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Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Data to Explore the Key Role of Fatty Acid Metabolism in Breast Cancer.
Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines.
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Single-cell RNA sequencing defines prognostic subtypes and identifies AIF1L as a therapeutic target in colorectal cancer.
Colorectal cancer (CRC) progression and therapy resistance are driven by heterogeneous tumor cell populations and microenvironmental interactions. However, a comprehensive single-cell atlas across patients that captures this heterogeneity, and its clinical implications has been lacking. Such an atlas could reveal rare tumor subpopulations that underpin disease aggressiveness and offer new prognostic biomarkers or therapeutic targets. We integrated two high-quality single-cell RNA sequencing datasets from 29 CRC patients to construct a cellular atlas encompassing immune, stromal and epithelial compartments. Malignant epithelial cells were distinguished via inferCNV-based copy number alteration analysis and reclustered, yielding seven transcriptionally distinct malignant subpopulations. One malignant epithelial cluster, marked by high expression of the long non-coding RNA ELFN1-AS1, exhibited the highest stemness signature. From this stem-like cluster, we derived a four-gene prognostic model (RPL21, GAL, ELFN1-AS1, AIF1L). In the TCGA-COAD cohort, this model stratified patients into high-risk and low-risk groups with significantly different survival outcomes, with the high-risk group experiencing significantly worse survival. High-risk tumors were enriched for metabolic and translational pathways and displayed distinct immune and genomic features. AIF1L was identified as a hub gene within the signature, and its knockdown in CRC cells enhanced migration and invasion, functionally validating its role in tumor progression. Our study provides a high-resolution single-cell atlas of CRC and identifies a previously unrecognized stem-like tumor cell subpopulation with prognostic significance. These findings highlight novel prognostic biomarkers and suggest potential therapeutic targets (such as AIF1L) that could inform patient stratification and the development of targeted therapies for CRC.
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A single-cell and spatial atlas of early human olfactory development.
The human nasal region arises from neural crest and placodal lineages, yet its early development remains poorly understood owing to limited fetal tissue access and structural complexity. Here we present an integrated single-nucleus and spatial transcriptomic atlas of the human fetal nasal region, generated from male and female fetuses between 7 and 12 post-conceptional weeks. Single-nucleus RNA sequencing [snRNA-seq] resolved 32 distinct cell types, while integration with multiplexed error-robust fluorescence in situ hybridization (MERFISH) enabled spatial and temporal mapping of gene expression dynamics across the olfactory epithelium (OE) and adjacent tissues. We identify markers of olfactory sensory neuron differentiation and pathways governing epithelial patterning and OE morphogenesis. Notably, spatially resolved snRNA-seq profiles of 169 olfactory receptor genes reveal molecular support for the "one neuron-one receptor" principle already in the first trimester. Together, this work establishes a molecular and spatial framework of early human olfactory development and provides a resource for studies of sensory neurogenesis and congenital disorders.
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SiCmiR Atlas: Single-Cell miRNA Landscape Reveals Hub-miRNA and Network Signatures in Human Cancers.
MicroRNAs (miRNAs) are pivotal post‑transcriptional regulators whose single‑cell behavior has remained largely inaccessible due to technical barriers in single-cell small‑RNA profiling. We present SiCmiR, a two‑layer neural network that predicts miRNA expression profiles from only 977 LINCS L1000 landmark genes, thereby reducing sensitivity to dropout in single-cell RNA-seq (scRNA-seq) data. Proof‑of‑concept analyses illustrate how SiCmiR can uncover candidate hub‑miRNAs in bulk-seq cell lines and hepatocellular carcinoma, scRNA-seq pancreatic ductal carcinoma, and ACTH‑secreting pituitary adenoma and extracellular vesicle (EV)‑mediated crosstalk in glioblastoma. Trained on 6,462 TCGA paired miRNA-mRNA samples, SiCmiR attains state‑of‑the‑art accuracy on cancers and generalizes to unseen cancer types and drug perturbations. We next construct SiCmiR‑Atlas, containing 362 public datasets, 9.36 million cells, and 726 cell types, which is the first dedicated database of single‑cell mature miRNA expression, providing interactive visualization, biomarker identification, and cell‑type‑resolved miRNA-target networks. SiCmiR transforms bulk‑derived statistical power into a single‑cell view of miRNA biology and provides a community resource for biomarker discovery. SiCmiR Atlas is available at https://awi.cuhk.edu.cn/∼SiCmiR/.
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A single-cell atlas revealing cellular heterogeneity across healthy and diseased human thymus.
