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Decoding cardiac homeostasis and injury: the evolving landscape of spatial transcriptomics.
The heart is a structurally complex organ where function is intimately tied to the precise spatial organization of diverse cell types. While single-cell RNA sequencing (scRNA-seq) has revolutionized cardiovascular research by providing a high-resolution "parts list" of the heart, the requisite tissue dissociation destroys the critical spatial context of intercellular communication and microenvironmental niches. Spatial transcriptomics (ST) has emerged as a transformative technology that bridges this gap, enabling the mapping of gene expression back to its histological coordinates. This review discusses the rapidly evolving landscape of spatial technologies, categorizing them into sequencing-based methods (e.g., Visium, Stereo-seq) which offer transcriptome-wide discovery, and imaging-based methods (e.g., MERFISH, Xenium, CosMx) which provide subcellular resolution with high sensitivity. We highlight recent applications of these tools in uncovering the spatial architecture of the heart at homeostasis and following injury. Furthermore, we explore the next frontier of spatial multi-omics, including simultaneous profiling of the proteome via sequential immunofluorescence (seq-IF) and expansion proteomics (iPEX), as well as chromatin accessibility. We conclude by discussing the computational challenges and future perspectives of integrating these multi-modal datasets to construct comprehensive atlases of the healthy and injured heart.
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Deep generative models in single-cell omics.
Deep Generative Models (DGMs) are becoming instrumental for inferring probability distributions inherent to complex processes, such as most questions in biomedical research. For many years, there was a lack of mathematical methods that would allow this inference in the scarce data scenario of biomedical research. The advent of single-cell omics has finally made square the so-called "skinny matrix", allowing to apply mathematical methods already extensively used in other areas. Moreover, it is now possible to integrate data at different molecular levels in thousands or even millions of samples, thanks to the number of single-cell atlases being collaboratively generated. Additionally, DGMs have proven useful in other frequent tasks in single-cell analysis pipelines, from dimensionality reduction, cell type annotation to RNA velocity inference. In spite of its promise, DGMs need to be used with caution in biomedical research, paying special attention to its use to answer the right questions and the definition of appropriate error metrics and validation check points that confirm not only its correct use but also its relevance. All in all, DGMs provide an exciting tool that opens a bright future for the integrative analysis of single-cell -omics to understand health and disease.
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KLB as a potential prognostic biomarker and therapeutic target in colorectal cancer: insights from multi omics and single cell analyses.
BACKGROUND: Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies, with a rising global incidence. Despite advances in diagnosis and treatment, effective biomarkers for early detection and targeted therapy remain limited. β-Klotho (KLB), a co-receptor in the FGF signaling pathway, has been implicated in tumor progression and poor prognosis in several cancers. However, its role in CRC is still poorly understood. METHODS: To investigate the function of KLB in CRC, we performed a comprehensive pan-cancer analysis using datasets from TCGA, GTEx, Human Protein Atlas, cBioPortal, UALCAN, and TIMER2.0. Analyses included KLB expression profiles, promoter methylation, genetic alterations, immune checkpoint associations, and immune cell infiltration. Functional enrichment was conducted via GO/KEGG and GSEA. A prognostic model was constructed using biostatistical methods. In vitro and in vivo assays-including CCK-8, colony formation, wound healing, and subcutaneous tumor formation in mice-were conducted to evaluate the biological effects of KLB on CRC cells. Additionally, single-cell RNA sequencing data were analyzed to explore the cell-type-specific expression of KLB within the tumor microenvironment. RESULTS: KLB expression was significantly downregulated in CRC tissues compared to normal tissues and negatively correlated with TNM stage and overall prognosis. Promoter methylation and somatic mutations were observed in CRC, suggesting epigenetic and genetic regulation of KLB. Functional enrichment revealed that KLB is involved in regulating the cell cycle, chromatin remodeling, and immune responses. The prognostic nomogram based on KLB expression showed improved predictive performance for progression-free interval (C-index = 0.778). Experimentally, KLB overexpression inhibited CRC cell proliferation and migration. Single-cell RNA-seq analysis demonstrated that KLB is primarily expressed in fibroblasts and stromal cells within the CRC tumor microenvironment, implicating its role in intercellular communication and immune modulation through pathways such as MIF and MK signaling. CONCLUSION: KLB functions as a potential tumor suppressor in CRC, with diagnostic, prognostic, and therapeutic implications. Its expression in stromal components of the tumor microenvironment suggests a regulatory role in immune response and tumor progression. These findings support KLB as a promising biomarker and therapeutic target for CRC.
