Quantification of SAHA-Dependent Changes in Histone Modifications Using Data-Independent Acquisition Mass Spectrometry

Histone post-translational modifications (PTMs) are important regulators of chromatin structure and gene expression. Quantitative analysis of histone PTMs by mass spectrometry remains extremely challenging due to the complex and combinatorial nature of histone PTMs. The most commonly used mass spectrometry-based method for high-throughput histone PTM analysis is data-dependent acquisition (DDA). However, stochastic precursor selection and dependence on MS1 ions for quantification impede comprehensive interrogation of histone PTM states using DDA methods. To overcome these limitations, we utilized a data-independent acquisition (DIA) workflow that provides superior run-to-run consistency and postacquisition flexibility in comparison to DDA methods. In addition, we developed a novel DIA-based methodology to quantify isobaric, co-eluting histone peptides that lack unique MS2 transitions. Our method enabled deconvolution and quantification of histone PTMs that are otherwise refractory to quantitation, including the heavily acetylated tail of histone H4. Using this workflow, we investigated the effects of the histone deacetylase inhibitor SAHA (suberoylanilide hydroxamic acid) on the global histone PTM state of human breast cancer MCF7 cells. A total of 62 unique histone PTMs were quantified, revealing novel SAHA-induced changes in acetylation and methylation of histones H3 and H4

Quantification of SAHA-Dependent Changes in Histone Modifications Using Data-Independent Acquisition Mass Spectrometry

Histone post-translational modifications (PTMs) are important regulators of chromatin structure and gene expression. Quantitative analysis of histone PTMs by mass spectrometry remains extremely challenging due to the complex and combinatorial nature of histone PTMs. The most commonly used mass spectrometry-based method for high-throughput histone PTM analysis is data-dependent acquisition (DDA). However, stochastic precursor selection and dependence on MS1 ions for quantification impede comprehensive interrogation of histone PTM states using DDA methods. To overcome these limitations, we utilized a data-independent acquisition (DIA) workflow that provides superior run-to-run consistency and postacquisition flexibility in comparison to DDA methods. In addition, we developed a novel DIA-based methodology to quantify isobaric, co-eluting histone peptides that lack unique MS2 transitions. Our method enabled deconvolution and quantification of histone PTMs that are otherwise refractory to quantitation, including the heavily acetylated tail of histone H4. Using this workflow, we investigated the effects of the histone deacetylase inhibitor SAHA (suberoylanilide hydroxamic acid) on the global histone PTM state of human breast cancer MCF7 cells. A total of 62 unique histone PTMs were quantified, revealing novel SAHA-induced changes in acetylation and methylation of histones H3 and H4

Quantification of SAHA-Dependent Changes in Histone Modifications Using Data-Independent Acquisition Mass Spectrometry

Histone post-translational modifications (PTMs) are important regulators of chromatin structure and gene expression. Quantitative analysis of histone PTMs by mass spectrometry remains extremely challenging due to the complex and combinatorial nature of histone PTMs. The most commonly used mass spectrometry-based method for high-throughput histone PTM analysis is data-dependent acquisition (DDA). However, stochastic precursor selection and dependence on MS1 ions for quantification impede comprehensive interrogation of histone PTM states using DDA methods. To overcome these limitations, we utilized a data-independent acquisition (DIA) workflow that provides superior run-to-run consistency and postacquisition flexibility in comparison to DDA methods. In addition, we developed a novel DIA-based methodology to quantify isobaric, co-eluting histone peptides that lack unique MS2 transitions. Our method enabled deconvolution and quantification of histone PTMs that are otherwise refractory to quantitation, including the heavily acetylated tail of histone H4. Using this workflow, we investigated the effects of the histone deacetylase inhibitor SAHA (suberoylanilide hydroxamic acid) on the global histone PTM state of human breast cancer MCF7 cells. A total of 62 unique histone PTMs were quantified, revealing novel SAHA-induced changes in acetylation and methylation of histones H3 and H4

Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition

Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time- and batch correction strategies. From 3 µg to 5 µg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5-8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale

Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time

Label-free quantification (LFQ) and isobaric labeling quantification (ILQ) are among the most popular protein quantification workflows in discovery proteomics. Here, we compared the TMT SPS/MS3 10-plex workflow to a label free single shot data-independent acquisition (DIA) workflow on a controlled sample set. The sample set consisted of ten samples derived from 10 biological replicates of mouse cerebelli spiked with the UPS2 protein standard in five different concentrations. For a fair comparison, we matched the instrument time for the two workflows. The LC–MS data were acquired at two facilities to assess interlaboratory reproducibility. Both methods resulted in a high proteome coverage (>5000 proteins) with low missing values on protein level (<2%). The TMT workflow led to 15–20% more identified proteins and a slightly better quantitative precision, whereas the quantitative accuracy was better for the DIA method. The quantitative performance was benchmarked by the number of true positives (UPS2 proteins) within the top 100 candidates. TMT and DIA showed a similar performance. The quantitative performance of the DIA data stayed in a similar range when searching the spectra against a fasta database directly, instead of using a project-specific library. Our experiments also demonstrated that both workflows are readily transferrable between facilities

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling

Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time

Label-free quantification (LFQ) and isobaric labeling quantification (ILQ) are among the most popular protein quantification workflows in discovery proteomics. Here, we compared the TMT SPS/MS3 10-plex workflow to a label free single shot data-independent acquisition (DIA) workflow on a controlled sample set. The sample set consisted of ten samples derived from 10 biological replicates of mouse cerebelli spiked with the UPS2 protein standard in five different concentrations. For a fair comparison, we matched the instrument time for the two workflows. The LC–MS data were acquired at two facilities to assess interlaboratory reproducibility. Both methods resulted in a high proteome coverage (>5000 proteins) with low missing values on protein level (<2%). The TMT workflow led to 15–20% more identified proteins and a slightly better quantitative precision, whereas the quantitative accuracy was better for the DIA method. The quantitative performance was benchmarked by the number of true positives (UPS2 proteins) within the top 100 candidates. TMT and DIA showed a similar performance. The quantitative performance of the DIA data stayed in a similar range when searching the spectra against a fasta database directly, instead of using a project-specific library. Our experiments also demonstrated that both workflows are readily transferrable between facilities

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling

Systematic Comparison of Strategies for the Enrichment of Lysosomes by Data Independent Acquisition

In mammalian cells, the lysosome is the main organelle for the degradation of macromolecules and the recycling of their building blocks. Correct lysosomal function is essential, and mutations in every known lysosomal hydrolase result in so-called lysosomal storage disorders, a group of rare and often fatal inherited diseases. Furthermore, it is becoming more and more apparent that lysosomes play also decisive roles in other diseases, such as cancer and common neurodegenerative disorders. This leads to an increasing interest in the proteomic analysis of lysosomes for which enrichment is a prerequisite. In this study, we compared the four most common strategies for the enrichment of lysosomes using data-independent acquisition. We performed centrifugation at 20,000 × g to generate an organelle-enriched pellet, two-step sucrose density gradient centrifugation, enrichment by superparamagnetic iron oxide nanoparticles (SPIONs), and immunoprecipitation using a 3xHA tagged version of the lysosomal membrane protein TMEM192. Our results show that SPIONs and TMEM192 immunoprecipitation outperform the other approaches with enrichment factors of up to 118-fold for certain proteins relative to whole cell lysates. Furthermore, we achieved an increase in identified lysosomal proteins and a higher reproducibility in protein intensities for label-free quantification in comparison to the other strategies

Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time

Label-free quantification (LFQ) and isobaric labeling quantification (ILQ) are among the most popular protein quantification workflows in discovery proteomics. Here, we compared the TMT SPS/MS3 10-plex workflow to a label free single shot data-independent acquisition (DIA) workflow on a controlled sample set. The sample set consisted of ten samples derived from 10 biological replicates of mouse cerebelli spiked with the UPS2 protein standard in five different concentrations. For a fair comparison, we matched the instrument time for the two workflows. The LC–MS data were acquired at two facilities to assess interlaboratory reproducibility. Both methods resulted in a high proteome coverage (>5000 proteins) with low missing values on protein level (<2%). The TMT workflow led to 15–20% more identified proteins and a slightly better quantitative precision, whereas the quantitative accuracy was better for the DIA method. The quantitative performance was benchmarked by the number of true positives (UPS2 proteins) within the top 100 candidates. TMT and DIA showed a similar performance. The quantitative performance of the DIA data stayed in a similar range when searching the spectra against a fasta database directly, instead of using a project-specific library. Our experiments also demonstrated that both workflows are readily transferrable between facilities