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Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data. The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4+ and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion: In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease. [ABSTRACT FROM AUTHOR]

Cellular senescence, a state of persistent growth arrest, is closely associated with aging and age‐related diseases. Deciphering the heterogeneity within senescent cell populations and identifying therapeutic targets are paramount for mitigating senescence‐associated pathologies. In this study, proteins on the surface of cells rendered senescent by replicative exhaustion and by exposure to ionizing radiation (IR) were identified using mass spectrometry analysis, and a subset of them was further studied using single‐cell CITE‐seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) analysis. Based on the presence of proteins on the cell surface, we identified two distinct IR‐induced senescent cell populations: one characterized by high levels of CD109 and CD112 (cluster 3), the other characterized by high levels of CD112, CD26, CD73, HLA‐ABC, CD54, CD49A, and CD44 (cluster 0). We further found that cluster 0 represented proliferating and senescent cells in the G1 phase of the division cycle, and CITE‐seq detection of cell surface proteins selectively discerned those in the senescence group. Our study highlights the heterogeneity of senescent cells and underscores the value of cell surface proteins as tools for distinguishing senescent cell programs and subclasses, paving the way for targeted therapeutic strategies in disorders exacerbated by senescence. [ABSTRACT FROM AUTHOR]

CD3δ SCID is a devastating inborn error of immunity caused by mutations in CD3D, encoding the invariant CD3δ chain of the CD3/TCR complex necessary for normal thymopoiesis. We demonstrate an adenine base editing (ABE) strategy to restore CD3δ in autologous hematopoietic stem and progenitor cells (HSPCs). Delivery of mRNA encoding a laboratory-evolved ABE and guide RNA into a CD3δ SCID patient's HSPCs resulted in a 71.2% ± 7.85% (n = 3) correction of the pathogenic mutation. Edited HSPCs differentiated in artificial thymic organoids produced mature T cells exhibiting diverse TCR repertoires and TCR-dependent functions. Edited human HSPCs transplanted into immunodeficient mice showed 88% reversion of the CD3D defect in human CD34+ cells isolated from mouse bone marrow after 16 weeks, indicating correction of long-term repopulating HSCs. These findings demonstrate the preclinical efficacy of ABE in HSPCs for the treatment of CD3δ SCID, providing a foundation for the development of a one-time treatment for CD3δ SCID patients.

Abstract Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

Abstract Background The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called “gating”, and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data. Results We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations. Conclusions CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

Long considered to fluctuate between pro‐ and anti‐inflammatory states, it has now become evident that microglia occupy a variegated phenotypic landscape with relevance to aging and neurodegeneration. However, whether specific microglial subsets converge in or contribute to both processes that eventually affect brain function is less clear. To investigate this, we analyzed microglial heterogeneity in a tauopathy mouse model (K18‐seeded P301L) and an accelerated aging model (Senescence‐Accelerated Mouse‐Prone 8, SAMP8) using cellular indexing of transcriptomes and epitopes by sequencing. We found that widespread tau pathology in K18‐seeded P301L mice caused a significant change in the number and morphology of microglia, but only a mild overrepresentation of disease‐associated microglia. At the cell population‐level, we observed a marked upregulation of the calprotectin‐encoding genes S100a8 and S100a9. In 9‐month‐old SAMP8 mice, we identified a unique microglial subpopulation that showed partial similarity with the disease‐associated microglia phenotype and was additionally characterized by a high expression of the same calprotectin gene set. Immunostaining for S100A8 revealed that this population was enriched in the hippocampus, correlating with the cognitive impairment observed in this model. However, incomplete colocalization between their residence and markers of neuronal loss suggests regional specificity. Importantly, S100A8‐positive microglia were also retrieved in brain biopsies of human AD and tauopathy patients as well as in a biopsy of an aged individual without reported pathology. Thus, the emergence of S100A8‐positive microglia portrays a conspicuous commonality between accelerated aging and tauopathy progression, which may have relevance for ensuing brain dysfunction. [ABSTRACT FROM AUTHOR]

Introduction: B cells play a pivotal role in adaptive immunity which has been extensively characterised primarily via flow cytometry-based gating strategies. This study addresses the discrepancies between flow cytometry-defined B cell subsets and their high-confidence molecular signatures using single-cell multiomics approaches. Methods: By analysing multi-omics single-cell data from healthy individuals and patients across diseases, we characterised the level and nature of cellular contamination within standard flow cytometric-based gating, resolved some of the ambiguities in the literature surrounding unconventional B cell subsets, and demonstrated the variable effects of flow cytometric-based gating cellular heterogeneity across diseases. Results: We showed that flow cytometric-defined B cell populations are heterogenous, and the composition varies significantly between disease states thus affecting the implications of functional studies performed on these populations. Importantly, this paper draws caution on findings about B cell selection and function of flow cytometric-sorted populations, and their roles in disease. As a solution, we developed a simple tool to identify additional markers that can be used to increase the purity of flow-cytometric gated immune cell populations based on multi-omics data (AlliGateR). Here, we demonstrate that additional non-linear CD20, CD21 and CD24 gating can increase the purity of both naïve and memory populations. Discussion: These findings underscore the need to reconsider B cell subset definitions within the literature and propose leveraging single-cell multi-omics data for refined characterisation. We show that single-cell multi-omics technologies represent a powerful tool to bridge the gap between surface marker-based annotations and the intricate molecular characteristics of B cell subsets. [ABSTRACT FROM AUTHOR]

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data. [ABSTRACT FROM AUTHOR]

Background: The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called "gating", and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data. Results: We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations. Conclusions: CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation. [ABSTRACT FROM AUTHOR]

