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PubMed2025

Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.

BACKGROUND: Cell clustering is an essential step in uncovering cellular architectures in single-cell RNA sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. RESULTS: Here, we develop a multiscale clustering (MSC) approach to construct a sparse cell-cell correlation network for unsupervised identification of de novo cell types and subtypes across multiple resolutions. Based upon simulated silver- and gold-standard data as well as real scRNA-seq data in diseases, MSC demonstrates significantly improved performance compared to established benchmark methods and reveals a biologically meaningful cell hierarchy to facilitate the discovery of novel disease-associated cell subtypes and mechanisms. CONCLUSIONS: We present MSC as a new single-cell multiscale clustering framework as a powerful tool for advancing discoveries in disease-associated cell populations using single-cell sequencing data.

Single-Cell Analysis↗

PubMed2026

Advancing insect research through cell line transcriptomics.

This review emphasizes the significance of insect cell lines in transcriptomic research, highlighting their role as vital tools for uncovering cellular and molecular mechanisms of insect physiology, immune responses, and adaptation to environmental stressors. Cell lines derived from tissues such as the midgut, fat body, nervous system, and reproductive organs enable researchers to examine gene expression changes in a controlled setting, making discoveries that are difficult to achieve through whole-organism studies. High-throughput sequencing and single-cell RNA sequencing (scRNA-seq) have identified genes linked to detoxification, stress response, development, and immune defense, offering valuable insights for future applications in agriculture, pest control, and biotechnology. To organize this information clearly, we have summarized key findings in a table, providing an accessible overview of each cell line's important roles in transcriptomic research. This method not only highlights the adaptability of insect cell lines in functional genomics but also underscores their usefulness as model systems in pest management, virology, and bioengineering. Through utilizing transcriptomics, insect cell lines continue to advance our understanding of insect biology and foster the development of innovative strategies for sustainable crop protection and biotechnological use.

Animals↗

PubMed2025

Multi-omics analyses reveal DjTcf4 critical for proper timing of differentiation in planarian regeneration.

The blastema is key to forming complete tissues in regenerating Dugesia japonica (D. japonica). However, the dynamic changes in cellular compositions and transcription landscapes in blastema during regeneration are understudied. Here, through genome reannotation, 3D spatial transcriptome construction, single-cell RNA sequencing (scRNA-seq), and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) analyses of changes in gene expression and chromatin structures, we delineate key transcription factors regulating the developmental trajectories of major cell clusters in the regenerating head. Importantly, we find that the T cell factor 4 (DjTcf4)-positive cells highly accumulate at wound areas, and its gene network is critical for the proper timing of development during regeneration in multiple progenitor cells. Depletion of DjTcf4 and its target genes leads to singular eye and/or dull tail phenotypes and delays regeneration. Taken together, we build multi-omics atlases in D. japonica and reveal the noncanonical function of the DjTcf4 network in developmental pattern formation, laying a foundation for studies of regeneration in D. japonica.

Animals↗

PubMed2025

Next-generation sequencing in breast cancer: current clinical applications and future directions.

INTRODUCTION: Breast cancer is a heterogeneous disease that claims 670,000 lives by 2022. Omic technologies, particularly next generation sequencing (NGS) offers promising avenues for precision medicine. American Society of Clinical Oncology (ASCO) outlines genomic testing's utility, emphasizing prognostic and diagnostic potential. OBJECTIVES: This review succinctly explores NGS's evolution and clinical applications of NGS in breast cancer, thereby guiding future research to enhance patient care. METHODS: Comprehensive literature searches were conducted using databases such as PubMed, Google Scholar, and ResearchGate, focusing on keywords including breast cancer, HER-2 low breast cancer, circulating tumour DNA, single-cell RNA sequencing, and next-generation sequencing. Peer-reviewed, high-quality articles published in English were selected for inclusion. RESULTS: Previous studies have explored the evolution of NGS technology and its clinical applications in breast cancer, including genomic and transcriptomic characterization, treatment guidance, and resistance prediction. Molecular profiling of challenging entities such as early-onset breast cancer and HER-2 low tumours was summarized, with key findings highlighted. This review also discusses emerging technologies, including circulating DNA and single-cell sequencing, as promising avenues for discovery. CONCLUSION: NGS has revealed the genomic and transcriptomic diversity of breast cancer, identifying actionable alterations associated with chemotherapy response and resistance to therapies such as trastuzumab, TKIs, and CDK4/6 inhibitors. Circulating tumour DNA (ctDNA) shows potential for diagnosis, prediction, prognosis, and monitoring, despite tumour heterogeneity. Single-cell analysis enables exploration of individual cell transcriptomes, though high costs and low throughput remain barriers to widespread adoption. HER2-low tumours continue to pose significant research challenges.

