Cristian
Piras
ae,
Oliver J.
Hale‡
a,
Christopher K.
Reynolds
b,
A. K. (Barney)
Jones
b,
Nick
Taylor
c,
Michael
Morris
d and
Rainer
Cramer
*a
aDepartment of Chemistry, University of Reading, Whiteknights, Reading RG6 6DX, UK. E-mail: r.k.cramer@reading.ac.uk; Tel: +44 (0)118 378 4550
bSchool of Agriculture, Policy and Development, University of Reading, Whiteknights, Reading RG6 6EU, UK
cVeterinary Epidemiology and Economics Research Unit (VEERU), PAN Livestock Services Ltd, School of Agriculture, Policy and Development, University of Reading, Whiteknights, Reading RG6 6EU, UK
dWaters Corporation, Stamford Avenue, Wilmslow, SK9 4AX, UK
eDepartment of Health Sciences, "Magna Græcia University" of Catanzaro, Campus Universitario "Salvatore Venuta" Viale Europa, 88100, Catanzaro, Italy
First published on 18th January 2022
Large-scale population screening for early and accurate detection of disease is a key objective for future diagnostics. Ideally, diagnostic tests that achieve this goal are also cost-effective, fast and easily adaptable to new diseases with the potential of multiplexing. Mass spectrometry (MS), particularly MALDI MS profiling, has been explored for many years in disease diagnostics, most successfully in clinical microbiology but less in early detection of diseases. Here, we present liquid atmospheric pressure (LAP)-MALDI MS profiling as a rapid, large-scale and cost-effective platform for disease analysis. Using this new platform, two different types of tests exemplify its potential in early disease diagnosis and response to therapy. First, it is shown that LAP-MALDI MS profiling detects bovine mastitis two days before its clinical manifestation with a sensitivity of up to 70% and a specificity of up to 100%. This highly accurate, pre-symptomatic detection is demonstrated by using a large set of milk samples collected weekly over six months from approximately 500 dairy cows. Second, the potential of LAP-MALDI MS in antimicrobial resistance (AMR) detection is shown by employing the same mass spectrometric setup and similarly simple sample preparation as for the early detection of mastitis.
Mass spectrometry (MS) has repeatedly been championed as a diagnostic method for highly specific and sensitive measurement of disease-specific biomarkers.7,8 Its exquisite mass measurement accuracy and resolution can provide the biomarker specificity and detection space for (multiplexed) disease detection at a level conventional antigen tests cannot compete.9–11 Particularly in combination with additional analysis modes such as MS/MS or the use of ion mobility, MS approaches can be extremely powerful with respect to detecting specific disease biomarkers unambiguously (e.g. see Singh et al.12).
However, most of the published MS methods are based on rather laborious and complex upfront sample separation and preparation methods as found in LC-ESI MS/MS with the additional disadvantage of demanding a minimum of several minutes of the mass analyser's time per sample. Upfront sample preparation for LC-ESI MS/MS typically involves enzymatic digestion, often leading to the loss of information related to the exact proteoform.13 Alternatively, MALDI MS profiling using axial linear TOF instrumentation, as established in microbiological testing, can allow for simpler upfront sample preparation and short MS analysis time but cannot provide the same high detection performance as modern atmospheric pressure (AP) ESI instruments with MS/MS capability. In this context, a major hindrance is the formation of predominately singly charged ions in (solid) MALDI while ESI generates abundant peptide/protein ions with a higher number of charges, resulting in superior fragmentation14 and (lower) m/z values that allow the employment of high-performing instruments with Q-TOF and orbitrap mass analysers.
Ideally, the speed and simplicity of MALDI is combined with the advantages of ESI, enabling the use of high-performing MS instrumentation. With this ideal in mind we developed a mass spectrometric platform that is based on the exploitation of liquid AP (LAP)-MALDI, which facilitates the generation of multiply charged ions and therefore the effective use of MALDI for MS(/MS) analysis on these types of instruments.15 In addition, the liquid nature of the MALDI sample provides an extremely stable ion yield, consuming only minute amounts of sample material (picoliters) while maintaining the speed of MALDI.15,16 This methodology has been further developed to include a one-pot/two-step sample preparation applicable to crude liquid biopsies, leading to an analyte solution that can be directly used for MALDI sample preparation.17
Here, we show the application of the above methodology for the fast and sensitive detection of pre-clinical mastitis in dairy cows using only small amounts of milk from the daily milking routine. In general, many liquid biopsies such as milk samples can be collected non-invasively and are known to harbour biomarkers that originate from pathogens and/or the host's response to infectious disease. Due to the low sample volume (μL) consumed virtually all types of mammalian milk, including human and rodent milk, are equally well suited for the analysis by this methodology.
