J

State of the Art for Autonomous Detection
Systems Using Mass Spectrometry

Peter Snyder, Ph.D., and Rabih E. Jabbour, Ph.D.

A white paper prepared for the June 25–26, 2013, workshop on Strategies for Cost-Effective and Flexible Biodetection Systems That Ensure Timely and Accurate Information for Public Health Officials, hosted by the Institute of Medicine’s Board on Health Sciences Policy and the National Research Council’s Board on Life Sciences. The authors are responsible for the content of this article, which does not necessarily represent the views of the Institute of Medicine or the National Research Council.

Mass spectrometry (MS) is being considered as a candidate for the Tier 1, Tier 2, and Tier 3 autonomous detection systems. Candidate status depends on how the figures of merit compare to the given desirable characteristics. Samples to be analyzed include a pathogenic and simulant spore former and a pathogen and simulant vegetative cell bacterium.

BACKGROUND

Mass Spectrometry

The mass spectrometer is an instrument designed to separate gas-phase ions according to their mass-to-charge ratio, m/z. The heart of the mass spectrometer is the analyzer. This element separates the gas-phase ions. The analyzer usually uses time-of-flight, quadrupole, ion trap, or a



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 215
J State of the Art for Autonomous Detection Systems Using Mass Spectrometry Peter Snyder, Ph.D., and Rabih E. Jabbour, Ph.D. A white paper prepared for the June 25–26, 2013, workshop on Strate- gies for Cost-Effective and Flexible Biodetection Systems That Ensure Timely and Accurate Information for Public Health Officials, hosted by the Institute of Medicine’s Board on Health Sciences Policy and the Na- tional Research Council’s Board on Life Sciences. The authors are re- sponsible for the content of this article, which does not necessarily represent the views of the Institute of Medicine or the National Research Council. Mass spectrometry (MS) is being considered as a candidate for the Tier 1, Tier 2, and Tier 3 autonomous detection systems. Candidate sta- tus depends on how the figures of merit compare to the given desirable characteristics. Samples to be analyzed include a pathogenic and simu- lant spore former and a pathogen and simulant vegetative cell bacterium. BACKGROUND Mass Spectrometry The mass spectrometer is an instrument designed to separate gas- phase ions according to their mass-to-charge ratio, m/z. The heart of the mass spectrometer is the analyzer. This element separates the gas-phase ions. The analyzer usually uses time-of-flight, quadrupole, ion trap, or a 215

OCR for page 215
216 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH combination of all three to move the ions from the region where they are created to a detector, where they produce a signal that is amplified. The m/z, and not only the mass, is of importance. A mass spectrometer can measure the mass of a molecule only after it converts the neutral mole- cule to a gas-phase ion. To do so, it imparts an electrical charge to mole- cules and converts the resultant flux of electrically charged ions into a proportional electrical current that a data system converts to digital in- formation, displaying it as a mass spectrum. Ions can be created in a number of ways:  Laser ablation of a compound dissolved in a matrix on a planar surface by matrix-assisted, laser-desorption ionization (MALDI).  Interaction with an energized particle or electron such as in elec- tron ionization.  Electrospray ionization (ESI) where the eluent from a liquid chromatography (LC) system receives a high voltage, resulting in an aerosol of ions. ESI is useful in producing ions from neutral macromolecules, because it overcomes the propensity of these molecules to fragment when ionized. ESI is advantageous over other atmospheric pressure ionization process- es (e.g., MALDI) because it may produce multiply charged ions, effec- tively extending the mass range of the analyzer to accommodate the kilodalton to megadalton orders of magnitude observed in proteins and their associated polypeptide fragments. ESI is a so-called soft ionization technique since there is very little fragmentation of the parent compound. This can be advantageous in the sense that the molecular ion (or, more accurately, a pseudo-molecular ion) is almost always observed; however, very little structural infor- mation can be gained from the simple mass spectrum obtained. This dis- advantage can be overcome by coupling ESI with tandem mass spectrometry (MS-MS). The development of ESI for the analysis of biological macromole- cules was rewarded with the Nobel Prize in chemistry to John Bennett Fenn in 2002. The analyzer is operated under high vacuum, so that the ions can travel to the detector with a sufficient yield. MS-MS is the combination of two or more MS experiments. The aim is either to get structural in- formation by fragmenting the ions isolated during the first experiment or to achieve better selectivity and sensitivity for quantitative analysis. MS-

OCR for page 215
APPENDIX J 217 MS is done either by coupling multiple analyzers (of the same kind or different kinds) or with an ion trap, by doing the various experiments within the trap. The unit of measure has become the dalton, displacing other terms such as amu. One dalton is one-twelfth of the mass of a single atom of carbon-12 (12C). Liquid Chromatography Liquid chromatography (LC) technology gives analytical access to about 80 percent of the chemical universe that is unreachable by GC. In its simplest form, LC relies on the ability to predict and reproduce com- peting interactions between analytes in solution (the mobile or condensed phase) being passed over a bed of packed particles (the stationary phase). The development in recent years of columns packed with a variety of functional moieties and of solvent delivery systems able to deliver the mobile phase has enabled LC to become the analytical backbone for many industries. The abbreviation HPLC for high-pressure liquid chro- matography was coined in 1970 by Csaba Horváth to indicate that high pressure is used to generate the flow required for LC in packed columns. The technique is now generally referred to as high-performance liquid chromatography, with the same abbreviation being used. HPLC is an important separation technique for the analysis of proteins and peptides because it can easily be coupled to a mass spectrometer. Moreover, the compatibility of solvents used in HPLC separations with ESI makes this hyphenated technique most commonly used in the final stage of prote- omics analysis. TIER 1: 2012 TO 2016 Currently there are two MS-based systems that may be viewed as having parameters that come close to satisfying the proposed figures of merit for the autonomous detection system using mass spectrometry. The relevant characteristics include actual system size, performance, and quantitative figures of merit, such as the probability of false-positives approaching one false-positive per year. Three significant differences between the two systems are size, the state of the sample collected, and liquid expendable.

