The Analyzers: Single-cell Superheroes of Therapy Development
The Analyzers: Single-cell Superheroes of Therapy Development
Supporting the single-cell revolution with rare, timely, annotated, and applicable biospecimens, from its origins to multiomics
Like Marvel’s Avengers, the heroic rise of single-cell analysis techniques has rescued translational research and medicine from the pitfalls of bulk measurements. Collectively, single-cell RNA sequencing (scRNA-seq), single-cell multiomics, and spatial transcriptomics provide more context to how specific cell signatures and lineages contribute to health and disease, allowing for more effective intervention strategies for preventing and treating complex diseases.
Despite their advantages, such techniques come at a significant cost per sample. So, the real heroes are the researchers taking the necessary steps to obtain biomarker-rich datasets cost-effectively.
Single-Cell Analysis: An Origin Story
If the single-cell saga were the Avengers, Lee Hood would be its Nick Fury. As if developing the first automated DNA sequencer wasn’t enough, Hood’s lab at Seattle’s Institute for Systems Biology laid the groundwork for single-cell resolution sequencing methods and the spinout of multiple pioneering tool providers, including Applied Biosystems.
In 2009, an academic-private company partnership from Cambridge University and Applied Biosystems (now a Thermo Fisher brand) described the first scRNA-seq method generating transcriptomes from individual cells [Tang 2009]. Since then, innovations in single-cell techniques have improved sensitivity while driving down costs, leading to widespread adoption in research and medicine. ScRNA-seq enables researchers to better understand the complex relationships among heterogeneous cell populations, compared to more traditional methods such as bulk RNA-seq, by capturing the differences in gene expression at individual cell resolution. Accordingly, single-cell analysis has become part of the toolbox for immunotherapy development, among other applications.
For instance, a study investigating T-cell epitopes as novel targets for COVID-19 vaccine development required a method of delineating T-cell responses against human leukocyte antigen (HLA) peptides. Using peripheral blood mononuclear cells (PBMCs) collected from healthy and convalescent SARS-CoV-2 patient blood samples obtained through Sanguine’s patient donor network, researchers screened patients for the expression of the HLA-A∗02:01 allele, which elicited the most robust CD8+ response in preliminary in vitro analyses. Subsequently, single-cell sequencing of epitope-reactive CD8+ T-cells in conjunction with a barcoded tetramer assay was used to evaluate the reactivity of CD8+ T-cells, the sequence of the T-cell receptors, and gene expression of reactive CD8+ T-cells in the presence of various HLA-I peptides [Weingarten-Gabbay 2021]. Surprisingly, the authors identified multiple peptides from out-of-frame canonical open reading frames that elicited robust T-cell responses, highlighting their potential as a target for next-generation COVID-19 vaccines.
Access to Sanguine’s donor community provided timely and specific convalescent sera at the height of the pandemic, when access to clinical facilities was limited, speeding up the research project and saving on costly single-cell methods and reagents.
Bigger Market, Bigger Data
Given the powerful implications of single-cell analysis in translational research, demand is escalating for new and innovative techniques that expand the specimens investigated and throughput.
In a 2023 study investigating a BACH1 inhibitor for treating sickle cell disease (SCD), an academic and commercial team used Sanguine’s extensive autoimmune patient network to collect human erythroid CD34+ positive cells from PBMCs that were isolated from whole blood samples of SCD patients. Monitoring the gene expression changes of single cells using hybridized fluorescent probes, researchers discovered that BACH1 inhibition led to the activation of NRF2-responsive genes, which are associated with SCD symptom improvement by reducing levels of plasma heme and inflammatory cytokines [Belcher 2023].
Single-cell analysis significantly evolved with “Cytoseq,” developed by Stephen Fodor. By using collections of beads containing cellular and molecular barcodes that hybridize with mRNA in various cell types, Cytoseq enabled single-cell gene expression characterization on a larger scale than previous sc-RNA seq methods, such as individual cells within large heterogeneous populations.
