 Fluorescence correlation spectroscopy (FCS) is a relatively new and advanced subsegment of the fluorescence spectroscopy market. FCS can provide a wealth of very specific physiochemical information that is most useful to those in biology and related sectors. Recent technical advancements have allowed the FCS market to begin to develop rapidly.

Fluorescence correlation spectroscopy demand for 2011

FCS measures the fluctuation of fluorescence of very small particles and molecules, and can provide both the concentration and size of the particles or molecules being analyzed, as well as a wealth of other physiochemical information. FCS systems are typically integrated microscopy systems, but some are sold as stand-alone detectors that can be connected with a microscopy system of choice. Configurations and system prices can vary widely, largely depending on the number and types of lasers used.

FCS can determine properties such as diffusion coefficients, average concentrations, and reaction rates, and it is attractive for drug discovery and development applications. One of the biggest strengths of the technique is characterizing the mobility-related parameters of relevant biological molecules. The strongest demand for this relatively new technique is still largely centered on academic institutions performing basic research. 

Although first developed in the 1970s, early systems were extremely complex to operate and faced a number of performance limitations. Technological improvements that came about in the mid-1990s allowed for the development of much more user friendly commercial instruments. The annual worldwide market for FCS is still less than $5 million, but it should have strong double-digit growth over the next several years as more competitors enter the segment and it becomes a more widely accepted analytical technique.

 The foregoing data were based on SDi’s market analysis and perspectives report entitled 

Global Assessment Report, 11th Edition: The Laboratory Life Science and Analytical Instrument Industry, October 2010.

 For more information, contact Stuart Press, Vice President – Strategic Analysis, Strategic Directions International, Inc., 6242 Westchester Parkway, Suite 100, Los Angeles, CA 90045, (310) 641-4982, fax: (310) 641-8851, www.strategic-directions.com. 

Articles

Market Profile Column

Fluorescence correlation spectroscopy (FCS) is a relatively new and advanced subsegment of the fluorescence spectroscopy market. FCS can provide a wealth of very specific physiochemical information that is most useful to those in biology and related sectors.

Market Profile: Fluorescence Correlation Spectroscopy

BaySpec's Nomadic Raman microscope is available with 532-, 785-, 1064-nm, and custom excitation wavelengths. The microscope reportedly features a high-throughput volume phase grating; a dispersive, no moving parts design; optimized detector cooling; and software for data and image analysis. 

The Tango FT-NIR spectrometer from Bruker Optics is designed for QC analysis of liquids and solids. The spectrometer reportedly provides fast measurements for high sample throughput, simultaneous evaluation of different components, and intuitive operation for nonexperts. According to the company, the instrument can be used with an integrated PC and monitor or as a separate analysis station. 

Spex CertiPrep's USP-TXM1 consumer safety compliance standards kit is designed for use in complying with recent USP regulations for elemental impurities in pharmaceuticals and nutraceuticals. The company also offers consumer safety compliance standards designed to test for levels of heavy metals and phthalates in waste electronics, consumer, and children's products. 

B&W Tek and Gnosys have partnered to provide the PolymerIQ Raman spectroscopy system for real-time, nondestructive identification and quantification of polymers. The system combines the i-Raman spectrometer with PolymerIQ chemometric software. 

Milestone's DMA-80 mercury analyzer is designed to enable an operator to analyze for mercury in matrixes that include solids, liquids, and gases. According to the company, the system can be used in laboratories serving industries that include toxicology, environmental, energy, chemical, quality control, and gases. 

PD-LD's LS-2 LabSource benchtop Raman module has two laser sources. According to the company, the laser sources are VBG-stabilized, and are available in standard and custom wavelengths. When the two laser sources' wavelengths are closely spaced together, the module reportedly is capable of performing surface enhanced Raman differential spectroscopy. 

The AvaGigE USB-to-Ethernet converter, from Avantes, enables the company's Avaspec USB-controlled spectrometers to plug into an Ethernet network. According to the company, the hardware device supports the connection of as many as eight spectrometers (via USB hub) and a web-based configuration utility. 

CEM Corporation's Discover SP-D pressurized microwave digestion system includes a venting system that safely vents excess gaseous by-products while allowing a digestion to continue uninterrupted. The digestion system is designed to process a typical digest in 10 min, including cool down. According to the company, the system can be placed on a benchtop or in a fume hood. 

The 20/20 PV UV–vis–NIR microspectrophotometer from Craic Technologies is designed to nondestructively analyze microscopic samples at wavelengths ranging from the deep ultraviolet to the near infrared. According to the company, analysis of samples can be done by absorbance, reflectance, luminescence, and fluorescence. 

The LifeSpec II fluorescence spectrometer from the Photonics Division of Edinburgh Instruments is designed to measure decays from 5 ps to 50 µs using high-repetition rate pulsed femtosecond, picosecond, or supercontinuum lasers. According to the company, measurements can be made from liquid, solid, film, and powder samples. 

Teledyne Leeman's Hydra II mercury analyzers are designed to provide configuration flexibility. According to the company, the analyzers can be configured to conduct the analysis of liquids by sample digestion followed by cold vapor atomic absorption or cold vapor atomic fluorescence, and the direct analysis of solid or semisolid sample matrices through thermal decomposition followed by cold vapor atomic absorption. 

Product Resources

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This is the fifth installment in a series devoted to explaining Maxwell's equations, the four mathematical statements upon which the classical theory of electromagnetic fields — and light — is based. Previous installments can be found on 

www.spectroscopyonline.com/The+Baseline+Column

Maxwell's equations are expressed in the language of vector calculus, so a significant part of previous columns have been dedicated to explaining vector calculus, not spectroscopy. For better or worse, that's par for the course, and it's my job to explain it well — I trust readers will let me know if I fail! The old adage "the truth will set you free" might be better stated, for our purposes, as "math will set you free." And that's the truth. 

In mid-1820, Danish physicist Hans Oersted discovered that a current in a wire can affect the magnetic needle of a compass. These experiments were quickly confirmed by François Arago and, more exhaustively, André Marie Ampère. Ampère's work, which defined a so-called "magnetic field" (labeled 

in Figure 31), demonstrated that the effects generated were centered on the wire, perpendicular to the wire, and circularly symmetric about the wire. By convention, the vector component of the field had a direction given by the right hand rule: If the thumb of the right hand were pointing in the direction of the current, the curve of the fingers on the right hand gives the direction of the vector field. 

Figure 31: The "shape" of a magnetic field about a wire with a current running through it. 

Other careful experiments by Jean-Baptiste Biot and Félix Savart established that the strength of the magnetic field was directly related to the current 

 in the wire and inversely related to the radial distance from the wire 

where "∝" means "proportional to". To make a proportionality an equality, we introduce a proportionality constant. However, because of the axial symmetry of the field, we typically include a factor of 2π (the radian angle of a circle) in the denominator of any arbitrary proportionality constant. As such, our new equation is 

where the constant µ is our proportionality constant and is called the 

 the magnetic field is in. In a vacuum, the permeability is labeled µ

 are defined, is equal to exactly 4π × 10

Figure 32: A wire loop generates a magnetic field B when a current I runs through the wire. In this case, the magnetic field is an axial field about the central axis of the loop. 

Not long after the initial demonstrations, Ampère had another idea: curve the wire into a circle. Sure enough, inside the circle, the magnetic field increased in strength as the concentric circles of the magnetic field overlapped on the inside (Figure 32). Biot and Savart found that the magnetic field 

 created by the loop was related to the current 

Multiple loops can be joined in sequence to increase 

, and in 1824–1825 English inventor William Sturgeon wrapped loops around a piece of iron, creating the first electromagnet. Even by then, Ampère had the thought that it was the current — that is, moving charges — that caused the magnetic field. 