OCR for page 215
218 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH Hamilton Sundstrand Mass Spectrometer The modified Hamilton Sundstrand mass spectrometer (HSMS) (Basile et al., 1998; Griest et al., 2001; Hart et al., 2000) is fairly small in size, uses a liquid expendable, and collects bulk samples. The system was originally manufactured by Bruker-Franzen in the 1980–1990 time frame and then transferred to Oak Ridge National Laboratories in the late 1990s. It subsequently came into the hands of Hamilton Sundstrand. The system has matured, and the key feature is that the aerosol sample is col- lected in bulk in a few minutes onto a small filter paper. Microliter amounts of liquid are injected onto the spot. The liquid is the reagent tetramethylammonium hydroxide (TMAH). The spot is then heated (pyrolyzed) in a ballistic fashion to about 500–550oC within 14 seconds and maintained at that temperature for 4 minutes, and TMAH reacts with any fatty acids that are included in the contents of the spot. TMAH reacts and derivatizes the fatty acids, and fatty acid methyl esters (FAMEs) re- sult. Fatty acids are typically found in all bacteria. There are certain fatty acids that are fairly unique to certain bacteria, but the better discriminator is the pattern of approximately 20 to 30 FAMEs that are produced to the exclusion of almost all other material in the spot. Residual char is heated to a higher temperature so as to develop a clean, fresh spot for the next cycle of aerosol collection. Meanwhile the vapors are transported into the ion trap system (MS-MS) for analysis. There is a resident database, and the system can produce an output with the likeliest bacteria present. It is the 20 to 30 derivatized fatty acids that are the fundamental data used to describe, analyze, and identify the biological sample. It is not yet known if that is enough information to correctly identify all the agents of interest and to exclude all others at the desired probability of detection (Pd) and probability of false-positive (Pfp) levels. Very few Pd and Pfp analyses were performed by the HSMS team. There has been none published. The HSMS has the following features:  Vibration tested in a way that simulates the high-mobility multi- purpose wheeled vehicle: HMMWV Model 1097 “Humvee.”  Chemical ionization in addition to electron ionization. Chemical ionization significantly reduces background chemical noise (such as diesel and gasoline vapors).  The vacuum system operates at about 35 W.

OCR for page 215
APPENDIX J 219  The HSMS has four separate modules, and each module is further compartmentalized for easy troubleshooting and replacement.  The entire HSMS is approximately 5.8 cubic feet and draws an average of 500 W power.  Two-stage virtual impaction system to deliver bioaerosol to the heating area from 300 L/min to 1 L/min with 50–90 percent effi- ciency for aerosol aerodynamic diameters between 2 and 10 microns.  Sample deposition, heating, and processing take place in a quartz tube, and the vapors are directed into the MS-MS system.  The 330-L/min aerosol input flow rate with a 2-minute sampling time and 50 agent-containing particles per liter of air (ACPLA) equates to 33,000 particles collected, which produces S/N = 5. This is laboratory data.  Outdoor Joint Field Trials-6 (JFT-6) at Defence Research Estab- lishment Suffield (DRES) in Medicine Hat, Alberta, Canada, during August 2000 had Bacillus globigii (BG) spore and Erwinia herbicola aerosol releases at the 30 ACPLA level. S/N of the JFT-6 bacterial FAME spectra were between 7 and 10.  No real quantitative figures of merit have been done with respect to false-positive (FP) and probability of false-positive (Pfp).  The four main Category A pathogenic bacteria and numerous spore and vegetative simulants have been performed. Bioaerosol Mass Spectrometer The bioaerosol mass spectrometer (BAMS) produces low-end bac- terial biochemical information such as basic dipicolinic acid spore in- formation. The BAMS needs a significant reduction in size. However, the system has had a significant degree of success in real-time analysis situations. Lawrence Livermore National Laboratory (LLNL) documented suc- cess in tackling the challenge of detection and identification of bacterial aerosols by time-of-flight mass spectrometry (TOF-MS). Much of the suc- cess is due to the multivariate data-analysis methods that delineate the simultaneously captured positive- and negative-ion mass spectra. The im- petus for developing a bioaerosol-TOF-MS system originated in the modi- fication of a system based on the analysis of ambient inorganic and

OCR for page 215
220 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH organic aerosols (Liu et al., 1997; Su et al., 2004) naturally found, gener- ated, and released into the environment. The bioaerosol-TOF-MS system developed at LLNL does not use chemical matrix, liquid, or solid consumables (Russell et al., 2005; Steele et al., 2003). Bioaerosol BG spores were drawn through a sizing region consisting of two lasers. A frequency-quadrupled, Q-switched Nd:YAG ablated, desorbed, and ionized each particle. Both positive and negative ions are scanned because two separate TOF-MS tubes emanate from the ion source at a separation of 180o. Typical mass spectra for a BG spore showed positive ions <150 Da and negative ions <200 Da. This was the first-ever recording of positive- and negative-ion mass spectra from the same biological particle. The development of the BAMS (Fergenson et al., 2004) saw scrutiny on the concept of analyzing every particle in succession. This is a key point that lends itself to the autonomous detection system using mass spectrometry Pd and Pfp figures of merit. Spores of BG and Bacillus thuringiensis (BT) were used with no liquid reagent. The data analysis starts with positive and negative mass spectra of 350 total masses (ele- ments). The experimental spectra are then compared to a database of or- ganisms. If multiple standards match, then the closest spectrum match (positive and negative ions) was considered as the experimental analyte. Reproducibility depended strongly on laser wavelength and fluence. The wavelength of 266 nm was chosen because it was absorbed by dipicolinic acid (DPA) in the spores. In nature, DPA is only found in Gram-positive bacterial spores. Different bacterial growth media for both spores saw minimal mass spectral differences within each species. Determination of individual particles was made in real time in two steps. First, a prescreening stage eliminated nonbacterial particles by analysis of the acquired spectra. The microbial-related spectra then went through mass-related criteria to refine and provide a database match to the experimental spectra. BG spores were identified 93.2 percent when compared to BT. Commonly found and commercially available white powders were separately mixed with BG and BT to test the data analysis algorithm for interference properties. For specificity information, the spores were recognized 91 percent of the time with Gold Bond powder, 86 percent with growth media, 78 per- cent with Equal sweetener, 56 percent with fungal spores, and 46 percent of the time with Knox gelatin.

