Heterogeneity Of Immune Cells Research Articles

Single-cell transcriptomic profiling reveals immune cell heterogeneity in acute myeloid leukaemia peripheral blood mononuclear cells after chemotherapy.

Acute myeloid leukaemia (AML) is a heterogeneous disease characterised by the rapid clonal expansion of abnormally differentiated myeloid progenitor cells residing in a complex microenvironment. However, the immune cell types, status, and genome profile of the peripheral blood mononuclear cell (PBMC) microenvironment in AML patients after chemotherapy are poorly understood. In order to explore the immune microenvironment of AML patients after chemotherapy, we conducted this study for providing insights into precision medicine and immunotherapy of AML. In this study, we used single-cell RNA sequencing (scRNA-seq) to analyse the PBMC microenvironment from five AML patients treated with different chemotherapy regimens and six healthy donors. We compared the cell compositions in AML patients and healthy donors, and performed gene set enrichment analysis (GSEA), CellPhoneDB, and copy number variation (CNV) analysis. Using scRNA-seq technology, 91,772 high quality cells of 44,950 PBMCs from AML patients and 46,822 PBMCs from healthy donors were classified as 14 major cell clusters. Our study revealed the sub-cluster diversity of T cells, natural killer (NK) cells, monocytes, dendritic cells (DCs), and haematopoietic stem cell progenitors (HSC-Prog) in AML patients under chemotherapy. NK cells and monocyte-DCs showed significant changes in transcription factor expression and chromosome copy number variation (CNV). We also observed significant heterogeneity in CNV and intercellular interaction networks in HSC-Prog cells. Our results elucidated the PBMC single-cell landscape and provided insights into precision medicine and immunotherapy for treating AML.

Interrogating drug action and immune response in organotypic glioma microenvironment.

2068 Background: The glioma tumor microenvironment is characteristic of a high level of heterogeneity and a profoundly immune suppressive phenotype as driven by the presence of glioma stem cells and abundant innate immune cells of the myeloid lineages, rendering glioblastomas (GBM) recalcitrant to conventional treatments and immunotherapies. To address the full complexity of the brain tumor with its diverse components, we have developed an organotypic slice culture system to overcome several limitations of the other preclinical models and allow interrogation of drug action and immunomodulatory agents in human GBM ex vivo. Methods: We performed extensive characterization of the organotypic glioma slice system and optimization of the ex vivo tissue culture conditions, and determined the longitudinal dynamics of slice cell viability, relative tumor versus immune cellularity, and responses to a selection of therapeutic agents and cytokines by multiplexed flow cytometry and single cell transcriptome (scRNA seq) profiling. Results: Single cell transcriptomic analysis showed that both tumor and immune cell heterogeneity were preserved ex vivo for the two-week observation time, which exhibited tumor-type specific gene expression profiles suggesting cooperative interactions among cell populations. Consistent with the findings in fresh tumor specimens and an immune suppressive microenvironment, we found a scarcity of T lymphocytes in IDH mutant gliomas, whereas abundant T cells were found in recurrent GBM slices that persisted ex vivo. Additional analyses revealed that T cell infiltration in GBM was associated with the mesenchymal-like molecular phenotype, which exhibited remarkable intratumor heterogeneity highlighting the presence of stem-like cells and a unique cell population expressing aberrant cancer genes and antigen presenting molecules. We further found that T cells in organotypic GBM slices acquired a potent effector phenotype in response to cytokine-induced reprogramming of microglia and tumor-associated macrophages. In addition, scRNA seq also revealed a population of cancer-associated fibroblast-like cells that are present in all tumors sequenced, which may function to mediate the interactions between immune and tumor cells as our data suggested. Conclusions: We demonstrated that the organotypic slice culture system we developed is compatible with genomic, pharmacological, and immunotherapeutic manipulations and provided data showing that organotypic slice culture preserves glioma microenvironment ex vivo allowing acquisition of crucial information of response of heterogeneous patient tumor tissue to the agent tested. This method represents a unique mechanism by which therapy development can be facilitated in an ex vivo setting.

Identification of cervical cancer stem cells using single-cell transcriptomes of normal cervix, cervical premalignant lesions, and cervical cancer.

