Isotope Cluster-Based Compound Matching in Gas Chromatography/Mass Spectrometry for Non-Targeted Metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has emerged as a powerful tool in metabolomics studies. A major bottleneck in current data analysis of GC/MS-based metabolomics studies is compound matching and identification, as current methods generate high rates of false positive and false -negative identifications. This is especially true for data sets containing a high amount of noise. In this work, a novel spectral similarity measure based on the specific fragmentation patterns of electron impact mass spectra is proposed. An important aspect of these algorithmic methods is the handling of noisy data. The performance of the proposed method compared to the dot product, the current gold standard, was evaluated on a complex biological data set. The analysis results showed significant improvements of the proposed method in compound matching and chromatogram alignment compared to the dot product

Testing Cooperative enhanced Scatter Search (CeSS).

101 evenly spaced, simulated, noise-free datapoints of 41 variables were generated with model version 2.0.0 (circles). Dynamic parameters were estimated within a [0.1 · θtrue, 10 · θtrue] search window, where θtrue denotes the ground truth parameter vector. Initial conditions were estimated within a [eps, 1.5 · θtrue] search window, where eps denotes machine epsilon (except for Apc, where the upper boundary was 5). a Histogram showing distribution of all optimized parameters θ versus ground truth θtrue, except for Apc: Cdh and pCdh (see text), and kDpE2f1 and kPhC25A (θtrue = 0 and θ ≤ eps). b Simulated time courses for three of the 41 variables, using the parameter vector θ found by CeSS.</p

Models of individual cell cycle transitions.

a, b Reaction network and bifurcation diagram of the restriction point. Lighter colour indicates reactions omitted in Figs A-a and A-b in S1 Appendix. c, d Reaction network and bifurcation diagram of the G1/S transition. Lighter colour indicates reactions omitted in Fig B-b in S1 Appendix. e, f Reaction network and bifurcation diagram of the G2/M transition as developed by Vinod and Novak [25]. tCycB: total cyclin B. g, h Reaction network and bifurcation diagram of the combined G2/M and M/A transition. Lighter colour represents a simplified schematic of the G2/M transition network shown in (e). In the reaction network diagrams conversions are represented by full arrows and catalytic interactions by dashed arrows. Four circles indicate degraded proteins. Red letters represent the species used as bifurcation parameters. In the bifurcation diagrams solid lines show stable and dotted lines unstable steady states. Unphysiological regions are semi-transparent. Line endings within the axes limits indicate disappearance of a steady state. For variable abbreviations please refer to Table A in S1 Appendix. Cdh1 and Cdh1:Emi in the G1/S transition network correspond to (p)Apc:Cdh1 and (p)Apc:Cdh1:Emi1, respectively in the full cell cycle model. Models incl. parameters are available in the /versions/v0.0.1/ directory of the cell_cycle_model GitHub repository.</p

Time courses of the core cell cycle model.

a, b Core cell cycle model at CycD = 1 AUD. c, d Simulation of mitogen deprivation. The full cell core model was simulated as in (a, b) until 610 min (c) and 620 min (d), respectively. CycD as proxy for mitogen availability was then turned to 0 AUD and the simulation was continued until 1500 min. e Full cell cycle model with CycE knockout. f Full cell cycle model with CycA knockout. Blue ticks at the top indicate the approximate location of the M/A transition. All models use identical parametrization (except for CycD). For variable abbreviations please refer to Table A in S1 Appendix. The model and parameters are available in the /versions/v1.0.0/ directory of the cell_cycle_model GitHub repository.</p

Comparison of this work with existing cell cycle models.

Comparison of this work with existing cell cycle models.</p

Supporting tables, equations, figures and notes.

The Supporting Tables describe the variables of the core model and the changelog of model versions. The ODE systems for each cell cycle submodel are provided in Equations A-D in S1 Appendix. The Supporting Figures show additional analysis of the models, trajectory reconstruction and parameter estimation. Supporting Texts in S1 Appendix. discuss merging of submodels, naming conventions in the BioNetGen model, adding CDKN1B to the cell cycle model, introducing compartmentalisation, considerations on the effect of cell cycle arrest and trajectory reconstruction, and handling of real-world data in parameter estimation. (PDF)</p

Time courses with alternating TP53 levels.

DNA damage was simulated by activating TP53. The first activation corresponds to G1 phase, the second to G2 phase. Inactivation of TP53 allows continued proliferation. For better visualisation the G1 checkpoint was lifted before the steady state was reached. Model available in the /versions/v3.1.0/cell_cycle_v3.1.0.bngl file of the cell_cycle_model GitHub repository.</p

Model version 4.0.0 fitted to pseudo-time courses of RPE-1 cells.

Experimental measurements and simulation results using estimated parameters. For better visibility, only every 10th measurement is shown. a, c, e Nuclear compartment. b, d, f Cytoplasmic compartment. g Convergence curve. PEtab problem incl. parameter table and SBML file with optimized parameters are available in the /versions/v4.0.0/ directory of the cell_cycle_petab GitHub repository.</p

Time course of cell cycle regulators in RPE-1 cells.

Proliferating cells of publicly available 4i measurements of an asynchronously dividing RPE-1 population [50] were gated in PHATE space (Fig I in S1 Appendix). For better visibility the variables are normalised to a mean of one, and axes are clipped. a Dot plot of 300 untreated RPE-1 cells. b-d Reconstructed time course of the cell population from (a). reCAT was provided with 36 variables. Time was calculated using Eq (2) and data was smoothened using a Kalman filter.</p

Cell cycle trajectory reconstruction from noise-free simulated data with reCAT.

a 300 cells (i.e. discrete time points) were sampled across one cell cycle model simulation (version ). Of all 49 model variables, cyclin E, cyclin A and B55 are shown. b Sketch of the reCAT algorithm showing the sampled cell population projected on the CycA-B55 plane. The true cell cycle time was not provided. reCAT reconstructed the trajectory by grouping the cells into 8 clusters (sketched as blue ovals) with a Gaussian mixture model and heuristically finding the shortest circular path that visits all cluster centers (sketched as red arrow). c Reconstructed cell cycle trajectory. d Correlation between true and reconstructed cell cycle time for each cell.</p