Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

OBJECTIVE: What is the molecular landscape underlying the functional decline of human testicular ageing?
CONCLUSIONS: The present study provides a comprehensive single-cell transcriptomic atlas of testes from young and old humans and offers insights into the molecular mechanisms and potential targets for human testicular ageing.
BACKGROUND: Testicular ageing is known to cause male age-related fertility decline and hypogonadism. Dysfunction of testicular cells has been considered as a key factor for testicular ageing.
METHODS: Human testicular biopsies were collected from three young individuals and three old individuals to perform single-cell RNA sequencing (scRNA-seq). The key results were validated in a larger cohort containing human testicular samples from 10 young donors and 10 old donors.
METHODS: scRNA-seq was used to identify gene expression signatures for human testicular cells during ageing. Ageing-associated changes of gene expression in spermatogonial stem cells (SSCs) and Leydig cells (LCs) were analysed by gene set enrichment analysis and validated by immunofluorescent and functional assays. Cell-cell communication analysis was performed using CellChat.
RESULTS: The single-cell transcriptomic landscape of testes from young and old men was surveyed, revealing age-related changes in germline and somatic niche cells. In-depth evaluation of the gene expression dynamics in germ cells revealed that the disruption of the base-excision repair pathway is a prominent characteristic of old SSCs, suggesting that defective DNA repair in SSCs may serve as a potential driver for increased de novo germline mutations with age. Further analysis of ageing-associated transcriptional changes demonstrated that stress-related changes and cytokine pathways accumulate in old somatic cells. Age-related impairment of redox homeostasis in old LCs was identified and pharmacological treatment with antioxidants alleviated this cellular dysfunction of LCs and promoted testosterone production. Lastly, our results revealed that decreased pleiotrophin signalling was a contributing factor for impaired spermatogenesis in testicular ageing.
METHODS: The scRNA-seq sequencing and processed data reported in this paper were deposited at the Genome Sequence Archive (https://ngdc.cncb.ac.cn/), under the accession number HRA002349.
CONCLUSIONS: Owing to the difficulty in collecting human testis tissue, the sample size was limited. Further in-depth functional and mechanistic studies are warranted in future.
CONCLUSIONS: These findings provide a comprehensive understanding of the cell type-specific mechanisms underlying human testicular ageing at a single-cell resolution, and suggest potential therapeutic targets that may be leveraged to address age-related male fertility decline and hypogonadism.
BACKGROUND: This work was supported by the National Key Research and Development Program of China (2022YFA1104100), the National Natural Science Foundation of China (32130046, 82171564, 82101669, 82371611, 82371609, 82301796), the Natural Science Foundation of Guangdong Province, China (2022A1515010371), the Major Project of Medical Science and Technology Development Research Center of National Health Planning Commission, China (HDSL202001000), the Open Project of NHC Key Laboratory of Male Reproduction and Genetics (KF202001), the Guangdong Province Regional Joint Fund-Youth Fund Project (2021A1515110921, 2022A1515111201), and the China Postdoctoral Science Foundation (2021M703736). The authors declare no conflict of interest.

The generation of quality data from a single-nucleus profiling experiment requires nuclei to be isolated from tissues in a gentle and efficient manner. Nuclei isolation must be carefully optimized across tissue types to preserve nuclear architecture, prevent nucleic acid degradation, and remove unwanted contaminants. Here, we present an optimized workflow for generating a single-nucleus suspension from ocular tissues of the embryonic chicken that is compatible with various downstream workflows. The described protocol enables the rapid isolation of a high yield of aggregate-free nuclei from the embryonic chicken eye without compromising nucleic acid integrity, and the nuclei suspension is compatible with single-nucleus RNA and ATAC sequencing. We detail several stopping points, either via cryopreservation or fixation, to enhance workflow adaptability. Further, we provide a guide through multiple QC points and demonstrate proof-of-principle using two commercially available kits. Finally, we demonstrate that existing in silico genotyping methods can be adopted to computationally derive biological replicates from a single pool of chicken nuclei, greatly reducing the cost of biological replication and allowing researchers to consider sex as a variable during analysis. Together, this tutorial represents a cost-effective, simple, and effective approach to single-nucleus profiling of embryonic chicken eye tissues and is likely to be easily modified to be compatible with similar tissue types.

