Faced with the COVID-19 pandemic, the American Society for • From 2:30 to 3:30 pm CDT, the Opening Plenary Lecture, “Mars Mass Spectrometry (ASMS) Board canceled the face-to-face ASMS Conference on Mass Spectrometry & Allied Topics, originally scheduled for May 31 to June 4 in Houston, Texas, and announced that they would shift the program to an on-line format. 

“Although the program would be delivered in a new and different manner, we remain committed to hosting and sharing brilliant science for a re-imagined ASMS 2020,” Richard A. Yost, the ASMS president, said in a statement.

With the event redubbed “The ASMS 2020 Reboot,” talks and posters will be presented in a virtual and interactive format using the familiar online planner and mobile app tools used at the annual conference in past years, with a schedule as follows:

On-demand videos of orals will begin on June 1.

Poster PDFs and optional poster presentation videos will also begin on that date.

Live webinars will be conducted for plenary lectures, tutorials, award lectures, and presentations, approximately 50 workshops, with live question-and-answer (Q&A) sessions for each of the planned 64 oral sessions, and the annual ASMS meeting (including board updates and additional award presentations.). These live webinars will be scheduled Monday–Friday, June 1–5 and June 8–12, from 10:00 am to 3:00 pm+ Central Daylight Time (CDT) on most of these days. Optional one hour Corporate Member activities will be held at start and end of each day.

The live webinars will be in the format of a “watch party” of each oral session (6 talks) followed by live Q&A with speakers. Attendees may also watch the talks individually on-demand at any time beginning June 1, but still join any Q&A at the scheduled times during the week of June 8.

Highlights of the Conference are outlined below.

• Two tutorials will be conducted. From 12:30 to 1:15 pm CDT, “Single-Cell Mass Spectrometry” will be presented by Peter Nemes of the University of Maryland, College Park, and from 1:30 pm to 2:15, “Glycoprotein Analysis for Understanding Human Disease” will be presented by Heather Desaire of the University of Kansas.

• A special keynote lecture, “Is There Still Gender Bias in Academic Science (and Does It Matter)? What the Scientific Studies Say,” will be delivered by Corinne Moss-Racusin from Skidmore College from 1:30 to 2:15 pm CDT.

• From 2:30 to 3:30 pm CDT, the Opening Plenary Lecture, "Mars 2020,” will be delivered by Patricia M. Beauchamp, the Chief Technologist of the Engineering and Science Directorate, at the Jet Propulsion Laboratory of the California Institute of Technology.

• From 9:00 to 10:00 am and again from 3:00 to 4:00 pm CDT, there will be featured corporate member activities, including on- line seminars, product launches, and demonstrations

• The first 16 of 56 webinar-style workshops will be conducted by interest groups or independent organizers, in two sessions, with workshops 1–8 held concurrently from 10:00 to 11:30 am CDT, and workshops 9–16 held concurrently from 12:00 to 1:30 pm CDT. This schedule will continue with 16 workshops per day, Tuesday through Thursday, with 8 on Friday.

• From 1:45 to 3:15 CDT, The Al Yergey MS Scientist Award will be presented to Rachel Ogorzalek, a research biological chemist at UCLA. This will be followed by the Award Lecture for the John B. Fenn Distinguished Contribution in Mass Spectrometry Award, presented by Michael L. Gross, of Washington University in St. Louis, Missouri.

• Corporate events will be held 9:00–10:00 am and 3:15–4:15 pm.

• From 1:45 to 3:15 pm CDT, Research Awards will be presented to Jace Jones of the University of Maryland, Miklos Guttman of the University of Washington, and Ian K. Webb of Indiana University and Purdue University. Also, the Research at Primarily Undergraduate Institutions (PUIs) Award will be presented to Christine Hughey of James Madison University in Harrisonburg, Virginia, and the Biemann medal will be presented to Ying Ge, a Professor of Cell and Regenerative Biology and Chemistry at the University of Wisconsin at Madison.

• Workshops 17–24 and 25–32 will be held from 10:00 to 11:30 am and 12:00 to 1:30 pm CDT.

• From 2:00 to 3:30 pm CDT, awards for Postdoctorate Career Development will be presented to Juan Aristizabal Henao of the University of Florida in Gainesville, Florida, Wout Bittremieux of the University of California–San Diego, Kevin Clark of the Beck- man Institute for Advanced Science and Technology (part of the University of Illinois at Urbana-Champaign), Kelly Karch of the Ohio State University, and Lindsay Pino of the University of Pennsylvania. In addition, the Ron Hites Award for Outstanding JASMS Publication will be presented to Stephen J. Valentine of West Virginia University and his co-authors Samaneh Ghassabi Kondalaji and Mahdiar Khakinejad for their paper, “Comprehensive Peptide Ion Structure Studies Using Ion Mobility Techniques: Part 3. Relating Solution-Phase to Gas- Phase Structures.”

• Workshops 33–40 and 41–48 will be held from 10:00 to 11:30 am and 12:00 to 1:30 pm CDT.

• The ASMS plans a 4-hour virtual “exhibit hall” from 12:00 to 4:00 pm CDT. This will be a specific timeframe when the society encourages all registrants of the Reboot to peruse exhibitor listings in the mobile app or on-line planner. ASMS corporate members are encouraged to host their own chat room or event during this period. Registrants can look for links within each Corporate Member or Exhibitor listing in the mobile app or on-line planner.

• From Monday through Thursday of the second week of the conference, two groups of oral sessions (eight sessions conducted in parallel in each) will be conducted from 10:00 am to 12 noon and from 1:15 to 3:15 pm CDT.

• These oral presentations will be followed by live Q&A periods from 12:15 pm to 1:00 pm and 3:30 to 4:15 pm, respectively, with one Q&A period covering the six talks in each oral session. Registrants may submit questions via the on-line planner or mobile app. Attendees may also watch the talks individually on-demand at any time beginning June 1, but still join any Q&A at the scheduled times during the week of June 8.

• From 10:00 to 11:00 am CDT, Stephen Brusatte of the University of Edinburgh will give the closing plenary lecture, “New Dinosaur Discoveries.”

The deadline to register is May 22. The society hopes to offer late registration at increased fees, but, as we go to press, they are not certain that this will be an option. On-demand content will remain available to registrants through August 31.

More information, including on-line event schedules, can be found at https:// asms.org/conferences/asms-2020-reboot.

 is the Managing Editor of Spectroscopy and LCGC North America. Direct correspondence to JChasse@MJHLifeSciences. com.

Articles

A preview of this year’s ASMS conference, in its new online format.

