Single-cell Resolution Research Articles

Understanding metabolic plasticity at single cell resolution.

It is increasingly clear that cellular metabolic function varies not just between cells of different tissues, but also within tissues and cell types. In this essay, we envision how differences in central carbon metabolism arise from multiple sources, including the cell cycle, circadian rhythms, intrinsic metabolic cycles, and others. We also discuss and compare methods that enable such variation to be detected, including single-cell metabolomics and RNA-sequencing. We pay particular attention to biosensors for AMPK and central carbon metabolites, which when used in combination with metabolic perturbations, provide clear evidence of cellular variance in metabolic function.

Single cell multi-omics analysis unveils a distinct mechanism of exercise-mediated cardioprotection in ischemic cardiomyopathy

Abstract Background Exercise is pivotal in heart disease prevention, with proven cardioprotective effects in clinical trials. However, its precise molecular mechanisms remain elusive. Recent advancements in single-cell omics have revealed cellular heterogeneity within heart tissues, offering a deeper understanding of the pathophysiology of cardiac diseases. Purpose To investigate the exercise-induced physiological changes in heart tissue at the single-cell level and elucidate the molecular mechanisms underlying its cardioprotective effect on ischemic cardiomyopathy. Methods Murine myocardial infarction (MI) model was established by ligating the left ascending coronary artery, and a six-week voluntary wheel running exercise regimen was implemented to investigate the effect of aerobic exercise on ischemic cardiomyopathy. Integrated multi-omics analyses of single-cell DNA methylation profiling, single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq) and single-nucleus RNA sequencing (snRNA-seq) were conducted to clarify the molecular mechanism of exercise-mediated cardioprotection at single-cell resolution. Overexpression and short hairpin RNA-mediated knockdown of gene X were conducted for murine MI model using cardiomyocyte-specific Myo-AAV2a vector. snRNA-seq of heart tissues from heart failure patients was performed to validate the clinical significance of the expression of gene X in cardiomyocytes. Results Exercise attenuates pathological cardiac remodeling, inducing physiological hypertrophy in non-infarcted areas while suppressing pathological remodeling of cardiomyocytes in infarct border zones. Single-cell epigenetic analysis unveiled intricate molecular mechanisms underlying exercise-induced transcriptome changes, providing insights into the gene regulatory networks modulated by exercise in the heart. In addition, integrated with snRNA-seq, we identified exercise-specific cardiomyocyte clusters, highlighting the substantial role of gene X in the molecular mechanism of exercise. Functional studies confirmed the essential role of gene X in exercise-induced cardioprotection, as its overexpression suppressed the adverse remodeling after MI and knockdown abolished the beneficial effects of exercise. Furthermore, snRNA-seq of heart failure patients revealed that higher expression of gene X was associated with ventricular reverse remodeling and favorable clinical outcomes, underscoring its significance in the pathophysiology of patients with heart failure. Conclusion Our study elucidates exercise-specific gene expression regulation networks in ischemic cardiomyopathy by unraveling the cellular heterogeneity and molecular changes associated with exercise. We identified gene X as a central player in exercise-mediated cardioprotection and further confirmed its significance in clinical settings, providing a potential therapeutic target for heart failure.

Overcoming fixation and permeabilization challenges in flow cytometry by optical barcoding and multi-pass acquisition.

The fixation and permeabilization of cells are essential for labeling intracellular biomarkers in flow cytometry. However, these chemical treatments often alter fragile targets, such as cell surface and fluorescent proteins (FPs), and can destroy chemically-sensitive fluorescent labels. This reduces measurement accuracy and introduces compromises into sample workflows, leading to losses in data quality. Here, we demonstrate a novel multi-pass flow cytometry approach to address this long-standing problem. Our technique utilizes individual cell barcoding with laser particles, enabling sequential analysis of the same cells with single-cell resolution maintained. Chemically-fragile protein markers and their fluorochrome conjugates are measured prior to destructive sample processing and adjoined to subsequent measurements of intracellular markers after fixation and permeabilization. We demonstrate the effectiveness of our technique in accurately measuring intracellular FPs and methanol-sensitive antigens and fluorophores, along with various surface and intracellular markers. This approach significantly enhances assay flexibility, enabling accurate and comprehensive cellular analysis without the constraints of conventional one-time measurement flow cytometry. This innovation paves new avenues in flow cytometry for a wide range of applications in immuno-oncology, stem cell research, and cell biology.

Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model.