The presented example focuses on dairy milk with its substantial impact on health and the economy. Assuming an incidence of ∼36 clinical mastitis cases per 100 cow-years and a cost of >US$200 per case and year for US dairy herds,18 an estimated economic loss of US$77 per cow per year was recently reported.19 With >100 million dairy cows world-wide (>20 million in the EU; >9 million in the USA)20 the economic impact amounts to billions of US dollars each year.
More importantly, antibiotic treatment of farm animals, in particular dairy cows, was also identified as a major human health risk.21 It has been suggested that antibiotic residues in milk can lead to allergic reactions, and zoonotic transmission of AMR strains of bacteria is a real threat and can add to AMR in humans.21
Thus, we also employed LAP-MALDI MS for AMR testing based on monitoring the appearance of products of antimicrobial β-lactam-containing compounds (ampicillin) as a result of bacterial lactamase activity, an approach previously applied in AMR detection by MALDI MS,22 using the same milk samples as for the detection of mastitis. This multi-test capacity, achieving both pre-clinical detection of mastitis and AMR testing, in combination with the method's speed and simplicity in sample preparation addresses important aspects of large-scale population screening for early and effective infectious disease intervention.
Longitudinal milk sample collection was undertaken in 2018 over a period of 6 months (24 weeks). Around 500 individual cows were sampled weekly at the Centre for Dairy Research (CEDAR) of the University of Reading (UoR). Sampling was performed following the standard operating procedure (SOP) provided in ESI SOP 1.†
Briefly, each sample was directly taken from the sampling bottle of the 50-point rotary milking parlour (Dairymaster 50; Dairymaster (UK) Ltd, Bromsgrove, UK). Composite milk samples were obtained as a fixed proportion of the total milk collected from all four quarters of the udder during routine milking. After shaking the sampling bottle, 2 mL of the composite milk was transferred to a labelled cryovial and placed in a polystyrene box containing dry ice within 5 min of milking. After the collection of all samples for each session, the samples were placed in cryoboxes (100-Well Microtube Storage Boxes, Fisherbrand™, product code 15579811; Fisher Scientific) for long-term storage at −80 °C.
Through this continuous weekly collection procedure a dairy milk biobank was assembled that consists of approximately 12000 samples. In addition, meta-data such as milk protein content and SCC were obtained monthly from each cow through the farm's routine milk analysis.
In general, aliquots in each plate were subjected to a precipitation step carried out with 100 μL of 5% (w/v) trichloroacetic acid (TCA), which was mixed with the aliquot by pipetting (dispensing/aspirating) the mixture 10 times using a 96-head CyBi™-Disk liquid handling robot (CyBio AG, Jena, Germany). Sample plates were then centrifuged for 5 min at 3000g. Supernatants were discarded and sample pellets were re-suspended in 80 μL of water/acetonitrile/isopropanol (1/1/1; v/v/v) by pipetting the mixtures 30 times, again using a CyBi™-Disk robot. After a sonication step of 60 s, sample plates were stored at −20 °C prior to LAP-MALDI sample preparation and MS analysis.
For analyte extraction, LAP-MALDI sample preparation and MS analysis of the longitudinally collected samples, the procedures were based on an SOP that can be found in ESI SOP 2.†
Aliquots of 5 μL of each sample extract were transferred from their storage plate to a new 96-well microtiter plate (product code 650101; GREINER BIO-ONE Ltd, Stonehouse, UK), keeping their plate positions. An equal volume of 5 μL of the LSM was added to each sample aliquot and mixed 10 times by pipetting. Droplets of 1.2 μL of the obtained sample mixtures were spotted on a 96-well Waters MALDI sample plate (Waters Corporation, Wilmslow, UK), keeping the original plate positions.
The instrument was employed in sensitivity, ion mobility-TOF mode. Ion mobility wave velocity was set at 650 m s−1 with a 40 V height and a nitrogen flow of 90 mL min−1. The complete set of ion mobility separation and MS parameters can be found in the “_extern.inf” files recorded by the instrument's software in each raw data folder. These files are available in the University of Reading's Research Data Archive entry that is associated with this project (https://researchdata.reading.ac.uk/id/eprint/279). The MS scan rate was set to 1 Hz for combined lipids and proteins analysis. Positive ions were recorded over an m/z range of 100–2000. External TOF calibration was performed manually by AP-LDI MS as previously described,23 using sodium iodide, an acquisition time of 3 min with an m/z range of 100–2000 and Intellistart software (MassLynx™; Waters). All samples were analysed with an acquisition time of 1 min. For large-scale whole-plate analyses, samples were analysed column-wise, thus crossing several times during a whole-plate MS profiling run the various sample groups that were spotted row-wise.