The 2020 ASMS Conference Moved Online in the Face of the COVID-19 Pandemic

Advancements in technology enable deployment of field-friendly gas chromatography–mass spectrometry (GC–MS), so the value of the technique can be realized at the sample site. A primary benefit of deployment to the scene is the ability to analyze trace levels of threats, providing real-time information to support scene safety and other intelligence, so mission-critical decisions using confirmed information may be made. This capability is especially useful in situations where a sample degrades, changes, or is unstable during storage or transport, or when access to a laboratory is not an option. When explosives are encountered on the battlefield, GC–MS may be useful. In this research, a variety of commercial, military, and homemade explosives were analyzed using portable ion-trap GC–MS. Data from both ion-trap and quadrupole mass-spectral libraries were compared to establish whether portable ion-trap GC–MS is useful for the identification of explosives. Ion-trap mass spectral data can be different from quadrupole data due to ion chemistry and other ion interactions, including space charge that may occur during ion trapping.  These results show that, although mass spectral data are sometimes different from quadrupole mass spectral data, portable ion-trap GC–MS is useful for performing confirmatory identification of explosives in the field. The forensic value of this capability is also considered.

	
	Portable gas chromatography–mass spectrometry (GC–MS) instrumentation has enabled the confirmatory analysis of explosive residues in the field. For battlefield forensics, portable GC–MS instruments are used for the detection and confirmatory identification of pre- and post-detonation threats. In addition, these systems provide information regarding the source of explosives based on the detection and identification of trace-level chemicals in the sample. The use of portable GC–MS is valuable for supporting scene safety, gathering intelligence, and providing investigative and adjudicative information about explosives events.

	It is important to recognize that GC–MS in the laboratory has historically been used for some of these same purposes, but there are significant benefits to performing the analysis at the scene. First, on-scene analysis enables the development of render-safe procedures based upon real-time threat identification and assessment. Second, intelligence turnaround time to support offensive operations is significantly improved. Commanders are empowered to make actionable decisions using reliable, confirmed information. Third, GC–MS results in real time can help guide and optimize scene processing. Finally, analysis at the sample site enables the most accurate evaluation of the scene. Results are representative of the scene at the time of analysis, not at some later date when samples are received and analyzed at the laboratory. This timeliness is especially relevant for this type of physical evidence, because, aside from the danger associated with storage and transport of explosive evidence, some explosives have relatively high vapor pressures, and will evaporate or sublimate during transport to the laboratory.

	Portable GC–MS systems were initially introduced to the market almost 25 years ago. Since this time, the design and performance of these systems has evolved (1,2), and there are currently a number of portable GC–MS options available to both scientists and non-scientists performing field analysis. These systems include both quadrupole and ion-trap designs. Quadrupole systems offer the benefit of generating classical mass spectra that are more easily interpreted and directly comparable with spectra in the National Institute of Standards and Technology (NIST) MS database. Ion-trap systems are desirable for field use, because they can run at higher operating pressures. The pumps required to achieve and maintain these higher pressures are more field friendly than the chemical pumps that were historically needed to achieve and maintain the operating pressures required of portable quadrupole systems. However, spectra generated using ion-trap systems may exhibit ion-chemistry events such as dimer formations occurring in the trap (2), and, therefore, are not always directly comparable with mass spectra within the NIST MS database. In addition, space charge is sometimes observed. Space charge occurs when the force of ion-ion repulsions inside the trap become too great, and can result in incorrect 

 assignments. Space charge further complicates spectral library matching. Library search results are frequently critical to end users of portable GC–MS systems who rely on library matching to achieve successful results in the field.

	To evaluate the potential use of portable ion-trap GC–MS analysis for the on-scene evaluation of explosive evidence, explosive standards were analyzed and compared with the proprietary library of the system and with the NIST MS database spectra to establish effectiveness of search results for these explosive standards. In addition, explosives samples received from international law-enforcement organizations were analyzed using portable ion-trap GC–MS, and results generated were reviewed to evaluate capabilities of the technology for analyzing the explosive-related components in these recovered samples.

	The portable GC–MS system used in this research directly links a low thermal mass resistively heated capillary column with a miniaturized toroidal ion-trap mass spectrometer (3–5). The GC column enables fast separation and thermal recovery (3-min analysis time) resulting in high throughput (approximately 5 min between injections). High throughput is always a desirable feature of analysis, but especially for field applications where scene-processing time may be limited or the scene itself may be dangerous. The toroidal ion-trap design is intended to increase trapping volume and, therefore, to minimize ion-ion repulsions. Regardless, the impact of both space charge and ion chemistry on the mass spectra generated may still be observed when using this system.

	The following equipment was used in this research:

		Smiths Detection Guardion portable GC–MS system with a GC capillary column; 5 m-long, Restek Corporation MXT-5 resistively heated, crossbond diphenyl-dimethylpolysiloxane, inner diameter 0.1 mm, film thickness 0.4 μm

		Leland Limited Incorporated disposable helium cartridge, 95 cc x 5/8 in helium filled, 2.4 g at 2500 psi

		Torion Technologies Custodian solid-phase microextraction (SPME) holder

		Supelco 23-gauge, 65-μm polydimethylsiloxane/divinylbenzene (PDMS/DVB) SPME fiber assembly

		Smiths Detection Guardion performance validation mixture sample

	Explosive standards were purchased from AccuStandard, Inc., in New Haven, Connecticut, and include:

		2,3-dimethyl-2,3-dinitrobutane (DMNB), 100 µg/mL in acetonitrile, CAS#3964-18-9

		2,4-dinitrotoluene (2,4-DNT), 1000 µg/mL in methanol, CAS#121-14-2

		ethylene glycol, dinitrate (EGDN), 100 µg/mL in acetonitrile:methanol (95:5), CAS#628-96-6

		hexamethylene triperoxide diamine (HMTD), 100 µg/mL in acetonitrile, CAS#283-66-9

		pentaerythritol tetranitrate (PETN), 1000 µg/mL in methanol, CAS#78-11-5

		1,3,5-trinitro-1,3,5-triazinane (RDX), 1000 µg/mL in methanol:acetonitrile (50:50), CAS#121-82-4

		triacetone triperoxide (TATP), 0.1 mg/mL in acetonitrile, CAS#17088-37-8

		1,3,5-trinitrobenzene (TNB), 1000 µg/mL in methanol:acetonitrile (50:50), CAS# 99-35-4

		2,4,6-trinitrotoluene (TNT), 1000 µg/mL in methanol:acetonitrile (50:50), CAS#118-96-7.

	Law-enforcement explosive evidence samples were received from various international law-enforcement organizations. These samples are detailed in Table I. The 20 samples are each from a different origin. The explosive type, as well as the sample description for each, was provided by the contributing organization. Aside from the details shown in Table I, no further information about the history of these samples was provided to the authors.

	Prior to all sampling, the SPME fiber was verified to be clean by performing an analysis using the GC–MS method described below. If chemicals were detected, the SPME fiber was continually cleaned by heating, until no chemicals were detected on the fiber. Explosive standard solutions were sampled using the direct-deposition method. Law-enforcement explosive samples were analyzed using either SPME headspace sampling, direct deposition, or both methods.

