Single-cell Resolution Research Articles

See the power in kidney cells with ATP biosensor

Yamamoto etal. developed an exciting technical advance to examine intracellular adenosine triphosphate levels with single-cell resolution in intact living kidney tissue, including in tubular and vascular segments that lie deep under the kidney surface. The work is a significant advance on prior invivo biosensor studies, and it allows for mechanistic investigation of alterations in cell metabolism, kidney disease pathobiology, and the effects of drug treatments on energy sources in different kidney cell types.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained throughscResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

T-cell receptor architecture and clonal tiding provide insight into the transformation trajectory of peripheral T-cell lymphomas.

While T cell lymphomas are classified as mature neoplasms, emerging evidence indicates that malignant transformation may occur at an earlier stage of T cell maturation. In this study, we determined clonal architectures in a broad range of T cell lymphomas. Our multidimensional profiling indicates that a large part of these lymphomas in fact emerge from an immature lymphoid T cell precursor at a maturation stage prior to V(D)J rearrangement that undergoes branching evolution. Consequently, at single cell resolution we observed considerable clonal tiding under selective therapeutic pressure. T cell receptor next-generation sequencing suggested a highly biased usage of TRBV20-1 gene segments as part of multiple antigen receptor rearrangements per patient. The predominance of TRBV20-1 was found across all major T cell lymphoma subtypes analyzed. This suggested that this particular V gene - independently of complementarity-determining region 3 (CDR3) configuration - may represent a driver of malignant transformation. Together, our data indicate that T cell lymphomas derive from immature lymphoid precursors and display considerable intratumoral heterogeneity that may provide the basis for relapse and resistance in these hard-to-treat cancers.

Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling

The integration of spatial omics technologies can provide important insights into the biology of tissues. Here we combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on a single tissue section to reveal metabolic heterogeneity at single-cell resolution within tissues and its association with specific cell populations such as cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components.

ImAge quantitates aging and rejuvenation.

For efficient, cost-effective and personalized healthcare, biomarkers that capture aspects of functional, biological aging, thus predicting disease risk and lifespan more accurately and reliably than chronological age, are essential. We developed an imaging-based chromatin and epigenetic age (ImAge) that captures intrinsic age-related trajectories of the spatial organization of chromatin and epigenetic marks in single nuclei, in mice. We show that such trajectories readily emerge as principal changes in each individual dataset without regression on chronological age, and that ImAge can be computed using several epigenetic marks and DNA labeling. We find that interventions known to affect biological aging induce corresponding effects on ImAge, including increased ImAge upon chemotherapy treatment and decreased ImAge upon caloric restriction and partial reprogramming by transient OSKM expression in liver and skeletal muscle. Further, ImAge readouts from chronologically identical mice inversely correlated with their locomotor activity, suggesting that ImAge may capture elements of biological and functional age. In sum, we developed ImAge, an imaging-based biomarker of aging with single-cell resolution rooted in the analysis of spatial organization of epigenetic marks.

Progress in multifactorial single-cell chromatin profiling methods.

Chromatin states play a key role in shaping overall cellular states and fates. Building a complete picture of the functional state of chromatin in cells requires the co-detection of several distinct biochemical aspects. These span DNA methylation, chromatin accessibility, chromosomal conformation, histone posttranslational modifications, and more. While this certainly presents a challenging task, over the past few years many new and creative methods have been developed that now enable co-assay of these different aspects of chromatin at single cell resolution. This field is entering an exciting phase, where a confluence of technological improvements, decreased sequencing costs, and computational innovation are presenting new opportunities to dissect the diversity of chromatin states present in tissues, and how these states may influence gene regulation. In this review, I discuss the spectrum of current experimental approaches for multifactorial chromatin profiling, highlight some of the experimental and analytical challenges, as well as some areas for further innovation.

CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images.

Recent studies show that cellular neighborhoods play an important role in evolving biological events such as cancer and diabetes. Therefore, it is critical to accurately and efficiently identify cellular neighborhoods from spatially-resolved single-cell transcriptomic data or single-cell resolution tissue imaging data. In this work, we develop CNTools, a computational toolbox for end-to-end cellular neighborhood analysis on annotated cell images, comprising both the identification and analysis steps. It includes state-of-the-art cellular neighborhood identification methods and post-identification smoothing techniques, with our newly proposed Cellular Neighbor Embedding (CNE) method and Naive Smoothing technique, as well as several established downstream analysis approaches. We applied CNTools on three real-world CODEX datasets and evaluated identification methods with smoothing techniques quantitatively and qualitatively. It shows that CNE with Naive Smoothing overall outperformed other methods and revealed more convincing biological insights. We also provided suggestions on how to choose proper identification methods and smoothing techniques according to input data.