UNASSIGNED: Ferroptosis, as a novel form of programmed cell death, plays a crucial role in the occurrence and development of bladder cancer (BCa). However, the regulatory mechanisms of ferroptosis in the tumor microenvironment (TME) of BCa remain to be elucidated.
UNASSIGNED: Based on single-cell RNA (scRNA) transcriptomic data of BCa, we employed non-negative matrix factorization (NMF) dimensionality reduction clustering to identify novel ferroptosis-related cell subtypes within the BCa TME, aiming to explore the biological characteristics of these TME cell subtypes. Subsequently, we conducted survival analysis and univariate Cox regression analysis to explore the prognostic significance of these cell subtypes. We investigated the relationship between specific subtypes and immune infiltration, as well as their implications for immunotherapy. Finally, we discovered a valuable and novel biomarker for BCa, supported by a series of in vitro experiments.
UNASSIGNED: We subdivided cancer-associated fibroblasts (CAFs), macrophages, and T cells into 3-5 small subpopulations through NMF and further explored the biological features. We found that ferroptosis played an important role in the BCa TME. Through bulk RNA-seq analysis, we further verified that ferroptosis affected the progression, prognosis, and immunotherapy response of BCa by regulating the TME. Especially ACSL4+CAFs, we found that high-level infiltration of this CAF subtype predicted worse prognosis, more complex immune infiltration, and less response for immunotherapy. Additionally, we found that this type of CAF was associated with cancer cells through the PTN-SDC1 axis, suggesting that SDC1 may be crucial in regulating CAFs in cancer cells. A series of in vitro experiments confirmed these inferences: SDC1 promoted the progression of BCa. Interestingly, we also discovered FTH1+ macrophages, which were closely related to SPP1+ macrophages and may also be involved in the regulation of BCa TME.
UNASSIGNED: This study revealed the significant impact of ferroptosis on bladder cancer TME and identified novel ferroptosis-related TME cell subpopulations, ACSL4+CAFs, and important BCa biomarker SDC1.

UNASSIGNED: Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the precise involvement of LIG1 in BLCA remains elusive. This pioneering investigation delves into the uncharted territory of LIG1\'s impact on BLCA. Our primary objective is to elucidate the intricate interplay between LIG1 and BLCA, alongside exploring its correlation with various clinicopathological factors.
UNASSIGNED: We retrieved gene expression data of para-carcinoma tissues and bladder cancer (BLCA) from the GEO repository. Single-cell sequencing data were processed using the \"Seurat\" package. Differential expression analysis was then performed with the \"Limma\" package. The construction of scale-free gene co-expression networks was achieved using the \"WGCNA\" package. Subsequently, a Venn diagram was utilized to extract genes from the positively correlated modules identified by WGCNA and intersect them with differentially expressed genes (DEGs), isolating the overlapping genes. The \"STRINGdb\" package was employed to establish the protein-protein interaction (PPI) network.Hub genes were identified through the PPI network using the Betweenness Centrality (BC) algorithm. We conducted KEGG and GO enrichment analyses to uncover the regulatory mechanisms and biological functions associated with the hub genes. A machine-learning diagnostic model was established using the R package \"mlr3verse.\" Mutation profiles between the LIG1^high and LIG1^low groups were visualized using the BEST website. Survival analyses within the LIG1^high and LIG1^low groups were performed using the BEST website and the GENT2 website. Finally, a series of functional experiments were executed to validate the functional role of LIG1 in BLCA.
UNASSIGNED: Our investigation revealed an upregulation of LIG1 in BLCA specimens, with heightened LIG1 levels correlating with unfavorable overall survival outcomes. Functional enrichment analysis of hub genes, as evidenced by GO and KEGG enrichment analyses, highlighted LIG1\'s involvement in critical function such as the DNA replication, cellular senescence, cell cycle and the p53 signalling pathway. Notably, the mutational landscape of BLCA varied significantly between LIG1high and LIG1low groups.Immune infiltrating analyses suggested a pivotal role for LIG1 in immune cell recruitment and immune regulation within the BLCA microenvironment, thereby impacting prognosis. Subsequent experimental validations further underscored the significance of LIG1 in BLCA pathogenesis, consolidating its functional relevance in BLCA samples.
UNASSIGNED: Our research demonstrates that LIG1 plays a crucial role in promoting bladder cancer malignant progression by heightening proliferation, invasion, EMT, and other key functions, thereby serving as a potential risk biomarker.

