OBJECTIVES: Recent advancements in single-cell RNA sequencing (scRNA-seq) have revolutionized the study of cellular heterogeneity, particularly within the hematological system. However, accurately annotating cell types remains challenging due to the complexity of immune cells. To address this challenge, we develop a PAN-blood single-cell Data Annotator (scPANDA), which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation. METHODS: The atlas, constructed from data collected in 16 studies, incorporated rigorous quality control, preprocessing, and integration steps to ensure a high-quality reference for annotation. scPANDA utilizes a three-layer inference approach, progressively refining cell types from broad compartments to specific clusters. Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis. Furthermore, the performance of scPANDA was evaluated in three external datasets. RESULTS: The atlas was structured hierarchically, consisting of 16 compartments, 54 classes, 4,‍460 low-level clusters (pd_cc_cl_tfs), and 611 high-level clusters (pmid_cts). Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets, analyzing immune-tumor coexisting clusters in renal cell carcinoma, and identifying conserved cell clusters across species. CONCLUSIONS scPANDA exemplifies effective reference mapping with a large-scale atlas, enhancing the accuracy and reliability of blood cell type identification.
Humans ; Single-Cell Analysis/methods* ; Sequence Analysis, RNA/methods* ; Blood Cells

The regulatory processes in developmental biology research are significantly influenced by long non-coding RNAs (lncRNAs). However, the dynamics of lncRNA expression during human tooth development remain poorly understood. In this research, we examined the lncRNAs present in the dental epithelium (DE) and dental mesenchyme (DM) at the late bud, cap, and early bell stages of human fetal tooth development through bulk RNA sequencing. Developmental regulators co-expressed with neighboring lncRNAs were significantly enriched in odontogenesis. Specific lncRNAs expressed in the DE and DM, such as PANCR, MIR205HG, DLX6-AS1, and DNM3OS, were identified through a combination of bulk RNA sequencing and single-cell analysis. Further subcluster analysis revealed lncRNAs specifically expressed in important regions of the tooth germ, such as the inner enamel epithelium and coronal dental papilla (CDP). Functionally, we demonstrated that CDP-specific DLX6-AS1 enhanced odontoblastic differentiation in human tooth germ mesenchymal cells and dental pulp stem cells. These findings suggest that lncRNAs could serve as valuable cell markers for tooth development and potential therapeutic targets for tooth regeneration.
Humans ; RNA, Long Noncoding/metabolism* ; Odontogenesis/genetics* ; Tooth Germ/embryology* ; Cell Differentiation ; Gene Expression Regulation, Developmental ; Mesoderm/metabolism* ; Tooth/embryology* ; Gene Expression Profiling ; Sequence Analysis, RNA ; Dental Pulp/cytology*

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.
Humans ; Gastrointestinal Microbiome/genetics* ; Bacteriophages/physiology* ; High-Throughput Nucleotide Sequencing ; Sequence Analysis, RNA/methods* ; Bacteria/virology*

Identification of disease-specific cell subtypes (DSCSs) has profound implications for understanding disease mechanisms, preoperative diagnosis, and precision therapy. However, achieving unified annotation of DSCSs in heterogeneous single-cell datasets remains a challenge. In this study, we developed the gPRINT algorithm (generalized approach for cell subtype identification with single cell's voicePRINT). Inspired by the principles of speech recognition in noisy environments, gPRINT transforms gene position and gene expression information into voiceprints based on ordered and clustered gene expression phenomena, obtaining unique "gene print" patterns for each cell. Then, we integrated neural networks to mitigate the impact of background noise on cell identity label mapping. We demonstrated the reproducibility of gPRINT across different donors, single-cell sequencing platforms, and disease subtypes, and its utility for automatic cell subtype annotation across datasets. Moreover, gPRINT achieved higher annotation accuracy of 98.37% when externally validated based on the same tissue, surpassing other algorithms. Furthermore, this approach has been applied to fibrosis-associated diseases in multiple tissues throughout the body, as well as to the annotation of fibroblast subtypes in a single tissue, tendon, where fibrosis is prevalent. We successfully achieved automatic prediction of tendinopathy-specific cell subtypes, key targets, and related drugs. In summary, gPRINT provides an automated and unified approach for identifying DSCSs across datasets, facilitating the elucidation of specific cell subtypes under different disease states and providing a powerful tool for exploring therapeutic targets in diseases.
Humans ; Algorithms ; Single-Cell Analysis ; Databases, Genetic ; Molecular Sequence Annotation

