The winners of the 6th Annual Thermo Scientific Tandem Mass Tag (TMT) Research Awards 2020 will be announced.

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue specific or enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.

We have recently shown that using fragment ion intensities can drastically increase the confidence in peptide identification and reduce false positive matches compared to classic database searching. Further peptide properties such as precursor charge and the rather vague term ‘proteotypicity’, influence the detectability of peptides. With the advent of deep learning and the wealth of data stored in public repositories, one may eventually be able to predict all peptide properties that are relevant for the detection and identification of peptides. Here we report on the extension of Prosit, a deep learning architecture for peptide property prediction, to deal with non-tryptic and multiply modified peptides as well as predicting proteotypicity and precursor charge.

In recent years, life science research has experienced a significant shift, moving away from conducting bulk cell interrogation towards single-cell analysis. It is only through single-cell analysis that a complete understanding of cellular heterogeneity, and the interplay between various cell types that are fundamental to specific biological phenotypes, can be achieved. The hematopoietic system is a prime example of such a complex hierarchy, where Hematopoietic stem cells (HSC) are the origin of all cell lineages contained therein. Acute myeloid leukemia (AML), a perturbed state of hematopoiesis, is also hierarchically organized, with leukemia stem cells (LSC) at the apex. Successful eradication of AML will likely depend on specific targeting of these tumour-initiating cells, in turn requiring their molecular characterization. Currently, we are able to isolate LSC based on CD34 and CD38 expression levels of primary AML cells. These sorted fractions, however, vary in their levels of LSC, and thus should be considered LSC-enriched, rather than pure LSC. Consequently, this significant level of heterogeneity within the LSC-enriched fractions hampers the molecular characterization of true LSC. Here, we have taken a well-characterized primary AML patient sample and subjected it to fluorescence-activated cell sorting, combined with a newly developed single-cell proteomics strategy to identify the protein landscapes of individual cells within the LSC, progenitor and blast populations of this patient. By using the latest state-of-the-art LC-MS instrumentation with intelligent data acquisition, combined with the new TMTPro reagents, this has resulted in an unprecedented map of protein expression in individual AML cells. Furthermore, we developed a computational pipeline (SCeptre) that effectively normalizes the data, clusters the cells, integrates available FACS data (i.e immunophenotype) and permits the extraction of cell-specific proteins. We found a strong enrichment of stem cell specific proteins in the LSC and progenitor compartments compared to blasts, and the resulting protein signatures clearly distinguish the various differentiation stages that were isolated. The approach presented here lays a solid foundation for implementing global single-cell proteomics studies in proteomics labs across the world.

While top down mass spectrometry has become synonymous with the direct analysis of intact proteins and their complexes, the term more generally denotes an approach to measurement that recognizes the value of retaining as much information as possible about a system prior to analysis. By avoiding proteolytic digestion, proteoform-specific identifications can be made -directly. A series of vignettes will focus on both denatured and native modes of Top Down Proteomics. I will also describe a few recent advances of top down MS, like the most recent breakthroughs in individual ion mass spectrometry (i2MS) in orbitraps (joint work with the group of Mike Senko at Thermo Fisher Scientific). I will also describe a new sample introduction approach called ‘SampleStream’ that enables simultaneous buffer exchange and >20x concentration of samples in either denaturing or native modes. Providing information from intact complex mass (MS1) to subunits and their backbone fragment ions, native top-down MS even enables identification and characterization of unknown protein complexes. By more faithfully preserving post-translational modifications and non-covalent interactions throughout the measurement process, top down mass spectrometry is positioned to make basic and translational proteomics more efficient and valuable, particularly in the detection and assignment of function to proteoforms and their PTMs underlying human wellness and disease.

Tyrosine phosphorylation (pTyr) plays a pivotal role in signal transduction and is commonly dysregulated in cancer, however existing MS methods to profile pTyr require a comprise between broad coverage, reproducibility, and quantitively accuracy. As a result, we developed “SureQuant pTyr,” a targeted approach coupling low input pTyr enrichment with a panel of isotopically labeled, tyrosine phosphorylated internal standard (IS) peptides for IS-guided acquisition using the SureQuant platform. We compare SureQuant pTyr to traditional acquisition approaches and demonstrate the method’s clinical utility in human colorectal tumors.

