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Through the Art of Proteomics, Scientists Paint a Portrait of the Mouse Uterus

Through the Art of Proteomics, Scientists Paint a Portrait of the Mouse Uterus content piece image
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Scientists from the Pacific Northwest National Laboratory (PNNL) recently published a study in Nature Communications outlining their successful feat in mapping the proteome of the exceptionally small mouse uterus.

The research was a collaborative effort, utilizing the skills of biologists, chemists and data analysts. Not only have the researcher's scientists been able to map the proteome of the tiny multicellular organ, but they have also managed to do it in extremely high detail.

The team, led by biochemist Kristin Burnum-Johnson,
Senior Research Scientist in Biological Sciences at Pacific, collected substantial information regarding the individual cells and tissues within the organ and converted it into a comprehensive portrait of protein abundance.

The mouse uterus may be small, but this research marks a huge success in biology. The toolbox of analytical methods adopted by the team, and their success in using them, encourages their wide-spread adoption for proteomics research in a variety of scientific fields.

Technology Networks spoke with Burnum-Johnson to learn more about the study rationale, the analytical technologies used and the challenges encountered along the way.

Molly Campbell (MC): Can you tell us about the rationale behind the study?

Kristin Burnum-Johnson (KBJ):
Biological tissues are some of the most complex assemblies in nature and, to better understand these tissues, we need to visualize with high spatial resolution the location of each biomolecule, including proteins, and how they combine to carry out functions. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping these molecules across tissues, but technical challenges have limited applying this to proteins. In this study, we demonstrate an automated proteome imaging approach that utilizes label-free proteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across thin (10-12 µm) tissue sections.

MC: Why did you opt to explore the utility of this novel technology in mouse uterus specifically?

KBJ: This study was funded in part by the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The mouse uterine tissue was provided by Professor Sudhansu K. Dey, Director of Cincinnati Children’s Division of Reproductive Sciences. Starting when we were both at Vanderbilt University in 2003, Dey and I have a long-lasting collaboration utilizing IMS technologies to decipher embryo-uterine interactions during embryo implantation.  The rationale for these studies is that a better understanding of uterine-derived mediators that influence the window of receptivity to implantation will offer multiple targets for uterine receptivity, which may in turn be used to determine appropriate times for embryo transfer in the in vitro fertilization clinic.

Our previous IMS studies visualized the lipidomic landscape of the uterus orchestrating embryo implantation with matrix-assisted laser desorption/ionization (MALDI) and nanospray desorption electrospray ionization (nano-DESI) technologies. In this first-of-its-kind application, we analyzed uterine cross sections from pregnant mice prior to the adhesion of early embryos. Our proteomic measurements distinguished between luminal epithelial cells which line the uterine cavity and are the first cells to make direct contact with an embryo in its early form; stromal cells, which support an embryo’s growth during early pregnancy and other cells known as dispersed glandular epithelial cells. We were also able to correlate these proteomic images with our previous lipidomic images by mapping luminal epithelial specific arachidonic acid metabolism from phospholipids into bioactive prostaglandins.

MC: You used automated PNNL technology coupled with mass spectrometry in this research. Can you expand on the methodology and why you adopted this approach?

The first step was to build upon previous work by colleagues Zhu (co-first author) and Kelly (co-corresponding author, now at Brigham Young University) to measure thousands of proteins from a small number (<100) of mammalian cells with nanoPOTS – nanodroplet Processing in One pot for Trace Samples. To create our proteomic images, we focused on tiny regions of tissue, or voxels, each measuring 100 microns long x 100 microns wide and just 10 microns thick. We used nanoPOTS to carry out all sample processing, protein extraction, reduction, alkylation, and proteolysis, on each voxel in just 200 nanoliters of fluid, then used MS to measure levels of more than 2,000 proteins across each sample.

MC: In the study, you mapped the molecular distribution of proteins across the microenvironments of the mouse uterus. Can you tell us more about the molecular map? Do you envision applying this approach to other tissues?

: In this study the peptides resulting from nanoPOTS processing of each voxel are transferred into 96-well PCR plates. For each well, our homebuilt liquid chromatography (LC) system automatically performed sample injection, sample cleanup, LC separation and MS/MS data acquisition for 97 minutes on QExactive Plus Orbitrap MS. To help interpret the data, molecular maps were created by colleagues Bramer, Stratton, and Webb-Robertson, who are experts at aggregating and analyzing large amounts of data into forms that can be interpreted more readily and accurately. They used Trelliscope, an open-source platform that PNNL developed for data visualization and management  to convert the mounds of numbers into a portrait of protein abundance.  Now that these foundational methods and tools exist, we are well positioned to apply nanoPOTS imaging to other biological tissues. 

MC: Which finding from your study excited you the most?

The most exciting findings from this study are that we can voxelate a tissue into pieces measuring 100 microns long x 100 microns wide x 10 microns thick and visualize how over 2,300 proteins change across heterogenous tissue sections – and even detect changes that can be missed by histological examination. Our protein images were able to characterize unique tissue microenvironments within the same cell populations by visualizing the gradient expression increase of stroma proteins along the mesometrial (top)–antimesometrial (bottom) axis of the uterus.  

MC: You are part of a large initiative to map the entire human body at single-cell resolution. Can you tell us more about this initiative and progress that has been made so far?

The development of the nanoPOTS imaging platform detailed in this study and ongoing efforts to increase its measurement throughput and spatial resolution are supported by the Human Biomolecular Atlas program, or HuBMAP, as a transformative technology that will comprehensively map human tissues. The PNNL portion of HuBMAP is in collaboration with former PNNL scientist Julia Laskin, now at Purdue. PNNL’s HuBMAP team members include myself, Charles Ansong, and co-first authors of the manuscript, Paul Piehowski and Ying Zhu. This collaborative HuBMAP project supports the advancement and integration of nano-DESI metabolomic and lipidomic imaging (Purdue) and nanoPOTS proteomic imaging (PNNL) to map the molecular landscape of human tissues.

MC: What are your next steps with this research?

The next steps with this research are to increase throughput and spatial resolution of the nanoPOTS proteome imaging platform by incorporating multiplexed isobaric labeling. Tandem Mass Tag (TMT) labeling is an approach developed for multiplexed identification and quantification of proteins from multiple different samples in a single LC−MS/MS analysis/run. We are already showing the feasibility of this approach with preliminary data generated by Zhu and Piehowski utilizing Thermo Scientific TMT10plex™ Isobaric Mass Tag Labeling Reagents Sets. These initial experiments give 10 times higher throughput. Because 10 different voxels are combined in a single LC-MS analysis, the proteome identification sensitivity is greatly improved as well, which facilitates mapping of much smaller tissue voxels without compromising proteome coverage.

Kristin Burnum-Johnson, Senior Research Scientist in Biological Sciences at Pacific Northwest National Laboratory, was speaking with Molly Campbell, Science Writer, Technology Networks.