Cervical cancer is the fourth leading cause of mortality among gynecological malignancies. However, the identification of cervical cancer stem cells remains unclear. We performed single-cell mRNA sequencing on ∼122,400cells from 20 cervical biopsies, including 5 healthy controls, 4 high-grade intraepithelial neoplasias, 5 microinvasive carcinomas of the cervix, and 6 invasive cervical squamous carcinomas. Bioinformatic results were validated by multiplex immunohistochemistry (mIHC) in cervical cancer tissue microarrays (TMA) (n=85). We identified cervical cancer stem cells and highlighted the functional changes in cervical stem cells during malignant transformation. The original non-malignant stem cell properties (characterized by high proliferation) gradually diminished, whereas the tumor stem cell properties (characterized by epithelial-mesenchymal transformation and invasion) were enhanced. The mIHC results of our TMA cohort confirmed the existence of stem-like cells and indicated that cluster correlated with neoplastic recurrence. Subsequently, we investigated malignant and immune cell heterogeneity in the cervical multicellular ecosystem across different disease stages. We observed global upregulation of interferon responses in the cervical microenvironment during lesion progression. Our results provide more insights into cervical premalignant and malignant lesion microenvironments. This research was supported by the Guangdong Provincial Natural Science Foundation of China (2023A1515010382), Grant 2021YFC2700603 from the National Key Research & Development Program of China and the Hubei Provincial Natural Science Foundation of China (2022CFB174 and 2022CFB893).

Heterogeneous baseline immune cell infiltration landscape as a predictor of pathological complete response in locally advanced esophageal squamous cell carcinoma (ESCC) following neoadjuvant chemotherapy and immunotherapy: Results from a single-arm, phase II clinical trial (SEEK-01).

4047 Background: Neoadjuvant immunotherapy and chemotherapy have shown promise in improving outcomes for patients with locally advanced ESCC, but the response to treatment can vary among patients. Therefore, there is a need to identify predictive biomarkers that can guide treatment decisions and improve patient outcomes. The infiltration of immune cells within the tumor microenvironment has been shown to be an important factor in cancer progression and response to treatment. In this study, we sought to explore the immune microenvironment prospectively related to treatment response of Neoadjuvant immunotherapy by scRNAseq. Methods: This was a single-arm phase II clinical trial, planned to enroll 20 locally advanced ESCC patients (cT2-4N0-1M0). After neoadjuvant chemotherapy (paclitaxel-albumin + carboplatin) combined with tislelizumab, patients received minimal invasive esophagectomy (MIE).Prior to neoadjuvant therapy, fresh tumor tissues were obtained via endoscopy and used for scRNAseq analysis, to evaluate the predictive capacity of the immune microenvironment. Pathological outcomes were assessed and divided into pCR, MPR defined as <10% residual tumor excluding pCR, and non-MPR defined as >10%. Results: Out of 18 enrolled patients, 15 completed neoadjuvant therapy and MIE surgery since March 2022. Of these 15 patients, 3 achieved pCR, 2 got MPR, and 10 were non-MPR, with a total of 51,208 single cells sequenced. Both groups showed similarly low expression of PD-L1 (5.7% vs. 2.3%). Comparing pCR to non-MPR patients, the former showed a significant increase in T lymphocytes (CD8+(17.1% vs.1.1%), Regulatory T(20.2% vs.4.0%)), plasma cell(22.0% vs.8.3%), and mature dendritic cell(1.5% vs.0.5%); a significant decrease in B cells(0.9% vs. 1.9%), neutrophils(1.7% vs.11.0%), and tumor cell, as well as a similar expression of macrophage(5.6% vs.6.6%) and monocyte, indicating a clear difference in the immune cell infiltration landscape between two groups. Furthermore, we identified a new subtype of plasma cell which was highly enriched in the non-MPR group, even though the pCR group showed a great increase in plasma cells in total. Conclusions: This study demonstrates the heterogeneity of baseline immune cell infiltration landscape in Locally Advanced ESCC, in which pCR and non-MPR patients exhibited entirely different profiles, and provide a potential predictor of response to neoadjuvant immunotherapy. Our findings suggest that baseline immune cell infiltration status, rather than pd-L1 expression, could accurately predict pCR or non-MPR before treatment, which could guide personalizing and minimize the need for trial-and-error approaches.

Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA.

Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.