	Between 100 and 500 mg of sample was transferred to a GC–MS headspace vial. The vial was closed and then stored at 22 °C for at least 2 h. The headspace was then sampled by piercing the vial cap with the needle end of the SPME holder containing the SPME fiber and exposing the fiber for a specified period of time. Sampling times were variable and ranged from 10 min to 40 min.

	For explosive standards, explosive amounts between 20 and 200 ng were directly deposited using a microsyringe onto the coated portion of the SPME fiber, and allowed to dry for up to 5 min to allow evaporation of the solvent. Although attempts were made to control the amount of sample deposited onto the fiber, no attempts to provide quantitative analysis of these data were made. For law-enforcement samples, between 100 and 500 mg of sample was dissolved in 10 mL of acetone and stored at 22 °C for at least 2 h. Then, 10 µL of the sample solution was directly deposited using a microsyringe onto the coated portion of the SPME fiber, and allowed to air dry for up to 5 min, to allow evaporation of the solvent.

	Performance of the system was tested at the start of every work day. Performance testing evaluates GC, MS, and library search performance. To perform this testing, a standard containing 13 chemicals is analyzed. These 13 chemicals have expected retention-time values between 0 and 90 s. GC performance acceptance criteria requires that the retention times for all 13 chemicals must be the stated value ±2 s. MS performance includes tests for spectral quality, mass calibration, mass resolution, ion statistics, space charge, signal-to-noise ratio, and sensitivity. Library- search testing verifies the system is matching substances as expected against the system’s proprietary library. The system always passed all performance-test criteria prior to use. The performance-test procedure used was the procedure recommended by the instrument manufacturer.

	The system used in this research is deployed with a single GC–MS method. This method is linked to a proprietary library, as well as a condensed version of the NIST MS database. It is required that, if the user wants to search these libraries at the time of analysis, the deployed GC–MS method must be used. Although there is some deviation from that method that is tolerated, the use of some GC settings, including ramp rate, begin temperature, and begin hold time are required. This is because library search criteria use retention times when searching the proprietary library, and retention index when searching the condensed NIST MS database. It is possible to change these settings, but this would require either library development (if automatic library searches were required), or manual interpretations of GC–MS results.

	The method’s standard GC settings (which were used for this research) areas follows:

	A manual review of all data from the explosive standard samples was performed, and chromatographic peaks and characteristic mass spectra for seven of the nine samples were reliably detected. For these seven samples, detections were possible at both low and high concentrations, and at all inlet temperatures. For two of the nine standards, no chromatographic peak for the explosive was detected. In addition, expected target ions were actively searched in the region of their expected retention and across the entire chromatogram in each data file to confirm the negative findings of the chromatographic data review. Table II summarizes the detection results from the manual review of the GC–MS data from the explosive standards.

	RDX was not detected in the standard sample. As is described later, it also was not detected in any law-enforcement samples expected to contain RDX. Although no RDX was detected, it is assumed that the system in its current configuration is capable of detecting RDX because this substance is programmed into the proprietary library for the system. This library is purported to contain library data generated using the system with its deployed method. The library entry describes GC retention values and MS fragments consistent with those reported in the NIST MS database for RDX, so it is assumed the system is capable of detecting RDX, not just its degradation products, impurities, or other contaminants. The failure to detect this compound using the portable GC–MS may be due the inlet temperature or transfer line temperatures being too high. As has been reported by other researchers, the thermally sensitive nature of some explosives can cause them to degrade when introduced to the high temperatures of the inlet (6). To address this potential expectation, the systems inlet temperature was lowered to 210 °C, but no RDX was detected. It is likely that a method for this substance at even lower temperature would be required for its detection.

	HMTD was also not detected in the standard sample under any analysis conditions. It was also not detected in any of the law-enforcement samples, although none of the law-enforcement samples were expected to contain this explosive. In accordance with the NIST MS database, HMTD has an estimated non-polar retention index value of 1619, and its three most intense mass fragments at 

 values in descending order of intensity of 208, 88, and 45. As such, this explosive would be expected to exhibit a retention time in this portable GC–MS under these analysis conditions of approximately 101.5 s. No chromatographic peaks were observed at or around this retention time. It is also important to note that the entry for this explosive in the proprietary library of the portable GC–MS system listed the anticipated retention time of 34.4 s for HMTD (RI value of approximately 770), with its most intense mass fragments in descending order of intensity of 73, 74, and 44. The significant discrepancy between the retention values reported in the NIST MS database and the proprietary library entry of the portable GC–MS system indicate the entry in the proprietary library is not representative of HMTD, and is potentially a degradation product, impurity, or other contaminant.

	In addition to performing a manual review of the data, all GC–MS data files for the seven detected explosives were searched against the library of the portable GC–MS system. This search uses its own proprietary search algorithm. Of the seven samples detected, only six were in the library. For the six explosives in the library, an automatic identification of the explosive sample was reliably made. The automatic identification occurred at both low and high concentrations, and at all inlet temperatures.

	A search of each GC–MS data file for the seven detected explosives was also performed by comparing the mass spectrum at the chromatographic peak maximum of the explosive against the NIST MS database using the NIST MS database search software. For six of the seven samples, the highest probability hit always correctly identified the explosive substance. However, TATP was only identified as the top hit about 50% of the time. The ability to correctly identify TATP as the top hit against the NIST MS database was not attributable to sample load. It is important to note that searches against the NIST MS database only consider mass spectral data when generating a hit list. A summary of library searching results is shown in Table III.

	Finally, the mass-spectral data from the ion-trap system were manually compared with NIST MS database quadrupole spectra. In most cases, as is evidenced in the automatic library search results, no significant differences were observed between the two types of spectra. Figure 1 shows the mass spectrum from the portable ion-trap system, along with the NIST MS database spectrum of TNT. Although small differences in peak intensities were observed across the spectrum, these spectra are generally consistent with each other. The most significant difference between the ion-trap and quadrupole spectra were observed in the spectra of DMNB. The ion-trap spectrum of DMNB had an additional ion fragment at 

 130 that was not observed in the NIST MS database spectrum. This fragment had an intensity of about 13% of the most intense fragment in the spectrum (

41 was not observed in the ion-trap data. However, the ion-trap system is only specified to detect fragments of 

43 to 500. Therefore, not too much should be interpreted from its absence in this data. The ion-trap spectrum of DMNB from the portable ion-trap system and the NIST MS database spectrum of DMNB are shown in Figure 2.

	The TATP data was also reviewed to investigate the cause of the missed identifications when searched against the NIST MS database. It is likely that the missed identifications are due to the simplicity of the mass spectrum of this explosive. The most intense fragment in the NIST MS database spectrum is 

43, followed by fragments at 58, 59, and 70, which have relative intensities to 43 of 13%, 11%, and 10%, respectively. There are spectra of many chemicals within the NIST MS database that have very similar spectra to TATP. The incorrect identifications of TATP when searched against the NIST MS database were all from chemicals that had very similar spectra to TATP. It was possible to sometimes, but not always, exclude the missed identifications due to inconsistencies between the retention time observed in the chromatogram from the ion-trap GC–MS system and the retention index reported in the NIST MS database.

