It is no question why we sat down with Sarah for this edition of the CDN Researcher Spotlight…her work, passion and recent success speaks for itself. She has worked on a vast amount of research projects in her professional career and recently has published a preprint on the relation between rhinovirus infections and childhood-onset asthma.

 

The study that became the preprint:

Could you summarize to the general public why you decided to carry out this study and what are the main takeaways?

“We are interested in understanding the molecular mechanisms of immune-mediated diseases by using a combination of genetic and multi-omics approaches.

Asthma is one of the themes we are studying in the lab since we know it is a disease influenced by genetics and the environment, however we only understand some part of the  genetic origin. Only 47% of risk loci co-localize with leukocyte T-cell, a cell type that is part of the immune system, and has been associated with the genetics of asthma.  So we decided to tackle this question by thinking about other cell types that are under-studied in the context of asthma and in functional genomics studies. Epithelial cells for instance, is another main cell type studied for asthma but not usually from the genetic perspective; so we decided to focus on those cell types; because epithelial cells are the first line of contact for respiratory viruses and allergens. Moreover, epidemiological studies have shown that viral infections in early life are associated with childhood-onset asthma development and it remains unclear whether rhinovirus is causal in asthma or whether it is a biomarker for children already predisposed to asthma.”

Keeping in mind the general public, could you briefly explain what are GWAS and how are you incorporating them into this study?

“I will start with an introduction about GWAS, which stands for Genome-wide association study and it aims to identify associations of genotypes with phenotypes by testing for differences in the allele frequency of genetic variants between patients and healthy controls. The summary statistic obtained from a genome-wide association study is what use for our analysis; you can think of millions of variants that will have an associated P-value and then we can assess which of those variants are associated with the disease. 

In our study we are combining single-cell RNA-seq data and GWAS data by looking at genes that are over-expressed in a specific context (ex: genes being unregulated upon rhinovirus infection or genes being upregulated upon asthma). And then we check if those genes carry more risk variants than expected by chance. In this way, we are identifying cell states that are likely mediating genetic risk for a disease; and in our case we found an enrichment of genes in childhood-onset asthma risk loci in epithelial cells infected with rhinovirus. And thanks to single cell RNA-seq, we found that it is the non-ciliated airway epithelial cells that are likely driving the genetic susceptibility to childhood-onset asthma.”

What do you think is the power of using single-cell technologies?

“Single-cell technologies are great for many reasons, compared to more ancestral methods where we would analyze bulk populations of cells, single cells allow researchers to study individual cells. Bulk methods provide a snapshot of the overall population, whereas single-cell analysis captures the dynamics of individual cells over time. These single-cells technologies allow to identify cell subset/state in a more fine grain manner, this permit to identify rare cell types and understand better their roles and potential mechanisms associated with them. We can also use single-cell technology better understand cell-to-cell interactions and communication.”

Could you elaborate on how identifying epithelial cells as key cells expressing asthma-associated genes can lead to better treatment or prevention of asthma?

“The identification of airway epithelial cells infected with rhinovirus, and specifically non-ciliated cells as being the one upregulating asthma-associated genes help to better understand the genetic origin of childhood-onset asthma. It has been shown in the literature that drug targets that have genetic association evidence are more likely to be approved, and then move forward to clinical trial.” 

If you could carry out further analysis, what would you like to have done?

“Now our results represent preliminary results from cells taken from a few individuals (10-20 individuals), if we could replicate our results in cohorts of larger sample size (hundreds to thousands of individuals) it would be ideal. In that way we could identify the likely causal genes of risk variants and this will help to characterize more precisely the underlying molecular mechanisms. We would also need functional validations to prove and understand better our findings, we could think about CRISPR strategy for example. Finally, if our findings are further validated we could imagine the development of a rhinovirus vaccine or other protective intervention in order to prevent childhood-onset asthma.”

Cells are the basic building blocks of all living organisms. As biologists we are in a continuous endeavor to characterize and unravel how they come together to form and maintain complex tissue structures and organisms. Understanding the underlying molecular mechanisms ruling these systems is critical to understanding how cells communicate, interact, respond to stimuli, and evolve in steady-state and disease conditions (Wagner et al., 2016).

Single-cell RNA sequencing (scRNAseq) technologies have revolutionized our ability to understand cells. scRNAseq is an untargeted approach to profile individual cells at the whole transcriptome level. In 2009, Tang, et al. showed how an mRNA sequencing assay of mouse blastomeres increased transcript detection sensitivity, and enabled the identification of more than 1,700 previously unknown transcript isoforms2. Already, at this nascent stage, scRNAseq was showing its strengths and hinting at the powerful revolution about to come. Currently, there are many different approaches to generating scRNAseq data. All of them follow, broadly, the same basic steps: 1) single cell isolation and capture, 2) cell lysis and barcoding, 3) reverse transcription, 4) pre-amplification, and 5) library preparation and sequencing. For more detailed information on the technologies refer to established literature such as Svensson et al. 2018 and Mereu & Lafzi et al. 2020.

