scholarly journals Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immunity

2016 ◽  
Author(s):  
Santiago J. Carmona ◽  
Sarah A. Teichmann ◽  
Lauren Ferreira ◽  
Iain C. Macaulay ◽  
Michael J.T. Stubbington ◽  
...  

AbstractThe immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analysed gene conservation of all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans-membrane proteins in NK cells compared to other immune cell populations, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA-Sequencing of lck:GFP cells in zebrafish to obtain the first transcriptome of specific immune cell types in a non-mammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T-cells, a novel type of NK-like cells and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immune cell specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed a higher gene turnover and lower conservation of trans-membrane proteins in NK cells compared to T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 9-9
Author(s):  
Michael Abadier ◽  
Jose Estevam ◽  
Deborah Berg ◽  
Eric Robert Fedyk

Background Mezagitamab is a fully human immunoglobulin (Ig) G1 monoclonal antibody with high affinity to CD38 that depletes tumor cells expressing CD38 by antibody- and complement-dependent cytotoxicity. CD38 is a cell surface molecule that is highly expressed on myeloma cells, plasma cells, plasmablasts, and natural killer (NK) cells, and is induced on activated T cells and other suppressor cells including regulatory T (Tregs) and B (Bregs) cells. Data suggest that immune landscape changes in cancer patients and this may correlate with disease stage and clinical outcome. Monitoring specific immune cell subsets could predict treatment responses since certain cell populations either enhance or attenuate the anti-tumor immune response. Method To monitor the immune landscape changes in RRMM patients we developed a mass cytometry panel that measures 39-biomarkers to identify multiple immune cell subsets, including T cells (naïve, memory, effector, regulatory), B cells (naïve, memory, precursors, plasmablasts, regulatory), NK cells, NKT cells, gamma delta T cells, monocytes (classical, non-classical and intermediate), dendritic cells (mDC; myeloid and pDC; plasmacytoid) and basophils. After a robust analytical method validation, we tested cryopreserved peripheral blood and bone marrow mononuclear cells from 19 RRMM patients who received ≥ 3 prior lines of therapy. Patients were administered 300 or 600 mg SC mezagitamab on a QWx8, Q2Wx8 and then Q4Wx until disease progression schedule (NCT03439280). We compared the percent change in immune cell subsets at baseline versus week 4 and week 16. Results CD38 is expressed at different levels on immune cells and sensitivity to depletion by mezagitamab generally correlates positively with the density of expression. CD38 is expressed at high densities on plasmablasts, Bregs, NK-cells, pDC and basophils at baseline and this was associated with reductions in peripheral blood and bone marrow (plasmablasts, 95%, Bregs, 90%, NK-cells, 50%, pDC, 55% and basophils, 40%) at week 4 post treatment. In contrast, no changes occurred in the level of total T-cells and B-cells, which is consistent with low expression of CD38 on most cells of these large populations. Among the insensitive cell types, remaining NK-cells acquired an activated, proliferative and effector phenotype. We observed 60-150% increase in activation (CD69, HLA-DR), 110-200% increase in proliferation (Ki-67), and 40-375% increase in effector (IFN-γ) markers in peripheral blood and bone marrow. Importantly, NK-cells which did not express detectable CD38, also exhibited a similar phenotype possibly by a mechanism independent of CD38. Consistent with these data, the remaining CD4 and CD8 T-cell populations exhibited an activated effector phenotype as observed by 40-200% increase in activation, 60-200% increase in proliferation and 40-90% increase in effector markers in peripheral blood. A potential explanation for this acquisition of activated effector phenotypes could be a reduction in suppressive regulatory lymphocytes. Next, we measured levels of Tregs and Bregs, and observed that Bregs which are CD24hiCD38hi were reduced to 60-90% in peripheral blood and bone marrow. In contrast, total Tregs were reduced by only 5-25% because CD38 expression in Tregs appears as a spectrum where only ~10-20% are CD38+, and thus CD38+ Tregs were reduced more significantly (45-75%), reflecting the selectively of mezagitamab to cells expressing high levels of CD38. CD38+ Tregs are induced in RRMM patients, thus we looked at the phenotype of CD38-, CD38mid, and CD38high -expressing Tregs. We observed higher level of markers that correlate with highly suppressive Tregs such as Granzyme B, Ki-67, CTLA-4 and PD-1 in CD38high Tregs. Accordingly, the total Treg population exhibited a less active phenotype after exposure to mezagitamab, which selectively depleted the highly suppressive CD38+ Tregs. Conclusions Chronic treatment with mezagitamab is immunomodulatory in patients with RRMM, which is associated with reductions in tumor burden, subpopulations of B and T regulatory cells, and characterized by conventional NK and T cells exhibiting an activated, proliferative and effector phenotype. The immune landscape changes observed is consistent with the immunologic concept of converting the tumor microenvironment from cold-to-hot and highlights a key mechanistic effect of mezagitamab. Disclosures Berg: Takeda Pharmaceuticals Inc: Current Employment.


