Single-cell Gene Expression Research Articles

CACNA1A haploinsufficiency leads to reduced synaptic function and increased intrinsic excitability.

Haploinsufficiency of the CACNA1A gene, encoding the pore-forming α1 subunit of P/Q-type voltage-gated calcium channels, is associated with a clinically variable phenotype ranging from cerebellar ataxia, to neurodevelopmental syndromes with epilepsy and intellectual disability. To understand the pathological mechanisms of CACNA1A loss-of-function variants, we characterized a human neuronal model for CACNA1A haploinsufficiency, by differentiating isogenic induced pluripotent stem cell lines into glutamatergic neurons, and investigated the effect of CACNA1A haploinsufficiency on mature neuronal networks through a combination of electrophysiology, gene expression analysis, and in silico modeling. We observed an altered network synchronization in CACNA1A+/- networks alongside synaptic deficits, notably marked by an augmented contribution of GluA2-lacking AMPA receptors. Intriguingly, these synaptic perturbations coexisted with increased non-synaptically driven activity, as characterized by inhibition of NMDA and AMPA receptors on micro-electrode arrays. Single-cell electrophysiology and gene expression analysis corroborated this increased intrinsic excitability through reduced potassium channel function and expression. Moreover, we observed partial mitigation of the CACNA1A+/- network phenotype by 4-aminopyridine, a therapeutic intervention for episodic ataxia type 2. Positive modulation of KCa2 channels could reverse the CACNA1A+/- network electrophysiological phenotype. In summary, our study pioneers the characterization of a human induced pluripotent stem cell-derived neuronal model for CACNA1A haploinsufficiency, and has unveiled novel mechanistic insights. Beyond showcasing synaptic deficits, this neuronal model exhibited increased intrinsic excitability mediated by diminished potassium channel function, underscoring its potential as a therapeutic discovery platform with predictive validity.

Defining the transcriptome of PIK3CA-altered cells in a human capillary malformation using single cell long-read sequencing

PIK3CA-related overgrowth spectrum (PROS) disorders are caused by somatic mosaic variants that result in constitutive activation of the phosphatidylinositol-3-kinase/AKT/mTOR pathway. Promising responses to molecularly targeted therapy have been reported, although identification of an appropriate agent can be hampered by the mosaic nature and corresponding low variant allele frequency of the causal variant. Moreover, our understanding of the molecular consequences of these variants—for example how they affect gene expression profiles—remains limited. Here we describe in vitro expansion of a human capillary malformation followed by molecular characterization using exome sequencing, single cell gene expression, and targeted long-read single cell RNA-sequencing in a patient with clinical features consistent with Megalencephaly-Capillary Malformation Syndrome (MCAP, a PROS condition). These approaches identified a targetable PIK3CA variant with expression restricted to PAX3+ fibroblast and undifferentiated keratinocyte populations. This study highlights the innovative combination of next-generation single cell sequencing methods to better understand unique transcriptomic profiles and cell types associated with MCAP, revealing molecular intricacies of this genetic syndrome.

Single-cell Sequencing of Circulating Human Plasmablasts during Staphylococcus aureus Bacteremia.

Staphylococcus aureus is the major cause of healthcare-associated infections, including life-threatening conditions as bacteremia, endocarditis, and implant-associated infections. Despite adequate antibiotic treatment, the mortality of S. aureus bacteremia remains high. This calls for different strategies to treat this infection. In past years, sequencing of Ab repertoires from individuals previously exposed to a pathogen emerged as a successful method to discover novel therapeutic monoclonal Abs and understand circulating B cell diversity during infection. In this paper, we collected peripheral blood from 17 S. aureus bacteremia patients to study circulating plasmablast responses. Using single-cell transcriptome gene expression combined with sequencing of variable heavy and light Ig genes, we retrieved sequences from >400 plasmablasts revealing a high diversity with >300 unique variable heavy and light sequences. More than 200 variable sequences were synthesized to produce recombinant IgGs that were analyzed for binding to S. aureus whole bacterial cells. This revealed four novel monoclonal Abs that could specifically bind to the surface of S. aureus in the absence of Ig-binding surface SpA. Interestingly, three of four mAbs showed cross-reactivity with Staphylococcus epidermidis. Target identification revealed that the S. aureus-specific mAb BC153 targets wall teichoic acid, whereas cross-reactive mAbs BC019, BC020, and BC021 target lipoteichoic acid. All mAbs could induce Fc-dependent phagocytosis of staphylococci by human neutrophils. Altogether, we characterize the active B cell responses to S. aureus in infected patients and identify four functional mAbs against the S. aureus surface, of which three cross-react with S. epidermidis.