For the initial LAP-MALDI MS analysis (m/z 300–400) of milk compounds, ampicillin spiked into milk and lactamase-induced ampicillin products, a volume of 20 μL of pooled milk was used from 100 control milk samples of the pre-clinical mastitis sample collection that were defrosted for 2 h at room temperature. Some aliquots of the pooled milk were prepared as described above while others were prepared as described above without incubation with penicillinase or as described above without both ampicillin and incubation with penicillinase.
A training set of LAP-MALDI mass spectral profiles was used for every machine learning model generated. A separate set of LAP-MALDI mass spectral profiles was then used to test these models. Both data sets were obtained by the same mass spectral acquisition (see LAP-MALDI MS analysis), using the same sample preparation (see LAP-MALDI sample preparation). After all LAP-MALDI mass spectral profiles were acquired, an appropriately blinded classification analysis was achieved by applying fully randomised or randomised representative sampling for the training and test sets.
In Fig. 1c, a mass spectrum of dairy milk is shown that was acquired on a modern Q-TOF mass analyser in less than 1 min by LAP-MALDI MS, employing an equally fast and simple sample preparation protocol (Fig. 1d), which only requires two steps and can be undertaken in the same vial prior to MALDI sample spotting. The m/z region of 250–450 is mostly populated by sugars, metabolites and other small endogenous and exogenous compounds such as antibiotics and their products (see below). Lipids can be typically found in the m/z region of 450–850. These two areas of the spectrum are mainly dominated by singly charged [M + H]+, [M + Na]+ or [M + K]+ ion species. The region above m/z 800 is mostly populated by multiply charged peptides and proteins, which is one of the unique features of LAP-MALDI. The LAP-MALDI source also offers rapid movement between samples. If the laser continuously fires, the total ion current (TIC) is elevated when the laser beam hits the samples but is reduced to baseline-level intensities when the laser beam hits the blank sample plate between samples. Fig. 1e displays the TIC obtained by rapidly moving between samples and acquiring data from a central spot of each liquid MALDI sample droplet for approximately 1 min with a continuous laser pulse repetition rate of 20 Hz. The drop in TIC between samples (see Fig. 1e) conveniently separates the acquired data of each sample.
The set-up used for this study allowed data acquisition with a laser pulse repetition rate of 20 Hz and the acquisition of informative MS profiles within 1 min, switching between samples in just 2 s and without any detectable carry-over. Fig. 2c displays the richness of the proteoforms obtained in the LAP-MALDI MS profiles in the m/z range of 900–2000. Deconvolution of the spectrum in Fig. 2c reveals abundant milk proteoforms, including caseins, lactalbumins, and lactoglobulins (Fig. 2d). Larger proteins are detected with higher charge states, essentially to the same extent as in ESI† (cf.Fig. 2e). For β-casein (∼24 kDa), ions are detected with 15–25 charges. Ion signal trains of these proteins can be easily observed in charge-vs.-m/z plots (Fig. 2e).
For detecting clinical mastitis, a total of 60 clinical mastitis samples and 329 control samples were prepared for LAP-MALDI MS analysis (see Methods section for details). The control samples included 223 low-SCC (lSCC; defined as <200000 somatic cells per mL of milk) samples and 106 high-SCC (hSCC; defined as ≥200000 somatic cells per mL of milk) samples. Both lSCC and hSCC samples were used for the control group. Using a training set for multivariate machine learning, a prediction model was obtained and applied to the LAP-MALDI MS data from a test set. Details about the make-up of the control group as well as the training and test sets can be found in ESI data 1.† The analysis of the test set allowed the detection of clinical mastitis with a classification accuracy of 98.9% (Fig. 3a).
