Research Projects

Rapid Discrimination of Foodborne Pathogens by Ion Mobility Mass Spectrometry

Foodborne diseases are significantly common; roughly 1 in 10 people worldwide experience illness after consuming contaminated food leading to 420,000 deaths annually as reported by World Health Organization (WHO) [1-2]. They are caused by agents that enter the body through the consumption of contaminated food or beverages followed by growth inside the host organism. They are caused by pathogenic bacteria, parasites, or viruses. Bacterial foodborne infections are currently diagnosed using the symptoms and subsequently, empirical antibiotics are being provided to patients which are usually inefficient, and they mostly lead to antibiotic resistance [3].

Bacterial food poisoning is commonly caused by pathogenic bacterial species such as Escherichia coli, Salmonella spp., Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, Clostridium perfringens, Campylobacter spp., Shigella spp., Streptococcus spp., Vibrio spp., and Yersinia spp. [2, 4]. In order to increase the recovery rates of patients suffering from foodborne infection and to reduce potential antibiotic resistance, fast robust diagnosis techniques that can work for various classes of microorganisms with high performance is required [3]. In addition, two relatively extensive studies were performed on wider sets of microorganisms with the support of advanced statistical models. The first study proved PS-MS to achieve the needed performance leading to high prediction rates for a wide set of microorganisms including multiple foodborne pathogenic bacteria. Gram-positive bacteria have been differentiated with a prediction rate of 98% utilizing negative ions solely while the sole utilization of positive ions has yielded a prediction rate of 96%, as shown in Figures 1a and 1b, respectively. Similarly, the capability to achieve high successful prediction rate was obtained for Gram-negative bacteria through data fusion statistical methodology of the positive and negative ion modes’ information which has led to a prediction rate of 87%, as shown in Figure 1c [5].

Because of the structural diversity of lipids, the addition of ion mobility separations is expected to lead to enhanced prediction rates resulting from the separation of lipid isomers. Recently, we reported the coupling of ambient ionization techniques with a commercial drift tube ion mobility mass spectrometer and demonstrated their capability for rapid separation of constitutional and geometric isomers [6].

Figure 1. Principal Component Analysis of (a) Gram-positive bacteria using negative ion mode information. (b) Gram-positive bacteria using positive ion mode information. (c) Gram-negative bacteria using fused positive and negative ion modes’ information.

We utilized our optimized methods (ambient ionization-IM-MS and ambient ionization-IM-MS/MS) in the discrimination of closely-related microorganisms, where ambient ionization techniques include, paper spray (PS), and leaf spray (LS).
We have demonstrated the ability of PS-MS/MS and PS-IM-MS/MS to rapidly discriminate five Bacillus Spp. [7]. We optimized parameters such as the incubation time (4 h) and the spray solvent (isopropyl alcohol). Bacillus species have been differentiated with a prediction rate of 92.4 % and 97.6% using the negative and positive ion information of PS-MS/MS, respectively, as shown in Figure 2 (A). Including the ion mobility separations, i.e., PS-IM-MS/MS, prediction rates of 99.7% and 100% were obtained using the negative and positive ion information, respectively, as shown in Figure 2 (B). We attributed the improvement in prediction rates to the ability of IM separations to resolve isomers.

Figure 2. Principal Component Analysis of the 5 Bacillus species utilizing ion mobility, mass spectrometry, and tandem MS from PS-IM-MS and PS-MS/MS experiments in the (A) Negative and (B) Positive ion modes. Species are indicated by color as follows: B. altitudinis (blue), B. pumilus (red), B. subtilis (green), B. thuringiensis (black), B. velezensis (pink).

When we analyzed seven Escherichia coli to evaluate the capability of PS-IM-MS/MS in discriminating bacteria in strain-level, the prediction rate by PS-IM-MS/MS were 62.5% and 73.5% in the negative and positive ion modes, respectively. Therefore, we combined liquid chromatography with IM-MS/MS (LC-IM-MS/MS) to accurately discriminate the E. coli strains. Prediction rates of 96.1% and 100% were achieved with LC-IM-MS/MS in negative and positive ion modes, respectively, as shown in Figure 3, highlighting the high accuracy and selectivity of the LC-IM-MS/MS method.

Figure 3. Principal Component Analysis of the 7 E. coli strains in the (A) negative and (B) positive ion modes of the LC-IM-MS/MS method. The E. coli strains are indicated by color as follows: BL21 (Black), C41 (Red), CSH23 (Blue), DH10B (Green), DH5α (Magenta), K12 (Orange), and S17–1 λpir (Wine).