Law-Enforcement Explosive Evidence Samples

	Table IV details the explosive-related compounds included in the proprietary library of the portable ion-trap GC–MS system; Table V details the automatic library search results for the explosives-related identifications from the 20 law-enforcement samples. A manual review of all data was also performed. As previously mentioned, the system did not detect RDX. This is important because a number of samples contain RDX, including Semtex and C4. In addition, the system failed to automatically identify tetryl, even though tetryl was in the library. This is because this sample was slightly overloaded, and does not chromatograph well on this column. The poor chromatography coupled with column overload caused a shift in the observed retention time. This observed retention time was outside of the retention-time window of the library entry. Therefore, the substance was not identified.

	Aside from missing RDX and tetryl, the system returned a few false automatic identifications including for EGDN in two of the three samples of C4 and one of the six samples of Semtex, PETN in one of the three dynamite samples, dinitroglycerin in four of the six samples of Semtex, and RDX in both TNT samples. Identifications were considered false if no chromatographic peak was present, but the software identified the substance. Such a result may occur when spectra are noisy and the chemical for identification has few or common mass fragments. For instance, the proprietary ion-trap library entry for dinitroglycerin contains only three fragments with 

 values of 46, 76, and 64, at 100%, 8%, and 1% intensities, respectively. It is reasonable to expect that these three fragments will be present in noisy data.

	A review of the results also shows that there is value to analyzing explosive samples using both headspace and direct-deposition sampling. For instance, dinitroglycerin in dynamite samples was always observed in the headspace samplings, but never in the direct-deposition sampling. For TNT, DNT degradation products were observed in the headspace sampling, but not in the direct-deposition sampling. The reverse was true for the TNT that was not observed in the headspace sampling, but was reliably observed in the direct-disposition samples. The different results that occur from using the two sampling methods are not contradictory to each other; they enable a more complete assessment of the sample. Headspace sampling prior to sample dilution is useful for identifying the components of the sample with higher vapor pressures. Vapor-pressure values of explosives reported in the literature, for a number of reasons, are variable and sometimes contradictory (7–9), but Figure 3 summarizes the ranking order of vapor-pressure values of many explosives at 25 ËC, calculated by Ewing and associates after an extensive review of values reported in the literature. (8)

When explosives are encountered on the battlefield, the use of portable GC–MS is valuable for the detection and confirmatory identification of pre- and post-detonation threats. In addition, this technique provides information about the source of explosives based on the detection and identification of trace-level chemicals in the sample. The data presented here confirm this capability.

Optimized Explosives Analysis Using Portable Gas Chromatography–Mass Spectrometry for Battlefield Forensics

The treatment of water for human use and consumption can lead to the generation of potentially hazardous transformation products (TPs) from organic micropollutants (OMPs), a group of synthetic chemicals present in water sources at very low concentrations. Despite the potential toxicity of TPs, they are not measured routinely, due to the analytical challenges associated with their identification. One of the major challenges is confidently identifying OMPs and their TPs given the plethora of possible compounds known to exist. With advances in analytical methodology, a combination of targeted and nontargeted screening can be applied to identify the “known,” “known unknown,” and “unknown unknown” contaminants present within a sample. In this article, we present a study performed using liquid chromatography coupled with high-resolution mass spectrometry (LC–HRMS), in combination with a comprehensive data analysis workflow, to screen water samples for TPs to determine the efficacy of different water treatment methods. 

	Organic micropollutants (OMPs) are synthetic chemicals found in water sources and treated water at microgram-per-liter concentrations, or below (1). These persistent contaminants may be hazardous in themselves, and can also give rise to a wide variety of transformation products (TPs) (2,3,4). Potentially hazardous TPs may be formed from OMPs during drinking water treatment, but are not routinely measured, because of the lack of standards and significant analytical challenges involved in their identification. Ascertaining what TPs are removed or created during drinking water treatment is important for public health, as is determining their identity and toxicity. Advanced analytical methods based on liquid chromatography coupled with high- resolution mass spectrometry (LC–HRMS) are now being applied to aid in monitoring drinking water treatment steps, and to support a better understanding of these compounds.

	A major issue in measuring OMPs and their TPs is the large number of possible compounds that can exist. OMPs and TPs can be considered as “knowns,” “known unknowns,” or “unknown unknowns.” “Knowns” are, for example, those regulated priority pollutants that are actively screened for and which can be detected through liquid chromatography–mass spectrometry (LC–MS) based target analyses (5). However, target analyses do not seek to identify new or emerging contaminants; they solely focus on looking for what is known. For the “known unknowns,” a suspect screening approach enables the detection of already described compounds that are present in chemical databases (6,7). Finally, screening for TPs means dealing with many millions of potential compounds, and for these “unknown unknowns,” a reliable nontargeted screening (NTS) approach is essential. LC–HRMS is highly relevant here, enabling NTS that is fundamental to broader detection and accurate identification of TPs (8).

	NTS identifies all measurable features (accurate mass-retention time pairs) within water samples (8). Features can be clustered based on feature intensity trend profiles from treatment processes, and can then be further categorized by risk. This approach facilitates the investigation of the identified clusters and their respective responses to relevant bioassays, making it possible to relate the chemistry to biological processes and fully assess the risks these compounds pose.

	This study used LC–HRMS in combination with a novel data analysis workflow to screen water samples for OMPs and their TPs to examine the effectiveness of different water treatment methods, using bioassays to relate toxic effects to the detected compounds.

	The focus of this study was to detect and identify OMPs and their TPs at different stages of the water treatment processes using NTS. Figure 1 illustrates the overall NTS identification approach used, which involved moving from a confidence level of 5, representing all the features identified in a sample (based on mass, retention time, and intensity), to a confidence level of 1, where the unique structure of an individual TP is determined, and its identity confirmed (9). The process includes looking at molecular formula and structure, based on multiple different possible structures, the examination of spectral similarity, and final confirmation with a reference standard. Because NTS generates large numbers of features, prioritization criteria were applied to reduce the number requiring identification.

	Three different drinking water treatment pilot installations were studied, each of which employed a different treatment protocol (Table I). To assess the effectiveness of each water treatment approach, 48 different OMPs, selected based on their presence in surface water, toxicity, available knowledge of treatment options, chemical properties, detectability, and availability, were spiked into the source water of each installation.

	Pre- and post-treatment samples (with sampling after each treatment stage for installations 1 and 2) were analyzed using LC–HRMS. Targeted analysis and NTS workflows were applied to determine OMP removal rates, the presence of parent compounds, and formation of TPs.