Targeting immune-fibroblast cell communication in heart failure.

Inflammation and tissue fibrosis co-exist and are causally linked to organ dysfunction1,2. However, the molecular mechanisms driving immune-fibroblast cell communication in human cardiac disease remain unexplored and there are at present no approved treatments that directly target cardiac fibrosis3,4. Here we performed multiomic single-cell gene expression, epitope mapping and chromatin accessibility profiling in 45 healthy donor, acutely infarcted and chronically failing human hearts. We identified a disease-associated fibroblast trajectory that diverged into distinct populations reminiscent of myofibroblasts and matrifibrocytes, the latter expressing fibroblast activator protein (FAP) and periostin (POSTN). Genetic lineage tracing of FAP+ fibroblasts in vivo showed that they contribute to the POSTN lineage but not the myofibroblast lineage. We assessed the applicability of experimental systems to model cardiac fibroblasts and demonstrated that three different in vivo mouse models of cardiac injury were superior compared with cultured human heart and dermal fibroblasts in recapitulating the human disease phenotype. Ligand-receptor analysis and spatial transcriptomics predicted that interactions between C-C chemokine receptor type 2(CCR2) macrophages and fibroblasts mediated by interleukin-1β (IL-1β) signalling drove the emergence of FAP/POSTN fibroblasts within spatially defined niches. In vivo, we deleted the IL-1 receptor on fibroblasts and the IL-1β ligand in CCR2+ monocytes and macrophages, and inhibited IL-1β signalling using a monoclonal antibody, and showed reduced FAP/POSTN fibroblasts, diminished myocardial fibrosis and improved cardiac function. These findings highlight the broader therapeutic potential of targeting inflammation to treat tissue fibrosis and preserve organ function.

Mapping lineage-traced cells across time points with moslin

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.

The Integrated Transcriptome Bioinformatics Analysis of Energy Metabolism-Related Profiles for Dorsal Root Ganglion of Neuropathic Pain.

Neuropathic pain (NP) is a debilitating disease and is associated with energy metabolism alterations. This study aimed to identify energy metabolism-related differentially expressed genes (EMRDEGs) in NP, construct a diagnostic model, and analyze immune cell infiltration and single-cell gene expression characteristics of NP. GSE89224, GSE123919, and GSE134003 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) analysis and an intersection with highly energy metabolism-related modules in weighted gene co-expression network analysis (WGCNA) was performed in GSE89224. Least absolute shrinkage and selection operator (LASSO), random forest, and logistic regression were used for model genes selection. NP samples were divided into high- and low-risk groups and different disease subtypes based on risk score of LASSO algorithm and consensus clustering analysis, respectively. Immune cell composition was estimated in different risk groups and NP subtypes. Datasets 134,003 were performed for identification of single-cell DEGs and functional enrichment. Cell-cell communications and pseudo-time analysis to reveal the expression profile of NP. A total of 38 EMRDEGs were obtained and are majorly enriched in metabolism about glioma and inflammation. LASSO, random forest, and logistic regression identified 6 model genes, which were Itpr1, Gng8, Socs3, Fscn1, Cckbr, and Camk1. The nomogram, based on six model genes, had a good predictive ability, concordance, and diagnostic value. The comparisons between different risk groups and NP subtypes identified important pathways and different immune cells component. The immune infiltration results majorly associated with inflammation and energy metabolism. Single-cell analysis revealed cell-cell communications and cells differentiation characteristics of NP. In conclusion, our results not only elucidate the involvement of energy metabolism in NP but also provides a robust diagnostic tool with six model genes. These findings might give insight into the pathogenesis of NP and provide effective therapeutic regimens for the treatment of NP.

Identification and Targeting of Regulators of SARS-CoV-2-Host interactions in the Airway Epithelium.

