What is spatial proteomics?
Spatial proteomics is the study of protein structures and functions. It deals with proteins’ structural, structural dynamics, and functional properties in their native environment, which are typically cell-free or cell-based environments.
Spatial proteomics is a type of bioinformatics. The term “spatial” refers to studying how proteins are arranged in space. Proteins found in a specific location (e.g., at the surface of cells) tend to behave differently than those that aren’t so localized (e.g., in macromolecular complexes).
Spatial proteomics is also known as “cell-free proteomics” because it uses different approaches and instruments from conventional molecular biology methods, such as mass spectroscopy and nuclear magnetic resonance (NMR). Spatial proteomics has been used to measure protein structure and function at the single molecule level, revealing information about protein interactions at atomic resolution. It can also infer structure-function relationships at higher resolution levels than obtained through conventional mass spectroscopy/NMR techniques.
2. What is spatial proteomics?
Spatial proteomics is a way of understanding the gene expression dynamics in living cells. It has been used to study various diseases and developmental disorders like cancer, Alzheimer’s disease, Parkinson’s disease, diabetes, and other diseases.
Let me first explain what a proteome is to make it easier for you to understand what spatial proteomics is. Every cell has its unique set of proteins responsible for translating the ordered information encoded in DNA into cellular functions. Proteins are assembled of amino acids that determine the shape of each amino acid chain. According to University College London (UCL) researchers, a protein only exists when functional peptides interact at specific places on the cell surface. Cells interacting with other cells more frequently than their functions require smaller proteomes. Proteins play an essential role as they interact very efficiently in living cells.
Spatial proteomics aims to identify protein interactions between different cell types or tissues within a region of space and time rather than within a single cell. This can be accomplished by placing protein interactions among spatially distinct cells within an area of space and time.
This type of analysis can reveal how proteins affect the growth of tumor cells, how tumors work together to grow or regrow in response to stimuli such as hormones and drugs, how cancer cells respond to therapy and radiation therapy, and whether cancerous tumors are malignant or not, how tumors affect one another when they sprout from different parts of the body or organ and so on.
3. The need for spatial proteomics
It’s been told that the human brain is a spatial organ. The cortex is the brain’s outer layer, while the hippocampus and cerebellum are inside it. A spatial proteomic approach can explore how this organ is structured and how these structures interact to form biological networks.
Spatial proteomics has shown that different network parts are related to other factors, partly due to interactions between proteins or other molecules in these networks. The underlying principles can be used as a bioinformatics tool for understanding biological processes like transcriptional regulation or protein-protein interactions.Another essential advantage of spatial proteomics is that it can reveal how structure relates to function to develop new drugs — for example, novel drugs with specific target regions or side effects — which conventional approaches have not yet identified.
This paper reviews recent advances in spatial proteomics and presents several examples of new drug discovery applications for understanding the brain’s spatiotemporal organization. We also discuss current gene regulation and protein dynamics findings by spatial proteomics, which could provide insights into dynamic systems biology and regulatory networks.
4. How spatial proteomics works
Do you know all those little “spots” on your skin? These are called “spatial proteomes.” They are multi-dimensional features that map our bodies to the world around us and help scientists understand what makes us tick. In my opinion, the term is a bit of a misnomer because an actual “proteome” is a collection of proteins. A proteome is usually a collection of proteins coded for by genes (the instruction book).
Spatial proteomics offers a much broader view of what we do with our bodies. Spatial proteomics can tell us why we do it, whether there is anything we can do to avoid getting sick or suffering from pain, and how long it takes for the effects to wear off — even when you don’t feel like it.
5. The benefits of spatial proteomics
Spatial proteomics is an emerging field. As of yet, there isn’t a definitive consensus on the processes involved.
We know that biological and physical phenomena occupy far different domains. Physiological systems are at the base of many complex biological processes, fundamental mechanisms determining how cells interact with each other and the environment.
In contrast, in spatial proteomics, the aim is to understand interactions between cells and their environment — how they travel from point A to B, what happens when they stop and where they end up next time.
The spatial organization of cells can predict spatial proteomics within a tissue or organ (e.g., blood vessels), which external factors can further modulate (e.g., gravity). It is an interdisciplinary field that includes genomics, genetics, crystallography, biophysics, cell biology, and physics (e.g., optics).
In case you’re curious about spatial proteomics: it’s also known as “spatiotemporal proteomics” or SXP for short. Spatiotemporal proteomics is another subfield of spatial proteomics that addresses the correlation between spatiotemporal patterns of protein expression and cellular functions in living cellular systems such as brain tissue or muscle cells.
6. The challenges of spatial proteomics
We may still be in the early years of exploring spatial proteomics, but we have been able to build several successful collaborations in this field.We have long known that proteins can interact with other proteins. At the same time, it is indistinct what that means or why these interactions occur. In this article, I will attempt to address some of these issues by explaining how we are starting to use spatial proteomics as a tool for understanding protein interactions.
Spatial Proteomics was initially born out of the need to understand how two different proteins interact, which can be very important in biological processes like cancer and aging. For example, it is known that senescence-associated secretory phosphoprotein (secretory phosphoprotein SASP) interacts with both senescence-associated complex 1 (SAC1) and mitogen-activated protein kinase kinase 3 (MAPK3). However, what drives this interaction has not yet been determined.
“Spatial Proteomics” is a new term coined by Professor of Immunology and Biophysics at Indiana University, Dr. Neil Risch. It is a new field of study that reveals how the cellular structures inside your body affect your health and how it affects your immune system.
This is a significant field of study because it provides a deeper understanding of the relationship between the diseases of old age and the aging process. The results show that specific molecules are associated with a slowdown in aging and an increase in cancer, heart disease, diabetes, and depression in older adults.
Spatial proteomics is conducted through one or more imaging methods such as electron microscopy, x-ray crystallography, fluorescence correlation spectroscopy, confocal microscopy (using confocal laser scanning microscopy), nuclear magnetic resonance (NMR) spectroscopy/spectroscopy-based spectroscopy, multi-spectral imaging (MSI) or hyperspectral imaging (HSI). Multiple techniques can be used to identify specific spatial proteomic features within cells:
MSI combines spectral information with samples that are imaged with multiple spectral sensitivities.
MSI combines spectral information with pieces that are reflected at different excitation wavelengths.
HSI combines spectral information with essays reproduced using various wavelengths of light simultaneously.
HELIOS combines spectral information with works that are produced using multiple wavelengths simultaneously.
In general, there are three types of analysis: whole cell analysis (WCA), quantification of protein content within cells (QPCA), quantification of protein content within lysosomes (QLCA), and quantification of protein content within endosomes and lysosomes (QLCA/LACA).
The goal of WCA is to quantify proteins from cells using optical microscopy. This allows scientists to identify protein molecules and their location within the cell/organelle, such as organelles and vesicles, thus allowing researchers to determine if there may be any changes in levels or function associated with these proteins after aging or disease.
As far as QPCA goes, this type of quantitative analysis allows researchers to compare the quantity/quantity level between two or more biological samples based on a fixed amount of sample volume and can be applied when comparing organelle sizes between different species. Quantitative analysis may also be used to compare relative quantity levels between two or more individuals or groups by manipulating sample volumes to allow for greater statistical.