OCR for page 215
APPENDIX J 221 Single-Particle Aerosol Mass Spectrometer The BAMS evolved into the single-particle aerosol mass spectrome- ter (SPAMS) system (Steele et al., 2008). The suite of biological sub- stances originally investigated was expanded to include chemical, biological, radiological, nuclear, and explosive (CBRNE) materials as well as clandestine and illegal drug substances. The SPAMS system uses three continuous-wave laser beams to produce particle sizing properties. The SPAMS can track up to 10,000 particles per second. The particle’s position and velocity are used to predict when it passes through subsequent regions of the instrument. After the sizing region, the particle is interrogated by a laser-induced fluorescence (LIF) region to determine the presence of ultraviolet (UV) fluorescence, which indicates a biological nature. If the particle produces UV fluorescence, it is then ionized by a 266-nm laser beam. Identification of a particle occurs by mass spectral pattern matching with a database. The tested substances produce significantly different experimental mass spectra, and no false identification or false alarms have been ob- served with sequential challenges of the CBRNE materials. In addition to the analytes, there is a constant background of ambient outdoor particles such that the background particles dominate or are equivalent in temporal signal responses to the particular CBRNE challenge signals. The SPAMS system was tested at the San Francisco, California, air- port. The aerosol collector and particle inlet were cleaned once per week. The ambient atmosphere internal to the airport was sampled every mi- nute, and the spectra were recorded. Approximately 1 million particles were tracked and recorded over a 7-week period. After the recording and storage of the aerosol data, it was analyzed in the laboratory. No real- time analyses or decisions were made in the field. In any 1-minute inter- rogation, no more than two particles were identified as BG or pentaerythritoltetranitrate (PETN) explosive, and this resulted in zero false alarms because the 2 particles/minute was below the alarm thresh- old. Thus specificity is excellent. Here are some figures of merit and comments on SPAMS:  Disadvantages: Very large size and high cost. However, operat- ing costs are very low.  For SPAMS, it is essentially a single ACPLA detector and clas- sifier with limited identification.

OCR for page 215
222 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH  SPAMS allows particles in the 0.7- to 10-micron-diameter range to pass into the system.  Six laser beams size and track the aerosol particles.  A second series of lasers, if necessary, produces LIF.  Particles enter into a bipolar TOFMS that can track 50 particles/s.  The system is autonomous and can track 10,000 particles/s up front. Most difficult when trying to detect nothing when no threat agent present! SPAMS accomplished high sensitivity and low Pfp and is very fast. After release in the lab of certain simulants in the air, it took on average of 34 s for the SPAMS to respond. A comprehensive set of receiver operating characteristics (ROCs) curves has been performed to detail the Pfp and detection of false- negatives (Pfn) figures of merit (Gard et al., DARPA proposal, 2007 [unpublished]). To the best of our knowledge, there is no published work showing any ROC curves or Pfp and Pd figures of merit. Their ROC curves have only been presented in a DARPA proposal for the BAND program (Gard et al., DARPA proposal, 2007 [unpublished]). The system can currently detect and identify a limited suite of sam- ples. The time to detection is 1 minute at 1 ACPLA, and the aerosol size range is 1–10 microns in diameter. To detect 1 ACPLA in 1 minute and sampling 100 L/min with sampling efficiency of 10 percent yields 10 agent-containing particles per minute. Alarm conditions are based on the measurement and assignment of more than 1 particle. The probability of misclassifying a particle as an agent is vanishingly small because no false-positives were observed in 5,000 BG, BT, and background particle cases. The probability of a single BT particle being wrongly identified as BG is 10−3. Concerning the probability for a false alarm, the alarm conditions are based on the measurement and assign- ment of more than 1 particle. The probability of correctly classifying a particle as an agent can be inferred from the BG spore challenge, in which the system correctly identified the particles 93 percent of the time over BT and other background aerosols. BG remained unidentified in 7 percent of the cases. The BAMS system has a good chance at performing to and meeting most of the autonomous detection system using mass spectrometry technical requirements but with some false-negatives. This assessment can also be considered for organisms in addition to Bacillus subtilis. The main issues for the BAMS and SPAMS are the size and power requirements.

OCR for page 215
APPENDIX J 223 TIER 2: 2016–2020 Tier 2 and Tier 3 systems cannot begin to address the analytical fig- ures of merit that are requested by DHS. Future systems just have not addressed any final or mature numbers because of the very fluid makeup and constitution of the basic research laboratory apparatus. For example, sensitivity and specificity change constantly depending on the hardware, methods, sample handling processes, and data analysis packages used. There is so much change as well as new modifications for the hardware and techniques. Aerosol Collector Candidates Possible candidates for aerosol collector are the Micromachined Virtual Impactor Collector and the Micromachined Radial Virtual Impactor Collector from MesoSystems Technology, Inc. However, es- sentially no sampling technique can ensure that the collected microbial specimen reflects the original state and can be directly used in bioanalysis (Heidelberg et al., 2000; Lee et al., 2004; Pasanen, 2001; Ren et al., 2001). Pathogen agar plate culturing is generally the necessary step before analysis, mainly because the concentration of the collected pathogen is too low for direct bioanalysis by the methods mentioned above. Because of the size difference, the water used to rinse or wash the samplers is generally too much compared with the small amount of the pathogens collected in the samplers. Consequently, the concentration of the collect- ed pathogens in aqueous media is too low for direct bioanalysis. There are various possible solutions. Microfluidics, which handles liquid in the micrometer dimension, corresponding to nanoliters in vol- ume, appears to concentrate pathogens in a relatively small amount of liquid (Baoa et al., 2008; Bhagat et al., 2011; Qi et al., 2010; You et al., 2011). Microfluidics is also economical, with much less reagent con- sumption, suitable for a large-scale deployment and field application (Holmes and Morgan, 2010; Li et al., 2006; Liu, 2010; Park et al., 2011). Cell capture by microfluidic chip has been reported (Jang et al., 2012; Lim et al., 2012; Loutherback et al., 2012; Reisewitz et al., 2010), but most reports focused on tissue cell capture and rarely refer to airborne bacteria cells captured directly from air. A simple microfluidic device that is capable of fast and efficient air- borne bacteria enrichment has recently been reported (Jing et al., 2013).

OCR for page 215
224 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH The initial concentration of E. coli bacteria suspension was 106 cell/mL, and an aerosol generator was used to generate the bioaerosol for 2 minutes. Under the vacuum created by a micropump, the bacteria aerosol is drawn into the channels of the microfluidic chip. At the same time, an LB culture dish is placed next to the microfluidic chip as a parallel con- trol (sedimentation method). Bacteria may be captured through their adhering to the inside walls of the microchannels in the chip. The uncap- tured bacteria will pass through the chip and enter the resuspension solution. After enrichment, 2 μL of buffer are loaded into the microchannel to wash the captured bacteria inside the microfluidic chip, and the solution is collected at the outlet for statistical analysis. When the concentration of E. coli bacteria suspension was 105 cell/mL, there were 254 cells collected by the microfluidic chip, which is more than 4.53 times higher than the 66 cells collected by the agar-plate, direct-from-the-air sedimentation method. When the concentration of E. coli bacteria suspension was 104 cell/mL, there were still 130 bacterial particles collected by the microfluidic chip, which is 4 times higher than the 26 cells collected by the plate sedimentation method. When the concentration of E. coli bacteria suspension decreased to 103 cells/mL, the microfluidic chip collected 56 bacteria cells, which is 55 times higher. The 130 E. coli bacteria captured by the microfluidic chip were enough for rapid detection methods, such as an ELISA-based test (100 bacteria are enough for ELISA and polymerase chain reaction [PCR]-based tests). Moreover, the detection limit of the microfluidic device is much lower than that of the agar-plate sedimentation method. It can collect enough bacteria at a low aerosol concentration for a direct ELISA, loop- mediated isothermal amplification (LAMP) test, which is essential for rapid bacteria detection, especially compared with traditional bioaerosol collection techniques that need the downstream culturing or PCR ampli- fication because of the relatively high capture limit. Bacterial Proteome Analysis The bacterial proteome represents the collection of functional and structural proteins that are present in the cell. The protein content of the cell represents the majority of the cell dry weight, which makes it an ideal cellular component to be utilized for bacterial characterization (Loferer-Krobacher et al., 1998).