2021 ◽  
Vol 12 ◽  
Author(s):  
Lixing Huang ◽  
Ying Qiao ◽  
Wei Xu ◽  
Linfeng Gong ◽  
Rongchao He ◽  
...  

Fish is considered as a supreme model for clarifying the evolution and regulatory mechanism of vertebrate immunity. However, the knowledge of distinct immune cell populations in fish is still limited, and further development of techniques advancing the identification of fish immune cell populations and their functions are required. Single cell RNA-seq (scRNA-seq) has provided a new approach for effective in-depth identification and characterization of cell subpopulations. Current approaches for scRNA-seq data analysis usually rely on comparison with a reference genome and hence are not suited for samples without any reference genome, which is currently very common in fish research. Here, we present an alternative, i.e. scRNA-seq data analysis with a full-length transcriptome as a reference, and evaluate this approach on samples from Epinephelus coioides-a teleost without any published genome. We show that it reconstructs well most of the present transcripts in the scRNA-seq data achieving a sensitivity equivalent to approaches relying on genome alignments of related species. Based on cell heterogeneity and known markers, we characterized four cell types: T cells, B cells, monocytes/macrophages (Mo/MΦ) and NCC (non-specific cytotoxic cells). Further analysis indicated the presence of two subsets of Mo/MΦ including M1 and M2 type, as well as four subsets in B cells, i.e. mature B cells, immature B cells, pre B cells and early-pre B cells. Our research will provide new clues for understanding biological characteristics, development and function of immune cell populations of teleost. Furthermore, our approach provides a reliable alternative for scRNA-seq data analysis in teleost for which no reference genome is currently available.


2020 ◽  
Author(s):  
Xuan Liu ◽  
Sara J.C. Gosline ◽  
Lance T. Pflieger ◽  
Pierre Wallet ◽  
Archana Iyer ◽  
...  

AbstractSingle-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3515-3515
Author(s):  
Muntasir M Majumder ◽  
Aino Maija Leppä ◽  
Caroline A Heckman