Myeloid-derived suppressor cells (MDSCs) are pathologically activated immature myeloid cells with immunosuppressive activity that expand during chronic inflammation, such as cancer and prosthetic joint infection (PJI). Myeloid-derived suppressor cells can be broadly separated into 2 populations based on surface marker expression and function: monocytic myeloid-derived suppressor cells (M-MDSCs) and granulocytic myeloid-derived suppressor cells (G-MDSCs). Granulocytic myeloid-derived suppressor cells are the most abundant leukocyte infiltrate during PJI; however, how this population is maintained in vivo and cellular heterogeneity is currently unknown. In this study, we identified a previously unknown population of Ly6G+Ly6C+F4/80+MHCII+ MDSCs during PJI that displayed immunosuppressive properties ex vivo. We leveraged F4/80 and MHCII expression by these cells for further characterization using cellular indexing of transcriptomes and epitopes by sequencing, which revealed a distinct transcriptomic signature of this population. F4/80+MHCII+ MDSCs displayed gene signatures resembling G-MDSCs, neutrophils, and monocytes but had significantly increased expression of pathways involved in cytokine response/production, inflammatory cell death, and mononuclear cell differentiation. To determine whether F4/80+MHCII+ MDSCs represented an alternate phenotypic state of G-MDSCs, Ly6G+Ly6C+F4/80−MHCII− G-MDSCs from CD45.1 mice were adoptively transferred into CD45.2 recipients using a mouse model of PJI. A small percentage of transferred G-MDSCs acquired F4/80 and MHCII expression in vivo, suggesting some degree of plasticity in this population. Collectively, these results demonstrate a previously unappreciated phenotype of F4/80+MHCII+ MDSCs during PJI, revealing that a granulocytic-to-monocytic transition can occur during biofilm infection. This study highlights the plasticity of myeloid-derived suppressor cell phenotypes that may influence the chronicity of prosthetic joint infection. [ABSTRACT FROM AUTHOR]

The role of Erythroid cells in immune regulation and immunosuppression is one of the emerging topics in modern immunology that still requires further clarification as Erythroid cells from different tissues and different species express different immunoregulatory molecules. In this study, we performed a thorough investigation of human bone marrow Erythroid cells from adult healthy donors and adult acute lymphoblastic leukemia patients using the state-of-the-art single-cell targeted proteomics and transcriptomics via BD Rhapsody and cancer-related gene copy number variation analysis via NanoString Sprint Profiler. We found that human bone marrow Erythroid cells express the ARG1, LGALS1, LGALS3, LGALS9, and C10orf54 (VISTA) immunosuppressive genes, CXCL5, CXCL8, and VEGFA cytokine genes, as well as the genes involved in antimicrobial immunity and MHC Class II antigen presentation. We also found that ARG1 gene expression was restricted to the single erythroid cell cluster that we termed ARG1-positive Orthochromatic erythroblasts and that late Erythroid cells lose S100A9 and gain MZB1 gene expression in case of acute lymphoblastic leukemia. These findings show that steady-state erythropoiesis bone marrow Erythroid cells express myeloid signature genes even without any transdifferentiating stimulus like cancer.

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE‐Seq) is a single‐cell phenotyping method that uses antibody‐derived tags (ADTs) to quantitatively detect cell surface protein expression and generate transcriptomic data at the single‐cell level. Despite the increased popularity of this technique to study cellular heterogeneity and dynamics, detailed methods on how to choose ADT markers and ensuring reagent performance in biological relevant systems prior to sequencing is not available. Here we describe a novel and easy‐to‐use multiplex flow proxy assay in which multiple protein markers can be measured simultaneously using a combination of ADT reagents and dye‐oligo conjugates by flow cytometry. Using dye‐oligo conjugates with sequences complementary to the ADT reagents, we can achieve specific binding and evaluate protein marker expression in a multiplex way. This quality control assay is useful for guiding ADT marker choice and confirming protein expression prior to sequencing. Importantly, the labeled cells can be directly isolated based on the specific fluorescence from dye‐oligo conjugates using a flow cytometry cell sorter and processed for downstream single‐cell multiomics. Using this streamlined workflow, we sorted natural killer cells and T cells efficiently using only ADT and dye‐oligo reagents, avoiding the possibility of decreased marker resolution from co‐staining cells with ADT and fluorescent antibodies. This novel workflow provides a viable option for improving ADT marker choice and cell sorting efficiency, allowing subsequent CITE‐Seq. [ABSTRACT FROM AUTHOR]

IntroductionB cells play a pivotal role in adaptive immunity which has been extensively characterised primarily via flow cytometry-based gating strategies. This study addresses the discrepancies between flow cytometry-defined B cell subsets and their high-confidence molecular signatures using single-cell multi-omics approaches.MethodsBy analysing multi-omics single-cell data from healthy individuals and patients across diseases, we characterised the level and nature of cellular contamination within standard flow cytometric-based gating, resolved some of the ambiguities in the literature surrounding unconventional B cell subsets, and demonstrated the variable effects of flow cytometric-based gating cellular heterogeneity across diseases.ResultsWe showed that flow cytometric-defined B cell populations are heterogenous, and the composition varies significantly between disease states thus affecting the implications of functional studies performed on these populations. Importantly, this paper draws caution on findings about B cell selection and function of flow cytometric-sorted populations, and their roles in disease. As a solution, we developed a simple tool to identify additional markers that can be used to increase the purity of flow-cytometric gated immune cell populations based on multi-omics data (AlliGateR). Here, we demonstrate that additional non-linear CD20, CD21 and CD24 gating can increase the purity of both naïve and memory populations.DiscussionThese findings underscore the need to reconsider B cell subset definitions within the literature and propose leveraging single-cell multi-omics data for refined characterisation. We show that single-cell multi-omics technologies represent a powerful tool to bridge the gap between surface marker-based annotations and the intricate molecular characteristics of B cell subsets.

Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease.

Ankylosing spondylitis (AS) is an immune-mediated inflammatory disorder that primarily affects the axial skeleton, especially the sacroiliac joints and spine. This results in chronic back pain and, in extreme cases, ankylosis of the spine. Despite its debilitating effects, the pathogenesis of AS remains to be further elucidated. This study used single cell CITE-seq technology to analyze peripheral blood mononuclear cells (PBMCs) in AS and in healthy controls. We identified a number of molecular features associated with AS. CD52 was found to be overexpressed in both RNA and surface protein expression across several cell types in patients with AS. CD16+ monocytes overexpressed TNFSF10 and IL-18Rα in AS, while CD8+ TEM cells and natural killer cells overexpressed genes linked with cytotoxicity, including GZMH, GZMB, and NKG7. Tregs underexpressed CD39 in AS, suggesting reduced functionality. We identified an overrepresented NK cell subset in AS that overexpressed CD16, CD161, and CD38, as well as cytotoxic genes and pathways. Finally, we developed machine learning models derived from CITE-seq data for the classification of AS and achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of > 0.95. In summary, CITE-seq identification of AS-associated genes and surface proteins in specific cell subsets informs our understanding of pathogenesis and potential new therapeutic targets, while providing new approaches for diagnosis via machine learning.