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Single cell profiling of human airway identifies tuft-ionocyte progenitor cells displaying cytokine-dependent differentiation bias in vitro.
Human airways contain specialized rare epithelial cells including CFTR-rich ionocytes that regulate airway surface physiology and chemosensory tuft cells that produce asthma-associated inflammatory mediators. Here, using a lung cell atlas of 311,748 single cell RNA-Seq profiles, we identify 687 ionocytes (0.45%). In contrast to prior reports claiming a lack of ionocytes in the small airways, we demonstrate that ionocytes are present in small and large airways in similar proportions. Surprisingly, we find only 3 mature tuft cells (0.002%), and demonstrate that previously annotated tuft-like cells are instead highly replicative progenitor cells. These tuft-ionocyte progenitor (TIP) cells produce ionocytes as a default lineage. However, Type 2 and Type 17 cytokines divert TIP cell lineage in vitro, resulting in the production of mature tuft cells at the expense of ionocyte differentiation. Our dataset thus provides an updated understanding of airway rare cell composition, and further suggests that clinically relevant cytokines may skew the composition of disease-relevant rare cells.
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Single cell atlas for 11 non-model mammals, reptiles and birds.
The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs.
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SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics.
Gene regulatory network (GRN) inference is an integral part of understanding physiology and disease. Single cell/nuclei RNA-seq (scRNA-seq/snRNA-seq) data has been used to elucidate cell-type GRNs; however, the accuracy and speed of current scRNAseq-based GRN approaches are suboptimal. Here, we present Single Cell INtegrative Gene regulatory network inference (SCING), a gradient boosting and mutual information-based approach for identifying robust GRNs from scRNA-seq, snRNA-seq, and spatial transcriptomics data. Performance evaluation using Perturb-seq datasets, held-out data, and the mouse cell atlas combined with the DisGeNET database demonstrates the improved accuracy and biological interpretability of SCING compared to existing methods. We applied SCING to the entire mouse single cell atlas, human Alzheimer's disease (AD), and mouse AD spatial transcriptomics. SCING GRNs reveal unique disease subnetwork modeling capabilities, have intrinsic capacity to correct for batch effects, retrieve disease relevant genes and pathways, and are informative on spatial specificity of disease pathogenesis.
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A Highly Sensitive, Accurate, and Automated Single-Cell RNA Sequencing Platform with Digital Microfluidics.
Single-cell RNA sequencing (scRNA-seq) is a powerful method in investigating single-cell heterogeneity to reveal rare cells, identify cell subpopulations, and construct a cell atlas. Conventional benchtop methods for scRNA-seq, including multistep operations, are labor intensive, reaction inefficient, contamination prone, and reagent consuming. Here we report a digital microfluidics-based single-cell RNA sequencing (digital-RNA-seq) for simple, efficient, and low-cost single-cell mRNA measurements. Digital-RNA-seq automates fluid handling as discrete droplets to sequentially perform protocols of scRNA-seq. To overcome the current problems of single-cell isolation in efficiency, integrity, selectivity, and flexibility, we propose a new strategy, passive dispensing method, relying on well-designed hydrophilic-hydrophobic microfeatures to rapidly generate single-cell subdroplets when a droplet of cell suspension is encountered. For sufficient cDNA generation and amplification, digital-RNA-seq uses nanoliter reaction volumes and hydrophobic reaction interfaces, achieving high sensitivity in gene detection. Additionally, the stable droplet handling and oil-closed reaction space featured in digital-RNA-seq ensure highly accurate measurement. We demonstrate the functionality of digital-RNA-seq by quantifying heterogeneity among single cells, where digital-RNA-seq shows excellent performance in rare transcript detection, cell type differentiation, and essential gene identification. With the advantages of automation, sensitivity, and accuracy, digital-RNA-seq represents a promising scRNA-seq platform for a wide variety of biological applications.