Figure 33:  A magnet inside a coil of wire (top) does not generate a current. A magnet moving through a coil of wire (bottom) does generate a current. 

Joseph Henry was an American scientist who eventually became the first secretary of the Smithsonian Institution. In 1830, he performed some experiments showing how a magnetic field can induce electricity and did not publish it. Because of this, he lost a larger place in scientific history when in 1831, Michael Faraday announced that a changing magnetic field could produce an electrical current. (Henry's work has not gone unnoticed, as the SI unit of inductance is named the henry.) Note that Faraday (followed by others) found that a changing magnetic field is required; a static, nonchanging magnetic field produces no current (Figure 33). This strongly suggests that an electric current 

 is related to a varying magnetic field, or 

Actually, this is not far from the truth (which would then be another of Maxwell's equations if it were), but the more complete truth is expressed in a different, more applicable form. 

This is fine for straight-line motion, but what about if the motion occurred on a curve (Figure 34 in two dimensions), with perhaps a varying force? Then calculating the work is not as straightforward, especially since force and displacement are both vectors. However, it can easily be justified that the work is the integral, from initial point to final point, of the dot product force vector 

 with the unit vector tangent to the curve, which we will label 

Because of the dot product, only the force component in the direction of the tangent to the curve contributes to the work. That makes sense if you remember the definition of the dot product, a∙b = |a||b|cosΘ: if the force is parallel to the displacement, work is maximized (because the cosine of the angle between the two vectors is cos[0°] = 1) while if the force is perpendicular to the displacement, work is zero (because now the cosine is cos[90°] = 0). 

Figure 34:  If the force F is not parallel to the displacement s (shown here as variable, but F can be variable too), then the work performed is not as straightforward to calculate. 

Now consider two random points inside of an electrostatic field 

 (Figure 35). Keep in mind that we have defined 

; that is, not moving or changing. Imagine that an electric particle with charge 

 (this is from Coulomb's law), we have for an imagined two-step process: 

Each integral covers one pathway, but eventually you end up where you started. 

Figure 35: Two arbitrary points in an electric field. The relative strength of the field is indicated by the darkness of the color. 

This last statement is a crucial one: Eventually you end up where you started. According to Coulomb's law, the only variable that the force or electric field between the two particles depends on is the radial distance, 

, depends only on the radial distance between any two points in the electric field. Furthermore, this implies that if you start and end at the same point, as we are in our example, the overall work is zero because you are starting and stopping at the same radial point 

. Thus, the equation above must be equal to zero: 

Because we are starting and stopping at the same point, the combined paths s

. Notice too that, other than being closed, we have not imposed any requirement on the overall path 

 itself: It can be any path. We say that this integral, which must equal zero, is 

The symbol for an integral over a closed path is ∮. Thus, we have

This is one characteristic of an electrostatic field: the path-independent integral over any closed path in an electrostatic field is exactly zero. 

The key word in the above statement is "any." You can select any random closed path in an electric field, and the integral of 

 over that path is exactly zero. How can we generalize this for any closed path? 

Figure 36: A closed, two-dimensional path around a point. 

Let us start with a closed path in one plane, as shown by Figure 36. The complete closed path has four parts, labeled T, B, L, and R for top, bottom, left, and right, and it surrounds a point at some given coordinates (

 axis, while L and R are parallel to the 

 (these will be useful shortly). Right now the area enclosed by the path is arbitrary, but later on we will want to shrink the closed path down so that the area goes to zero. Finally, the path is immersed in a three dimensional field 

Let us evaluate the work of each straight segment of the path separately, starting with path B. The work is 

, since path B points along the positive 

 (see expression above), the result is simply 

. (Can you verify this?) Finally, since the displacement 

 can vary as you go across path B — in fact, it is better labeled as 

) — let us assume some average value of 

 value ½ of the height of the box below the point in the center. Thus, we have 

where we have replaced the infinitesimal d

We can do the same for the work at the top of the box, which is path T. There are only two differences: first, the tangent vector is –

, because the path is moving in the negative direction, and second, the average value of 

/2, which is ½ of the height of the box above the center point. Hence, we can simply write 

The sum of the work on the top and bottom are thus

Rearranging this so that it is in the form "top minus bottom" and factoring out the Δ

Let us multiply this expression by 1, in the form of Δ

Recall that this work is actually a sum of two integrals involving, originally, the integrand 

. Reminding ourselves, this last expression can be written as 

. Dividing the area to the other side of the equation, we have 

The Baseline

Here, we continue the discussion of electromagnetism and Maxwell's second equation.

Maxwell's Equations, Part V

Recent advancements in photomultiplier tubes and detectors, along with improved filter contrast and transmission, have allowed what was once impossible: detection and analysis of spontaneous movements on the nanometer level using fluorescence correlation spectroscopy (FCS). Here, the basic techniques of FCS are discussed along with several novel applications, such as nanoparticle dispersion studies. Insights into solving potential problems, such as photobleaching, also are provided.   

Fluorescence correlation spectroscopy (FCS) is an extremely powerful, versatile technique that is useful both in vivo and in vitro for biochemistry and biophysics. Using FCS, one can determine and obtain a great deal of quantitative information, such as diffusion coefficients, hydrodynamic radii, kinetic chemical reaction rates, photophysical processes, and a wide variety of internal and external dynamic characteristics of biological molecules. These measurements can be done quickly and easily by modifying any standard fluorescence or confocal microscope. Here, we cover the important details of FCS in a few specific examples as well as focus on the material side and see how improved filters and detectors have resurrected this once popular technique. 

The technique of fluorescence correlation spectroscopy, in conjunction with confocal or two-photon excitation microscopy, begins, as most fluorescent microscopy systems do, with focusing light on a sample. The sample's emission is measured over time and the fluorescence intensity fluctuations are analyzed using what is called 

(1). Temporal autocorrelation stems from spatial dependency and analysis of demographics or geography and is a very useful method of measurement and analysis when working with small fluctuations in a known sample size. Essentially, it refers to the association between time-shifted values given a voxel time series, and those particular signal values at present time are independent of past and future signals (2). Temporal autocorrelation is important to consider because cellular and subcellular activity is completely random and minute. Analysis of the signal during a short time domain should assist in improving the understanding of intracellular activity. The intensity fluctuations vary depending on the sample under inspection. This is crucial because the measured fluorescence is directly related to the intensity of the entity's overall fluctuations. In this technique, it is very important to ensure that the number of molecules remains constant during inspection; no samples should enter or leave the observation volume. Additionally, when the threshold of units is exceeded, fluctuations in the observation volume become undetectable. This threshold is not a specific value but rather an observational determinant. If the inspection region is saturated with noise and light, or if there is no noticeable flux, then the threshold more than likely has been crossed. A consistent concentration ratio and observation volume of a variety of specimens allows researchers to conduct experiments in a wide variety of fields, such as materials science and biology. This is not the easiest of tasks but as the saying goes, "practice makes perfect."  

Typical FCS setups consist of a laser line, a focusing objective, dichroic and emission filters, and a photomultiplier tube (PMT) or detector. Depending on the sample, the laser line can be a continuous wave in the visible spectrum or a pulsed beam in the near-infrared (NIR) wavelengths. NIR wavelengths are less harmful to cells than UV–vis wavelengths, so when working with biological material it is better to expose them to longer wavelengths if possible. Companies have been producing and advancing PMTs for many years, resulting in wider spectral responses, faster time responses, lower dark noise, and higher sensitivity. For many years, traditional coated filters have dominated the market and have performed adequately. However, a recently developed coating technique (advanced plasma reactive sputtering coating, Edmund Optics, Barrington, New Jersey) is now yielding higher quality filters. These hard-sputtered filters are produced on a single-substrate and can provide deeper blocking, higher transmission, and steeper slopes than what was traditionally available. Their dense coating design also helps prevent filter degradation. The recent advancements in PMTs and detectors, along with improved filter contrast and transmission, have allowed for the detection and analysis of minute and spontaneous movements on the nanometer level, which was once impossible. 

