Accurately and efficiently connecting molecular changes to pathogenic phenotypes is paramount in toxicological research. Transcriptomic and proteomic analyses are traditional investigatory approaches, though exact transcript and protein content can vary from cell to cell in any given tissue. Therefore, conventional bulk molecular analyses can give nonspecific averages because of the presence of heterogenic cell populations. Heterogenic populations within bulk analyses may obscure effects in certain cell subpopulations that could be more sensitive toxicity targets. Single cell analyses, on the other hand, can identify complex cell subpopulations within a single sample and identify rare target populations. Some traditional methods can be modified to allow for single cell analysis, while other methods have been amended to include single cell capabilities to broaden investigatory horizons. During the 2020 SOT Virtual Meeting, the Workshop Session “Single Cell Applications in Mechanistic Toxicology,” chaired by Dr. Cody Smith, featured efforts to capitalize on investigatory opportunities that single cell approaches provide.
The first speaker, Dr. Cody Smith of Rutgers, The State University of New Jersey, gave an overview of traditional bulk analytical methods as well as an introduction to how single cell applications could be used to expand investigations. Using an acute lung injury study as an example, Dr. Smith discussed how lung tissue is populated by a diverse cell population that becomes activated during acute cell injury. Exploiting the sorting parameters of traditional flow cytometry methodology (i.e., size, cell protein expression, digital register of fluorescent label signal intensity) allowed for identification of lung cell subpopulations within a given heterogenous sample. Heterogeneous samples also could be assessed via single cell western blot analysis, where individual cells are separated into microwells, lysed in situ, and subsequently subjected to gel electrophoresis and blotting. Single cell western blotting offers a useful approach for samples with low starting cell numbers (e.g., primary cultures, rare cell populations) and is capable of both multiplexing and quantitative analyses. Within both approaches, using specific combinations of antibodies and/or machine parameters can distinguish individual populations within samples. Furthermore, these approaches allow for identification of and changes within sensitive population subtypes that may otherwise go undetected in bulk analyses.
The second and third speakers, Dr. Douglas Ruden of Wayne State University and Dr. Oswaldo Lozoya of the National Institute of Environmental Health Sciences, both discussed how next-generation sequencing technology with single cell analyses could reveal deeper layers of information in Seq investigations. Using a Drosophilia model of traumatic brain disease, Dr. Ruden described how single cell RNA-seq approaches increase analytical transcriptomic throughput while simultaneously allowing for the detection of diverse cell subpopulation types using gene x cell matrices. Dr. Lozoya followed by addressing the challenges of utilizing single cell RNA-seq with human subject samples. Dr. Lozoya presented work exploring novel epigenetic signature changes in the macrophages of smokers versus nonsmokers. Single cell RNA-seq can prove challenging as human subject samples may vary broadly from person to person and make it difficult to integrate individuals and libraries. Dr. Lozoya described progress made with a unique data processing workflow called single-cell amalgamation by latent semantic analysis (SALSA). SALSA functions to identify subsets of informative data within gene x cell matrices, highlight genes where expression has changed the most, and ascertain rare transcript expression within a sample. The SALSA workflow by design also reduced noise associated with traditional Seq matrix hyperplexing and reduced analytical difficulty and time. Using such a workflow approach, Dr. Lozoya maintained, has potential for cluster-type analyses based on variation patterns to ascribe cell types and population patterns and detect new biomarkers. While more data had the potential to amplify noise and analytical complexity, more sequencing and deeper data dives could yield higher quality analysis.
Single cell analytical approaches have the potential to revolutionize mechanistic studies and open a channel to more informative data yields when compared with bulk analyses alone. As demonstrated by this session, information gleaned from single cell approaches can develop more precise investigations that explore regulatory relationships between exposures, genes, and proteins as well as allow for cell lineage tracing. By balancing single cell methodologies and improved data workflow pipelines, investigators can enhance their ability to identify novel biomarkers for disease and injury and unexplored toxicity pathways.
This blog was prepared by an SOT Reporter and represents the views of the author. SOT Reporters are SOT members who volunteer to write about sessions and events in which they participate during the SOT Annual Meeting and ToxExpo or 2020 SOT Virtual Meeting. SOT does not propose or endorse any position by posting this article. If you are interested in participating in the SOT Reporter program in the future, please email Giuliana Macaluso.