scholarly journals Global immune characterization of HBV/HCV-related hepatocellular carcinoma identifies macrophage and T-cell subsets associated with disease progression

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Guohe Song ◽  
Yang Shi ◽  
Meiying Zhang ◽  
Shyamal Goswami ◽  
Saifullah Afridi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
T Cell Subsets ◽  
M2 Macrophage ◽  
Advanced Hcc ◽  
Immune Cell Subsets

AbstractDiverse immune cells in the tumor microenvironment form a complex ecosystem, but our knowledge of their heterogeneity and dynamics within hepatocellular carcinoma (HCC) still remains limited. To assess the plasticity and phenotypes of immune cells within HBV/HCV-related HCC microenvironment at single-cell level, we performed single-cell RNA sequencing on 41,698 immune cells from seven pairs of HBV/HCV-related HCC tumors and non-tumor liver tissues. We combined bio-informatic analyses, flow cytometry, and multiplex immunohistochemistry to assess the heterogeneity of different immune cell subsets in functional characteristics, transcriptional regulation, phenotypic switching, and interactions. We identified 29 immune cell subsets of myeloid cells, NK cells, and lymphocytes with unique transcriptomic profiles in HCC. A highly complex immunological network was shaped by diverse immune cell subsets that can transit among different states and mutually interact. Notably, we identified a subset of M2 macrophage with high expression of CCL18 and transcription factor CREM that was enriched in advanced HCC patients, and potentially participated in tumor progression. We also detected a new subset of activated CD8+ T cells highly expressing XCL1 that correlated with better patient survival rates. Meanwhile, distinct transcriptomic signatures, cytotoxic phenotypes, and evolution trajectory of effector CD8+ T cells from early-stage to advanced HCC were also identified. Our study provides insight into the immune microenvironment in HBV/HCV-related HCC and highlights novel macrophage and T-cell subsets that could be further exploited in future immunotherapy.

Single Cell Profiling Reveals Unique CXCL13 Positive T Cell Subsets in the Tumor Microenvironment of Lymphocyte Rich Classic Hodgkin Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-33
Author(s):  
Tomohiro Aoki ◽  
Lauren C. Chong ◽  
Katsuyoshi Takata ◽  
Katy Milne ◽  
Elizabeth Chavez ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
B Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Expression Patterns ◽  
The Other ◽  
Tfh Cells