Despite the decades-old knowledge that males and people with diabetes mellitus (DM) are at increased risk for coronary artery disease (CAD), the reasons for this association are only partially understood. Among the immune cells involved, recent evidence supports a critical role of T cells as drivers and modifiers of CAD. CD4+ T cells are commonly found in atherosclerotic plaques. We aimed to understand the relationship of CAD with sex and DM by single-cell RNA (scRNA-Seq) and antibody sequencing (CITE-Seq) of CD4+ T cells. Peripheral blood mononuclear cells (PBMCs) of 61 men and women who underwent cardiac catheterization were interrogated by scRNA-Seq combined with 49 surface markers (CITE-Seq). CAD severity was quantified using Gensini scores, with scores above 30 considered CAD+ and below 6 considered CAD-. Four pairs of groups were matched for clinical and demographic parameters. To test how sex and DM changed cell proportions and gene expression, we compared matched groups of men and women, as well as diabetic and non-diabetic subjects. We analyzed 41,782 single CD4+ T cell transcriptomes for sex differences in 16 women and 45 men with and without coronary artery disease and with and without DM. We identified 16 clusters in CD4+ T cells. The proportion of cells in CD4+ effector memory cluster 8 (CD4T8, CCR2+ Em) was significantly decreased in CAD+, especially among DM+ participants. This same cluster, CD4T8, was significantly decreased in female participants, along with two other CD4+ T cell clusters. In CD4+ T cells, 31 genes showed significant and coordinated upregulation in both CAD and DM. The DM gene signature was partially additive to the CAD gene signature. We conclude that (1) CAD and DM are clearly reflected in PBMC transcriptomes, and (2) significant differences exist between women and men and (3) between subjects with DM and non-DM.

Dysregulated apoptosis and proliferation are fundamental properties of cancer, and microRNAs (miRNA) are critical regulators of these processes. Loss of miR-15a/16-1 at chromosome 13q14 is the most common genomic aberration in chronic lymphocytic leukemia (CLL). Correspondingly, the deletion of either murine miR-15a/16-1 or miR-15b/16-2 locus in mice is linked to B cell lymphoproliferative malignancies. However, unexpectedly, when both miR-15/16 clusters are eliminated, most double knockout (DKO) mice develop acute myeloid leukemia (AML). Moreover, in patients with CLL, significantly reduced expression of miR-15a, miR-15b, and miR-16 associates with progression of myelodysplastic syndrome to AML, as well as blast crisis in chronic myeloid leukemia. Thus, the miR-15/16 clusters have a biological relevance for myeloid neoplasms. Here, we demonstrate that the myeloproliferative phenotype in DKO mice correlates with an increase of hematopoietic stem and progenitor cells (HSPC) early in life. Using single-cell transcriptomic analyses, we presented the molecular underpinning of increased myeloid output in the HSPC of DKO mice with gene signatures suggestive of dysregulated hematopoiesis, metabolic activities, and cell cycle stages. Functionally, we found that multipotent progenitors (MPP) of DKO mice have increased self-renewing capacities and give rise to significantly more progeny in the granulocytic compartment. Moreover, a unique transcriptomic signature of DKO MPP correlates with poor outcome in patients with AML. Together, these data point to a unique regulatory role for miR-15/16 during the early stages of hematopoiesis and to a potentially useful biomarker for the pathogenesis of myeloid neoplasms. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) is a major cause of death worldwide. The role of CD8+ T cells in CAD is unknown. Recent studies suggest a breakdown of tolerance in atherosclerosis, resulting in active T cell receptor (TCR) engagement with self-antigens. We hypothesized that TCR engagement would leave characteristic gene expression signatures. In a single cell RNA-sequencing analysis of CD8+ T cells from 30 patients with CAD and 30 controls we found significant enrichment of TCR signaling pathways in CAD+ subjects, suggesting recent TCR engagement. We also found significant enrichment of cytotoxic and exhaustion pathways in CAD cases compared to controls. Highly significant upregulation of TCR signaling in CAD indicates that CD8 T cells reactive to atherosclerosis antigens are prominent in the blood of CAD cases compared to controls. [ABSTRACT FROM AUTHOR]

Abstract Gangliogliomas are brain tumors composed of neuron-like and macroglia-like components that occur in children and young adults. Gangliogliomas are often characterized by a rare population of immature astrocyte-appearing cells expressing CD34, a marker expressed in the neuroectoderm (neural precursor cells) during embryogenesis. New insights are needed to refine tumor classification and to identify therapeutic approaches. We evaluated five gangliogliomas with single nucleus RNA-seq, cellular indexing of transcriptomes and epitopes by sequencing, and/or spatially-resolved RNA-seq. We uncovered a population of CD34+ neoplastic cells with mixed neuroectodermal, immature astrocyte, and neuronal markers. Gene regulatory network interrogation in these neuroectoderm-like cells revealed control of transcriptional programming by TCF7L2/MEIS1-PAX6 and SOX2, similar to that found during neuroectodermal/neural development. Developmental trajectory analyses place neuroectoderm-like tumor cells as precursor cells that give rise to neuron-like and macroglia-like neoplastic cells. Spatially-resolved transcriptomics revealed a neuroectoderm-like tumor cell niche with relative lack of vascular and immune cells. We used these high resolution results to deconvolute clinically-annotated transcriptomic data, confirming that CD34+ cell-associated gene programs associate with gangliogliomas compared to other glial brain tumors. Together, these deep transcriptomic approaches characterized a ganglioglioma cellular hierarchy—confirming CD34+ neuroectoderm-like tumor precursor cells, controlling transcription programs, cell signaling, and associated immune cell states. These findings may guide tumor classification, diagnosis, prognostication, and therapeutic investigations.

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Background Microglia have been implicated in the pathophysiology of neuropathic pain. Here, we sought to investigate whether cerebrospinal fluid (CSF) might be used as a proxy-measure of microglial activation in human participants. Methods We preformed fluorescence-activated cell sorting (FACS) of CSF immune cell populations derived from individuals who experienced pain with neuropathic features. We sorted CD4+, CD8+ T cells and monocytes and analyzed their transcriptome using RNA sequencing. We also performed Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing to characterize the expression of all CSF immune cells in a patient with postherpetic neuralgia and in a patient with neuropathic pain after failed back surgery. Results Immune cell numbers and phenotypes were not obviously different between individuals regardless of the etiology of their pain. This was true when examining our own dataset, as well as when comparing it to previously published single-cell RNA sequencing data of human CSF. In all instances, CSF monocytes showed expression of myeloid cell markers commonly associated with microglia (P2RY12, TMEM119 and OLFML3), which will make it difficult to ascertain the origin of CSF proteins: do they derive directly from circulating CSF monocytes or could some originate in spinal cord microglia in the parenchyma? Conclusions We conclude that it will not be straightforward to use CSF as a biomarker for microglial function in humans.