Thresholding CITE-Seq-based Antibody Expression Data to Improve Cell Type Identification in scRNA-Seq

Abstract scRNA-seq is widely used to assess immune cell heterogeneity by measuring individual cell transcriptomes. Surface markers have been used for decades to type immune cells by flow and more recently mass cytometry. Expression of surface markers is poorly correlated with the mRNA levels of their encoding transcripts. CITE-Seq provides a systematic approach to analyze surface phenotypes along with cell transcriptomes by using oligonucleotide-tagged antibodies. Like flow cytometry, CITE-seq-based assessment of surface markers is complicated by non-specific binding. Here, we introduce and validate a systematic method of thresholding the levels of protein marker expression to address the issue of non-specific antibody binding. To remove the non-specific signal, a threshold value separating noise from expression for each surface marker is obtained by comparing the antibody signal in a cell type known to not express this surface marker. If no negative cell type is available, we use deconvolution of overlapping normal distributions to find the best separator in density plots (Ridgeline plots) by deconvolution (normalmixEM tool) of overlapping normal distributions. In total, 34 of 49 antibodies tested were reliably thresholded, thus effectively removing the noise from the signal. In a validation experiment testing 188 antibodies at 4 different concentrations, 124 were successfully thresholded, including all 34 mentioned above. Supported by the National Institutes of Health (NIH) P01 HL136275

Integrated Proteomics and Single-Cell Mass Cytometry Analysis Dissects the Immune Landscape of Ankylosing Spondylitis.

Mass cytometry is a powerful single-cell technology widely adopted to depict immune cell heterogeneity in different contexts. However, this method is only capable of examining several dozens of proteins simultaneously and requires a prior knowledge of the markers to be analyzed. Here we propose that the integration of mass cytometry with shot-gun proteomics may serve as a valuable tool to achieve an in-depth understanding of the immune system. By implementing such a strategy, we investigated the immune landscape of ankylosing spondylitis (AS), a chronic inflammatory arthritis with unclear etiology. The proteome alteration in peripheral blood mononuclear cells (PBMCs) was investigated by quantitative proteomics, and then mass cytometry analysis was conducted to decipher the immunome by considering the signaling molecules identified with differential expression by proteomics. As a result, we identified a wide spectrum of proteins dysregulated in AS, e.g., upregulation of glycolytic enzymes, downregulation of lipid transporters, and dysregulation of chemokine signaling molecules involved in proinflammatory cytokine production and leucocyte migration. Moreover, the single-cell analysis showed the upregulation of chemokine signaling regulators in subclusters of both innate and adaptive immune cells in AS. In addition, correlation analysis unveiled the interplay among Phenograph-identified subclusters of monocytes, CD4+ T cells, and CD8+ T cells. Taken together, our findings demonstrated that the integration of mass spectrometry-based proteomics and single-cell mass cytometry may serve as a useful tool to reveal clinically relevant information regarding useful targets and cellular phenotypes that could be further exploited to develop novel therapeutic strategies.

Conserved Transcriptional Signatures of Microglia in Health and Disease (P2-3.011)

<h3>Objective:</h3> To determine signatures of microglia in humans and mice in the context of central nervous system (CNS) inflammation and homeostasis. <h3>Background:</h3> Single cell profiling of gene expression has illuminated the heterogeneity of immune cells and accessible phenotypes across organs and under a myriad of conditions. These efforts—typified by recent studies aiming to identify signatures of microglia—typically entail pre-enrichment of cell populations, even from distinct species, using a limited set of surface markers or morphological traits. This step introduces potential bias and subjectivity that may alter the gene expression states that are discerned. <h3>Design/Methods:</h3> We queried the NCBI GEO for datasets of single-cell transcription within CNS tissue from healthy subjects, multiple sclerosis (MS) patients, and mice afflicted with experimental autoimmune encephalomyelitis (EAE). In total, this yielded data on over 135,000 human cells—encompassing a spectrum of lesion chronicity in patients with progressive and relapsing-remitting MS—and 12,000 murine cells. Datasets were pre-processed for quality control and harmonized according to species prior to cross-species integration. We developed classifier models of microglia using logistic regression and support vector machine frameworks. <h3>Results:</h3> Unexpectedly, markers closely linked in the literature to microglia, including Iba-1, were found to be expressed in non-microglial myeloid cells. Other markers previously ascribed to microglia in one species, such as Hexb, failed to represent useful markers in the other species. We identified intra- and inter-species signatures of microglia using machine-learning methods that form the basis of future studies. <h3>Conclusions:</h3> Whole-brain, single-cell gene expression data, unencumbered by bias introduced by prior cell enrichment, enable identification of transcriptional signatures of cell types across species. Signatures of microglia during CNS inflammation identify potentially targetable pathways to understand their role in disease and limit tissue injury. <b>Disclosure:</b> The institution of an immediate family member of Dr. Salinas has received research support from Veterans Health Administration. Navid Manouchehri has nothing to disclose. Rehana Hussain has nothing to disclose. Dr. Stuve has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for EMD Serono. Dr. Stuve has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for Novartis. Dr. Stuve has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for Roche Genentech. Dr. Stuve has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for TG Therapeutics. Dr. Stuve has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for TG Therapeutics. Dr. Stuve has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for EMD Serono. Dr. Stuve has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for VYNE. Dr. Stuve has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Therapeutic Advances in Neurological Diseases. Dr. Stuve has received research support from US Department of Veterans Affairs. Dr. Stuve has received research support from National Multiple Sclerosis Society (US). Dr. Stuve has received research support from Exalys. Dr. Stuve has received research support from Merck KGaA.

