Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing To Define Immune Cell Chimerism in the Rejecting Kidney Transplant

BackgroundIn solid organ transplantation, donor-derived immune cells are assumed to decline with time after surgery. Whether donor leukocytes persist within kidney transplants or play any role in rejection is unknown, however, in part because of limited techniques for distinguishing recipient from donor cells.MethodsWhole-exome sequencing of donor and recipient DNA and single-cell RNA sequencing (scRNA-seq) of five human kidney transplant biopsy cores distinguished immune cell contributions from both participants. DNA-sequence comparisons used single nucleotide variants (SNVs) identified in the exome sequences across all samples.ResultsAnalysis of expressed SNVs in the scRNA-seq data set distinguished recipient versus donor origin for all 81,139 cells examined. The leukocyte donor/recipient ratio varied with rejection status for macrophages and with time post-transplant for lymphocytes. Recipient macrophages displayed inflammatory activation whereas donor macrophages demonstrated antigen presentation and complement signaling. Recipient-origin T cells expressed cytotoxic and proinflammatory genes consistent with an effector cell phenotype, whereas donor-origin T cells appeared quiescent, expressing oxidative phosphorylation genes. Finally, both donor and recipient T cell clones within the rejecting kidney suggested lymphoid aggregation. The results indicate that donor-origin macrophages and T cells have distinct transcriptional profiles compared with their recipient counterparts, and that donor macrophages can persist for years post-transplantation.ConclusionsAnalysis of single nucleotide variants and their expression in single cells provides a powerful novel approach to accurately define leukocyte chimerism in a complex organ such as a transplanted kidney, coupled with the ability to examine transcriptional profiles at single-cell resolution.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/JASN/2020_08_07_JASN2020030326.mp3

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Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing to Define Immune Cell Chimerism in the Rejecting Kidney Transplant

10.1101/2020.03.10.986075 ◽

2020 ◽

Author(s):

Andrew F. Malone ◽

Haojia Wu ◽

Catrina Fronick ◽

Robert Fulton ◽

Joseph P. Gaut ◽

...

Keyword(s):

T Cells ◽

Single Cell ◽

Rna Sequencing ◽

Kidney Transplant ◽

Solid Organ Transplantation ◽

Cell Phenotype ◽

Solid Organ ◽

Single Nucleotide ◽

Transcriptional Profiles ◽

Single Cell Rna Sequencing

AbstractIn solid organ transplantation, donor derived immune cells are assumed to decline with time after surgery. Whether donor leukocytes persist within kidney transplants or play any role in rejection is unknown, however, in part because of limited techniques for distinguishing recipient and donor cells. To address this question, we performed paired whole exome sequencing of donor and recipient DNA and single cell RNA sequencing (scRNA-seq) of 5 human kidney transplant biopsy cores. Exome sequences were used to define single nucleotide variations (SNV) across all samples. By analyzing expressed SNVs in the scRNA-seq dataset we could define recipient vs. donor cell origin for all 81,139 cells. The leukocyte donor to recipient ratio varied with rejection status for macrophages and with time post-transplant for lymphocytes. Recipient macrophages were characterized by inflammatory activation and donor macrophages by antigen presentation and complement signaling. Recipient origin T cells expressed cytotoxic and pro-inflammatory genes consistent with an effector cell phenotype whereas donor origin T cells are likely quiescent expressing oxidative phosphorylation genes relative to recipient T cells. Finally, both donor and recipient T cell clones were present within the rejecting kidney, suggesting lymphoid aggregation. Our results indicate that donor origin macrophages and T cells have distinct transcriptional profiles compared to their recipient counterparts and donor macrophages can persist for years post transplantation. This study demonstrates the power of this approach to accurately define leukocyte chimerism in a complex tissue such as the kidney transplant coupled with the ability to examine transcriptional profiles at single cell resolution.