Introduction: Classic Hodgkin lymphoma (CHL) features a unique crosstalk between malignant cells and different types of normal immune cells in the tumor-microenvironment (TME). On the basis of histomorphologic and immunophenotypic features of the malignant Hodgkin and Reed-Sternberg (HRS) cells and infiltrating immune cells, four histological subtypes of CHL are recognized: Nodular sclerosing (NS), Mixed cellularity, Lymphocyte-rich (LR) and Lymphocyte-depleted CHL. Recently, our group described the high abundance of various types of immunosuppressive CD4+ T cells including LAG3+ and/or CTLA4+ cells in the TME of CHL using single cell RNA sequencing (scRNAseq). However, the TME of LR-CHL has not been well characterized due to the rarity of the disease. In this study, we aimed at characterizing the immune cell profile of LR-CHL at single cell resolution. METHODS: We performed scRNAseq on cell suspensions collected from lymph nodes of 28 primary CHL patients, including 11 NS, 9 MC and 8 LR samples, with 5 reactive lymph nodes (RLN) serving as normal controls. We merged the expression data from all cells (CHL and RLN) and performed batch correction and normalization. We also performed single- and multi-color immunohistochemistry (IHC) on tissue microarray (TMA) slides from the same patients. In addition, an independent validation cohort of 31 pre-treatment LR-CHL samples assembled on a TMA, were also evaluated by IHC. Results: A total of 23 phenotypic cell clusters were identified using unsupervised clustering (PhenoGraph). We assigned each cluster to a cell type based on the expression of genes described in published transcriptome data of sorted immune cells and known canonical markers. While most immune cell phenotypes were present in all pathological subtypes, we observed a lower abundance of regulatory T cells (Tregs) in LR-CHL in comparison to the other CHL subtypes. Conversely, we found that B cells were enriched in LR-CHL when compared to the other subtypes and specifically, all four naïve B-cell clusters were quantitatively dominated by cells derived from the LR-CHL samples. T follicular helper (TFH) cells support antibody response and differentiation of B cells. Our data show the preferential enrichment of TFH in LR-CHL as compared to other CHL subtypes, but TFH cells were still less frequent compared to RLN. Of note, Chemokine C-X-C motif ligand 13 (CXCL13) was identified as the most up-regulated gene in LR compared to RLN. CXCL13, which is a ligand of C-X-C motif receptor 5 (CXCR5) is well known as a B-cell attractant via the CXCR5-CXCL13 axis. Analyzing co-expression patterns on the single cell level revealed that the majority of CXCL13+ T cells co-expressed PD-1 and ICOS, which is known as a universal TFH marker, but co-expression of CXCR5, another common TFH marker, was variable. Notably, classical TFH cells co-expressing CXCR5 and PD-1 were significantly enriched in RLN, whereas PD-1+ CXCL13+ CXCR5- CD4+ T cells were significantly enriched in LR-CHL. These co-expression patterns were validated using flow cytometry. Moreover, the expression of CXCR5 on naïve B cells in the TME was increased in LR-CHL compared to the other CHL subtypes We next sought to understand the spatial relationship between CXCL13+ T cells and malignant HRS cells. IHC of all cases revealed that CXCL13+ T cells were significantly enriched in the LR-CHL TME compared to other subtypes of CHL, and 46% of the LR-CHL cases showed CXCL13+ T cell rosettes closely surrounding HRS cells. Since PD-1+ T cell rosettes are known as a specific feature of LR-CHL, we confirmed co-expression of PD-1 in the rosetting cells by IHC in these cases. Conclusions: Our results reveal a unique TME composition in LR-CHL. LR-CHL seems to be distinctly characterized among the CHL subtypes by enrichment of CXCR5+ naïve B cells and CD4+ CXCL13+ PD-1+ T cells, indicating the importance of the CXCR5-CXCL13 axis in the pathogenesis of LR-CHL. Figure Disclosures Savage: BeiGene: Other: Steering Committee; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy. Scott:Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy. Steidl:AbbVie: Consultancy; Roche: Consultancy; Curis Inc: Consultancy; Juno Therapeutics: Consultancy; Bayer: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding.

scholarly journals Potent Anti-Tumor Activity of Bcma CAR-T Therapy Against Heavily Treated Multiple Myeloma and Dynamics of Immune Cell Subsets Using Single-Cell Mass Cytometry

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1859-1859 ◽  
Author(s):  
Yongxian Hu ◽  
Zhang Yanlei ◽  
Guoqing Wei ◽  
Chang alex Hong ◽  
He Huang
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Mass ◽  
Mass Cytometry ◽  
Car T Cells ◽  
Car T Cell ◽  
Immune Cell Subsets ◽  
Car T

