Traditional immunoassays, such as ELISA, have been widely adopted over the years to detect and quantify specific biomarkers or analytes within samples. Although these conventional assays suffice for the detection of abundantly expressed biomarkers, they offer a limited dynamic range and low sensitivity; therefore, lowly expressed biomarkers in complex samples may be undetectable by traditional immunoassays.
Single-molecule counting is a fast and robust single-molecule counting technology offering a high dynamic range, accuracy, reproducibility, and sensitivity. The lower limit of quantification of single-molecule counting is lower than that of ELISA and other traditional immunoassays.
Therefore, single-molecule counting has emerged as a promising method for the discovery of novel biomarkers with low abundance and the accurate quantification of ultra-low concentrations of analytes.
The technology: How does it work?
The extraordinarily high sensitivity and precision of the single-molecule counting technology are achieved by the use of biomarker-specific antibodies and the integration of microparticle and microplate-based immunoassays with digital detection.1
The technology was originally developed by Singulex Inc. and is now commercially available at Millipore Sigma. Currently available kits include those for the detection of various cytokines (e.g., IFNγ, IL-1β, and TNF-α), growth factors (e.g., BDNF, VEGF, G-CSF, and GM-CSF), and signaling molecules (e.g., AKT1).
The initial steps of single-molecule counting are similar to traditional ELISA or bead-based immunoassays. Samples containing the analyte are captured onto magnetic beads coated with biomarker-specific antibodies. After washing off unbound molecules, a biomarker-specific detection antibody directly labeled with a fluorescent dye is added. Detection antibodies bind to the captured analytes to generate immunocomplexes consisting of the capture antibody, the analyte, and the detection antibody.1
In contrast to ELISA and other traditional immunoassays, in single-molecule counting, these immunocomplexes are dissociated, and the beads are magnetically removed. The solution containing the antibodies is then transferred into multi-well plates for detection.
In the detection system of the SMCxPRO and Erenna instruments, the sample runs through a narrow capillary, and a confocal laser is used to scan each well to excite the fluorescently labeled detection antibodies. The emitted photos are detected and digitally counted as single molecules. The detection of single molecules passing through the laser is a key feature of this technology, and it contributes to its improved quantification at the lower end of the dynamic range.1
The digital counting of individual fluorescently labeled detection antibodies enables the generation of low background and a high peak signal profile with a broad dynamic range. This profile is automatically translated into biomarker concentration, with a sensitivity reaching sub-picogram/mL or femtogram/mL levels.1
Owing to its high sensitivity, reproducibility, accuracy, and precision, the technology allows scientists to accelerate drug discovery and development programs by identifying previously undetectable clinically relevant biomarkers, which could serve as therapeutic targets. The technology can also be used to acquire a deeper insight into various diseases and therapeutic areas, including cancer, cardiovascular diseases, neurodegeneration, and infectious diseases.
How can this technology help improve diagnosis and clinical research?
Single-molecule counting can be applied to complex biological samples, including serum, plasma, cerebral spinal fluid, cell lysates, and fine-needle biopsies. Using this technology, researchers and clinicians can accurately classify tissues and disease states based on the robust detection of biomarkers with low abundance, which could have been missed by previous biomarker analysis methods.
Moreover, the ultra-quantitative nature of the single-molecule counting technology allows its use for the precise measurement of diagnostic and prognostic biomarkers associated with disease development or progression. Hence, the clinical implementation of the method can improve diagnostic accuracy, as well as the risk-stratification of patients.1
Todd et al. evaluated the performance of the signal-molecule counting technology for numerous clinically relevant biomarkers, including cardiac troponin I (cTnI), in 50–150 μL of serum samples.2 They found that the technology provided detection limits of 10–100 pg/L for most protein biomarkers analyzed. For cTnI, in particular, the limit of detection was 0.2 ng/L, and the dynamic range was over 4.5 logs.2
In a large prospective cohort study, McCarthy et al. used single-molecule counting to better understand the prognostic value of high-sensitivity troponin I (hsTnI). In 991 patients without acute myocardial infarction, high levels of hsTnI were associated with a higher prevalence of angiographic coronary artery disease (CAD). Furthermore, hsTnI levels significantly predicted obstructive CAD, myocardial infarction, and cardiovascular death.3
cTnI, a contractile protein expressed in cardiac monocytes, is released at low levels (0.1 to 10 pg/mL) during the lifecycle of cells. However, in cases of damage to the cardiac monocytes due to acute myocardial infarction, the levels of circulating cTnI are increased by 10–1000 times. Early diagnosis of acute myocardial infarction is key to prevent myocardial damage and heart failure. cTnI levels have also been associated with the risk of death due to cardiovascular disease.4 Therefore, detecting small changes in cTnI over time can help improve the early diagnosis of acute myocardial infarction and to stratify patients in terms of risk.