Coronary artery disease (CAD) is a major cause of death worldwide. The role of CD8+ T cells in CAD is unknown. Recent studies suggest a breakdown of tolerance in atherosclerosis, resulting in active T cell receptor (TCR) engagement with self-antigens. We hypothesized that TCR engagement would leave characteristic gene expression signatures. In a single cell RNA-sequencing analysis of CD8+ T cells from 30 patients with CAD and 30 controls we found significant enrichment of TCR signaling pathways in CAD+ subjects, suggesting recent TCR engagement. We also found significant enrichment of cytotoxic and exhaustion pathways in CAD cases compared to controls. Highly significant upregulation of TCR signaling in CAD indicates that CD8 T cells reactive to atherosclerosis antigens are prominent in the blood of CAD cases compared to controls.

Simultaneous measurement of multiple modalities in single-cell analysis, represented by CITE-seq, is a promising approach to link transcriptional changes to cellular phenotype and function, requiring new computational methods to define cellular subtypes and states based on multiple data types. Here, we design a flexible single-cell multimodal analysis framework, called CITEMO, to integrate the transcriptome and antibody-derived tags (ADT) data to capture cell heterogeneity from the multi omics perspective. CITEMO uses Principal Component Analysis (PCA) to obtain a low-dimensional representation of the transcriptome and ADT, respectively, and then employs PCA again to integrate these low-dimensional multimodal data for downstream analysis. To investigate the effectiveness of the CITEMO framework, we apply CITEMO to analyse the cell subtypes of Cord Blood Mononuclear Cells (CBMC) samples. Results show that the CITEMO framework can comprehensively analyse single-cell multimodal samples and accurately identify cell subtypes. Besides, we find some specific immune cells that co-express multiple ADT markers. To better describe the co-expression phenomenon, we introduce the co-expression entropy to measure the heterogeneous distribution of the ADT combinations. To further validate the robustness of the CITEMO framework, we analyse Human Bone Marrow Cell (HBMC) samples and identify different states of the same cell type. CITEMO has an excellent performance in identifying cell subtypes and states for multimodal omics data. We suggest that the flexible design idea of CITEMO can be an inspiration for other single-cell multimodal tasks. The complete source code and dataset of the CITEMO framework can be obtained from https://github.com/studentiz/CITEMO.

Gangliogliomas are brain tumors composed of neuron-like and macroglia-like components that occur in children and young adults. Gangliogliomas are often characterized by a rare population of immature astrocyte-appearing cells expressing CD34, a marker expressed in the neuroectoderm (neural precursor cells) during embryogenesis. New insights are needed to refine tumor classification and to identify therapeutic approaches. We evaluated five gangliogliomas with single nucleus RNA-seq, cellular indexing of transcriptomes and epitopes by sequencing, and/or spatially-resolved RNA-seq. We uncovered a population of CD34+ neoplastic cells with mixed neuroectodermal, immature astrocyte, and neuronal markers. Gene regulatory network interrogation in these neuroectoderm-like cells revealed control of transcriptional programming by TCF7L2/MEIS1-PAX6 and SOX2, similar to that found during neuroectodermal/neural development. Developmental trajectory analyses place neuroectoderm-like tumor cells as precursor cells that give rise to neuron-like and macroglia-like neoplastic cells. Spatially-resolved transcriptomics revealed a neuroectoderm-like tumor cell niche with relative lack of vascular and immune cells. We used these high resolution results to deconvolute clinically-annotated transcriptomic data, confirming that CD34+ cell-associated gene programs associate with gangliogliomas compared to other glial brain tumors. Together, these deep transcriptomic approaches characterized a ganglioglioma cellular hierarchy—confirming CD34+ neuroectoderm-like tumor precursor cells, controlling transcription programs, cell signaling, and associated immune cell states. These findings may guide tumor classification, diagnosis, prognostication, and therapeutic investigations. [ABSTRACT FROM AUTHOR]

Defining the immunological landscape of human tissue is an important area of research, but challenges include the impact of tissue disaggregation on cell phenotypes and the low abundance of immune cells in many tissues. Here, we describe methods to troubleshoot and standardize Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) for studies involving enzymatic digestion of human tissue. We tested epitope susceptibility of 92 antibodies commonly used to differentiate immune lineages and cell states on human peripheral blood mononuclear cells following treatment with an enzymatic digestion cocktail used to isolate islets. We observed CD4, CD8a, CD25, CD27, CD120b, CCR4, CCR6, and PD1 display significant sensitivity to enzymatic treatment, effects that often could not be overcome with alternate antibodies. Comparison of flow cytometry-based CITE-seq antibody titrations and sequencing data supports that for the majority of antibodies, flow cytometry accurately predicts optimal antibody concentrations for CITE-seq. Comparison by CITE-seq of immune cells in enzymatically digested islet tissue and donor-matched spleen not treated with enzymes revealed little digestion-induced epitope cleavage, suggesting increased sensitivity of CITE-seq and/or that the islet structure may protect resident immune cells from enzymes. Within islets, CITE-seq identified immune cells difficult to identify by transcriptional signatures alone, such as distinct tissue-resident T cell subsets, mast cells, and innate lymphoid cells (ILCs). Collectively this study identifies strategies for the rational design and testing of CITE-seq antibodies for single-cell studies of immune cells within islets and other tissues. [ABSTRACT FROM AUTHOR]

