Ying Wang

Accurate inference of absolute copy numbers beyond simple gains and losses from single-cell chromatin accessibility (scATAC-seq) data remains challenging, thereby obscuring the distinction between genetic and epigenetically driven oncogenic dependencies. Here, we present TeaCNV, a computational framework that reconstructs clonal absolute copy number profiles and tumor clonal architectures from scATAC-seq data without matched DNA baselines. Through validation both in silico and against bulk whole-genome sequencing in renal cell carcinomas, TeaCNV resolved subclonal absolute copy number profiles with less than 10% error and detected copy number variations (CNVs) with 98.6% accuracy, outperforming existing methods. Applied to six cancer types including renal, breast, pancreatic, head and neck, colorectal, and ovarian cancers, TeaCNV delineated polyclonal architectures and revealed distinct chromatin accessibility patterns driven by CNVs in key driver genes, including AKT2, ZNF217, and SOX2. By enabling absolute copy number profiling and clonal deconvolution from epigenomic assays, TeaCNV bridges critical gaps in studying oncogenic dependencies and genotype–phenotype relationships at single-cell resolution.

Cancer is characterized by aneuploidy, often resulting from chromosomal instability (CIN) due to events such as whole-genome duplication (WGD), breakage-fusion-bridge (BFB) cycles, and chromothripsis. These catastrophic events drive de novo copy number alterations (CNAs) with different chromosomal rearrangement patterns, contributing to tumor heterogeneity and evolution. The lack of methods capable of dissecting complex patterns of CNAs at single-cell resolution limits the understanding of how chromosome aneuploidy influences tumor evolution. To address this, we developed ChromoPattern, a computational framework to quantify four distinct chromosomal rearrangement patterns associated with complex CNA mechanisms based on single-cell copy number profiles. ChromoPattern effectively differentiates these patterns through simulations and real-world data. We applied ChromoPattern to single-cell DNA sequencing data from a liver cancer cell line and breast cancer patients. Our analysis identified the oscillation pattern of CNAs as a significant contributor to tumor heterogeneity and evolution. This pattern was associated with a 20% increase in the de novo CNA rate during tumor evolution and was linked to increased intratumor heterogeneity and subclonality. Further analysis using single-cell RNA sequencing data demonstrated that tumor clones with an enriched oscillation pattern display increased cell cycle activity and a stem-like phenotype. These findings underscore the significant and differential roles of chromosomal rearrangement patterns in tumor heterogeneity and evolution. The identification of the oscillation pattern as a key driver of tumor heterogeneity and evolution opens new avenues for developing targeted therapies and diagnostic tools.

Accurate inference of absolute copy numbers beyond simple gains and losses from single-cell chromatin accessibility (scATAC-seq) data remains challenging, thereby obscuring the distinction between genetic and epigenetically driven oncogenic dependencies. Here, we present TeaCNV, a computational framework that reconstructs clonal absolute copy number profiles and tumor clonal architectures from scATAC-seq data without matched DNA baselines. Validated against bulk whole-genome sequencing in renal cell carcinomas, TeaCNV resolved subclonal absolute copy number profiles with less than 10% error and detected copy number variations (CNVs) with 98.6% accuracy, outperforming existing methods. Applied to six cancer types including renal, breast, pancreatic, head and neck, colorectal, and ovarian cancers, TeaCNV delineated polyclonal architectures and revealed distinct chromatin accessibility patterns driven by CNVs in key driver genes, including AKT2, ZNF217 and SOX2. By enabling absolute copy number profiling and clonal deconvolution from epigenomic assays, TeaCNV bridges critical gaps in studying oncogenic dependencies and genotype-phenotype relationships at single-cell resolution.

Cleavage and polyadenylation are essential processes that can impact many aspects of mRNA fate. Most eukaryotic genes have alternative polyadenylation (APA) events. While the heterogeneity of mRNA polyadenylation isoform choice has been studied in specific tissues, less attention has been paid to the neuronal heterogeneity of APA selection at single-nucleus resolution. APA is highly controlled during development and neuronal activation, however, to what extent APA events vary in a specific neuronal cell population and the regulatory mechanisms are still unclear. In this paper, we investigated dynamic APA usage in different cell types using snRNA-seq data of 1424 human brain cells generated by single-cell 3′ RNA sequencing. We found that distal APA sites are not only favored by global neuronal cells, but that their usage also varies between the principal types of neuronal cell populations (excitatory neurons and inhibitory neurons). A motif analysis and a gene functional analysis indicated the enrichment of RNA-binding protein (RBP) binding sites and neuronal functions for the set of genes with neuron-enhanced distal PAS usage. Our results revealed the extensive involvement of APA regulation in neuronal populations at the single-nucleus level, providing new insights into roles for APA in specific neuronal cell populations, as well as utility in future functional studies.

This paper describes a method that addresses the transient loss of observations in sea surface target state estimations. A six degrees of freedom swing platform fixed with a MiniRadaScan is used to simulate the loss of observations. The state transition model based on the historical observation data fit prediction is designed because the existing state estimation method can only use the system model prediction while the observation is missing. An observation data sliding window width adaptive adjustment strategy is proposed that can improve the fitting accuracy of the state transition model. To solve the problem where the weight value of the Gaussian components of the Gaussian mixture filter is not changed in the time update stage while the observation is missing, an adaptive adjustment strategy for the weight is proposed based on the Chapman‐Kolmogorov equation, which can improve the estimation precision under the conditions of the missing observation. The simulation test demonstrates the proposed accuracy and real‐time performance of the proposed algorithm.