Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro

For bottom-up based proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third approach compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth approach increases confidence of peptide identifications by a simple strategy to simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. These projects have been developed in Python using Numpy and Pandas and open sourced with a main aim being to broaden appeal to the data science community and lower the barrier to experimentation and algorithm improvements.

BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems

Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (<50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems.

Enhanced Electrospray In-source Fragmentation for Higher Sensitivity Data Independent Acquisition and Autonomous METLIN Molecular Identification

Electrospray ionization (ESI) in-source fragmentation (ISF) has traditionally been minimized to promote precursor molecular ion formation, and therefore its value in molecular identification underappreciated. Recently a METLIN-guided in-source annotation (MISA) algorithm was introduced to increase confidence in putative identifications by using ubiquitous in-source fragments. However, MISA is limited by ESI sources that are generally designed to minimize ISF. In this study, enhanced ISF with MISA (eMISA) was created by tuning the ISF conditions to generate in-source fragmentation patterns comparable with higher energy fragments generated at higher collision energies as deposited in the METLIN MS/MS library, without compromising the intensity of precursor ions (median loss ≤ 10% in both positive and negative ionization modes). The analysis of 50 molecules was used to validate the approach in comparison to MS/MS spectra produced via data dependent acquisition (DDA) and data independent acquisition mode (DIA) with quadrupole time-of-flight mass spectrometry (QTOF-MS). Enhanced ISF as compared to QTOF DDA, enables for higher peak intensities for the precursor ions (median: 18 times at negative mode and 210 times at positive mode), with the eMISA fragmentation patterns consistent with METLIN for over 90% of the molecules with respect to fragment relative intensity and <i>m/z</i>. eMISA also provides higher peak intensity as opposed to QTOF DIA with a median increase of 20% at negative mode and 80% at positive mode for all precursor ions. Metabolite identification with eMISA was also successfully validated from the analysis of a metabolic extract from macrophages. An interesting side benefit of enhanced ISF is that it significantly improved the compound identification confidence with low resolution single quadrupole mass spectrometry-based untargeted LC/MS experiments. Overall, enhanced ISF allowed for eMISA to be used as a more sensitive alternative to other QTOF DIA and DDA approaches, and further, it enables the acquisition of ESI TOF and ESI single quadrupole mass spectrometry instrumentation spectra with higher sensitivity and improved molecular identification confidence.

Enhanced Electrospray In-source Fragmentation for Higher Sensitivity Data Independent Acquisition and Autonomous METLIN Molecular Identification

Electrospray ionization (ESI) in-source fragmentation (ISF) has traditionally been minimized to promote precursor molecular ion formation, and therefore its value in molecular identification underappreciated. Recently a METLIN-guided in-source annotation (MISA) algorithm was introduced to increase confidence in putative identifications by using ubiquitous in-source fragments. However, MISA is limited by ESI sources that are generally designed to minimize ISF. In this study, enhanced ISF with MISA (eMISA) was created by tuning the ISF conditions to generate in-source fragmentation patterns comparable with higher energy fragments generated at higher collision energies as deposited in the METLIN MS/MS library, without compromising the intensity of precursor ions (median loss ≤ 10% in both positive and negative ionization modes). The analysis of 50 molecules was used to validate the approach in comparison to MS/MS spectra produced via data dependent acquisition (DDA) and data independent acquisition mode (DIA) with quadrupole time-of-flight mass spectrometry (QTOF-MS). Enhanced ISF as compared to QTOF DDA, enables for higher peak intensities for the precursor ions (median: 18 times at negative mode and 210 times at positive mode), with the eMISA fragmentation patterns consistent with METLIN for over 90% of the molecules with respect to fragment relative intensity and <i>m/z</i>. eMISA also provides higher peak intensity as opposed to QTOF DIA with a median increase of 20% at negative mode and 80% at positive mode for all precursor ions. Metabolite identification with eMISA was also successfully validated from the analysis of a metabolic extract from macrophages. An interesting side benefit of enhanced ISF is that it significantly improved the compound identification confidence with low resolution single quadrupole mass spectrometry-based untargeted LC/MS experiments. Overall, enhanced ISF allowed for eMISA to be used as a more sensitive alternative to other QTOF DIA and DDA approaches, and further, it enables the acquisition of ESI TOF and ESI single quadrupole mass spectrometry instrumentation spectra with higher sensitivity and improved molecular identification confidence.

Investigating the Ease-of-Retrieval Effect in an Eyewitness Context

The way in which individuals think about their own cognitive processes plays an important role in various domains. When eyewitnesses assess their confidence in identification decisions, they could be influenced by how easily relevant information comes to mind. This ease-of-retrieval effect has a robust influence on people’s cognitions in a variety of contexts (e.g., attitudes), but it has not yet been applied to eyewitness decisions. In three studies, we explored whether the ease with which eyewitnesses recall certain memorial information influenced their identification confidence assessments and related testimony-relevant judgements (e.g., perceived quality of view). We manipulated the number of reasons participants gave to justify their identification (Study 1; N = 343), and also the number of instances they provided of a weak or strong memory (Studies 2a &amp; 2b; Ns = 350 &amp; 312, respectively). Across the three studies, ease-of-retrieval did not affect eyewitnesses’ confidence or other testimony-relevant judgements. We then tried—and failed—to replicate Schwarz et al.’s (1991) original ease-of-retrieval finding (Study 3; N = 661). In three of the four studies, ease-of-retrieval had the expected effect on participants’ perceived task difficulty; however, frequentist and Bayesian testing showed no evidence for an effect on confidence or assertiveness ratings.