Protein Profiling: Protein Biomarker Discovery

Protein Biomarker Discovery Applications

Protein Biomarker Monitoring Tumor Progression

Protein biomarkers monitoring human liver tumor progressions: 2D DIGE protein profiling was used to identify differentially expressed proteins between normal, early stage, and late stage liver tumor. These changed proteins are potential protein biomarkers monitoring liver tumor progressions.

Normal human liver

Early-stage liver tumor

Late-stage liver tumor

Figure 1: Global protein profiling of human liver proteome from normal liver, early and late stage liver tumor.

Normal / Early stage

Normal / Late stage

Figure 2: Overlay images of Normal/Early stage and Normal/Late stage. The green and red spots are proteins with decreased and increased expressions, respectively, due to early/late stage tumor.

Tumor Diagnostic Protein Biomarker

Protein biomarkers diagnosing late stage liver tumor: 2D DIGE protein profiling was used to identify commonly changed proteins in patients with late stage liver cancers. These proteins could be candidate markers to screen late stage liver cancer.

Patient 1: Normal tissue / Tumor tissue

Patient 2: Normal tissue / Tumor tissue

Figure 1: Differential protein expression was analyzed by 2D DIGE between normal liver and 2 patient livers with late stage liver cancer. Circled spots are proteins with common changes in 2 patients vs. normal.

Drug Toxicity Protein Biomarkers

Identify protein biomarkers causing drug toxicity: 2D DIGE protein profiling was used to analyze the protein expression changes induced by different drug treatment on human embryonic body (EB) cells . The study leads to the discovery of potential protein biomarkers for evaluating drug toxicity.

Control / Drug 1

Control / Drug 2

Drug 1 / Drug 2

Control / Drug 3

Control / Drug 4

Drug 3 / Drug 4

Figure 1: Protein expression changes between control and drug treatment. Here we only showed 4 out of 18 drugs tested.

Figure 2: 18 drugs are divided into 3 groups based on their toxicity: Liver and kidney toxic, liver toxic and liver non-toxic. A total of 1480 proteins are shown for each of the 18 drugs. Cluster analysis was performed with Genespring software (Redwood City, CA) on the expression change (obtained from 2D DIGE experiment) between normal and drug on 1480 proteins for each drug. Blue and red indicated down- and up- regulation, respectively. Similar protein patterns are observed within each toxicity group.

Drug Mechanism

Protein biomarkers for drug mechanisms: 2D DIGE protein profiling was used to compare protein expression levels of human cells treated with different types of drugs. Clustering analysis shows distinct protein pathways for each treatment, which leads to better understanding of compound’s mechanism of action, effective dose and toxicity.

Type A

Type B

Figure 1: Two drug compounds of each type (A1, A2 and B1, B2) were used to treat a human cell line. The toxicity indicators (proteins) were analyzed by 2D DIGE cross-gel analysis.

Type A

Type B

Fold Change

Figure 2: Clustering analysis of protein expression levels. The data illustrate different protein pathways for each type of compound. The protein pathway helps to understand compound’s mechanism of action, effective dose and toxicity.

Disease Protein Biomarkers

Protein biomarkers of different liver diseases: 2D DIGE protein profiling was used to compare protein expression levels of mouse livers with different diseases. The proteins showing distinct change in specific diseases could be potential disease-specific protein biomarkers.

• Normal: normal liver (control mice)
• Disease models: MDB (DDC+), fatty liver, CH7

Normal Liver

MDB (DDC+)

Normal / MDB (DDC+)

Figure 1: Black/white 2D DIGE image of Normal, MDB (DDC+) liver proteome and color overlay image of Normal/MDB (DDC+)

Normal Liver

MDB (DDC+)

Normal / MDB (DDC+)

Figure 2: Color 2D DIGE image of Normal, MDB (DDC+) liver proteome and color overlay image of Normal / MDB (DDC+)

Normal / MDB (DDC+)

Normal / Fat liver

CH7 / MDB (DDC+)

Figure 3: Zoomed-in view of overlay color images of Normal/MDB, Normal/Fat liver, and CH7/MDB

Therapeutic Target Protein Biomarkers

Identify therapeutic targets: 2D DIGE protein profiling was used to identify changed proteins in treated CD8+ T Cells at different time points with and without therapeutic functions. The commonly changed proteins at different time points with therapeutic functions are drug target candidates.

