The importance of the surfaceome and its interactors

We love to hear how our customers are using Progenesis QI and Progenesis QI for proteomics. It’s great to learn what they are researching and how Progenesis can help them. It’s also nice to find out more about the people behind the research. Our latest blog post features Dr Maria Pavlou and her interesting work with Dualsystems Biotech AG. First, here’s some background about Maria:

Dr Maria Pavlou

Maria Pavlou received her PhD in translational proteomics from the Department of Laboratory Medicine and Pathobiology at University of Toronto, Canada. Upon PhD completion, Maria moved to Switzerland to pursue a post-doctoral fellowship in the Institute of Molecular Systems Biology at the Swiss Federal Institute of Technology (ETH) in Zurich focusing on host-pathogen interactions. In 2017, she joined Dualsystems Biotech AG as a senior scientist and a year later, she was promoted to Chief Scientific Officer leading the research team to develop further the Ligand-based Receptor Capture (LRC) methodology and establish new services.

Now, onto the research:

The importance of the surfaceome and its interactors

If the plasma membrane is considered the gateway through which cells communicate and interact with their environment, then proteins associated with the surface – referred as the surfaceome – can be seen as the gatekeepers. The surfaceome largely dictates the shape, polarity, differentiation and motility of cells. It also mediates cellular behaviors such as cell-cell communication, self and non-self recognition, and cell signalling. Given the crucial role of surface-associated proteins in every aspect of cellular life, it is not surprising that they are the molecular targets for roughly 70% of FDA approved drugs [1].

The original concept, depicting the plasma membrane as a homogeneous fluid bilayer with freely diffusing proteins, has been evolved to another depicting a highly organized and crowded mosaic of interacting lipids and glycoproteins. This higher organization modulates the biological processes occurring on the cell surface, exemplified by receptors being active only when they form dimers, hetero-dimers or higher order oligomers [2]. Interactions of proteins in the cell membrane of the same cell (cis), and interactions of proteins of neighboring cells, the extracellular matrix and circulating ligands (trans) are collectively referred to as extracellular protein-protein interactions (ePPIs).

Elucidating ePPIs in a systemic fashion is pivotal to gain a better understanding of the surfaceome function. More specifically, identifying the targets of key ligands on the cell surface provides valuable mechanistic information about signal transduction, drug action or off-target effects. For instance, pathogen or growth factor interactions are important for developing novel therapies. Additionally, numerous ligands exist – both biologics and small molecules – involved in biological functions mediated at the cell surface through still unknown protein targets.

Towards target identification, an advanced cell-based chemo-proteomic approach has been developed namely ligand-based receptor capture (LRC) [3-4]. In this approach, the endogenous receptor repertoire of a given cell serves as an existing bait library that can be probed for ligand interaction. The key component of the LRC methodology is a trifunctional compound (TriCEPS or its latest development named HATRIC [3-4]) that utilizes the extensive glycosylation displayed by the majority of cell surface proteins to capture receptor interactions on living cells. Experimentally, the first arm of TriCEPS is conjugated with the primary amines of a ligand and the conjugates are added on living cells (mildly oxidized). There the ligand binds to its target(s) and the second arm of TriCEPS is covalently crosslinked to the glycans of the binding partner. The third arm facilitates target purification for mass spectrometric analysis.

In a typical LRC-TriCEPS experiment, at least two treatment arms are performed in parallel: one with the ligand of interest and a second with a control ligand (that is, a ligand with a known target). Upon identification, the relative abundance of cell surface proteins in the ligand samples is compared to those in the control samples using MS1-based label-free quantification. Randomly identified cell surface proteins are expected to have equal abundance in both samples, whereas the corresponding receptors are found enriched in the ligand sample.

Progenesis QIP gives the user full control but does not require advanced computational skills

Progenesis QI for proteomics (QIP) has been the workhorse when it comes to data analysis. Performing MS1-based label-free quantitation in Progenesis is extremely straightforward through an intuitive user-friendly interface.

