A comprehensive understanding of the evolution of the immune landscape in humans across the entire lifespan at single-cell transcriptional and protein levels, during development, maturation and senescence is currently lacking. We recruited a total of 220 healthy volunteers from the Shanghai Pudong Cohort (NCT05206643), spanning 13 age groups from 0 to over 90 years, and profiled their peripheral immune cells through single-cell RNA-sequencing coupled with single T cell and B cell receptor sequencing, high-throughput mass cytometry, bulk RNA-sequencing and flow cytometry validation experiments. We revealed that T cells were the most strongly affected by age and experienced the most intensive rewiring in cell–cell interactions during specific age. Different T cell subsets displayed different aging patterns in both transcriptomes and immune repertoires; examples included GNLY⁺CD8⁺ effector memory T cells, which exhibited the highest clonal expansion among all T cell subsets and displayed distinct functional signatures in children and the elderly; and CD8⁺ MAIT cells, which reached their peaks of relative abundance, clonal diversity and antibacterial capability in adolescents and then gradually tapered off. Interestingly, we identified and experimentally verified a previously unrecognized ‘cytotoxic’ B cell subset that was enriched in children. Finally, an immune age prediction model was developed based on lifecycle-wide single-cell data that can evaluate the immune status of healthy individuals and identify those with disturbed immune functions. Our work provides both valuable insights and resources for further understanding the aging of the immune system across the whole human lifespan.

Introduction: Reliable biomarkers are in need to predict the prognosis of hepatocellular carcinoma (HCC). Whilst recent evidence has established the critical role of copper homeostasis in tumor growth and progression, no previous studies have dealt with the copper-related genes (CRGs) signature with prognostic potential in HCC.Methods: To develop and validate a CRGs prognostic signature for HCC, we retrospectively included 353 and 142 patients as the development and validation cohort, respectively. Copper-related Prognostic Signature (Copper-PSHC) was developed using differentially expressed CRGs with prognostic value. The hazard ratio (HR) and the area under the time-dependent receiver operating characteristic curve (AUC) during 3-year follow-up were utilized to evaluate the performance. Additionally, the Copper-PSHC was combined with age, sex, and cancer stage to construct a Copper-clinical-related Prognostic Signature (Copper-CPSHC), by multivariate Cox regression. We further explored the underlying mechanism of Copper-PSHC by analyzing the somatic mutation, functional enrichment, and tumor microenvironment. Potential drugs for the high-risk group were screened.Results: The Copper-PSHC was constructed with nine CRGs. Patients in the high-risk group demonstrated a significantly reduced overall survival (OS) (adjusted HR, 2.65 [95% CI, 1.83–3.84] and 3.30, [95% CI, 1.27–8.60] in the development and validation cohort, respectively). The Copper-PSHC achieved a 3-year AUC of 0.74 [95% CI, 0.67–0.82] and 0.71 [95% CI, 0.56–0.86] for OS in the development and validation cohort, respectively. Copper-CPSHC yield a 3-year AUC of 0.73 [95% CI, 0.66–0.80] and 0.72 [95% CI, 0.56–0.87] for OS in the development and validation cohort, respectively. Higher tumor mutation burden, downregulated metabolic processes, hypoxia status and infiltrated stroma cells were found for the high-risk group. Six small molecular drugs were screened for the treatment of the high-risk group.Conclusion: Copper-PSHC services as a promising tool to identify HCC with poor prognosis and to improve disease outcomes by providing potential clinical decision support in treatment.

Comparing gene expressions among parasitic plants infecting different host species can have significant implications for understanding host–parasite interactions. Taxillus nigrans is a common hemiparasitic species in Southwest China that parasitizes a variety of host species. However, a lack of nucleotide sequence data to date has hindered transcriptome-level research on T. nigrans. In this study, the transcriptomes of T. nigrans individuals parasitizing four typical host species (Broussonetia papyrifera (Bpap), a broad-leaved tree species; Cryptomeria fortunei (Cfor), a coniferous tree species; Cinnamomum septentrionale (Csep), an evergreen tree species; and Ginkgo biloba (Gbil), a deciduous-coniferous tree species) were sequenced, and the expression profiles and metabolic pathways were compared among hosts. A total of greater than 400 million reads were generated in nine cDNA libraries. These were de novo assembled into 293823 transcripts with an N50 value of 1790 bp. A large number of differentially expressed genes (DEGs) were identified when comparing T. nigrans individuals on different host species: Bpap vs. Cfor (1253 DEGs), Bpap vs. Csep (864), Bpap vs. Gbil (517), Cfor vs. Csep (259), Cfor vs. Gbil (95), and Csep vs. Gbil (40). Four hundred and fifteen unigenes were common to all six pairwise comparisons; these were primarily associated with Cytochrome P450 and environmental adaptation, as determined in a KEGG enrichment analysis. Unique unigenes were also identified, specific to Bpap vs. Cfor (808 unigenes), Bpap vs. Csep (329 unigenes), Bpap vs. Gbil (87 unigenes), Cfor vs. Csep (108 unigenes), Cfor vs. Gbil (32 unigenes), and Csep vs. Gbil comparisons (23 unigenes); partial unigenes were associated with the metabolism of terpenoids and polyketides regarding plant hormone signal transduction. Weighted gene co-expression network analysis (WGCNA) revealed four modules that were associated with the hosts. These results provide a foundation for further exploration of the detailed molecular mechanisms involved in plant parasitism.

Point clouds are a widely available and canonical data modality which convey the 3D geometry of a scene. Despite significant progress in classification and segmentation from point clouds, policy learning from such a modality remains challenging, and most prior works in imitation learning focus on learning policies from images or state information. In this paper, we propose a novel framework for learning policies from point clouds for robotic manipulation with tools. We use a novel neural network, ToolFlowNet, which predicts dense per-point flow on the tool that the robot controls, and then uses the flow to derive the transformation that the robot should execute. We apply this framework to imitation learning of challenging deformable object manipulation tasks with continuous movement of tools, including scooping and pouring, and demonstrate significantly improved performance over baselines which do not use flow. We perform 50 physical scooping experiments with ToolFlowNet and attain 82% scooping success. See https://tinyurl.com/toolflownet for supplementary material.