OCR for page 215
APPENDIX J 225 Most of the Category A, B, and C biological threats from the Centers for Disease Control and Prevention (CDC) have their genomes fully se- quenced and available for bioinformatics-based proteomics methods. The predominant MS techniques used for bacterial identification and differentiation include ESI-MS-MS, MALDI-TOF-MS, and one- or two- dimensional sodium dodecylsulfatepolyacrylamide gel electrophoresis (1D or 2D SDS-PAGE). MS techniques for bacterial identification and differentiation (Kollipara et al., 2011) rely on the comparison of the proteome infor- mation generated from either intact protein profiles (top-down) or the product ion mass spectra of digested peptide sequences (bottom-up) analyses (Fox et al., 2002; Pennington et al., 1997; Zhou et al., 2012). The different approaches include  Top-down from intact proteins: Bacterial differentiation and identification are accomplished through the comparison of the MS data of intact proteins with an experimental mass spectral database (fingerprint spectrum) containing the mass spectral pro- tein masses of the microorganisms (Demirev et al., 1999; Fenselau and Demirev 2001; Jabbour et al., 2005; Pineda et al., 2000).  Bottom-up around 1.5 kDa: Bacterial differentiation using prod- uct ion mass spectral data of peptide sequences from the trypsin- digested proteins is accomplished through the use of search en- gines against publicly available sequence databases to infer iden- tification (Williams et al., 2002).  Middle-down: around 5 kDa (Zhou et al., 2012). This is the most mature method for the MS identification of bacterial proteins.  Shotgun proteomics: Trypsin is used to digest and separate all peptides in an LC system for MS-MS analysis. This is the pre- ferred method because it yields the most data.  Peptide mass fingerprinting or peptide fingerprinting from pro- teins: Only for predominately expressed bacterial proteins in a MALDI-MS spectrum. Thus, either intact bacterial mass or pro- tein extract can be trypsin-digested without any purification or separation of the proteins. This approach is usually used to target certain proteins that are overexpressed in a bacterium. Then one can compare a theoretical table of peptide masses with experi- mental masses; small acid-soluble proteins (SASPs) are a good example (Castanha et al., 2006; Demirev and Fenselau, 2008).

OCR for page 215
234 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH The results showed that the method was effective in identifying bac- teria whether the sample was composed of one organism or a mixture or even if the sample was not resident in the database. No false-positives were observed for any of the blind samples that were analyzed, including the blank sample. There are some major advantages to the proteomic method over other molecular biology methods, such as the DNA-based methods, in that (a) no prior information about the sample is required for analysis, (b) no specific reagents are needed in the analysis process, (c) proteomics MS is capable of identifying an organism when a pri- mer/probe set is not available, (d) proteomics MS requires less rigorous sample preparation than PCR, and (e) proteomics MS can provide a pre- sumptive identification of a true unknown organism by mapping its phy- logenetic relationship with other, known pathogens. Sensitivity Performance of Mass Spectrometry–Based Proteomics The MS proteomics method has shown promising results in specifici- ty and sensitivity. While the latter parameter is highly dependent on the MS physical limit of detection, enhancing the biological sample pro- cessing is a crucial step to ease such dependency. Table J-1 shows bacte- ria and their sensitivity limits for the MS proteomics method. TIER 3: BEYOND 2020 Future systems may combine various techniques, such as PCR with LC-ESI-MS-MS or MALDI-MS-MS or ESI-TOF-MS or antigen-antibody with LC-ESI-MS-MS or MALDI-MS-MS or ESI-TOF-MS. The PLEX-ID is a product (Havlicek et al., 2013) that, although re- cently introduced (Jacob et al., 2012), has had a short-lived life in the commercial market. It and its predecessor, the Ibis T5000, were devel- oped by Ibis Biosciences, Inc., which was acquired in 2009 by a subsidi- ary of the Abbott Diagnostics Group (Abbott Molecular Inc.). The product featured nucleic acid amplification (PCR) coupled with ESI- TOF-MS to carry out base-composition analysis (Ecker et al., 2008). The PLEX-ID instrument received the CE marking in March 2012 along with three assays for use on the system: PLEX-ID Viral IC Spectrum, PLEX-ID BAC Spectrum BC, and PLEX-ID Flu (Ibis, n.d.). In 2011 the company

OCR for page 215
APPENDIX J 235 TABLE J-1 Sensitivity Limits of Mass Spectrometry Proteomics Method Reproducibility N=8 Specificity Blind CV Biological Agent Analytical % (95% Positive % Tested Sensitivity Detected confidence ID detected ID) Yersinia pestis 1.04 cfu 100 5%  100 CO92 B. anthracis Ames, 6,000 cfu 100 5%  100 Sterne Burkholderia mal- 2,000 cfu 100 4.2%  100 lei/pseudomallei C. burnetii NMQ 8,000 cfu 90 5%  100 4 F. tularensis type- 1.1×10 100 5%  100 A/type-B cfu E. coli 1,000 cfu 100 2%  100 O157/O104/O111 and O26 Dengue virus 1,200 cpu 100 12%  88 Vaccinia 850 100 8%  92 Ricin toxin 75 pg 100 11%  95 SEB toxin 25 pg 100 9%  95 also introduced the PLEX-ID Biothreat Assay. The system was reported to enable the identification and quantification of a broad set of patho- gens, including bacteria (Sampath et al., 2012), all major groups of path- ogenic fungi (Kaleta et al., 2011), protozoa, and the major families of viruses (MacInnes et al., 2011). In September 2012 Abbott discontinued the production of the PLEX-ID system; however, it did not exclude the possibility of developing a smaller, cheaper, and faster device based on similar principles in the future. There are many research reports on viral typing performed by MALDI (Gijavanekar et al., 2012) or ESI (Jeng et al., 2012). There is a market gap in this field, although there are speculations about the renais- sance of commercial techniques based on nucleic acid sequencing (Jeng et al., 2012) because the past Abbott PLEX-ID product was discontinued in September 2012.