Although the impact of SARS-CoV-2 in the lung has been extensively studied, the molecular regulators and targets of the host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. This is in part ascribed to the use of non-primary cell systems, overreliance on single-cell gene expression profiling that not ultimately reflect protein activity and bias toward the downstream effects rather than their mechanistic determinants. Here we address these issues by network-based analysis of single cell transcriptomic profiles of pathophysiologically relevant human adult basal, ciliated and secretory cells to identify master regulator (MR) protein modules controlling their SARS-CoV-2-mediated reprogramming. This uncovered chromatin remodeling, endosomal sorting, ubiquitin pathway as well as proviral factors identified by CRISPR analyses as components of the host response collectively or selectively activated in these cells. Large-scale perturbation assays, using a clinically-relevant drug library, identified 11 drugs able to invert the entire MR signature activated by SARS-CoV-2 in these cell types. Leveraging MR analysis and perturbational profiles of human primary cells, represents a novel mechanism-based approach and resource that can be directly generalized to interrogate signatures of other airway conditions for drug prioritization.

Single-cell and spatial detection of senescent cells using DeepScence.

Accurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, substantially outperforming existing methods.

Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations

Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations.

Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls

Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop chemoresistance, leading to poor outcomes due to the disease's high heterogeneity. In this study, we used publicly available single-cell RNA sequencing data and machine learning to classify AML patients and healthy, monocytes, dendritic and progenitor cells population. We found that gene expression profiles of AML patients and healthy controls can be classified at the individual level with high accuracy (>70 %) when using progenitor cells, suggesting the existence of subject-specific single cell transcriptomics profiles. The analysis also revealed molecular determinants of patient heterogeneity (e.g. TPSD1, CT45A1, and GABRA4) which could support new strategies for patient stratification and personalized treatment in leukaemia.

Single-cell stimulus-response gene expression trajectories reveal the stimulus specificities of dynamic responses by single macrophages.

Macrophages induce the expression of hundreds of genes in response to immune threats. However, current technology limits our ability to capture single-cell inducible gene expression dynamics. Here, we generated high-resolution time series single-cell RNA sequencing (scRNA-seq) data from mouse macrophages responding to six stimuli, and imputed ensembles of real-time single-cell gene expression trajectories (scGETs). We found that dynamic information contained in scGETs substantially contributes to macrophage stimulus-response specificity (SRS). Dynamic information also identified correlations between immune response genes, indicating biological coordination. Furthermore, we showed that the microenvironmental context of polarizing cytokines profoundly affects scGETs, such that measuring response dynamics offered clearer discrimination of the polarization state of individual macrophage cells than single time-point measurements. Our findings highlight the important contribution of dynamic information contained in the single-cell expression responses of immune genes in characterizing the SRS and functional states of macrophages.

Reduction of myeloid-derived suppressor cells in prostate cancer murine models and patients following white button mushroom treatment.

In a previously reported Phase I trial, we observed therapy-associated declines in circulating myeloid-derived suppressor cells (MDSCs) with the administration of white button mushroom (WBM) tablets in prostate cancer (PCa) patients. These observations led us to hypothesise that WBM could mitigate PCa progression by suppressing MDSCs. We performed bidirectional translational research to examine the immunomodulatory effects of WBM consumption in both syngeneic murine PCa models and patients with PCa participating in an ongoing randomised Phase II trial (NCT04519879). In murine models, WBM treatment significantly suppressed tumour growth with a reduction in both the number and function of MDSCs, which in turn promoted antitumour immune responses mediated by T cells and natural killer (NK) cells. In patients, after consumption of WBM tablets for 3 months, we observed a decline in circulating polymorphonuclear MDSCs (PMN-MDSCs), along with an increase in cytotoxic CD8+ T and NK cells. Furthermore, single immune cell profiling of peripheral blood from WBM-treated patients showed suppressed STAT3/IRF1 and TGFβ signalling in circulating PMN-MDSCs. Subclusters of PMN-MDSCs presented transcriptional profiles associated with responsiveness to fungi, neutrophil chemotaxis, leukocyte aggregation, and regulation of inflammatory response. Finally, in mouse models of PCa, we found that WBM consumption enhanced the anticancer activity of anti-PD-1 antibodies, indicating that WBM may be used as an adjuvant therapy with immune checkpoint inhibitors. Our results from PCa murine models and patients provide mechanistic insights into the immunomodulatory effects of WBM and provide a scientific foundation for WBM as a nutraceutical intervention to delay or prevent PCa progression. White button mushroom (WBM) treatment resulted in a reduction in pro-tumoural MDSCs, notably polymorphonuclear MDSCs (PMN-MDSCs), along with activation of anti-tumoural T and NK cells. Human single immune cell gene expression profiling shed light on the molecular alterations induced by WBM, specifically on PMN-MDSCs. A proof-of-concept study combining WBM with PD-1 blockade in murine models revealed an additive effect on tumour regression and survival outcomes, highlighting the clinical relevance of WBM in cancer management.

