Maryam Clausen

Bio: Maryam Clausen is an academic researcher from AstraZeneca. The author has contributed to research in topics: Induced pluripotent stem cell & Genome editing. The author has an hindex of 12, co-authored 26 publications receiving 1494 citations. Previous affiliations of Maryam Clausen include University of North Carolina at Chapel Hill.

Tracking replication enzymology in vivo by genome-wide mapping of ribonucleotide incorporation

Abstract: HydEn-seq, a new sequencing method that maps the distribution of ribonucleotides misincorporated by low-fidelity DNA polymerases in budding yeast, reveals unexpected strand-specific replication patterns in both nuclear and mitochondrial genomes.

The lung environment controls alveolar macrophage metabolism and responsiveness in type 2 inflammation.

Abstract: Fine control of macrophage activation is needed to prevent inflammatory disease, particularly at barrier sites such as the lungs. However, the dominant mechanisms that regulate the activation of pulmonary macrophages during inflammation are poorly understood. We found that alveolar macrophages (AlvMs) were much less able to respond to the canonical type 2 cytokine IL-4, which underpins allergic disease and parasitic worm infections, than macrophages from lung tissue or the peritoneal cavity. We found that the hyporesponsiveness of AlvMs to IL-4 depended upon the lung environment but was independent of the host microbiota or the lung extracellular matrix components surfactant protein D (SP-D) and mucin 5b (Muc5b). AlvMs showed severely dysregulated metabolism relative to that of cavity macrophages. After removal from the lungs, AlvMs regained responsiveness to IL-4 in a glycolysis-dependent manner. Thus, impaired glycolysis in the pulmonary niche regulates AlvM responsiveness during type 2 inflammation.

Extracellular vesicles induce minimal hepatotoxicity and immunogenicity.

Abstract: Extracellular vesicles (EVs) mediate cellular communication through the transfer of active biomolecules, raising interest in using them as biological delivery vehicles for therapeutic drugs. For drug delivery applications, it is important to understand the intrinsic safety and toxicity liabilities of EVs. Nanoparticles, including EVs, typically demonstrate significant accumulation in the liver after systemic administration in vivo. We confirmed uptake of EVs derived from Expi293F cells into HepG2 cells and did not detect any signs of hepatotoxicity measured by cell viability, functional secretion of albumin, plasma membrane integrity, and mitochondrial and lysosomal activity even at high exposures of up to 5 × 1010 EVs per mL. Whole genome transcriptome analysis was used to measure potential effects on the gene expression in the recipient HepG2 cells at 24 h following exposure to EVs. Only 0.6% of all genes were found to be differentially expressed displaying less than 2-fold expression change, with genes related to inflammation or toxicity being unaffected. EVs did not trigger any proinflammatory cytokine response in HepG2 cells. However, minor changes were noted in human blood for interleukin (IL)-8, IL-6, and monocyte chemotactic protein 1 (MCP-1). Administration of 5 × 1010 Expi293F-derived EVs to BALB/c mice did not result in any histopathological changes or increases of liver transaminases or cytokine levels, apart from a modest increase in keratinocyte chemoattractant (KC). The absence of any significant toxicity associated with EVs in vitro and in vivo supports the prospective use of EVs for therapeutic applications and for drug delivery.

Fast, sensitive and accurate integration of single-cell data with Harmony.

Abstract: The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony ( https://github.com/immunogenomics/harmony ), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data. Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.

Comprehensive integration of single cell data

Abstract: Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

Determining cell type abundance and expression from bulk tissues with digital cytometry.

Abstract: Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells. CIBERSORTx, a suite of computational tools, enables inference of cell type abundance and cell-type-specific gene expression profiles from bulk RNA profiles.

Regulatory T Cells

Abstract: Despite the skepticism that once prevailed among immunologists, it is now widely accepted that the normal immune system harbors a T-cell population, called regulatory T cells (Treg cells), specialized for immune suppression. It was first shown that depletion of a T-cell subpopulation from normal rodents produced autoimmune disease. Search for a molecular marker specific for such autoimmune-preventive Treg cells has revealed that the majority, if not all, of them constitutively express the CD25 molecule as depletion of CD25+CD4+ T cells spontaneously evokes autoimmune disease in otherwise normal rodents. The expression of CD25 by Treg cells has made it possible to delineate their developmental pathways, in particular their thymic development, and establish simple in vitro assay for assessing their suppressive activity. The marker and the in vitro assay have helped to identify human Treg cells with similar functional and phenotypic characteristics. Recent efforts have shown that natural Treg cells specifically express the transcription factor Foxp3 and that mutations of the Foxp3 gene produce a variety of immunological diseases in humans and rodents. Specific expression of Foxp3 in natural Treg cells has enabled their functional and developmental characterization by genetic approach. These studies altogether have provided firm evidence for Foxp3+CD25+CD4+ Treg cells as an indispensable cellular constituent of the normal immune system for establishing and maintaining immunologic self-tolerance and immune homeostasis. Treg cells are now within the scope of clinical use to treat immunological diseases and control physiological and pathological immune responses.