The Need To Scale Single-Cell Functional Studies

The following article is an opinion piece written by Kathrin Herbst. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position of Technology Networks.

The ability to examine individual cells within a population and provide a more detailed and accurate understanding of cellular diversity is essential in today’s drug discovery. Single-cell analysis enables the identification and characterization of rare cell types or subpopulations that may play critical roles in disease progression or in drug responses. By understanding the variability among individual cells, researchers can identify specific cellular targets or pathways associated with key traits, allowing for the design of more targeted and personalized therapies.

Taking advantage of current single-cell technologies, scientists have been able to make big gains in understanding disease mechanisms and in biomarker discovery. The identification of specific biomarkers linked to disease states or drug responses at the single-cell level has pushed the boundaries in the discovery of new therapeutic targets and the development of diagnostic tools.

But even as the life science community reaps the rewards of single-cell analysis technology, we must acknowledge that it suffers from an inherent limitation. The single-cell tools used today can report on expressed genes or relative protein levels – allowing scientists to infer biological function – but they cannot give a true functional readout. Cellular function is heavily influenced by non-template-driven factors and complex molecular and cellular interaction networks. How does a cell’s microenvironment influence its interaction with a neighboring cell? How do changes in a cell’s gene expression network influence the function, localization and stability of a key protein? These are important questions that cannot be answered directly with current single-cell analysis platforms.

One of the biggest gaps in drug discovery today is achieving a deep understanding of function where predictive models fail. While the evolution to interrogating individual cells has been a huge leap forward for the life sciences, today’s single-cell platforms should not – and indeed cannot – be the end of this journey. Scientists need not just single-cell analysis, but single-cell functional analysis. In order to accelerate therapeutic development, researchers have to be able to move from a genotypic approach to a phenotypic one.

Single-cell platforms

There are different approaches to analyzing individual cells with today’s technology platforms. Droplet-based systems, for instance, offer the enormous advantage of performing large-scale studies that allow users to process thousands or millions of cells at a time. However, most droplet approaches suffer from a lack of fine control, making it impossible to ensure the exact desired cellular content of each droplet. These tools suffer from the limitations of the Poisson distribution, meaning many droplets contain no cells at all. Finally, the scale itself can be difficult for scientists to manage; barcode-based information systems must be implemented to track each cell’s journey.

Traditional microfluidic technologies can also be used to analyze single cells. These offer much greater control and manipulation, with dedicated channels, valves and chambers. But that control comes at a cost: microfluidic devices usually have very low throughput. They are also poorly suited to the kinds of experimental change that often happen in science, particularly in early-stage discovery; a new approach might require an entirely new fabrication with custom channels designed just for that interrogation.

Innovation needed

For the next evolution in single-cell analysis, many of these limitations must be overcome. Single-cell platforms must offer the benefits of high-precision manipulation at single-cell resolution along with scalability and flexibility. Ideally, they would make it possible to create environments to study cell-to-cell interactions in a way that no rare cell goes to waste, while simultaneously offering the scalability necessary to screen whole cell populations. Most importantly, though, is the need to go beyond gene or protein readouts to true functional or phenotypic readouts.

Today, adding functional results to a single-cell analysis study requires a separate experimental setup, but it should be technically feasible to merge these into one platform that makes it possible to accomplish both goals at once. Currently, specific functional assays typically require their own experimental setup and bespoke platform. Therefore, multifunctional assay workflows may require carrying out a primary assay on one platform followed by retrieval and repreparation of cells for analysis on the next platform. However, transferring cells from one platform to another can be cumbersome and can result in a considerable loss of cell performance and viability. A better approach would involve one functional analysis platform that is flexible enough to run a broad range of assays at the single-cell level, all without damaging the cells. This could even enable running assays sequentially to learn much more about each cell.

For optimal utility, any platform should also allow users to capture cells of interest, preserving their viability for additional downstream analyses to help understand why certain subpopulations may behave differently from others. The ability to query each individual cell, and to do so repeatedly with a variety of assays, will be essential for a true functional understanding.

Key research areas

A single-cell functional analysis platform would provide a next frontier of analysis for specific life science applications where single-cell analysis tools are already widely used.

This is particularly true in the discovery, development and manufacture of cell and gene therapies. For this new class of treatments, understanding behavior at the cellular level – and, where possible, the biological microenvironment where these therapies are expected to work – is imperative. Often, researchers need to assess not just one function but several functions in combination, such as exhaustion profile, cancer killing effectiveness, and ability to recruit other cells. With conventional tools, it can take several years to screen for just one of those functions. Screening for all of them together is exponentially more challenging. Ideally, single-cell technologies would allow researchers to create cell villages, each perhaps containing a T cell, a B cell and a dendritic cell, and then evaluate their behavior with certain perturbations.

Applications such as antibody discovery, cell line development and CRISPR genome editing would also benefit from better functional analysis tools operating at the level of the individual cell. Each involves a functional requirement, such as assessing antibody efficacy in mechanism of action studies or assessing whether a gene edit had the desired effect, so having a functional readout would allow scientists to move through experiments more quickly, fail bad candidates faster and accelerate drug discovery timelines.

Looking ahead

The next generation of single-cell platforms will need built-in functional capabilities to enable the kinds of experiments that will truly accelerate drug discovery and development. There is tremendous opportunity here for technology developers and scientists alike to deliver on the promise of advanced therapies, precision medicine and so much more.

About the author:
 

Kathrin Herbst, PhD, has been working in cell and gene therapy development and using single-cell analysis tools for more than a decade. She serves as director of science and business development at Lightcast Discovery.