However, in order to fully understand a cell’s behavior and role in the tissue, it is critical to look beyond the cell in isolation. It is necessary to understand where it is located and how it is interacting with its neighbors. Ultimately, maintaining the spatial context is key to understanding tissue architecture of single cells in health and disease. However, single-cell analysis methods lose this spatial disposition in the cell dissociation step. The spatial neighborhood of a cell defines its interaction universe through juxtacrine and paracrine signaling and determines which biological processes that cell will carry out. Bulk RNAseq and scRNAseq deliver the promise of transcriptomics at the tissue and single-cell level, but at the cost of dissociating cells and removing the spatial context. Therefore, technologies aimed at capturing mRNAs while retaining the spatial context have been developed for a more comprehensive analysis. One of the most popular spatial transcriptomics technologies used nowadays is Visium. Originally developed by Ståhl et al. in 2016 and commercialized by 10X Genomics in 2020 (Figure 1).

The Visium technology, allows the user to profile tissue sections 6.5mm x 6.5mm, using ~5,000 spots. These spots are 55µm in diameter and therefore do not provide single-cell resolution. For reference immune cells range from 8-20µm in diameter while epithelial cells can go up to 25µm. Therefore, these spots are believed to capture the mRNA from 1-10 cells and sometimes even more, depending on the cellular density of the tissue.

Therefore, during his Ph.D. Marc set out to leverage the strengths of scRNAseq and spatial transcriptomics by integrating them and inferring the location of cell types and stateswithin complex tissues. To do so he developed SPOTLight (Elosua-Bayes et al,. 2021), an NMF-based regression model that learns gene signatures from the single cell data and uses them to decompose the cell types found within each spot. He then proceeded to apply it in different scenarios, for example, to better understand the tumor microenvironment in oropharyngeal carcinoma (Nieto et al., 2021). There he showed how the tumor presented different immune landscapes within and surrounding it. Some of these regions were immune active with cytotoxic CD8 T cells present while in others tumor cells had managed to dampen the immune response and showed a higher prevalence of regulatory and exhausted T cells (Figure 2).

These results highlight the heterogeneous nature of tumors and the importance of characterizing the different compartments found within them. Reaching this level of spatial resolution will enable us to better understand tumors and their heterogeneity, serve as prognostic markers, and ultimately deliver more effective personalized treatments.

The continuous advance of single-cell and spatial technologies will keep transforming our understanding of biology in health and disease. However, these advances must be translated to the clinic in order to have an impact on the general population. Nowadays, there is an abundance of data that comes from multiple modalities, tissues, and conditions, and much more is yet to come. The ability to integrate this information and draw the line from genomic variants to specific cell types in specific spatial niches to disease onset and treatment will be a grand challenge in the years to come.

 

References

• Wagner, Allon, Aviv Regev, and Nir Yosef. 2016. “Revealing the Vectors of Cellular Identity with Single-Cell Genomics.” Nature Biotechnology 34 (11): 1145–60. https://doi.org/10.1038/nbt.3711

• Tang, Fuchou, Catalin Barbacioru, Yangzhou Wang, Ellen Nordman, Clarence Lee, Nanlan Xu, Xiaohui Wang, et al. 2009. “MRNA-Seq Whole-Transcriptome Analysis of a Single Cell.” Nature Methods 6 (5): 377–82. https://doi.org/10.1038/nmeth.1315

• Svensson, Valentine, Roser Vento-Tormo, and Sarah A. Teichmann. 2018. “Exponential Scaling of Single-Cell RNA-Seq in the Past Decade.” Nature Protocols 13 (4): 599–604. https://doi.org/10.1038/nprot.2017.149

• Mereu, Elisabetta, Atefeh Lafzi, Catia Moutinho, Christoph Ziegenhain, Davis J. McCarthy, Adrián Álvarez-Varela, Eduard Batlle, et al. 2020. “Benchmarking Single-Cell RNA-Sequencing Protocols for Cell Atlas Projects.” Nature Biotechnology 38 (6): 747–55. https://doi.org/10.1038/s41587-020-0469-4

• Ståhl, Patrik L., Fredrik Salmén, Sanja Vickovic, Anna Lundmark, José Fernández Navarro, Jens Magnusson, Stefania Giacomello, et al. 2016. “Visualization and Analysis of Gene Expression in Tissue Sections by Spatial Transcriptomics.” Science (New York, N.Y.) 353 (6294): 78–82. https://doi.org/10.1126/science.aaf2403

• Elosua-Bayes, Marc, Paula Nieto, Elisabetta Mereu, Ivo Gut, and Holger Heyn. 2021. “SPOTlight: Seeded NMF Regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes.” Nucleic Acids Research 49 (9): e50. https://doi.org/10.1093/nar/gkab043

• Nieto, Paula, Marc Elosua-Bayes, Juan L. Trincado, Domenica Marchese, Ramon Massoni-Badosa, Maria Salvany, Ana Henriques, et al. 2021. “A Single-Cell Tumor Immune Atlas for Precision Oncology.” Genome Research 31 (10): 1913–26. https://doi.org/10.1101/gr.273300.120