Effects of Immune Cell Heterogeneity and Protein Corona on the Cellular Association and Cytotoxicity of Gold Nanoparticles: A Single-Cell-Based, High-Dimensional Mass Cytometry Study.

Understanding how nanoparticles (NPs) interact with biological systems is important in many biomedical research areas. However, the heterogeneous nature of biological systems, including the existence of numerous cell types and multitudes of key environmental factors, makes these interactions extremely challenging to investigate precisely. Here, using a single-cell-based, high-dimensional mass cytometry approach, we demonstrated that the presence of protein corona has significant influences on the cellular associations and cytotoxicity of gold NPs for human immune cells, and those effects vary significantly with the types of immune cells and their subsets. The altered surface functionality of protein corona reduced the cytotoxicity and cellular association of gold NPs in most cell types (e.g., monocytes, dendritic cells, B cells, natural killer (NK) cells, and T cells) and those immune cells selected different endocytosis pathways such as receptor-mediated endocytosis, phagocytosis, and micropinocytosis. However, even slight alterations in the major cell type (phagocytic cells and non-phagocytic cells) and T cell subsets (e.g., memory and naive T cells) resulted in significant protein corona-dependent variations in their cellular dose of gold NPs. Especially, naive T killer cells exhibited additional heterogeneity than memory T killer cells, with clusters exhibiting distinct cellular association patterns in single-cell contour plots. This multi-parametric analysis of mass cytometry data established a conceptual framework for a more holistic understanding of how the human immune system responds to external stimuli, paving the way for the application of precisely engineered NPs as promising tools of nanomedicine under various clinical settings, including targeted drug delivery and vaccine development.

Abstract 6664: Comprehensive immunoprofiling of peripheral blood reveals five conserved immunotypes with implications for immunotherapy in cancer patients

Abstract Recent advances in immunotherapy demonstrate the need to further understand the characteristics of an individual cancer patient’s immune system and how it influences responses to cancer treatment. Here, we developed an immunoprofiling platform to evaluate the features in the blood of cancer patients to test the hypothesis that peripheral immune cell heterogeneity could be used to stratify these patients into different categories or immunotypes to monitor disease progression and treatment response. To that end, we established a unique diagnostic immunoprofiling assay and analytical framework based on the analysis of leukocytes in the peripheral blood using multiparameter flow cytometry. Supervised manual gating of flow cytometry data from a cohort of 50 healthy donors identified 415 cell types and immune activation states that were used to train and later independently validate machine learning models to automatically identify immune cell subsets from raw cytometry data. By applying this tool to peripheral blood samples from a mixed cohort of 299 healthy donors and 323 cancer patients, we developed a machine-learning classification model that can differentiate between these two groups with 93% accuracy. This model was further refined using spectral clustering with bootstrapping, revealing 5 clusters, or immunotypes, characterized by specific physiological immune profiles: (1) Myeloid-derived suppressor/NK cell, (2) Terminally-differentiated CD8+ T cells, (3) Mixed CD4+ T helper cells, (4) CD4+ Th1 &amp; CD8+ T cell memory, and (5) Naive T and B lymphocytes. Interestingly, very few healthy donors could be found in clusters 1 and 2 but were assigned most frequently to cluster 5. Matched RNA-seq was used to further validate these profiles using the cellular deconvolution algorithm, Kassandra, and differential gene expression analysis revealed immunotype-specific signatures that are consistent with immune response potential. Patients in the terminally-differentiated CD8+ T cell cluster had a narrower range of HLA-types than the other clusters, and TCR repertoire analysis indicated significantly increased clonality and reduced clonotype diversity. Within this cluster there was a high degree of overlap between TCR sequences in the peripheral blood and the tumor, indicating a relationship between peripheral blood immunotype and tumor infiltration. Altogether, the establishment of these immunotypes using peripheral blood immunoprofiling represents a promising signature that can be used to identify and stratify cancer patients that will benefit from immune-based therapies. Citation Format: Daniiar Dyikanov, Iris Wang, Tatiana Vasileva, Polina Shpudeiko, Polina Turova, Arseniy A. Sokolov, Olga Golubeva, Evgenii Tikhonov, Anna Kamysheva, Ilya Krauz, Mary Abdou, Madison Chasse, Tori Conroy, Nicholas R. Merriam, Boris Shpak, Anastasia Radko, Anastasiia Kilina, Lira Nigmatullina, Linda Balabanian, Christopher J. Davitt, Alexander A. Ryabykh, Olga Kudryashova, Cagdas Tazearslan, Ravshan Ataullakhanov, Alexander Bagaev, Aleksandr Zaitsev, Nathan Fowler, Michael F. Goldberg. Comprehensive immunoprofiling of peripheral blood reveals five conserved immunotypes with implications for immunotherapy in cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6664.