Chronic tendinopathy represents a growing healthcare burden in the ageing global population. Curative therapies remain elusive as the mechanisms that underlie chronic inflammation in tendon disease remain unclear. Identifying and isolating key pathogenic and reparative cells is essential in developing precision therapies and implantable materials for improved tendon healing. Multiple discrete human tendon cell populations have been previously described ex vivo. To determine if these populations persist in vitro, healthy human hamstring tenocytes were cultured for 8 d on either tissue culture plastic or aligned electrospun fibres of absorbable polydioxanone. Novel single-cell surface proteomics combined with unbiased single-cell transcriptomics (CITE-Seq) was used to identify discrete tenocyte populations. 6 cell populations were found, 4 of which shared key gene expression determinants with ex vivo human cell clusters: PTX3_PAPPA, POSTN_SCX, DCN_LUM and ITGA7_NES. Surface proteomics found that PTX3_PAPPA cells were CD10+CD26+CD54+. ITGA7_NES cells were CD146+ and POSTN_SCX cells were CD90+CD95+CD10+. Culture on the aligned electrospun fibres favoured 3 cell subtypes (DCN_LUM, POSTN_SCX and PTX3_ PAPPA), promoting high expression of tendon-matrix-associated genes and upregulating gene sets enriched for TNF-a and IL-6/STAT3 signalling. Discrete human tendon cell subpopulations persisted in in vitro culture and could be recognised by specific gene and surface-protein signatures. Aligned polydioxanone fibres promoted the survival of 3 clusters, including pro-inflammatory PTX3-expressing CD10+CD26+CD54+ cells found in chronic tendon disease. These results improved the understanding of preferred culture conditions for different tenocyte subpopulations and informed the development of in vitro models of tendon disease.

Background & Aim: CAR T-cell therapy has demonstrated remarkable success particularly for hematological malignancies. Nonetheless, treatment failure and toxicities remain obstacles to optimal response. Intrinsic CAR T-cell characteristics may play a pivotal role in their effectiveness. Therefore, studying infusion products may reveal insights into how clinical efficacy and toxicities stem from inherent CAR T-cell factors. Eleven autologous CD22 CAR T-cell products manufactured to treat children and young adults with B-ALL enrolled in a phase II study (NCT02315612) were analyzed by CITE-seq on the BD Rhapsody platform (Fig.1a). Illumina sequencing data was processed using BD Rhapsody Targeted Analysis Pipeline on Sevenbridge. Analysis and visualization were performed with Seurat and ggplot2 in Rstudio. Non-responders were defined as patients who failed to achieve or maintain a measurable residual disease (MRD)-negative remission 28-30 days after CAR T-cell infusion. Sequencing of 17,866 cells identified 19 distinct T-cell subpopulations (Fig.1b). Samples from the non-responder group exhibited a reduced number of clusters compared to the responder group, suggesting lower T-cell diversity among non-responders (Fig.1c). Non-responder sample NR4 was an outlier for unknown reasons. A distinctive pattern emerged between non-responders and responders. Samples from non-responders displayed a higher abundance of subpopulations (red arrow) which lacked CCR7 expression and were identified as effector T-cells (Teff) and effector memory T cells (Tem). While more prevalent subpopulations (red circle) in the responder group had CCR7high expression that were characterized as stem cell-like memory T-cells (Tscm) and central memory T cells (Tcm) (Fig.2). In the context of CAR-related hemophagocytic lymphohistiocytosis (HLH) toxicity (carHLH), cells demonstrated 12 clusters with no clear separation between carHLH (+) and carHLH(-) groups (Fig.3a). Analysis of cell percentages and subtypes revealed cluster10 had 82% cells from the carHLH(+) group, characterized as Tem, while cluster4 and cluster7 contained 29% and 34% cells, respectively, from the carHLH(+) group and were classified as Tscm and Tcm (Fig.3b/c/d). [ABSTRACT FROM AUTHOR]

Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well.

Defining the immunological landscape of human tissue is an important area of research, but challenges include the impact of tissue disaggregation on cell phenotypes and the low abundance of immune cells in many tissues. Here, we describe methods to troubleshoot and standardize Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) for studies involving enzymatic digestion of human tissue. We tested epitope susceptibility of 92 antibodies commonly used to differentiate immune lineages and cell states on human peripheral blood mononuclear cells following treatment with an enzymatic digestion cocktail used to isolate islets. We observed CD4, CD8a, CD25, CD27, CD120b, CCR4, CCR6, and PD1 display significant sensitivity to enzymatic treatment, effects that often could not be overcome with alternate antibodies. Comparison of flow cytometry-based CITE-seq antibody titrations and sequencing data supports that for the majority of antibodies, flow cytometry accurately predicts optimal antibody concentrations for CITE-seq. Comparison by CITE-seq of immune cells in enzymatically digested islet tissue and donor-matched spleen not treated with enzymes revealed little digestion-induced epitope cleavage, suggesting increased sensitivity of CITE-seq and/or that the islet structure may protect resident immune cells from enzymes. Within islets, CITE-seq identified immune cells difficult to identify by transcriptional signatures alone, such as distinct tissue-resident T cell subsets, mast cells, and innate lymphoid cells (ILCs). Collectively this study identifies strategies for the rational design and testing of CITE-seq antibodies for single-cell studies of immune cells within islets and other tissues.

Abstract Background Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called “hashing.” Results Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects. Conclusions Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.

The liver provides a unique niche of lymphocytes enriched with a large proportion of innate-like T cells. However, the heterogeneity and functional characteristics of the hepatic T-cell population remain to be fully elucidated. We obtained liver sinusoidal mononuclear cells from the liver perfusate of healthy donors and recipients with HBV-associated chronic liver disease (CLD) during liver transplantation. We performed a CITE-seq analysis of liver sinusoidal CD45+ cells in combination with T cell receptor (TCR)-seq and flow cytometry to examine the phenotypes and functions of liver sinusoidal CD8+ T cells. We identified a distinct CD56hiCD161-CD8+ T-cell population characterized by natural killer (NK)-related gene expression and a uniquely restricted TCR repertoire. The frequency of these cells among the liver sinusoidal CD8+ T-cell population was significantly increased in patients with HBV-associated CLD. Although CD56hiCD161-CD8+ T cells exhibit weak responsiveness to TCR stimulation, CD56hiCD161-CD8+ T cells highly expressed various NK receptors, including CD94, killer immunoglobulin-like receptors, and NKG2C, and exerted NKG2C-mediated NK-like effector functions even in the absence of TCR stimulation. In addition, CD56hiCD161-CD8+ T cells highly respond to innate cytokines, such as IL-12/18 and IL-15, in the absence of TCR stimulation. We validated the results from liver sinusoidal CD8+ T cells using intrahepatic CD8+ T cells obtained from liver tissues. In summary, the current study found a distinct CD56hiCD161-CD8+ T-cell population characterized by NK-like activation via TCR-independent NKG2C ligation. Further studies are required to elucidate the roles of liver sinusoidal CD56hiCD161-CD8+ T cells in immune responses to microbial pathogens or liver immunopathology. The role of different immune cell populations in the liver is becoming an area of increasing interest. Herein, we identified a distinct T-cell population that had features similar to those of natural killer (NK) cells – a type of innate immune cell. This distinct population was expanded in the livers of patients with chronic liver disease and could thus have pathogenic relevance. [Display omitted] • Single-cell analysis revealed heterogeneity among liver sinusoidal CD8+ T cells. • The CD56hiCD161-CD8+ T-cell population expands in HBV-associated chronic liver disease. • CD56hiCD161-CD8+ T cells have NK-like transcriptomes and unique TCR repertoire. • CD56hiCD161-CD8+ T cells exert TCR-independent, NKG2C-mediated effector functions. • CD56hiCD161-CD8+ T cells exhibit hyper-responsiveness to IL-12/IL-18 and IL-15. [ABSTRACT FROM AUTHOR]