Filtering in a fluorescent microscope system typically uses three specific filter types: excitation, emission, and a dichroic filter (Figure 1). The excitation filter rests within the optical path between the illumination source and the sample of interest. This ensures that the wavelengths transmitted through the filter closely match the fluorophore's absorption bands. The dichroic filter reflects excitation wavelengths, transmits emission wavelengths, and is positioned at 45° within the optical path. The combination of the dichroic and emission filters prevents additional energy and noise from propagating through to the detector. 

 Figure 1: Standard filtering for fluorescence microscopy.  

By incorporating a filter with a high optical density (OD), narrow full width-half maximum (FWHM), and high transmission at the center wavelength (CWL), it will be easier to excite particular fluorophores and quantify their emission without photobleaching or damaging the sample. 

 is the destruction of a fluorophore and its ability to produce a light emission. This destructive effect is especially problematic in time-lapse microscopy techniques and can be easily avoided. As mentioned above, the appropriate filter or reduced light exposure intensity can prevent this effect from occurring. This is increasingly important for FCS as molecular concentrations and fluid volumes are further reduced. One of the biggest problems today for fluorescence microscopy is photobleaching or burning of the sample because of overexposure to a source or laser line. One common fluorophore is the green fluorescent protein (GFP). GFP is a protein that emits a bright green fluorescence when exposed to a blue excitation source. The two excitation bands in the blue spectrum are 395 nm and 475 nm, which result in an emission band of 509 nm. There are a number of GFPs in nature, including one derived from jellyfish (

). The average lifetime of a GFP is only 0.1–1 s when exposed to blue light whereas typical organic dyes have a longer lifetime of approximately 1–10 s. To avoid this risk, it is important to identify the CWL, make sure that it is narrow enough to incorporate the necessary bandwidth, and have a high OD to reject unwanted light and energy. When selecting the appropriate filter for an FCS setup, it is crucial to know what your peak excitation and peak emission wavelengths are. The excitation filter should have a CWL that is closely matched to the absorption band of the fluorophore and should cover as much of the bandwidth as possible. The minimum transmission of this filter will determine how much fluorescence can be attained in the system. When discussing transmission, it is important to focus on OD. Transmissions >85% are recommended and ODs close to 6.0 will ensure the best signal-to-noise ratio. The OD of the filter ensures the blocking efficiency of the filter outside of the specified CWL and FWHM. 

There are a large number of applications and experimental setups for FCS. Molecular concentration analysis and aggregation measurements are typically applied to protein analysis. Mobility and molecular interactions such as diffusion, active transport, molecular interactions, and conformational changes are measured for a variety of biological molecules. Another important application involves the association or dissociation of enzyme kinetics that can be analyzed using two-photon excitation microscopy. Finally, FCS can explore the molecular dynamics and conditions in the microenvironment that affect the activation and photoluminescence of particular fluorophores in solution (1).  

FCS varies from standard fluorescence techniques because it focuses less on the emission wavelength and concentrates more on the spontaneous intensity fluctuation that is caused by small deviations in size, shape, or molecular interactions. These recorded deviations can provide invaluable information on how a protein folds and what induced such a reaction; how a clinical drug trial can affect cellular vitality; or how a particular fluorophore decays over time under exposure to light. FCS offers high spatial resolution while providing an accurate analysis of very low concentrations of biomolecular elements in solution. FCS is an exceptionally sensitive analytical tool because it examines such a small sample of molecules in an extremely tiny solution bed. The advent of confocal technology has certainly assisted in the advancement of FCS's spatial resolution and high-throughput screening. That is why one typically sees FCS used in conjunction with confocal or two-photon excitation microscopy. Improved resolution and rapid screening, along with the combination of short data acquisition times and direct measurement analysis has pushed FCS into the field of intracellular analysis. Intracellular imaging has regained popularity, especially as researchers have begun studying nucleic acid segments in DNA and RNA in greater detail and investigating protein–protein interactions (3). Proteins can fold and alter their native conformation via their amino acid sequences or compound interactions. When this happens randomly with tertiary or quaternary protein structures, there can be serious consequences. The protein transformations in tissue and the nucleic makeup of cells are thought to be the key to a number of deadly and incurable genetic disorders, such as Parkinson's disease. The new ability of FCS to detect intracellular changes may aid in the research of such diseases by allowing researchers to trace and follow biochemical pathways in live, vital cells and organs. 

Another novel application involving FCS is the characterization and quantification of nanoparticle dispersion. This is important to mention here considering the advantages of nanoparticles in a fluorescent system and the fact that they are superior labels and markers that do not pose the risk of photobleaching. Nanoparticles are an incredible advancement because they bring scientists one step closer to accessing atomic and molecular structures. Effectively, the use of nanoparticles could allow the human species to alter the state of elements and compounds without a chemical reaction or catalyst. They are currently used and will be continuously developed in many biomedical and optical fields. To use nanoparticles, however, it is crucial to determine and understand their diffusion effects in a liquid environment. The best method of analysis is through FCS.   

Understanding nanoparticle diffusion will help with applications in vivo and in vitro. Diffusion properties vary under different environments and physical conditions such as core size and shape, capping materials, and viscous or electrically induced fields. Today, one of the biggest problems with using nanoparticles in vivo is the toxicity of the shell and core. Identifying substrates with reduced toxicity is not a simple task, and understanding the rate at which the particles travel in a biological system will help determine appropriate sample sizes and methods of delivery. In addition, the fluorescence and photon correlation will provide a better understanding of the physics and chemistry and how nanoparticles affect protein folding, crystal growth, and general aggregation processes (4). 

Another application where fluorescence correlation spectroscopy can be advantageous is in bioassays. A 

 is a method for measuring and assaying molecular interactions in solution. Key types of molecular interactions assessed through bioassays include protein-to-protein binding, nucleic acid binding, and protein-to-nucleic acid binding. These applications are specifically useful in drug discovery, medical diagnostics, and environment and biological warfare screening. 

In comparison to polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), the technique of FCS offers improvements in quantitative data, normalization time, measurement time, detection, distinction, specificity, and overall design. FCS does not require any normalization before measurement, nor does it require a standard curve to compare present and future data. The measurement time is significantly decreased from about 30 min for real-time PCR and 3–6 h for ELISA down to roughly 10–20 min for FCS. Additionally, the time it takes to assess the assay quality falls from 1–2 h for PCR and 3–6 h for ELISA to <1 min for FCS. There is very little sample handling required for a FCS analysis in comparison to the tedious prework and preparation required for PCR and ELISA, in which DNA amplification and enzyme washing are required. The biggest advantage of using FCS as a bioassay, however, is the reduction of false positives and negatives because of the improvement in the ability to separate signal from noise. Perhaps the only advantage PCR and ELISA have on FCS is the cost of a full system and instrumentation — PCR and ELISA run between $20–30K, while FCS can cost upwards of $100K.  