Exosomal miRNAs-mediated macrophage polarization and its potential clinical application.

Macrophages are highly heterogeneous and plastic immune cells that play an important role in the fight against pathogenic microorganisms and tumor cells. After different stimuli, macrophages can polarize to the M1 phenotype to show a pro-inflammatory effect and the M2 phenotype to show an anti-inflammatory effect. The balance of macrophage polarization is highly correlated with disease progression, and therapeutic approaches to reprogram macrophages by targeting macrophage polarization are feasible. There are a large number of exosomes in tissue cells, which can transmit information between cells. In particular, microRNAs (miRNAs) in the exosomes can regulate the polarization of macrophages and further affect the progression of various diseases. At the same time, exosomes are also effective "drug" carriers, laying the foundation for the clinical application of exosomes. This review describes some pathways involved in M1/M2 macrophage polarization and the effects of miRNA carried by exosomes from different sources on the polarization of macrophages. Finally, the application prospects and challenges of exosomes/exosomal miRNAs in clinical treatment are also discussed.

Label-free macrophage phenotype classification using machine learning methods

Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.

Transcriptional control of ILC identity.

Innate lymphoid cells (ILCs) are heterogeneous innate immune cells which participate in host defense, mucosal repair and immunopathology by producing effector cytokines similarly to their adaptive immune cell counterparts. The development of ILC1, 2, and 3 subsets is controlled by core transcription factors: T-bet, GATA3, and RORγt, respectively. ILCs can undergo plasticity and transdifferentiate to other ILC subsets in response to invading pathogens and changes in local tissue environment. Accumulating evidence suggests that the plasticity and the maintenance of ILC identity is controlled by a balance between these and additional transcription factors such as STATs, Batf, Ikaros, Runx3, c-Maf, Bcl11b, and Zbtb46, activated in response to lineage-guiding cytokines. However, how interplay between these transcription factors leads to ILC plasticity and the maintenance of ILC identity remains hypothetical. In this review, we discuss recent advances in understanding transcriptional regulation of ILCs in homeostatic and inflammatory conditions.

New Insights into the Role of Synovial Fibroblasts Leading to Joint Destruction in Rheumatoid Arthritis

Rheumatoid arthritis (RA), one of the most common autoimmune diseases, is characterized by multiple-joint synovitis with subsequent destruction of bone and cartilage. The excessive autoimmune responses cause an imbalance in bone metabolism, promoting bone resorption and inhibiting bone formation. Preliminary studies have revealed that receptor activator of NF-κB ligand (RANKL)-mediated osteoclast induction is an important component of bone destruction in RA. Synovial fibroblasts are the crucial producers of RANKL in the RA synovium; novel analytical techniques, primarily, single-cell RNA sequencing, have confirmed that synovial fibroblasts include heterogeneous subsets of both pro-inflammatory and tissue-destructive cell types. The heterogeneity of immune cells in the RA synovium and the interaction of synovial fibroblasts with immune cells have recently received considerable attention. The current review focused on the latest findings regarding the crosstalk between synovial fibroblasts and immune cells, and the pivotal role played by synovial fibroblasts in joint destruction in RA.