Humans↗

PubMed2003

Marked mitochondrial DNA sequence heterogeneity in single CD34+ cell clones from normal adult bone marrow.

Somatic mitochondrial DNA (mtDNA) mutations accumulate with age in postmitotic tissues but have been postulated to be diluted and lost in continually proliferating tissues such as bone marrow (BM). Having observed marked sequence variation among healthy adult individuals' total BM cell mtDNA, we undertook analysis of the mtDNA control region in a total of 611 individual CD34+ clones from 6 adult BM donors and comparison of these results with the sequences from 580 CD34+ clones from 5 umbilical cord blood (CB) samples. On average, 25% (range, 11% to 50%) of individual CD34+ clones from adult BM showed mtDNA heterogeneity, or sequence differences from the aggregate mtDNA sequence of total BM cells of the same individual. In contrast, only 1.6% of single CD34+ clones from CB showed mtDNA sequence variation from the aggregate pattern. Thus, age-dependent accumulation of mtDNA mutations appears relatively common in a mitotically active human tissue and may provide a method to approximate the mutation rate in mammalian cells, to assess the contribution of reactive oxygen species to genomic instability, and for natural "marking" of hematopoietic stem cells; our data also have important implications for the aging process, forensic identifications, and anthropologic conclusions dependent on the mtDNA sequence.

Adult↗

PubMed2025

CryoSCAPE: Scalable immune profiling using cryopreserved whole blood for multi-omic single cell and functional assays.

BACKGROUND: The field of single cell technologies has rapidly advanced our comprehension of the human immune system, offering unprecedented insights into cellular heterogeneity and immune function. While cryopreserved peripheral blood mononuclear cell (PBMC) samples enable deep characterization of immune cells, challenges in clinical isolation and preservation limit their application in underserved communities with limited access to research facilities. We present CryoSCAPE (Cryopreservation for Scalable Cellular And Proteomic Exploration), a scalable method for immune studies of human PBMC with multi-omic single cell assays using direct cryopreservation of whole blood. RESULTS: Comparative analyses of matched human PBMC from cryopreserved whole blood and density gradient isolation demonstrate the efficacy of this methodology in capturing cell proportions and molecular features. The method was then optimized and verified for high sample throughput using fixed single cell RNA sequencing and liquid handling automation with a single batch of 60 cryopreserved whole blood samples. Additionally, cryopreserved whole blood was demonstrated to be compatible with functional assays, enabling this sample preservation method for clinical research. CONCLUSIONS: The CryoSCAPE method, optimized for scalability and cost-effectiveness, allows for high-throughput single cell RNA sequencing and functional assays while minimizing sample handling challenges. Utilization of this method in the clinic has the potential to democratize access to single-cell assays and enhance our understanding of immune function across diverse populations.

Humans↗

PubMed2002

A comparison of hydraulic and laser capture microdissection methods for collection of single B cells, PCR, and sequencing of antibody VDJ.

