Race for the Yellow Jersey: Crowning a Protein Detection System Champion

By: Geoffrey Feld, Ph.D. | Geocyte LLC
Anna Berdine | Alamar Biosciences

What system yields the best balance of speed, breadth, and detection parameters?

One of the iconic global summer pastimes is the Tour de France cycling race. Taking place across the French countryside each July since 1903, the tour tests the world’s best road cyclists’ endurance, quickness, and camaraderie across mountains, cities, and farmland. Each day’s overall leader sports the coveted Yellow Jersey, while individual champions in sprinting, mountain climbing, and youth (riders under age 26) are identified with Green, Polka Dot, and White Jerseys, respectively. Translational scientists tasked with identifying and validating biomarkers similarly evaluate protein detection systems, choosing the solution that fits their needs based on performance in dynamic range and multiplexing, sensitivity and specificity, or automation amenability. Do researchers need to compromise, or is there a clear “Yellow Jersey” among protein detection systems? Meet the competition For the scope of this blog, we are focusing on systems that do not rely on mass spectrometry, which is generally considered complementary to the methods described here and the topic of a later discussion. Here, you will learn about single-molecule arrays (SIMOA), aptamer-enabled assays (SomaScan), proximity ligation assays (PLA), proximity extension assay (OLINK), and nucleic acid-linked immune-sandwich assay (NULISA™). For a brief introduction to these methods and how they read out proteins, see our previous blog on immunoassays. The Polka Dot Jersey: dynamic range and multiplexing If you’ve ever watched the Tour de France on a mountain-heavy day, you probably felt similarly to biomarker scientists years ago who thought they would never cover the breadth of blood-based protein diversity. Circulating proteins come in a plethora of shapes, sizes, and concentrations, covering a dynamic range of ~12 orders of magnitude. Like conquering a mountain in the Pyrenees on a bicycle, capturing sufficient dynamic range for protein detection is challenging and rewarding. At the top, the literal “10,000-ft view” (or 3,000 meters) of the terrain below is analogous to surveying the multitude of circulating proteins that inform on pathways, systems, cell types, and disease states (i.e., multiplexing). By design, the single-molecule methods, SIMOA and SMC, are not competing for the Polka Dot Jersey. As targeted kits, they cover dynamic ranges suited to the anticipated analytes (usually 3–4 logs around the ambient concentration), and each kit, at most, covers on the order of tens of proteins. As discovery platforms, SomaScan, OLINK, and NULISA are designed to encompass the wide dynamic range and diversity of circulating protein analytes. All three can cover ~10 orders of magnitude and hundreds or thousands of relevant targets. OLINK requires the dilution of high abundant proteins and segregates proteins into pools of similarly expressed targets.  Therefore, SomaScan and NULISA methods equally take home the Polka Dot Jersey in identifying low and high-abundance proteins across healthy and disease-state levels that may present in the blood. The White Jersey: sensitivity and specificity Until recently, protein detection methods were all pretty vanilla regarding sensitivity and specificity. With typical sensitivities in the pg/ml range and specificities that may or may not be human-specific antigens, classic ELISA immuno-sandwich assays and their evolution into PLA methods were “good enough” for mainly validation purposes. Single-molecule SIMOA and SMC methods relying on fluorescence pushed the detection limits into the femtogram (fg/ml) range. SomaScan and PEA have detection limits in the “classic” low pg/ml range By combining nucleic acid barcoding with magnetic bead capture and a unique biotin-streptavidin “recapture” step, relevant newcomer NULISA pushes the detection limit into the low fg/ml (attomolar) range while demonstrating superior specificity for human targets (Feng 2023). Hands down, NULISA earns the honor of wearing the White Jersey. Interestingly, recent Tour de France events have seen White Jersey combatants fiercely competing for the top prize; is the youngest protein detection system entrant also poised to wear the Yellow? The Green Jersey: automation augmentation Three primary goals drive the march toward sample prep and assay automation in biotech:
  1. Increase measurement reproducibility by reducing the variability inherent to manual processes
  2. Increase productivity by reducing FTE hands-on time, thereby shifting the focus from the bench to the science
  3. Increase the speed of assay performance by accumulating more measurements per second.
As assays and the analytes they measure become more complex, additional steps are needed to ensure biomolecules are maintained in relatively ambient conditions that resemble in situ. Each additional handling or transformation step can systematically introduce measurement errors that accumulate throughout the assay, starting with the specimen collection and ending at the point of diagnosis. By its nature, manual processing can significantly contribute to error propagation, complicating the precision and accuracy of the overall measurement (Figure 1). Automating the steps of an analysis process reduces the variability of individual measurements, which can result in massive cost savings in the form of fewer repeat measurements and even smaller clinical trials.

Figure 1: Example “peacock plot” demonstrating the accumulation of systematic errors in a measurement that requires multiple handling steps.

All the methods described have taken on automated steps in some shape or form. SomaScan must be performed by a certified laboratory, so we will not discuss its automation compatibility. PLA and SMC methods have been adapted to numerous liquid-handling robotic systems, sample prep stations, plate hotels, movers, and readers. While this bespoke automation comes in handy when such systems are already present in a laboratory, they typically require a high level of automation and programming expertise to build, augment, and maintain assay productivity. SIMOA, PEA, and NULISA methods have purpose-built systems and associated reagent kits for researchers to achieve turnkey automation specific to the technology. You can perform SIMOA on one of three systems: a fully automated, large-footprint instrument, a semi-automated benchtop apparatus, and a small benchtop device for measurement only. PEA and NULISA methods can be performed in dedicated benchtop systems with walkaway automation. NULISA has the added advantage of automating a dilution step necessary for achieving high dynamic range; thus, it edges out the competition for the Green Jersey in offering researchers a single system capable of running all its current and future assays with the highest level of automation available in a space-saving benchtop instrument. The Yellow Jersey: compromise on nothing In sharing Polka Dot Jersey honors while squeaking by with the Green Jersey and running away with the White Jersey, NULISA cruises to the Tour de Protein detection system finish line wearing the Yellow Jersey. For the first time, translational scientists no longer must compromise on the biophysical limitations of protein analysis (dynamic range, multiplexing, sensitivity, and specificity) nor on achieving an automated workflow (see Summary Table). Tune in for the next series of blogs focusing on specific disease indication areas and how NULISA technology can unlock the biomarker potential of liquid biopsy proteomics.

Summary Table: Tour de Protein Detection System


References
Katz et al. Science Advances (2022). Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods. 8(33): eabm5164.

Feng et al. Nature Communications (2023) NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing. 14: 7238.