The alignment of features is performed by sophisticated algorithms but at the same time the software provides the user with visual inspection of the whole procedure. This is extremely useful as the user has full control of the data and a better understanding on how the samples are processed. It can also reveal technical issues related to the liquid chromatography separation prior to mass spectrometric analysis or sample quality. Notably, proper feature alignment is pivotal for robust quantitation. Moreover, the feature-picking algorithm (peak picking) has been developed to minimize missing values therefore the requirement for imputation; another asset for robust quantitation.

Through the various filtering options, the user can eliminate features that are not of interest (such as polymers or contaminants) focusing on what really matters. The QC metrics tab provides a qualitative overview of the experiment giving the opportunity (once more) to assess whether the LC and MS parameters used were optimal.

Upon protein inference and calculation of relative abundances, the user can easily review protein characteristics (such as number of peptides, peptide sequence and modifications, expression profiles, see figure 1) and confirm results or flag outliers. Once more, this option gives the user full control over the data and eliminates, to a great extent, experimental artefacts.

LRC-TriCEPS analysis to identify the receptors of Insulin and Transferrin on HEK293 cells; a screenshot of Progenesis QIP.

Two LRC experiments were performed using insulin and transferrin as ligands of interest on HEK293 cells with receptor capture at two different pH (6.5 and 7.4).

(A) Following the intuitive and straightforward progenesis pipeline, the identification and quantitation (MS-1 based) was completed within 6 hours.

(B) A total of approximately 300 surface proteins were identified and quantified across all samples. Transferrin receptor (TFR1), the known target of Transferrin, was also identified with roughly 40 unique peptides.

(C) Using the protein filter the relative abundance of TFR1 across the four conditions was visualized; TFR1 is clearly enriched in the Transferrin samples.

(D) For more detailed information the user can check the quantitation of every peptide identified and spot any irregularities.

(E) The user can use the statistics run by Progenesis or export the data for post-hoc analysis.

As statistical testing is incorporated in the software, the final outcome of an analysis provides immediate information regarding proteins being significantly regulated. However, there is still the option to export all necessary information in order to perform post-hoc statistical analysis using different tools. This increases greatly the flexibility of the user.

Finally, Progenesis QIP is readily scalable when it comes to number of samples and performs analysis in a time-efficient manner, allowing for complete label-free quantitation in the course of a working day. This is extremely important given that data analysis is usually the beginning of a series of experiments aiming to verify and interpret the identified quantitative differences. It provides a variety of different plots and graphs that can be readily used for publications or reports.

In summary, the analysis of LRC-TriCEPS data with Progenesis QIP offers unique advantages. The software is user-friendly and intuitive therefore can be used by researchers with experience in data analysis but also by users that are just starting or do not perform data analysis daily. Progenesis QIP provides the user with full control over data analysis which is very important to spot and resolve experimental artefacts and to understand how the final outcome is reached. At the same time, the sophisticated algorithms provide high quality label-free quantitation and robust results. Finally, the nice visualization aspects can generate high quality graphs that can be used to communicate the results of each study.

1. Uhlén M, et al. Tissue-based map of the human proteome. Science (80- ). 2015.

2. Milligan G, G protein-coupled receptor dimerisation: molecular basis and relevance to function, Biochim Biophys Acta. 1768(4):825-35, 2007

3. Frei AP, Moest H, Novy K, Wollscheid B. Ligand-based receptor identification on living cells and tissues using TRICEPS. Nat Protoc. 2013;8:1321–36.

4. Sobotzki N, et al. HATRIC-based identification of receptors for orphan ligands. Nat Commun. 2018;9:1–16.

Thank you to Maria for a very interesting blogpost. Finally, please get in touch

• If you are a user with an interesting research project using Progenesis. We are keen to share user stories via our blog.

• If you would like to try Progenesis on your own data

Thank you

Acknowledgement: Maria Pavlou, PhD, Paul Helbling, PhD

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