Single cell technologies can complement average measurements of a response determined through assessment of bulk cell populations. Different cells, tissues, and organs can respond to toxic exposures differently. Single cell technologies allow investigators to unmask this heterogeneity in response and ascertain biological response to an insult at the cell-type level.
During the SOT Virtual Meeting webinar “Single Cell Technologies: A Potentially Transformative Tool for Toxicology,” Dr. Grace Zheng from ArsenalBio described recent developments in technology that have been used for measuring multiple endpoints from a single cell. In T cells, this technique can measure the transcriptomic profile, antigen binding regions (V(D)J), surface proteins, and antigens, all from the same cell population. This can be useful in profiling immune cells and uncovering immune response to various infections and can also be applied to identify patient-specific tumor cell clusters.
Endocrine-disrupting chemicals (EDCs) have been associated with increased risk of breast cancer. Dr. Justin Colacino from the University of Michigan described how the number of stem cells in breast tissue (“stemness” of breast tissue) also is associated with risk of breast cancer. Using single cell technology, their team reported a large heterogeneity in the number of stem cells in breast tissue, and this stemness was associated with an increased risk of breast cancer in African American women, a demography known to have a disproportionally high burden of breast cancer. “Stemness” of tissue could also be investigated as a mediator of the effect of EDC exposure and the risk of breast cancer.
Dr. Patrick Allard’s lab at the University of California Los Angeles is leveraging the power of experimentation to complement this fine-grained technique to discern how different organs, tissues, and cells respond to common exposures. Using the nematode model, Caenorhabditis elegans, they are taking a whole-organism approach to measure single-nucleus RNA (snRNA). This approach has allowed them to capture changes in transcription that result from low-dose exposures. The short life cycle of the nematode makes it a good model to study transgenerational effects of exposure. Their team has been able to measure the effect of low-dose ethanol exposure on nuclear transcription in germ line cells, mechanosensory and motor neurons in the F1 generation, replicating previous findings associated with FASD.
Dr. Nikita Joshi of Northwestern University described the use of cell-lineage tracing, single cell transcriptomics, and in situ hybridization to determine the role of macrophages in asbestos-induced pulmonary fibrosis (AIPF). Their group reported that deleting monocyte-derived alveolar macrophages ameliorated AIPF. Further, using these techniques, they reported that M-CSFR (macrophage colony-stimulating factor receptor) is a druggable target that can disrupt the contribution of monocyte-derived alveolar macrophages to the fibrosis associated with asbestos exposure.
Dr. Suzanne Martos of the National Institute of Environmental Health Sciences described the effects of a different respiratory toxicant, smoking. Using single cell transcriptomics, they investigated whether smoking is associated with a change in regulation versus composition of circulating CD8 T-lymphocytes. They found that the major cell type frequency did not differ between smokers and nonsmokers. However, they found differentiation state shifts, seen as reduced diversity of T cell receptor repertoire in smokers. Further, they reported higher expression of genes linked to senescence and cytolytic activity, possibly related to atherosclerosis and aging.
Single cell technologies also can be applied to uncover the effect of exposure on the epigenetic regulatory landscape of a cell and discern the spatial organization of gene expression. As a metabolomics researcher, I am curious whether high-resolution mass spectrometers can be used to measure the metabolomic profile of different cell populations. Some of the challenges of single cell technologies were laid out by the Co-Chair Sudin Bhattacharya at the outset of the webinar. The data generated tend to be noisy, sparse and with high dimensionality. He also noted that understanding the nature of normal or healthy cellular state will allow researchers to better determine a cell that has departed from health. Further, technologies and algorithms that allow for prediction of this departure will be an incredible asset.