The levels of IL-6, TNF-α, and IL-17A have also been associated with cardiovascular disease. However, biological variations in their levels limit their utility as biomarkers in clinical practice. Todd et al. used single-molecule counting to determine the reference range biological variation in IL-6, TNF-α, and IL-17A levels. They found an intra-patient variability of 21%–57%, while the inter-patient variation ranged from 22% to 53%.5 In a similar way, Gilbert et al. used the method to determine the reference limits of IL-4 and IL-10, two key immunosuppressive cytokines.6
The technology can also be used to optimize drug delivery methods. In a recent study, Horacek used dynamic single-molecule counting to optimize nanoparticles, a promising drug delivery tool. Specifically, they quantified the number of ligands on the surface of individual nanoparticles to precisely determine the particle functionalization.7
Another important advantage of this technology is that it requires reduced sample volumes (10–100 μL per well for human samples) due to its improved limit of quantification. This feature makes single-molecule counting an ideal method for the analysis of clinical samples, which are often available in limited amounts.
In addition to minimal sample dilution, the high accuracy and reproducibility of the platform make it a powerful tool that can help basic and clinical scientists to better understand the molecular basis of diseases.1
Limitations of the technology
Although single-molecule counting technology provides improved sensitivity and a broader dynamic range of detection, the protocol is more complex than the protocols used in ELISA and other conventional immunoassays.
For instance, beads and immunocomplexes need to be thoroughly washed, and sufficient time should be allowed for the beads to settle before the next step.
In addition, this technology requires specialized equipment, which might not be available in many laboratories. In addition, the use of a capture surface in single-molecule counting technology makes the assay considerably more expensive than conventional immunoassays, limiting its wide use in clinical laboratories.
Another key disadvantage of the single-molecule counting technology over traditional immunoassays is its limited multiplexing ability. Future efforts to improve the multiplexing ability of single-molecule counting are needed to increase the cost-effectiveness of the technology.
References
- Hwang J, Banerjee M, Venable AS, et al. Quantitation of low abundant soluble biomarkers using high sensitivity Single Molecule Counting technology. Methods. 2019;158:69-76. doi:https://doi.org/10.1016/j.ymeth.2018.10.018
- Todd J, Freese B, Lu A, et al. Ultrasensitive Flow-based Immunoassays Using Single-Molecule Counting. Clin Chem. 2007;53(11):1990-1995. doi:10.1373/clinchem.2007.091181
- McCarthy CP, Ibrahim NE, Lyass A, et al. Single-Molecule Counting of High-Sensitivity Troponin I in Patients Referred for Diagnostic Angiography: Results From the CASABLANCA (Catheter Sampled Blood Archive in Cardiovascular Diseases) Study. J Am Heart Assoc. 2018;7(6). doi:10.1161/JAHA.117.007975
- Babuin L, Jaffe AS. Troponin: the biomarker of choice for the detection of cardiac injury. C Can Med Assoc J = J l’Association medicale Can. 2005;173(10):1191-1202. doi:10.1503/cmaj/051291
- Todd J, Simpson P, Estis J, Torres V, Wub AHB. Reference range and short- and long-term biological variation of interleukin (IL)-6, IL-17A and tissue necrosis factor-alpha using high sensitivity assays. Cytokine. 2013;64(3):660-665. doi:10.1016/j.cyto.2013.09.018
- Gilbert M, Livingston R, Felberg J, Bishop JJ. Multiplex single molecule counting technology used to generate interleukin 4, interleukin 6, and interleukin 10 reference limits. Anal Biochem. 2016;503:11-20. doi:https://doi.org/10.1016/j.ab.2016.03.008
- Horáček M, Engels DJ, Zijlstra P. Dynamic single-molecule counting for the quantification and optimization of nanoparticle functionalization protocols. Nanoscale. 2020;12(6):4128-4136. doi:10.1039/C9NR10218C
Christos Evangelou, PhD
Christos received his Masters in Cancer Biology from Heidelberg University and PhD from the University of Manchester. After working as a scientist in cancer research for ten years, Christos decided to switch gears and start a career as a medical writer and editor. He is passionate about communicating science and translating complex science into clear messages for the scientific community and the wider public.