For the investigation of pre-clinical mastitis, milk sample aliquots (20 μL) were taken from a biobank of around 12000 milk samples that were collected weekly on the same day for a period of 24 weeks (July 2018–December 2018). The collection date was compared to the date of clinical mastitis detection. From this longitudinal sample collection a total of 500 control samples and 90 mastitis samples that were collected between 0 and 7 days before the clinical event were analysed. As before, both lSCC and hSCC samples were used for the control group, and various training sets based on the days before mastitis was clinically diagnosed were subjected to multivariate machine learning to build prediction models for early (pre-clinical) detection of mastitis. Details about the make-up of the control group as well as the training and test sets used for the data in Fig. 3 can be found in ESI data 1.†
The prediction models that included mastitis samples collected more than 3 days before clinical diagnosis had a sensitivity of less than 50%. There was a substantial increase in sensitivity for models that were built using mastitis samples that were collected within 2–3 days before mastitis was clinically detected. For instance, the model that was built using the mastitis cases collected 0 and 1 day before the clinical event showed a specificity of 100% and sensitivity of 56.3% for the prediction of pre-clinical cases from analysis of samples obtained 2 days before the clinical event (Fig. 3b). The associated Volcano plot of its classification data is provided in ESI Fig. S1.† Other analyses using training and test sets that were mixtures of mastitis samples collected at various time points up to 2 days before the clinical event showed an even higher sensitivity of up to 70%.
In both LDA models of Fig. 3, the most important feature for mastitis detection is the isracidin-containing peptide ion, an α-s1-casein fragment, whose [M + 6H]6+ monoisotopic ion signal was detected at m/z 723.9 with a measurement accuracy of <5 ppm for most profiles (see ESI Fig. S2†). The majority of its isotopologues' ion signal can be found in the AMX mass bin at m/z 724.5 (see Methods section for data processing details). The box plots for the normalised signal intensity of its mass bin for the various sample sets show that the lSCC control sample set is well separated from the mastitis samples for the detection of both clinical and pre-clinical mastitis with respect to the third quartile of the lSCC control samples and first quartile of the mastitis samples (see Fig. 3c and d). Furthermore, the normalised mass bin signal intensity was significantly different between the clinical/pre-clinical mastitis sample group and the two sub-groups (lSCC, hSCC) of the control sample group (see Fig. 3c and d).
The m/z bins of 543.5, 620.5, 724.5, and 868.5/869.5, which can be assigned to the isracidin-containing peptide ions with a charge state of 5–8, have all been found with significantly differential signal intensities between pre-clinical mastitis and control samples with a change of >20% (>100% for 724.5; see ESI Fig. S1†). Other m/z bins that show significantly different signal intensities between pre-clinical mastitis and control samples are the m/z bins 728.5 and 784.5–788.5, which can be putatively assigned to another α-s1-casein fragment with the amino acid residues R1-F24 and a C-terminal fragment of β-casein with the amino acid residues T126–V209. Furthermore, the m/z bins 753.5/754.5 and 811.5–814.5 also fall into the same category and cover the m/z values for the 14+ and 13+ ions of a previously identified clinical mastitis marker.17
Since low m/z values for all analytes enabled the use of a high-performing orthogonal (hybrid) mass analyser, important advantages such as the simultaneous detection of the entire biomarker panel (metabolites, lipids and peptides/proteins) were achieved. This uniquely widened the range of simultaneously accessible biomarker signals for MALDI MS profiling analysis. Conventional solid MALDI with its predominately singly charged ions typically fails in detecting proteins and large peptides on these instruments since the m/z values are too high.26
These new functionalities gained through LAP-MALDI were explored on a commercial Q-TOF mass spectrometer, whose standard ESI ion source was simply modified by adding a heated transfer tube and MALDI stage.25,27,28 The milk spectra which were obtained from a fast and simple one-pot/two-step sample preparation (see Fig. 1) are extremely rich in biomarkers across a wide mass range, from metabolites and lipids to peptides and proteins, with excellent signal-to-noise ratios and high mass accuracy and resolution, as typically recorded for ESI-Q-TOF MS. The simple and fast (off-line) MALDI sample preparation as well as swift sample introduction are however unrivalled by ESI while conventional solid MALDI MS as found in clinical biotyping29 would not be able to detect simultaneously the full profile (set of biomarkers) due to the limitations of axial TOF mass analysers. Signal stability, which is important for profile comparison, and the added speed advantage by not loading the sample plate into a vacuum chamber are additional advantages of LAP-MALDI.
Initial tests of LAP-MALDI for the detection of clinical mastitis revealed an extremely high test accuracy of 98.9% with only two false test results. Importantly, the false positive was an hSCC sample, which is not too surprising, considering that hSCC is frequently used as a marker for mastitis.30–32 The fact that the percentage of hSCC samples in the test set was higher than what is typically found in the entire herd might have further increased the chance to obtain a false positive.