Single-Cell Analyses of the Oral Mucosa Reveal Immune Cell Signatures

Inflammatory bowel disease (IBD) is a common immune-related disease of the gastrointestinal tract that affects many people around the world. Extraintestinal manifestations of IBD have been frequently observed in recent years; one of these, periodontitis, has gained increasing attention. Periodontitis is a chronic inflammatory disease characterized by inflammation and destruction of periodontal tissues due to the disruption of host immune homeostasis. Clinical studies have revealed that periodontal inflammation is associated with IBD. However, the detailed heterogeneity of immune cells and their developmental relationships remain poorly understood at the single-cell level. In this study, we performed single-cell RNA (scRNA) sequencing to assess the transcriptome heterogeneity in periodontal tissues. We found the cellular composition and subclusters with specific gene expression profiles by uniform manifold approximation and projection. Pseudo-time analysis combined with gene enrichment analysis was performed to reveal cell states and key pathways. Ligand–receptor pairs revealed cell–cell communication among the immune cell types in periodontal tissues. Based on our analysis, we identified an essential role for Tcr+ macrophage, Prdx1+ neutrophil, and Mif+ T subpopulations with proinflammatory phenotype infiltration. Moreover, we examined the heterogeneity of monocytic cells and B cells. Collectively, the mapping of scRNA revealed the complex cellular landscape of oral mucosa immune cells and highlighted these immune cells as a previously unrecognized factor that may aggravate inflammation. Our analysis proves that periodontitis could exacerbate colitis and provides novel ideas for controlling and preventing IBD exacerbations.

Abstract 15: Single-cell Immune Landscape Of Human Clot Retrieved At Mechanical Thrombectomy. Association With Stroke Origin.

Background: Mechanical thrombectomy (MT) has significantly improved clinical outcomes in acute ischemic stroke. We used single-cell RNA sequencing (scRNA-seq) to describe the landscape of human clots at single cell resolution seeking to provide greater mechanistic insight into stroke pathophysiology and to uncover an association with stroke origin. Methods: scRNA-seq was performed on 14 clots retrieved from 14 patients undergoing MT between October 2020-July 2022. We followed standard molecular biology and bioinformatics workflows for sample processing and data to characterize differences in cellular composition between clots of different stroke origin (atrial fibrillation (AF) and carotid atherosclerosis (CA)). Results: Clots revealed a significant heterogeneity of immune cells with major constituents including cells of the mononuclear phagocyte system (MPS) and neutrophils. The cellular populations most enriched in CA clots were B-cells and CD4+ T-cells. The cellular population most enriched in AF clots was red blood cells. When looking at cellular subpopulations of the MPS component, we found that CD16+ monocytes (intermediate and non-classical) were significantly more abundant than CD14+CD16- (classical) monocytes in all clots. Moreover, the proportions of CD16+ monocytes varied with stroke origin: the proportion of non-classical (CD14-CD16+) monocytes was higher in CA clots (p=0.05) whereas the proportion of intermediate monocytes (CD14+CD16+) was higher in FA clots (p=0.03). AF clots had upregulated biomarkers and pathways involved with AF or contractile dysfunction (IFI30, FABP4, PPAR, HOTAIR, ILK, Integrin). CA clots had upregulated biomarkers and pathways associated with cardiovascular diseases and atherogenesis (RNSE1, POLR2J3.1, PLTP, Sirtuin, FAK). (Figure) Conclusions: These results reveal a significant heterogeneity of immune cells and point to important differences in cellular composition associated with stroke etiology.

The role of immune cells and associated immunological factors in the immune response to spinal cord injury.

Spinal cord injury (SCI) is a devastating neurological condition prevalent worldwide. Where the pathological mechanisms underlying SCI are concerned, we can distinguish between primary injury caused by initial mechanical damage and secondary injury characterized by a series of biological responses, such as vascular dysfunction, oxidative stress, neurotransmitter toxicity, lipid peroxidation, and immune-inflammatory response. Secondary injury causes further tissue loss and dysfunction, and the immune response appears to be the key molecular mechanism affecting injured tissue regeneration and functional recovery from SCI. Immune response after SCI involves the activation of different immune cells and the production of immunity-associated chemicals. With the development of new biological technologies, such as transcriptomics, the heterogeneity of immune cells and chemicals can be classified with greater precision. In this review, we focus on the current understanding of the heterogeneity of these immune components and the roles they play in SCI, including reactive astrogliosis and glial scar formation, neutrophil migration, macrophage transformation, resident microglia activation and proliferation, and the humoral immunity mediated by T and B cells. We also summarize findings from clinical trials of immunomodulatory therapies for SCI and briefly review promising therapeutic drugs currently being researched.