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Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

10.1101/2021.05.22.443646 ◽

2021 ◽

Author(s):

Alex Rogozhnikov ◽

Pavan Ramkumar ◽

Saul Kato ◽

Sean Escola

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Single Cell Sequencing ◽

Additional Information ◽

Genotype Information ◽

Single Cell Rna Sequencing ◽

Genotype Inference

Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

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Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

Genome Biology ◽

10.1186/s13059-019-1863-4 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 12

Author(s):

Fenglin Liu ◽

Yuanyuan Zhang ◽

Lei Zhang ◽

Ziyi Li ◽

Qiao Fang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Allele Frequencies ◽

Cellular Heterogeneity ◽

Variant Allele ◽

Detection Methods ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Single Cell Rna Sequencing

Abstract Background Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Results Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

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Single-Cell RNA Sequencing Reveals the Immunological Profiles of Renal Allograft Rejection in Mice

Frontiers in Immunology ◽

10.3389/fimmu.2021.693608 ◽

2021 ◽

Vol 12 ◽

Author(s):

Qixia Shen ◽

Yucheng Wang ◽

Jiaoyi Chen ◽

Lifeng Ma ◽

Xiaoru Huang ◽

...

Keyword(s):

T Cells ◽

B Cells ◽

Single Cell ◽

Rna Sequencing ◽

Allograft Rejection ◽

Renal Allograft ◽

Immune Cell ◽

Fold Increase ◽

Renal Allograft Rejection ◽

Single Cell Rna Sequencing

Allograft rejection is a common immunological feature in renal transplantation and is associated with reduced graft survival. A mouse renal allograft rejection model was induced and single-cell RNA sequencing (scRNA-seq) data of CD45+ leukocytes in kidney allografts on days 7 (D7) and 15 (D15) after operation was analyzed to reveal a full immunological profiling. We identified 20 immune cell types among 10,921 leukocytes. Macrophages and CD8+ T cells constituted the main populations on both timepoints. In the process from acute rejection (AR) towards chronic rejection (CR), the proportion of proliferating and naïve CD8+ T cells dropped significantly. Both B cells and neutrophils decreased by about 3 folds. On the contrary, the proportion of macrophages and dendritic cells (DCs) increased significantly, especially by about a 4.5-fold increase in Ly6cloMrc1+ macrophages and 2.6 folds increase in Ly6cloEar2+ macrophages. Moreover, myeloid cells harbored the richest ligand and receptor (LR) pairs with other cells, particularly for chemokine ligands such as Cxcl9, Cxcl10, Cxcl16 and Yars. However, macrophages with weak response to interferon gamma (IFNg) contributed to rejection chronicization. To conclude, reduction in CD8 T cells, B cells and neutrophils while increasing in Ly6cloMrc1+ macrophages and Ly6cloEar2+ macrophages, may contribute significantly to the progress from AR towards CR.

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

Cell Discovery ◽

10.1038/s41421-020-00225-2 ◽

2020 ◽

Vol 6 (1) ◽

Cited By ~ 3

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.

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Monocytes and Macrophages in Kidney Transplantation and Insights from Single Cell RNA-Seq Studies

Kidney360 ◽

10.34067/kid.0003842021 ◽

2021 ◽

pp. 10.34067/KID.0003842021

Author(s):

Andrew F Malone

Keyword(s):

Kidney Transplantation ◽

Single Cell ◽

Rna Sequencing ◽

Kidney Transplant ◽

New Technologies ◽

Immune Cell ◽

Human Kidney ◽

Cell Types ◽

Single Cell Rna Sequencing ◽

And Function

Single-cell RNA sequencing (scRNA-seq) is a powerful technology that allows for the identification of minority cell types in complex tissues, such as immune cells in the kidney. Previously, gene expression from infrequent cell types was missed using bulk RNA-sequencing methods due to an averaging effect. Additionally, single-cell RNA sequencing facilitates assignment of cell origin in a sample, a shortcoming of previous bulk sequencing technologies. Thus, scRNA-seq is ideal to study the immune cell landscape and the alloimmune response in the human kidney transplant. However, there are few studies published to date. Macrophages are known to play an important role in health and disease in the kidney. Furthermore, it is known that macrophages play key roles in rejection of the kidney transplant. The definition, ontogeny, and function of these cells is complex and nomenclature has evolved as new technologies have become available. In this review, an overview of monocyte and macrophage nomenclature, ontogeny, and function with a specific focus on kidney transplantation is provided with novel scRNA-seq findings included. Single-cell RNA sequencing offers an unbiased transcriptional approach to defining macrophages and provides insights into macrophage ontogeny and function not possible with contemporary methods.

