FAIMS

Field Asymmetric Ion Mobility (FAIMS) - Mass Spectrometry

Field Asymmetric Ion Mobility Spectrometer (FAIMS) is a high speed, gas phase ion separation technique. When interfaced to a Mass Spectrometer, the FAIMS chip provides an additional separation stage, making it suitable for a applications ranging from drug development to proteomics.

The FAIMS ion filter is orthogonal to both LC and MS, so has the potential to separate analytes that are difficult to distinguish using only LC-MS. In some cases, the FAIMS stage can replace the LC and associated sample preparation steps.

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Orbitrap/LTQ signal threshold

We evaluate the effect of ion-abundance threshold settings for data dependent acquisition on a hybrid
LTQ-Orbitrap mass spectrometer, analyzing features such as the total number of spectra collected,
the signal to noise ratio of the full MS scans, the spectral quality of the tandem mass spectra acquired,
and the number of peptides and proteins identified from a complex mixture. We find that increasing
the threshold for data dependent acquisition generally decreases the quantity but increases the quality
of the spectra acquired. This is especially true when the threshold setting is set above the noise level
of the full MS scan. We compare two distinct experimental configurations: one where full MS scans
are acquired in the Orbitrap analyzer, while tandem MS scans are acquired in the LTQ analyzer and
one where both full MS and tandem MS scans are acquired in the LTQ analyzer. We examine the
number of spectra, peptides, and proteins identified under various threshold conditions, and we find
that the optimal threshold setting is at or below the respective noise level of the instrument regardless
of whether the full MS scan is performed in the Orbitrap or in the LTQ analyzer. When comparing
the high-throughput identification performance of the two analyzers, we conclude that, used at
optimal threshold levels, the LTQ and the Orbitrap identify similar numbers of peptides and proteins.
The higher scan speed of the LTQ, which results in more spectra being collected, is roughly
compensated by the higher mass accuracy of the Orbitrap, which results in improved database
searching and peptide validation software performance.

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mass accuracy of Orbitrap: internal communications

From communications with one colleague.


It was mentioned during the training course last month that, while performing DDA, the Orbitrap performs a "pre-scan" of the ions entering the mass spectrometer. This pre-scan is really just a part of the full scan. Rather than waiting for the completion of the full scan at a high resolution, instead, part way through, it registers the pre-scan (at a resolution of about 15,000) for the purpose of selecting ions for MS/MS. The full scan continues to completion, but the MS/MS scans on the linear ion trap already are underway by the time this happens.

The point here is that the precursor masses reported for the MS/MS spectra are from the pre-scan, not the higher resolution (if one was specified in the methods file) full scan.

At my request, he installed a utility named extractMSn on the computer associated with OT1. This utility takes a .RAW data file and extracts all of the MS and MS/MS spectra, creating a set of .dta files. The first line of each .dta file representing a MS/MS file contains the m/z value of the precursor as determined by the full scan, not the pre-scan.


The purpose of the pre-scan is to speed up the process of data-dependent acquisition (DDA). Instead of waiting for an entire full scan to be completed before doing the first MS/MS, it collects data when the full scan is partially complete. This doesn't mean it only has covered a part of the scan range. The full range of ions are present in both the pre-scan and the full scan; they are just better resolved in the full scan (and a little more accurate).

The pre-scan is not inherently more useful than the full scan. In fact, the peaks of the full scan should be better resolved. The problem is that the precursor m/z values reported for the MS/MS spectra (i.e. the values that appear in the .dtas) are the less-resolved pre-scan values. So something is needed to go back to the full scan and pick out the better resolved values if those are what one wants.

I decided to have a look at whether it makes any signficant difference. So, for 63 MS/MS spectra representing BSA peptides, I calculated the ppm error for precursor m/z values taken from the pre-scan and the full scan. This data is attached as a spreadsheet.

For the pre-scan precursor m/z values, the ppm error ranged from -2.1 ppm to as high as 10.9 ppm in what seems to be a uniform distribution.

For the full scan precursor values, most fell into the range -2.8 ppm to 3.4 ppm, with an average of -0.8. This matches what I've observed for y ions of MS/MS spectra. However, there was a second range or errors, from -31.9 ppm to -22.1 ppm, for the precursors of 11 peptides.

Possibly, the second range arises from the utility selecting an interfering peak rather than the proper one in certain instances. I'll look into it further.

That is what ExtractMSn does (generates peak lists with the full scan m/z value for the precursor included instead of the pre-scan value), but it apparently doesn't always do it well. If it did it with 100% success, I would recommend using it for applications where resolution greater than 15,000 is important. But it doesn't.