Abstract Introduction Off-target cytotoxicity resulting in severe side effects and compromising patient survival often hampers the development of new cancer therapeutics. Understanding the complete drug response landscape of different cell populations is crucial to identify drugs that selectively eradicate the malignant cell population, but spare healthy cells. Here, we developed a high content, no wash, multi-parametric flow cytometry based assay that enables testing of blood cancer patient samples and simultaneously monitors the effects of several drugs on 11 hematopoietic cell types. The assay can be used to i) dissect malignant from healthy cell responses and predict off-target effects; ii) assess drug effects on immune cell subsets; iii) identify drugs that can potentially be repositioned to new blood cancer indications. Methods Mononuclear cells were prepared from bone marrow aspirates of 7 multiple myeloma (MM) and 3 acute myeloid leukemia (AML) patients plus the peripheral blood from a healthy donor, which were collected following informed consent and in compliance with the Declaration of Helsinki. Optimal cell density, antibody dilutions, incubation time, and wash versus no wash assay conditions for the selected antibody panels were determined. Cells were incubated at a density of 2 million cells/ml in either 96- or 384-well plates for 3 days. The antibodies were tested in two panels to study the effects of 6 drugs in 5 dilutions (1-10000 nM) (clofarabine, bortezomib, dexamethasone, navitoclax, venetoclax and omipalisib) on 11 cell populations, namely hematopoietic stem cells (HSCs) (CD34+CD38-), common progenitor cells (CPCs) (CD34+CD38+), monocytes (CD14+), B cells (CD45+CD19+), cytotoxic T cells (CD45+CD3+CD8+), T helper cells (CD45+CD3+CD4+), NK-T cells (CD45+CD3+CD56+), NK cells (CD45+CD56+CD3-), clonal plasma cells (CD138+CD38+), other plasma cells (CD138+CD38-) and granulocytes (CD45+, SSC++). Annexin-V and 7AAD were used to distinguish live cell populations from apoptotic and dead cells. After 1 h incubation with antibodies, the plates were read with the iQue Screener PLUS instrument (Intellicyt). Counts for each population were used to generate four parameter nonlinear regression fitted dose response curves with GraphPad Prism 7. Three samples were tested in duplicate to assess reproducibility. Results To decrease the complexity of the assay, we tested all antibodies under wash and no wash conditions, and found that results from both conditions were comparable. To minimize the amount of sample needed as well as maximize the number of drugs tested and cell populations that can be detected, we set up the assay in both 96- and 384-well plates. The assay was highly reproducible when samples were tested in replicate and was scalable to a 384-well format without compromising sensitivity to detect rare populations such as plasma cells. Due to the differentiation of immature cells to specialized cell types, the drug responses of specific populations tended to drift. HSCs (CD34+CD38-) were shown to be refractory to the tested drugs compared to CPCs characterized as (CD34+CD38+) and other cell types. Interestingly, the proteasome inhibitor bortezomib was cytotoxic to all cell populations except for CD138+CD38- plasma cells. Clofarabine, a nucleoside analog used to treat ALL, effectively targeted CPC, NK and B cells, while HSCs and plasma cells were resistant. The glucocorticoid and immunosuppressive drug dexamethasone specifically targeted B and NK cells compared to T cell populations (CD8+, CD4+), while NK-T cells were modestly sensitive. The cell population response patterns were similar in samples derived from MM, AML and healthy individuals, highlighting that the drug responses are highly cell type specific. Summary Using a high content, multi-parametric assay, we could rapidly assess the effect of several drugs on specific cell populations in individual patient samples. Our results demonstrate that many drugs preferentially affect different hematological cell lineages. Although heterogeneity was observed between individual patients, the pattern of cytotoxic response exhibited by specific cell types was consistent among samples derived from MM, AML and healthy donors. The assay will be useful to identify drugs with maximal on-target and minimal off-target specificity, and can potentially be used to guide treatment decision and predict patient response Disclosures Heckman: Celgene: Research Funding; Pfizer: Research Funding.


2021 ◽  
Vol 12 ◽  
Author(s):  
Juber Herrera-Uribe ◽  
Jayne E. Wiarda ◽  
Sathesh K. Sivasankaran ◽  
Lance Daharsh ◽  
Haibo Liu ◽  
...  

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 34-35
Author(s):  
Anna Kalff ◽  
Sam Norton ◽  
Tiffany Khong ◽  
Malarmathy Ramachandran ◽  
Mary H. Young ◽  
...  