Quade, P.; Nsoesie, E. O., A platform for crowdsourced foodborne illness surveillance: description of users and reports. JMIR public health and surveillance 2017, 3 (3).
Ellis, D. I.; Muhamadali, H.; Chisanga, M.; Goodacre, R., Omics Methods For the Detection of Foodborne Pathogens. Encyclopedia of food chemistry, 2015, 1, 364-370.
Caliendo, A. M.; Gilbert, D. N.; Ginocchio, C. C.; Hanson, K. E.; May, L.; Quinn, T. C.; Tenover, F. C.; Alland, D.; Blaschke, A. J.; Bonomo, R. A., Better tests, better care: improved diagnostics for infectious diseases. Clinical Infectious Diseases 2013, 57 (suppl_3), S139-S170.
Quintela-Baluja, M.; Böhme, K.; Fernández-No, I. C.; Alnakip, M. E.; Caamaño, S.; Barros-Velázquez, J.; Calo-Mata, P., MALDI-TOF Mass Spectrometry, a Rapid and Reliable Method for the Identification of Bacterial Species in Food-Microbiology Laboratories. Novel Food Preservation and Microbial Assessment Techniques 2014, 353.Hamid, A. M.; Jarmusch, A. K.; Pirro, V.; Pincus, D. H.; Clay, B. G.; Gervasi, G.; Cooks, R. G., Rapid discrimination of bacteria by paper spray mass spectrometry. Analytical chemistry 2014, 86 (15), 7500-7507.
Olajide, O. E.; Donkor, B.; Hamid, A. M., Systematic Optimization of Ambient Ionization Ion Mobility Mass Spectrometry for Rapid Separation of Isomers. Journal of the American Society for Mass Spectrometry 2021.
Olajide, O. E.; Yi, Y.; Zheng, J.; Hamid, A. M. Species-level discrimination of microorganisms by high-resolution paper spray–Ion mobility–Mass spectrometry. International Journal of Mass Spectrometry 2022, 478, 116871.
Olajide, O. E.; Yi, Y.; Zheng, J.; Hamid, A. M. Strain-Level Discrimination of Bacteria by Liquid Chromatography and Paper Spray Ion Mobility Mass Spectrometry. Journal of the American Society for Mass Spectrometry 2023, 34 (6), 1125-1135.

Diagnosis of Alzheimer's Disease by Ion Mobility Mass Spectrometry

Alzheimer’s disease (AD) is a progressive neurodegenerative disease in which neurons in the brain die and tangles form in the brain. AD accounts for 60-80% of dementia cases and can be recognized by common symptoms such as memory loss, misplacing objects, withdrawal from social activities, etc. [1]. Unfortunately, there is still no cure that can prevent Alzheimer's disease. However, its treatments are symptomatic in that they improve symptoms and have no effect on the development of Alzheimer's disease once a certain neuropathological threshold is reached. Apart from this, it has become an economic burden in the U.S. For many years, biomarkers for Alzheimer's disease have been studied in clinical practice using imaging techniques (e.g., positron emission tomography (PET) and magnetic resonance imaging (MRI)) and cerebrospinal fluid (CSF) analysis [2]. However, these methods are expensive, and CSF collection is invasive. Therefore, there is a need to use other biofluids such as blood, plasma, serum, etc. for the detection of AD. Common protein biomarkers analyzed in Alzheimer's disease are amyloid beta and tau. Recently, small molecules such as lipids and metabolites have gained much attention as promising AD biomarkers. The study of these biomarkers by LC, MS and IM is the subject of much research [3, 4]. The simultaneous application of these three methods can provide us with structural information about these biomarkers in four dimensions, such as polarity, mass, fragmentation pattern, shape and size [5-8]. Therefore, LC-IM-MS/MS was used in this project to investigate the protein biomarkers (tau and amyloid beta 42 Aβ1-42) and lipid biomarkers (phospholipids and gangliosides). As shown in our preliminary studies, when we examined the IM spectrum, Aβ1-42 provided us with the information that multiple conformers were present (Figure 1). The same was observed for ganglioside GD 38:1 (Figure 2).

Figure 1. LC-IM-MS/MS of protein biomarkers in the positive ion mode. (A) LC spectra between tau-441 and Aβ1-42, (B) MS spectrum of Aβ1-42, (C) MS/MS of Aβ1-42, and (D) IM spectrum of Aβ1-42.

Figure 2. IM-MS/MS of ganglioside, GD (38:1) in the negative ion mode. (A) MS spectrum of GD (38:1), (B) MS/MS spectrum of GD (38:1), and (C) IM spectrum of GD (38:1).