There are many advantages of working with fluorescence correlation spectroscopy. However, there are some pitfalls and areas that need improvement. First, when working with biological material, specifically proteins, preparing the sample for inspection can be challenging. Proteins, much like cells, tend to adhere to surfaces that they interact with; proteins can stick to cover slips or the observation chamber because of molecular surface charges (1). Protein adhesion is very noticeable when working with extremely small concentration ratios, and not noticeable at all when working with a large mass of proteins, so it is very important to find a happy medium between the two. Not only is there a risk of the proteins adhering to surfaces, but they also are prone to aggregating with one another and forming a larger mass. If aggregation occurs, one has to account for fluorescence enhancement of the larger aggregate. Photobleaching, discussed earlier, is another frequent problem. Photophysical damage to a specimen will decrease the number of active molecules. Inactive molecules will not fluoresce, and instead will remain idle and possibly inhibit the dynamics of molecules in its vicinity. Although the three pitfalls listed above are minor problems that can easily be resolved or accounted for, it is important to point them out to ensure that the highest quality data are captured and analyzed with FCS (2). 

Fluorescence correlation spectroscopy is a very powerful measurement and analytical tool for biological material in vivo and in vitro. The procedure is minimally invasive, which is extremely beneficial, and works on a confocal system to ensure very high spatial and temporal resolution. The information gathered helps determine molecular stability, diffusion coefficients, and a number of internal dynamics, along with photophysical methods. The typical concentration ratio and sample size is a few molecules per nanoliter, resulting in very fast measurements that range from a few nanoseconds to hundreds of milliseconds. As mentioned above, there are a few challenges involving molecular adhesion, aggregation, and photobleaching, but these are avoidable and certainly do not inhibit this advanced technique from evolving further. In the future, FCS may provide groundbreaking information or shed more light on unknown intracellular activity.  

 is the biomedical product line engineer responsible for the microscopy product line and life sciences segmentation at Edmund Optics. He holds a bachelors of science in biomedical engineering from Drexel University and is currently working toward his masters of science. Direct correspondence to: 

Fluorescence Correlation Spectroscopy — An Introduction to its Concepts and Applications

 (Experimental Biophysics Group, Gottingen, Germany: Biophysics Textbook Online, 2004). 

, (July 9, 2002), Retrieved October 3, 2011, from National Institute of Health: 

http://afni.nimh.nih.gov/afni/afniboard/index.html

Handbook of Biological Confocal Microscopy

Lasers and Optical Characterization Lab, 

Columns: Lasers and Optics Interface

The basic techniques of fluorescence correlation spectroscopy (FCS) are discussed as well as several applications, such as nanoparticle dispersion studies.

Tracking Unknown Intracellular Activity with Fluorescence Correlation Spectroscopy

Urinary metal concentrations may be effective indicators of particulate metal fume exposure and provide a better understanding of specific exposure-response relationships if an analytical method can be developed that is sufficiently sensitive to measure levels at or near background concentrations. Analyzing urine samples by inductively coupled plasma–mass spectrometry (ICP-MS) poses several distinct challenges as the matrix contains components likely to cause false-positives and reduced analytical sensitivity. This study will demonstrate a method that mitigates these problems and produces quantifiable results even at low parts-per-trillion concentrations.

When metals are rapidly liquefied by electric arc forced-gas techniques, such as industrial electric arc induction furnaces and gas metal arc welding, they frequently volatilize into an aerosol of metal particles that can be <1 µm in diameter; such aerosols are sometimes referred to as 

. Previous investigations established that the formation rate and the precise chemical composition of particulate metal fumes are highly dependent on the parameters of specific processes (for example, temperature, gas flow rate, and raw materials) and can contain various metals, including aluminum, cadmium, chromium, copper, iron, lead, magnesium, manganese, molybdenum, nickel, titanium, vanadium, and zinc (1). 

For workers that are frequently exposed to the complex metal oxides found in particulate metal fumes (for example, welders, boilermakers, and steel mill workers), the occupational respiratory health hazards and potentially long-term adverse health effects of exposure are of considerable concern. The United States Occupational Safety and Health Administration (Washington, D.C.) recognizes that symptoms such as eye, nose, and throat irritation, fever, chills, headache, nausea, shortness of breath, and muscle pain can result from even brief acute exposure to particulate metal fumes (an illness commonly referred to as 

). It is suspected that chronic occupational exposure is related to several cardiopulmonary diseases and cancer (2). Although the characteristics of particulate metal fumes and the symptoms of exposure have already been investigated, few studies have identified precise exposure-response relationships between specific metals and adverse health effects. An understanding of these relationships is essential for risk assessment and the development of effective exposure prevention strategies.  

Depending on the circumstances, various measures are employed to minimize occupational exposure to particulate metal fumes, such as ventilation and personal respirators, but the effectiveness of these protective measures is typically determined by testing the resulting air for concentrations of metals. However, information regarding actual biological exposure to particulate metal fumes is required to understand exposure–response relationships. Biomarkers are often used in epidemiological and toxicological studies to establish biological exposure to various substances in the environment. In contrast to air measurements, information regarding the concentrations of soluble metals in the urine of workers exposed to particulate metal fumes should provide a better understanding of actual biological exposure, exposure–response relationships, and the effectiveness of protective measures.  

As part of a study funded by the National Institute of Environmental Health Sciences (Research Triangle Park, North Carolina) and conducted by the Department of Environmental Health at the Harvard School of Public Health (Boston, Massachusetts), urine samples were collected from nearly 350 individuals known to be occupationally exposed to particulate metal fumes (see Acknowledgments). One phase of this study included analyses of the samples for urinary metal concentrations to investigate the relationship between particulate metal fume exposure and urinary metal concentrations; validate the use of this biomarker as an indicator of exposure; and to contribute further to the investigation of specific exposure-response relationships. Brooks Rand Labs (Seattle, Washington) was contracted to perform analyses of the collected urine samples for the determination of Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn. For the results of these analyses to have adequate significance to this study, an analytical method was required that would be sufficiently sensitive to achieve quantifiable results even at low parts-per-trillion concentrations with a high degree of accuracy and precision.  

Analysis by inductively coupled plasma–mass spectrometry (ICP-MS) with a quadrupole MS system is widely recognized as one of the most advanced techniques currently available for the determination of trace element concentrations. However, analysis by ICP-MS also has traditionally been challenging in two aspects: the potential formation of polyatomic spectral interferences and an intolerance for elevated concentrations of dissolved solids. 

The accurate determination of a small number of elements using ICP-MS can be severely compromised because of spectral interferences. These interferences are caused by various elements found in the plasma gas, solvents, or sample matrix forming polyatomic ions that have the same atomic masses as the isotopes of the analytes of interest. The elements As, Cr, Fe, Ni, Se, and V are all known to be frequently affected by these spectral interferences, resulting in false positives and elevated detection limits (3). Although some techniques to minimize these interferences have been developed, including sample dilution, correction equations, matrix separation, and cold plasma technology, these solutions can in turn lead to an unacceptable increase in detection limits and a greater degree of uncertainty in the data. In some instances, less abundant isotopes that may not be affected as strongly by these interferences can be targeted for detection, but not without the potential for a significant loss in analytical sensitivity. 

Because this study required quantifiable results for several of the metals known to be affected, an investigation into the potential formation of polyatomic spectral interferences was undertaken. The primary sources of potential interferences when analyzing urine samples occurs when components in the urine matrix (that is, dissolved salts or urea) combine in the argon plasma to form polyatomic ions with the same atomic masses as the major isotopes of Cr, Fe, Ni, and V (4). Examples of the effect of polyatomic spectral interferences on the most abundant isotopes of each element are listed in Table I. 