Immune dysregulation plays a major role in cancer progression. The quantification of lymphocytic spatial inflammation may enable spatial system biology, improve understanding of therapeutic resistance, and contribute to prognostic imaging biomarkers. In this paper, we propose a knowledge-guided deep learning framework to measure the lymphocytic spatial architecture on human H&E tissue, where the fidelity of training labels is maximized through single-cell resolution image registration of H&E to IHC. We demonstrate that such an approach enables pixel-perfect ground-truth labeling of lymphocytes on H&E as measured by IHC. We then experimentally validate our technique in a genetically engineered, immune-compromised Rag2 mouse model, where Rag2 knockout mice lacking mature lymphocytes are used as a negative experimental control. Such experimental validation moves beyond the classical statistical testing of deep learning models and demonstrates feasibility of more rigorous validation strategies that integrate computational science and basic science. Using our developed approach, we automatically annotated more than 111,000 human nuclei (45,611 CD3/CD20 positive lymphocytes) on H&E images to develop our model, which achieved an AUC of 0.78 and 0.71 on internal hold-out testing data and external testing on an independent dataset, respectively. As a measure of the global spatial architecture of the lymphocytic microenvironment, the average structural similarity between predicted lymphocytic density maps and ground truth lymphocytic density maps was 0.86 ± 0.06 on testing data. On experimental mouse model validation, we measured a lymphocytic density of 96.5 ± %1% in a Rag2+/- control mouse, compared to an average of 16.2 ± %5% in Rag2-/- immune knockout mice (p<0.0001, ANOVA-test). These results demonstrate that CD3/CD20 positive lymphocytes can be accurately detected and characterized on H&E by deep learning and generalized across species. Collectively, these data suggest that our understanding of complex biological systems may benefit from computationally-derived spatial analysis, as well as integration of computational science and basic science.

Single-cell chemoproteomics identifies metastatic activity signatures in breast cancer.

Protein activity state, rather than protein or mRNA abundance, is a biologically regulated and relevant input to many processes in signaling, differentiation, development, and diseases such as cancer. While there are numerous methods to detect and quantify mRNA and protein abundance in biological samples, there are no general approaches to detect and quantify endogenous protein activity with single-cell resolution. Here, we report the development of a chemoproteomic platform, single-cell activity-dependent proximity ligation, which uses automated, microfluidics-based single-cell capture and nanoliter volume manipulations to convert the interactions of family-wide chemical activity probes with native protein targets into multiplexed, amplifiable oligonucleotide barcodes. We demonstrate accurate, reproducible, and multiplexed quantitation of a six-enzyme (Ag-6) panel with known ties to cancer cell aggressiveness directly in single cells. We further identified increased Ag-6 enzyme activity across breast cancer cell lines of increasing metastatic potential, as well as in primary patient-derived tumor cells and organoids from patients with breast cancer.

MALDI MSI Protocol for Spatial Bottom-Up Proteomics at Single-Cell Resolution.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) started with spatial mapping of peptides and proteins. Since then, numerous bottom-up protocols have been developed. However, achievable spatial resolution and sample preparation with many wet steps hindered the development of single cell-level workflows for bottom-up spatial proteomics. This study presents a protocol optimized for MALDI-MSI measurements of single cells within the context of their 2D culture. Sublimation of CHCA, followed by a dip in ice-cold ammonium phosphate monobasic (AmP), produced peptide-rich mass spectra while maintaining matrix crystal sizes around 400 nm. This enables MALDI-MSI imaging of proteins in single cells grown on an ITO slide with a throughput of approximately 7800 cells per day. 89 peptide-like features corresponding to a single MDA-MB-231 breast cancer cell were detected. Furthermore, by combining the MALDI-MSI data with LC-MS/MS data obtained on cell pellets, we have successfully identified 24 peptides corresponding to 17 proteins, including actin, vimentin, and transgelin-2.

Single-cell transcriptome analysis identifies a novel tumor-associated macrophage subtype predicting better prognosis in pancreatic ductal adenocarcinoma.