Tuberculosis (TB) has one of the highest mortality rates among infectious diseases worldwide. The immune response in the host after infection is proposed to contribute significantly to the progression of TB, but the specific mechanisms involved remain to be elucidated. Single-cell RNA sequencing (scRNA-seq) provides unbiased transcriptome sequencing of large quantities of individual cells, thereby defining biological comprehension of cellular heterogeneity and dynamic transcriptome state of cell populations in the field of immunology and is therefore increasingly applied to lung disease research. Here, we first briefly introduce the concept of scRNA-seq, followed by a summarization on the application of scRNA-seq to TB. Furthermore, we underscore the potential of scRNA-seq for clinical biomarker exploration, host-directed therapy, and precision therapy research in TB and discuss the bottlenecks that need to be overcome for the broad application of scRNA-seq to TB-related research.
Humans ; Single-Cell Analysis/methods* ; Tuberculosis/genetics* ; Sequence Analysis, RNA/methods* ; Transcriptome/genetics*

BACKGROUND: Several studies have demonstrated the occurrence of secondary tumors as a rare but significant complication of chimeric antigen receptor T (CAR-T) cell therapy, underscoring the need for a detailed investigation. Given the limited variety of secondary tumor types reported to date, a comprehensive characterization of the various secondary tumors arising after CAR-T therapy is essential to understand the associated risks and to define the role of the immune microenvironment in malignant transformation. This study aims to characterize the immune microenvironment of a newly identified secondary tumor post-CAR-T therapy, to clarify its pathogenesis and potential therapeutic targets. METHODS: In this study, the bone marrow (BM) samples were collected by aspiration from the primary and secondary tumors before and after CD19 CAR-T treatment. The CD45 + BM cells were enriched with human CD45 microbeads. The CD45 + cells were then sent for 10× genomics single-cell RNA sequencing (scRNA-seq) to identify cell populations. The Cell Ranger pipeline and CellChat were used for detailed analysis. RESULTS: In this study, a rare type of secondary chronic myelomonocytic leukemia (CMML) were reported in a patient with diffuse large B-cell lymphoma (DLBCL) who had previously received CD19 CAR-T therapy. The scRNA-seq analysis revealed increased inflammatory cytokines, chemokines, and an immunosuppressive state of monocytes/macrophages, which may impair cytotoxic activity in both T and natural killer (NK) cells in secondary CMML before treatment. In contrast, their cytotoxicity was restored in secondary CMML after treatment. CONCLUSIONS This finding delineates a previously unrecognized type of secondary tumor, CMML, after CAR-T therapy and provide a framework for defining the immune microenvironment of secondary tumor occurrence after CAR-T therapy. In addition, the results provide a rationale for targeting macrophages to improve treatment strategies for CMML treatment.
Humans ; Lymphoma, Large B-Cell, Diffuse/therapy* ; Tumor Microenvironment/genetics* ; Antigens, CD19/metabolism* ; Leukemia, Myelomonocytic, Chronic/genetics* ; Immunotherapy, Adoptive/adverse effects* ; Male ; Single-Cell Analysis/methods* ; Female ; Sequence Analysis, RNA/methods* ; Receptors, Chimeric Antigen ; Middle Aged

