Strategies for optimizing CITE-seq for human islets and other tissues
Ontology highlight
ABSTRACT: Defining the immunological landscape of human tissue is an important area of research, but challenges include the impact of tissue disaggregation on cell phenotypes and the low abundance of immune cells in many tissues. Here, we describe methods to troubleshoot and standardize Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) for studies involving enzymatic digestion of human tissue. We tested epitope susceptibility of 92 antibodies commonly used to differentiate immune lineages and cell states on human peripheral blood mononuclear cells following treatment with an enzymatic digestion cocktail used to isolate islets. We observed CD4, CD8a, CD25, CD27, CD120b, CCR4, CCR6, and PD1 display significant sensitivity to enzymatic treatment, effects that often could not be overcome with alternate antibodies. Comparison of flow cytometry-based CITE-seq antibody titrations and sequencing data supports that for the majority of antibodies, flow cytometry accurately predicts optimal antibody concentrations for CITE-seq. Comparison by CITE-seq of immune cells in enzymatically digested islet tissue and donor- matched spleen not treated with enzymes revealed little digestion-induced epitope cleavage, suggesting increased sensitivity of CITEseq and/or that the islet structure may protect resident immune cells from enzymes. Within islets, CITEseq identified immune cells difficult to identify by transcriptional signatures alone, such as distinct tissue-resident T cell subsets, mast cells, and innate lymphoid cells (ILCs). Collectively this study identifies strategies for the rational design and testing of CITE-seq antibodies for single-cell studies of immune cells within islets and other tissues.
Project description:Type 1 diabetes (T1D) is caused by the autoimmune destruction of insulin-producing pancreatic beta cells, leading to life-long dependence on exogenous insulin. Profiling immune cells that infiltrate islets would be invaluable to understanding how beta cell destruction occurs. However, human pancreatic samples demonstrating active infiltration and beta cell destruction are rare. Alternatively, peri-pancreatic lymph nodes (pLNs) or other secondary lymphoid organs may harbor immune cells which participate in memory responses that drive T1D autoimmunity. To study the immune response throughout T1D onset and disease, lymphocytes from pLNs, mesenteric lymph nodes (mesLNs), and the spleen were collected from human T1D, auto-antibody positive (AAb+), and normal donors (NDs) enrolled in the Human Pancreas Analysis Program (HPAP). Tissue immune cell identity, phenotype, and transcriptional status was analyzed using Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITEseq). Lymphocytes from 17 pLN, 9 mesLN, and 15 spleen samples spanning 7 ND, 5 AAb+, and 7 T1D donors were thawed and processed through the CITEseq pipeline. 5 donors per disease group had a paired pLN and spleen sample, with 3 of the 5 donors having a paired mesLN sample, allowing for cross-tissue immune status comparison spanning multiple stages of disease onset. The dataset provides one of the first and largest CITEseq datasets on human AAb+ and T1D samples publicly available.
Project description:Synovial biopsies were taken using 14G Precisa Needle (HS Hospital Service, Italy) in ultrasound-guided protocol, and digested as described previously (Alivernini et al, 2020). Live CD45+ cells from the synovial tissue (ST) of healthy donors (n=3), patients with active RA (n=5) and patients in sustained remission (n=2) were sorted for single cell RNA (scRNA) or CITE (scCITE) sequencing using the 10x Genomics platform. Maximum 20,000 immune cells from tissue were sorted into Protein LoBind 1.5 ml Eppendorf tube containing 300 μl of RPMI media with 10% of FSC. Cells were loaded onto a Chromium Controller (10X Genomics) for single-cell partitioning, followed by library preparation using Single-Cell 3’ Reagent Kits v3.1. For 3 healthy ST and 4 ST from active RA (Figure S1), Totalseq Hashtag were used (Biolegend #394601, #394603, #394605, #394607, #394609) to combine 2 samples per run, and TotalSeq™-A Human Universal Cocktail (V1.0) was used to collect protein expression (CITEseq) data together with transcriptome data. Single-cell libraries were sequenced on the Illumina HiSeq 4000 system to a minimum depth of 50k reads/cell.