Advances in single-cell RNA sequencing (scRNA-seq) technologies has provided an unprecedent opportunity for cell-type identification. As clustering is an effective strategy towards cell-type identification, various computational approaches have been proposed for clustering scRNA-seq data. Recently, with the emergence of cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), the cell surface expression of specific proteins and the RNA expression on the same cell can be captured, which provides more comprehensive information for cell analysis. However, existing single cell clustering algorithms are mainly designed for single-omic data, and have difficulties in handling multi-omics data with diverse characteristics efficiently. In this study, we propose a novel deep embedded multi-omics clustering with collaborative training (DEMOC) model to perform joint clustering on CITE-seq data. Our model can take into account the characteristics of transcriptomic and proteomic data, and make use of the consistent and complementary information provided by different data sources effectively. Experiment results on two real CITE-seq datasets demonstrate that our DEMOC model not only outperforms state-of-the-art single-omic clustering methods, but also achieves better and more stable performance than existing multi-omics clustering methods. We also apply our model on three scRNA-seq datasets to assess the performance of our model in rare cell-type identification, novel cell-subtype detection and cellular heterogeneity analysis. Experiment results illustrate the effectiveness of our model in discovering the underlying patterns of data. [ABSTRACT FROM AUTHOR]

Within the thymus, regulation of the cellular crosstalk directing T cell development depends on spatial interactions within specialized niches. To create a spatially defined map of tissue niches guiding human postnatal T cell development, we employed the multidimensional imaging platform co-detection by indexing (CODEX) as well as cellular indexing of transcriptomes and epitopes sequencing (CITE-seq) and assay for transposase accessible chromatin sequencing (ATAC-seq). We generated age-matched 4- to 5-month-old human postnatal thymus datasets for male and female donors, identifying significant sex differences in both T cell and thymus biology. We demonstrate a possible role for JAG ligands in directing thymic-like dendritic cell development, identify important functions of a population of extracellular matrix (ECM) - fibroblasts, and characterize the medullary niches surrounding Hassall's corpuscles. Together, these data represent an age-matched spatial multiomic resource to investigate how sex-based differences in thymus regulation and T cell development arise, providing an essential resource to understand the mechanisms underlying immune function and dysfunction in males and females., Competing Interests: Declaration of interests P.W.Z. is a cofounder of Notch Therapeutics and a member of its scientific advisory board., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Cell surface proteins serve as primary drug targets and cell identity markers. Techniques such as CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) have enabled the simultaneous quantification of surface protein abundance and transcript expression within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability of these computational approaches across diverse contexts (e.g., different tissues/disease states) impede their widespread adoption. Here, we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA sequencing), a context-agnostic zero-shot deep ensemble model, which enables large-scale protein abundance prediction and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2,500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER, including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition characterized by painful nodules, abscesses, and scarring, predominantly affecting intertriginous regions and it is often underdiagnosed. This study aimed to utilize single cell RNA and cell-surface protein sequencing (CITE-Seq) to delineate the immune composition of circulating cells in Hidradenitis suppurativa (HS) peripheral blood compared to healthy controls. CITE-Seq was used to analyze the gene and protein expression profiles of peripheral blood mononuclear cells (PBMCs) from 9 HS and 29 healthy controls. The study identified significant differences cell composition between HS patients and healthy controls, including increased proportions of CD14+ and CD16+ monocytes, cDC2, plasmablasts, and proliferating CD4+ T cells in HS patients. Differential expression analysis revealed upregulation of inflammatory markers such as TNF, IL1B , and NF-κB in monocytes, as well as chemokines and cell adhesion molecules involved in immune cell recruitment and tissue infiltration. Pathway enrichment analysis highlighted the involvement of IL-17, IL-26 and TNF signaling pathways in HS pathogenesis. Machine learning identified key markers for diagnostics and therapeutic development. The findings also support the potential for machine learning models to aid in the diagnosis of HS based on immune cell markers. These insights may inform future therapeutic strategies targeting specific immune pathways in HS., Competing Interests: Conflict of Interest WL has received research grant funding from Abbvie, Amgen, Janssen, Leo, Novartis, Pfizer, Regeneron, and TRex Bio. CP has received honoraria and/or travel support from MSD, BMS, Pierre Fabre, MERCK, Sanofi, Almirall, AbbVie, Pelpharma, Amgen, DSD, Takeda, Pfizer, Novartis, Leo, Janssen, Astra Zeneca, and Boehringer Ingelheim. AW received honoraria and travel support from AbbVie, Amgen, Biogen, Janssen, Leo, Novartis, UCB and Sanofi. HN has received grant support from AbbVie; consulting fees from 23andme, Abbvie, Aristea Therapeutics, Nimbus Therapeutics, Medscape, Sonoma Biotherapeutics, DAVA Oncology, Boehringer Ingelheim, Union Chimique Belge’s (UCB) and Novartis; investigator fees from Pfizer; and holds shares in Radera, Inc. She is also an Associate Editor for JAMA Dermatology and Vice President of the Hidradenitis Suppurativa Foundation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems., Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data. The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed., Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8 + T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset., Conclusion: In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease., Competing Interests: The authors have no potential conflicts of interest to disclose., (© Copyright: Yonsei University College of Medicine 2024.)

