Single-cell Multiomic Analysis Research Articles

Lymphoma Driver Mutations in the Pathogenic Evolution of an Iconic Human Autoantibody

Pathogenic autoantibodies arise in many autoimmune diseases, but it is not understood how the cells making them evade immune checkpoints. Here, single-cell multi-omics analysis demonstrates a shared mechanism with lymphoid malignancy in the formation of public rheumatoid factor autoantibodies responsible for mixed cryoglobulinemic vasculitis. By combining single-cell DNA and RNA sequencing with serum antibody peptide sequencing and antibody synthesis, rare circulating B lymphocytes making pathogenic autoantibodies were found to comprise clonal trees accumulating mutations. Lymphoma driver mutations in genes regulating B cell proliferation and V(D)J mutation (CARD11, TNFAIP3, CCND3, ID3, BTG2, and KLHL6) were present in rogue B cells producing the pathogenic autoantibody. Antibody V(D)J mutations conferred pathogenicity by causing the antigen-bound autoantibodies to undergo phase transition to insoluble aggregates at lower temperatures. These results reveal a pre-neoplastic stage in human lymphomagenesis and a cascade of somatic mutations leading to an iconic pathogenic autoantibody.

Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia.

Identifying the causes of human diseases requires deconvolution of abnormal molecular phenotypes spanning DNA accessibility, gene expression and protein abundance1-3. We present a single-cell framework that integrates highly multiplexed protein quantification, transcriptome profiling and analysis of chromatin accessibility. Using this approach, we establish a normal epigenetic baseline for healthy blood development, which we then use to deconvolve aberrant molecular features within blood from patients with mixed-phenotype acute leukemia4,5. Despite widespread epigenetic heterogeneity within the patient cohort, we observe common malignant signatures across patients as well as patient-specific regulatory features that are shared across phenotypic compartments of individual patients. Integrative analysis of transcriptomic and chromatin-accessibility maps identified 91,601 putative peak-to-gene linkages and transcription factors that regulate leukemia-specific genes, such as RUNX1-linked regulatory elements proximal to the marker gene CD69. These results demonstrate how integrative, multiomic analysis of single cells within the framework of normal development can reveal both distinct and shared molecular mechanisms of disease from patient samples.

Single Cell Multiomic Analysis of T Cell Exhaustion <i>in vitro</i>

A key step in the clinical production of CAR-T cells is the expansion of engineered T cells. To generate enough cells for a therapeutic product, cells must be chronically stimulated, which raises the risk of inducing T-cell exhaustion and reducing therapeutic efficacy. As protocols for T-cell expansion are being developed to optimize CAR T cell yield, function and persistence, fundamental questions about the impact of in vitro manipulation on T-cell identity are important to answer. Namely: 1) what types of cells are generated during chronic stimulation? 2) how many unique cell states can be defined during chronic stimulation? We sought to answer these fundamental questions by performing single-cell multiomic analysis to simultaneously measure expression of 39 proteins and 399 genes in human T cells expanded in vitro. This approach allowed us to study – with unprecedented depth - how T cells change over the course of chronic stimulation. Comprehensive immunophenotypic and transcriptomic analysis at day 0 enabled a refined characterization of T-cell maturational states (from naive to TEMRA cells) and the identification of a donor-specific subset of terminally differentiated T-cells that would have been otherwise overlooked using canonical cell classification schema. As expected, T-cell activation induced downregulation of naive-associated markers and upregulation of effector molecules, proliferation regulators, co-inhibitory and costimulatory receptors. Our deep kinetic analysis further revealed clusters of proteins and genes identifying unique states of activation defined by markers temporarily expressed upon 3 days of stimulation (PD-1, CD69, LTA), markers constitutively expressed throughout chronic activation (CD25, GITR, LGALS1), and markers uniquely upregulated upon 14 days of stimulation (CD39, ENTPD1, TNFDF10). Notably, different ratios of cells expressing activation or exhaustion markers were measured at each time point. These data indicate high heterogeneity and plasticity of chronically stimulated T cells in response to different kinetics of activation. In this study, we demonstrate the power of a single-cell multiomic approach to comprehensively characterize T cells and to precisely monitor changes in differentiation, activation and exhaustion signatures in response to different activation protocols.

Deep characterization of tumor microenvironments using single cell multi-omics analysis

Abstract Understanding the complexity of cell interactions in the tumor microenvironment (TME) requires the ability to distinguish each cell type, and is a prerequisite of personalized cancer treatments. Here, we use a fully integrated system for single cell RNA sequencing, to simultaneously profile the transcriptome, cell surface proteins and immune repertoire of cells from primary colorectal cancer (CRC), non-small cell lung cancer (NSCLC), and mucosa-associated lymphoid tissue (MALT) lymphoma. Each tumor varied in type and proportion of its cellular components, and in particular in the proportion of immune cells. The CRC tumor consisted of T (3% CD4+, 3% CD8+), B lymphocytes (5% CD79A+) and plasma B cells (11% IGH high). Repertoire sequencing identified a clonal expansion (>4% of B cell clonotypes) suggesting a strong B cell response in this tumor. The NSCLC tumor displayed a marked immune cell infiltration containing predominantly B cells (30% CD79A+ and 8% IGH high plasma B) with a very limited clonal expansion. The MALT lymphoma consisted of only T and B lymphocytes (31% CD4+, 8% CD8+, 57% CD79A+). In addition to the activated CD4+ and CD8+ T cells, Tfh and Treg cells were clearly identified as well as two distinct large B cell populations showing plasmacytic differentiation. Analysis of the B cell repertoire revealed a large expanded clone bearing an IGHV segment associated with parotid MALT lymphoma. These findings emphasize the importance of combining repertoire and gene expression sequencing data to determine the nature and clonality of an immune response. This method enables full characterization of tumor heterogeneity and the adaptive immune response to the TME and will be key in the development of successful immunotherapies.

Single cell transcriptomics: moving towards multi-omics.

As the basic units of life, cells present dramatic heterogeneity which, although crucial to an organism's behavior, is undetected by bulk analysis. Recently, much attention has been paid to reveal cellular types and states at the single-cell level including genome, transcriptome, epigenome or proteome-based on sequencing and immunological methods, etc. Among these approaches, transcriptomic analysis provides knowledge of the molecular linkages between genetic information and the proteome, leading to a comprehensive understanding of biological processes and diseases. Compared to single-dimensional inspection, multi-dimensional analysis combines the transcriptome with other "omics" to enable a comprehensive understanding of single-cell processes and functions. Moreover, compared to separate observations or single snapshots, spatial dimension and lineage time tracing can provide a more multifaceted understanding of the micro-environment and dynamic processes. In this review, we will introduce current transcriptomic analysis methods, as well as their combination with other omics methods including genomic, proteomic, and epigenetic approaches. Multi-dimensional analysis using these approaches for spatial positioning and lineage tracing applications will be reviewed. The future perspectives of single-cell multi-omics analysis based on the transcriptome will also be discussed.