The LEOPARD trial evaluated lenalidomide and alternate day prednisolone (RAP) as post ASCT maintenance in newly diagnosed transplant eligible MM patients (TE NDMM). 60 patients were recruited. Estimated median potential follow-up was 44 months (IQR 26m - 52m). Median PFS from time of commencing RAP was 38.3m (95% CI, 25.8 to 54.8); median OS was not reached (71.4% of patients were alive at 36 months). Here we present the findings from correlative immune studies of this trial. Aims: To undertake mass cytometry (CyTOF) based immune profiling in patients with TE NDMM treated with RAP maintenance post ASCT. Methods: The LEOPARD trial was a phase II, multi centre, open label, single arm study of RAP maintenance after a single melphalan conditioned (200mg/m2) ASCT as part of up-front therapy. Patients were restaged at D+42 ASCT, and if eligible, were commenced on RAP maintenance (LEN 10mg daily increasing to 15mg daily after 8 weeks and alternate day prednisolone 50mg) within 8 weeks of D+0 of the ASCT. Therapy continued until toxicity/progression. CyTOF was performed in sequential samples in two selected groups of patients: long runners (LR, n=7), defined as those with PFS > 36 months (median) and early relapsers (ER, n=8), defined as those who progressed/died before reaching the lower quartile of PFS. [All patients had peripheral blood collected at baseline (pre-ASCT), 6w post-ASCT and weeks 4, 8, 12, 20, 28 and 40 of RAP]. Cells were barcoded using the Cell-ID 20-Plex Pd barcoding kit (Fluidigm) followed by staining with sub-set/function defining antibodies (targeting myeloid, B, T and NK cells: CD45, CD3, CD19, CD5, CD1c, CD226. CD8, CD11c, CD16, CD127, CD138, CD123, NKG2A, TIGIT, TIM3, CD45RA. CD274, CD27, CD197, CD28, Ki67, CD66b, CD183, KLRG1, CD43, NKG2D, CD38, CD278/ICOS, CD25, HLA-DR, CD4, CD57, GramB, PD-1, CD14, CD56, CD11b, Tbet, CD33). Samples were acquired on the Helios instrument. Supervised analysis was performed to determine differences in canonical immune cell populations. Unsupervised analysis was then performed: data were clustered in the VORTEX package. Significant differences in cluster frequency were assessed by Mann-Whitney test for statistical significance. Cluster phenotypes were determined and validated via multiple visualisation approaches. Results: Median age was 56yrs for LR versus 63yrs for ER. Median PFS for LR was 46.3m (38.4 - 51.5m) versus 10.2 m (2.1 - 21.3m) for ER. Supervised analysis was performed on all samples, dichotomized into baseline and last time point sampled for each patient. At baseline, Ki67+CD8+ T cells, ICOS+CD8+ T cells, HLA-DR+CD4+ T cells and CD11c+ myeloid cells were enriched in LR compared to ER. At the last timepoint sampled, Ki67+CD8+ T cells and ICOS+CD8+ T cells were again enriched in LR compared to ER. Conversely, B-reg-like cells (CD19+CD5+CD43-) were enriched in ER compared to LR at the last timepoint sampled. Unsupervised analysis was performed on all samples (all timepoints were pooled). Five clusters were significantly enriched in LR compared to ER. Four of these clusters represented activated/cytotoxic NK cells: CD56 dim, CD16-, NKG2A(CD159a)+, NKG2D(CD314)+, Granzyme B+ and CD38+, and additional expression of CD57 on one cluster; one cluster represented a mature myeloid population, with high expression of HLA-DR, CD11b and CD11c and low expression of CD33. One cluster was significantly enriched in ER compared to LR, representing activated neutrophils, with high expression of CD66b, CD11b and CD16. The clusters that were enriched were then assessed longitudinally over all time points. There was no difference in the kinetics of these populations between groups. Conclusions Significant differences in both T-cell and NK cell populations were demonstrable at baseline in LR versus ER patients. Subsequently, durable responses to post-ASCT lenalidomide maintenance were associated with a cytotoxic, controlled immune response whereas early relapse was characterised by a more uncontrolled inflammatory response and the emergence of B-reg-like cells prior to relapse. We conclude that immune profiling at baseline and after initiation of therapy may help to predict a more sustained response to lenalidomide maintenance enabling pre-emptive tailored treatment decisions. Disclosures Kalff: Roche: Honoraria; Janssen: Honoraria; Amgen: Honoraria; CSL: Honoraria; Celgene: Honoraria. Young:Bristol Meyers Squibb: Current Employment, Current equity holder in publicly-traded company. Pierceall:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Thakurta:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company; Oxford University: Other: visiting professor. Oppermann:Bristol Meyers Squibb: Research Funding. Guo:Bristol Meyers Squibb: Research Funding. Reynolds:Novartis AG: Current equity holder in publicly-traded company. Spencer:AbbVie, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Consultancy; AbbVie, Amgen, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Honoraria; Amgen, Celgene, Haemalogix, Janssen, Servier and Takeda: Research Funding; Celgene, Janssen and Takeda: Speakers Bureau.