Abstract We identified an autosomal dominant progranulin mutation carrier without symptoms of dementia in her lifetime (Reduced Penetrance Mutation Carrier, RedPenMC). This resistance to develop expected pathology presents a unique opportunity to interrogate neurodegenerative mechanisms. We performed multimodal single-nuclei analyses of post-mortem frontal cortex from RedPenMC, including transcriptomics and global levels of chromatin marks. RedPenMC had an increased ratio of GRN-expressing microglia, higher levels of activating histone mark H3k4me3 in microglia and lower levels of the repressive chromatin marks H3k9me1 and H3k9me3 in the frontal cortex than her affected mutation carrier son and evidence of higher protein levels of progranulin in both plasma and brain homogenates. Although the study is limited to one case, the results support that restoring brain progranulin levels may be sufficient to escape neurodegeneration and FTD. In addition to previously identified modifier genes, it is possible that epigenetic marks may contribute to the increased progranulin expression in cases of reduced penetrance. These findings may stimulate similar follow-up studies and new therapeutic approaches.

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

Ankylosing spondylitis (AS) is an immune-mediated inflammatory disorder that primarily affects the axial skeleton, especially the sacroiliac joints and spine. This results in chronic back pain and, in extreme cases, ankylosis of the spine. Despite its debilitating effects, the pathogenesis of AS remains to be further elucidated. This study used single cell CITE-seq technology to analyze peripheral blood mononuclear cells (PBMCs) in AS and in healthy controls. We identified a number of molecular features associated with AS. CD52 was found to be overexpressed in both RNA and surface protein expression across several cell types in patients with AS. CD16+ monocytes overexpressed TNFSF10 and IL-18Rα in AS, while CD8+ TEM cells and natural killer cells overexpressed genes linked with cytotoxicity, including GZMH, GZMB, and NKG7. Tregs underexpressed CD39 in AS, suggesting reduced functionality. We identified an overrepresented NK cell subset in AS that overexpressed CD16, CD161, and CD38, as well as cytotoxic genes and pathways. Finally, we developed machine learning models derived from CITE-seq data for the classification of AS and achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of > 0.95. In summary, CITE-seq identification of AS-associated genes and surface proteins in specific cell subsets informs our understanding of pathogenesis and potential new therapeutic targets, while providing new approaches for diagnosis via machine learning.

Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease.

Simultaneous measurement of multiple modalities in single-cell analysis, represented by CITE-seq, is a promising approach to link transcriptional changes to cellular phenotype and function, requiring new computational methods to define cellular subtypes and states based on multiple data types. Here, we design a flexible single-cell multimodal analysis framework, called CITEMO, to integrate the transcriptome and antibody-derived tags (ADT) data to capture cell heterogeneity from the multi omics perspective. CITEMO uses Principal Component Analysis (PCA) to obtain a low-dimensional representation of the transcriptome and ADT, respectively, and then employs PCA again to integrate these low-dimensional multimodal data for downstream analysis. To investigate the effectiveness of the CITEMO framework, we apply CITEMO to analyse the cell subtypes of Cord Blood Mononuclear Cells (CBMC) samples. Results show that the CITEMO framework can comprehensively analyse singlecell multimodal samples and accurately identify cell subtypes. Besides, we find some specific immune cells that co-express multiple ADT markers. To better describe the co-expression phenomenon, we introduce the co-expression entropy to measure the heterogeneous distribution of the ADT combinations. To further validate the robustness of the CITEMO framework, we analyse Human Bone Marrow Cell (HBMC) samples and identify different states of the same cell type. CITEMO has an excellent performance in identifying cell subtypes and states for multimodal omics data. We suggest that the flexible design idea of CITEMO can be an inspiration for other single-cell multimodal tasks. The complete source code and dataset of the CITEMO framework can be obtained from https://github. com/studentiz/CITEMO. [ABSTRACT FROM AUTHOR]

Problem: Mucosal‐Associated Invariant T (MAIT) cells have been recently identified at the maternal‐fetal interface. However, transcriptional programming of decidual MAIT cells in pregnancy remains poorly understood. Method of study: We employed a multiomic approach to address this question. Mononuclear cells from the decidua basalis and parietalis, and control PBMCs, were analyzed via flow cytometry to investigate MAIT cells in the decidua and assess their transcription factor expression. In a separate study, both decidual and matched peripheral MAIT cells were analyzed using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE‐seq) coupled with gene expression analysis. Lastly, decidual MAIT cells were stimulated with E.coli and expression of MR1 by antigen presenting cells was measured to evaluate decidual MAIT cell function. Results: First, we identified MAIT cells in both the decidua basalis and parietalis. CITE‐seq, coupled with scRNA‐seq gene expression analysis, highlighted transcriptional programming differences between decidual and matched peripheral MAIT cells at a single cell resolution. Transcription factor expression analysis further highlighted transcriptional differences between decidual MAIT cells and non‐matched peripheral MAIT cells. Functionally, MAIT cells are skewed towards IFNγ and TNFα production upon stimulation, with E.coli leading to IFNγ production. Lastly, we demonstrate that MR1, the antigen presenting molecule restricting MAIT cells, is expressed by decidual APCs. Conclusion: MAIT cells are present in the decidua basalis and obtain a unique gene expression profile. The presence of MR1 on APCs coupled with in vitro activation by E.coli suggests that MAIT cells might be involved in tissue‐repair mechanisms at the maternal‐fetal interface. [ABSTRACT FROM AUTHOR]

The role of Erythroid cells in immune regulation and immunosuppression is one of the emerging topics in modern immunology that still requires further clarification as Erythroid cells from different tissues and different species express different immunoregulatory molecules. In this study, we performed a thorough investigation of human bone marrow Erythroid cells from adult healthy donors and adult acute lymphoblastic leukemia patients using the state-of-the-art single-cell targeted proteomics and transcriptomics via BD Rhapsody and cancer-related gene copy number variation analysis via NanoString Sprint Profiler. We found that human bone marrow Erythroid cells express the ARG1, LGALS1, LGALS3, LGALS9 , and C10orf54 (VISTA) immunosuppressive genes, CXCL5, CXCL8 , and VEGFA cytokine genes, as well as the genes involved in antimicrobial immunity and MHC Class II antigen presentation. We also found that ARG1 gene expression was restricted to the single erythroid cell cluster that we termed ARG1-positive Orthochromatic erythroblasts and that late Erythroid cells lose S100A9 and gain MZB1 gene expression in case of acute lymphoblastic leukemia. These findings show that steady-state erythropoiesis bone marrow Erythroid cells express myeloid signature genes even without any transdifferentiating stimulus like cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Perik-Zavodskii, Perik-Zavodskaia, Alrhmoun, Volynets, Shevchenko, Nazarov, Denisova and Sennikov.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