Genetically engineered CD8+ T cells are being explored for the treatment of various cancers. Analytical characterization represents a major challenge in the development of genetically engineered cell therapies, especially assessing the potential off-target editing and product heterogeneity. As conventional sequencing techniques only provide information at the bulk level, they are unable to detect off-target CRISPR translocation or editing events occurring in minor cell subpopulations. In this study, we report the analytical development of a single-cell multi-omics DNA and protein assay to characterize genetically engineered cell products for safety and genotoxicity assessment. We were able to quantify on-target edits, off-target events, and potential translocations at the targeting loci with per-cell granularity, providing important characterization data of the final cell product. Conclusion: A single-cell multi-omics approach provides the resolution required to understand the composition of cellular products and identify critical quality attributes (CQAs).

Enhlink is a computational tool for scATAC-seq data analysis, facilitating precise interrogation of enhancer function at the single-cell level. It employs an ensemble approach incorporating technical and biological covariates to infer condition-specific regulatory DNA linkages. Enhlink can integrate multi-omic data for enhanced specificity, when available. Evaluation with simulated and real data, including multi-omic datasets from the mouse striatum and novel promoter capture Hi-C data, demonstrate that Enhlink outperfoms alternative methods. Coupled with eQTL analysis, it identified a putative super-enhancer in striatal neurons. Overall, Enhlink offers accuracy, power, and potential for revealing novel biological insights in gene regulation.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common type of tumor globally and the leading cause of cancer-related deaths. Although treatment strategies such as immune checkpoint inhibitors and chemotherapy have advanced, the heterogeneity among NSCLC patients results in significant variability in treatment outcomes. Studies have shown that certain patients respond poorly to immune checkpoint inhibitors, indicating that treatment response is closely related to multiple factors. Therefore, it is necessary to develop predictive models to stratify patients based on gene expression and clinical characteristics, aiming for precision therapy.
OBJECTIVE: This study aims to construct a stratified prognostic model for NSCLC patients based on lysosome-dependent cell death (LDCD) scoring by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data. By analyzing the immune-related characteristics of high-risk and low-risk groups, we further explored the impact of cell death patterns on lung cancer and identified potential therapeutic targets.
METHODS: This study obtained single-cell RNA sequencing data and gene expression data of NSCLC patients and normal lung tissues from the GEO and TCGA databases. We used R packages such as Seurat and CellChat for data preprocessing and analysis, and performed dimensionality reduction and visualization through Principal Component Analysis (PCA) and UMAP algorithms. LASSO regression analysis was used to construct the predictive model, followed by cross-validation and ROC curve analysis. The model\'s effectiveness was validated through survival analysis and immune microenvironment analysis.
RESULTS: The study showed a significant increase in the proportion of monocytes in NSCLC tissues, suggesting their important role in cancer progression. Cell communication analysis indicated that macrophages, smooth muscle cells, and myeloid cells exhibit strong intercellular communication during cancer progression. Using the constructed prognostic model based on 12 LDCD-related genes, we found significant differences in overall survival and immune microenvironment between the high-risk and low-risk groups.

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

Monoclonal antibodies (mAbs) hold significant potential as therapeutic agents and are invaluable tools in biomedical research. However, the lack of efficient high-throughput screening methods for single antibody-secreting cells (ASCs) has limited the diversity of available antibodies. Here, we introduce a novel, integrated workflow employing self-seeding microwells and an automated microscope-puncher system for the swift, high-throughput screening and isolation of single ASCs. The system allows for the individual screening and isolation of up to 6,400 cells within approximately one day, with the opportunity for parallelization and efficient upscaling. We successfully applied this workflow to both hybridomas and human patient-derived B cells, enabling subsequent clonal expansion or antibody sequence analysis through an optimized, single-cell nested reverse transcription-polymerase chain reaction (RT-PCR) procedure. By providing a time-efficient and more streamlined single ASC screening and isolation process, our workflow holds promise for driving forward progress in mAb development.