Background BCMA CAR-T cells have demonstrated substantial clinical activity against relapsed/refractory multiple myeloma (RRMM). In different clinical trials, the overall response rate (ORR) varied from 50% to 100%. Complete remission (CR) rate varied from 20% to 80%. Here we developed a BCMA CAR-T cell product manufactured via lentiviral vector-mediated transduction of activated T cells to express a second-generation CAR with 4-1BB costimulatory domain and evaluated the efficacy and safety, moreover, dynamics of immune cell subsets using single-cell mass cytometry during treatment were analyzed. Methods Our trial (ChiCTR1800017404) is a phase 1, single-arm, open-label single center study to evaluate the safety and efficacy of autologous BCMA CAR-T treatment for RRMM. Patients were subjected to a lymphodepleting regimen with Flu and Cy prior to CAR-T infusion. BCMA CAR-T cells were administered as a single infusion at a median dose of 3.5 (1 to 6) ×106/kg. MM response assessment was conducted according to the International Uniform Response Criteria. Cytokine-release syndrome (CRS) was graded as Lee DW et al described (Blood.2014;124(2):188-195). Phenotypic analysis of peripheral blood mononuclear cells (PBMCs), frozen BCMA CAR-T aliquots, phenotype and in vivo kinetics of immune cell subsets after CAR-T infusion were performed by single-cell mass cytometry. Results As of the data cut-off date (August 1st, 2019), 33 patients, median age 62.5 (49 to 75) years old were infused with BCMA CAR-T cells. The median observation period is 8.0 (0.7 to 18) months. ORR was 100% (The patient who died of infection at 20 days after CAR-T infusion were excluded). All the 32 patients achieved MRD negative in bone marrow by flow cytometry in 2 weeks after CAR-T infusion. Partial response (4 PR, 12.1%), VGPR (7 VGPR, 21.2%), and complete response (21 CR, 63.6%) within 12 weeks post CAR-T infusion were achieved. Durable responses from 4 weeks towards the data cut-off date were found in 28/33 patients (84.8%) (Figure 1a). All patients had detectable CAR-T expansion by flow cytometry from Day 3 post CAR-T cell infusion. The peak CAR-T cell expansion in CD3+ lymphocytes of peripheral blood (PB) varied from 35% to 95% with a median percentage of 82.9%. CRS was reported in all the 33 patients, including 4 with Grade 1, 13 with Grade 2 and 16 with Grade 3. During follow-up, 1-year progression-free survival (PFS) was 70.7% (Figure 1b) and overall survival (OS) was 71.7% (Figure 1c). Multivariate analysis of patients with PR and patients with CR+VGPR revealed that factors including extramedullary infiltration, age>60 years old, high-risk cytogenetics, late stage and CAR-T cell dose were not associated with clinical response (P>0.05). Single-cell mass cytometry revealed that the frequency of total T cells, CD8+ T cells, NK cells and CD3+CD56+ NKT cells in PB was not associated with BCM CAR-T expansion or clinical response. CD8+ Granzyme B+ Ki-67+ CAR-T cells expanded prominently in CRS period. As serum cytokines increased during CRS, non-CAR-T immune cell subsets including PD1+ NK cells, CD8+ Ki-67+ ICOS+ T cells expanded dominantly implying that non-CAR-T cells were also activated after CAR-T treatment. After CRS, stem cell like memory CAR-T cells (CD45RO+ CCR7- CD28- CD95+) remain the main subtype of CAR-T cells (Figure 1d). Conclusions Our data showed BCMA CAR-T treatment is safe with prominent efficacy which can overcome the traditional high-risk factors. We also observed high expansion level and long-term persistence of BCMA CAR-T cells contribute to potent anti-myeloma activity. Stem cell like memory CAR-T cells might be associated with long-term persistence of BCMA CAR-T cells. These initial data provide strong evidence to support the further development of this anti-myeloma cellular immunotherapy. Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing

2020 ◽  
Author(s):  
Gang Xu ◽  
Furong Qi ◽  
Hanjie Li ◽  
Qianting Yang ◽  
Haiyan Wang ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Close Association ◽  
Suppressor Cells ◽  
T Cell Subsets ◽  
Peripheral Blood Mononuclear ◽  
Peripheral T Cells

Understanding the mechanism that leads to immune dysfunction induced by SARS-CoV2 virus is crucial to develop treatment for severe COVID-19. Here, using single cell RNA-seq, we characterized the peripheral blood mononuclear cells (PBMC) from uninfected controls and COVID-19 patients, and cells in paired broncho-alveolar lavage fluid (BALF). We found a close association of decreased dendritic cells (DC) and increased monocytes resembling myeloid-derived suppressor cells (MDSC) which correlated with lymphopenia and inflammation in the blood of severe COVID-19 patients. Those MDSC-like monocytes were immune-paralyzed. In contrast, monocyte-macrophages in BALFs of COVID-19 patients produced massive amounts of cytokines and chemokines, but secreted little interferons. The frequencies of peripheral T cells and NK cells were significantly decreased in severe COVID-19 patients, especially for innate-like T and various CD8+ T cell subsets, compared to health controls. In contrast, the proportions of various activated CD4+ T cell subsets, including Th1, Th2 and Th17-like cells were increased and more clonally expanded in severe COVID-19 patients. Patients' peripheral T cells showed no sign of exhaustion or augmented cell death, whereas T cells in BALFs produced higher levels of IFNG, TNF, CCL4 and CCL5 etc. Paired TCR tracking indicated abundant recruitment of peripheral T cells to the patients' lung. Together, this study comprehensively depicts how the immune cell landscape is perturbed in severe COVID-19.