Single-Cell RNA Sequencing Maps Immune Cell Heterogeneity in Mice with Allogeneic Cardiac Transplantation

Objective: Immune cells play important roles in mediating allograft rejection and tolerance after cardiac transplantation. However, immune cell heterogeneity at the single-cell level, and how immune cell states shape transplantation immunity, remain incompletely characterized. Methods: We performed single-cell RNA sequencing (scRNA-seq) on immune cells in LNs from a mouse syngeneic and allogeneic cardiac transplantation model. Nine T cell clusters were identified through unsupervised analysis. Pathway enrichment analysis was used to explore the functional differences among cell subpopulations and to characterize the metabolic heterogeneity of T cells. Results: We comprehensively determined the transcriptional landscape of immune cells, particularly T cells, and their metabolic transcriptomes in LNs during mouse cardiac transplantation. On the basis of molecular and functional properties, we also identified T cell types associated with transplantation-associated immune processes, including cytotoxic CD8+ T cells, activated conventional CD4+ T cells, and dysfunctional Tregs. We further elucidated the contribution of JunB to the induction of Th17 cell differentiation and restriction of Treg development, and identified that HIF-1a participates in T cell metabolism and function. Conclusions: We present the first systematic single-cell analysis of transcriptional variation within the T cell population, providing new insights for the development of novel therapeutic targets for allograft rejection.

Personalized Immunotherapy of Patients: Defining by Single-cell RNA-seq with Artificial Intelligence.

Immunotherapy, including immune cell therapy and targeted therapy, is gradually developed through the ongoing discovery of molecular compounds or immune cells. Choosing the best one or the best combination of target compounds and immune-cell therapy is a challenge for clinical scientists and clinicians. We have found variable efficacy individually after tumor-infiltrating lymphocyte (TIL) therapy, and now TILs have been discovered in a group of heterogeneous immune cells. To select the best immunotherapy for each patient, we started to study TIL genomics, including single-cell mRNA differential display from TIL published in 2007 and single-cell RNA-seq from TIL published in 2013, set up TIL quantitative network in 2015, researched machine-learning model for immune therapy in 2022. These manual reports single-cell RNA-seq data combined with machine learning to evaluate the optimal compounds and immune cells for individual patients. The machine-learning model, one of artificial intelligence, can estimate targeting genomic variance from single-cell RNA-seq so that they can cover thirteen kinds of immune cell therapies and ongoing FDA-approved targeted therapies such as PD1 inhibitors, PDL1 inhibitors, and CTLA4 inhibitors, as well as other different treatments such as HDACI or DNMT1 inhibitors, FDA-approved drugs. Moreover, also cover Phase-1, Phase-2, Phase-3, and Phase-4 of clinical trials, such as TIL, CAR T-cells, TCR T-cells. Single-cell RNA-seq with an Artificial intelligence estimation system is much better than our published models from microarrays or just cell therapy. The medical goal is to address three issues in clinical immunotherapy: the increase of efficacy; the decrease of adverse effects and the decrease of the cost in clinical applications.

Specialized functions and sexual dimorphism explain the functional diversity of the myeloid populations during glioma progression

Malignant gliomas are aggressive, hard-to-treat brain tumors. Their tumor microenvironment is massively infiltrated by myeloid cells, mostly brain-resident microglia, bone marrow (BM)-derived monocytes/macrophages, and dendritic cells that support tumor progression. Single-cell omics studies significantly dissected immune cell heterogeneity, but dynamics and specific functions of individual subpopulations were poorly recognized. We use Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) to precisely dissect myeloid cell identities and functionalities in murine GL261 gliomas. We demonstrate that the diversity of myeloid cells infiltrating gliomas is dictated by cell type and cell state. Glioma-activated microglia are the major source of cytokines attracting other immune cells, whereas BM-derived cells show the monocyte-to-macrophage transition in the glioma microenvironment. This transition is coupled with a phenotypic switch from the IFN-related to antigen-presentation and tumor-supportive gene expression. Moreover, we found sex-dependent differences in transcriptional programs and composition of myeloid cells in murine and human glioblastomas.