Hosts manipulate lifestyle switch and pathogenicity heterogeneity of opportunistic pathogens in the single-cell resolution

Host-microbe interactions are virtually bidirectional, but how the host affects their microbiome is poorly understood. Here, we report that the host is a critical modulator to regulate the lifestyle switch and pathogenicity heterogeneity of the opportunistic pathogens Serratia marcescens utilizing the Drosophila and bacterium model system. First, we find that Drosophila larvae efficiently outcompete S. marcescens and typically drive a bacterial switch from pathogenicity to commensalism toward the fly. Furthermore, Drosophila larvae reshape the transcriptomic and metabolic profiles of S. marcescens characterized by a lifestyle switch. More importantly, the host alters pathogenicity and heterogeneity of S. marcescens in the single-cell resolution. Finally, we find that larvae-derived AMPs are required to recapitulate the response of S. marcescens to larvae. Altogether, our findings provide an insight into the pivotal roles of the host in harnessing the life history and heterogeneity of symbiotic bacterial cells, advancing knowledge of the reciprocal relationships between the host and pathogen.

Hosts manipulate lifestyle switch and pathogenicity heterogeneity of opportunistic pathogens in the single-cell resolution.

Host-microbe interactions are virtually bidirectional, but how the host affects their microbiome is poorly understood. Here, we report that the host is a critical modulator to regulate the lifestyle switch and pathogenicity heterogeneity of the opportunistic pathogens Serratia marcescens utilizing the Drosophila and bacterium model system. First, we find that Drosophila larvae efficiently outcompete S. marcescens and typically drive a bacterial switch from pathogenicity to commensalism toward the fly. Furthermore, Drosophila larvae reshape the transcriptomic and metabolic profiles of S. marcescens characterized by a lifestyle switch. More importantly, the host alters pathogenicity and heterogeneity of S. marcescens in the single-cell resolution. Finally, we find that larvae-derived AMPs are required to recapitulate the response of S. marcescens to larvae. Altogether, our findings provide an insight into the pivotal roles of the host in harnessing the life history and heterogeneity of symbiotic bacterial cells, advancing knowledge of the reciprocal relationships between the host and pathogen.

Mapping sagittal-plane reference brain atlas of the cynomolgus macaque (Macaca fascicularis) based on consecutive cytoarchitectonic images

The brain atlas is essential for exploring the anatomical structure and function of the brain. Non-human primates, such as cynomolgus macaque, have received increasing attention due to their genetic similarity to humans. However, current macaque brain atlases only offer coarse sections with intervals along the coronal direction, failing to meet the needs of single-cell resolution studies in functional and multi-omics research of the macaque brain. To address this issue, we utilized fluorescence micro-optical sectioning tomography to obtain sub-micron resolution cytoarchitectonic images of the macaque brain at the sagittal plane. Based on the obtained 8000 image sequences, a reference brain atlas comprising 45 sagittal sections was created, delineating 270 brain regions other than the cortex. Additionally, a website was established to share the reference atlas corresponding image data. This study is expected to provide an essential dataset and tool for scientists studying the macaque brain.

Twisted cell flow facilitates three-dimensional somite morphogenesis in zebrafish

Tissue elongation is a fundamental morphogenetic process to construct complex embryonic structures. In zebrafish, somites rapidly elongate in both dorsal and ventral directions, transforming from a cuboidal to a V-shape within a few hours of development. Despite its significance, the cellular behaviors that directly lead to somite elongation have not been examined at single-cell resolution. Here, we describe the motion and shapes of all cells composing the dorsal half of the somite in three-dimensional space using lightsheet microscopy. We identified two types of cell movements—in horizontal and dorsal directions—that occur simultaneously within individual cells, creating a complex, twisted flow of cells during somite elongation. Chemical inhibition of Sdf1 signaling disrupted the collective movement in both directions and inhibited somite elongation, suggesting that Sdf1 signaling is crucial for this cell flow. Furthermore, three-dimensional computational modeling suggested that horizontal cell rotation accelerates the perpendicular elongation of the somite along the dorsoventral axis. Together, our study offers novel insights into the role of collective cell migration in tissue morphogenesis, which proceeds dynamically in the three-dimensional space of the embryo.

Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart

Sex differences and age-related changes in the human heart at the tissue, cell, and molecular level have been well-documented and many may be relevant for cardiovascular disease. However, how molecular programs within individual cell types vary across individuals by age and sex remains poorly characterized. To better understand this variation, we performed single-nucleus combinatorial indexing (sci) ATAC- and RNA-Seq in human heart samples from nine donors. We identify hundreds of differentially expressed genes by age and sex and find epigenetic signatures of variation in ATAC-Seq data in this discovery cohort. We then scale up our single-cell RNA-Seq analysis by combining our data with five recently published single nucleus RNA-Seq datasets of healthy adult hearts. We find variation such as metabolic alterations by sex and immune changes by age in differential expression tests, as well as alterations in abundance of cardiomyocytes by sex and neurons with age. In addition, we compare our adult-derived ATAC-Seq profiles to analogous fetal cell types to identify putative developmental-stage-specific regulatory factors. Finally, we train predictive models of cell-type-specific RNA expression levels utilizing ATAC-Seq profiles to link distal regulatory sequences to promoters, quantifying the predictive value of a simple TF-to-expression regulatory grammar and identifying cell-type-specific TFs. Our analysis represents the largest single-cell analysis of cardiac variation by age and sex to date and provides a resource for further study of healthy cardiac variation and transcriptional regulation at single-cell resolution.

Accurate long-read transcript discovery and quantification at single-cell, pseudo-bulk and bulk resolution with Isosceles

Accurate detection and quantification of mRNA isoforms from nanopore long-read sequencing remains challenged by technical noise, particularly in single cells. To address this, we introduce Isosceles, a computational toolkit that outperforms other methods in isoform detection sensitivity and quantification accuracy across single-cell, pseudo-bulk and bulk resolution levels, as demonstrated using synthetic and biologically-derived datasets. Here we show Isosceles improves the fidelity of single-cell transcriptome quantification at the isoform-level, and enables flexible downstream analysis. As a case study, we apply Isosceles, uncovering coordinated splicing within and between neuronal differentiation lineages. Isosceles is suitable to be applied in diverse biological systems, facilitating studies of cellular heterogeneity across biomedical research applications.

Up-regulated DSG2 promotes tumor growth and reduces immune infiltration in cervical cancer

BackgroundDesmoglein-2 (DSG2) has been reported to play pivotal roles in various diseases. However, its roles in cervical cancer (CC) remain insufficiently elucidated. Here, we aimed to comprehensively explore the functional mechanisms of DSG2 in CC using bioinformatics and experimental methods. MethodsSeveral online databases, including Gene Expression Profiling Interactive Analysis (GEPIA), ONCOMINE, LinkedOmics, MetaScape, Human protein atlas (HPA), OMICS and single-cell RNA sequencing (scRNA-seq) data were used to explore the expression, prognosis, gene mutations, and potential signaling pathway of DSG2 in CC. Quantitative real-time PCR (qRT-PCR) and western blotting were used to measure DSG2 expression in collected samples. Experimental assays were conducted to verify the effects of dysregulated DSG2 on cervical cell lines in vitro. ResultsBioinformatic analyses revealed that DSG2 was significantly up-regulated in CC compared to normal cervical tissues at both mRNA and protein levels. Elevated DSG2 levels were also associated with poor prognosis and clinical parameters (e.g., cancer stages, tumor grade, nodal metastasis status, etc.). DSG2 expression was predominantly observed in epithelial cells, increasing with disease progression on a single-cell resolution. Additionally, up-regulation of DSG2 significantly enhanced tumor purity by reducing the infiltration of immune cells (e.g., B cells, T cells, NK cells, etc.). Over-expression of DSG2 was further validated in collected CC samples at both mRNA and protein levels. Knockdown of DSG2 markedly reduced the proliferation and invasion of CC cell lines in vitro. ConclusionsIn summary, elevated levels of DSG2 were significantly associated with poor prognosis and diminished immune infiltration in CC. Thus, DSG2 may serve as a potential therapeutic and diagnostic biomarker for CC.

An approach to analyze spatiotemporal patterns of gene expression at single-cell resolution in Candida albicans-infected mouse tongues.

Candida albicans is a fungal pathobiont inhabiting multiple mucosal surfaces of the human body. Immunosuppression, antibiotic-induced microbial dysbiosis, or implanted medical devices can impair mucosal integrity enabling C. albicans to overgrow and disseminate, causing either mucosal diseases such as oropharyngeal candidiasis or life-threatening systemic infections. Profiling fungal genes that are expressed in the infected mucosa or in any other infected organ is paramount to understand pathogenesis. Ideally, these transcript profiling measurements should reveal the expression of any gene at the single-cell level. The resolution typically achieved with current approaches, however, limits most gene expression measurements to cell population averages. The approach described in this report provides a means to dissect fungal gene expression in infected tissues at single-cell resolution.

Nondestructive and three-dimensional visualization by identifying elements using synchrotron radiation microscale X-ray CT reveals microbial and cavity distributions in anaerobic granular sludge.