2023 Alzheimer's disease facts and figures; Alzheimer’s & Dementia -The journal of Azlheimer's Association, 2023. https://pubmed.ncbi.nlm.nih.gov/36918389/#:~:text=This%20number%20could%20grow%20to,death%20in%20the%20United%20States.DOI: 10.1002/alz.13016.
Porsteinsson, A.; Isaacson, R.; Knox, S.; Sabbagh, M.; Rubino, I. Diagnosis of early Alzheimer’s disease: clinical practice in 2021. The journal of prevention of Alzheimer's disease 2021, 8, 371-386.
Reveglia, P.; Paolillo, C.; Ferretti, G.; De Carlo, A.; Angiolillo, A.; Nasso, R.; Caputo, M.; Matrone, C.; Di Costanzo, A.; Corso, G. Challenges in LC–MS-based metabolomics for Alzheimer’s disease early detection: Targeted approaches versus untargeted approaches. Metabolomics 2021, 17 (9), 78.
Wang, Y.-Y.; Sun, Y.-P.; Luo, Y.-M.; Peng, D.-H.; Li, X.; Yang, B.-Y.; Wang, Q.-H.; Kuang, H.-X. Biomarkers for the clinical diagnosis of Alzheimer’s disease: Metabolomics analysis of brain tissue and blood. Frontiers in pharmacology 2021, 12, 700587.
Olajide, O. E.; Donkor, B.; Hamid, A. M. Systematic optimization of ambient ionization ion mobility mass spectrometry for rapid separation of isomers. Journal of the American Society for Mass Spectrometry 2021, 33 (1), 160-171.
Olajide, O. E.; Yi, Y.; Zheng, J.; Hamid, A. M. Species-level discrimination of microorganisms by high-resolution paper spray–Ion mobility–Mass spectrometry. International Journal of Mass Spectrometry 2022, 478, 116871.
Olajide, O. E.; Yi, Y.; Zheng, J.; Hamid, A. M. Strain-Level Discrimination of Bacteria by Liquid Chromatography and Paper Spray Ion Mobility Mass Spectrometry. Journal of the American Society for Mass Spectrometry 2023, 34 (6), 1125-1135.
Kartowikromo, K. Y.; Olajide, O. E.; Hamid, A. M. Collision cross section measurement and prediction methods in omics. Journal of Mass Spectrometry 2023, 58 (9), e4973.

Direct Identification of Pesticides from fruits and vegetables

Contamination of food with pesticides can be a significant source of many serious diseases such as cancer, malformations, and damage to the endocrine, nervous and immune systems. Therefore, different communities have established maximum residue limits of pesticides, and subsequently sensitive and reliable analytical methods are required to determine the level of pesticide residues [1]. Currently, tandem mass spectrometry coupled to LC (LC-MS/MS) presents the standard method for the detection and quantitation of various pesticides’ residues [2]. It is noteworthy that the mass spectrometric and chromatographic analyses are typically performed after sample extraction and cleanup steps which are laborious and expensive steps [4-5]. In fact, the complexity in LC-MS analysis is caused by the need to transfer different analytes from matrices to vacuum as ions, while ambient ionization techniques connect the sample pretreatment and ionization into one step [3].

Utilizing Leaf Spray (LS), pesticides can be detected directly from the surface of leaves [4]. In addition, ion mobility can be used to separate isomers while computational studies can be used to identify the structures, as shown below.

1. Stachniuk, A.; Fornal, E., Liquid chromatography-mass spectrometry in the analysis of pesticide residues in food. Food Analytical Methods 2016, 9 (6), 1654-1665.

2. Sivaperumal, P.; Anand, P.; Riddhi, L., Rapid determination of pesticide residues in fruits and vegetables, using ultra-high-performance liquid chromatography/time-of-flight mass spectrometry. Food chemistry 2015, 168, 356-365.

3. Evard, H.; Kruve, A.; Lõhmus, R. n.; Leito, I., Paper spray ionization mass spectrometry: Study of a method for fast-screening analysis of pesticides in fruits and vegetables. Journal of Food Composition and Analysis 2015, 41, 221-225.

4. Olajide, O. E.; Donkor, B.; Hamid, A. M., Systematic Optimization of Ambient Ionization Ion Mobility Mass Spectrometry for Rapid Separation of Isomers. Journal of the American Society for Mass Spectrometry 2021.

Figure 1. (Left) Schematic of LS-IM-MS, (Center) LS–IM–MS analysis of terbuthylazine and propazine pesticide isomers. (Right) Optimized structures of (A-B) terbuthylazine and (C-D) propazine with their relative energies (kcal/mol) at B3LYP/6-31G(d,p) level of theory and their corresponding collision cross section values.