 Table I: Example interferences for urine analyses by ICP-MS			

Relatively recent advances have resulted in several applications with the potential for overcoming the challenges associated with these interferences. One approach in particular, using a collision– reaction cell, provides conventional detection by ICP-MS and can be configured using a combination of focusing and filtering mechanisms to eliminate spectral interferences. We explored the optimal configuration of the collision–reaction cell to resolve many of the polyatomic spectral interferences that were anticipated to contribute to elevated method detection limits for Cr, Fe, Ni, and V in samples that contain large amounts of calcium, carbon, and chlorine. 

Located between the ion optics and the instrument's mass analyzer, the dynamic collision–reaction cell is an enclosed and positively pressurized chamber with an rf/dc quadrupole independent of the mass analyzer quadrupole. After exiting the plasma, the ion beam enters the interface in the normal manner — passing through the sampler or skimmer cones and the ion lens before entering the collision–reaction cell chamber (see Figure 1). A highly reactive gas is injected into the chamber, resulting in gas-phase chemical reactions that breakdown polyatomic ions into neutral elemental species and the rf/dc quadrupole ejects potential sources of new interferences, focusing the analyte ions through to the mass analyzer quadrupole for detection. 

Figure 1: Location of the dynamic reaction cell.			

, reactive gasses are introduced into the chamber as the ion beam passes through and can result in numerous ion-molecule reactions. Under the correct conditions, ions entering the collision–reaction cell will interact with the polar molecules of the reaction gas and effectively transfer their charge. According to the specifics of thermodynamic reaction kinetics, this charge transfer reaction depends on its exothermicity (5). The difference in the ionization potential (eV) of the ions and the polar reaction gas molecules determines whether the reaction will be exothermic and proceed to occur. Table II lists the ionization potentials for some of the elements and reactive gases that are relevant to this investigation. 

It can be predicted that charge transfer reactions between ions and polar molecules will happen exothermically if the ionization potential of the reaction gas molecule is significantly less than the analyte. This process is equally applicable to argide interferences and polyatomic ions; that is to say, when they have a greater ionization potential than the reaction gas molecules they will undergo charge transfer reactions and breakdown into neutral elemental atoms. Conversely, if the ionization potential of the ion is less than the reaction gas molecules, an endothermic reaction will generally not occur. Examples of this process also can be expressed as 

The reaction gas that is ultimately selected is based on its predictability to undergo a controlled and specific gas-phase chemical reaction with the polyatomic ions responsible for spectral interferences at a rate considerably faster than it will react with the analyte ion. Ammonia is commonly used as a reaction gas because it has an intermediate ionization potential that is between those of many analyte elements and most polyatomic ions, making it highly effective at chemical resolution. 

However, the resulting products of this chemical resolution process can maintain the potential to undergo secondary reactions and form further polyatomic interferences if they are not effectively removed from the chamber. In addition to focusing the analyte ions through the collision–reaction cell, the electrical fields of the rf/dc quadrupole can be optimized to behave as a selective bandpass mass filter. In a process called 

, the amplitude and frequency of the quadrupole are adjusted to establish precisely defined stability regions that act as a low-mass or high-mass cut off. Ions that do not fall within this window will be ejected from the chamber, preventing the secondary reactions that can lead to new interferences (6). 

By configuring the collision–reaction cell with the appropriate reaction gas for chemical resolution and sweeping the bandpass rf/dc quadrupole of the collision–reaction cell in tandem with the mass analyzer quadrupole, polyatomic spectral interferences are largely eliminated and the optimal transmission of the analyte ions into the mass analyzer quadrupole for conventional mass separation and detection can occur. 

Traditionally, analysis by ICP-MS also is prone to additional limitations because of the instrument's intolerance for dissolved solids. If samples with elevated concentrations of total dissolved solids (TDS) are introduced to the system, material can begin to deposit on the tip of the sample injector, the small orifices of the sampler and skimmer cones, the ion optics, and so forth. These deposits can quickly build up, leading to blockages that cause a degradation of signal stability and a loss of analytical sensitivity. Therefore, it is generally recommended that samples contain no more than 0.2% TDS for optimal instrument performance and signal stability (7).  

Samples containing concentrations of TDS that exceed the recommended level can be diluted with a suitable diluent, solvent, or aerosol gas to reduce the concentrations to a level that the instrument can tolerate better. As might be expected, urine samples can contain more than 5% suspended and dissolved solids, including elevated concentrations of dissolved calcium, sodium, chlorine, proteins, and other organic matter. Therefore, these samples typically require significant dilution before the concentrations of TDS fall to recommended levels. These dilutions can lead to elevated method detection limits that may exceed the concentrations of the analytes.  

Because this study required quantifiable results even at background levels, we investigated a means to prevent the elevated TDS concentrations in urine from leading to significantly elevated method detection limits. Recent innovations offer solutions to address this issue, primarily by reducing the length of time that the instrument is exposed to the samples and reducing the volume of sample that is introduced to the system, thereby minimizing the amount of solids that can accumulate and affect instrument performance. 

The precise configuration of the sample introduction system that delivers samples to the ICP-MS for analyses can vary widely. Conventional designs are typically configured to deliver a relatively constant flow of sample to the instrument until the required measurements have been confirmed and then perform a thorough rinse cycle to prevent any contamination of the subsequent sample. This process can take as long as 10 min depending on the application, during which the instrument may be exposed to the sample matrix for a significant amount of time. 

Other innovations in sample introduction configurations, in conjunction with appropriate nebulizers, now allow for a dramatic reduction in the sample volume that must be injected for accurate measurements and the time that each analysis requires. High-throughput sample injection systems allow for precisely determined amounts of sample, depending on the required analyses, to be rapidly pumped into a short sample loop located in close proximity to the nebulizer. The sample is then injected into the nebulizer along with a constant flow of carrier solution, ensuring that no air is introduced into the sample line. This process minimizes the amount of time required for signal stabilization to occur and the volume of sample that must be introduced. 

For efficient vaporization and ionization, liquid samples are typically injected into the argon plasma of an ICP-MS instrument as fine aerosols using a concentric nebulizer that shears the liquid stream into small droplets with argon gas and a spray chamber that rejects all but the smallest droplets. The length of time it takes for the instrument's signal to stabilize after sample injection begins, and therefore the amount of sample that the instrument is effectively exposed to, depends in part on how efficiently this aerosol generation process occurs. 

Conventional concentric nebulizers are typically constructed of quartz and aspirate samples at flow rates of approximately 1 mL/min. As little as 2% of the aspirated sample may actually achieve the required droplet size and successfully pass through the spray chamber. This relatively low sample transport efficiency requires that the instrument be continuously exposed to the sample until signal stability is eventually achieved, increasing the potential for dissolved solids to deposit in amounts sufficient to cause lowered instrument sensitivity. This issue raises even greater concerns when the analyses of large quantities of samples are required, because it can necessitate increased instrument maintenance and very often reanalysis of the samples.  

More recently, specialized high-efficiency nebulizers that use higher gas pressures have been developed that can aspirate samples at flow rates as low as 20 µL/min. With nearly all of the aspirated sample successfully passing through the spray chamber, these microflow nebulizers provide a significantly higher sample transport efficiency than conventional concentric nebulizers. Additionally, microflow nebulizers are typically constructed from fluoropolymers that are extremely resistant to corrosion, which reduces the potential for contamination and makes them highly appropriate for applications that require extremely low detection limits in complex sample matrices. 

By configuring a sample introduction system that incorporates a high throughput sample injection device and a microflow nebulizer, the issues associated with elevated concentrations of TDS in sample matrices should be eliminated without compromising the goal of low detection limits. 

