Rapid, Sensitive and Reliable Ricin Identification in Serum Samples Using LC–MS/MS

Ricin, a protein derived from the seeds of the castor bean plant (Ricinus communis), is a highly lethal toxin that inhibits protein synthesis, resulting in cell death. The widespread availability of ricin, its ease of extraction and its extreme toxicity make it an ideal agent for bioterrorism and self-poisoning. Thus, a rapid, sensitive and reliable method for ricin identification in clinical samples is required for applying appropriate and timely medical intervention. However, this goal is challenging due to the low predicted toxin concentrations in bio-fluids, accompanied by significantly high matrix interferences. Here we report the applicability of a sensitive, selective, rapid, simple and antibody-independent assay for the identification of ricin in body fluids using mass spectrometry (MS). The assay involves lectin affinity capturing of ricin by easy-to-use commercial lactose–agarose (LA) beads, following by tryptic digestion and selected marker identification using targeted LC–MS/MS (Multiple Reaction Monitoring) analysis. This enables ricin identification down to 5 ng/mL in serum samples in 2.5 h. To validate the assay, twenty-four diverse naive- or ricin-spiked serum samples were evaluated, and both precision and accuracy were determined. A real-life test of the assay was successfully executed in a challenging clinical scenario, where the toxin was identified in an abdominal fluid sample taken 72 h post self-injection of castor beans extraction in an eventual suicide case. This demonstrates both the high sensitivity of this assay and the extended identification time window, compared to similar events that were previously documented. This method developed for ricin identification in clinical samples has the potential to be applied to the identification of other lectin toxins

Rapid, Sensitive and Reliable Ricin Identification in Serum Samples Using LC–MS/MS

Ricin, a protein derived from the seeds of the castor bean plant (Ricinus communis), is a highly lethal toxin that inhibits protein synthesis, resulting in cell death. The widespread availability of ricin, its ease of extraction and its extreme toxicity make it an ideal agent for bioterrorism and self-poisoning. Thus, a rapid, sensitive and reliable method for ricin identification in clinical samples is required for applying appropriate and timely medical intervention. However, this goal is challenging due to the low predicted toxin concentrations in bio-fluids, accompanied by significantly high matrix interferences. Here we report the applicability of a sensitive, selective, rapid, simple and antibody-independent assay for the identification of ricin in body fluids using mass spectrometry (MS). The assay involves lectin affinity capturing of ricin by easy-to-use commercial lactose–agarose (LA) beads, following by tryptic digestion and selected marker identification using targeted LC–MS/MS (Multiple Reaction Monitoring) analysis. This enables ricin identification down to 5 ng/mL in serum samples in 2.5 h. To validate the assay, twenty-four diverse naive- or ricin-spiked serum samples were evaluated, and both precision and accuracy were determined. A real-life test of the assay was successfully executed in a challenging clinical scenario, where the toxin was identified in an abdominal fluid sample taken 72 h post self-injection of castor beans extraction in an eventual suicide case. This demonstrates both the high sensitivity of this assay and the extended identification time window, compared to similar events that were previously documented. This method developed for ricin identification in clinical samples has the potential to be applied to the identification of other lectin toxins

Discriminative Identification of SARS-CoV-2 Variants Based on Mass-Spectrometry Analysis

