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, single-cell analysis techniques have rescued translational research and medicine from the pitfalls of bulk measurements. Single-cell RNA sequencing (scRNA-seq), single-cell multiomics, and spatial transcriptomics show how specific cell signatures and lineages contribute to health and disease. Together, they help researchers design more effective ways to prevent and treat complex diseases.

These techniques come at a significant cost per sample, though. So the real heroes are the researchers who find ways 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. Hood developed the first automated DNA sequencer. On top of that, his lab at Seattle’s Institute for Systems Biology laid the groundwork for single-cell resolution sequencing methods and spun out multiple pioneering tool providers, including Applied Biosystems.

In 2009, an academic-private partnership between Cambridge University and Applied Biosystems (now a Thermo Fisher brand) described the first scRNA-seq method for generating transcriptomes from individual cells [Tang 2009]. Since then, innovations in single-cell techniques have improved sensitivity while driving down costs. This has led to widespread adoption in research and medicine. Compared to traditional methods like bulk RNA-seq, scRNA-seq lets researchers better understand the complex relationships among heterogeneous cell populations by capturing gene expression differences at individual cell resolution. As a result, single-cell analysis has become part of the toolbox for immunotherapy development, among other applications.

For instance, a study investigating T-cell epitopes as new targets for COVID-19 vaccine development needed a way to map T-cell responses against human leukocyte antigen (HLA) peptides. Researchers used peripheral blood mononuclear cells (PBMCs) collected from healthy and convalescent SARS-CoV-2 patient blood samples through Sanguine’s patient donor network. They screened patients for the HLA-A∗02:01 allele, which triggered the strongest CD8+ response in preliminary in vitro tests. Then, using single-cell sequencing of epitope-reactive CD8+ T-cells along with a barcoded tetramer assay, they evaluated the reactivity of CD8+ T-cells, the sequence of the T-cell receptors, and gene expression of reactive CD8+ T-cells across various HLA-I peptides [Weingarten-Gabbay 2021]. Surprisingly, the authors found multiple peptides from out-of-frame canonical open reading frames that triggered strong T-cell responses. This highlights their potential as targets 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. This sped up the research project and saved on costly single-cell methods and reagents.

Bigger Market, Bigger Data

Given how powerful single-cell analysis is for translational research, demand keeps growing for new techniques that expand the specimens researchers can investigate and the throughput they can achieve.

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 isolated from the whole blood of SCD patients. By monitoring gene expression changes in single cells using hybridized fluorescent probes, researchers found that BACH1 inhibition activated NRF2-responsive genes. These genes are linked to SCD symptom improvement because they reduce levels of plasma heme and inflammatory cytokines [Belcher 2023].

Single-cell analysis took a major step forward with “Cytoseq,” developed by Stephen Fodor. Cytoseq uses collections of beads containing cellular and molecular barcodes that hybridize with mRNA in various cell types. This let researchers characterize single-cell gene expression on a larger scale than earlier scRNA-seq methods, even within large heterogeneous cell 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. They compared a population of untreated B donor cells to one spiked with Ramos lymphoma cells, and reported the RNA capture efficiency using a panel of B cell genes [Fan 2015]. Interestingly, the Cytoseq assay picked up the upregulation of mRNA transcripts related to lymphoma compared to the control cells. This confirmed the method’s usefulness for measuring and profiling gene expression in large, heterogeneous cell populations.

The Rise of the Multiverse

Researchers today aren’t limited to characterizing transcripts at single-cell resolution. They can also run “multiomics” studies that integrate gene expression with other key measurements in the central dogma, including proteomics, epigenomics, and metabolomics. Single-cell multiomics methods give a full picture of cellular phenotype, which makes them invaluable for applications like cell lineage tracing, immunology studies [Baysoy 2023], and cell therapy development. Combine these “-omics” with AI, and researchers have a powerful set of tools for finding robust biomarkers across the therapy landscape.

A key development in the single-cell multiomic field is 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 to quantify cell surface protein expression and transcriptomic data in individual cells [Stoeckius 2017]. This lets researchers better understand cell function by measuring post-transcriptional and translational changes to proteins — something transcriptomics alone can’t do. CITE-Seq and other single-cell multiomics methods are growing in popularity for evaluating and classifying cell phenotypes and gene editing workflows. They offer new modalities, target cells, and quality metrics across the therapeutic development spectrum.

Endgame: Designing Cost-Effective and Efficient Single-Cell Analysis

Single-cell analysis is an essential tool for translational research. But reagents, sequencing, and specialized equipment cost a lot, so researchers need to design the optimal experimental approach to save resources and get data efficiently. Access to enough of the “right” samples — whether whole blood from healthy donors or PBMCs from patients with rare diseases — helps researchers get more relevant data for their investment. Obtaining large quantities of enriched immune cells from one donor (e.g., leukopaks) works well for many applications, especially cell & gene therapy development.

Sanguine built a nationwide system for collecting minimally invasive biospecimens in patients’ homes, making single-cell analysis studies more effective. Researchers can choose from a diverse network of disease-state patients and healthy donors. This removes the need for clinical site access and lets hard-to-reach patients with rare conditions take part in therapy development.

Alternatively, healthy onsite collection programs give researchers on-demand access to healthy biospecimens like PBMCs or serum, supporting single-cell workflow and analysis optimization studies [Yi 2023]. These programs make recruitment faster and more convenient for both researcher and patient. In both at-home and onsite studies, recallable and engaged donors in Sanguine’s network consent to share clinically relevant medical records. This enables longitudinal studies and more effective inclusion/exclusion criteria than traditional procurement methods like biobanks.

Find out more about how Sanguine can help power your single-cell analysis.

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: mRNA-Seq whole-transcriptome analysis of a single cell

[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: The technological landscape and applications of single-cell multi-omics

[6] Stoeckius, M. (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14, 865–868 DOI: Simultaneous epitope and transcriptome measurement in single cells

[7] Yi Ping-Cheng (2023) Impact of delayed PBMC processing on functional and genomic assays. Journal of Immunological Methods 519, 113514. DOI: Impact of delayed PBMC processing on functional and genomic assays