As a major program of the Center for Disease Control and Prevention's (CDC, Atlanta, Georgia) National Center for Health Statistics, the National Health and Nutrition Examination Survey (NHANES) is a large-scale study designed to assess the health of people throughout the United States. As part of the survey, urine samples were collected from participants to measure exposure to environmentally toxic substances, including trace metals. The laboratory method employed by the National Center for Environmental Health's Division of Laboratory Sciences in support of NHANES (CDC Method No: ITU001) was developed to achieve rapid and accurate quantification of As, Ba, Be, Cd, Cs, Co, Mo, Pb, Pt, Sb, Tl, W, and U by ICP-MS in more than 5000 urine samples annually (8).  

With one exception, this method was not developed to address any of the specific trace metals analyses requested of Brooks Rand Labs (Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn), but because of the broad acceptance of the method an effort was made to replicate it to the extent achievable. However, because of the presence of polyatomic spectral interferences and elevated concentrations of dissolved solids, strict adherence to the method led to elevated method detection limits. We attempted to resolve those issues through the optimization of the collision–reaction cell parameters and implementation of various sample introduction configurations. 

The CDC method describes the use of an ELAN DRC II MS system (Perkin-Elmer LAS, Shelton, Connecticut), a Cetac ASX 500 series autosampler (Cetac Technologies, Omaha, Nebraska), and a Meinhard Type A quartz concentric nebulizer TQ-30-A3 (Meinhard Glass Products, Golden, Colorado). Additionally, our experiments included an ESI SC-4 autosampler with an ESI SC-FAST valve, an ESI PFA-ST MicroFlow nebulizer, and an ESI PC3 Peltier-cooled spray chamber (Elemental Scientific Inc., Omaha, Nebraska). 

The urine samples for this study had been stored frozen (-80 °C) in 15-mL polypropylene centrifuge tubes for more than three years before submission to the laboratory. The sample tubes were placed in a Class 100 laminar flow clean hood and allowed to reach room temperature. An aliquot of each sample was added to an autosampler tube and diluted 1:9 with 1% HNO

; matrix spikes were added at the same time as the diluent. To dissolve any suspended material, the aliquots were capped and heated to 90 °C for 30 min or until particulates were no longer visible. The vials were allowed to cool to room temperature and then loaded directly into the autosampler for analysis. 

In an attempt to overcome the recognized potential for polyatomic spectral interferences, ammonia was selected as the appropriate reaction gas for the collision–reaction cell based on the reasons outlined earlier and a compromise flow rate of 0.8 mL/min was chosen in the interest of throughput efficiency. Optimal RPq values for the electrical fields of the rf/dc quadrupole were selected on an element-specific basis (see Table III). The magnitude of interferences (both polyatomic and isobaric) for the most abundant isotope of Ni were so significant that superior analytical sensitivity was achieved by monitoring the next most abundant isotope, 

 Ni, in which far less interference was observed. 

Table III: Analytical instrument parameters (PerkinElmer Elan II DRC) 			

Three sample introduction configurations were implemented to measure the effectiveness of several variables to minimize the amount of time the instrument was exposed to the sample matrix and mitigate the issues associated with dissolved solids. The first configuration closely followed the specifications outlined in the CDC analytical method, using a Cetac ASX 520HS autosampler and a Meinhard Type A quartz nebulizer with a sample flow rate of 1.2 mL/min, and included element-specific collision–reaction cell reaction gas flows. The second configuration replaced the specified nebulizer with a microflow model, the ESI PFA-ST MicroFlow nebulizer, and utilized the previously mentioned common collision–reaction cell reaction gas flow, also with a sample flow rate of 1.2 mL/min. The third configuration maintained the microflow nebulizer and replaced the specified autosampler with one that included a high-throughput sample injection system, the ESI SC-4 FAST system, with a sample flow rate of 0.3 mL/min. 

In Figure 2, the total analysis time per sample has been segmented into consecutive stages, including integration, sample flush, read delay, collision–reaction cell gas flow stabilization, and wash times. The first configuration required more than 150 s per analysis and exposed the instrument to the sample matrix for more than 120 s. The second configuration eliminated the necessity for collision–reaction cell gas flow stabilization time but still required more than 120 s per analysis and exposed the instrument to the sample matrix for more than 90 s. The third configuration dramatically reduced the sample flush time, required less than 50 s per analysis, and exposed the instrument to the sample matrix for less than 40 s.  

Figure 2: Total analysis times for the different sample introduction configurations. 			

As suggested in the CDC method, the low-level and high-level versions of standard reference material NIST SRM 2670a (toxic elements in urine) were analyzed in conjunction with the samples to establish method accuracy. However, the certificate of analysis for this standard reference material does not provide any values for Fe and only "information values" for Cr, Ni, and V. The results of the analyses for each of the sample introduction configurations appear in Table IV. Configurations including the microflow nebulizer appear to have slightly increased the recoveries of Cr and Ni, particularly for the low-level SRM. The recoveries of V appear to be unchanged regardless of the configuration or concentration. 

Table IV: NIST 2670a toxic elements in urine SRM results (Âµg/L) 			

Method precision was evaluated by analyzing 15 pairs of urine samples spiked at levels 2–5 times the method detection limits (refer to Table VI) for each sample introduction configuration. The combined mean recoveries of these analyses for the first and second sample introduction configurations (both used the Cetac ASX 520HS autosampler) and separately for the third sample introduction configuration (ESI SC-4 FAST autosampler) are shown in Table V. The mean matrix spike recoveries appear to have significantly improved for Cr, Fe, and Ni with the use of the third configuration. The mean relative percent differences (RPD) between results for matrix spike pairs also appears to have significantly improved for Cr, Fe, and V with the use of the third configuration. 

Table V: Matrix spike results for concentrations 2â5Ãthe MDL (%) 			

The recoveries for specific pairs of matrix spike samples analyzed for Cr are displayed in Figure 3. Again, the results of the analyses from the first and second sample configurations have been averaged. The mean recovery of the matrix spikes for the first and second configurations is only 84.7%, with a mean RPD between results for matrix spike pairs of 9.7%. Moreover, the analyses were halted after the eighth pair because of poor internal standard recoveries (see more below). The mean recovery of the matrix spikes for the third configuration was significantly improved at 98.5%, with a mean RPD between results for matrix spike pairs of 2.4%. 

Figure 3: Chromium recoveries for specific pairs of spiked samples. 			

Although adjustments in the sample introduction system did not appear to significantly affect the recoveries of the standard reference materials, the recoveries of the matrix spikes did, in some cases, improve substantially. This inconsistency is explained primarily because of the order in which these samples were analyzed. The standard reference materials were analyzed immediately subsequent to the method blanks and before the instrument had been exposed to any of the urine samples; whereas the pairs of matrix spikes were analyzed after approximately every set of 10 urine samples. The degradation in analytical sensitivity caused by the exposure of the instrument to the greater levels of dissolved solids using the first and second sample introduction configurations is apparent when the recoveries of the internal standard (10 µg/L Ga) are observed in Figure 4. However, the recoveries for the third sample introduction configuration remain highly consistent and only begin to degrade as the number of samples approaches 200. 

Methods and results from a study analyzing metal concentrations in urine samples using inductively coupled plasma–mass spectrometry (ICP-MS)

Optimization of an Analytical Method for the Determination of Trace Metals in Urine by ICP-MS

 Integrated data management is a central element of industrial research and manufacturing. However, data management within the university research setting is less well-defined. Variation in record keeping practices may be caused by the range of experience of researchers (from new undergraduate students to postdoctoral research associates), the exploratory nature of the research, and also perhaps a lack of consensus within the community in setting reasonable expectations. Here, we explore some aspects of integrated data management specifically for mass spectrometric data, touching on issues such as ever-increasing spectral dataset size, the breadth and complexity of associated laboratory documents, and evolving publication and funding requirements for archiving and sharing research data. 