Over the past decade, high-throughput RNA sequencing (RNA-seq) has vastly expanded our understanding of transcriptome dynamics in human physiology and disease. As a powerful tool for investigating systematic changes in RNA biology, RNA-seq has facilitated the discovery of novel functional RNA species. Mature RNA transcripts, which transmit genetic information from DNA to proteins, undergo intricate transcriptional and post-transcriptional regulation. This process allows a single gene to produce multiple RNA transcripts, each performing specific functions depending on the physiological or pathological context. Specific RNA transcripts (SRTs) are uniquely expressed in particular tissues or tumors and are closely associated with tissue-specific functions or disease states, particularly cancer. This review explores the generation of SRTs through key mechanisms, such as alternative splicing (AS), transcriptional regulation, polyadenylation (polyA), and the influence of transposable elements (TEs). We also examine their critical roles in normal tissue development and diseases, with an emphasis on their relevance to cancer. Furthermore, the potential applications of SRTs in diagnosing and treating diseases, especially malignancies, are discussed. By serving as diagnostic markers and therapeutic targets, SRTs hold significant promise in the development of personalized medicine and precision therapies. This review aims to provide new insights into the importance of SRTs in advancing the understanding and treatment of human diseases.
Humans ; Neoplasms/genetics* ; Alternative Splicing/genetics* ; RNA/genetics* ; Animals ; Sequence Analysis, RNA/methods* ; Polyadenylation/genetics*

Humans ; Consensus ; High-Throughput Nucleotide Sequencing/standards* ; Neoplasms/diagnosis* ; Quality Control ; Sequence Analysis, DNA/methods* ; Sequence Analysis, RNA/methods*

OBJECTIVE: To confirm the sequence of a null allele HLA-C*08:127N produced by a base insertion. METHODS: PCR sequence-specific oligonucleotide probe (SSOP) and PCR sequence-based typing (SBT) were used for HLA routine detection, which discovered abnormal sequence maps of HLA-C in one acute myeloid leukemia patient. The sequence of the above loci was confirmed by next generation sequencing (NGS) technology. RESULTS: The SSOP typing result showed that HLA-C locus was C*03:04, C*08:01, while the sequence was suspected to be inserted or deleted in exon 3 by SBT, and finally confirmed by NGS as C*03:04, C*08:127N. CONCLUSION When base insertion produces HLA null alleles, SBT analysis software cannot provide correct results, but NGS technology can more intuitively obtain accurate HLA typing results.
Humans ; Alleles ; High-Throughput Nucleotide Sequencing ; HLA-C Antigens/genetics* ; Histocompatibility Testing ; Polymerase Chain Reaction ; Leukemia, Myeloid, Acute/genetics* ; Sequence Analysis, DNA ; Mutagenesis, Insertional ; Exons

OBJECTIVE: To distinguish the ambiguous genotyping results of human leukocyte antigen (HLA), identify a novel HLA-B allele and analyze the nucleotide sequence. METHODS: A total of 2 076 umbilical core blood samples from the Zhejiang Cord Blood Bank in 2022 were detected using the next generation sequencing technology (NGS) based on the Ion Torrent S5 platform. Among these a rare HLA-B allele with ambiguous combination result containing a base mutation was identified, and was further confimed by the third-generation sequencing (TGS) based on the nanopore technology. RESULTS: The NGS typing result of HLA-B locus showed HLA-B* 46:18, 54:06 or HLA-B*46:01, 54:XX (including a base mutation), and nanopore sequencing confirmed the typing as HLA-B*46:01, 54:XX (including a base mutation). Compared with HLA-B*54:01:01:01, the HLA-B*54:XX allele showed one single nucleotide substitution at position 1014 T>C in exon 6, with no amino acid change. The nucleotide sequence of the novel HLA-B*54:XX has been submitted to the GenBank nucleotide sequence database and the accession number OP853532 was assigned. CONCLUSION A ambiguous genotyping of the HLA-B Locus detected by NGS was distinguished by nanopore sequencing and a new HLA-B allele was successfully identified, which was officially named as HLA-B*54:01:11 by the World Health Organization Nomenclature Committee for Factors of the HLA System.
Humans ; High-Throughput Nucleotide Sequencing ; Alleles ; HLA-B Antigens/genetics* ; Genotype ; Mutation ; Sequence Analysis, DNA ; Base Sequence