Paired Single Cell RNA and TCR Sequencing Analysis Following PD-L1 Blockade in Cutaneous T-Cell Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 21-22
Author(s):  
Christiane Querfeld ◽  
Xiwei Wu ◽  
Hanjun Qin ◽  
Chingyu Su ◽  
Zhen Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Differential Expression ◽  
Cell Population ◽  
Immune Cells ◽  
Cd4 T Cells ◽  
T Cell Subsets ◽  
Malignant T Cells

Introduction: T cell exhaustion is a hallmark of CTCL and alterations in mRNA profiles correlate with immune checkpoint expression, with potential clinical relevance (Querfeld et al. 2018). There is no immunophenotypic marker that can distinguish malignant CD4+ T cells from benign CD4+ T cells in the infiltrate and intratumoral heterogeneity poses a major challenge to treatments and long-term remissions. The microenvironment in CTCL harbors multiple immune cells that may contribute to the development of resistance to drug treatments; however, the genomic and molecular determinants of response to therapeutic agents remain incompletely understood. The aim of our study was to distinguish malignant from non-malignant T cells based on TCR α/β repertoires and to understand the transcriptional landscapes of malignant and non-malignant cells in the TME while on anti-PD-L1 therapy. Methods: Migrated cells from skin explants were harvested and subsequently analyzed by our paired single-cell RNA and T cell receptor (TCR; alpha/beta) sequencing on ~3000-4000 cells from skin lesions of 6 patients with mycosis fungoides at baseline and cycle 1 day 15 with anti-PD-L1 + lenalidomide. Results: We identified 14 gene clusters. Differential expression (DE) of genes in each of the unique clusters were identified by comparing gene expression from cells in each cluster to that of all other cells in the dataset, using a cut-off of P < 0.05 and further requiring expression of the gene in >25% of cells in the cluster. Thus, DE-identified genes are expressed either uniquely or by a large proportion of cells within each cluster compared to all other clusters. TCR clones in these cells were also characterized. Through this combined analysis, we demonstrated differences in the diversity, clonal expansion and T cell phenotypes that differentiated expanded malignant T cell populations (cluster 0-3) from non-malignant T cells including tumor infiltrating lymphocytes (TILs), regulatory T cells (Tregs), NK/T cells, and from immune cells such as B cells, antigen presenting cells (dendritic cells, macrophages) and other cells (stromal, epithelial cells) (cluster 4-13). Comparing baseline to C1D15 we were able to identify microenvironmental changes that occurred during treatment, specifically characterized the expression and significance of PD1, LAG3, CTLA4, TIM3 and ICOS in malignant and non-malignant T cell clusters, which demonstrated differential expression of these targets in malignant T cells (clusters 0-4). Non-malignant T cell phenotyping revealed an enriched tumor-infiltrating CD8+ T cell population at baseline with upregulation of LAG3 gene expression, and FOXP3+ CD4+ regulatory T cell population with high expression of CTLA4 and ICOS consistent with inducible Tregs (iTregs) in all, but one baseline sample that did not resolve during treatment (C1D15). Conclusions: Paired scRNA and TCRseq revealed distinctive functional composition of T cells and other immune cells. Combined scRNA expression and scTCR analysis identified malignant from non-malignant T cell subsets. Malignant T cell clones diminished in responders during treatment, while shifted or emerged in non-responders. Clonal enrichment of iTregs and exhausted CD4 and CD8 T cells were identified that did not resolve during treatment. suggesting that potential targeting of ICOS, CTLA4 and/or LAG3 will reverse T cell dysfunction in TILS and iTregs, respectively and increase clinical benefit of anti-PD-L1 blockade. Disclosures Querfeld: Stemline: Consultancy; MiRagen: Consultancy; Kyowa Kirin: Consultancy; Bioniz: Consultancy; Helsinn: Consultancy; Trillium: Consultancy; Celgene: Research Funding. Rosen:Novartis: Consultancy; Pebromene: Consultancy; Aileron Therapeutics: Consultancy; Celgene: Speakers Bureau; paradigm Medical Communications: Speakers Bureau; Abbvie: Speakers Bureau; Seattle Genetics: Consultancy; NeoGenomics: Consultancy.

scholarly journals Identification of Human Immune Cell Subtypes Most Vulnerable to IL-1β-induced Inflammatory Signaling Using Mass Cytometry

2020 ◽  
Author(s):  
Hema Kothari ◽  
Corey M. Williams ◽  
Chantel McSkimming ◽  
Mythili Vigneshwar ◽  
Eli R. Zunder ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
T Cell Subsets ◽  
Effector Memory ◽  
High Morbidity ◽  
Cytokine Storm ◽  
Mass Cytometry ◽  
Immune Cell Subsets