OCR for page 215
236 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH Memory effect has not been found for the lab-made devices (Wu et al., 2004). After each digestion, the substrate and products left in the microchannels were cleaned out by pumping fresh water through the microchannels for a few minutes. Subsequently, a blank solution of 2- mM NH4HCO3 (pH 8.0) buffer was allowed to flow into the microchannels as for sample digestion, then collected and checked for any remaining samples in the channels using MALDI-TOF-MS detection procedures. No detectable peptide fragments were found after using the cleaning procedures described above, which indicates that the micro- reactors are less susceptible to memory effect. The lab-made micro- reactor devices can be used at least 50 times in one week without notice- able loss of activity with a proper storage at 4oC; the two ends of the microchannel were sealed to avoid drying-induced enzyme degradation (Shi et al., 1999). In fact, the ability to alter the digestion time by varying the flow rate could provide a powerful means to achieve the desired ex- tent of digestion or to compensate for enzyme activity loss. Finally, a new MS system currently being developed by Torion could be either Tier 2 or Tier 3. Torion has introduced a portable, ruggedized 32-pound, battery-operated GC-MS capable of providing two to three hours of operation on a single battery. The next-generation system is proposed to be on the order of 1,200 cubic inches, 15 pounds, and 40 watts of power. REFERENCES Amado, F. M. L., P. Domingues, M. G. Santana-Marques, A. J. Ferrer-Correia, and K. B. Tomer. 1997. Discrimination effects and sensitivity variations in matrix-assisted laser desorption/ionization. Rapid Communications in Mass Spectrometry 11:1337–1352. Andersson, H., and A. van den Berg. 2004. Microtechnologies and nanotechnol- ogies for single-cell analysis. Current Opinion in Biotechnology 15:44–49. Baoa, N., B. Jagadeesan, A. K. Bhunia, Y. Yao, and C. Lu. 2008. Quantification of bacterial cells based on autofluorescence on a microfluidic platform. Journal of Chromatography A 1181:153–158. Basile, F., M. B. Beverly, C. Abbas-Hawkes, C. D. Mowry, K. J. Voorhees, and T. L. Hadfield. 1998. Direct mass spectrometric analysis of in situ thermally hydrolyzed and methylated lipids from whole bacterial cells. Analytical Chemistry 70:1555–1562. Bhagat, A. A. S., H. W. Hou, L. D. Li, C. T. Lim, and J. Han. 2011. Pinched flow coupled shear-modulated inertial microfluidics for high-throughput rare blood cell separation. Lab on a Chip 11:1870−1878.

OCR for page 215
APPENDIX J 237 Bright, J. J., M. A. Claydon, M. Suofian, and D. B. Gordon. 2002. Rapid typing of bacteria using matrix assisted laser desorption ionization time-of-flight mass spectrometry and pattern recognition software. Journal of Microbio- logical Methods 48:127–138. Castanha, E. R., A. Fox, and K. F. Fox. 2006. Rapid discrimination of Bacillus anthracis from outer members of the B. cereus group by mass and sequence of ”intact” small acid soluble proteins (SASPs) using mass spectrometry. Journal of Microbiological Methods 67:230–240. Demirev, P. A., and C. Fenselau. 2008. Mass spectrometry in biodefense. Jour- nal of Mass Spectrometry 43:1441–1457. Demirev, P. A., Y.-P. Ho, V. Ryzhov, and C. Fenselau. 1999. Microorganism identification by mass spectrometry and protein database searches. Analyti- cal Chemistry 71:2732–2738. Demirev, P. A., J. S. Lin, F. J. Pineda, and C. Fenselau. 2001. Bioinformatics and mass spectrometry for microorganism identification: Proteome-wide post-translational modifications and database search algorithms for charac- terization of intact H. pylori. Analytical Chemistry 73:4566–4573. Deshpande, S. V., R. E. Jabbour, P. A. Snyder, M. Stanford, C. H. Wick, and A. W. Zulich. 2011. ABOid: A software for automated identification and phyloproteomics classification of tandem mass spectrometry data. Journal of Chromatography and Separation Techniques S5:001. Dickinson, D. N., M. T. La Duc, M. Satomi, J. D. Winefordner, D. H. Powell, and K. Venkateswaran. 2004. MALDI-TOFMS compared with other polyphasic taxonomy approaches for the identification and classification of Bacillus pumilus spores. Journal of Microbiological Methods 58:1–12. Dittich, P. S., K. Tachikawa, and A. Manz. 2006. Micro total analysis systems: Latest advancements and trends. Analytical Chemistry 78:3887–3908. Domin, M. A., K. J. Welham, and D. S. Ashton. 1999. The effect of solvent and matrix combinations on the analysis of bacteria by matrix-assisted laser de- sorption/ionisation time-of-flight mass spectrometry. Rapid Communica- tions in Mass Spectrometry 13:222–226. Dworzanski, J. P., and A. P. Snyder. 2005. Classification and identification of bacteria using mass spectrometry-based proteomics. Expert Reviews of Pro- teomics 2:863–878. Ecker, D. F., R. Sampath, L. B. Blyn, M. W. Eshoo, C. Ivy, J. A. Ecker, B. Libby, V. Samant, K. A. Sannes-Lowery, R. E. Melton, K. Russell, N. Freed, C. Barrozo, J. Wu, K. Rudnick, A. Desai, E. Moradi, D. J. Knize, D. W. Robbins, J. C. Hannis, P. M. Harrell, C. Massire, T. A. Hall, Y. Jiang, R. Ranken, J. J. Drader, N. White, J. A. McNeil, S. T. Crooke, and S. A. Hofstadler. 2005. Rapid identification and strain-typing of respiratory pathogens for epidemic surveillance. Proceedings of the National Academy of Science of the United States of America 102:8012–8017.