Project description:Type 1 diabetes is an autoimmune disease caused by the destruction of the insulin producing beta cells. We characterized the gene expression profile of pancreatic tissue from four type 1 diabetes patients (Cases 1-4), who died at different stages of the disease (onset and longstanding) by microarray analysis. All samples from patients and controls were obtained after death, at the time of organ donation. Pancreatic blocks from the four diabetic patients and three organ donors (Controls 1-3) were obtained and snap frozen. At the same time, islets from Cases 1 and 4 were isolated from a piece of the pancreas tail by enzymatic digestion (using automated method and handpicking) and snap frozen. Islets from three different organ donors (Controls 4-6) were obtained similarly to those of the diabetic patients. Both pancreatic blocks and islets were stored in liquid nitrogen until the mRNA extraction. For pancreas gene expression profiles, we compared data from three blocks of each of the four type 1 diabetic pancreas with pancreases data from the Controls 1-3. For islets expression profile, we compared data from Cases 1 and 4 islets with islets data from the Controls 4-6. <br><br>
Project description:SARS-CoV-2 infection triggers adaptive immune responses from both T and B cells. However, most studies focus on peripheral blood, which may not fully reflect immune responses in lymphoid tissues at the site of infection. To evaluate both local and systemic adaptive immune responses to SARS-CoV-2, we collected peripheral blood, tonsils, and adenoids from 110 children undergoing tonsillectomy/adenoidectomy during the COVID-19 pandemic. In order to investigate the adaptive immune response locally, we measured the neutralizing antibodies against to multiple SARS-CoV-2 virus variants and performed high dimensional flow cytometry and CITEseq including TCR and BCR analysis for deeper characterization of the immune response to SARS-CoV-2. In our CITE-seq analyses, we sorted SARS-CoV-2 S1 (spike 1) binding (Spike1+) and non-binding (Spike1-) CD19+ B cells from tonsils, adenoids, and PBMCs from two subjects with a history of COVID-19 and one uninfected control. To assess T cells at the single cell level, we sorted CD95+ CD4+ and CD95+ CD8+ T cells, identified expanded T cell clonotypes, and then compared the CDR3 sequences to those previously reported to recognize SARS-CoV-2 antigens. Our results provide evidence for persistent tissue-specific immunity to SARS-CoV-2 in the upper respiratory tract of children weeks to months after acute infection.
Project description:Single-cell RNA-sequencing (scRNA-Seq) is widely used to characterize immune cell populations. However, mRNA levels correlate poorly with expression of surface proteins, which are well established to define immune cell types. CITE-Seq (cellular indexing of transcriptomes and epitopes by sequencing) utilizes oligonucleotide-tagged antibodies to simultaneously analyze surface phenotypes and transcriptomes. Considering the high costs of adding surface phenotyping to scRNA-Seq, we aimed to determine which of 188 tested CITE-Seq antibodies can detect their antigens on human peripheral blood mononuclear cells (PBMCs), a commonly interrogated cell population in immunology, and find the optimal concentration for staining. The recommended concentration was optimal for 76 antibodies, whereas staining quality of 7 antibodies improved when the concentration was doubled. 33 and 8 antibodies still worked well when the concentration was reduced to 1/5 or 1/25, respectively. 64 antigens were not detected at any antibody concentration. Optimizing the antibody panel by removing antibodies not able to detect their target antigens and adjusting concentrations of the remaining antibodies could enable a cost reduction of almost 50%. In conclusion, our data are a resource for building an informative and cost-effective panel of CITE-Seq antibodies and use them at their optimal concentrations in future CITE-seq experiments on human PBMCs.
Project description:While great efforts are being made to establish single-cell transcriptomics profiling of clinical material, protein expression of clinically relevant biomarkers has been harder to integrate into existing pipelines. CITE-seq bridges the RNA-protein gap, but has so far primarily been applied to liquid biopsies, which do not require tissue dissociation. Processing of solid biopsies to characterize the tumor microenvironment is an essential next step in applying single-cell technologies to translational studies. Here, we demonstrate CITE-seq performance and protocol customizations on dissociated tissues such as human skin and primary and metastatic melanoma biopsies on a total of 52,672 cells from 11 solid and 6 liquid primary samples. Analogous to fluorescently activated cell sorting (FACS), we describe gating of cell populations based on transcriptome signatures and setting thresholds for protein expression using cell type-aware ridge plot visualization. For a panel of 97 antibodies, we report on gene and protein expression correlation in liquid and solid sample cohorts. Using peripheral blood mononuclear cells (PBMCs) as a model, we show the effect of enzymatic digestion on transcriptome and epitope expression in immune cell populations. Additionally, we optimized digestion protocols for healthy skin and tumor tissues that yield various cell populations within a given tissue type. Finally, we demonstrate the applicability of CITE-seq for biomarker discovery on metastatic melanoma. Our work provides a blueprint and pipeline for CITE-seq to a broad range of clinically relevant samples, thus allowing for an increasingly detailed resolution of solid tissue specimens and enabling translational studies where protein biomarker profiling could provide better functional descriptions of cell states. We believe that the described protocol will find wide-ranging applications for basic and clinical research.