A gene signature was previously found to be correlated with mosaic adenovirus 26 vaccine protection in simian immunodeficiency virus and simian-human immunodeficiency virus challenge models in non-human primates. In this report, we investigated the presence of this signature as a correlate of reduced risk in human clinical trials and potential mechanisms of protection. The absence of this gene signature in the DNA/rAd5 human vaccine trial, which did not show efficacy, strengthens our hypothesis that this signature is only enriched in studies that demonstrated protection. This gene signature was enriched in the partially effective RV144 human trial that administered the ALVAC/protein vaccine, and we find that the signature associates with both decreased risk of HIV-1 acquisition and increased vaccine efficacy (VE). Total RNA-seq in a clinical trial that used the same vaccine regimen as the RV144 HIV vaccine implicated antibody-dependent cellular phagocytosis (ADCP) as a potential mechanism of vaccine protection. CITE-seq profiling of 53 surface markers and transcriptomes of 53,777 single cells from the same trial showed that genes in this signature were primarily expressed in cells belonging to the myeloid lineage, including monocytes, which are major effector cells for ADCP. The consistent association of this transcriptome signature with VE represents a tool both to identify potential mechanisms, as with ADCP here, and to screen novel approaches to accelerate the development of new vaccine candidates.

Summary: Multimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells). Motivation: Multimodal single-cell sequencing enables multiple aspects for characterizing the dynamics of cell states and developmental processes. Properly integrating information from multiple modalities is a crucial step for interpreting cell heterogeneity. Here, we present LinQ-View, a computational workflow that provides an effective solution for integrating multiple modalities of CITE-seq data for downstream interpretation. LinQ-View balances information from multiple modalities to achieve accurate clustering results and is specialized in handling CITE-seq data with routine numbers of surface protein features.

We identified an autosomal dominant progranulin mutation carrier without symptoms of dementia in her lifetime (Reduced Penetrance Mutation Carrier, RedPenMC). This resistance to develop expected pathology presents a unique opportunity to interrogate neurodegenerative mechanisms. We performed multimodal single-nuclei analyses of post-mortem frontal cortex from RedPenMC, including transcriptomics and global levels of chromatin marks. RedPenMC had an increased ratio of GRN-expressing microglia, higher levels of activating histone mark H3k4me3 in microglia and lower levels of the repressive chromatin marks H3k9me1 and H3k9me3 in the frontal cortex than her affected mutation carrier son and evidence of higher protein levels of progranulin in both plasma and brain homogenates. Although the study is limited to one case, the results support that restoring brain progranulin levels may be sufficient to escape neurodegeneration and FTD. In addition to previously identified modifier genes, it is possible that epigenetic marks may contribute to the increased progranulin expression in cases of reduced penetrance. These findings may stimulate similar follow-up studies and new therapeutic approaches. [ABSTRACT FROM AUTHOR]

Atherosclerosis is initiated by the accumulation of lipids in the arterial wall that trigger a complex and poorly understood network of inflammatory processes. At the same time, recent clinical findings reveal that targeting specific immune alterations in patients with cardiovascular disease (CVD) represents a promising approach to preventing recurrent cardiovascular events. In order to achieve these tailored therapies, it is critical to resolve the heterogenous environment of the atherosclerotic lesion and decipher the complex structural and functional changes which immune cells undergo throughout disease progression. Recently, single-cell approaches including single cell mass cytometry by time of flight (CyTOF), single cell RNA sequencing (scRNA-seq) and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) have emerged as valuable tools in resolving cellular plasticity within atherosclerotic lesions. In this review, we will discuss the most important insights that have been gleaned from the application of these single-cell approaches to validated experimental models of atherosclerosis. Additionally, as clinical progress in treatment of the disease depends on the translation of discoveries to human tissues, we will also examine the challenges associated with the application of single-cell approaches to human vascular tissue and the discoveries made by the initial efforts in this direction. Finally, we will analyze the advantages and limitations of dissociative single-cell approaches and how novel in-situ technologies could advance the field by allowing for the investigation of individual cells while preserving the heterogenous architecture of the atherosclerotic lesion. [Display omitted] • Atherosclerosis is initiated by the accumulation of lipids in the arterial wall that trigger a complex network of inflammatory processes. • Recent clinical trials revealed that targeting inflammation in patients with cardiovascular disease is a promising approach to preventing recurrent clinical events. • Single-cell technologies can resolve the heterogenous environment of the atherosclerotic lesion and the functional changes of several immune cells throughout disease progression and regression. [ABSTRACT FROM AUTHOR]

Despite the decades-old knowledge that males and people with diabetes mellitus (DM) are at increased risk for coronary artery disease (CAD), the reasons for this association are only partially understood. Among the immune cells involved, recent evidence supports a critical role of T cells as drivers and modifiers of CAD. CD4+ T cells are commonly found in atherosclerotic plaques. We aimed to understand the relationship of CAD with sex and DM by single-cell RNA (scRNA-Seq) and antibody sequencing (CITE-Seq) of CD4+ T cells. Peripheral blood mononuclear cells (PBMCs) of 61 men and women who underwent cardiac catheterization were interrogated by scRNA-Seq combined with 49 surface markers (CITE-Seq). CAD severity was quantified using Gensini scores, with scores above 30 considered CAD+ and below 6 considered CAD−. Four pairs of groups were matched for clinical and demographic parameters. To test how sex and DM changed cell proportions and gene expression, we compared matched groups of men and women, as well as diabetic and non-diabetic subjects. We analyzed 41,782 single CD4+ T cell transcriptomes for sex differences in 16 women and 45 men with and without coronary artery disease and with and without DM. We identified 16 clusters in CD4+ T cells. The proportion of cells in CD4+ effector memory cluster 8 (CD4T8, CCR2+ Em) was significantly decreased in CAD+, especially among DM+ participants. This same cluster, CD4T8, was significantly decreased in female participants, along with two other CD4+ T cell clusters. In CD4+ T cells, 31 genes showed significant and coordinated upregulation in both CAD and DM. The DM gene signature was partially additive to the CAD gene signature. We conclude that (1) CAD and DM are clearly reflected in PBMC transcriptomes, and (2) significant differences exist between women and men and (3) between subjects with DM and non-DM.