OCR for page 215
238 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH Ecker, D. J., R. Sampath, C. Massire, L. B. Blyn, T. A. Hall, M. W. Eshoo, and S. A. Hofstadler. 2008. Ibis T5000: A universal biosensor approach for mi- crobiology. Nature Reviews Microbiology 6:553−558. Ethier, F., W. Hou, H. S. Duewel, and D. Figeys. 2006. The proteomic reactor:  A microfluidic device for processing minute amounts of protein prior to mass spectrometry analysis. Journal of Proteome Research 5:2754–2759. Feng, X., W. Du, Q. Luo, and B.-F. Liu. 2009. Microfluidic chip: Next- generation platform for systems biology. Analytica Chimica Acta 650:83– 97. Fenselau, C., and P. A. Demirev. 2001. Characterization of intact microorgan- isms by MALDI mass spectrometry. Mass Spectrometry Reviews 20:157– 171. Fergenson, D. P., M. E. Pitesky, H. J. Tobias, P. T. Steele, G. A. Czerwieniec, S. C. Russell, C. B. Lebrilla, J. M. Horn, K. R. Coffee, A. Srivastava, S. P. Pillai, M. T. P. Shih, H. L. Hall, A. J. Ramponi, J. T. Chang, R. G. Langlois, P. L. Estacio, R. T. Hadley, M. Frank, and E. E. Gard. 2004. Reagentless detection and classification of individual bioaerosol particles in seconds. Analytical Chemistry 76:373–378. Fox, A., M. Anderson, J. Dunn, B. Guenther, L. Parks, R. Pinnick, C. Reed, J. Rowe, R. Luftig, G. W. Long, R. Lontz, G. L. Marchin, A. T. McManus, P. Setlow, J. Siedow, A. J. Sievers, M. L. Tchikindas, and S. Tove. 2002. Re- port of the “Bioterrorism Workshop”—Duke University Thomas Center on 2–4 April 2002 organized by U.S. Army Research Office. Journal of Mi- crobiological Methods 51:247–254. Freire, S. L. S., and A. R. Wheeler. 2006. Proteome-on-a-chip: Mirage, or on the horizon? Lab on a Chip 6:1415–1423. Gantt, S. L., N. B. Valentine, A. J. Saenz, M. T. Kingsley, and K. L. Wahl. 1999. Use of internal control for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis of bacteria. Journal of the Ameri- can Society of Mass Spectrometry 10:1131–1137. Gijavanekar, C., R. Drabek, M. Soni, G. W. Jackson, U. Strych, G. E. Fox, Y. Fofanov, and R. C. Willson. 2012. Detection and typing of viruses using broadly sensitive cocktail-PCR and mass spectrometric cataloging: Demon- stration with dengue virus. Journal of Molecular Diagnostics 4:402−407. Griest, W. H., M. B. Wise, K. J. Hart, S. A. Lammert, C. V. Thompson, and A. A. Vass. 2001. Biological agent detection and identification by the Block II chemical biological mass spectrometer. Field Analytic Chemistry and Technology 5:177–184. Halden, R. U., D. R. Colquhoun, and E. S. Wisniewski. 2005. Identification and phenotypic characterization of Sphingomonas wittichii strain RW1 by pep- tide mass fingerprinting using matrix-assisted laser desorption ionization- time of flight mass spectrometry. Applied and Environmental Microbiology 71(5):2442–2451.

OCR for page 215
APPENDIX J 239 Hart, K. J., M. B. Wise, W. H. Griest, and S. A. Lammert. 2000. Design, devel- opment, and performance of a fieldable chemical biological agent detector. Field Analytical Chemistry and Technology 4:93–110. Havlicek, V., K. Lemr, and K. A. Schug. 2013. Current trends in microbial di- agnostics based on mass spectrometry. Analytical Chemistry 85:790–797. Heidelberg, J. F., J. A. Eisen, W. C. Nelson, R. A. Clayton, M. L. Gwinn, R. J. Dodson, D. H. Haft, E. K. Hickey, J. D. Peterson, L. Umayam, S. R. Gill, K. E. Nelson, T. D. Read, H. Tettelin, D. Richardson, M. D. Ermolaeva, J. Vamathevan, S. Bass, H. Y. Qin, I. Dragoi, P. Sellers, L. McDonald, T. Utterback, R. D. Fleishmann, W. C. Nierman, O. White, S. L. Salzberg, H. O. Smith, R. R. Colwell, J. J. Mekalanos, J. C. Venter, and C. M. Fraser. 2000. DNA sequence of both chromosomes of the cholera pathogen Vibrio cholerae. Nature 406:477−483. Henion, J. 2009. The reality of lab-on-a-chip technology for the mass spec- trometry laboratory. LCGC North America 27:900–915. Hettick, J. M., M. L. Kashon, J. P. Simpson, P. D. Siegel, G. H. Mazurek, and D. N. Weissman. 2004. Proteomic profiling of intact Mycobacteria by ma- trix-assisted laser desorption/ionization time-of-flight mass spectrometry. Analytical Chemistry 76:5769–5776. Holmes, D., and H. Morgan. 2010. Single cell impedance cytometry for identifi- cation and counting of CD4 T-cells in human blood using impedance labels. Analytical Chemistry 82:1455−1461. Huikko, K., R. Kostiainen, and T. Kotiaho. 2003. Introduction to microanalytical systems: Bioanalytical and pharmaceutical applications. European Jour-nal of Pharmaceutical Science 20:149–171. Ibis. n.d. Welcome page. http://www.ibisbiosciences.com/index.html (accessed on October 27, 2012). Jabbour, R., J. P. Dworzanski, S. V. Deshpande, A. P. Snyder, and C. H. Wick. 2005. Effect of gas phase fractionation of peptide ions on bacterial identifi- cation using mass spectrometry-based proteomics approach. Proceedings of the 53rd ASMS Conference on Mass Spectrometry and Allied Topics, San Antonio, TX, TP31. Jabbour, R. E., S. V. Deshpande, M. M. Wade, M. F. Stanford, C. H. Wick, A. W. Zulich, E. W. Skowronski, and A. P. Snyder. 2010. Double blind characterization of non-genome-sequenced bacteria by mass spectrometry- based proteomics. Applied and Environmental Microbiology 76:3637–3644. Jabbour, R. E., S. V. Deshpande, M. F. Stanford, C. H. Wick, A. W. Zulich, and A. P. Snyder. 2011. A protein processing filter method for bacterial identifi- cation by mass spectrometry-based proteomics. Journal of Proteome Re- search 10:907–912. Jacob, D., U. Sauer, R. Housley, C. Washington, K. Sannes-Lowery, D. J. Ecker, R. Sampath, and R. Grunow. 2012. Rapid and high-throughput de- tection of highly pathogenic bacteria by Ibis PLEX-ID technology. PLoS ONE 7(6):e39928.