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Multi-omics reveals immune features in immune and non-immune cells, an IFN-γ/IFN-α-B2M positive feedback loop, and targeted metabolic therapy in multiple myeloma.
Multiple myeloma (MM) is highly heterogeneous, with relapse occurring in the majority of cases, and recent advancements in single-cell RNA sequencing (scRNA-seq), sc-metabolism profiling, and bulk RNA-seq have facilitated the identification of cell subpopulations and metabolic reprogramming at the single-cell level, uncovering novel molecular mechanisms. This study aims to establish a multi-omics atlas of MM, characterizing the cell subpopulations and signaling pathways that drive immune evasion and disease progression. Additionally, sc-metabolic profiling identifies reprogramming patterns and informs therapeutic screening. We integrated scRNA-seq and bulk RNA-seq data using R to analyze immune and non-immune cell features and pathways in MM. Metabolic reprogramming was assessed via sc-metabolic profiling, and drug candidates were screened through multi-omics integration, with efficacy evaluated in vitro using CCK-8 assays, flow cytometry, Western blotting, and CalcuSyn software. Novel MM subpopulations were identified, including myeloma-activated hematopoietic stem cells and ISG15+ B cells, which correlated with survival and were validated by multiplex immunofluorescence. IFN-γ is primarily secreted by effector memory CD8+T cells, and IFN-α is primarily secreted by non-classical monocytes, driving an IFN-γ/α-B2M feedback loop. Multi-omics identified four drug candidates, each demonstrating anti-tumor effects against myeloma cell lines.
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A Strategy to Compare Single-Cell RNA Sequencing Data Sets Provides Phenotypic Insight into Cellular Heterogeneity Underlying Biological Similarities and Differences Between Samples.
Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the known phenotypes, facilitating potential novel cell type detection. In a comparison of our tool using scRNA-seq data sets from human peripheral blood mononuclear cells (PBMCs), we show that scCompare outperforms single-cell variational inference (scVI) in higher precision and sensitivity for most of the cell types. scCompare was used on a cardiomyocyte data set where it confirmed the discovery of a distinct cluster of cells that differed between the 2 protocols for differentiation. Further use of scCompare on cell atlas data sets revealed insights into the cellular heterogeneity underpinning biological diversity between samples. In addition, we used a cell atlas to better understand the effect of key parameters used in the scCompare pipeline. We envision that scCompare will be of value to the research community when comparing large scRNA-seq data sets.
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Single-cell RNA sequencing reveals a high-resolution cell atlas of petals in Prunus mume at different flowering development stages.
Prunus mume (mei), a traditional ornamental plant in China, is renowned for its fragrant flowers, primarily emitted by its petals. However, the cell types of mei petals and where floral volatile synthesis occurs are rarely reported. The study used single-cell RNA sequencing to characterize the gene expression landscape in petals of P. mume 'Fenhong Zhusha' at budding stage (BS) and full-blooming stage (FS). Six major cell types of petals were identified: epidermal cells (ECs), parenchyma cells (PCs), xylem parenchyma cells, phloem parenchyma cells, xylem vessels and fibers, and sieve elements and companion cells complex. Cell-specific marker genes in each cell type were provided. Floral volatiles from mei petals were measured at four flowering development stages, and their emissions increased from BS to FS, and decreased at the withering stage. Fifty-eight differentially expressed genes (DEGs) in benzenoid/phenylpropanoid pathway were screened using bulk RNA-seq data. Twenty-eight DEGs expression increased from BS to FS, indicating that they might play roles in floral volatile synthesis in P. mume, among which PmBAHD3 would participate in benzyl acetate synthesis. ScRNA-seq data showed that 27 DEGs mentioned above were expressed variously in different cell types. In situ hybridization confirmed that PmPAL2, PmCAD1, PmBAHD3,5, and PmEGS1 involved in floral volatile synthesis in mei petals are mainly expressed in EC, PC, and most vascular tissues, consistent with scRNA-seq data. The result indicates that benzyl acetate and eugenol, the characteristic volatiles in mei, are mostly synthesized in these cell types. The first petal single-cell atlas was constructed, offering new insights into the molecular mechanism of floral volatile synthesis.