• T-cells Tmart-1 D20 and D27: displays the therapeutic functions
• T-cells Tmart-1 D13: without the therapeutic functions

Tmart-1-D13 / Tmart-1-D20

Tmart-1-D13 / Tmart-1-D27

Figure 1: T cells play a pivotal role in the immune response. Here we use 2D DIGE to identify the commonly changed proteins from Day 20 and Day 27 Tmart-1–specific CD8+ T Cells versus Mart-1 Day 13. The circled spots clearly showed the change.

Diagnostic Protein Biomarkers

Identify prenatal diagnostic protein biomarkers in human aminotic fluid: early prenatal diagnostic markers from human aminotic fluids can be used as the genetic and/or developmental markers to monitor the embryo development. 2D DIGE protein profiling was used to identify changed proteins between Normal and Disease human amniotic fluid samples. These proteins are potential disease diagnostic markers of human amniotic fluid.

Normal / Disease

Quality Control Testing

Detect Batch-to-Batch Variations: Another important application of 2D DIGE protein profiling allows comparing different batches of any protein samples. Such analysis provides direct visual comparisons as well as accurate quantification of the differences.

Transgenic Mouse Protein Biomarker

Transgenic mouse is widely used in studying gene functions or as animal models of human diseases. 2D DIGE protein profiling on transgenic mouse proteome will identify protein biomarkers differentially expressed due to the gene alteration, providing in-depth knowledge of protein functions and pathways.

SiRNA Protein Profiling

Small interfering RNA (siRNA) is widely used to study the function of specific genes by preventing the gene translation. 2D DIGE protein profiling on samples with siRNA will identify protein biomarkers differentially expressed due to siRNA interference, providing in-depth knowledge of protein functions and pathways.

Control

+siRNA

Control / +siRNA

Figure 1: Black/white images of Control, sample with siRNA and color overlay image.

Monitoring Protein Expression

2D DIGE protein profiling is very useful in monitoring protein expression changes under different conditions. The following 12 images showed how different treatments can change the proteome in different ways.

Time Course Protein Biomarker

Due to the multiplexity nature, 2D DIGE protein profiling has been widely used in time course studies. Here we show an example of monitoring time-dependent proteome changes during cell differentiation by 2D DIGE protein profiling. The data shows that 5 protein spots had different behaviors during the time course.

Kinase Target / Substrate

Protein phosphorylation by kinase shifts the protein spot to the acidic side. 2D DIGE is the only platform that enables visualization of both unmodified and phosphorylated protein spots between Control and Kinase-stabilized cell proteome. The example here can be used to monitor kinase activity and identify kinase substrates.

Gene Function Analysis

Transfection is a powerful tool for study of gene and protein functions. 2D DIGE protein profiling allows comparing the protein profiling before and after gene transfection, and identifying proteins that are changed due to transfection. The data provides great insight into the function of the transfected gene.

Mock

Transfected

Mock / Transfected

Figure 1: 2D DIGE color images of Mock (green), Tranfected (red) and their overlay images. The red and green spots in the overlay image represent increased and decreased expressed proteins due to gene transfection.

Gene Knockout

Gene knockout (KO) is a genetic approach to inactivate a specific gene in order to understand it functionality. 2D DIGE protein profiling can compare the proteome between the knockout and normal subjects, in order to identify protein biomarkers differentially expressed due to the gene knock-out, thus providing in-depth knowledge of functions and pathways of the targeted gene.

Example 1: Effects of gene knockout on 4 different mouse tissue proteomes

Example 2: Effects of gene knockout on a bacteria proteome

WT / KO

Yeast Protein Profiling

2D DIGE analysis of Yeast (S. cerevisiae) proteome before and after treatment