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Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

Nature Communications ◽

10.1038/s41467-021-24730-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

David S. Fischer ◽

Meshal Ansari ◽

Karolin I. Wagner ◽

Sebastian Jarosch ◽

Yiqi Huang ◽

...

Keyword(s):

T Cells ◽

Single Cell ◽

Rna Sequencing ◽

Ex Vivo ◽

Severe Disease ◽

Human Leukocyte ◽

Cell Receptor ◽

Single Cell Rna Sequencing

AbstractThe in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for ‘reverse phenotyping’. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

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Red panda: a novel method for detecting variants in single-cell RNA sequencing

BMC Genomics ◽

10.1186/s12864-020-07224-3 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Adam Cornish ◽

Shrabasti Roychoudhury ◽

Krishna Sarma ◽

Suravi Pramanik ◽

Kishor Bhakat ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Articular Chondrocytes ◽

Genetic Diseases ◽

Simulated Data ◽

Single Nucleotide ◽

Single Cell Rna Sequencing ◽

Red Panda ◽

Novel Method ◽

Rare Cells

Abstract Background Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. Results In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools—FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus—ranged from 5.8–41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. Conclusions We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently existing software. However, methods for identification of genomic variants using scRNA-seq data can be still improved.

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MBRS-46. CHARTING NEOPLASTIC AND IMMUNE CELL HETEROGENEITY IN HUMAN AND GEM MODELS OF MEDULLOBLASTOMA USING scRNAseq

Neuro-Oncology ◽

10.1093/neuonc/noaa222.555 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii406-iii406

Author(s):

Andrew Donson ◽

Kent Riemondy ◽

Sujatha Venkataraman ◽

Ahmed Gilani ◽

Bridget Sanford ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Genetically Engineered ◽

Cellular Heterogeneity ◽

Cell Heterogeneity ◽

Transcriptomic Data ◽

Single Cell Rna Sequencing ◽

Transcript Profiles

Abstract We explored cellular heterogeneity in medulloblastoma using single-cell RNA sequencing (scRNAseq), immunohistochemistry and deconvolution of bulk transcriptomic data. Over 45,000 cells from 31 patients from all main subgroups of medulloblastoma (2 WNT, 10 SHH, 9 GP3, 11 GP4 and 1 GP3/4) were clustered using Harmony alignment to identify conserved subpopulations. Each subgroup contained subpopulations exhibiting mitotic, undifferentiated and neuronal differentiated transcript profiles, corroborating other recent medulloblastoma scRNAseq studies. The magnitude of our present study builds on the findings of existing studies, providing further characterization of conserved neoplastic subpopulations, including identification of a photoreceptor-differentiated subpopulation that was predominantly, but not exclusively, found in GP3 medulloblastoma. Deconvolution of MAGIC transcriptomic cohort data showed that neoplastic subpopulations are associated with major and minor subgroup subdivisions, for example, photoreceptor subpopulation cells are more abundant in GP3-alpha. In both GP3 and GP4, higher proportions of undifferentiated subpopulations is associated with shorter survival and conversely, differentiated subpopulation is associated with longer survival. This scRNAseq dataset also afforded unique insights into the immune landscape of medulloblastoma, and revealed an M2-polarized myeloid subpopulation that was restricted to SHH medulloblastoma. Additionally, we performed scRNAseq on 16,000 cells from genetically engineered mouse (GEM) models of GP3 and SHH medulloblastoma. These models showed a level of fidelity with corresponding human subgroup-specific neoplastic and immune subpopulations. Collectively, our findings advance our understanding of the neoplastic and immune landscape of the main medulloblastoma subgroups in both humans and GEM models.