	Following preparation in flasks pre-rinsed with acetone and petroleum ether, and containing internal standards, each sample was filtered and split into three separate vials. From each vial, 100 µL of the sample was injected per LC–HRMS analysis. A range of predefined bioassays were used to examine compound toxicity, to help prioritize the identification of unknowns.

	Chromatographic separations were performed on a Thermo Scientific Vanquish ultrahigh-pressure liquid chromatography (UHPLC) system, using an XBridge BEH C18 XP reversed-phase column (Waters Corporation) operating at 25 °C with an acetonitrile gradient.

	Mass spectrometric data were collected on a Thermo Scientific Orbitrap Fusion Tribrid mass spectrometer, with a heated electrospray ionization source, using 120,000 FWHM resolution in full scan acquisition over the range of 50–1000 

. This was followed by data-dependent tandem mass spectrometry acquisition (DDA), selecting the top eight most intense peaks for fragmentation using stepped higher-energy collisional dissociation (HCD) energies of 20, 35, 50, and 15,000 FWHM resolution. Samples were measured in triplicate, with blank samples run every 5–10 samples to check for any carryover or contamination, as well as system performance through checking internal standards (IS).

	A mix of commercial software, open-source packages, and R-scripts were used for data analysis. The NTS data analysis workflow utilized Thermo Scientific Compound Discoverer software for peak picking, feature building, componentization, and identification of “knowns,” “known unknowns,” and elucidation of “unknown unknowns.” Suspect screening was based on elemental formula, accurate mass and retention time, and fragmentation spectra via tandem mass spectrometry (MS/MS) data searching, using the Thermo Scientific mzCloud mass spectral fragment library and Thermo Scientific mzLogic data analysis algorithm to rank suspect candidates when there were no library matches. Data were then exported to the “R” programming language for hierarchical clustering, trend analyses, and integration with bioassay data (Figure 2).

	LC–HRMS was used to assess different water treatments for their effects on TP formation and degradation. Data analysis enabled compound detection followed by suspect screening using a variety of approaches (Figure 3). These approaches included taking predicted compositions and searching molecular formulas in compound databases, and screening acquired spectra against spectral libraries of environmentally relevant compounds. Suspect screening was also conducted based on accurate mass, using the NORMAN SusDat database (10) of around 40,000 environmentally relevant compounds with their structures, which were imported into Compound Discoverer, as well as tailored lists of experimentally detected and predicted TP suspects.

	For compounds that do not exist in any databases, the “unknown unknowns,” there is a need to predict TPs, for which a range of tools were used based on rules for biotic and abiotic transformation processes (11,12) and metabolic logic (13). Fragmentation spectra were assessed against the experimental spectra in mzCloud with the application of mzLogic to rank order the suspect candidates retrieved from the multiple data sources used facilitating identification of compounds not in a spectral library. 

	Figure 4 shows the results of assessing influent and effluent samples for feature count and suspect versus parent (spiked in compounds) matches. Notably, advanced oxidation treatment resulted in 144 features in the final effluent, 28 of which were suspect matches and 12 were parent matches. Ultrafiltration, in combination with reverse osmosis, resulted in 37 features, of which 11 were suspect matches and 9 were parent matches. The overall high number of features across all three treatments meant that some form of prioritization would be required. Data also showed that degradation of the parent compounds and TP formation were water-source specific, that ultrafiltration reverse osmosis (UFRO) removed most compounds, and in cluster analysis, UFRO samples aligned with the experimental blanks (Figure 5).

	Ahead of any prioritization, these data were processed with mzCloud, which allowed immediate identification of some OMPs, but many more were not present in the database. The first step in prioritizing the identification of the remaining features was to apply hierarchical clustering analysis to the data from all the samples. This enabled the detection of feature intensity trend profiles. Compounds clustered depending on whether they were removed or formed by a particular water treatment step.

	The profiles for the advanced oxidation treatment steps O

, are visualized in Figure 6. These show the influent for the process and effluent after O

. Nine clusters emerged where the features either increased or decreased. Clusters 2, 4, 6, 7, and 9 showed an increase in features indicating these should be identified as a priority. An alternative approach to assessing the effectiveness of treatment is to look at the overall increase, reduction, or no change in features. The combined value in this treatment indicates that about 20% of features increase, and although this means identifying a fifth of features, it also represents a fivefold reduction in features through the treatment process.

	Further prioritization can be achieved by including effect-based data. Here, NTS data were integrated with the results of bioassays, in this case, the standard Ames test for mutagenicity and a range of chemical activated luciferase expression (CALUX) bioassays to identify toxic endpoints for substances, such as hormone disruption and anti-estrogenic receptors (14).

	Integration of the data highlighted the presence of compounds that have potentially harmful effects, with correlation between the bioassay results and clusters 4, 7, and 9. The features occurring in clusters 4, 7, and 9 were therefore revisited in Compound Discoverer to try to identify them, with the associated data including peak shape, MS1 spectrum, isotopic pattern, and whether it provided a hit on the mass list (NORMAN SusDat). Suspect TP candidates were then ranked and identified based on spectral similarity. For one such TP with a molecular formula of C

, the search results returned two possible compounds with differing mzLogic scores of 67 and 21 (Figure 7), providing insights as to the potential structural characteristics and putative structure for this unknown. Figure 8 indicates the acquired sample spectrum compared with the library spectrum for bis(4-ethybenzylidene)sorbitol, demonstrating that NTS can facilitate the confident identification of TPs, something that would not have been possible through other established means.

	Increasing chemical use, and the discharge of industrial and personal waste into watercourses, are responsible for the growing presence of OMPs and their TPs in drinking water sources. This increase is driving the need to detect and remove these hazardous contaminants from water to ensure it is safe for human consumption. The large number of potential compounds, along with their extensive chemical diversity, poses significant challenges, and means targeted chemical analysis alone is no longer sufficient to determine drinking water quality and assessing the effects of treatment processes.

	The data from this study demonstrate the utility of NTS in enabling a more complete analysis of samples, ensuring higher standards of water quality. Incorporating powerful NTS methods, using high-resolution accurate mass spectrometry into water quality assessment workflows, supports the development of an optimized, risk-based water monitoring strategy. This approach identifies features that potentially pose a risk to human health or the environment, and improves the identification of “unknown unknowns,” allowing decisions to be made in relation to water treatment processes. It also provides the opportunity to monitor water treatment steps and improve drinking water monitoring for known and unknown contaminants to protect public health.

	Consequently, NTS using high-resolution accurate mass spectrometry is becoming an essential tool for water quality monitoring due to the increased presence of emerging contaminants with water sources. With the ability to rapidly provide in-depth, high-quality MS

 data, and comprehensively triangulate multiple data sources and cheminformatics tools to determine contaminants and identify the compounds, HRAM MS will continue to facilitate the detection of established and emerging contaminants in water sources.