Characterized by an immune-suppressive tumor microenvironment (TME), pancreatic ductal adenocarcinoma (PDAC) is well-known for its poor prognosis. Tumor associated macrophages (TAMs) play a critical role in PDAC TME. An in-depth understanding of TAMs is helpful to develop new strategies for immunotherapy. A large number of single-cell RNA sequencing data and bulk RNA sequencing data of PDAC were collected for systematic bioinformatics analysis. Characterize subtypes of TAMs at single-cell resolution and its effect on prognosis. Differential gene analysis and cell-cell communication were used to describe the effect on prognosis and validated by the TCGA dataset. We used two prognosis-favorable genes, SLC12A5 and ENPP2, to identify a benign M2-like TAMs (bM2-like TAMs), which shared similarities with C1QC + TAMs, CXCL9+ TAMs and CD169+ TAMs, by analyzing scRNA-seq data and bulk RNA data of PDAC. The bM2-like TAMs were revealed to promote T cell activation and proliferation through ALCAM/CD6 interaction. Meanwhile, the bM2-like TAMs were responsible for stroma modeling by altering αSMA+/αSMA-cell ratio. On the contrast, the rest of the M2-like TAMs were defined as malignant M2-like TAMs (mM2-like TAMs), partly overlapping with SPP1+ TAMs. mM2-like TAMs were revealed to promote tumor progression by secretion of MIF and SPP1. Our study used two prognosis-favorable genes to divide M2-like TAMs of PDAC into anti-tumor bM2-like TAMs and pro-tumor mM2-like TAMs. The bM2-like TAMs activate T cells through ALCAM/CD6 and generate prognosis-favorable αSMA+ myofibroblasts through secreting TGFβ, which brings insight into heterogeneity of TAMs, prognosis prediction and immunotherapy of PDAC.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective.

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Dynamics of microbial-induced oil degradation at the microscale.

Microbial-induced oil degradation (MIOD) has a wide range of applications, such as microbial enhanced oil recovery and bioremediation of oil pollution. However, our understanding of MIOD is still far from complete. Particularly, how is the dynamics of degradation process at the microscale level with a single-cell resolution remains to be disclosed. In this work, using hexadecane droplets in water as a model system, we have studied the dynamics of hexadecane degradation by different strains, including Pseudomonas aeruginosa PAO1, IMP68, O-2-2, and Dietzia sp. DQ12-45-1b, at the microscale. Based on visualization of MIOD, the dynamics of MIOD can be characterized by a three-stage process, including adhesion, adaptation, and degradation stages. Although different strains showed similar three-stage dynamics of MIOD, the effective degradation rate varied and followed an order of PAO1 > O-2-2 > IMP68 > DQ12-45-1b under aerobic conditions. Different oxygen conditions were also tested, and the dynamics of MIOD was slowed down under anaerobic conditions in comparison to under aerobic conditions. Further investigations at the degradation stage revealed that biofilms formed at the oil-water interface enhanced oil degradation, but a prerequisite for such enhanced degradation is proper stimulation of biofilm cells in the course of biofilm formation. The findings in this work provided a detailed picture on the dynamics of MIOD at the microscale and would be beneficial for better applications of MIOD.IMPORTANCEMicrobial-induced oil degradation is environmental friendly and economic and has become a promising technique in the fields of enhanced oil recovery and remediation of crude oil-polluted environments. For better applications of microbial-induced oil degradation, understanding the degradation dynamics particularly at the microscale is crucial. In this study, we investigated the degradation dynamics of hexadecane oil droplets incubated with different strains, including Pseudomonas aeruginosa PAO1, O-2-2, IMP68, and Dietzia sp. DQ12-45-1b at the microscale by employing microdroplet-based methods and bacterial tracking techniques. The findings in this study provided a detailed picture on the dynamics of microbial-induced oil degradation at the microscale, which will deepen our understandings on the biodegradation mechanisms of alkanes and shed insights for developing more effective biodegradation techniques.

Single-cell resolution of intestinal regeneration in pythons without crypts illuminates conserved vertebrate regenerative mechanisms

Canonical models of intestinal regeneration emphasize the critical role of the crypt stem cell niche to generate enterocytes that migrate to villus ends. Burmese pythons possess extreme intestinal regenerative capacity yet lack crypts, thus providing opportunities to identify noncanonical but potentially conserved mechanisms that expand our understanding of regenerative capacity in vertebrates, including humans. Here, we leverage single-nucleus RNA sequencing of fasted and postprandial python small intestine to identify the signaling pathways and cell-cell interactions underlying the python's regenerative response. We find that python intestinal regeneration entails the activation of multiple conserved mechanisms of growth and stress response, including core lipid metabolism pathways and the unfolded protein response in intestinal enterocytes. Our single-cell resolution highlights extensive heterogeneity in mesenchymal cell population signaling and intercellular communication that directs major tissue restructuring and the shift out of a dormant fasted state by activating both embryonic developmental and wound healing pathways. We also identify distinct roles of BEST4+ enterocytes in coordinating key regenerative transitions via NOTCH signaling. Python intestinal regeneration shares key signaling features and molecules with mammalian gastric bypass, indicating that conserved regenerative programs are common to both. Our findings provide different insights into cooperative and conserved regenerative programs and intercellular interactions in vertebrates independent of crypts which have been otherwise obscured in model species where temporal phases of generative growth are limited to embryonic development or recovery from injury.