During the development of B lymphocytes, a series of gene rearrangements assemble the sequences that encode immunoglobulin heavy and light chains (VDJ). Earlier studies of VDJ sequence diversification during expansion of cells in splenic or appendix germinal centers used hydraulic micromanipulation (HM) to collect single B cells for PCR amplification of rearranged antibody heavy and light chain genes. PCR products were directly sequenced without a cloning step. Hydraulic micromanipulation is a very tedious method. Once capability to collect single cells by laser capture microdissection (LCM) was developed, we modified previous tissue staining and fixation methods so that we could collect cells from a given stained tissue section by HM and LCM and directly compare our success rates using these two methods. Cells were alkaline lysed and after two rounds of nested PCR products were recovered and directly sequenced. Because each rearrangement of genomic DNA that occurs to form the immunoglobulin heavy-chain-encoding sequence in developing B cells is unique, this system allowed us to verify our success rate in recovering single lymphocytes from tissue sections and amplifying a single allele. The methods developed have now made LCM an efficient alternative to HM for the collection of single B cells.

Animals↗

PubMed2024

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

Algorithms↗

PubMed2027

Teratoma Formation and Genomic Profiling Using Multi-Omics Approaches.

Teratoma formation is the gold standard assay for evaluating the developmental pluripotency of human and mouse embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). Following subcutaneous injection into immunodeficient mice, pluripotent stem cells spontaneously differentiate into derivatives representing all three embryonic germ layers-ectoderm, mesoderm, and endoderm. Beyond serving as a functional assay for pluripotency, teratomas provide a unique three-dimensional model system for studying early human development and lineage specification in vivo. This chapter describes comprehensive protocols for teratoma formation in immunodeficient mice, tissue processing for multiple downstream genomic applications, and multi-omics profiling approaches. We detail methods for embryonic stem cell culture, teratoma generation via subcutaneous injection, tissue dissection and processing for chromatin immunoprecipitation followed by sequencing (ChIP-Seq), RNA sequencing (RNA-Seq), single-cell multiome profiling combining chromatin accessibility (ATAC-Seq) and gene expression (scRNA-Seq), and histological analysis using hematoxylin and eosin (H&E) staining. Additionally, we provide bioinformatics workflows for analyzing the resulting genomic datasets to characterize the epigenetic and transcriptional landscapes of teratoma-derived tissues. These methods enable comprehensive molecular characterization of developmental processes and provide valuable resources for stem cell biologists studying pluripotency, differentiation, and early embryonic development.

Teratoma↗

PubMed2026

Translation of scRNA-seq to a clinical blood test for infection diagnostics.

INTRODUCTION: Early and accurate triage of patients with febrile illness is crucial for appropriate treatment. While standard inflammatory biomarkers are often nonspecific, transcriptome analysis of peripheral blood has diagnostic potential. However, bulk gene expression data is often confounded by changes in cell count proportions, a more robust quantification of gene expression in specific single-cell types, such as monocytes, is required to serve as a reliable clinical biomarker. AREAS COVERED: Various methods to obtain single-cell-type gene expression results, including the gold standard of gene expression analysis after cell sorting and single-cell RNA sequencing, which are difficult to implement in the routine settings are discussed. Other method to interrogate gene expression of a single cell-type is needed. Finally, monocyte cell-type specific ratio-based biomarker (RBB, called Direct Leukocyte Single cell-type Transcript Abundance, or DIRECT LS-TA) which can estimate single cell-type (monocyte) specific gene expression without cell sorting is introduced. EXPERT OPINION: Traditional diagnostic test for differentiating infection has several limitations requiring breakthrough including turn-around time and cost. DIRECT LS-TA provides a reliable way to quantify monocyte-specific gene expression that strongly correlates with gold-standard methods. It is more affordable than single-cell RNA sequencing and can be readily implemented in clinical laboratories using widely available quantitative PCR or digital PCR machines.

Humans↗

PubMed2025

Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencing.