ABSTRACTIL-1β has emerged as a key mediator of the cytokine storm linked to high morbidity and mortality from COVID-19 and blockade of the IL-1 receptor (IL-1R) with Anakinra has entered clinical trials in COVID-19 subjects. Yet, knowledge of the specific immune cell subsets targeted by IL-1β and IL-1β-induced signaling pathways in humans is limited. Utilizing mass cytometry (CyTOF) of human peripheral blood mononuclear cells, we identified effector memory CD4 T cells and CD4−CD8low/-CD161+ T cells as the circulating immune subtypes with the greatest expression of p-NF-κB in response to IL-1β stimulation. Notably, CCR6 distinctly identified T cells most responsive to IL-1β. Other subsets including CD11c myeloid dendritic cells (mDCs), classical monocytes (CM), two subsets of natural killer cells (CD16−CD56brightCD161− and CD16−CD56dimCD161+) and a population of lineage−(Lin-) cells expressing CD161 and CD25 also showed IL-1β-induced expression of p-NF-kB. The IL-1R antagonist, Anakinra significantly inhibited IL-1β-induced p-NF-kB in the CCR6+ T cells and CD11c mDCs with a trending inhibition in CD14 monocytes and Lin−CD161+CD25+ cells. IL-1β also induced a rapid but much less robust increase in p-p38 expression as compared to p-NF-kB in the majority of these same immune cell subsets. Prolonged IL-1β stimulation greatly increased p-STAT3 and to a much lesser extent p-STAT1 and p-STAT5 in T cell subsets, monocytes, DCs and the Lin−CD161+CD25+ cells suggesting IL-1β-induced production of downstream STAT-activating cytokines, consistent with its role in cytokine storm. Interindividual heterogeneity and inhibition of this activation by Anakinra raises the intriguing possibility that assays to measure IL-1β-induced p-NF-kB in CCR6+ T cell subtypes could identify those at higher risk of cytokine storm and those most likely to benefit from Anakinra therapy.

scholarly journals Role of Circulating T Follicular Helper Cells and Stem-Like Memory CD4+ T Cells in the Pathogenesis of HIV-2 Infection and Disease Progression

2021 ◽  
Vol 12 ◽  
Author(s):  
Sivasankaran Munusamy Ponnan ◽  
K.K. Vidyavijayan ◽  
Kannan Thiruvengadam ◽  
Nancy Hilda J ◽  
Manikannan Mathayan ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Disease Progression ◽  
Neutralizing Antibodies ◽  
Immune Cell ◽  
Adaptive Immune Response ◽  
T Cell Subsets ◽  
Follicular Helper ◽  
Immune Cell Subsets

CD4+ T cells are critical players in the host adaptive immune response. Emerging evidence suggests that certain CD4+ T cell subsets contribute significantly to the production of neutralizing antibodies and help in the control of virus replication. Circulating T follicular helper cells (Tfh) constitute a key T cell subset that triggers the adaptive immune response and stimulates the production of neutralizing antibodies (NAbs). T cells having stem cell-like property, called stem-like memory T cells (Tscm), constitute another important subset of T cells that play a critical role in slowing the rate of disease progression through the differentiation and expansion of different types of memory cell subsets. However, the role of these immune cell subsets in T cell homeostasis, CD4+ T cell proliferation, and progression of disease, particularly in HIV-2 infection, has not yet been elucidated. The present study involved a detailed evaluation of the different CD4+ T cell subsets in HIV-2 infected persons with a view to understanding the role of these immune cell subsets in the better control of virus replication and delayed disease progression that is characteristic of HIV-2 infection. We observed elevated levels of CD4+ Tfh and CD4+ Tscm cells along with memory and effector T cell abundance in HIV-2 infected individuals. We also found increased frequencies of CXCR5+ CD8+ T cells and CD8+ Tscm cells, as well as memory B cells that are responsible for NAb development in HIV-2 infected persons. Interestingly, we found that the frequency of memory CD4+ T cells as well as memory B cells correlated significantly with neutralizing antibody titers in HIV-2 infected persons. These observations point to a more robust CD4+ T cell response that supports B cell differentiation, antibody production, and CD8+ T cell development in HIV-2 infected persons and contributes to better control of the virus and slower rate of disease progression in these individuals.

