Experimental and Bioinformatic Approaches to Studying DNA Methylation in Cancer

DNA methylation is an essential epigenetic mark. Alterations of normal DNA methylation are a defining feature of cancer. Here, we review experimental and bioinformatic approaches to showcase the breadth and depth of information that this epigenetic mark provides for cancer research. First, we describe classical approaches for interrogating bulk DNA from cell populations as well as more recently developed approaches for single cells and multi-Omics. Second, we focus on the computational analysis from primary data processing to the identification of unique methylation signatures. Additionally, we discuss challenges such as sparse data and cellular heterogeneity.

Single cell immune profiling reveals a developmentally distinct CD4+ GM-CSF+ T cell lineage that induces GI tract GVHD

Gastrointestinal (GI) tract involvement is a major determinant for subsequent morbidity and mortality arising during graft versus host disease (GVHD). CD4+ T cells that produce GM-CSF have emerged as central mediators of inflammation in this tissue site as GM-CSF serves as a critical cytokine link between the adaptive and innate arms of the immune system. However, cellular heterogeneity within the CD4+ GM-CSF+ T cell population due to the concurrent production of other inflammatory cytokines has raised questions as to whether these cells have a common ontology or if there exists a unique CD4+ GM-CSF+ subset that differs from other defined T helper (TH) subtypes. Using single cell RNA sequencing analysis, we identified two CD4+ GM-CSF+ T cell populations that arose during GVHD and were distinguishable by the presence or absence of IFN-γ co-expression. CD4+ GM-CSF+ IFN-γ- T cells which emerged preferentially in the colon had a distinct transcriptional profile, employed unique gene regulatory networks, and possessed a non-overlapping TCR repertoire when compared to CD4+ GM-CSF+ IFN-γ+ T cells as well as all other transcriptionally defined CD4+ T cell populations in the colon. Functionally, this CD4+ GM-CSF+ T cell population contributed to pathological damage in the GI tract which was critically dependent upon signaling through the IL-7 receptor but was independent of type 1 interferon signaling. Thus, these studies help to unravel heterogeneity within CD4+ GM-CSF+ T cells that arise during GVHD and define a developmentally distinct colitogenic TH GM-CSF+ subset that mediates immunopathology.

Mapping Cell Phenomics with Multiparametric Flow Cytometry Assays

Phenomics explores the complex interactions among genes, epigenetics, symbiotic microorganisms, diet, and environmental exposure based on the physical, chemical, and biological characteristics of individuals and groups. Increasingly efficient and comprehensive phenotyping techniques have been integrated into modern phenomics-related research. Multicolor flow cytometry technology provides more measurement parameters than conventional flow cytometry. Based on detailed descriptions of cell phenotypes, rare cell populations and cell subsets can be distinguished, new cell phenotypes can be discovered, and cell apoptosis characteristics can be detected, which will expand the potential of cell phenomics research. Based on the enhancements in multicolor flow cytometry hardware, software, reagents, and method design, the present review summarizes the recent advances and applications of multicolor flow cytometry in cell phenomics, illuminating the potential of applying phenomics in future studies.

Moment-Based Estimation of State-Switching Rates in Cell Populations

In isogenic cell populations, cells can switch back and forth between different gene expression states. These expression states can be biologically relevant. For example, a certain expression state may cause a tumor cell to be resistant to treatment, while another state may leave it vulnerable to treatment. However, estimating the rates of state-switching can be difficult, because experimentally measuring a cell's transcriptome often involves destroying the cell, so it can only be measured once. In this paper, we propose a computational method to estimate the rate of switching between expression states, given data from a Luria-Delbrück style fluctuation test that is experimentally simple and feasible. We then benchmark this method using simulated data to test its efficacy, with varying assumptions made about cell cycle timing distribution in the simulations.