Interestingly, results from both the clinical and pre-clinical mastitis detection analyses reveal that the ion signal of a single, isracidin-containing peptide is significantly different between the lSCC, hSCC and mastitis samples. Single molecular markers can be useful for the design of non-MS tests such as antigen-based lateral flow tests for point-of-care testing. The <5 ppm mass measurement accuracy achieved in this study is unique for MS analysis of large peptides from a crude biofluid using only a simple and quick analyte extraction. The high mass resolving power of >10000 throughout the entire m/z range is rarely reported for MALDI MS profiling of biofluids and substantially superior compared to analyses on axial MALDI-TOF instruments as currently employed in clinical laboratories, even when optimised for a specific m/z range.33 In combination with MS/MS, this analytical performance supports effective multiple reaction monitoring (MRM) of both small molecules and (large) peptides/proteins, and thus introduces entirely new diagnostic opportunities for MALDI-based platforms.
The isracidin-containing peptide was also found as highly discriminative in an earlier study on MS-based mastitis detection.17 Isracidin has been frequently reported as an antimicrobial peptide (AMP)34 but it is only more recently that the isracidin-containing peptide as found in this study has also been identified as AMP,35 displaying similar antimicrobial activity as isracidin. It should be noted that reported antimicrobial activity is typically determined in vitro against specific pathogens (bacterial strains) and can therefore not be compared to direct host response data as obtained here. However, as can be seen from the data in Fig. 3c and d, the ion signal of this peptide on its own is an insufficient biomarker for high-accuracy mastitis testing. Our data show that the entire LAP-MALDI MS profile is far more powerful than individual biomarker signals, which is further supported by the Volcano plots obtained from the MS profiles.
It is worth noting that the possibility to perform top-down MS analysis of proteins,36 directly from the samples used for disease detection by MS profiling, allows greater characterisation of biomarkers compared to bottom-up approaches using enzymatic digestion as used in LC-ESI MS/MS. In the study presented here, the superior top-down analysis afforded by LAP-MALDI is exemplified by the identification of a casein peptide as one of the most valuable diagnostic markers, containing isracidin and an additional 14 amino acids. The peptide's low prominence in the literature is arguably also due to the limitations of bottom-up proteomics, which generally impedes the identification and quantification of specific proteoforms.13 This kind of knowledge gain could warrant new peptide validations with respect to enhanced antimicrobial activity.
To test LAP-MALDI MS profiling for early disease detection, a longitudinal bovine mastitis study was initiated using simple machine learning models. The data from this study resulted in an overall sensitivity of up to 70% for the detection of mastitis 2 days before its clinical manifestation. In all tested models, sensitivity was greater than 50% and specificity was up to 100%. From model building, it was evident that the sensitivity of detecting mastitis 3 or more days in advance was well below 50%. Please note that this rapid decline in sensitivity was expected as numerous in vivo challenge studies have shown that inoculation of even small amounts of bacteria leads to clinical symptoms within 1–2 days.30,31,37 Cytokine and other host protein levels showed a similar, in some cases even earlier, temporal response.30,32,38 Thus, it is virtually impossible to predict individual bovine mastitis more than 2–3 days before symptoms occur unless the infection event itself can be predicted.
Given the above test performance, daily testing using LAP-MALDI MS profiling should enable the detection of at least half of the pre-symptomatic cases. The test's high specificity would only produce a few, if any, false positives and can therefore support effective mastitis control by early pharmacological (antibiotic, steroidal and non-steroidal drugs) and/or non-pharmacological (such as isolation and separate milking) intervention, dramatically reducing transmission of infection and number of cases in the herd.
Importantly, the test is also simple, fast and cost-effective, using only a microcentrifuge tube (or microtiter plate at scale-up), a couple of tips (∼US$0.02 per sample) and minute amounts of inexpensive solvents/reagents (≤US$0.01 per sample). At large scale, it is therefore significantly less costly than current biomolecular methods for bovine mastitis detection. Moreover, data acquisition time can be significantly shortened by at least a factor of 10, using a laser with a faster pulse repetition rate without losing any ion signal intensity,39 and a throughput of 5–10 million samples per year per platform is feasible. The capital cost of an adequate Q-TOF mass spectrometer and sample preparation robotics (≤US$300000) and the associated running costs (including staff) would add another ≤US$0.02 per sample based on 5 years of depreciation, leading to an estimated overall cost per sample of ≤US$0.067 (for milking parlours that can automatically collect small amounts of milk samples). ESI Table S2† provides a detailed costing schedule. Interestingly, the highest contributions to the cost per sample originate from the plastic ware (tips, microtiter plates) while the instrument capital and running expenditures as well as the staff and transportation expenditures together only add around a quarter to the overall cost per sample. In summary, daily testing would cost approximately US$24 per cow per year, i.e. less than a third of the estimated cost of mastitis of US$77 per cow per year.19 Most of the costing is intrinsically the same for the analysis of milk from other mammals, including human milk analysis.