scholarly journals The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Gang Xu ◽  
Furong Qi ◽  
Hanjie Li ◽  
Qianting Yang ◽  
Haiyan Wang ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Close Association ◽  
T Cell Subsets ◽  
Peripheral T Cells ◽  
Single Cell Rna Sequencing

Abstract Understanding the mechanism that leads to immune dysfunction in severe coronavirus disease 2019 (COVID-19) is crucial for the development of effective treatment. Here, using single-cell RNA sequencing, we characterized the peripheral blood mononuclear cells (PBMCs) from uninfected controls and COVID-19 patients and cells in paired broncho-alveolar lavage fluid (BALF). We found a close association of decreased dendritic cells (DCs) and increased monocytes resembling myeloid-derived suppressor cells (MDSCs), which correlated with lymphopenia and inflammation in the blood of severe COVID-19 patients. Those MDSC-like monocytes were immune-paralyzed. In contrast, monocyte-macrophages in BALFs of COVID-19 patients produced massive amounts of cytokines and chemokines, but secreted little interferons. The frequencies of peripheral T cells and NK cells were significantly decreased in severe COVID-19 patients, especially for innate-like T and various CD8+ T cell subsets, compared to healthy controls. In contrast, the proportions of various activated CD4+ T cell subsets among the T cell compartment, including Th1, Th2, and Th17-like cells were increased and more clonally expanded in severe COVID-19 patients. Patients’ peripheral T cells showed no sign of exhaustion or augmented cell death, whereas T cells in BALFs produced higher levels of IFNG, TNF, CCL4, CCL5, etc. Paired TCR tracking indicated abundant recruitment of peripheral T cells to the severe patients’ lung. Together, this study comprehensively depicts how the immune cell landscape is perturbed in severe COVID-19.

targetSCAPE and ultraSCAPE: Simultaneous identification and deep profiling of human antigen-specific T cells and other immune cell subsets by mass cytometry.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15204-e15204
Author(s):  
Brian Abel ◽  
Faris Kairi ◽  
Alessandra Nardin ◽  
Evan Newell ◽  
Michael Fehlings
Keyword(s):  
T Cells ◽  
T Cell ◽  
Immune Cell ◽  
T Cell Subsets ◽  
Single Sample ◽  
High Dimensional ◽  
Cell Populations ◽  
Mass Cytometry ◽  
Immune Cell Subsets

e15204 Background: During clinical trial immune monitoring, especially in the field of immunotherapy, it is critical to collect in-depth phenotypic information from multiple immune cell populations in order to assess the biological activity of the immunotherapy, to identify biomarkers of response or disease progression, and/or to identify new drug targets. However, patient samples such as peripheral blood mononuclear cells or tissues, are often scarce and current methods face limitations in either achieving a sufficient depth of analysis and/or cell throughput. Methods: In order to identify therapy-relevant antigens and to facilitate a concurrent in-depth characterization of T cells directed towards these targets, immunoSCAPE leverages the high-dimensional immune profiling capabilities of mass cytometry and a unique methodology allowing the identification and characterization of rare antigen-specific T-cell subsets (targetSCAPE). By implementing a new cutting-edge technology that combines flow and mass cytometry in parallel with a combinatorial live cell barcoding strategy, we further increased the high-dimensional phenotyping capacities to over 100 different marker molecules on up to four different immune cell subsets simultaneously within the same sample. Results: We isolated 4 different immune cell populations from a single sample and combined 3 different phenotypic panels consisting of 35 makers each together with a combinatorial tetramer multiplex and phenotyping panel for deep profiling of myeloid cells, NK cells, B cells and T cells. We demonstrate the potential of this novel immuno-phenotyping method, by tracking virus-specific T cells while simultaneously characterizing 4 immune cell subsets with over 100 distinct phenotypic markers from a single sample, which is currently impossible employing modern flow cytometers or classical mass cytometry methods. Conclusions: With its ability to provide an unprecedented picture of the immune status within a single sample, including T cell specificity information and in-depth profiling of relevant immune cell subsets, ultraSCAPE in combination with targetSCAPE can provide detailed insights on the effects of immunotherapy on the immune cell population. Information learned from in-depth immune phenotyping of several immune cell subsets such as T, B, NK and myeloid cell subsets can be leveraged for the development of novel diagnostics, biomarker discovery and monitoring therapeutic strategies in immunotherapy clinical trials.

