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Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.
Imaging mass spectrometry is a powerful technology that combines the molecular measurements of mass spectrometry with the spatial information inherent to microscopy. This unique combination of capabilities is ideally suited for the analysis of metabolites and lipids from single cells. This chapter describes a methodology for the sample preparation and analysis of single cells using high performance MALDI FTICR MS. Using this approach, we are able to generate profiles of lipid and metabolite expression from single cells that characterize cellular heterogeneity. This approach also enables the detection of variations in the expression profiles of lipids and metabolites induced by chemical stimulation of the cells. These results demonstrate that MALDI IMS provides an insightful view of lipid and metabolite expression useful in the characterization of a number of biological systems at the single cell level.
To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.
Cells of any organism are consistently exposed to changes over time in their environment. The kinetics by which these changes occur are critical for the cellular response and fate decision. It is therefore important to control the temporal changes of extracellular stimuli precisely to understand biological mechanisms in a quantitative manner. Most current cell culture and biochemical studies focus on instant changes in the environment and therefore neglect the importance of kinetic environments. To address these shortcomings, we developed two experimental methodologies to precisely control the environment of single cells. These methodologies are compatible with standard biochemistry, molecular, cell and quantitative biology assays. We demonstrate applicability by obtaining time series and time point measurements in both live and fixed cells. We demonstrate the feasibility of the methodology in yeast and mammalian cell culture in combination with widely used assays such as flow cytometry, time-lapse microscopy and single-molecule RNA Fluorescent in-situ Hybridization (smFISH). Our experimental methodologies are easy to implement in most laboratory settings and allows the study of kinetic environments in a wide range of assays and different cell culture conditions.
p63 is a transcriptional regulator of ectodermal development that is required for basal cell proliferation and stem cell maintenance. p73 is a closely related p53 family member that is expressed in select p63-positive basal cells and can heterodimerize with p63. p73-/- mice lack multiciliated cells and have reduced numbers of basal epithelial cells in select tissues; however, the role of p73 in basal epithelial cells is unknown. Herein, we show that p73-deficient mice exhibit delayed wound healing despite morphologically normal-appearing skin. The delay in wound healing is accompanied by decreased proliferation and increased levels of biomarkers of the DNA damage response in basal keratinocytes at the epidermal wound edge. In wild-type mice, this same cell population exhibited increased p73 expression after wounding. Analyzing single-cell transcriptomic data, we found that p73 was expressed by epidermal and hair follicle stem cells, cell types required for wound healing. Moreover, we discovered that p73 isoforms expressed in the skin (ΔNp73) enhance p63-mediated expression of keratinocyte genes during cellular reprogramming from a mesenchymal to basal keratinocyte-like cell. We identified a set of 44 genes directly or indirectly regulated by ΔNp73 that are involved in skin development, cell junctions, cornification, proliferation, and wound healing. Our results establish a role for p73 in cutaneous wound healing through regulation of basal keratinocyte function.
Transcript levels powerfully influence cell behavior and phenotype and are carefully regulated at several steps. Recently developed single cell approaches such as RNA single molecule fluorescence in-situ hybridization (smFISH) have produced advances in our understanding of how these steps work within the cell. In comparison to single-cell sequencing, smFISH provides more accurate quantification of RNA levels. Additionally, transcript subcellular localization is directly visualized, enabling the analysis of transcription (initiation and elongation), RNA export and degradation. As part of our efforts to investigate how this type of analysis can generate improved models of gene expression, we used smFISH to quantify the kinetic expression of STL1 and CTT1 mRNAs in single Saccharomyces cerevisiae cells upon 0.2 and 0.4 M NaCl osmotic stress. In this Data Descriptor, we outline our procedure along with our data in the form of raw images and processed mRNA counts. We discuss how these data can be used to develop single cell modelling approaches, to study fundamental processes in transcription regulation and develop single cell image processing approaches.
The immune monocyte/phagocyte system (MPS) includes numerous cell subsets of the myeloid lineage including monocyte, macrophage, and dendritic cell (DC) populations that are heterogeneous both phenotypically and functionally. Previously, we characterized these diverse MPS phenotypes with multi-parametric mass cytometry (CyTOF). In order to expansively characterize monocytes, macrophages, and dendritic cells, a CyTOF panel was designed to measure 35 identity-, activation-, and polarization-markers. Here we provide a protocol to define a reference map for the myeloid compartment, including sample preparation, to produce reference cell subsets from the monocyte/phagocyte system. In particular, we focused on monocyte-derived macrophages that were further polarized in vitro with cytokine stimulation (i.e., M-CSF, GM-CSF, IL-4, IL-10, IFNγ, and LPS), as well as monocyte-derived DCs, and myeloid-derived suppressor cells (MDSCs), generated in vitro from human bone marrow and/or peripheral blood.
Single-cell technologies that can quantify features of individual cells within a tumor are critical for treatment strategies aiming to target cancer cells while sparing or activating beneficial cells. Given that key players in protein networks are often the primary targets of precision oncology strategies, it is imperative to transcend the nucleic acid message and read cellular actions in human solid tumors. Here, we review the advantages of multiplex, single-cell mass cytometry in tissue and solid tumor investigations. Mass cytometry can quantitatively probe nearly any cellular feature or target. In discussing the ability of mass cytometry to reveal and characterize a broad spectrum of cell types, identify rare cells, and study functional behavior through protein signaling networks in millions of individual cells from a tumor, this review surveys publications of scientific advances in solid tumor biology made with the aid of mass cytometry. Advances discussed include functional identification of rare tumor and tumor-infiltrating immune cells and dissection of cellular mechanisms of immunotherapy in solid tumors and the periphery. The review concludes by highlighting ways to incorporate single-cell mass cytometry in solid tumor precision oncology efforts and rapidly developing cytometry techniques for quantifying cell location and sequenced nucleic acids.
© 2018 Federation of European Biochemical Societies.