Genetic variants (both coding and noncoding) can impact gene function and expression, driving disease mechanisms such as cancer progression. The systematic study of endogenous genetic variants is hindered by inefficient precision editing tools, combined with technical limitations in confidently linking genotypes to gene expression at single-cell resolution. We developed single-cell DNA-RNA sequencing (SDR-seq) to simultaneously profile up to 480 genomic DNA loci and genes in thousands of single cells, enabling accurate determination of coding and noncoding variant zygosity alongside associated gene expression changes. Using SDR-seq, we associate coding and noncoding variants with distinct gene expression in human induced pluripotent stem cells. Furthermore, we demonstrate that in primary B cell lymphoma samples, cells with a higher mutational burden exhibit elevated B cell receptor signaling and tumorigenic gene expression. SDR-seq provides a powerful platform to dissect regulatory mechanisms encoded by genetic variants, advancing our understanding of gene expression regulation and its implications for disease.

Humans↗

PubMed2003

Application of optical trapping for cells grown on plates: optimization of PCR and fidelity of DNA sequencing of p53 gene from a single cell.

BACKGROUND: Optical trapping has traditionally been used to visually select and isolate nonadherent cells grown in suspension because cells grown in monolayers will rapidly reattach to surfaces if suspended in solution. We explored methods to slow cell reattachment that are also compatible with high-fidelity PCR. METHODS: Using HeLa cells grown on plates and suspended after trypsinization, we measured the efficiency of capture by retention and movement of the cell by the laser. Success for removing a captured cell by pipette was determined by PCR amplification of the 5S rRNA gene. After optimizing PCR amplification of a 2049-bp region of the p53 gene, we determined PCR fidelity by DNA sequencing. RESULTS: Addition of bovine serum albumin to suspended cells slowed reattachment from seconds to minutes and allowed efficient trapping. The success rate of removing a cell from the trap by pipette to a PCR tube was 91.5%. The 5S PCR assay also revealed that DNA and RNA that copurify with polymerases could give false-positive results. Sequence analysis of four clones derived from a single cell showed only three polymerase errors in 7200 bp of sequence read and revealed difficulties in reading the correct number in a run of 16 A:T. Comparison of the HeLa and wild-type human sequences revealed several previously unreported base differences and an (A:T)(n) length polymorphism in p53 introns. CONCLUSIONS: These results represent the first use of optical trapping on adherent cells and demonstrate the high accuracy of DNA sequencing that can be achieved from a single cell.

Cell Separation↗

PubMed2026

Complement expression profiles in human glomerular mesangial cells, endothelial cells, podocytes and proximal tubular epithelial cells.

BACKGROUND: Local expression of complement components in the kidney has been reported sporadically in both diseased and normal kidneys. This study aimed to comprehensively characterize the expression of complement components in human glomerular mesangial cells (GMCs), glomerular endothelial cells (GECs), podocytes, and proximal tubular epithelial cells (PTECs) in non-diseased renal tissue. METHODS: Complement expression in cultured human renal intrinsic cells was initially evaluated using reverse transcription polymerase chain reaction and immunofluorescence staining. These findings were further examined using publicly available single-cell RNA-sequencing datasets and 10×Genomics single-cell RNA sequencing of non-diseased human kidney tissue. The analyses focused on complement components involved in the initiation of the classical, lectin, and alternative pathways, as well as components shared among these activation pathways, terminal pathway components, complement regulators, and complement receptors. RESULTS: Complement components unique to the initial phase for classical pathway (C1S, C1R, C2, C4), lectin pathway (MBL2, FCN1, MASP1), alternative pathway (CFB, CFD), and the C3 component shared by the three activation pathways were detected in these cells. The components shared by the terminal pathways including C5, C6, C7, C8 and C9 exhibited lower expression, while complement regulators (CFH, CFI, CD55/DAF, CD46/MCP, CD59, C4BPB, PROS1/Protein S) or receptors (CD93/C1QR1, CR1), particularly membrane-bound proteins, such as DAF, MCP and CD59, which inhibit complement activation and the formation of the membrane attack complex, showed relatively high expression. CONCLUSION: These results showed that all four types of intrinsic renal cells expressed multiple complement components associated with the classical, lectin, and alternative pathways. In non-diseased kidney tissue, complement regulatory molecules involved in the control of complement activation showed relatively higher expression, whereas components of the terminal complement pathway were expressed at relatively lower levels, suggesting that renal intrinsic cells maintain a locally poised but tightly regulated complement system.