In my talk, I will outline how generation of personalised genome or transcriptome sequencing information is essential for neoantigen discovery using LC-MS technology, and how we were able to map novel, tumour-specific human papillomavirus-derived antigens in cervical cancer using this approach.

Major breakthrough in cancer treatment has been achieved by harnessing the capabilities of the immune system to both identify and eliminate cancer, even metastasized cancer. Mutations in cancer leading to altered proteins are well documented as cancer neoantigens. However, exiting data indicates that genomic aberration can generate other types of tumor specific proteins like those derived from intron retention, unexpected novel coding regions, premature start codons, activated retroviral elements and other events.1,2 This poses a new challenge since effective discovery and analysis of such complex neoantigens require true integration of genomics and proteomics. We have generated tools that take advantage of massive genomics data by incorporating sequence information to the proteomics data-analysis pipeline. This will allow protein level analysis of gene variants as well as detection of novel protein coding regions1. To control error rate in variant detection, we have a combined experimental isoelectric point data from peptide fractions (HiRIEF LC-MS/MS) and bioinformatics approaches into the proteogenomics workflow (IPAW)2. A proteogenomics analysis of human tissues using the IPAW pipeline reveals novel protein coding regions. When applied on breast cancer tumor samples, we could demonstrate in-depth quantitative analysis revealing drug target correlations as well as discovers putative cancer neoantigens3. To gain knowledge of these novel proteins, we have analyzed subcellular location of these in human cell line models. For location analysis, we used SubCellBarcode based proteome wide location analysis4. Another application of the pipeline enabled detection of proteins transferring placenta during pregnancy, the relying on paternal protein variant detection, suggesting molecular communication between fetus and mother5. Finally, the systems level analysis of the tumor proteome represents the combined effect of epigenetic, transcriptional and translational regulation in relation to host immune response and will therefore provide an important molecular phenotype data layer on immune state and evasion mechanisms of tumors.

1. Branca R.M., Orre L-M., Johansson H.J., Granholm V., Huss M., Pérez-Bercoff Å., Forshed J., Käll L., Lehtiö J. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nature Methods, 2014 Jan;11(1):59-62.

2. Zhu Y, Orre LM, Johansson HJ, Huss M, Boekel J, Vesterlund M, Fernandez-Woodbridge A, Branca RMM, Lehtiö J. Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow. Nature Commun. 2018 Mar 2;9(1):903.

3. Johansson HJ, Socciarelli F, Vacanti NM, Haugen MH, Zhu Y, Siavelis I, Fernandez A, Aure MR, Sennblad B, Vesterlund M, Branca RM, Orre LM, Huss M, Fredlund E, Beraki E, Garred Ø, Boekel J, Sauer T, Zhao W, Nord S, Höglander EK, Jans CD, Brismar H, Haukaas TH, Bathen TF, Schlichting E, Naume B, OSBREAC, Luders T, Borgen E, Kristensen VN, Russnes HG, Lingjærde OC, Mills GB, Sahlberg KK, Børresen-Dale A.L, Lehtiö J., Breast cancer quantitative proteome and proteogenomic landscape. Nature Comm, 10 Apr, 2019.

4. Orre LM, Vesterlund M, Pan Y, Arslan T, Zhu Y, Fernandez Woodbridge A, Frings O, Fredlund E, Lehtiö J. SubCellBarCode: Proteome-wide mapping of protein localization and relocalization. Mol Cell. 2019 Jan 3.

5. Pernemalm M, Sandberg A, Zhu Y, Boekel J, Tamburro D, Schwenk JM, Bjork A, Wahren-Herlenius M, Amark H, Ostenson C.G, Westgren M, Lehtiö J. In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta. eLife, Apr. 8., 2019.

High-field asymmetric-waveform ion-mobility spectrometry (FAIMS) allows separation of ion populations at athmospheric pressure just in front of the mass spectrometer. The combination of this ion mobility separation with data independent acquisition (DIA) results in increased proteome coverage. Here, we show applications of FAIMS DIA to human body fluids and in particular to plasma for cancer biomarker detection.

