BACKGROUND: Liver cancer (LC) is a prevalent malignancy and a leading cause of cancer-related mortality worldwide. Extensive research has been conducted to enhance patient outcomes and develop effective prevention strategies, ranging from molecular mechanisms to clinical interventions. Single-cell sequencing, as a novel bioanalysis technology, has significantly contributed to the understanding of the global cognition and dynamic changes in liver cancer. However, there is a lack of bibliometric analysis in this specific research area. Therefore, the objective of this study is to provide a comprehensive overview of the knowledge structure and research hotspots in the field of single-cell sequencing in liver cancer research through the use of bibliometrics.
METHODS: Publications related to the application of single-cell sequencing technology to liver cancer research as of December 31, 2023, were searched on the web of science core collection (WoSCC) database. VOSviewers, CiteSpace, and R package \"bibliometrix\" were used to conduct this bibliometric analysis.
RESULTS: A total of 331 publications from 34 countries, primarily led by China and the United States, were included in this study. The research focuses on the application of single cell sequencing technology to liver cancer, and the number of related publications has been increasing year by year. The main research institutions involved in this field are Fudan University, Sun Yat-Sen University, and the Chinese Academy of Sciences. Frontiers in Immunology and Nature Communications is the most popular journal in this field, while Cell is the most frequently co-cited journal. These publications are authored by 2799 individuals, with Fan Jia and Zhou Jian having the most published papers, and Llovet Jm being the most frequently co-cited author. The use of single cell sequencing to explore the immune microenvironment of liver cancer, as well as its implications in immunotherapy and chemotherapy, remains the central focus of this field. The emerging research hotspots are characterized by keywords such as \'Gene-Expression\', \'Prognosis\', \'Tumor Heterogeneity\', \'Immunoregulation\', and \'Tumor Immune Microenvironment\'.
CONCLUSIONS: This is the first bibliometric study that comprehensively summarizes the research trends and developments on the application of single cell sequencing in liver cancer. The study identifies recent research frontiers and hot directions, providing a valuable reference for researchers exploring the landscape of liver cancer, understanding the composition of the immune microenvironment, and utilizing single-cell sequencing technology to guide and enhance the prognosis of liver cancer patients.

The 2023 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 12 invited reviews on topics of current interest in pathology. This year, our subjects include immuno-oncology and computational pathology approaches for diagnostic and research applications in human disease. Reviews on the tissue microenvironment include the effects of apoptotic cell-derived exosomes, how understanding the tumour microenvironment predicts prognosis, and the growing appreciation of the diverse functions of fibroblast subtypes in health and disease. We also include up-to-date reviews of modern aspects of the molecular basis of malignancies, and our final review covers new knowledge of vascular and lymphatic regeneration in cardiac disease. All of the reviews contained in this issue are written by expert groups of authors selected to discuss the recent progress in their particular fields and all articles are freely available online (https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896). © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Droplet microfluidic technology has revolutionized biomolecular analytical research, as it has the capability to reserve the genotype-to-phenotype linkage and assist for revealing the heterogeneity. Massive and uniform picolitre droplets feature dividing solution to the level that single cell and single molecule in each droplet can be visualized, barcoded, and analyzed. Then, the droplet assays can unfold intensive genomic data, offer high sensitivity, and screen and sort from a large number of combinations or phenotypes. Based on these unique advantages, this review focuses on up-to-date research concerning diverse screening applications utilizing droplet microfluidic technology. The emerging progress of droplet microfluidic technology is first introduced, including efficient and scaling-up in droplets encapsulation, and prevalent batch operations. Then the new implementations of droplet-based digital detection assays and single-cell muti-omics sequencing are briefly examined, along with related applications such as drug susceptibility testing, multiplexing for cancer subtype identification, interactions of virus-to-host, and multimodal and spatiotemporal analysis. Meanwhile, we specialize in droplet-based large-scale combinational screening regarding desired phenotypes, with an emphasis on sorting for immune cells, antibodies, enzymatic properties, and proteins produced by directed evolution methods. Finally, some challenges, deployment and future perspective of droplet microfluidics technology in practice are also discussed.

Alzheimer\'s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.