Hugh Kim & Soo Yeon Chae
The cancer disease is associated with various intracellular protein changes related to cell proliferation, migration, or death. Due to genetic abnormalities, cancer cells undergo uncontrolled proliferation. These changes occur through altered protein expression, and proteomic analysis can provide critical information for cancer diagnosis, prognosis prediction, and understanding of disease mechanisms throughout the treatment process.
As explained in the previous post “Proteom Analysis Methods, Part II”, the remarkable advances in the resolution and sensitivity of mass spectrometers have revolutionized the field of proteome profiling. Mass spectrometry is now a vital tool for addressing biological challenges. However, proteomic analyses based on mass spectrometry still face concerns about oversimplifying the heterogeneity of cancer cells due to the bulk analysis of large amounts of proteins. That is, there are limitations in identifying subtle changes that arise depending on the microenvironment surrounding each individual cancer cell.
Even within a single cancer type, heterogeneity can be observed due to differences in metastasis processes and microenvironments. The problem is that both less aggressive (good prognosis) and more aggressive (poor prognosis) cancer cells can be found simultaneously in the same patient. Cancer can be broadly classified into hematological cancers and solid tumors, with 85% of cancers being solid tumors. Solid tumors can be described as abnormal tissues composed of cancer cells, various other cells, and the extracellular matrix.
One of the challenges in interpreting proteomes from tumors is that cancer cells reside in different cellular layers within diverse microenvironments, creating variability. Depending on the microenvironment, tumor cells can be classified into proliferative cells (outer region), quiescent cells (middle region), or necrotic core cells. Each of these regions receives different levels of nutrients and oxygen, leading to different cellular characteristics. This cellular diversity significantly affects the efficacy of chemotherapy through intrinsic or acquired tumor resistance.
The 3D cell culture model, which artificially recreates tumor tissues, is one method to mimic the complexity of solid tumors. A thorough understanding of the complex characteristics of the tumor microenvironment and technologies that can accurately replicate it are essential. The use of these models is important because samples from tumor tissues can only be obtained through invasive procedures like surgery, and the sample quantities are limited. To rapidly interpret proteogenomic data obtained from actual patient tumors and apply it to treatment, building a comprehensive database covering numerous cases is crucial. The 3D cell model can facilitate the construction of such databases.
Recent advances in 3D cancer cell model research are providing abundant insights into mechanisms related to cancer development. Proteomic analysis using these 3D models can be used to predict individual cell responses to chemotherapy in environments closely resembling in vivo tumors. These models offer more diverse information than traditional 2D cell cultures or animal-based xenograft models and help reduce the ethical concerns often associated with animal experiments.
The methods for culturing 3D cell models were covered in the previous chapter. By using various culture techniques to alter the microenvironment of cancer cells, they exhibit different protein expression profiles in response to environmental stimuli. These intercellular differences in protein expression may be the key to answering many questions in cancer research.
Single-cell analysis, which analyzes individual cells one by one, enables the quantification of protein levels within a single cell and provides detailed information on how cellular heterogeneity affects function and fate. This helps us better understand variations among cancer cells. For example, in studies profiling adenocarcinoma cells, changes in the proteome during cancer cell differentiation were observed, and it was confirmed that each mutated cell type exhibited unique molecular and functional characteristics.
The main reason single-cell proteomics has become possible is the dramatic increase in the sensitivity of mass spectrometers. However, the small sample volume within a single cell remains a challenge. To address this, great effort is required in sample preparation to minimize sample loss. Techniques such as ultrasonic single-cell lysis and minimal proteomic sample preparation (mPOP) have been developed for this purpose.
Unlike commonly used chemical or mechanical lysis methods, ultrasonic lysis involves briefly applying ultrasonic waves (typically less than 50 seconds) to disrupt the cell membrane and extract intracellular materials. Recently, a method using mild detergents (e.g., digitonin) to pre-treat cells, followed by just 3 seconds of ultrasonic lysis, has also been developed. Another technique, mPOP, involves repeatedly freezing and heating cells between –80°C and 90°C in very small droplets. This method enables simultaneous processing of multiple samples, thereby minimizing volume, sample loss, and reagent usage.
Single-cell proteomics allows the quantification of thousands of proteins within a single cell and enhances our understanding of the origin and classification of individual cancer cells. Unlike bulk proteome analyses, single-cell characterization of cancer cells provides more detailed insights into cellular interactions and heterogeneity within tumors. Moreover, single-cell proteomics can identify heterogeneity within histologically similar cells, offering valuable information on cellular diversity among cancer cells and the developmental stages of cancer.
Further improvements in the sensitivity and throughput of single-cell proteomics technologies will significantly enhance our understanding of cellular heterogeneity and contribute to better cancer prognosis and diagnosis.
Please visit the Hugh Kim Research Group homepage.
Reference
1. Migisha et al., “Mass spectrometry-based proteomics of single cells and organoids: The new generation of cancer research” Trends in Analytical Chemistry, 2020, 130, 116005
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