OCR for page 215
240 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH Jang, K., Y. Tanaka, J. Wakabayashi, R. Ishii, K. Sato, K. Mawatari, M. Nilsson, and T. Kitamori. 2012. Selective cell capture and analysis using shallow antibody-coated microchannels. Biomicrofluidics 6(4):044117. Jeng, K., C. Massire, T. R. Zembower, V. M. Deyde, L. V. Gubareva, Y. H. Hsieh, R. E. Rothman, R. Sampath, S. Penugonda, D. Metzgar, L. B. Blyn, J. Hardick, and C. A. Gaydos. 2012. Monitoring seasonal influenza A evo- lution: Rapid 2009 pandemic H1N1 surveillance with a reverse transcrip- tion-polymerase chain reaction/electro-spray ionization mass spectrometry assay. Journal of Clinical Virology 54:332−336. Jing, W., W. Zhao, S. Liu, L. Li, C.-T. Tsai, X. Fan, W. Wu, J. Li, X. Yang, and G. Sui. 2013. Microfluidic device for efficient airborne bacteria capture and enrichment. Analytic Chemistry 85:5255–5262. Kaleta, E. J., A. E. Clark, A. Cherkaoui, V. H. Wysocki, E. L. Ingram, J. Schrenzel, and D. M. Wolk. 2011. Use of PCR coupled with electrospray ionization mass spectrometry for rapid identification of bacterial and yeast bloodstream pathogens from blood culture bottles. Clinical Chemistry 57:1057−1067. Kollipara, S., N. Agarwal, B. Varshney, and J. Paliwal. 2011. Technological advancements in mass spectrometry and its impact on proteomics. Analyti- cal Letters 44:1498–1520. Krishnamurthy, T., P. L. Ross, and U. Rajamani. 1996. Detection of pathogenic and non-pathogenic bacteria by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spec- trometry 10(8):883–888. Krishnamurthy, T., U. Rajamani, P. L. Ross, R. Jabbour, H. Nair, J. Eng, J. Yates, M. T. D. Douglas, C. Stahl, and T. D. Lee. 2000. Mass spectral in- vestigation of microorganisms. Toxin Reviews 19:95–117. Lau, S. K. P., B. S. F. Tang, S. O. T. Curreem, T.-M. Chan, P. Martelli, C. W. S. Tse, A. K. L. Wu, K.-Y. P. Yuen, and C. Y. Woo. 2012. Matrix-assisted la- ser desorption ionization-time of flight mass spectrometry for rapid identifi- cation of Burkholderia pseudomallei: Importance of expanding databases with pathogens endemic to different localities. Journal of Clinical Microbi- ology 50:3142–3143. Lay, J. O., Jr. 2001. MALDI–TOF mass spectrometry of bacteria. Mass Spec- trometry Reviews 20:172–194. Lee, K., D. Bae, and D. Lim. 2002. Evaluation of parameters in peptide mass fingerprinting for protein identification by MALDI-TOF mass spectrome- try. Molecules and Cells 13:175–184. Lee, K. S., K. H. Bartlett, M. Brauer, G. M. Stephens, W. A. Black, and K. Teschke. 2004. A field comparison of four samplers for enumerating fungal aerosols I. Sampling characteristics. Indoor Air 14:360−366.

OCR for page 215
APPENDIX J 241 Li, G., M. Waltham, N. L. Anderson, E. Unsworth, A. Treston, and J. N. Weinstein. 1997. Rapid mass spectrometric identification of protein from two-dimensional polyacrylamide gels after in gel proteolytic digestion. Electrophoresis 18:391–402. Li, Y. B., and X. L. Su. 2006. Microfluidics-based optical biosensing method for detection of Escherichia coli O157:H7. Journal of Rapid Methods and Au- tomation in Microbiology 14:96−109. Lim, E., A. Tay, and A. G. Nicholson. 2012. Antibody independent microfluidic cell capture of circulating tumor cells for the diagnosis of cancer. Journal of Thoracic Oncology 7:E42−E43. Lion, N., T. C. Rohner, L. Dayon, I. L. Arnaud, E. Damoc, N. Youhnovski, Z.-Y. Wu, C. Roussel, J. Josserand, H. Jensen, J. S. Rossier, M. Przybylski, and H. H. Girault. 2003. Microfluidic systems in proteomics. Electrophore- sis 24:3533–3562. Liu, C. 2010. Rapid fabrication of three-dimensional microfluidic chip using natural lotus leaf template. Microfluidics and Nanofluidics 9:923−931. Liu, D.-Y., D. Rutherford, M. Kinsey, and K. A. Prather. 1997. Real time moni- toring of pyrotechnically derived aerosol particles in the troposphere. Ana- lytical Chemistry 69:1808–1814. Liu, H., Z. Du, J. Wang, and R. Yang. 2007. Universal sample preparation method for characterization of bacteria by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Applied Environmental Micro- biology 73:1899–1907. Liu, Y., H. Lu, W. Zhong, P. Song, J. Kong, P. Yang, H. H. Girault, and B. Liu. 2006. Multilayer-assembled microchip for enzyme immobilization as reac- tor toward low-level protein identification. Analytical Chemistry 78:801– 808. Loferer-Krobacher, M., J. Klima, and R. Psenner, R. 1998. Determination of bacterial cell dry mass by transmission electron microscopy and densitometric image analysis. Applied Environmental Microbiology 64:688–694. Loutherback, K. J. D’Silva, L. Y. Liu, A. Wu, R. H. Austin, and J. C. Sturm. 2012. Deterministic separation of cancer cells from blood at 10 mL/min. AIP Advances 2(4):e42107. Ma, J., J. Liu, L. Sun, L. Gao, Z. Liang, L. Zhang, and Y. Zhang, Y. 2009. Online integration of multiple sample pretreatment steps involving denatur- ation, reduction, and digestion with microflow reversed-phase liquid chro- matography−electrospray ionization tandem mass spectrometry for high- throughput proteome profiling. Analytical Chemistry 81:6534–6540. MacInnes, H., Y. Zhou, K. Gouveia, J. Cromwell, K. Lowery, R. C. Layton, M. Zubelewicz, R. Sampath, S. Hofstadler, Y. S. Liu, Y. S. Cheng, and F. Koster. 2011. Transmission of aerosolized seasonal H1N1 influenza A to ferrets. PLoS ONE 6(9):e24448.