Humans↗

PubMed2026

Plasticity of extrachromosomal DNA segregation during drug adaptation.

Uneven segregation during mitosis is a striking feature of extrachromosomal DNA (ecDNA). Because ecDNA lacks a centromere, it is thought to segregate stochastically, generating intratumoral heterogeneity in genomic copy number. Drug treatment can readily change ecDNA copy number, enabling cells to acquire drug resistance, yet whether these changes reflect static selection of pre-existing clones or active reconfiguration under stress remains unresolved. To address this, we develop a high-throughput framework combining single-cell DNA sequencing with cellular barcoding for clonal tracking. Single-cell cloning reveals that not all clones exhibit identical segregation modes even under drug-free conditions. Under treatment, resistant populations do not simply arise from pre-existing clones with favorable ecDNA states; instead, some clones actively reconfigure their segregation behavior to generate resistant cells. Thus, although ecDNA generally segregates stochastically, it can undergo nonrandom, actively regulated segregation under drug stress, raising the possibility of therapeutically targeting ecDNA segregation mechanisms to counteract adaptive resistance.

Extrachromosomal DNA↗

PubMed2026

scFANCL: Dual contrastive learning with false-negative correction at cell level for single-cell RNA-seq clustering.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables cellular characterization at single-cell resolution. However, its high dimensionality, sparsity, and noise make clustering challenging. Approaches utilizing contrastive learning and data augmentation have been introduced to improve representation quality for scRNA-seq clustering. In particular, dual contrastive frameworks combining instance- and cluster-level objectives can capture both cell-cell similarities and inter-cluster variations. However, existing dual contrastive frameworks focus primarily on discrete cluster boundaries, neglecting the biological continuity inherent in scRNA-seq data. METHODS: We propose scFANCL, a dual contrastive framework designed to capture biological continuity in scRNA data. Rather than treating all non-augmented samples as negatives, scFANCL applies a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, preserving continuous transcriptional relationships among them while maintaining inter-cluster separation. RESULTS: Extensive experiments across seven publicly available scRNA-seq datasets demonstrated that scFANCL achieves competitive clustering performance compared with existing baseline methods, consistently yielding high ARI and NMI scores across datasets of varying size and complexity. Ablation studies further confirmed the contribution of the false negative filtering component, showing measurable improvements over variants without filtering. Downstream analyses further suggest that the learned embeddings may reflect biologically meaningful transcriptional transitions, including continuous differentiation trajectories within related cell types. The source code is available at https://github.com/mjuailab/scFANCL . CONCLUSIONS: scFANCL addresses a key limitation of conventional contrastive learning by applying a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, thereby preserving biological continuity within cell types while maintaining inter-cluster separation. Evaluations across seven benchmark scRNA-seq datasets demonstrate competitive clustering performance, with learned embeddings capturing biologically meaningful transcriptional structure and characteristics of rare cell populations.

Clustering Algorithms↗

PubMed2025

Paradoxical Effect of Myosteatosis on the Immune Checkpoint Inhibitor Response in Metastatic Renal Cell Carcinoma.