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540 Transcriptionally defined immune landscape in human gliomas

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0540 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A576-A576

Author(s):

Pravesh Gupta ◽

Minghao Dang ◽

Krishna Bojja ◽

Huma Shehwana ◽

Tuan Tran ◽

...

Keyword(s):

T Cells ◽

Dendritic Cell ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Myeloid Cell ◽

Wild Type ◽

Cell Clusters ◽

Md Anderson ◽

Antigen Presenting

BackgroundBrain immunity is largely myeloid cell dominated rather than lymphoid cells in healthy and diseased state including malignancies of glial origins called as gliomas. Despite this skewed myeloid centric immune contexture, immune checkpoint and T cell based therapeutic modalities are generalizably pursued in gliomas ignoring the following facts i) T cells are sparse in tumor brain ii) glioma patients are lymphopenic iii) gliomas harbor abundant and highly complex myeloid cell repertoire. We recognized these paradoxes pertaining to fundamental understanding of constituent immune cells and their functional states in the tumor immune microenvironment (TIME) of gliomas, which remains elusive beyond a priori cell types and/or states.MethodsTo dissect the TIME in gliomas, we performed single-cell RNA-sequencing on ~123,000 tumor-derived sorted CD45+ leukocytes from fifteen genomically classified patients comprising IDH-mutant primary (IMP; n=4), IDH-mutant recurrent (IMR; n=4), IDH-wild type primary (IWP; n=3), or IDH-wild type recurrent (IWR; n=4) gliomas (hereafter referred as glioma subtypes) and two non-glioma brains (NGBs) as controls.ResultsUnsupervised clustering analyses delineated predominant 34-myeloid cell clusters (~75%) over 28-lymphoid cell clusters (~25%) reflecting enormous heterogeneity within and across glioma subtypes. The glioma immune diversity spanned functionally imprinted phagocytic, antigen-presenting, hypoxia, angiogenesis and, tumoricidal myeloid to classical cytotoxic lymphoid subpopulations. Specifically, IDH-mutant gliomas were predominantly enriched for brain-resident microglial subpopulations in contrast to enriched bone barrow-derived infiltrates in IDH-wild type especially in a recurrent setting. Microglia attrition in IWP and IWR gliomas were concomitant with invading monocyte-derived cells with semblance to dendritic cell and macrophage like transcriptomic features. Additionally, microglial functional diversification was noted with disease severity and mostly converged to inflammatory states in IWR gliomas. Beyond dendritic cells, multiple antigen-presenting cellular states expanded with glioma severity especially in IWP and IWR gliomas. Furthermore, we noted differential microglia and dendritic cell inherent antigen presentation axis viz, osteopontin, and classical HLAs in IDH subtypes and, glioma-wide non-PD1 checkpoints associations in T cells like Galectin9 and Tim-3. As a general utility, our immune cell deconvolution approach with single-cell-matched bulk RNA sequencing data faithfully resolved 58-cell states which provides glioma specific immune reference for digital cytometry application to genomics datasets.ConclusionsAltogether, we identified prognosticator immune cell-signatures from TCGA cohorts as one of many potential immune responsiveness applications of the curated signatures for basic and translational immune-genomics efforts. Thus, we not only provide an unprecedented insight of glioma TIME but also present an immune data resource that can be exploited for immunotherapy applications.Ethics ApprovalThe brain tumor/tissue samples were collected as per MD Anderson internal review board (IRB)-approved protocol numbers LAB03-0687 and, LAB04-0001. One non-tumor brain tissue sample was collected from patient undergoing neurosurgery for epilepsy as per Baylor College of Medicine IRB-approved protocol number H-13798. All experiments were compliant with the review board of MD Anderson Cancer Center, USA.ConsentWritten informed consent was obtained from the patient for publication of this abstract and any accompanying images. A copy of the written consent is available for review by the Editor of this journal

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