In any case, it seems the extractMSn utility isn't very helpful and should not be used.

Canadian National Proteomics Network

Lately, I was invited to join Canadian National Proteomics Network. CNPN is a not-for-profit federally incorporated organization created to provide a cooperative mechanism for building the proteomics research infrastructure in Canada to further a better understanding of proteomics in the Canadian life sciences community and to sponsor scientific conferences, seminars and forums to create a national focus for scientific collaboration and education.


It is unclear how many members it has now, but I would guess it is a small group, mainly consists of students, postodocs and professors. Anyway, I hope it can really grow and create more communications, collaborations, and career opportunities for bioinformatics professionals. It is a crucial especially for a less populated country like Canada.


click here to see what is new.

Bioinformatics: Global Market Forecast Updated

The global market for bioinformatics will surge at a compound annual growth rate (CAGR) of nearly 26 percent during 2011-2013, according to market research and information analysis firm RNCOS.

The research firm said that bioinformatics offers an indispensable technology for function assignment and is widely used for gene annotation. Initial efforts in bioinformatics were focused on the analysis of DNA sequence data.

However, presently, the scope and objectives of bioinformatics research and development have broadened owing to the high generation of data from various sources and for different cellular processes, continuously evolving analytical technologies, and increasing computational capability.

The global bioinformatics industry has been witnessing double-digit growth rate for the past decade due to ongoing research. With increasing R&D investments by companies on bioinformatics and regulatory support by various countries, the research firm anticipate that the bioinformatics market will post high growth rate in majority of the countries.

The research firm found that the technology has its applications in a variety of areas, including genomics, proteomics, pharmacogenomics, and cheminformatics.

Bioinformatics companies collaborate with other companies or with research institutes to enhance their operations. Partners may share the resources such as, products, intellectual property, project funding, knowledge, expertise, manufacturing capability, and capital equipment, added the research firm.

This research report categorizes the global bioinformatics market into the following segments:

Bioinformatics platforms

• Sequence alignment platforms
• Sequence manipulation platforms
• Sequence analysis platforms
• Structural analysis platforms

Content/Knowledge management tools

• Specialized knowledge management tools
• Generalized knowledge management tools

Services

• Data Analysis
• Sequencing Services
• Database & Management services

MALDI MS

MALDI (matrix-assisted laser desorption/ionization), a laser-based soft ionization method has proven to be one of the most successful ionization methods for mass spectrometric analysis and investigation of large molecules.[1,2] In addition analysis of post source decay (decay of proteins following the ionization) in many cases allows rapid sequencing of proteins. Thus MALDI has gained a crucial importance for protein analysis.
Its constituting feature is that the sample is embedded in a chemical matrix (ca. 1000 x molar excess) that greatly facilitates the production of intact gas-phase ions from large, nonvolatile, and thermally labile compounds such as proteins, oligonucleotides, synthetic polymers. and large inorganic compounds. The matrix plays a key role in this technique by absorbing the laser light energy and causing a small part of the target substrate to vaporize.

The most important applications of MALDI mass spectrometry are (in decreasing order of importance): peptides and proteins, synthetic polymers, oligonucleotides, oligosaccharides, lipids, inorganics.

In its current state, MALDI is primarily based on the laser desorption of solid matrix-analyte deposits [6]. The technique suffers from some disadvantages such as low shot-to-shot reproducibility, short sample life time and strong dependence on the sample preparation method.

MALDI MS-based systems have been developed to rapidly characterize microorganisms, such as intact  proteins in the 5K-20K Da range in a top-down proteomics. In such experiments, proteins are identified either by conducting a homology search of extracted sequence tags or by comparison of MS/MS spectra with fragmentations predicted from protein sequences in existing protoeme database.
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top-down vs. bottom-up protoemics

Bottom-Up Proteomics

In bottom-up proteomics, the analytes introduced into the mass spectrometer are peptides generated by enzymatic cleavage of one or many proteins. The proteins can first be separated by GE or chromatography, in which case the sample will contain only one or a few proteins. Alternatively, a complex protein mixture initially can be digested to the peptide level, then separated by on-line chromatography coupled to electrospray mass spectrometry (ESI–MS). In the latter case, the digest can contain thousands to hundreds of thousands of peptides, and require separation in two or more chromatographic dimensions before MS analysis. The identity of the original protein is determined by comparison of the peptide mass spectra with theoretical peptide masses calculated from a proteomic or genomic database. There are two approaches for protein identification using the bottom-up approach, peptide mass fingerprinting and tandem MS (MS–MS).