The human thymus plays a key role in the development of the adaptive immune system. Its development and pathologic aberrations with missing involution occupy the scientific world for years. Here, we present a comprehensive single-cell RNA sequencing (scRNA-seq) analysis of 453,727 cells across 53 datasets derived from healthy prenatal, pediatric, and adult thymic tissues, as well as six pathological conditions, including thymic hyperplasia and thymic epithelial tumors (types A, AB, B, C, and micronodular thymoma). We created a high-resolution cellular atlas revealing disease-specific cellular populations and transcriptional programs, particularly within fibroblast subsets and thymic epithelial cells. Comparative analysis uncovers distinct intercellular communication patterns and identifies transcriptional alterations associated with thymic pathology. Integration with published bulk RNA-seq datasets supports the robustness and translational relevance of our findings. This study provides a foundational resource for understanding the cellular and molecular landscape of thymic development and disease, offering avenues for diagnostic and therapeutic innovations.
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CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.
Exploratory analysis of single-cell RNA sequencing (scRNA-seq) typically relies on hard clustering over two-dimensional projections like uniform manifold approximation and projection (UMAP). However, such methods can severely distort the data and have many arbitrary parameter choices. Methods that can model scRNA-seq data as non-discrete "gene expression programs" (GEPs) can better preserve the data's structure, but currently, they are often not scalable, not consistent across repeated runs, and lack an established method for choosing key parameters. Here, we developed a GPU-based unsupervised learning approach, "consensus and scalable inference of gene expression programs" (CSI-GEP). We show that CSI-GEP can recover ground truth GEPs in real and simulated atlas-scale scRNA-seq datasets, significantly outperforming cutting-edge methods, including GPT-based neural networks. We applied CSI-GEP to a whole mouse brain atlas of 2.2 million cells, disentangling endothelial cell types missed by other methods, and to an integrated scRNA-seq atlas of human tumors and cell lines, discovering mesenchymal-like GEPs unique to cancer cells growing in culture.
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A single-cell immune atlas of primary and secondary lymphoid organs in pigs.
Single-cell RNA sequencing (scRNA-seq) has revolutionized understandings of cellular identities and functions due to the ability to study transcriptome-wide gene expression within individual cells. Multi-tissue scRNA-seq atlases have generated holistic understandings of body-wide cell dynamics and serve as key foundational resources for further scientific studies across a variety of species. Pigs are a valuable biomedical model, and pork is an essential global food source, but minimal understanding of immune cell identities and functions across anatomical locations limits agricultural and health advancements in pigs. To address current limitations, we apply scRNA-seq to create an atlas of immune cells recovered from key immune tissues including primary lymphoid organs (bone marrow and thymus) and secondary lymphoid organs (lymph node and spleen). Thymus data was compared to a previously published scRNA-seq dataset of pig thymus and shared a general consensus while also identifying several new thymic cell populations. Comparison of spleen to a human splenic scRNA-seq dataset also revealed conserved features, including two subsets of innate lymphoid cells conserved between pigs and humans. Inference of lymph node cell interactions and proximities from scRNA-seq data resembled some features of follicular organization and conventional germinal center reaction processes. To expand accessibility of the scRNA-seq atlas for biological query, we deploy an interactive application and demonstrate its use for non-computational exploration of diverse cell populations recovered from bone marrow. Overall, results expand current foundational understandings of immune cell identities and functions in pig lymphoid organs and demonstrate pig-to-human immune similarities to consider for future research applications. Materials associated with this work are made readily accessible for others to investigate individual queries requiring foundational knowledge pertaining to pig immunity.
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Single-cell atlas of AML reveals age-related gene regulatory networks in t(8;21) AML.
Acute myeloid leukemia (AML) is characterized by cellular and genetic heterogeneity, which correlates with clinical course. Although single-cell RNA sequencing (scRNA-seq) reflects this diversity to some extent, the low sample numbers in individual studies limit the analytic potential when comparing specific patient groups. We performed large-scale integration of published scRNA-seq datasets to create a unique single-cell transcriptomic atlas for AML (AML scAtlas), totaling 748,679 cells, from 159 AML patients and 51 healthy donors from 20 different studies. This is the largest single-cell data resource for human AML to our knowledge, publicly available at https://cellxgene.cziscience.com/collections/071b706a-7ea7-47a4-bddf-6457725839fc. This AML scAtlas allowed investigations into 20 patients with t(8;21) AML, where we explored the clinical importance of age, given the in-utero origin of pediatric disease. We uncovered age-associated gene regulatory network (GRN) signatures, which we validated using bulk RNA sequencing data to delineate distinct groups with divergent biological characteristics. Furthermore, using an additional multiomic dataset (scRNA-seq and scATAC-seq), we validated our initial findings and created a de-noised enhancer-driven GRN reflecting the previously defined age-related signatures. Applying integrated data analysis of the AML scAtlas, we reveal age-dependent gene regulation in t(8;21) AML, potentially reflecting immature/fetal HSC origin in prenatal origin disease vs postnatal origin. Our analysis revealed that BCLAF1, which is particularly enriched in pediatric AML with t(8;21) of inferred in-utero origin, is a promising prognostic indicator. The AML scAtlas provides a powerful resource to investigate molecular mechanisms underlying different AML subtypes.