2020 ◽  
Author(s):  
Bharat Panwar ◽  
Benjamin J. Schmiedel ◽  
Shu Liang ◽  
Brandie White ◽  
Enrique Rodriguez ◽  
...  

ABSTRACTThe systemic lupus erythematosus (SLE) is an incurable autoimmune disease disproportionately affecting women and may lead to damage in multiple different organs. The marked heterogeneity in its clinical manifestations is a major obstacle in finding targeted treatments and involvement of multiple immune cell types further increases this complexity. Thus, identifying molecular subtypes that best correlate with disease heterogeneity and severity as well as deducing molecular cross-talk among major immune cell types that lead to disease progression are critical steps in the development of more informed therapies for SLE. Here we profile and analyze gene expression of six major circulating immune cell types from patients with well-characterized SLE (classical monocytes (n=64), T cells (n=24), neutrophils (n=24), B cells (n=20), conventional (n=20) and plasmacytoid (n=22) dendritic cells) and from healthy control subjects. Our results show that the interferon (IFN) response signature was the major molecular feature that classified SLE patients into two distinct groups: IFN-signature negative (IFNneg) and positive (IFNpos). We show that the gene expression signature of IFN response was consistent (i) across all immune cell types, (ii) all single cells profiled from three IFNpos donors using single-cell RNA-seq, and (iii) longitudinal samples of the same patient. For a better understanding of molecular differences of IFNpos versus IFNneg patients, we combined differential gene expression analysis with differential Weighted Gene Co-expression Network Analysis (WGCNA), which revealed a relatively small list of genes from classical monocytes including two known immune modulators, one the target of an approved therapeutic for SLE (TNFSF13B/BAFF: belimumab) and one itself a therapeutic for Rheumatoid Arthritis (IL1RN: anakinra). For a more integrative understanding of the cross-talk among different cell types and to identify potentially novel gene or pathway connections, we also developed a novel gene co-expression analysis method for joint analysis of multiple cell types named integrated WGNCA (iWGCNA). This method revealed an interesting cross-talk between T and B cells highlighted by a significant enrichment in the expression of known markers of T follicular helper cells (Tfh), which also correlate with disease severity in the context of IFNpos patients. Interestingly, higher expression of BAFF from all myeloid cells also shows a strong correlation with enrichment in the expression of genes in T cells that may mark circulating Tfh cells or related memory cell populations. These cell types have been shown to promote B cell class-switching and antibody production, which are well-characterized in SLE patients. In summary, we generated a large-scale gene expression dataset from sorted immune cell populations and present a novel computational approach to analyze such data in an integrative fashion in the context of an autoimmune disease. Our results reveal the power of a hypothesis-free and data-driven approach to discover drug targets and reveal novel cross-talk among multiple immune cell types specific to a subset of SLE patients. This approach is immediately useful for studying autoimmune diseases and is applicable in other contexts where gene expression profiling is possible from multiple cell types within the same tissue compartment.