scholarly journals A Single-Cell Immune Atlas of Triple Negative Breast Cancer Reveals Novel Immune Cell Subsets

2020 ◽  
Author(s):  
Si Qiu ◽  
Ruoxi Hong ◽  
Zhenkun Zhuang ◽  
Linnan Zhu ◽  
Yuan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
Single Cell ◽  
Triple Negative Breast Cancer ◽  
Immune Cells ◽  
Triple Negative ◽  
Immune Cell ◽  
Breast Cancer Subtype ◽  
Cell Subset ◽  
Immune Cell Subsets

Abstract Background: Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, which recently attracts great interest for immune therapeutic development. In this context, in-depth understanding of TNBC immune landscape is highly demanded.Results: Here we report full-length single-cell RNA sequencing results of 9683 tumor-infiltrated immune cells isolated from 14 treatment naïve TNBC tumors, where 22 immune cell subsets, including T cells, macrophages, B cells, and DCs have been characterized. We identify a new T cell subset, CD8+CXCL8+ T cell, which associates with poor survival, and a subset of “pre-exhaustion” T cell cluster, which is predictive of favorable prognosis. A novel immune cell subset comprised of TCR+ macrophages, is found to be widely distributed in TNBC tumors. Further analyses reveal an up-regulation of molecules associated with TCR signaling and cytotoxicity in these immune cells.Conclusions: Altogether, our study provides a valuable resource to understand the immune ecosystem of TNBC. The novel immune cell subsets reported herein might be functionally important in cancer immunity. These data will be helpful for the immunotherapeutic strategy design of this disease.

scholarly journals Single-cell analysis reveals cell communication triggered by macrophages associated with the reduction and exhaustion of CD8+ T cells in COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lei He ◽  
Quan Zhang ◽  
Yue Zhang ◽  
Yixian Fan ◽  
Fahu Yuan ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Communication ◽  
Cell Types ◽  
Trial Registration ◽  
T Cell Subsets ◽  
Disease Group ◽  
Communication Analysis ◽  
Receptor Interactions

Abstract Background The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has become an ongoing pandemic. Understanding the respiratory immune microenvironment which is composed of multiple cell types, together with cell communication based on ligand–receptor interactions is important for developing vaccines, probing COVID-19 pathogenesis, and improving pandemic control measures. Methods A total of 102 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study. Clinical information, routine laboratory tests, and flow cytometry analysis data with different conditions were collected and assessed for predictive value in COVID-19 patients. Next, we analyzed public single-cell RNA-sequencing (scRNA-seq) data from bronchoalveolar lavage fluid, which offers the closest available view of immune cell heterogeneity as encountered in patients with varying severity of COVID-19. A weighting algorithm was used to calculate ligand–receptor interactions, revealing the communication potentially associated with outcomes across cell types. Finally, serum cytokines including IL6, IL1β, IL10, CXCL10, TNFα, GALECTIN-1, and IGF1 derived from patients were measured. Results Of the 102 COVID-19 patients, 42 cases (41.2%) were categorized as severe. Multivariate logistic regression analysis demonstrated that AST, D-dimer, BUN, and WBC were considered as independent risk factors for the severity of COVID-19. T cell numbers including total T cells, CD4+ and CD8+ T cells in the severe disease group were significantly lower than those in the moderate disease group. The risk model containing the above mentioned inflammatory damage parameters, and the counts of T cells, with AUROCs ranged from 0.78 to 0.87. To investigate the molecular mechanism at the cellular level, we analyzed the published scRNA-seq data and found that macrophages displayed specific functional diversity after SARS-Cov-2 infection, and the metabolic pathway activities in the identified macrophage subtypes were influenced by hypoxia status. Importantly, we described ligand–receptor interactions that are related to COVID-19 serverity involving macrophages and T cell subsets by communication analysis. Conclusions Our study showed that macrophages driving ligand–receptor crosstalk contributed to the reduction and exhaustion of CD8+ T cells. The identified crucial cytokine panel, including IL6, IL1β, IL10, CXCL10, IGF1, and GALECTIN-1, may offer the selective targets to improve the efficacy of COVID-19 therapy. Trial registration: This is a retrospective observational study without a trial registration number.

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