The Promise of Single-Cell and Single-Molecule Analysis Tools to Advance Environmental Health Research Proceedings of a Workshop—in Brief (2019) / Chapter Skim
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The Promise of Single-Cell and Single-Molecule Analysis Tools to Advance Environmental Health Research: Proceedings of a Workshop - in Brief
The Promise of Single-Cell and Single-Molecule Analysis Tools to Advance Environmental Health Research: Proceedings of a Workshop - in Brief
Pages 1-12
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Workshop participants explored the state of this rapidly evolving field of study, reviewed preliminary uses of single-cell and single-molecule analysis tools in environmental health studies, and discussed the resources needed to make the data generated most useful to the biomedical and public health fields and to regulatory decision makers. In his introductory remarks, Kim Boekelheide from Brown University noted that singlecell and single-molecule analysis tools are widely applicable to many areas of science, but have not yet made much of an inroad into environmental health sciences.
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Regev explained that two major lines of technological advances within the past few years are single-cell genomics, particularly single-cell RNA sequencing, and spatial genomics. Rather than classify cells on the basis of their location or their shape, single-cell gene expression profiling allows cells to be defined as a point in the 20,000-plus dimensional gene expression space.
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Vasiliou explained that tissue-imaging mass spectrometry can yield predictive, prognostic, diagnostic, and surrogate markers and provide information on the underlying molecular mechanisms of disease. For example, imaging mass spectrometry can reveal biomarkers for drug response phenotypes and define a metabolomic profile for a specific genotype.
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There are challenges associated with single-cell metabolomics, said Vasiliou, including the low abundance of the molecules of interest, the rapid turnover rates of metabolites in a cell, artifacts caused by the MALDI matrix, and the limited availability of software that can integrate mass spectrometry data with microscopy images and enable linking specific metabolites to cellular pathology. Single-cell metabolomic analysis may be in its infancy, said Vasiliou, but he anticipates it will provide unique insights into the molecular mechanisms governing cell proliferation and differentiation, environment-induced changes in cellular function, and intracellular differences in susceptibility to adverse effects.
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are a better feature to use for identifying tumor subpopulations than gene expression features alone. To visualize tumor subpopulation data, her team developed what it calls bipartite graph visualization, which treats individual cells as nodes within a tumor and then generates networks using genes that correlate with eeSNVs.
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Vadigepalli's group developed a model of the multiscale control of liver repair that integrates molecular regulation, cell phenotypes, and physiological response to better understand the functional state transitions that underlying cell phenotypes go through to trigger and orchestrate the repair process. One finding from this work was that shifting the dynamics of the transition that occurs in hepatic stellate cells in response to liver injury controls overall mass recovery in the liver.
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A NON-LINEAR MODEL FOR ANALYZING SINGLE-CELL RNA SEQUENCING DATA Single-cell analyses are challenging, said Barbara Engelhardt from Princeton University, because of poorly defined cell types and cell states; rare, continuous, and unseen cell types; batch effects, dropouts, and doublets; and a heterogeneous latent dimension resulting from the complexity of expression patterns obtained from certain tissues. To address these challenges, Engelhardt and her colleagues developed a low-dimensional model of scRNA-Seq to help understand variation in gene expression across a population of cells.
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Gilad and his colleagues have tackled the challenge of identifying cell type and cell cycle phases in the context of the continuum of cell states in the cell cycle. This is done by taking advantage of a system that allowed them to obtain independent information about cell cycle by using fluorescence to measure the expression of genes whose activities are known to be cyclical and RNA sequencing from the same cell.
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Early insights gained from this type of cell atlas include discovering previously unknown cell types, determining the order in which cells develop, and identifying the programs cells use during their lifetime. For example, Regev and her colleagues developed an algorithm that allows them to use gene expression data to work backward in a cell's development to identify a cell's origin.
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Andersen noted that the idea that cells respond in an all-or-none fashion points to the importance of subtyping cells as a means of identifying those cells that are responding to a chemical perturbation instead of measuring responses in a population of cells. This demonstrates how single-cell analysis applies to environmental health research, he said.
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Regarding environmental health science, it will be important to understand and characterize the "normal" state of cells in order to have a validated baseline against which to understand how chemical perturbations affect single cells, said Norbert Kaminski. Having said that, he also noted that while he can see using these new technologies to understand biology, he is not yet sure what role they can play to inform risk assessment and science policy decisions.
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The statements made are those of the rapporteurs or individual workshop participants and do not necessarily represent the views of all workshop participants, the workshop committee, or the National Academies of Sciences, Engineering, and Medicine. Workshop Committee on the Promise of Single-Cell and Single-Molecule Analysis Tools to Advance Environmental Health Research: Norbert Kaminski (Chair)
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