Plasma/Serum Proteomics Service

What Is Blood Proteomics

Proteins within the human blood circulation system hold paramount significance, intimately interconnected with all organs, tissues, and cells, their properties serving as direct reflections of the body's pathological and physiological states. A majority of disease-associated biomarkers find their origins intertwined with blood-based proteins. Blood proteomics entails the intricate exploration of protein ensembles residing within the circulating blood, constituting the most intricate facet of the human proteome.

Choice Between Serum and Plasma for Proteomic Analysis

Serum refers to the translucent yellowish fluid obtained after coagulation of blood, resulting in the separation of fibrinogen and certain coagulation factors from plasma. Plasma constitutes a vital component of blood, presenting as a pale yellow fluid with a complex composition, including substantial amounts of water, proteins, lipids, inorganic salts, among others. Plasma is acquired by centrifugation of blood after the addition of anticoagulants, distinguishing it from serum primarily due to its fibrin content. However, the presence of anticoagulants may potentially influence research outcomes. The decision to employ either serum or plasma for proteomic investigations depends on the research objectives. For instance, when studying a specific coagulation factor, plasma might be the preferred choice.

Exploring the Intricacies of Serum and Plasma Proteomics in Disease Research

Serum and plasma proteomics represent pivotal subdomains within the realm of proteomic investigation. By examining the complete protein expression profiles within plasma and serum of selected cohorts, differential proteins are identified, disease-associated proteins are discerned, consequently forging novel avenues for probing into the pathophysiological mechanisms of major diseases, identifying specific early diagnostic biomarkers, and uncovering drug-target interactions.

在疾病过程中,无论是早期diagnostics or throughout the course of pathogenesis, the body predominantly secretes low-abundance proteins into plasma and serum. Characteristics of plasma and serum proteins are as follows: 1) Proteins exhibit an intricate compositional diversity, with at least ten thousand protein forms present, and a vast majority of these proteins existing in exceedingly low abundances. 2) The abundance discrepancies among proteins are substantial, surpassing the sensitivities and dynamic ranges achievable by current separation and identification methodologies. However, the evolution of plasma and serum protein technologies, such as mass spectrometry-based proteomics and Data-Independent Acquisition (DIA), provides new possibilities for delving into the proteomic exploration of plasma and serum in the future.

The protein constituents within plasma and serum are exceedingly intricate, rendering a solitary mass spectrometry-based proteomic approach insufficient to unveil their entirety. Crucially, the focus of plasma and serum research resides in the quest for early disease diagnostic markers, frequently residing in the realm of lower abundance. To unveil more critical biomarkers, the following issues require consideration: 1) Both plasma and serum samples can undergo mass spectrometry-based proteomic analysis, albeit with slight differences in identified proteins. 2) EDTA plasma or citrate plasma, both devoid of platelets, are better suited for blood plasma proteomic analysis. 3) The removal of high-abundance proteins from plasma and serum is imperative to enhance the detectability of low-abundance proteins.

What Can We Do for You

Creative Proteomics offers proteomic analysis services for plasma/serum proteins utilizing mass spectrometry techniques based on Data Independent Acquisition (DIA) and Data-Dependent Acquisition (DDA).

Blood Proteomics Service Workflow

Sample Collection and Preparation

After blood collection, samples undergo preprocessing steps, including centrifugation, quality screening, protein extraction, and dehydration, ensuring sample purity and stability.

Mass Spectrometry Analysis

Following preprocessing, samples are introduced into a mass spectrometer for analysis. Mass spectrometry analysis is the cornerstone technology of clinical blood proteomics, primarily employing ionization, separation, and detection to analyze protein components within the sample. Commonly used mass spectrometers include MALDI-TOF MS and ESI-MS. The analysis yields an initial raw mass spectrum.

Bioinformatics Analysis

Due to the complexity of spectra generated by mass spectrometry, bioinformatics analysis is essential. This involves steps like feature extraction and analysis, database alignment, noise reduction, and more. The objective is to convert spectrum information into protein identification and relative abundance data.

Data Analysis

To derive statistically meaningful results from bioinformatics-analyzed data, further data analysis is conducted. This encompasses differential analysis, clustering analysis, pathway analysis, and more.

Result Output

The output of blood proteomics includes information at two levels. First, a quantitative results table is generated, listing detected proteins in the samples and their relative abundances. Second, a mass spectrum is provided, enabling in-depth scrutiny of identified proteins within the sample.

Blood Proteomics Sample Requirements

Whether in plasma proteomics or serum proteomics, the process begins with preparing plasma or serum from blood. Plasma is obtained by centrifuging blood with anticoagulants added, while serum is obtained by centrifuging coagulated blood. Thus, researchers need to choose the appropriate blood processing method based on their research objectives. If mass spectrometry analysis is planned, the use of EDTA as an anticoagulant is not recommended. Blood samples should be stored at -80°C and should not undergo repeated freeze-thaw cycles.

Report

Experimental steps
Relevant experiment parameters
Mass spectrometry spectra
Raw data
Proteomics analysis results

Multi-omic profiling of plasma reveals molecular alterations in children with COVID-19

Journal:Theranostics
Impact Factor (IF): 11.553

Research Background

COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is an acute infectious disease. As of June 23, 2021, it has resulted in nearly 180 million infections and 4 million deaths worldwide. The susceptibility to the novel coronavirus is widespread among all demographics, with no significant gender or age disparities. However, research from various countries has indicated that compared to adults, children with COVID-19 tend to exhibit milder symptoms, and severe conditions such as Acute Respiratory Distress Syndrome (ARDS) and Multisystem Inflammatory Syndrome are less common among pediatric cases. This study employs multi-omics techniques along with machine learning algorithms to uncover plasma molecular changes in pediatric COVID-19 patients compared to adult patients, providing novel insights into the mechanisms behind the manifestation of mild cases in pediatric COVID-19.

Research Methods

Utilizingproteomicsandmetabolomicstechniques

Experimental Strategy

(Figure 1)
Result 1: Molecular Alterations in Pediatric COVID-19 Patients

(Figure 2)
Result 2: Identification of Optimal Composite Model using Machine Learning Algorithms

(Figure 3)

(Figure 5)
Result 3: Validation of Plasma Molecular Changes in an In Vitro Viral Infection Model

(Figure 4)

Conclusion

In this study, a systematic analysis of plasma molecules in pediatric COVID-19 patients was conducted using a combination of multi-omics techniques and machine learning algorithms. This analysis not only revealed numerous unique changes in plasma molecules among pediatric COVID-19 patients but also offered possible explanations for the clinical differences observed between pediatric and adult patients. Additionally, the research identified various plasma molecules with antiviral and anti-inflammatory activities. These distinctive plasma molecules may play roles in preventing disease deterioration and immune regulation during the course of pediatric COVID-19 infection. Furthermore, they open new avenues for the development of COVID-19 therapeutic drugs and immunosuppressants.

The integration of proteomics and metabolomics in joint analysis has long been a focal point in multi-omics research. However, due to the inherent differences between proteome and metabolome, joint analysis remains challenging. This study demonstrates a prevalent approach in proteomics and metabolomics joint analysis, involving the annotation of proteins and metabolites to signaling pathways using KEGG, thereby associating differentially expressed proteins and metabolites with relevant pathways to establish a molecular model through joint analysis.