Cannabinoid type 2 receptor (CB2R) distribution in dermatomyositis skin and peripheral blood mononuclear cells (PBMCs) and in vivo effects of LenabasumTM

Background Lenabasum is a cannabinoid type 2 receptor (CB2R) reverse agonist that demonstrates anti-inflammatory effects in vivo and in vitro in dermatomyositis (DM) and is currently being investigated for therapeutic potential. The purpose of our study is to investigate CB2R distribution as well as the effects of lenabasum in DM. Methods Immunohistochemistry staining (IHC) was utilized to examine immune cell and cytokine production changes in lesional DM skin biopsies from lenabasum and placebo-treated patients. CB2R expression in various immune cell populations within DM skin was investigated with image mass cytometry (IMC), whereas flow cytometry elucidated CB2R expression in DM peripheral blood mononuclear cells (PBMCs) as well as cytokine production by CB2R-expressing cell populations. Results After 12 weeks of lenabasum treatment, IHC staining showed that CD4+ T cells, CB2R, IL-31, IFN-γ, and IFN-β cytokines were downregulated. IFN-γ and IFN-β mRNA decreased in lesional DM skin but not in PBMCs. IMC findings revealed that CB2R was upregulated in DM lesional skin compared to HC skin and DM PBMCs (p<0.05). In DM skin, CB2R was upregulated on dendritic cells, B cells, T cells, and macrophages while dendritic cells had the greatest expression in both DM skin and PBMCs (p<0.05). These CB2R+ cells in the skin produce IL-31, IL-4, IFN-γ, and IFN-β. Conclusion Our findings of differential CB2R expression based on location and cell type suggest modes by which lenabasum may exert anti-inflammatory effects in DM and highlights dendritic cells as potential therapeutic targets.

Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

Blood malignancies provide unique opportunities for longitudinal tracking of disease evolution following therapeutic bottlenecks and for the monitoring of changes in anti-tumor immunity. The expanding development of multi-modal single-cell sequencing technologies affords newer platforms to elucidate the mechanisms underlying these processes at unprecedented resolution. Furthermore, the identification of molecular events that can serve as in-vivo barcodes now facilitate the tracking of the trajectories of malignant and of immune cell populations over time within primary human samples, as these permit unambiguous identification of the clonal lineage of cell populations within heterogeneous phenotypes. Here, we provide an overview of the potential for chromosomal copy number changes, somatic nuclear and mitochondrial DNA mutations, single nucleotide polymorphisms, and T and B cell receptor sequences to serve as personal natural barcodes and review technical implementations in single-cell analysis workflows. Applications of these methodologies include the study of acquired therapeutic resistance and the dissection of donor- and host cellular interactions in the context of allogeneic hematopoietic stem cell transplantation.

Single-cell transcriptome reveals the redifferentiation trajectories of the early stage of de novo shoot regeneration in Arabidopsis thaliana

De novo shoot regeneration from a callus plays a crucial role in both plant biotechnology and the fundamental research of plant cell totipotency. Recent studies have revealed many regulatory factors involved in this developmental process. However. our knowledge of the cell heterogeneity and cell fate transition during de novo shoot regeneration is still limited. Here, we performed time-series single-cell transcriptome experiments to reveal the cell heterogeneity and redifferentiation trajectories during the early stage of de novo shoot regeneration. Based on the single-cell transcriptome data of 35,669 cells at five-time points, we successfully determined seven major cell populations in this developmental process and reconstructed the redifferentiation trajectories. We found that all cell populations resembled root identities and undergone gradual cell-fate transitions. In detail, the totipotent callus cells differentiated into pluripotent QC-like cells and then gradually developed into less differentiated cells that have multiple root-like cell identities, such as pericycle-like cells. According to the reconstructed redifferentiation trajectories, we discovered that the canonical regeneration-related genes were dynamically expressed at certain stages of the redifferentiation process. Moreover, we also explored potential transcription factors and regulatory networks that might be involved in this process. The transcription factors detected at the initial stage, QC-like cells, and the end stage provided a valuable resource for future functional verifications. Overall, this dataset offers a unique glimpse into the early stages of de novo shoot regeneration, providing a foundation for a comprehensive analysis of the mechanism of de novo shoot regeneration.