Single-cell spatiotemporal analysis reveals alveolar dendritic cell–T cell immunity hubs defending against pulmonary infection

How immune cells are spatiotemporally coordinated in the lung to effectively monitor, respond to, and resolve infection and inflammation in primed form needs to be fully illustrated. Here we apply immunocartography, a high-resolution technique that integrates spatial and single-cell RNA sequencing (scRNA-seq) through deconvolution and co-localization analyses, to the SARS-CoV-2-infected Syrian hamster model. We generate a comprehensive transcriptome map of the whole process of pulmonary infection from physiological condition, infection initiation, severe pneumonia to natural recovery at organ scale and single-cell resolution, with 142,965 cells and 45 lung lobes from 25 hamsters at 5 time points. Integrative analysis identifies that alveolar dendritic cell–T cell immunity hubs, where Ccr7+Ido1+ dendritic cells, Cd160+Cd8+ T cells, and Tnfrsf4+Cd4+ T cells physiologically co-localize, rapidly expand during SARS-CoV-2 infection, eliminate SARS-CoV-2 with the aid of Slamf9+ macrophages, and then restore to physiological levels after viral clearance. We verify the presence of these cell subpopulations in the immunity hubs in normal and SARS-CoV-2-infected hACE2 mouse models, as well as in publicly available human scRNA-seq datasets, demonstrating the potential broad relevance of our findings in lung immunity.

Integrating bulk and single-cell transcriptomics to elucidate the role and potential mechanisms of autophagy in aging tissue.

Autophagy is frequently observed in tissues during the aging process, yet the tissues most strongly correlated with autophagy during aging and the underlying regulatory mechanisms remain inadequately understood. The purpose of this study is to identify the tissues with the highest correlation between autophagy and aging, and to explore the functions and mechanisms of autophagy in the aging tissue microenvironment. Integrated bulk RNA-seq from over 7000 normal tissue samples, single-cell sequencing data from blood samples of different ages, more than 2000 acute myeloid leukemia (AML) bulk RNA-seq, and multiple sets of AML single-cell data. The datasets were analysed using various bioinformatic approaches. Blood tissue exhibited the highest positive correlation between autophagy and aging among healthy tissues. Single-cell resolution analysis revealed that in aged blood, classical monocytes (C. monocytes) are most closely associated with elevated autophagy levels. Increased autophagy in these monocytes correlated with a higher proportion of C. monocytes, with hypoxia identified as a crucial contributing factor. In AML, a representative myeloid blood disease, enhanced autophagy was accompanied by an increased proportionof C. monocytes. High autophagy levels in monocytes are associated with pro-inflammatory gene upregulation and Reactive Oxygen Species (ROS) accumulation, contributing to tissue aging. This study revealed that autophagy is most strongly correlated with aging in blood tissue. Enhanced autophagy levels in C. monocytes demonstrate a positive correlation with increased secretion of pro-inflammatory factors and elevated production of ROS, which may contribute to a more rapid aging process. This discovery underscores the critical role of autophagy in blood aging and suggests potential therapeutic targets to mitigate aging-related health issues.

Multiomics profiling of mouse polycystic kidney disease progression at a single-cell resolution

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and causes significant morbidity, ultimately leading to kidney failure. PKD pathogenesis is characterized by complex and dynamic alterations in multiple cell types during disease progression, hampering a deeper understanding of disease mechanism and the development of therapeutic approaches. Here, we generate a single-nucleus multimodal atlas of an orthologous mouse PKD model at early, mid, and late timepoints, consisting of 125,434 single-nucleus transcriptomic and epigenetic multiomes. We catalog differentially expressed genes and activated epigenetic regions in each cell type during PKD progression, characterizing cell-type-specific responses to Pkd1 deletion. We describe heterogeneous, atypical collecting duct cells as well as proximal tubular cells that constitute cyst epithelia in PKD. The transcriptional regulation of the cyst lining cell marker GPRC5A is conserved between mouse and human PKD cystic epithelia, suggesting shared gene regulatory pathways. Our single-nucleus multiomic analysis of mouse PKD provides a foundation to understand the earliest changes molecular deregulation in a mouse model of PKD at a single-cell resolution.