	The author acknowledges Dennis Vughs, Annemieke Kolkman, Thomas ter Laak, Milou Dingemans, Cheryl Bertelkamp, Bas Wols, Danny Harmsen, Wolter Siegers (KWR), Bram J.Martijn (PWN), Wim A.Oorthuizen (Dunea), Camilla di Marcantonio (Sapienza University of Rome) and the Dutch drinking water utilities Dunea, PWN, Evides and Waternet for funding.
	 

Removing Organic Micropollutants from Wastewater.

https://www.accessscience.com/content/removing-organic-micropollutants-from-wastewater/BR0927161 

		A.C. Belfroid, M. van Drunen, M.A. Beek, S.M. Schrap, C.A. van Gestel, and B. van Hattum, 

		C.M. de Jongh, P.J.F. Kooij, P. de Voogt, T.L. ter Laak, 

		W. Brack, J. Hollender, M.L. de Alda, C. Müller, T. Schulze, E. Schymanski, J. Slobodnik, and M. Krauss, 

		A.D. McEachran, J.R. Sobus, and A.J. Williams, 

Chemicals Environmental Science & Technology 

		J. Hollender, E.L. Schymanski, H.P. Singer, and P.L. Ferguson, 

		E.L. Schymanski, J. Jeon, R. Gulde, K. Fenner, M. Ruff, H.P. Singer, and J. Hollender, 

		Network of reference laboratories, research centres and related organisations for monitoring of emerging environmental substances. NORMAN Suspect List Exchange. 

https://www.norman-network.com/?q=suspect-list-exchange 

		K. Fenner, J. Gao, S. Kramer, L. Ellis, and L. Wackett, 

		L. Blum, L.C. Schmid, E. Fenner, and K. von Gunten, 

Linking Influent and Effluent Peaks from Biological Wastewater Treatment to Detect Relevant Nontarget Compounds

, SETAC Europe, Barcelona, Spain (2015), p. 2.	

		M.B. Heringa, D.J.H. Harmsen, E.F. Beerendonk, A.A. Reus, C.A.M. Krul, D.H. Metz, and G.F. Ijpelaar, 

		A.M. Brunner, C. Bertelkamp, M.M.L. Dingemans, A. Kolkman, B. Wols, D. Harmsen, W. Siegers, B.J. Martijn, W.A. Oorthuizen, and T.L. ter Laak, 

 is the deputy team leader of the Chemical Water Quality and Health team at KWR Water Research Institute, in Nieuwegein, in the Netherlands. Direct correspondence to: 

Liquid chromatography coupled with high-resolution mass spectrometry (LC–HRMS) is used in combination with a comprehensive data analysis workflow to screen water samples for potentially hazardous transformation products from organic micropollutants to determine the efficacy of different water treatment methods.

Nontargeted Screening as an Essential Tool for Drinking Water Quality Monitoring

Aggregates and fragments are common product-related impurities in biopharmaceuticals that have impact on efficacy, safety, and stability. Identification of these low-level impurities may involve labor-intensive chromatographic fraction collection and follow-up experiments. In addition, a significant part of the total protein aggregates can be caused by non-covalent molecular interactions, which are dissociable in denaturing buffers, and cannot be characterized by conventional mass spectrometry (MS). With the use of ammonium acetate buffer, size-exclusion chromatography (SEC) can be coupled on-line to electrospray ionization MS for the characterization of size variants of therapeutical monoclonal antibodies (mAbs). A quadrupole time-of-flight (QTOF) MS system was employed, and the MS method was optimized to achieve favorable sensitivity for high-mass detection, while maintaining the structural integrity of the aggregates. As a result, high quality native MS spectra could be acquired for drug substance samples containing as low as 0.4% of these species, with the molecular masses of all relevant size variants obtained using 10-min SEC–MS runs. It is expected that this method will have significant potential to be implemented in routine analysis of mAbs for the characterization of the aggregation mechanisms and degradation pathways.

	
	Recombinant monoclonal antibodies (mAbs), as the most common modality among protein therapeutics, have achieved great success in multiple disease areas, including immunology, oncology, and blood diseases, and potentially could provide therapies for neurodegenerative diseases such as Alzheimer’s disease (1–5). MAbs are large tetrameric molecules (approximately 146 kDa) composed of two heavy chains (~50 kDa) and two light chains (~23 kDa). The four chains are connected through interchain disulfide bonds. During manufacturing and storage, mAbs can form aggregates (or high molecular weight species [HMWs]) and fragments (or low molecular weight species [LMWs])(6). MAb aggregation can be formed through various mechanisms, such as molecular interactions and chemical cross-linking (6–9). The aggregates can be potentially immunogenic, and possibly promote the formation of particulates (10). MAb fragmentation can occur through several pathways, such as hydrolysis, free radical induced fragmentation, and enzymatic cleavage (11). The fragmented mAb may have reduced biological activity and pharmacokinetic compared to the intact mAb (12). Both aggregates and fragments are usually the critical quality attributes that need to be controlled to very low levels to ensure the efficacy, safety, and stability of mAb drugs.

	Characterization of aggregates and fragments is important to understand mAb degradation pathways and to establish an effective control strategy. Conventional characterization methods often include size-exclusion chromatography (SEC) with UV or multi-angle light scattering (MALS) detection, analytical ultracentrifugation, and capillary electrophoresis with sodium dodecyl sulfate (6,7,9). In-depth characterization and identification of these low-level species may involve labor-intensive fraction collection and mass spectrometric analyses. In addition, noncovalent aggregates formed by molecular interactions can be a significant portion of the total aggregates, but are dissociable, and cannot be characterized by conventional denaturing liquid chromatography–mass spectrometry (LC–MS) methods (7, 9).

	Native mass spectrometry has been an emerging technique in the past two decades for the study of protein complex assemblies (13,14), protein–protein interactions (15,16), protein–ligand interactions (15), glycoform analysis in mAbs (17,18), antibody–drug conjugates (19,20), bispecific antibodies (21), and mAb aggregates (22,23). For example, it has been specifically used for characterization of antibody–drug conjugates to determine the drug to antibody ratio (24,25), the study of product-related impurities and variants in bispecific antibodies (26), and study of aggregate formation in a coformulated drug containing two mAbs (27).

	There are two special features of native MS: The need to use non-denaturing MS-compatible buffer and gentle ionization conditions to preserve protein complexes; and the need to use MS with high 

 ranges, because protein complexes have higher molecular weights, and are in native states carrying fewer charges compared to conventional denaturing states. In theory, time-of-flight (TOF) instruments have unlimited mass ranges, and thus are a natural fit for native MS. Some orbital ion trap instruments also have native MS capability, due to their extended mass ranges.

	Apart from direct injection experiments, native MS can be coupled on-line to separation methods with the use of native or “near native” MS-compatible buffers, such as SEC (22,23,28), cation exchange chromatography (29), and capillary zone electrophoresis (30,31).

