MultiNotch MS3 Enables Accurate, Sensitive, and Multiplexed Detection of Differential Expression across Cancer Cell Line Proteomes

Multiplexed quantitation via isobaric chemical tags (e.g., tandem mass tags (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ)) has the potential to revolutionize quantitative proteomics. However, until recently the utility of these tags was questionable due to reporter ion ratio distortion resulting from fragmentation of coisolated interfering species. These interfering signals can be negated through additional gas-phase manipulations (e.g., MS/MS/MS (MS3) and proton-transfer reactions (PTR)). These methods, however, have a significant sensitivity penalty. Using isolation waveforms with multiple frequency notches (i.e., synchronous precursor selection, SPS), we coisolated and cofragmented multiple MS2 fragment ions, thereby increasing the number of reporter ions in the MS3 spectrum 10-fold over the standard MS3 method (i.e., MultiNotch MS3). By increasing the reporter ion signals, this method improves the dynamic range of reporter ion quantitation, reduces reporter ion signal variance, and ultimately produces more high-quality quantitative measurements. To demonstrate utility, we analyzed biological triplicates of eight colon cancer cell lines using the MultiNotch MS3 method. Across all the replicates we quantified 8 378 proteins in union and 6 168 proteins in common. Taking into account that each of these quantified proteins contains eight distinct cell-line measurements, this data set encompasses 174 704 quantitative ratios each measured in triplicate across the biological replicates. Herein, we demonstrate that the MultiNotch MS3 method uniquely combines multiplexing capacity with quantitative sensitivity and accuracy, drastically increasing the informational value obtainable from proteomic experiments

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Photoperiod-dependent changes in the phase of core clock transcripts and global transcriptional outputs at dawn and dusk in Arabidopsis

Plants use the circadian clock to sense photoperiod length. Seasonal responses like flowering are triggered at a critical photoperiod when a light-sensitive clock output coincides with light or darkness. However, many metabolic processes, like starch turnover, and growth respond progressively to photoperiod duration. We first tested the photoperiod response of 10 core clock genes and two output genes. qRT-PCR analyses of transcript abundance under 6, 8, 12 and 18 h photoperiods revealed 1-4 h earlier peak times under short photoperiods and detailed changes like rising PRR7 expression before dawn. Clock models recapitulated most of these changes. We explored the consequences for global gene expression by performing transcript profiling in 4, 6, 8, 12 and 18 h photoperiods. There were major changes in transcript abundance at dawn, which were as large as those between dawn and dusk in a given photoperiod. Contributing factors included altered timing of the clock relative to dawn, light signalling and changes in carbon availability at night as a result of clock-dependent regulation of starch degradation. Their interaction facilitates coordinated transcriptional regulation of key processes like starch turnover, anthocyanin, flavonoid and glucosinolate biosynthesis and protein synthesis and underpins the response of metabolism and growth to photoperiod

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Quantitative temporal viromics: an approach to investigate host-pathogen interaction

A systematic quantitative analysis of temporal changes in host and viral proteins throughout the course of a productive infection could provide dynamic insights into virus-host interaction. We developed a proteomic technique called “quantitative temporal viromics” (QTV), which employs multiplexed tandem-mass-tag-based mass spectrometry. Human cytomegalovirus (HCMV) is not only an important pathogen but a paradigm of viral immune evasion. QTV detailed how HCMV orchestrates the expression of >8,000 cellular proteins, including 1,200 cell-surface proteins to manipulate signaling pathways and counterintrinsic, innate, and adaptive immune defenses. QTV predicted natural killer and T cell ligands, as well as 29 viral proteins present at the cell surface, potential therapeutic targets. Temporal profiles of >80% of HCMV canonical genes and 14 noncanonical HCMV open reading frames were defined. QTV is a powerful method that can yield important insights into viral infection and is applicable to any virus with a robust in vitro model

Quantitative Proteomics of the Cancer Cell Line Encyclopedia Reveals Coordinated Post-Transcriptional Pathway Regulation

Cell lines are widely used for examining normal biological function and disease. To date, large-scale profiling of cell line collections including the Cancer Cell Line Encyclopedia (CCLE) has focused primarily on genetic information while deep interrogation of the proteome has remained out of reach. Here we expand the CCLE through quantitative profiling of thousands of proteins by mass spectrometry across 375 cell lines from diverse lineages. At present this is the largest single collection of deep proteomics experiments reported on any human model. We present several analyses as example applications of these data including relationships to mutations and surprising regulation patterns within the electron transport chain. We observe unexpected correlations within and between pathways that are largely absent from RNA, and provide a correlation network to explore these structured expression patterns. These data in conjunction with the wider CCLE are a unique resource to explore cellular behavior

An ultra-tolerant database search reveals that a myriad of modified peptides contributes to unassigned spectra in shotgun proteomics

Fewer than half of all tandem mass spectrometry (MS/MS) spectra acquired in shotgun proteomics experiments are typically matched to a peptide with high confidence. Here we determine the identity of unassigned peptides using an ultra-tolerant Sequest database search that allows peptide matching even with modifications of unknown masses up to ±500 Da. In a proteome-wide dataset on HEK293 cells (9,513 proteins and 396,736 peptides), this approach matched an additional 184,000 modified peptides, which were linked to biological and chemical modifications representing 523 distinct mass bins, including phosphorylation, glycosylation, and methylation. We localized all unknown modification masses to specific regions within a peptide. Known modifications were assigned to the correct amino acids with frequencies often >90%. We conclude that at least one third of unassigned spectra arise from peptides with substoichiometric modifications

Estimating the Selective Effects of Heterozygous Protein Truncating Variants from Human Exome Data

The dispensability of individual genes for viability has interested generations of geneticists. For some genes it is essential to maintain two functional chromosomal copies, while others may tolerate the loss of one or both copies. Exome sequence data from 60,706 individuals provide sufficient observations of rare protein truncating variants (PTVs) to make genome-wide estimates of selection against heterozygous loss of gene function. The cumulative frequency of rare deleterious PTVs is primarily determined by the balance between incoming mutations and purifying selection rather than genetic drift. This enables the estimation of the genome-wide distribution of selection coefficients for heterozygous PTVs and corresponding Bayesian estimates for individual genes. The strength of selection can discriminate the severity, age of onset, and mode of inheritance in Mendelian exome sequencing cases. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, putatively cell-essential genes, Mendelian disease genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that likely have critical function but have not yet been extensively annotated in published literature