Meet all of your Day 2 speakers and have them answer your questions live!

Recent developments in mass spectrometry-based single-cell proteomics (SCP) have resulted in dramatically improved sensitivity, yet the relatively low measurement throughput remains a limitation. Isobaric and isotopic labeling methods have been separately applied to SCP to increase throughput through multiplexing. Here we combined both forms of labeling to achieve multiplicative scaling for higher throughput. Two-plex stable isotope labeling of amino acids in cell culture (SILAC) and isobaric tandem mass tag (TMT) labeling enabled up to 28 single cells to be analyzed in a single liquid chromatography−mass spectrometry (LC−MS) analysis, in addition to carrier, reference, and negative control channels. A custom nested nanowell chip was used for nanoliter sample processing to minimize sample losses. Using a 145-min total LC−MS cycle time, ∼280 single cells were analyzed per day. This measurement throughput could be increased to ∼700 samples per day with a high-duty-cycle multicolumn LC system producing the same active gradient.

Quantitative mass spectrometry-based proteomic experiments are designed to answer increasingly complex questions. For example, we may want to characterize changes in protein abundance in biological samples collected repeatedly over time and across multiple conditions. Or we may want to distinguish changes in protein abundance from other changes, such as changes in post-translational modifications. At the same time, proteomics experiments increasingly incorporate Tandem Mass Tags (TMT) labeling, a multiplexing strategy that gains both accuracy of relative protein quantification and sample throughput. Combining complex designs and TMT multiplexing leads to unique interplays of multiple sources of systematic variation of interest, and of nuisance variation. This talk will advocate for multilevel modeling for statistical analyses of data in such complex situations, and its implementation in the open-source R/Bioconductor family of MSstats packages, specifically MSstatsTMT, MSstatsPTM and MSstatsShiny.

Isobaric labeling enables the multiplexed analysis of up to 18 proteomes or PTMomes in a single analysis and provides information on protein and peptide abundance across all samples simultaneously. For certain applications, reactive cysteine profiling and single cell proteomics, we sought to improve the throughput of analyses to 36-plex by employing a hyperplexing approach that combined both isobaric labels with additional mass offsets through light/heavy cysteine probes or SILAC. Previous implementations of hyperplexing have been challenged by the selection and quantification of both peptides within a hyperplexed pair, leading to some peptides/proteins only being quantified in 18 of the 36 samples. To ensure quantification of both peptide pairs we introduce PairQuant an intelligent data acquisition approach implemented through inSeqAPI, our own instrument API program. PairQuant sequences peptides within hyperplexed pairs in real time and only triggers quantification scans after both peptides within a hyperplexed pair have been identified. Furthermore, if the instrument does not select both peptides within a hyperplexed pair for MS/MS, PairQuant will send PairSeq scans in an attempt to sequence the missing peptide. We use pair quant for 36-plex cysteine profiling analyses as well as 28-pelx single cell analysis and demonstrate an increase in the number of hyperplexed pairs where each partner peptide was quantified.

Functional interactions between cytotoxic immune cells and tumor cells are central to anti-cancer immunity. Some of the proteins involved, particularly immune checkpoints expressed by T cells, serve as promising clinical targets in immunotherapy. This notwithstanding, our comprehension of the complexity and dynamics of the interactions between tumor cells and T cells is only rudimentary. To decipher intercellular communication at a molecular level and identify new drug targets for immunotherapy, better approaches are needed to measure signaling dynamics between engaged cells.

Here we present a method to quantify short-term protein and phosphorylation dynamics between and within physically interacting (heterotypic) cells. We barcode specific cell types using SILAC labeling prior to co-culture incubation. We next extract proteins from the entire mixed cell lysate and quantify cell-type-specific proteome and phosphorylation dynamics occurring during the initial hours of T cell:tumor cell engagement using a second quantification method, either LFQ or TMT. Adopting a SILAC-TMT approach has improved the throughput and sensitivity in our assay. By implementing real-time-search capability in our MS3-based TMT acquisition methods, we can specifically select for tumor-derived or T cell-derived peptides for quantification.

Using hybrid quantification we have been able to discover proteins contributing to functional T cell:tumor interactions and have identified drug targets that sensitize tumor cells to T cell killing. Thus, hybrid quantification is simple method to study short-term protein signaling dynamics in physically interacting cells, which can be easily extended to other biological systems.

Meet all of your Day 1 speakers and have them answer your questions live!

Targeted proteomics enables hypothesis-driven research by measuring the expression of protein cohorts related by function or disease in systems biology. Here, I highlight a strategy, termed GoDig, for targeting entire pathways of up to 200 proteins selected from among >10,000 expressed proteins, exploiting sample multiplexing to increase throughput. GoDig requires only a single-shot LC-MS analysis (equivalent to 7.5-min per sample), ~1 µg peptide material, and real-time analytics to simultaneously quantify hundreds of analytes across up to 18 samples. Importantly, MS3 scans are triggered without MS1 detection. We have applied the GoDig assay to study pathways in yeast, human, and mouse. For example, we have applied GoDig to investigate the impact of genetic variation on protein expression across 480 fully-genotyped diversity outbred (DO) mice fed a high-fat diet. We identified previously unknown quantitative trait loci and established potential linkages between specific proteins and lipid homeostasis.

Efficient protein turnover is essential for cell homeostasis and organ function. Loss of proteostasis is a hallmark of aging, which culminates as a severe reduction in protein turnover rates. To investigate protein turnover dynamics during aging, we performed continuous in vivo metabolic stable isotope labeling using 15N amino acid precursors and TMT-based multiplexed proteomic analysis. First, we discovered that the brain proteome uniquely experiences dynamic global turnover fluctuations during aging compared to heart and liver tissue. Second, in the brain proteome, global protein turnover trends across aging displayed sex-specific differences that were tightly linked to their cellular compartments. Next, parallel analyses of the insoluble proteome revealed that distinct cellular compartments experience hampered turnover, at least partially due to misfolding. Finally, we discovered that age-associated fluctuations in the activity of the ubiquitin proteasome system were linked to the turnover of the catalytic core subunits. Taken together, our study provides a proteome-wide atlas of protein turnover across the aging continuum and highlights a new link between the turnover of individual proteasome subunits and the age-associated decline in proteosome activity.

Learning Objectives

• Recognize the importance of using metabolic stable isotope labeling with multiplexed TMT-based quantification to probe age associated change in brain protein turnover.
• Describe the importance of protein degradation in hampering protein degradation.
• Understand sex specific change in brain protein turnover.
• Interpret change in protein levels and relative changes in protein turnover.

The multiplexing capability of tandem mass tags (TMT) has driven quantitative proteomic experimental designs towards larger numbers of samples. In order to efficiently process hundreds to thousands of samples, automated liquid handling platforms using standardized microwell plates are commonly employed to increase throughput and reproducibility. In this session, we will demonstrate rapid, efficient processing of low microgram amounts of cell and tissue samples using the Thermo Scientific™ EasyPep™ 96 Micro MS sample preparation kit in combination with multiplexed labeling using novel, solution-stabilized TMT™ and TMTpro reagents in ready-to-use 96-well microplates that are compatible with automated liquid handling platforms.

TMT and Tandem Mass Tag are trademarks of Proteome Sciences.