2019 ◽  
Author(s):  
Benjamin DeMeo ◽  
Bonnie Berger

AbstractSingle-cell RNA-sequencing (scRNA-seq) has grown massively in scale since its inception, presenting substantial analytic and computational challenges. Even simple downstream analyses, such as dimensionality reduction and clustering, require days of runtime and hundreds of gigabytes of memory for today’s largest datasets. In addition, current methods often favor common cell types, and miss salient biological features captured by small cell populations. Here we present Hopper, a single-cell toolkit that both speeds up the analysis of single-cell datasets and highlights their transcriptional diversity by intelligent subsampling, or sketching. Hopper realizes the optimal polynomial-time approximation of the Hausdorff distance between the full and downsampled dataset, ensuring that each cell is well-represented by some cell in the sample. Unlike prior sketching methods, Hopper adds points iteratively and allows for additional sampling from regions of interest, enabling fast and targeted multi-resolution analyses. In a dataset of over 1.3 million mouse brain cells, we detect a cluster of just 64 macrophages expressing inflammatory tissues (0.004% of the full dataset) from a Hopper sketch containing just 5,000 cells, and several other small but biologically interesting immune cell populations invisible to analysis of the full data. On an even larger dataset consisting of ~2 million developing mouse organ cells, we show even representation of important cell types in small sketch sizes, in contrast with prior sketching methods. By condensing transcriptional information encoded in large datasets, Hopper grants the individual user with a laptop the same analytic capabilities as large consortium.


2020 ◽  
Author(s):  
Nathan Lawlor ◽  
Djamel Nehar-Belaid ◽  
Jessica D.S. Grassmann ◽  
Marlon Stoeckius ◽  
Peter Smibert ◽  
...  

AbstractImmune cell activation assays have been widely used for immune monitoring and for understanding disease mechanisms. However, these assays are typically limited in scope. A holistic study of circulating immune cell responses to different activators is lacking. Here we developed a cost-effective high-throughput multiplexed single-cell RNA-seq combined with epitope tagging (CITE-seq) to determine how classic activators of T cells (anti-CD3 coupled with anti-CD28) or monocytes (LPS) alter the cell composition and transcriptional profiles of peripheral blood mononuclear cells (PBMCs) from healthy human donors. Anti-CD3/CD28 treatment activated all classes of lymphocytes either directly (T cells) or indirectly (B and NK cells) but reduced monocyte numbers. Activated T and NK cells expressed senescence and effector molecules, whereas activated B cells transcriptionally resembled autoimmune disease- or age-associated B cells (e.g., CD11c, T-bet). In contrast, LPS specifically targeted monocytes and induced two main states: early activation characterized by the expression of chemoattractants and a later pro-inflammatory state characterized by expression of effector molecules. These data provide a foundation for future immune activation studies with single cell technologies (https://czi-pbmc-cite-seq.jax.org/).Graphical abstract


2020 ◽  
Author(s):  
Momeneh Foroutan ◽  
Ramyar Molania ◽  
Aline Pfefferle ◽  
Corina Behrenbruch ◽  
Axel Kallies ◽  
...  

AbstractImmunotherapy success in colorectal cancer (CRC) is mainly limited to patients whose tumours exhibit high microsatellite instability (MSI). However, there is variability in treatment outcomes within this group, which is in part driven by the frequency and characteristics of tumour infiltrating immune cells. Indeed, the presence of specific infiltrating immune cell subsets has been shown to correlate with immunotherapy responses and is in many cases prognostic of treatment outcome. Tumour-infiltrating lymphocytes (TILs) can undergo distinct differentiation programs such as acquire features of tissue-residency or exhaustion, a process during which T cells upregulate inhibitory receptors such as PD-1 and loose functionality. While residency and exhaustion programs of CD8 T cells are relatively well-studied, these programs have only recently been appreciated in CD4 T cells and remain largely unknown in tumour-infiltrating natural killer (NK) cells. In this study, we use single cell RNA-seq data to identify signatures of residency and exhaustion in CRC infiltrating lymphocytes, including CD8, CD4 and NK cells. We then test these signatures in independent single cell data from tumour and normal tissue infiltrating immune cells. Further, we use versions of these signatures adapted for bulk RNA-seq data to identify a list of tumour intrinsic mutations associated with residency and exhaustion from TCGA data. Finally, using two independent transcriptomic data sets from patients with colon adenocarcinoma, we show that combinations of these signatures, in particular NK signatures, as well as tumour-associated signatures, such as TGF-β signalling, are associated with distinct survival outcomes in colorectal cancer patients.


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