Private information leakage from single-cell count matrices.

The increase in publicly available human single-cell datasets, encompassing millions of cells from many donors, has significantly enhanced our understanding of complex biological processes. However, the accessibility of these datasets raises significant privacy concerns. Due to the inherent noise in single-cell measurements and the scarcity of population-scale single-cell datasets, recent private information quantification studies have focused on bulk gene expression data sharing. To address this gap, we demonstrate that individuals in single-cell gene expression datasets are vulnerable to linking attacks, where attackers can infer their sensitive phenotypic information using publicly available tissue or cell-type-specific expression quantitative trait loci (eQTLs) information. We further develop a method for genotype prediction and genotype-phenotype linking that remains effective without relying on eQTL information. We show that variants from one study can be exploited to uncover private information about individuals in another study.

A human-specific progenitor sub-domain extends neurogenesis and increases motor neuron production.

Neurogenesis lasts ~10 times longer in developing humans compared to mice, resulting in a >1,000-fold increase in the number of neurons in the CNS. To identify molecular and cellular mechanisms contributing to this difference, we studied human and mouse motor neurogenesis using a stem cell differentiation system that recapitulates species-specific scales of development. Comparison of human and mouse single-cell gene expression data identified human-specific progenitors characterized by coexpression of NKX2-2 and OLIG2 that give rise to spinal motor neurons. Unlike classical OLIG2+ motor neuron progenitors that give rise to two motor neurons each, OLIG2+/NKX2-2+ ventral motor neuron progenitors remain cycling longer, yielding ~5 times more motor neurons that are biased toward later-born, FOXP1-expressing subtypes. Knockout of NKX2-2 converts ventral motor neuron progenitors into classical motor neuron progenitors. Such new progenitors may contribute to the increased production of human motor neurons required for the generation of larger, more complex nervous systems.

Single-cell RNA sequencing and immune repertoire analysis revealed dynamic immune characteristics associated with peripheral blood during sepsis

Sepsis is a potentially fatal condition arising from an abnormal immune response to an infection, which can result in organ failure and even death. To explore the mechanism underlying the dysregulated immune response during sepsis and identify potential therapeutic targets, single-cell RNA sequencing (scRNA-seq) and immune repertoire analysis were conducted to depict the cellular landscape of peripheral blood cells in septic mice. We observed significant alterations in the number and proportion of peripheral blood cell populations driven by sepsis. By combining single-cell gene expression profiles and B cell receptor (BCR) repertoire analysis, we discerned that infection inflicted serious damage on the antigen presentation ability of B cells and the diversity of BCR in a short time. In addition, we found that the cecal ligation and puncture procedure in mice inhibited the communication signals of CD4+ and CD8+ T cells and decreased the interactions between B cells and other cells. Our study provides detailed insights into the dynamic changes in the biological characteristics of peripheral blood cells driven by sepsis and provides important advances in our understanding of immune disorders during sepsis.

Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes.

Most phenotype-associated genetic variants map to non-coding regulatory regions of the human genome. Moreover, variants associated with blood cell phenotypes are enriched in regulatory regions active during hematopoiesis. To systematically explore the nature of these regions, we developed a highly efficient strategy, Perturb-multiome, that makes it possible to simultaneously profile both chromatin accessibility and gene expression in single cells with CRISPR-mediated perturbation of a range of master transcription factors (TFs). This approach allowed us to examine the connection between TFs, accessible regions, and gene expression across the genome throughout hematopoietic differentiation. We discovered that variants within the TF-sensitive accessible chromatin regions, while representing less than 0.3% of the genome, show a ~100-fold enrichment in heritability across certain blood cell phenotypes; this enrichment is strikingly higher than for other accessible chromatin regions. Our approach facilitates large-scale mechanistic understanding of phenotype-associated genetic variants by connecting key cis-regulatory elements and their target genes within gene regulatory networks.