Monoallelic de novo variants in DDX17 cause a neurodevelopmental disorder.

DDX17 is an RNA helicase shown to be involved in critical processes during the early phases of neuronal differentiation. Globally, we compiled a case-series of 11 patients with neurodevelopmental phenotypes harbouring de novo monoallelic variants in DDX17. All 11 patients in our case series had a neurodevelopmental phenotype, whereby intellectual disability, delayed speech and language, and motor delay predominated. We performed in utero cortical electroporation in the brain of developing mice, assessing axon complexity and outgrowth of electroporated neurons, comparing wild-type and Ddx17 knockdown. We then undertook ex vivo cortical electroporation on neuronal progenitors to quantitatively assess axonal development at a single cell resolution. Mosaic ddx17 crispants and heterozygous knockouts in Xenopus tropicalis were generated for assessment of morphology, behavioural assays, and neuronal outgrowth measurements. We further undertook transcriptomic analysis of neuroblastoma SH-SY5Y cells, to identify differentially expressed genes in DDX17-KD cells compared to controls. Knockdown of Ddx17 in electroporated mouse neurons in vivo showed delayed neuronal migration as well as decreased cortical axon complexity. Mouse primary cortical neurons revealed reduced axon outgrowth upon knockdown of Ddx17 in vitro. The axon outgrowth phenotype was replicated in crispant ddx17 tadpoles and in heterozygotes. Heterozygous tadpoles had clear neurodevelopmental defects and showed an impaired neurobehavioral phenotype. Transcriptomic analysis identified a statistically significant number of differentially expressed genes involved in neurodevelopmental processes in DDX17-KD cells compared to control cells. We have identified potential neurodevelopment disease-causing variants in a gene not previously associated with genetic disease, DDX17. We provide evidence for the role of the gene in neurodevelopment in both mammalian and non-mammalian species and in controlling the expression of key neurodevelopment genes.

Highly Branched Au Superparticles as Efficient Photothermal Transducers for Optical Neuromodulation.

Precise neuromodulation is critical for interrogating cellular communication and treating neurological diseases. Nanoscale transducers have emerged as effective interfaces to exert photothermal effects and modulate neural activities with a high spatiotemporal resolution. Ideal materials for this application should possess strong light absorption, high photothermal conversion efficiency, and great biocompatibility for clinical translation. Here, we show that the structurally designed 3D Au superparticles with a highly branched morphology can be promising candidates for nongenetic and remote neuromodulation. The structure-induced blackbody-like absorption endows Au superparticles with photothermal conversion efficiency over 90%, much higher than that of conventional Au nanorods. With the biocompatible polydopamine ligands, Au superparticles can be readily interfaced with primary mouse hippocampal neurons and other cells and can photostimulate or inhibit their activities in both cell networks or with a single-cell resolution. These findings highlight the importance of structural designs as powerful tools to promote the performance of plasmonic materials in neuromodulation and related research of neuroscience and neuroengineering.

CCL2-mediated endothelial injury drives cardiac dysfunction in long COVID.

Evidence linking the endothelium to cardiac injury in long coronavirus disease (COVID) is well documented, but the underlying mechanisms remain unknown. Here we show that cytokines released by endothelial cells (ECs) contribute to long-COVID-associated cardiac dysfunction. Using thrombotic vascular tissues from patients with long COVID and induced pluripotent stem cell-derived ECs (iPSC-ECs), we modeled endotheliitis and observed similar dysfunction and cytokine upregulation, notably CCL2. Cardiac organoids comprising iPSC-ECs and iPSC-derived cardiomyocytes showed cardiac dysfunction after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure, driven by CCL2. Profiling of chromatin accessibility and gene expression at a single-cell resolution linked CCL2 to 'phenotype switching' and cardiac dysfunction, validated by high-throughput proteomics. Disease modeling of cardiac organoids and exposure of human ACE2 transgenic mice to SARS-CoV-2 spike proteins revealed that CCL2-induced oxidative stress promoted post-translational modification of cardiac proteins, leading to cardiac dysfunction. These findings suggest that EC-released cytokines contribute to cardiac dysfunction in long COVID, highlighting the importance of early vascular health monitoring in patients with long COVID.

Hunting for Early Melanomas With a Maximum Clinical Surface Diameter up to 6 mm: A Prospective Study of Reflectance Confocal Microscopy Features in 68 Consecutive Cases.

