Advanced Genomics-Based Approaches for Defining Allograft Rejection With Single Cell Resolution

Solid organ transplant recipients require long-term immunosuppression for prevention of rejection. Calcineurin inhibitor (CNI)-based immunosuppressive regimens have remained the primary means for immunosuppression for four decades now, yet little is known about their effects on graft resident and infiltrating immune cell populations. Similarly, the understanding of rejection biology under specific types of immunosuppression remains to be defined. Furthermore, development of innovative, rationally designed targeted therapeutics for mitigating or preventing rejection requires a fundamental understanding of the immunobiology that underlies the rejection process. The established use of microarray technologies in transplantation has provided great insight into gene transcripts associated with allograft rejection but does not characterize rejection on a single cell level. Therefore, the development of novel genomics tools, such as single cell sequencing techniques, combined with powerful bioinformatics approaches, has enabled characterization of immune processes at the single cell level. This can provide profound insights into the rejection process, including identification of resident and infiltrating cell transcriptomes, cell-cell interactions, and T cell receptor α/β repertoires. In this review, we discuss genomic analysis techniques, including microarray, bulk RNAseq (bulkSeq), single-cell RNAseq (scRNAseq), and spatial transcriptomic (ST) techniques, including considerations of their benefits and limitations. Further, other techniques, such as chromatin analysis via assay for transposase-accessible chromatin sequencing (ATACseq), bioinformatic regulatory network analyses, and protein-based approaches are also examined. Application of these tools will play a crucial role in redefining transplant rejection with single cell resolution and likely aid in the development of future immunomodulatory therapies in solid organ transplantation.

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Genomic Analysis at the Single-Cell Level

Annual Review of Genetics ◽

10.1146/annurev-genet-102209-163607 ◽

2011 ◽

Vol 45 (1) ◽

pp. 431-445 ◽

Cited By ~ 140

Author(s):

Tomer Kalisky ◽

Paul Blainey ◽

Stephen R. Quake

Keyword(s):

Single Cell ◽

Genomic Analysis ◽

Single Cell Level ◽

Cell Level

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Toward an understanding of immune cell sociology: real-time monitoring of cytokine secretion at the single-cell level

IUBMB Life ◽

10.1002/iub.1110 ◽

2012 ◽

Vol 65 (1) ◽

pp. 28-34 ◽

Cited By ~ 4

Author(s):

Yoshitaka Shirasaki ◽

Mai Yamagishi ◽

Nanako Shimura ◽

Atsushi Hijikata ◽

Osamu Ohara

Keyword(s):

Single Cell ◽

Real Time ◽

Immune Cell ◽

Cytokine Secretion ◽

Single Cell Level ◽

Real Time Monitoring ◽

Cell Level

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Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis

Frontiers in Immunology ◽

10.3389/fimmu.2021.679521 ◽

2021 ◽

Vol 12 ◽

Author(s):

Brittany Rocque ◽

Arianna Barbetta ◽

Pranay Singh ◽

Cameron Goldbeck ◽

Doumet Georges Helou ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Human Liver ◽