Top-Down Proteomics

In top-down proteomics, intact protein molecular ions generated by ESI/MALDI are introduced into the mass analyzer and are subjected to gas-phase fragmentation. An obstacle to this approach is the determination of product ion masses from multiply charged product ions (1). These can vary in charge state up to that of the multiply charged protein precursor ion. This can introduce ambiguity in the interpretation of top-down MS-MS spectra. Two approaches have been used to circumvent this limitation. The first is charge state manipulation through gas phase ion–ion interactions, and the second is the use of instruments with high mass measurement accuracy (MMA). It provides an approach for large scale characterization of proteins, both types of FTMS instruments, ICR and Orbitrap have been used for this methodology. The molecular mass of the inact precursor protein along with fragment ions from MS/MS experiments enables high confidence mapping to database protein entries as well as PTM detection.


Advantages and limitations of bottom-up strategies: Bottom-up proteomics is the most mature and most widely used approach for protein identification and characterization. Reversed-phase HPLC provides high-resolution separations of peptide digests with solvents that are compatible with ESI. On-line nano-scale reversed-phase LC–ESI–MS–MS can be fully automated and is almost universally used for bottom-up proteomics. Commercial instruments with control software and bioinformatics tools optimized for bottom-up applications are available from several vendors. The bottom-up strategy using on-line multidimensional capillary HPLC–MS-MS has been most successful in the identification of proteins in digests derived from very complex mixtures such as cell lysates (6). Moreover, quantitative techniques have been developed using affinity tags and stable isotope labels for determination of up- and down-regulated proteins in expression proteomics (7).
There are several fundamental and practical limitations to the bottom-up strategy.
Most importantly, only a fraction of the total peptide population of a given protein is identified. Therefore, information on only a portion of the protein sequence is obtained. It is clear from genomic studies that each open reading frame can give rise to many protein isoforms, which can originate from alternative splicing products and varying types and locations of posttranslational modifications (PTMs). PTMs such as phosphorylation and glycosylation are known to be important in the regulation of protein function and cell metabolism. A consequence of the limited sequence coverage in bottom-up proteomics is loss of much information about PTMs. Moreover, PTMs are often labile in the CID process and require techniques such as neutral loss scanning to detect them.
Practical limitations are encountered when bottom-up methods are used for protein identification from very complex peptide mixtures. On-line multidimensional LC–MS-MS analyses using ion-exchange coupled to reversed-phase columns require extended run times of as long as 15 h or more. Although this can be automated, the throughput of multidimensional LC–MS-MS is quite limited. Other problems include the loss of information about low-abundance peptides in mass spectra dominated by high-abundance species. Finally, narrow chromatographic peak widths can compromise acquisition of adequate MS–MS information during elution.


Advantages and limitations of top-down strategies: The two major advantages of the top-down strategy are the potential access to the complete protein sequence and the ability to locate and characterize PTMs. In addition, the time-consuming protein digestion required for bottom-up methods is eliminated.
Top-down proteomics is a relatively young field compared to bottom-up proteomics, and currently suffers from several limitations. First, the very complex spectra generated by multiply charged proteins limits the approach to isolated proteins, or simple protein mixtures at best. Second, the favored instrumentation (FT-ICR, hybrid ion trap FT-ICR or hybrid ion trap–orbitrap) are expensive to purchase and operate. Third, the top-down approach does not work well with intact proteins larger than about 50 kDa. Fourth, the favored dissociation techniques (ECT, ETD) are low-efficiency processes requiring long ion accumulation, activation, and detection times. This limits the ability to couple top-down MS techniques with on-line separations. Fifth, the mechanisms of protein dissociation behavior are less well understood than those of peptide dissociation. If top-down approaches are to be adopted widely, a greater understanding of fragmentation of multiply charged ions is needed (1), including the influence of precursor ion charge state, the role of protein primary, secondary and tertiary structure, and the contribution of PTMs. Finally, bioinformatics tools for top-down proteomics are primitive compared to those for bottom-up proteomics.


Thermo Scientific* ProSightPC, the first stand-alone software for analyzing top-down proteomics data, has been enhanced to add support for middle-down and bottom-up experiments, making it an all-around tool for identification and characterization of both intact proteins and peptides.
ProSightPC* 2.0 software enables high-throughput processing of all accurate-mass MS/MS data, whether from top-down, middle-down or bottom-up experiments including the characterization of proteins with known PTMs. ProSightPC 2.0 software uses multiple search modes to determine the exact protein sequence including modifications and alternative splicing. It is the only proteomics software that allows the user to search their tandem MS data against proteome warehouses containing the known biological complexity present in UniProt.

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