	Early work in native MS has been described using a QTOF system (32). Recently, we used a TripleTOF QTOF instruments from Sciex to establish a native MS method for mAbs, and results are described in this paper. The method couples size-exclusion chromatography on-line with native MS using a MS-compatible SEC running buffer. The applications for the direct analysis of drug substance samples of therapeutic mAbs to obtain the molecular mass information of all the relevant size variants, including monomer, aggregates (in most cases the dimers), and fragments are demonstrated.

	MAb-1 is the NIST mAb. MAb-2, mAb-3, mAb-4, and mAb-5 are humanized mAbs expressed in Chinese hamster ovary (CHO) cells at Biogen. For native SEC-MS analyses, the mAbs were diluted to final concentrations of 10 mg/mL using their formulation buffers.

	Ammonium acetate (mass spectrometry grade) was purchase from Sigma. Milli Q water (18.2 MΩ) was used to prepare the ammonium acetate aqueous solutions.

	SEC separation was performed on a Shimadzu ultrahigh-pressure liquid chromatography (UHPLC) system with a Waters BEH200 SEC column (2.1 or 4.6 mm × 150 mm, 1.7-μm) heated to 30 °C. Typically, 50 or 100 μg mAb (50 µg for the 2.1 mm column; 100 µg for the 4.6 mm column) was loaded onto the column and eluted by a 25 mM ammonium acetate solution delivered at 100–200 μL/min (100 or 125 μL/min for the 2.1 mm column; 200 μL/min for the 4.6 mm column). The eluates were monitored sequentially by a PDA detector and a QTOF mass spectrometer.

	Unless otherwise mentioned, the native MS data were acquired on a Sciex TripleTOF 6600 QTOF mass spectrometer, and the instrument parameters are summarized in Table I. Data analysis was performed using PeakView 2.2 software (Sciex). Protein deconvolution was achieved using Bayesian algorithm in the Bio Tool Kit (Sciex).

	Shown in Figure 1 are the electrospray ionization mass spectra of mAb-1, acquired under native (Figure 1a) and denatured conditions (Figure 1b). In Figure 1b, as mAb-1 has been denatured by the organic solvent and the acidifier, more of the surface is exposed to the solvent, so that it takes on additional charges compared to the native state in a neutral aqueous solution (Figure 1a). As a result, the center charge states of the denatured and native mAb are 51 and 27, and their upper mass to charge ratio (

) ranges are approximately 4000 and 6000, respectively.

	Moreover, the native mass spectrum appears to have a narrower charge state distribution, and reveals a simplified spectrum compared to the one under denaturing conditions. In the native spectrum, there are more spaces between nearby charge states, which increase the chance to detect and quantitate post-translational modifications when their signals fall in these regions. This could be a major reason that native MS has been increasingly popular for the analysis of antibody–drug conjugates (20).

	Native SEC–MS method optimization was based on the mass spectra of both the monomer and dimer peaks of mAbs after SEC separation on a 2.1 mm x 150 mm BEH200 column with 25 mM ammonium acetate buffer delivered at 0.125 mL/min. Multiple instrument parameters, including gas settings, source temperature, and declustering potential (DP) were evaluated. Right levels of source temperature and declustering potential are critical for removing solvent-related adducts and enhancing resolution and sensitivity. However, the excess levels of source temperature or declustering potential could induce denaturation and fragmentation of the analytes, which are not desirable.

	Figure 2 shows the impact of source temperature on native SEC–MS analysis of mAb-2. Figure 2a shows the total ion chromatograms (TICs) under source temperature of 300 °C (blue trace) and 350 °C (purple trace), respectively. Two major analytes, the dimer and monomer, eluted at 2.0 min and 2.3 min, and their mass spectra are shown in Figure 2b and 2c, respectively. The MS signals of denatured monomer, native monomer, and dimer are in 

 windows of 1500–4000, 4500–6000, and 6500–8000, respectively. Compared to 300 °C, the higher source temperature doubled the sensitivity of the both the dimer and the monomer; however, it also denatured a significant portion of monomer. If we focus on dimer characterization, 350 °C could be chosen, otherwise, 300 °C or a temperature in between might be better choices to minimize monomer denaturation. Source temperature of 400 °C was also evaluated (data not shown). Under this elevated temperature, the recovery of dimer was not as good as that of 350 °C, since it became unstable and a portion of it decomposed into monomer and fragments.

SEC Separation is Critical  for Native MS Analysis of MAb Dimer

	In Figure 2b, there are a few low-level signals in the 6500–8500 

 range, which align exactly to the mass spectrum of the dimer in Figure 2c. The dimer in Figure 2b was believed to be an artifact formed in-source. It can be postulated that, in direct injection experiments, the signals of artifactual in-source dimer could mask the signals of in-solution dimer. Therefore, SEC separation is beneficial in differentiating in-source dimer from in-solution dimer based on their different SEC retention times. Additionally, in direct injection experiments mAbs often need to be buffer-exchanged into mass-spectrometry compatible native buffers. During extended storage in native buffers, some mAbs may have a tendency toward concentration-dependent self-association, resulting in the formation of new aggregates that would not have been formed (or formed at much lower rates) when stored in formulation buffers. With native on-line SEC–MS, mAbs remain in their formulation buffers prior to injection, thus minimizing this risk.

	Moreover, SEC provides separation and enrichment of the dimer peak, whose ionization could otherwise be suppressed by the monomer. The levels of dimer in drug substance samples are often very low, and so are their native MS signals. Overloading in SEC could be an approach used to enhance the signals of the dimer peaks. As shown in Figure 3a, 160 µg drug substance (DS) material, instead of a typical 50 µg material, was loaded on a 2.1 mm x 150 mm SEC column. Because the dimer is the first peak and a minor peak, overloading would not significantly impact its peak width and its separation from the monomer, and in theory the dimer response could be increased by approximately two- to threefold.

	Shown in Figure 3b, however, in addition to the monomer peak, many in-source artifacts are also detected, such as the denatured monomer, dimer, trimer, and tetramer. Although these artifacts are undesirable, the detection of trimer and tetramer suggests that the TOF analyzer can cover a wide range of aggregates including but not limited to the dimer.

Sensitive Detection of MAb Dimer by Native SEC-MS

	Even without sample overloading, the native SEC–MS method can achieve impressive sensitivity for the detection of mAb dimers. Shown in Figure 4, 100 µg DS sample of mAb-4 containing 0.4% dimer was analyzed by native SEC–UV-MS, with the use of a 4.6 mm x 150 mm SEC column. Figure 4a and 4b are the UV (280 nm) and TIC chromatograms of the DS sample, respectively. Figure 4c and 4d are the mass spectra of the monomer and dimer, respectively.