Immune Cell ◽

Solid Organ Transplantation ◽

Immune Homeostasis ◽

Solid Organ ◽

Immune Cell Subpopulations ◽

Cell Subpopulations ◽

Cellular Phenotypes

The liver is unique in both its ability to maintain immune homeostasis and in its potential for immune tolerance following solid organ transplantation. Single-cell RNA sequencing (scRNA seq) is a powerful approach to generate highly dimensional transcriptome data to understand cellular phenotypes. However, when scRNA data is produced by different groups, with different data models, different standards, and samples processed in different ways, it can be challenging to draw meaningful conclusions from the aggregated data. The goal of this study was to establish a method to combine ‘human liver’ scRNA seq datasets by 1) characterizing the heterogeneity between studies and 2) using the meta-atlas to define the dominant phenotypes across immune cell subpopulations in healthy human liver. Publicly available scRNA seq data generated from liver samples obtained from a combined total of 17 patients and ~32,000 cells were analyzed. Liver-specific immune cells (CD45+) were extracted from each dataset, and immune cell subpopulations (myeloid cells, NK and T cells, plasma cells, and B cells) were examined using dimensionality reduction (UMAP), differential gene expression, and ingenuity pathway analysis. All datasets co-clustered, but cell proportions differed between studies. Gene expression correlation demonstrated similarity across all studies, and canonical pathways that differed between datasets were related to cell stress and oxidative phosphorylation rather than immune-related function. Next, a meta-atlas was generated via data integration and compared against PBMC data to define gene signatures for each hepatic immune subpopulation. This analysis defined key features of hepatic immune homeostasis, with decreased expression across immunologic pathways and enhancement of pathways involved with cell death. This method for meta-analysis of scRNA seq data provides a novel approach to broadly define the features of human liver immune homeostasis. Specific pathways and cellular phenotypes described in this human liver immune meta-atlas provide a critical reference point for further study of immune mediated disease processes within the liver.

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Advances in single cell technologies in immunology

BioTechniques ◽

10.2144/btn-2020-0047 ◽

2020 ◽

Vol 69 (3) ◽

pp. 226-236

Author(s):

Jane Ru Choi

Keyword(s):

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Single Cell Level ◽

Future Perspectives ◽

Biological Mechanisms ◽

Cell Level ◽

Cell Responses ◽

Physiological Processes ◽

Cell Technologies

The immune system is composed of heterogeneous populations of immune cells that regulate physiological processes and protect organisms against diseases. Single cell technologies have been used to assess immune cell responses at the single cell level, which are crucial for identifying the causes of diseases and elucidating underlying biological mechanisms to facilitate medical therapy. In the present review we first discuss the most recent advances in the development of single cell technologies to investigate cell signaling, cell–cell interactions and cell migration. Each technology's advantages and limitations and its applications in immunology are subsequently reviewed. The latest progress toward commercialization, the remaining challenges and future perspectives for single cell technologies in immunology are also briefly discussed.

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Genomic Analysis of Circulating Tumor Cells at the Single-Cell Level

Journal of Molecular Diagnostics ◽

10.1016/j.jmoldx.2020.02.013 ◽

2020 ◽

Vol 22 (6) ◽

pp. 770-781 ◽

Cited By ~ 1

Author(s):

Shan Lu ◽

Chia-Jung Chang ◽

Yinghui Guan ◽

Edith Szafer-Glusman ◽

Elizabeth Punnoose ◽

...

Keyword(s):

Single Cell ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Genomic Analysis ◽

Single Cell Level ◽

Cell Level

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A rapid and robust method for single cell chromatin accessibility profiling

10.1101/309831 ◽

2018 ◽

Cited By ~ 2

Author(s):

Xi Chen ◽

Ricardo J Miragaia ◽

Kedar Nath Natarajan ◽

Sarah A Teichmann

Keyword(s):

Single Cell ◽

Immune Cell ◽

Cell Types ◽

Chromatin Accessibility ◽

Single Cell Level ◽

Cell Type ◽

Cell Level ◽

Regulatory Regions ◽

Cell Type Specific ◽

Accessible Chromatin

AbstractThe assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting. We demonstrated that our method worked robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3,000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors.

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A targeted multi-omic analysis approach measures protein expression and low abundance transcripts on the single cell level

10.1101/700534 ◽

2019 ◽

Cited By ~ 1

Author(s):

Florian Mair ◽

Jami R. Erickson ◽

Valentin Voillet ◽

Yannick Simoni ◽

Timothy Bi ◽

...

Keyword(s):

Protein Expression ◽

Single Cell ◽

Cell Function ◽

Immune Cell ◽

High Sensitivity ◽

Read Depth ◽

Single Cell Level ◽

Cell Level ◽

Protein Datasets ◽

Relationship Of

SummaryHigh throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets.Here, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.

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