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the increase of highly aggressive forms of skin cancer and metastases spread, and the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Targeted oncotherapy is one of the standard treatment for progressive stage 3 and 4 melanoma, and, e.g; vemurafenib, dabrafenib, encorafenib combined with MEK inhibitor, e.g: trametinib, cobimetinib, bibimetinib) can effectively counter BRAFV600E-mutated melanomas. We verify our current knowledge of the Melanoma disease mechanisms that regulates the functional divergence in cancer development on a molecular level along the axis of gene expression, with an emphasis on the tumor proteome. We establish a basis for the interdependence in-between different molecular expressions and how Melanoma progression links to functional diversity. Somatic mutations have been extensively characterized in malignant melanoma. Here we describe quantitative mass-spectrometry-based proteomic study interfaced to extensive and detailed pathological tumor characterization, associated with clinical meta-data, for the analysis of 500 annotated malignant melanoma tumor samples. We demonstrate that ProteoGenomic analysis of malignant melanoma cancer elucidates the functional consequences of somatic mutations, narrows candidate nominations for driver and passenger proteins, and identifies therapeutic candidate targets.

The causative agent of the coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions and killed hundreds of thousands of people worldwide, highlighting an urgent need to develop antiviral therapies. Here, we present a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in Vero E6 cells, revealing dramatic rewiring of phosphorylation on host and viral proteins. SARS-CoV-2 infection promoted casein kinase II (CK2) and p38 MAP kinase activation, production of diverse cytokines, and shutdown of mitotic kinases resulting in cell cycle arrest. Infection also stimulated a marked induction of CK2-containing filopodia protrusions possessing budding viral particles. Eighty-seven drugs and compounds were identified by mapping global phosphorylation profiles to dysregulated kinases and pathways. We found pharmacologic inhibition of p38, CK2, CDKs, AXL and PIKFYVE kinases to possess antiviral efficacy, representing potential COVID-19 therapies.

We developed a high-throughput targeted proteomics assay to detect SARS-CoV-2 proteins directly from nasopharyngeal and oropharyngeal swabs. Sample preparation was fully automated by using a modified magnetic particle-based proteomics approach implemented on a robotic liquid handler, enabling a fast processing of samples. The use of turbulent flow chromatography included four times multiplexed on-line sample cleanup and UPLC separation. MS/MS detection of two peptides from SARS-CoV-2 nucleoprotein and their isotope label standards and endogenous control peptides was achieved within 2.5 min, enabling the analysis of more than 500 samples per day.

We have been working for some time on two related ideas in transcriptomics and proteomics, the use of transcriptomes to infer proteomes and combining quantitative transcriptomics and proteomics to gain deeper understanding of how viral infections affect the host cell. These ideas were first put forward 2 years ago in the technique we named PIT analysis - Proteomics Informed by Transcriptomics. In this talk I will quickly outline how the concept works before providing some short examples of how we have used it to analyse viral infections from a deeper analysis of how a common cold virus (adenovirus) affects host cell ubiquitination to how a zoonotic virus (Hendra virus) distinctly affects bat cells and human cells during infection. Critically we will show how direct RNA sequencing (third generation transcriptomics) can be used to proteomically uncover unexpected deletions in the protein repertoire of SARS-CoV-2 with significant impact on our understanding of SARS-CoV-2 pathogenesis and how to conduct SARS-CoV-2 vaccine trials in animals.

Host responses to SARS-CoV-2 are dynamic and complex. Here I will present applications of advanced proteomics technologies to interrogate host responses in the sera, urine and FFPE tissue specimens collected from COVID-19 patients. Our proteomic analyses of COVID-19 specimens nominated potential intervention strategies that may be exploited to facilitate vaccine development and therapeutics against COVID-19.

The emergence of the betacoronavirus, SARS-CoV-2 that causes COVID-19, represents a significant threat to global human health. Vaccine development is focused on the principal target of the humoral immune response, the spike (S) glycoprotein, that mediates cell entry and membrane fusion. SARS-CoV-2 S gene encodes 22 N-linked glycan sequons per protomer, which likely play a role in immune evasion and occluding immunogenic protein epitopes. In this talk, I will discuss the site-specific mass spectrometric analysis of the glycan structures of recombinant SARS-CoV-2 S immunogens and that of an infectious virus. This analysis enables mapping of the glycan-processing states across the recombinant and native viral spikes and has implications for serological testing and vaccine design. Comparison of the glycosylation of SARS-CoV-2 with other viruses, such as HIV, also reveal fundamental differences in the utilization of glycosylation in viral pathobiology.