High efficiency, high temperature separations on silica based monolithic columns

The characterization of column heating effect in nanoflow liquid chromatography mass spectrometry (nanoLC-MS)-based proteomics

Column heating strategy is often applied in nano-high-performance liquid chromatography-mass spectrometer (nanoHPLC-MS) platform for enhancing the analytical efficiency of peptides or proteins. Nonetheless, the influence effects of column heating in peptides or proteins identification still lack of deep understanding. In this study, a systematic comparison of room temperature (RT) and column heating of nanoHPLC was done. Based on the data, under column heating condition, the backpressure of nanoHPLC can be decreased. Due to the increase of resolution, the peak widths of precursor ion were narrowed. As a result, in MS/MS data acquisition part, more time was spared for MS1 detecting and MS2 fragmenting, which eventually resulted in increased identification of peptides and proteins. Moreover, we also proposed the application scope of column heating by evaluating its influence on sample detection. On one hand, column heating significantly increased the identification of membrane proteins due to more efficient elution of highly hydrophobic peptides compared with RT. On the other hand, heating was not suitable for analyzing short or/and hydrophilic peptides with low retention time, which would be eluted out during sample loading process under high temperature and missed by mass spectrometric detection. In conclusion, our study provides a reference for rational application of column heating in proteomics research.

Arv1 promotes cell division by recruiting IQGAP1 and myosin to the cleavage furrow

Cell division is strictly regulated by a diversity of proteins and lipids to ensure proper duplication and segregation of genetic material and organelles. Here we report a novel role of the putative lipid transporter ACAT-related protein required for viability 1 (Arv1) during telophase. We observed that the subcellular localization of Arv1 changes according to cell cycle progression and that Arv1 is recruited to the cleavage furrow in early telophase by epithelial protein lost in neoplasm (EPLIN). At the cleavage furrow Arv1 recruits myosin heavy chain 9 (MYH9) and myosin light chain 9 (MYL9) by interacting with IQ-motif-containing GTPase-activating protein (IQGAP1). Consequently the lack of Arv1 delayed telophase-progression, and a strongly increased incidence of furrow regression and formation of multinuclear cells was observed both in human cells in culture and in follicle epithelial cells of egg chambers of Drosophila melanogaster in vivo. Interestingly, the cholesterol-status at the cleavage furrow did not affect the recruitment of either IQGAP1, MYH9 or MYL. These results identify a novel function for Arv1 in regulation of cell division through promotion of the contractile actomyosin ring, which is independent of its lipid transporter activity.

Proteomics tools reveal startlingly high amounts of oxytocin in plasma and serum

The neuropeptide oxytocin (OT) is associated with a plethora of social behaviors, and is a key topic at the intersection of psychology and biology. However, tools for measuring OT are still not fully developed. We describe a robust nano liquid chromatography-mass spectrometry (nanoLC-MS) platform for measuring the total amount of OT in human plasma/serum. OT binds strongly to plasma proteins, but a reduction/alkylation (R/A) procedure breaks this bond, enabling ample detection of total OT. The method (R/A + robust nanoLC-MS) was used to determine total OT plasma/serum levels to startlingly high concentrations (high pg/mL-ng/mL). Similar results were obtained when combining R/A and ELISA. Compared to measuring free OT, measuring total OT can have advantages in e.g. biomarker studies.

Comparison of commercial nanoliquid chromatography columns for fast, targeted mass spectrometry-based proteomics

AIM

We compared four commonly used, commercially available reverse phase nanoLC columns for identification/determination of Wnt/β-catenin-related pathway proteins.

MATERIALS & METHODS

The columns were: Chromolith® (silica monolith; Merke Millipore, MA, USA), PepMap™ (porous particles; Thermo Fisher Scientific, MA, USA), Accucore™ (solid core particles; Thermo Fisher Scientific) and PepSwift™ (organic monolith; Thermo Fisher Scientific).

RESULTS

The peak capacity of the columns varied from 100 (Pepswift) to 190 (Accucore) (for 30 min gradients). All columns enabled identification/detection of GSK3β and β-catenin in the complex samples. However, even the columns with higher peak capacities could not enable detection of the somewhat less abundant proteins AXIN2 and TNKS2. The monoliths were more prone to retention time instability when sample complexity increased.

CONCLUSION

We find that commercial nanoLC columns, although featuring different morphologies and peak capacities, provided surprisingly few practical differences for relatively fast, targeted determination of proteins.

Nano-LC in proteomics: recent advances and approaches

In proteomics, nano-LC is arguably the most common tool for separating peptides/proteins prior to MS. The main advantage of nano-LC is enhanced sensitivity, as compounds enter the MS in more concentrated bands. This is particularly relevant for determining low abundant compounds in limited samples. Nano-LC columns can produce peak capacities of 1000 or more, and very narrow columns can be used to perform proteomics of 1000 cells or less. Also, nano-LC can be coupled with online add-ons such as selective trap columns or enzymatic reactors, for faster and more automated analysis. Nano-LC is today an established tool for research laboratories; but can nano-LC-based systems soon be ready for more routine settings, such as in clinics?

Optimized nonlinear gradients for reversed-phase liquid chromatography in shotgun proteomics

Reversed-phase liquid chromatography has become the preferred method for separating peptides in most of the mass spectrometry-based proteomics workflows of today. In the way the technique is typically applied, the peptides are released from the chromatography column by the gradual addition of an organic buffer according to a linear function. However, when applied to complex peptide mixtures, this approach leads to unequal spreads of the peptides over the chromatography time. To address this, we investigated the use of nonlinear gradients, customized for each setup at hand. We developed an algorithm to generate optimized gradient functions for shotgun proteomics experiments and evaluated it for two data sets consisting each of four replicate runs of a human complex sample. Our results show that the optimized gradients produce a more even spread of the peptides over the chromatography run, while leading to increased numbers of confident peptide identifications. In addition, the list of peptides identified using nonlinear gradients differed considerably from those found with the linear ones, suggesting that such gradients can be a valuable tool for increasing the proteome coverage of mass spectrometry-based experiments.

Rapid chromatographic determination of caseins in milk with photometric and fluorimetric detection using a hydrophobic monolithic column

Reverse-phase liquid chromatographic methods using a hydrophobic C18 monolithic column and on-line photometric and fluorimetric detection for the determination of the major casein (CN) proteins in milk are presented. The separation of αs1-CN, αs2-CN, β-CN and κ-CN was achieved in only five minutes. Fluorimetric detection enabled better analytical results than photometric detection. Thus, the dynamic ranges of the calibration graphs and detection limits obtained using fluorimetric detection were (mgmL(-)(1)): αs1-CN (0.74-10.0, 0.22), αs2-CN (0.15-10.0, 0.045), β-CN (0.68-10.0, 0.20) and κ-CN (0.21-10.0, 0.06). The analytical features of the photometric method, which does not allow the quantification of β-casein, were (mgmL(-)(1)): αs1-CN (1.5-9.0, 0.45), αs2-CN (1.4-10.0, 0.43) and κ-CN (0.4-9.0, 0.12). Precision data, expressed as relative standard deviation, ranged between 0.6% and 5.3% for the fluorimetric method and between 2.4% and 6.2% for the photometric method. Both methods were applied to the analysis of three different milk samples, obtaining recoveries in the ranges of 86.6-103.2% and 92.0-106.5% using fluorimetric and photometric detection, respectively.