Early diagnosis of melanoma and prompt effective therapy optimizes prognosis. Reflectance confocal microscopy (RCM) facilitates diagnosis by providing immediate 3D single cell resolution down into the papillary dermis. Consecutive cases were examined using a Vivascope 1500 confocal microscope at a single referral medical practice in Sydney, Australia 2019-2023. Melanoma clinical surface diameters were recorded by 0.1 mm increments up to 6.0 mm. The RCM features recorded were: pagetoid single cells or nests, pleomorphic cell shape, atypical dendritic cells, non-edged papillae, variation in melanocyte size and confluent sheets of cells. All cases required diagnostic agreement by two dermatohistopathologists using hematoxylin and eosin staining followed by SOX 10 and/or PRAME stains if required. Total cases were 68: 38 males (mean age 57) and 30 females (mean age 64). Melanoma in situ (n = 65) compared to invasive melanoma (n = 3), all males, invasion depth (0.4-0.5 mm). Most frequent RCM features found in 50% or more of cases within all diameter increments were: pagetoid single cells n = 64/68 (94%), pleomorphic cell shape n = 63/68 (93%), epidermal disarray n = 58/68 (85%), and atypical dendritic cells n = 45/68 (66%). Non-edged dermal papillae were n = 42/68 (62%). Melanoma RCM features were found throughout the diameter ranges. Confocal examination may facilitate early melanoma recognition in these ranges.

Systematic benchmark of single-cell hashtag demultiplexing approaches reveals robust performance of a clustering-based method.

Single-cell technology opened up a new avenue to delineate cellular status at a single-cell resolution and has become an essential tool for studying human diseases. Multiplexing allows cost-effective experiments by combining multiple samples and effectively mitigates batch effects. It starts by giving each sample a unique tag and then pooling them together for library preparation and sequencing. After sequencing, sample demultiplexing is performed based on tag detection, where cells belonging to one sample are expected to have a higher amount of the corresponding tag than cells from other samples. However, in reality, demultiplexing is not straightforward due to the noise and contamination from various sources. Successful demultiplexing depends on the efficient removal of such contamination. Here, we perform a systematic benchmark combining different normalization methods and demultiplexing approaches using real-world data and simulated datasets. We show that accounting for sequencing depth variability increases the separability between tagged and untagged cells, and the clustering-based approach outperforms existing tools. The clustering-based workflow is available as an R package from https://github.com/hwlim/hashDemux.

TNFα has differential effects on the transcriptome profile of selected populations in murine cartilage

ObjectiveTo further our understanding of the role of tumor necrosis factor (TNF)α on the inflammatory response in chondrocytes. DesignWe explored the effects of TNFα on the transcriptome of epiphyseal chondrocytes from newborn C57BL/6 mice at the total and single cell (sc) resolution. ResultsGene set enrichment analysis of total RNA-Seq from TNFα-treated chondrocytes revealed enhanced response to biotic stimulus, defense and immune response and cytokine signaling and suppressed cartilage and skeletal morphogenesis and development. scRNA-Seq analyzed 14,239 ​cells and 24,320 genes and distinguished 16 ​cell clusters. The more prevalent ones were constituted by limb bud and chondrogenic cells and fibroblasts comprising ∼73 ​% of the cell population. Genes expressed by joint fibroblasts were detected in 5 clusters comprising ∼45 ​% of the cells isolated. Pseudotime trajectory finding revealed an association between fibroblast and chondrogenic clusters which was not modified by TNFα. TNFα decreased the total cells recovered by 18.5 ​% and the chondrogenic, limb bud and mesenchymal clusters by 32 ​%, 27 ​% and 7 ​%, respectively. TNFα had profound effects on the insulin-like growth factor (IGF) axis decreasing Igf1, Igf2 and Igfbp4 and inducing Igfbp3 and Igfbp5, explaining an inhibition of collagen biosynthesis, cartilage and skeletal morphogenesis. Ingenuity Pathway Analysis of scRNA-Seq data revealed that TNFα enhanced the osteoarthritis, rheumatoid arthritis, pathogen induced cytokine storm and interleukin 6 signaling pathways and suppressed fibroblast growth factor signaling. ConclusionsEpiphyseal chondrocytes are constituted by diverse cell populations distinctly regulated by TNFα to promote inflammation and suppression of matrix biosynthesis and growth.

SpatialDeX is a Reference-Free Method for Cell Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors.

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multi-cell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell type-specific spatial organization. To this end, we developed SpatialDeX, a regression model-based method for estimating cell type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring scRNA-seq references.