muscle. Clustering and pseudotemporal trajectories revealed six subpopulations in fibroblast/FAPs and 10 subclusters in myeloid‐derived cells. A subpopulation of myeloid‐derived cells expressing adipocyte‐enriched genes and Pdgfra−/Cd68+ cells displayed lipid droplets upon adipogenic induction, indicating their adipogenic potential. Lipidomic analysis revealed the changes of overall lipid classes composition (e.g. triglycerides (TAGs) increased by 19.3 times, P = 0.0098; sulfoquinovosyl diacylglycerol decreased by 83%, P = 0.0056) and in the distribution of lipids [e.g. TAGs (18:2/18:2/22:6) increased by 181.6 times, P = 0.021] between GLY‐group and saline control. RNA‐seq revealed 1847 up‐regulated genes and 321 down‐regulated genes and significant changes in lipid metabolism‐related pathways (e.g. glycerolipid pathway and glycerophospholipid pathway) in our model of GLY‐injured skeletal muscle. Notably, short‐term cold exposure altered fatty acid composition (e.g. saturated fatty acid decreased by 6.4%, P = 0.058) in fat‐infiltrated muscles through directly affecting lipid metabolism pathways including PI3K–AKT and MAPK signalling pathway. Conclusions Our results showed that a subpopulation of myeloid‐derived cells may contribute to IMAT infiltration. GLY‐induced IMAT infiltration changed the lipid composition and gene expression profiles. Short‐term cold exposure might regulate lipid metabolism and its related signalling pathways in fat‐infiltrated muscle. Our study provides a comprehensive resource describing the molecular signature of fat infiltration in skeletal muscle.

Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli ). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research. Population level transcriptomics measurements miss bacterial heterogeneity. Here the authors report smRandom-seq, a droplet-based high-throughput single-microbe RNA-seq assay, using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion.

Genome-wide association studies have identified thousands of genetic variants associated with non-small cell lung cancer (NSCLC), however, it is still challenging to determine the causal variants and to improve disease risk prediction. Here, we applied massively parallel reporter assays to perform NSCLC variant-to-function mapping at scale. A total of 1249 candidate variants were evaluated, and 30 potential causal variants within 12 loci were identified. Accordingly, we proposed three genetic architectures underlying NSCLC susceptibility: multiple causal variants in a single haplotype block (e.g. 4q22.1), multiple causal variants in multiple haplotype blocks (e.g. 5p15.33), and a single causal variant (e.g. 20q11.23). We developed a modified polygenic risk score using the potential causal variants from Chinese populations, improving the performance of risk prediction in 450,821 Europeans from the UK Biobank. Our findings not only augment the understanding of the genetic architecture underlying NSCLC susceptibility but also provide strategy to advance NSCLC risk stratification. Determining the causal variants at GWAS loci is crucial for understanding genetic disease mechanisms. Here, the authors apply MPRA to perform non-small cell lung cancer (NSCLC) variant-to-function mapping at scale and propose distinct genetic architectures underlying NSCLC susceptibility.

Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications for health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve a comprehensive understanding of complex microbial communities together with their hosts are therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive response states among species in Prevotella and Roseburia genera and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated that smRandom-seq2 is a high-throughput and high-resolution smRNA-seq technique that is highly adaptable to complex microbial communities in real-world situations and promises new perspectives in the understanding of human microbiomes.

Sleep loss may lead to negative bias during social interaction. In the current study, we conducted a revised social evaluation task experiment to investigate how sleep deprivation influences the self-referential and cognitive processes of social feedback. The experiment consisted of a first impression task and a social feedback task. Seventy-eight participants completed the first impression task and were divided into normal and poor sleep groups. The results of an independent samples t-test showed that participants who slept worse were less likely to socialize with others but did not evaluate others as less attractive. Afterward, 22 of the participants from the first impression task were recruited to complete the social feedback task during functional magnetic resonance imaging (fMRI) on the mornings following two different sleep conditions at night: one night of normal sleep and one night of sleep deprivation. The results of this within-subject design study showed that participants who experienced the latter condition showed increased activation within the default mode network (i.e. superior parietal lobule, precuneus, inferior parietal lobule, inferior temporal gyrus, and medial frontal gyrus) and anterior cingulate cortex (ACC) and stronger negative insula functional connectivity (FC) with the precuneus to negative feedback than positive feedback. The altered activation and behavioral pattern may indicate a negative bias for social cues. However, stronger negative coupling may indicate stronger cognitive control, which may protect against potential damage to self-concept. Our study suggested that sleep impairs most social functions, but may protect against impairment of important ones, such as self-concept.

SiC wafers, due to their hardness and brittleness, suffer from a low feed rate and a high failure rate during the dicing process. In this study, a novel dual laser beam asynchronous dicing method (DBAD) is proposed to improve the cutting quality of SiC wafers, where a pulsed laser is firstly used to introduce several layers of micro-cracks inside the wafer, along the designed dicing line, then a continuous wave (CW) laser is used to generate thermal stress around cracks, and, finally, the wafer is separated. A finite-element (FE) model was applied to analyze the behavior of CW laser heating and the evolution of the thermal stress field. Through experiments, SiC samples, with a thickness of 200 μm, were cut and analyzed, and the effect of the changing of continuous laser power on the DBAD system was also studied. According to the simulation and experiment results, the effectiveness of the DBAD method is certified. There is no more edge breakage because of the absence of the mechanical breaking process compared with traditional stealth dicing. The novel method can be adapted to the cutting of hard-brittle materials. Specifically for materials thinner than 200 μm, the breaking process in the traditional SiC dicing process can be omitted. It is indicated that the dual laser beam asynchronous dicing method has a great engineering potential for future SiC wafer dicing applications.