Project description:Purpose: Investigate cellular heterogeneity in a fresh human ovarian cancer tissue sample Methods: Enzymatic digestion of fresh tissue sample collected from the operating room to produce single cell suspension. Cells were labelled with fluorescent antibodies to CD3, CD14, CD19, CD20, CD56 and FACS sorted to remove immune cells. The negative population was used for sequencing. Single cells were processed using the Fluidigm C1 Chip to generate barcoded cDNA for each cell. Amplifed cDNA was sequenced using an Illumina HiSeq 2500 machine. Results: Single cell RNA sequence data was obtained for 92 cells and a "bulk" sample of 1000 cells. 26 cells were removed from analysis due to quality control standards. The remaining 66 cells and the bulk sample were analyzed. Conclusion: Single cell RNA sequence analysis reveals heterogeneity in gene expression in cells harvested from a high grade ovarian serous cancer
Project description:Different cell isolation techniques exist for transcriptomic and proteotype profiling of brain cells. Here, we provide a systematic investigation of the influence of different cell isolation protocols on transcriptional and proteotype profiles in mouse brain tissue by taking into account single-cell transcriptomics of brain cells, proteotypes of microglia and astrocytes, and flow cytometric analysis of microglia. We show that standard enzymatic digestion of brain tissue at 37°C induces profound and consistent alterations in the transcriptome and proteotype of neuronal and glial cells, as compared to an optimized mechanical dissociation protocol at 4°C. These findings emphasize the risk of introducing technical biases and biological artefacts when implementing enzymatic digestion-based isolation methods for brain cell analyses.
Project description:Assessing the self-peptides presented by susceptible major histocompatibility complex (MHC) molecules is crucial for evaluating the pathogenesis and therapeutics of tissue-specific autoimmune diseases. However, direct examination of such MHC-bound peptides displayed in the target organ remains largely impractical. Here, we demonstrate that the blood leukocytes from non-obese diabetic (NOD) mice presented peptide epitopes to autoreactive CD4 T cells. These peptides were bound to the autoimmune class II MHC molecule, I-Ag7, and originated from insulin B-chain and C-peptide. The presentation required a glucose challenge, which stimulated the release of insulin peptides from pancreatic islets. The circulating leukocytes, especially the B cells, promptly captured and presented these peptides. Although canonical intracellular processing of insulin was involved in the presentation, extracellular binding of catabolized insulin products to I-Ag7 gave rise to a unique pathogenic epitope. Administration of monoclonal antibodies recognizing insulin B-chain abolished the presentation and diminished diabetes incidence. Mass spectrometry analysis of the leukocyte MHC-II peptidomes revealed a series of beta cell derived peptides, with identical sequences to those previously in the islet MHC-II peptidome. Thus, the WBC peptidome echoes that found in islets and serves to identify immunogenic peptides in an otherwise inaccessible tissue.
Project description:We implemented a murine model of intestinal inflammation based on oral administration of multiple cycles of low dose dextran sulfate sodium (DSS) to induce epithelial injury and ECM deposition. Mice were subjected to 3 repetitive cycles of DSS displayed progressive accumulation of immune cell infiltrates associated with excessive deposition of collagen fibers. Lamina propria cells from water-fed and DSS-fed mice were isolated using enzymatic digestion, and enriched for stromal cells by FACS using antibodies excluding hematopoietic cells (CD45), epithelial cells (EpCAM), and erythrocytes. Prepared single cell suspensions were then profiled using the 10x Chromium V2 droplet-based single cell RNA sequencing platform. Tissues were collected from proximal and distal colon.