During lunch at a recent scientific conference, more folks at the table were checking their smart phones than eating their lunch. For those of us struggling to maintain some small part of the art of conversation, a science trivia question came up, at which point one of the "linked-in" generation diverted attention long enough to search for the answer on the web. Impressed, but trying to maintain the conversational thread, I ventured the opinion that the phones at the table had more interconnectivity and processing and data storage capability than the mass spectrometry (MS) data systems that I used at the start of my research career. Self-deprecation sparked interest from the younglings, and after explaining to the others what a Winchester disk drive was (1), I learned that the astrophysicist at the table simulated stellar accretion discs in a data file size of 1–10 terabytes, and that the protein sequencing scientist generated the same amount of data each and every month.  

Like any other modern instrument-based approach, MS analysis generates tremendous amounts of data. It is simple enough to examine the size of the files in our computers, or to examine storage requirements for archived measurements, and deduce that there has been a substantial increase in mass spectral dataset size over the last few decades. The number of experimental options also has increased. In the past, we may have focused on a single mass spectrum as the final result. Each compound was expected to have a distinct mass spectrum, usually an electron ionization mass spectrum recorded under standard conditions, and these spectra were gathered together in libraries (first printed, and now electronic) to serve as an archive of the spectral fingerprints used for compound identification. In the 1970s, it was gas chromatography coupled with mass spectrometry (GC–MS), and the associated necessity for measurement data in a computer for storage and processing, that represented the first of a series of major shifts in our approach. A GC–MS data file typically contains thousands of individual mass spectra, recorded sequentially. Spectra that span the time of an eluted peak could be averaged together, background-subtracted, and otherwise enhanced to produce the "fingerprint" mass spectrum that identifies the compound, or, given the synergism derived from over-determined data, provide clues about unresolved coeluted compounds. Fast forward 40 years, and consider all of the experimental options for recording mass spectra. The mass spectrum could be composed of positive ions, or negative ions, or possibly an alternating sequence. We might record low-resolution as well as high-resolution mass spectra. We might record product ion MS-MS spectra from any of several higher abundance ions in any of those mass spectra (recording those on the fly), or we might choose precursor ion or constant neutral loss MS-MS spectra. For certainty, we may elect to record the mass spectrum of a single compound with several different ionization methods or even with several different reagent gases in chemical ionization alone. We might derivatize the sample, age the sample, purify and extract and concentrate the sample, and complete another analysis. New instrumentation begets new chemistry; new insight begets new information. 

With this array of measurement options, an associated increase in size and complexity of mass spectral datasets is inevitable. Figure 1 summarizes the increase in MS dataset size over the last few decades and forecasts possible continued growth into the future with new and broader applications of MS. The first three chevrons document 30 years from 1980 to the present. Amazingly, this period roughly encompasses the first widespread use of data systems for GC–MS (and MS dataset sizes of 20–40 megabytes) through to the modern laboratory that generates 1–10 terabytes of data monthly, including MS-MS and high-resolution mass spectral (HRMS) measurements. It is unrealistic to assume that such expansive sets of data can be interpreted and assessed solely through direct human effort. Figure 1 includes a break in the chronological sequence to emphasize the need for 

, a generic term to include all of the processes in computer-assistance software to deal with large dataset sizes, perform certification and assurance tests, and to search for features and results, both apparent and hidden. Our ability to record massive amounts of data is a genuine challenge to automated procedures for assessment of that data, and as we will discuss in this column, managing it. The sequence outlined in Figure 1 also forecasts potential growth in MS datasets in the future. By 2020, for example, it is probable that sequencing datasets may expand to the petabyte threshold, especially as several sequencing experiments are linked together. Metabolomics analysis through MS includes complex experiments for hundreds of different metabolites. The analysis includes the use of isotopic tracers, with concordant control experiments, quantitation, and time-dependent analyses. Therefore, it is probable that by 2030, such uses of MS may reach the exabyte limit for summed dataset size.  

 Figure 1: Progression in the sizes of mass spectral datasets, linked to associated uses and decades. 

Managing Data at the Petabyte Level and Beyond

Managing all of this data, even in consideration of the dataset size and scale alone, is clearly a challenging task. However, methods and strategies developed for comprehensive data management in scientific research, including MS (2,3), can be applied to this MS challenge. As in other research areas, we know that there is a substantial data record associated with mass spectral data that is not MS data itself, and proper data management methods encompass these records. For instance, the broader set of documents includes laboratory notebooks, instrument logs, sample inventories, blank files, calibration files, certification files, and — in the ideal case — link files to analytical data obtained on other mass spectrometers and other analytical instruments. In any data storage and management system, the format of the data becomes an important issue. That topic alone could be the focus of an entire column in this series, including formats that have been developed in efforts to reduce the size of the dataset. Martens and colleagues (4) recently described a community standard for MS data called mzML, developed from the Proteomics Standards Initiative. This user-developed initiative, developed only for the data, and covering only a portion of the associated metadata, is a hint at larger potentials that will greatly impact MS in the next 20 years. For example, the Institute for the Future (Palo Alto, California) states in their recent report (5):  

Indeed, science has always been driven by data but now the sheer complexity and amount of information is demanding new practices and methods to translate the bits into knowledge. To that end, science will seek contributions from the networked public to tag raw data and make connections, seek patterns, and draw links between datasets. The speed of scientific discovery will be proportional to the availability of cross-disciplinary engagement with relevant data. 

Although this is a forecast, the reality is already upon us (6). 

Reality in our scientific development derives from vision, realistic capabilities, and sometimes regulation and requirement. The latter (ideally at least) derives from a consensus understanding of what scientists do and how they do it. For this column, that latter desideratum is what mass spectrometrists do and how they do it. Figure 2 illustrates a data management timeline (applicable for any progression of scientific research), tracking from conception of the idea through to dissemination of data after publication of the results. Responsibilities for data management are plotted on the 

-axis (note that this axis is nonlinear and has a break). There are issues of intellectual property, responsible mentoring, and community responsibilities all enfolded within the summed "data management" value, and many of these impact the university research environment. Research students, postdoctoral research associates, and university faculty working together to develop an idea and reduce it to practice all have interests in that intellectual property, and perhaps a patent application. There are specific record-keeping and data management practices to be followed.  

Figure 2: Data management responsibilities vary as research progresses from conception of experiment to dissemination of results. 

The research progression in Figure 2 is marked out in six steps: conception, inception, repetition, interpretation, publication, and dissemination. Conception is, of course, the "What if?" and "How about if we?" creative discussions at the beginning of new research efforts. Because many ideas don't work out, it may seem as though there is no need for data management, but there is. Those first discussions may become important documents of the chronology later when claims of priority arise. Certainly, as ideas are put into practice (inception), responsibilities for data management increase, as first experiments provide some information about what works and what does not. First experiments may be difficult to describe completely and accurately because all of the relevant variables have not yet been identified. But then that's why they call it research. When the MS researcher gets a desired result, and wants to repeat it, the data management responsibility rises on the steepest part of the curve. Reproducibility by other researchers is a core value of scientific progress, and accurate experimental and instrumental records are vital. Interpretation of results (the next step) also relies on those records, and publication (the penultimate step) may require the submission of associated data as supplementary files to accompany the publication. In a future column, we will survey electronic records policies for MS data for the various MS journals.  