OCR for page 215
242 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH Madonna, A. J., F. Basile, I. Ferrer, M. A. Meetani, J. C. Rees, and K. J. Voorhees. 2000. On-probe sample pretreatment for the detection of proteins above 15 KDa from whole cell bacteria by matrix-assisted laser desorp- tion/ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry 14:2220–2229. Owen, R. J., M. A. Claydon, J. Gibson, B. Burke, and A. Ferrus. 1999. Strain variation within Helicobacter pylori detected by mass spectrometry of cell wall surfaces. Gut 45(Suppl. 3):A28. Park, S., Y. Zhang, S. Lin, T.-H. Wang, and S. Yang. 2011. Advances in micro- fluidic PCR for point-of-care infectious disease diagnostics. Biotechnology Advances 29:830−839. Pasanen, A. L. 2001. A review: Fungal exposure assessment in indoor environ- ments. Indoor Air 11:87−98. Pennington, S. R., M. R. Wilkins, D. F. Hochstrasser, and M. J. Dunn. 1997. Proteome analysis: From protein characterization to biological function. Trends in Cell Biology 7:168–173. Pineda, F. J., J. S. Lin, C. Fenselau, and P. A. Demirev. 2000. Testing the signif- icance of microorganism identification by mass spectrometry and proteome database search. Analytical Chemistry 72:3739–3744. Qi, A., L. Yeo, J. Friend, and J. Ho. 2010. The extraction of liquid, protein mol- ecules and yeast cells from paper through surface acoustic wave atomiza- tion. Lab on a Chip 10:470−476. Reisewitz, S., H. Schroeder, N. Tort, K. A. Edwards, A. J. Baeumner, and C. M. Niemeyer. 2010. Capture and culturing of living cells on microstructured DNA substrates. Small 6:2162−2168. Ren, P., T. M. Jankun, K. Belanger, M. B. Bracken, and B. P. Leaderer. 2001. The relation between fungal propagules in indoor air and home characteris- tics. Allergy 56:419−424. Ruelle, V., B. E. Moualij, W. Zorzi, P. Ledent, and E. De Pauw. 2004. Rapid identification of environmental bacterial strains by matrix-assisted laser de- sorption/ionization time-of-flight mass spectrometry. Rapid Communica- tions in Mass Spectrometry 18:2013–2019. Russell, S. C., G. Czerwieniec, C. Lebrilla, P. Steele, V. Riot, K. Coffee, M. Frank, and E. E. Gard. 2005. Achieving high detection sensitivity (14 zmol) of biomolecular ions in bioaerosol mass spectrometry. Analytical Chemistry 77:4734-4741. Sampath, R., N. Mulholland, L. B. Blyn, C. Massire, C. A. Whitehouse, N. Waybright, C. Harter, J. Bogan, M. S. Miranda, D. Smith, C. Baldwin, M. Wolcott, D. Norwood, R. Kreft, M. Frinder, R. Lovari, I. Yasuda, H. Matthews, D. Toleno, R. Housley, D. Duncan, F. Li, R. Warren, M. W. Eshoo, T. A. Hall, S. A. Hofstadler, and D. J. Ecker. 2012. Comprehensive biothreat cluster identification by PCR/electrospray-ionization mass spec- trometry. PLoS ONE 7(6):e36528.

OCR for page 215
APPENDIX J 243 Sedgwick, H., F. Caron, P. B. Monaghan, W. Kolch, and J. M. Cooper. 2008. Lab-on-a-chip technologies for proteomic analysis from isolated cells. Journal of the Royal Society Interface 5:S123–S130. Shi, H., W. B. Tsai, M. D. Garrison, S. Ferrari, and B. D. Ratner. 1999. Template-imprinted nanostructured surfaces for protein recognition. Nature 398:593–597. Steele, P. T., H. J. Tobias, D. P. Fergenson, M. E. Pitesky, J. M. Horn, G. A. Czerwieniec, S. C. Russell, C. B. LeBrilla, E. E. Gard, and M. Frank. 2003. Laser power dependence of mass spectral signatures from individual bacte- rial spores in bioaerosol mass spectrometry. Analytical Chemistry 7:5480– 5487. Steele, P. T., G. R. Farquar, A. N. Martin, K. R. Coffee, V. J. Riot, S. I. Martin, D. P. Fergenson, E. E. Gard, and M. Frank. 2008. Autonomous, broad- spectrum detection of hazardous aerosols in seconds. Analytical Chemistry 80:4583–4589. Su, Y., M. F. Sipin, H. Furutani, and K. A. Prather. 2004. Development and characterization of an aerosol time-of-flight mass spectrometer with in- creased detection efficiency. Analytical Chemistry 76:712–719. Szita, N., K. Polizzi, N. Jaccard, and F. Baganz. 2010. Microfluidic approaches for systems and synthetic biology. Current Opinions in Biotechnology 21:517–523. Tian, R., X. D. Hoa, J.-P. Lambert, J. P. Pezacki, T. Veres, and D. Figeys. 2011. Development of a multiplexed microfluidic proteomic reactor and its appli- cations for studying protein-protein interactions. Analytical Chemistry 83:4095–4102. Wang, C., R. Oleschuck, F. Ouchen, P. Li, P. Thibault, and D. J. Harrison. 2000. Integration of immobilized trypsin bead beds for protein digestion within a microfluidic chip incorporating capillary electrophoresis separations and an electrospray mass spectrometry interface. Rapid Communications in Mass Spectrometry 14:1377–1383. Wang, Z., L. Russon, L. Li, D. C. Roser, and S. R. Long. 1998. Investigation of spectral reproducibility in direct analysis of bacteria proteins by matrix- assisted laser desorption/ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry 12:456–464. Williams, T. L., P. Leopold, and S. Musser. 2002. Automated post processing of electrospray LC/MS data for profiling protein expression in bacteria. Ana- lytical Chemistry 74:5807–5813. Williams, T., D. Andrzejewski, J. O. Lay, Jr., and S. M. Musser. 2003. Experi- mental factors affecting the quality and reproducibility of MALDI TOF mass spectra obtained from whole bacteria cells. Journal of the American Society of Mass Spectrometry 1:342–351. Wisniewski, J. R., A. Zougman, N. Nagaraj, and M. Mann. 2009. Universal sample preparation method for proteome analysis. Nature Methods 6:359– 362.

OCR for page 215
244 TECHNOLOGIES TO ENABLE AUTONOMOUS DETECTION FOR BIOWATCH Wu, H., J. Zhai, Y. Tian, H. Lu, X. Wang, W. Jin, B. Liu, P. Yang, Y. Xu, and H. Wang. 2004. Microfluidic enzymatic-reactors for peptide mapping: Strategy, characterization, and performance. Lab on a Chip 4:588–597. Yates, J. R. III. 2004. Mass spectral analysis in proteomics. Annual Reviews of Biophysics and Biomolecular Structures 33:297–316. Yates, J. R., C. I. Ruse, and A. Nakorchevsky. 2009. Proteomics by mass spec- trometry: Approaches, advance, and applications. Annual Review of Bio- medical Engineering 11:49–79. You, D. J., P. L. Tran, H.-J. Kwon, D. Patel, and J.-Y. Yoon. 2011. Very quick reverse transcription polymerase chain reaction for detecting 2009 H1N1 in- fluenza A using wire-guide droplet manipulations. Faraday Discussions 149:159−170. Zhou, H., Z. Ning, A. E. Starr, M. Abu-Farha, and D. Figeys. 2012. Advance- ments in top-down proteomics. Analytical Chemistry 84:720–734.