In a recent study published in Nature Biomedical Engineeringresearchers developed a multi-omics-based microsampling workflow for profiling proteins, metabolites, lipids, and specific cytokines and hormones, i.e., multiple analytes, in a single 10 μl blood sample (microsample).
The researchers sought to demonstrate how a microsampling method can effectively capture all lifestyle-related biological changes in an individual.
Background
Despite advances in multi-omics technologies, current sample collection and processing techniques are cumbersome. They require travel to a clinic and access to a phlebotomist and are not sufficiently flexible and non-invasive, leading to discomfort for the patient.
Furthermore, they require 10 to 50 ml of venous blood, which limits frequent collections, which in turn hinders the high-resolution analysis of biological processes that take place in the body over minutes or hours. Finally, high sample processing costs may be a reason for reluctance in people to participate in large-scale studies in remote areas.
Studies have shown that dried blood spot (DBS) sampling is often unreproducible, impeding analyte(s) analysis due to lack of sufficient sample volume.
However, volumetric absorptive microsampling (VAMS) is relatively more stable and achieves reliable analytical performance for targeted metabolites, such as lipids and proteins. Compared to DBS, VAMS is less invasive and easy to perform remotely in indigenous settings.
About the study
In the current study, researchers described two case studies to demonstrate the analytical capabilities of the multi-omics microsampling workflow in capturing and analyzing thousands of metabolites, lipids, cytokines, and proteins in 10 μl of blood. They used a solid matrix-based device, Mitra, to collect fixed volumes of blood.
Similarly, the team chose biphasic extraction with methyl-tert-butyl ether (MTBE) for the efficient extraction of lipids, proteins and metabolites from 10 μl of blood (micro) sample. This also ensured that these analytes were in the correct processed form for obtaining proteomics data. They used a different blood micro-sample for performing multiplex immunoassays.
Finally, the team analyzed this processed, annotated and curated omics dataset. Furthermore, the researchers used wearable sensors together with multi-omics microsampling to facilitate dynamic health profiles, for example intra-day physiological changes in heart rate (HR).
In the first case study, they assessed the response to a complex dietary intervention, ie Ensure Shake, to discover inflammatory and metabolic responses at an individual level. They screened 28 individuals from diverse backgrounds to develop six metabolic response metrics.
The second case study helped the researchers discover large-scale molecular fluctuations and multiple molecular associations related to physiological changes and physical activities during the day, with clinical biomarker levels, such as glucose and cortisol. They performed this high-resolution or very dense ’24 by 7′ profiling using 98 microsamples over a week. All participants collected blood micro-samples every one to two hours during waking hours over seven days, with some samples taken as little as 30 minutes apart. This helped the researchers collect 98 microsamples with wearable data from two devices, a smart watch and a continuous glucose monitor (CGM).
Study findings
In response to the ingestion of a mixed meal shake, with a complex metabolic profile, the researchers noted a high heterogeneity in individual metabolic and immune responses. The researchers detected 155 lipids, 560 metabolites and 54 cytokines/hormones in each micro-sample. Most significantly elevated metabolites and lipids reached the highest levels after approximately ∼60 and 120 min of shake consumption, respectively, and returned to baseline levels by 240 min. Future studies should uncover the reasons for these variations at the individual level to help optimize dietary and lifestyle changes for personal health, weight reduction, and management of metabolic disease.
Nevertheless, the proposed method can be valuable in research and large-scale stability studies, given that the analytes remain stable over time and with varying temperatures prior to their quantification. Most analytes, especially proteins, remained stable, while lipids were the least stable analytes. Some problems can arise with relatively unstable molecules. To deal with this, sample collection procedures can first include rapid and cold shipping, as processing them within 24 hours will minimize deterioration. Another way could be to quantify them from their unique degradation products.
In the second case study, the researchers collected 214,661 biochemical measurements in addition to wearable physiological data to analyze the human whole physiognomy and lifestyle, their changes on an hourly scale and relationships on a personal level. They grouped 2,213 internal molecular profiles into 11 clusters using fuzzy c-means clustering analysis, two of which followed circadian patterns. Cluster 4 enriched in various metabolites peaked during the day, while cluster 11, which consisted mainly of lipids, peaked at night. The microsampling method also captured the pharmacokinetics of aspirin, the drug consumed by a study participant in low amounts for four days. This detected its hydrolyzed product, salicylic acid, and revealed a clearance period of ∼24 hours.
Conclusions
Overall, the study method can help to reliably quantify thousands of molecules, including hormones and cytokines, even if they are in insignificant amounts in the blood.
The streamlined multi-omics profiling system of the present study enabled high-frequency molecular profiling with a fixed 10 μl sample volume. The finger prick blood collection method minimized pain and allowed sampling in a few minutes without traveling to a clinic or a laboratory technician. Moreover, it was scalable and easy to execute remotely.
Overall, it demonstrated the potential for large-scale comprehensive, dynamic molecular and digital biomarker discovery, monitoring and health profiling.