Recipient tissue microenvironment determines developmental path of intestinal innate lymphoid progenitors

Innate lymphoid cells (ILCs) are critical in maintaining tissue homeostasis, and during infection and inflammation. Here we identify, by using combinatorial reporter mice, a rare ILC progenitor (ILCP) population, resident to the small intestinal lamina propria (siLP) in adult mice. Transfer of siLP-ILCP into recipients generates group 1 ILCs (including ILC1 and NK cells), ILC2s and ILC3s within the intestinal microenvironment, but almost exclusively group 1 ILCs in the liver, lung and spleen. Single cell gene expression analysis and high dimensional spectral cytometry analysis of the siLP-ILCPs and ILC progeny indicate that the phenotype of the group 1 ILC progeny is also influenced by the tissue microenvironment. Thus, a local pool of siLP-ILCP can contribute to pan-ILC generation in the intestinal microenvironment but has more restricted potential in other tissues, with a greater propensity than bone marrow-derived ILCPs to favour ILC1 and ILC3 production. Therefore, ILCP potential is influenced by both tissue of origin and the microenvironment during development. This may provide additional flexibility during the tuning of immune reactions.

ScNODE : generative model for temporal single cell transcriptomic data prediction.

Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE's predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path. The data and code are publicly available at https://github.com/rsinghlab/scNODE.

Investigation and Confirmation of PYCARD as a Potential Biomarker for the Management of Psoriasis Disease.

Psoriasis is not yet completely curable, and its etiology and pathogenesis are unclear. Necroptosis, also known as programmed necrosis, is a regulated mode of necrotic cell death. The interaction between inflammatory diseases and necrotic apoptosis has recently attracted significant attention. We explored the molecular mechanisms of necrotic apoptosis-related genes in psoriasis using bioinformatics methods to identify potential biomarkers for psoriasis. In this study, we screened psoriasis differentially expressed genes from the datasets GSE13355 and GSE14905 and took intersections with necrotic apoptosis-related genes for the next analysis. We used multiple machine learning algorithms to screen key genes and perform enrichment analysis. In addition, we performed an immune infiltration analysis. Transcription factors were predicted by the R package "RcisTarget". We also observed the cellular clustering of key genes in different cell types at the single-cell sequencing level. We used real-time fluorescence-based quantitative-polymerase chain reaction, Western blot, and immunohistochemistry to analyze gene expression in clinical samples. We constructed an imiquimod-induced psoriasis-like dermatitis model in mice for further validation. Seven key genes were screened as follows: AIM2, CARD6, HPSE, MYD88, PYCARD, RAI14, and TNFSF10. Enrichment analysis showed that the key genes were mainly involved in inflammatory pathways. Immune infiltration analysis showed significantly higher levels of CD8 T cells, CD4 initial T cells, and CD4 memory-activated T cells in the disease group's samples than in the normal patients' samples. The key gene expression in single cells analyzed showed that PYCARD was significantly expressed in keratinocytes. PYCARD was selected for gene expression analysis; the results showed that its expression was significantly elevated in the skin lesion tissues of patients with psoriasis. We also verified that PYCARD might play a vital role in the development of psoriasis skin lesions using animal experiments. PYCARD plays a vital role in psoriasis development and is a potential biomarker for psoriasis.

Dissecting reversible and irreversible single cell state transitions from gene regulatory networks.

Understanding cell state transitions and their governing regulatory mechanisms remains one of the fundamental questions in biology. We develop a computational method, state transition inference using cross-cell correlations (STICCC), for predicting reversible and irreversible cell state transitions at single-cell resolution by using gene expression data and a set of gene regulatory interactions. The method is inspired by the fact that the gene expression time delays between regulators and targets can be exploited to infer past and future gene expression states. From applications to both simulated and experimental single-cell gene expression data, we show that STICCC-inferred vector fields capture basins of attraction and irreversible fluxes. By connecting regulatory information with systems' dynamical behaviors, STICCC reveals how network interactions influence reversible and irreversible state transitions. Compared to existing methods that infer pseudotime and RNA velocity, STICCC provides complementary insights into the gene regulation of cell state transitions.