In the Science publication describing the Cytoseq method, Fodor and colleagues at his startup Cellular Research demonstrated proof-of-concept in primary B cell samples collected in-home from a healthy donor in Sanguine’s network. In comparing a population of untreated B donor cells to one that contained a “spike-in” of Ramos lymphoma cells, the team reported the RNA capture efficiency using a panel of B cell genes [Fan 2015]. Interestingly, the Cytoseq assay identified the upregulation of mRNA transcripts related to lymphoma compared to the control cells, which verified the utility of this method for measuring and profiling gene expression in large heterogeneous populations of cells.
The Rise of the Multiverse
Researchers today are so spoiled they aren’t limited to characterizing transcripts at single-cell resolution. They can do so-called “multiomics” studies integrating gene expression with other valuable measurements in the central dogma, including proteomics, epigenomics, and metabolomics. As such, single-cell multiomics methods comprehensively characterize cellular phenotype, proving invaluable for applications such as cell lineage tracing, immunology studies [Baysoy 2023], and cell therapy development. Combining these “infinity stone” -omics with AI as their Thanos, researchers can deploy the ultimate weapon in tackling robust biomarkers across the therapy landscape.
A critical development in the single-cell multiomic field is the cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), developed by the New York Genome Center. CITE-Seq uses antibodies labeled with DNA barcodes that quantify cell surface protein expression and transcriptomic data in individual cells [Stoeckius 2017]. These features allow researchers to better elucidate cell function by measuring post-transcriptional and translational modifications to proteins, which is impossible through transcriptomics alone. As such, CITE-Seq and other multiomics methods at single-cell resolution are growing in popularity for the evaluation and classification of cell phenotypes and gene editing workflows, providing new modalities, target cells, and quality metrics across the therapeutic development spectrum.
Endgame: Designing Cost-Effective and Efficient Single-Cell Analysis
While single-cell analysis has proven to be an essential tool for translational research applications, high costs typically associated with reagents, sequencing, and specialized equipment mean that researchers must design the optimal experimental approach to save resources and obtain data efficiently. Access to sufficient numbers of the “right” type of samples, whether whole blood from healthy donors or PBMCs from patients with rare diseases, enables researchers to return more relevant data on their investment. Obtaining large quantities of enriched immune cells from one donor (e.g., leukopaks) has proven attractive for many applications, particularly cell & gene therapy development.
Sanguine developed a nationwide means for collecting minimally invasive biospecimens in patients’ homes to facilitate more effective single-cell analysis studies. Researchers choose among a diverse network of disease-state patients and healthy donors, alleviating the need for clinical site access and enabling hard-to-reach patients with rare conditions to participate in therapy development.
Alternatively, healthy onsite collection programs permit on-demand access to healthy biospecimens like PBMCs or serum, facilitating single-cell workflow and analysis optimization studies [Yi 2023]. These programs make the recruitment process faster and more convenient for the researcher and patient. In both at-home and onsite studies, recallable and engaged donors in Sanguine’s network consent to clinically relevant medical records that enable longitudinal studies and more effective inclusion/exclusion criteria than traditional procurement methods such as biobanks.
Find out more about how Sanguine can help power your single-cell analysis here.
By: William Lawrence, Ph.D.; Geocyte
References
[1] Tang, F. (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6, 377–382. DOI: https://doi.org/10.1038/nmeth.1315
[2] Weingarten-Gabbay, S. (2021) Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs. Cell. 184: 3962-3980. DOI: 10.1016/j.cell.2021.05.046
[3] Belcher, J. (2023) The BACH1 inhibitor ASP8731 inhibits inflammation and vaso-occlusion and induces fetal hemoglobin in sickle cell disease. Frontiers in Medicine. Volume 10. DOI: 10.3389/fmed.2023.1101501
[4] Fan, H. (2015) Combinatorial labeling of single cells for gene expression cytometry. Science. Volume 347,1258367 DOI:10.1126/science.1258367
[5] Baysoy, A. (2023) The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 24, 695–713 DOI: https://doi.org/10.1038/s41580-023-00615-w
[6] Stoeckius, M. (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14, 865–868 DOI: https://doi.org/10.1038/nmeth.4380
[7] Yi Ping-Cheng (2023) Impact of delayed PBMC processing on functional and genomic assays. Journal of Immunological Methods 519, 113514. DOI: https://doi.org/10.1016/j.jim.2023.113514