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Identification of a hypoxia-related gene prognostic signature in colorectal cancer based on bulk and single-cell RNA-seq.
Colorectal cancer (CRC) is the most common and fatal tumor in the gastrointestinal system. Its incidence and mortality rate have increased in recent years. Hypoxia, a persistent physiological tumor feature, plays a vital role in CRC tumorigenesis, metastasis, and tumor microenvironment (TME). Therefore, we constructed a hypoxia-related gene (HRG) nomogram to predict overall survival (OS) and explored the role of HRGs in the CRC TME. The Cancer Genome Atlas (TCGA) dataset was used as the training set, and two Gene Expression Omnibus datasets (GSE39582 and GSE103479) were used as the testing sets. HRGs were identified using the Gene Set Enrichment Analysis (GSEA) database. An HRG prognostic model was constructed in the training set using the least absolute shrinkage and selection operator regression algorithm and validated in the testing sets. Then, we analyzed tumor-infiltrating cells (TICs) using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Furthermore, single-cell next-generation RNA sequencing (RNA-seq) was used to investigate HRG expression in different TICs in the GSE139555 dataset. Finally, reverse transcription polymerase chain reactions (RT-PCR) were used to validate HRG mRNA expression in ten pairs of CRC normal and cancer tissue samples. A six HRG prognostic signature was constructed, with a superior OS prediction ability in CRC patients (area under the receiver operating characteristic curve (AUC) at one year: 0.693, AUC at three years: 0.712, and AUC at five years: 0.780). GSEA enrichment analysis identified six pathways enriched in the high-risk group. The TIC analysis indicated that the high-risk group had lower T-cell expression and higher neutrophil expression than the low-risk group. Furthermore, immune-related genes had an inseparable relationship with the HRG prognostic signature. Based on single-cell RNA-seq data, we found elevated hexokinase 1 (HK1) and glucose-6-phosphate isomerase (GPI) gene expression in natural killer (NK) and CD8+ T cells. RT-PCR in ten CRC normal-tumor tissue pairs showed that expression of the signature's six HRGs varied differently in cancerous and paracancerous tissues. The constructed HRG signature successfully predicted the OS of whole-stage CRC patients. In addition, we showed that the signature's six HRGs were closely associated with the TME in CRC, where hypoxia inhibits the antitumor function of T cells.
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Uncovering the role of RNA binding proteins in colorectal cancer through single-cell transcriptomic analysis.
Colorectal cancer (CRC) is a globally prevalent malignancy associated with high mortality rates. Despite the existence of various treatment modalities, the prognosis for CRC remains relatively poor. This study aims to explore the role of RNA-binding proteins (RBPs) in CRC progression and their potential as prognostic biomarkers and therapeutic targets. We first identified 166 prognosis-related RBPs, including LIN28B, PPARGC1A, RBM47, and AFF3, by performing univariate Cox regression analysis on bulk transcriptomic and clinical data from The Cancer Genome Atlas (TCGA). Next, single-cell RNA sequencing data from normal, adenoma, and CRC tissues of four patients were analyzed to determine cell type-specific expression patterns of RBPs. Ten upregulated RBPs (HSPB1, RBM47, HMGN2, BRD2, BST2, RBM6, YBX3, CANX, PLEC, and RNASET2) were identified as CRC-associated. Among them, HSPB1, RBM47, HMGN2, BRD2, BST2, and PLEC were predominantly expressed in epithelial cell subsets, whereas RNASET2, RBM6, YBX3, and G3BP2 showed higher expression in T cell subpopulations. Aberrant expression of these RBPs was significantly associated with clinical features such as age, cancer stage, and overall survival (p < 0.05). To validate the above findings, we performed qRT-PCR experiments on 15 paired CRC tumor and adjacent normal tissue samples. The results showed that the expression levels of CANX, RNASET2, YBX3, and BST2 were significantly higher in tumor tissues compared to adjacent normal tissues. Furthermore, we validated the expression levels and prognostic significance of these genes using the TCGA-COAD and READ cohorts, and found that their high expression was significantly associated with poorer overall survival. We suggest that RBPs may influence the occurrence and progression of CRC by affecting epithelial cells and T-cell subtypes, thus serving as promising prognostic biomarkers and potential targets for cancer immunotherapy in CRC.