BACKGROUND: Treatment for metastatic renal cell carcinoma (mRCC) has shifted from tyrosine kinase inhibitor (TKI) therapy to immune checkpoint inhibitor (ICI)-based therapy, improving outcomes but with variable individual responses. This study investigated the prognostic implications of pretreatment low skeletal muscle mass (LSMM) and myosteatosis in patients with mRCC undergoing first-line ICI-based therapies, comparing outcomes between PD-1 inhibitor + CTLA-4 inhibitor and PD-1 inhibitor + TKI, incorporating single-cell RNA sequencing. METHODS: A retrospective analysis was performed on 90 patients with mRCC treated with ICI-based therapies between November 2019 and March 2023. Patients were grouped based on whether they received PD-1 inhibitor + CTLA-4 inhibitor or PD-1 inhibitor + TKI combinations. LSMM was defined as skeletal muscle index below 40.8 cm2/m2 for men and 34.9 cm2/m2 for women. Myosteatosis was defined using skeletal muscle density, with cut-off values < 41 HU for BMI < 25 kg/m2 and < 33 HU for BMI ≥ 25 kg/m2. Progression-free survival (PFS) and overall survival (OS) were compared using Kaplan-Meier curves and multivariable models. Single-cell RNA sequencing was performed on pretreatment samples to compare the immune microenvironment between patients with and without myosteatosis. RESULTS: The study cohort (26.7% female; median age: 60.5 years) included 59 patients (65.6%) treated with PD-1 inhibitor + CTLA-4 inhibitor and 31 patients (34.4%) treated with PD-1 inhibitor + TKI. LSMM was present in 18.9% of patients, and myosteatosis in 41.1%, with comparable proportions across groups. During follow-up, 29 patients (32.2%) died: 16 in the PD-1 inhibitor + CTLA-4 inhibitor group and 13 in the PD-1 inhibitor + TKI group. The overall 1-year mortality rate was 22.2%, and PFS rate was 53.3%. Myosteatosis predicted poor OS (HR, 5.389; p = 0.008) and PFS (HR, 2.930; p = 0.022) in the PD-1 inhibitor + TKI group but was protective for PFS (HR, 0.461; p = 0.049) in the PD-1 inhibitor + CTLA-4 inhibitor group. LSMM did not significantly affect outcomes in either group. Single-cell RNA sequencing revealed higher CTLA-4 expression in regulatory T cells and more effector memory CD8+ T cells in patients with myosteatosis, whereas patients without myosteatosis had more anti-tumoural non-classical monocytes. CONCLUSIONS: Myosteatosis negatively impacts OS and PFS in patients with mRCC treated with PD-1 inhibitor + TKI therapy but is protective for PFS in those treated with PD-1 inhibitor + CTLA-4 inhibitor therapy. Altered checkpoint expression and immune cell composition associated with myosteatosis may contribute to these differential responses.

Humans↗

PubMed1989

Diagnosis of beta-thalassaemia by DNA amplification in single blastomeres from mouse preimplantation embryos.

Mouse preimplantation embryos were accurately diagnosed as normal or mutant at the beta-major haemoglobin locus by amplification of specific DNA sequences in a single cell. A DNA sequence containing the whole of exon 3 and some 3' untranslated sequences within the beta-major haemoglobin gene was amplified in single blastomeres by means of the polymerase chain reaction (PCR). Blastomeres were removed from embryos of four to eight cells from normal BALB/c mice and from mutant (thalassaemic) BALB/c mice homozygous for a deletion of the whole beta-major haemoglobin gene. The sensitivity of the amplification procedure was enhanced by the sequential use of two sets of oligonucleotide primers for 30 cycles of amplification each, the second pair being located within the segment amplified by the first pair. The product (204 base-pairs) could be easily visualised in ethidium bromide-stained agarose gels. Stringent precautions to prevent contamination were taken, and with these precautions the PCR amplification procedure could be carried out under normal laboratory conditions. These procedures for diagnosis of genetic disease before implantation should be applicable to preimplantation diagnosis of any monogenic disorder in man for which the affected DNA sequence is known.

Alleles↗

PubMed2024

A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.

Single-cell RNA sequencing (scRNA-seq) provides high resolution of cell-to-cell variation in gene expression and offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, technical challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we present a deep learning framework, called the dynamic batching adversarial autoencoder (DB-AAE), for denoising scRNA-seq datasets. First, we describe steps to set up the computing environment, training, and tuning. Then, we depict the visualization of the denoising results. For complete details on the use and execution of this protocol, please refer to Ko et al.1.

Deep Learning↗