Other important outcomes of early detection and intervention of microbial infections are a reduction of antibiotic usage and improved health and welfare. For bovine mastitis, these additional benefits are well aligned with the one health concept.40 As the use of antibiotics in farm animals has been linked to an increase of AMR bacterial strains in humans,41 any reduction of antibiotics on farms and a generally healthier livestock would also benefit human health, helping in the fight of more pandemics and the global threat of AMR.
With respect to AMR the data from this study also show that LAP-MALDI MS profiling can effectively detect lactamase activity from a simple mass spectral read-out, using crude milk spiked with a conventional beta-lactam antibiotic. Using the same MS set-up as for mastitis detection, lactamase-based AMR can be detected after a short incubation period (2 hours or less) and a sample preparation that is even simpler and faster than that for mastitis detection, acquiring the mass spectral data within seconds. This workflow substantially cuts the time to detect AMR compared to classical bacteriological AMR testing, which typically involves the growth of individual bacterial colonies, potentially missing AMR strains.
In general, incubating crude liquid biopsies such as raw milk with the antibiotic of interest will provide direct and universal detection of AMR and is easily extended to other antibiotic substrates either within the same vial or if needed in different aliquots, without a substantial increase in time or cost.
Applications of this additional mode of analysis are countless in both animal and human diagnostics and can be employed beyond bacterial infections in milk. As the screening is extremely rapid and can be multiplexed with a variety of antibiotics it will certainly save time and cost compared to individual, non-exhaustive bacteriological tests as currently applied to milk samples. Importantly, due to the variety of antibiotics that can be tested at the same time, it is far more likely to identify in a much shorter time frame the antibiotics that are still effective, which can then be administered at an earlier disease stage, thus accelerating the efficient treatment and ultimately healing process as well as reducing the risk of further AMR proliferation.
It is noteworthy that simple LAP-MALDI MS profiling was sufficient for this assay despite the high biomatrix background that crude biofluids such as milk provide. Thus, other liquid biopsies such as blood will also benefit from the robustness of LAP-MALDI MS profiling. Moreover, LAP-MALDI used on high-performing MS/MS analysers also provides the opportunity to employ MRM-like assays, providing additional analytical sensitivity and specificity, if needed. However, the most important proteinaceous classifier markers obtained in this study are mostly abundant (host response) proteins or protein fragments, although the diversity in analyte types and the number of individual analyte ions detected appear to be greater compared to conventional solid MALDI MS profiling. The latter is mainly due to the relatively low background ion signals of MALDI matrix, biomatrix and other non-specific (contaminant) compounds in LAP-MALDI MS as employed in this study.
Finally, it is reiterated that future tests based on LAP-MALDI MS profiling are envisaged to be applied to other biofluids, exploiting the MS/MS capabilities provided by multiply charged ions and the use of high-performing tandem mass spectrometers, e.g. for pathogen identification by MS/MS sequencing and superior disease detection by MRM of pathogen and/or host peptides. Combined with the high speed of analysis, low cost per sample and multiplexing capability as well as the potential to adapt the predictive read-out extremely fast to new infectious diseases, these platforms are an appealing proposition for highly specific, large-scale population screening not only of farm animals but other species, including the entire human population.
AMP | Antimicrobial peptide |
AMR | Antimicrobial resistance |
AP | Atmospheric pressure |
ESI | Electrospray ionisation |
HPLC | High-performance liquid chromatography |
LAP | Liquid AP |
LC | Liquid chromatography |
LDA | Linear discriminant analysis |
LOD | Limit of detection |
MALDI | Matrix-assisted laser desorption/ionisation |
MRM | Multiple reaction monitoring |
MS | Mass spectrometry |
MS/MS | Tandem MS |
PCR | Polymerase chain reaction |
TCA | Trichloroacetic acid |
TIC | Total ion current |
WREnS | Waters research enabled software |
Footnotes |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/d1sc05171g |
‡ Present address: School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. |
This journal is © The Royal Society of Chemistry 2022 |