Microorganisms inhabit diverse environments and often form biofilms. One factor that affects their community structure is the surrounding physical environment. The arrangement of residential space within the formed biofilm plays a crucial role in the supply and transportation of substances, as well as the discharge of metabolites. Conventional approaches, such as scanning electron microscopy and confocal laser scanning microscopy combined with fluorescence in situ hybridization, have limitations as they provide information primarily from the biofilm surface and cross-sections. In this study, we developed a method for detecting microorganisms in biofilms using synchrotron radiation X-ray microscale computer tomography. The developed method allows nondestructive three-dimensional observation of biofilms at a single-cell resolution (voxel size of approximately 200 nm), facilitating an understanding of the relationship between microorganisms and their physical habitats.

Host-pathogen interactions in the Plasmodium-infected mouse liver at spatial and single-cell resolution

Upon infecting its vertebrate host, the malaria parasite initially invades the liver where it undergoes massive replication, whilst remaining clinically silent. The coordination of host responses across the complex liver tissue during malaria infection remains unexplored. Here, we perform spatial transcriptomics in combination with single-nuclei RNA sequencing over multiple time points to delineate host-pathogen interactions across Plasmodium berghei-infected liver tissues. Our data reveals significant changes in spatial gene expression in the malaria-infected tissues. These include changes related to lipid metabolism in the proximity to sites of Plasmodium infection, distinct inflammation programs between lobular zones, and regions with enrichment of different inflammatory cells, which we term ‘inflammatory hotspots’. We also observe significant upregulation of genes involved in inflammation in the control liver tissues of mice injected with mosquito salivary gland components. However, this response is considerably delayed compared to that observed in P. berghei-infected mice. Our study establishes a benchmark for investigating transcriptome changes during host-parasite interactions in tissues, it provides informative insights regarding in vivo study design linked to infection and offers a useful tool for the discovery and validation of de novo intervention strategies aimed at malaria liver stage infection.

Single-cell resolution profiling of the immune microenvironment in primary and metastatic nasopharyngeal carcinoma

BackgroundNasopharyngeal carcinoma (NPC) is an assertive malignancy with partially understood underlying mechanisms, urging further study into its diverse and dynamic tumor microenvironment (TME) to bolster diagnosis, treatment, and prognostic accuracy.AimsTo track the evolutionary route of metastasis, here we perform a yielding scRNA-seq data from 24 primary carcinoma, 7 peripheral blood mononuclear cell (PBMC) nasopharyngeal carcinoma, and 7 metastatic carcinoma patients’ sample.Materials and methodsFollowing high quality control and filtration, a total of 292,298 cells from these tumors were classified into 10 clusters: T cells, B cells, Macrophages/Monocytes, Natural Killer (NK) cells, Plasma cells, plasmacytoid Dendritic Cells, Migratory Dendritic Cells, Mast cells, Cancer-Associated Fibroblasts, and Epithelial cells.ResultsBy comparing and analyzing the different functional capacities of cellular entities within primary and metastatic nasopharyngeal carcinoma, coupled with a detailed investigation into the heterogeneity and differential fate trajectories of T cells, B cells, and myeloid cells, as well as assessing the interactions of cell–cell communicative heterogeneity between these carcinogenic states, we established single-cell atlases for primary and metastatic tumors and identified a large number of potential therapeutic targets.ConclusionThis comprehensive analysis significantly advances our understanding of nasopharyngeal carcinoma (NPC) metastasis by detailing the evolutionary dynamics and the impact of the tumor microenvironment at a single-cell resolution, thereby laying a crucial foundation for future metastatic tumor research and providing new insights into immune heterogeneity, molecular interactions, and potential therapeutic strategies for NPC.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks.

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

MiniXL: An open-source, large field-of-view epifluorescence miniature microscope for mice capable of single-cell resolution and multi-brain region imaging.

Capturing the intricate dynamics of neural activity in freely behaving animals is essential for understanding the neural mechanisms underpinning specific behaviors. Miniaturized microscopy enables investigators to track population activity at cellular level, but the field of view (FOV) of these microscopes have been limited and does not allow multiple-brain region imaging. To fill this technological gap, we have developed the eXtra Large field-of-view Miniscope (MiniXL), a 3.5g lightweight miniaturized microscope with an FOV measuring 3.5 mm in diameter and an electrically adjustable working distance of 1.9 mm ± 200 μm. We demonstrated the capability of MiniXL recording the activity of large neuronal population in both subcortical area (hippocampal dorsal CA1) and deep brain regions (medial prefrontal cortex, mPFC and nucleus accumbens, NAc). The large FOV allows simultaneous imaging of multiple brain regions such as bilateral mPFCs or mPFC and NAc during complex social behavior and tracking cells across multiple sessions. As with all microscopes in the UCLA Miniscope ecosystem, the MiniXL is fully open-source and will be shared with the neuroscience community to lower the barriers for adoption of this technology.