	The 0.4 µg dimer on column generated a high-quality native mass spectrum with base peak intensity of approximately 80 (Figure 4d). From these data, it is likely that this SEC–MS method could be applied to a DS sample containing 0.1% dimer. The 0.1 ug (0.1%) dimer from 100 µg DS would generate a mass spectrum with base peak intensity of approximately 20, which is close to the quantitation limit (10–20 ion counts) of the mass detector.

	Size variants, including aggregates and fragments, are product-related impurities that are critical to the quality of protein drugs. Characterization of size variants is important for the understanding of the stability profiles and degradation pathways in the mAbs.

	To analyze the size variants in mAb-5, 100 µg of DS sample containing 1.6% dimer was analyzed by the native SEC–UV-MS method (Figure 5). Three major peaks were detected, including the monomer, dimer, and a low molecular weight (LWM) peak. Their raw mass spectra and deconvoluted mass spectra are shown in Figure 5c, 5e, 5g, and Figure 5d, 5f, 5h, respectively.

	In Figure 5d, the deconvoluted native mass spectrum supports the identification of major glycoforms of mAb-5. Deconvoluted mass spectrum of the dimer is shown in Figure 5f. Deconvoluted mass spectrum of the LMW-containing mAb missing a single Fab due to commonly observed hinge cleavage  is shown in Figure 5h (33). The mass accuracies are generally about 200 ppm (Table II), which are typically considered to be low but acceptable for the purpose of analysis presented here. For the dimer and LMW species, due to their low abundances and high levels of heterogeneities, the deconvoluted spectra (Figure 5f and 5h) contain a few additional harmonic peaks. It appears that the deconvolution algorithm has not been fully optimized for native spectra analysis. Nevertheless, the base peaks in the deconvoluted spectra accurately report the molecular masses of these analytes.

	The assignments of the glycoforms in the monomer, as well as the identification of dimer and LMW peaks, are summarized in Table II. From this data, all the major size variants in the mAb-5 DS sample can be identified using a 10-min native SEC–MS run.

	Native SEC–MS conditions were optimized for the analysis of mAb size variants, with the use of ammonium acetate SEC buffer and a TOF mass spectrometer. This method is fast and sensitive for direct size variant characterization of mAbs at levels as low as 0.4%. The method was successfully applied to characterize all the relevant size variants detected in various mAbs. Native SEC–MS has been demonstrated to be a useful technique for mAb aggregate characterization. It has significant potential to be routinely applied to study mAb aggregation mechanisms and degradation pathways, thus provides valuable information to guide purification and formulation process development, and supports the risk assessment of product-related impurities.

	The authors thank Sean McCarthy, Fan Zhang, Esme Candish, and Elliott Jones at Sciex for the technical support of this work.

		A.L. Nelson, E. Dhimolea, and J.M. Reichert, 

		N.A.P.S. Buss, S.J. Henderson, M. McFarlane, J.M. Shenton, and L. de Haan, 

		J. Sevigny, P. Chiao, T. Bussière, P.H. Weinreb, L. Williams, M. Maier, R. Dunstan, S. Salloway, T. Chen, Y. Ling, J. O’Gorman, F. Qian, M. Arastu, M. Li, S. Chollate, M.S. Brennan, O. Quintero-Monzon, R.H. Scannevin, H.M. Arnold, T. Engber, K. Rhodes, J. Ferrero, Y. Hang, A. Mikulskis, J. Grimm, C. Hock, R.M. Nitsch, and A. Sandrock, 

		A. Beck, E. Wagner-Rousset, D. Ayoub, A. Van Dorsselaer, and S. Sanglier-Cianférani, 

		C.-F. Xu, Y. Chen, L. Yi, T. Brantley, B. Stanley, Z. Sosic, and L. Zang, 

		K.D. Ratanji, J.P. Derrick, R.J. Dearman, and I. Kimber, 

		S. Rosati, Y. Yang, A. Barendregt, and A.J.R. Heck, 

		S. Rosati, E.T.J. v.d. Bremer, J. Schuurman, P.W.H.I. Parren, JP. Kamerling, and A.J.R. Heck, 

		A. Beck, V. D’Atri, A. Ehkirch, S. Fekete, O. Hernandez-Alba, R. Gahoual, E. Leize-Wagner, Y. François, D. Guillarme, and S. Cianférani, 

		G. Terral, A. Beck, and S. Cianférani, 

		B. Kükrer, V. Filipe, E. van Duijn, P.T. Kasper, R.J. Vreeken, A.J.R. Heck, and W. Jiskoot, 

		M. Haberger, M. Leiss, A.-K. Heidenreich, O. Pester, G. Hafenmair, M. Hook, L. Bonnington, H. Wegele, M. Haindl, D. Reusch, and P. Bulau, 

		J. Chen, S. Yin, Y. Wu, and J. Ouyang, 

		F. Debaene, A. Bœuf, E. Wagner-Rousset, O. Colas, D. Ayoub, N. Corvaïa, A. Van Dorsselaer, A. Beck, and S. Cianférani, 

		Y.-T Yan, T. Xing, S.-H. Wang, T.J. Daly, and N. Li, 

		T. Xing, Y.-T. Yan, S.-H. Wang, T J. Daly, and N. Li, “Identification and quantitation of heterodimer species in co-formulated protein drugs by lc-ms-based approaches” 67TH ASMS Conference on Mass Spectrometry and Allied Topics; June 6th; Atlanta, Georgia 2019.	

		K. Muneeruddin, J.J. Thomas, P.A. Salinas, and I.A. Kaltashov, 

		Y. Leblanc, V. Faid, M.A. Lauber, Q. Wang, N. Bihoreau, and G. Chevreux, 

		A.M. Belov, R. Viner, M.R. Santos, D.M. Horn, M. Bern, B.L. Karger, and A.R Ivanov, 

		E.A. Redman, J.S. Mellors, J.A. Starkey, and J.M. Ramsey, 

		N.J. Thompson, S. Rosati, and A.J.R. Heck, 

 are with the Department of Analytical Development at Biogen, in Cambridge, Massachussetts. Direct correspondence to 

Size-exclusion chromatography (SEC), with the use of ammonium acetate buffer, can be coupled on-line to electrospray ionization MS for the characterization of size variants of therapeutic monoclonal antibodies (mAbs). A quadrupole time-of-flight (QTOF) MS system was employed, and the MS method was optimized to achieve favorable sensitivity for high-mass detection, while maintaining the structural integrity of the aggregates (or high molecular weight species) and fragments (or low molecular weight species).

Special Issues-05-01-2020 | LCGC Supplements

The 2020 ASMS Conference Moved Online in the Face of the COVID-19 Pandemic

Optimized Explosives Analysis Using Portable Gas Chromatography–Mass Spectrometry for Battlefield Forensics

Nontargeted Screening as an Essential Tool for Drinking Water Quality Monitoring

Aggregate and Fragment Analysis in Therapeutic Monoclonal Antibodies Using On-Line Size-Exclusion Chromatography with Native Mass Spectrometry