The spread of SARS-CoV-2 variants of concern (VOCs) is of great importance since genetic changes may increase transmissibility, disease severity and reduce vaccine effectiveness. Moreover, these changes may lead to failure of diagnostic measures. Therefore, variant-specific diagnostic methods are essential. To date, genetic sequencing is the gold-standard method to discriminate between variants. However, it is time-consuming (taking several days) and expensive. Therefore, the development of rapid diagnostic methods for SARS-CoV-2 in accordance with its genetic modification is of great importance. In this study we introduce a Mass Spectrometry (MS)-based methodology for the diagnosis of SARS-CoV-2 in propagated in cell-culture. This methodology enables the universal identification of SARS-CoV-2, as well as variant-specific discrimination. The universal identification of SARS-CoV-2 is based on conserved markers shared by all variants, while the identification of specific variants relies on variant-specific markers. Determining a specific set of peptides for a given variant consists of a multistep procedure, starting with an in-silico search for variant-specific tryptic peptides, followed by a tryptic digest of a cell-cultured SARS-CoV-2 variant, and identification of these markers by HR-LC-MS/MS analysis. As a proof of concept, this approach was demonstrated for four representative VOCs compared to the wild-type Wuhan reference strain. For each variant, at least two unique markers, derived mainly from the spike (S) and nucleocapsid (N) viral proteins, were identified. This methodology is specific, rapid, easy to perform and inexpensive. Therefore, it can be applied as a diagnostic tool for pathogenic variants

PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging

In-source fragmentation (ISF) is a naturally occurring phenomenon in various ion sources including soft ionization techniques such as matrix-assisted laser desorption/ionization (MALDI). It has traditionally been minimized as it makes the dataset more complex and often leads to mis-annotation of metabolites. Here, we introduce an approach termed PICA (for pixel intensity correlation analysis) that takes advantage of ISF in MALDI imaging to increase confidence in metabolite identification. In PICA, the extraction and association of in-source fragments to their precursor ion results in “pseudo-MS/MS spectra” that can be used for identification. We examined PICA using three different datasets, two of which were published previously and included validated metabolites annotation. We show that highly colocalized ions possessing Pearson correlation coefficient (PCC) ≥ 0.9 for a given precursor ion are mainly its in-source fragments, natural isotopes, adduct ions, or multimers. These ions provide rich information for their precursor ion identification. In addition, our results show that moderately colocalized ions (PCC < 0.9) may be structurally related to the precursor ion, which allows for the identification of unknown metabolites through known ones. Finally, we propose three strategies to reduce the total computation time for PICA in MALDI imaging. To conclude, PICA provides an efficient approach to extract and group ions stemming from the same metabolites in MALDI imaging and thus allows for high-confidence metabolite identification

PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging

In-source fragmentation (ISF) is a naturally occurring phenomenon in various ion sources including soft ionization techniques such as matrix-assisted laser desorption/ionization (MALDI). It has traditionally been minimized as it makes the dataset more complex and often leads to mis-annotation of metabolites. Here, we introduce an approach termed PICA (for pixel intensity correlation analysis) that takes advantage of ISF in MALDI imaging to increase confidence in metabolite identification. In PICA, the extraction and association of in-source fragments to their precursor ion results in “pseudo-MS/MS spectra” that can be used for identification. We examined PICA using three different datasets, two of which were published previously and included validated metabolites annotation. We show that highly colocalized ions possessing Pearson correlation coefficient (PCC) ≥ 0.9 for a given precursor ion are mainly its in-source fragments, natural isotopes, adduct ions, or multimers. These ions provide rich information for their precursor ion identification. In addition, our results show that moderately colocalized ions (PCC < 0.9) may be structurally related to the precursor ion, which allows for the identification of unknown metabolites through known ones. Finally, we propose three strategies to reduce the total computation time for PICA in MALDI imaging. To conclude, PICA provides an efficient approach to extract and group ions stemming from the same metabolites in MALDI imaging and thus allows for high-confidence metabolite identification

Specific and Rapid SARS-CoV-2 Identification Based on LC-MS/MS Analysis

This study describes the development of a novel assay for SARS-CoV-2 identification using LC-MS/MS analysis. A multi-step procedure for the rational down-selection of a set of markers has leaded to the discovery of six SARS-CoV-2 specific and sensitive markers, enabling the reliable identification of the virus. A rapid and simple assay was developed, successfully applied to clinical nasopharyngeal samples. The assay may potentially serve as a complementary approach for SARS-CoV-2 identification