The progression illustrated in Figure 2 is the philosophical overview of the evolution of an idea, but it also might be used to chart the progress of a graduate student's research efforts leading (ideally) to a dissertation. A research publication or the successful completion of a dissertation represents the culmination of an effort. Data management responsibilities in the last step of dissemination continue, at least for the period that the data is seen as current and relevant to others. Data have a half-life of usefulness, and only a limited subset of mass spectral data is valuable enough to be archived indefinitely. But in the period immediately after publication of a new research result, colleagues may wish to examine supporting data. They may have questions, seek additional data, or have differing interpretations of the data. After acceptance of a dissertation, and student graduation, those who follow in a research laboratory may depend on good data records to continue and extend research.  

Therefore, there's an explicit expectation that scientific data and MS data will be shared within the community, and that expectation requires that records be kept and the data appropriately managed. Two related questions come to mind. First, if sharing is the expectation, then what's a reasonable time for maintaining records so that the data can be shared? (And what's the reason for the drop on the right side of the Figure 2 curve?) Second, who owns the data? 

How long are data relevant? The answer to this question varies by research field and type of data. Survey data, for example, would reasonably have a greater lifetime than spectral analysis data, because surveys are seldom exactly repeated and are compared with each other in a trending analysis. In instrumental analysis, the finite lifetime of instruments factors into the archival value of the data produced. We can estimate the useful life of mass spectral data by examining aggregate descriptors for mass spectral publications, most of which are associated with supporting spectral data. The commercial service 

 compiles metrics on science journals, including a metric called "journal cited half life." For any given publication year, this factor tallies all citations to publications in that journal. The half-life is the point at which 50% of citations are to more recent publications in that journal, and 50% to older. "Rapid communications" journals are expected to have a shorter half-life factor than review journals, but the values calculated for MS journals and the range informs the related question of how long data are relevant to our own community. In a nutshell, the half-life for MS data is about five years.   

The estimated half-life of five years helps to explain the drop off in the curve in Figure 2 after publication of mass spectral data. In a university research laboratory with a constant turnover of students and instruments, five years is also a reasonable estimate of data persistence. But a more prosaic reason contributes to the drop — regulations regarding the retention of research data. University policy may vary or may not exist at all. However, funding agencies impose requirements as a condition of receipt of research funding. As an example, the National Science Foundation (NSF, Arlington, Virginia) (7) requires that records (including data) pertinent to a grant be retained for three years from the date of submission of the "Final Report" for the award. Additionally, "NSF may request transfer to its custody of records not needed by the grantee when it determines that the records possess long-term retention value." 

Now on to the second question: Who owns the data? In federally-funded research at universities, the answer is clear. But interestingly, the responses from those who perform the research are not consistent with the correct answer. Most principal investigators on federal awards claim that they own the data; some of the postdoctoral research associates or graduate students who complete the research, perhaps as part of a dissertation, assert that they own the data. However, the correct answer is that the grantee owns the data; the grantee is the university to whom the research funds were awarded. It is the grantee that is responsible for the proper administration of the award, and proper administration includes compliance with data sharing requirements. For instance, with respect to research awards from NSF (8):  

Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing. 

The National Institutes of Health (NIH, Bethesda, Maryland) has a specific data sharing policy that requires a data management plan for proposals that seek more than $500,000 in direct costs annually. Specific types of projects funded by NIH have individualized data-sharing requirements. For NSF (9): 

Proposals submitted or due on or after January 18, 2011, must include a supplementary document of no more than two pages labeled "Data Management Plan." This supplementary document should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results 

As mass spectrometrists, we may tend to view our data as value-neutral, of interest to ourselves in analysis, method development, and creative research. But data are a valuable commodity and resource, and while it seems that there is an increasing modern emphasis on its archival and subsequent use, taking advantage of new computational capabilities, access to the data itself, and concerns about its quality, have been the subject of legislation in the United States for some time. In 1999, the "Shelby amendment" to an omnibus funding bill provided direction to the Office of Management and Budget (OMB) to take action so that the data that result from federally-funded research are available through Freedom-of-Information Act (FOIA) requests (10). The "Data Quality Act" (DQA) passed in 2001 as part of an appropriations bill; here the OMB was to create guidelines that "provide policy and procedural guidance to Federal agencies for ensuring and maximizing the quality, objectivity, utility, and integrity of information (including statistical information) disseminated by Federal agencies." Apparently, "quality" was a flashpoint term, with the DQA later coming under criticism for its lack of legislative depth and debate, and its potential for abuse (11,12). As this article was written, the U.S. Federal government issued a request for information "Public Access to Digital Data Resulting from Federally Funded Scientific Research" (13). Although the public comment period for this request ended January 12, 2012, a future Federal Register will include the public comments received. The request for information contains 13 insightful questions dealing with issues of data formats, competing interests of stakeholders, intellectual property, international communities, and the potential role of data standards. Interestingly, the issue of increasing dataset size is not represented within the list of questions, nor are questions presented that discuss physical aspects (cost and location) of any potential centralized data archive. Even as we have created data archives, the cost to maintain them has become problematic. In spring 2011, NIH announced plans to shutter bioinformatics databases, including the Sequence Reach Archive repository, because of budget restrictions. A company with some experience in large databases made a $15-million grant to maintain accessibility in a nongovernment hosting site. Thank you, Google (14).  

In the end, intellectual property concerns associated with data management and data sharing are likely to be the most challenging, and the most engaging. Must investigators share their datasets with their colleagues when they view their colleagues as competitors for research funding? What if special software or algorithms are needed to access or process the data, and those are under separate patent or proprietary protections? Data are indeed a treasure trove; after the mine is located, will data wildcatters appear to siphon value? Legislation has been passed, and guidelines put in place, for data management and data sharing. The assessment of reasonableness, driven in no small part by community consensus, lags behind. But we are moving inexorably into the large scale data universe, whether we are ready for it or not. 

 fearlessly forecasts MS dataset size 30 years into the future in this column. He is convinced that if it were not for the fact that mass spectral data has a relatively short half-life, we would be buried under paper copies of billions of mass spectra previously recorded. He concludes that most of the mass spectral data archived today was probably recorded within the past five years, and that newer data will overwhelm the archives unless explicit and reasonable efforts are taken to purge older data. He invites readers to check back with him in 2038 to see if his dataset size forecast for 2040 is on track. This column is the sole responsibility of the author, who can be reached at 

http://en.wikipedia.org/wiki/History_of_hard_disk_drives

 (2) E.E. Schadt, M.D. Linderman, J. Sorenson, L. Lee, and G.P. Nolan, 

Data Intensive Distributed Computing: Challenges and Solutions for Large-Scale Information Management

, (IGI Global, 2012; ISBN-10: 1615209719). 

 (4) L. Martens, M. Chambers, M. Sturm, D. Kessner, F. Levander, J. Shofstahl, W.H. Tang, A. Rompp, S. Neumann, A.D. Pizarro, L. Montecchi-Palazzi, N. Tasman, M. Coleman, F. Reisinger, P. Souda, H. Hermjakob, P.-A. Binz, and E.W. Deutsch, 

10: 10.1074/mcp.R110.000133, 1–7 (2011). 

 (5) "A Multiverse of Exploration: The Future of Science 2021," Institute for the Future, Technology Horizons, Fall 2011. 

Aspects of integrated data management specifically for mass spectrometric data are explored, including spectral dataset size, laboratory documents, and requirements for archiving and sharing research data.

Spectroscopy-01-01-2012

Market Profile: Fluorescence Correlation Spectroscopy

Products

Maxwell's Equations, Part V

Tracking Unknown Intracellular Activity with Fluorescence Correlation Spectroscopy

Optimization of an Analytical Method for the Determination of Trace Metals in Urine by ICP-MS

Managing Mass Spectrometric Data

Vol 27 No 1 Spectroscopy January 2012 Regular Issue PDF