Building Real-Time Proteome Simulations with Mass Spectrometry, Live-Seq, Raman Imaging, and Machine Learning
Table of Figures
List of Abbreviations
Abbreviation | Meaning |
---|---|
MS | Mass Spectrometry |
HPLC / LC | High-Performance / Liquid Chromatography |
HCD | High-energy Collisional Dissociation |
ESI | Electrospray Ionisation |
TIMS | Trapped-Ion Mobility Spectrometry |
FAIMS | Field Asymmetric Ion Mobility Spectrometry |
CCS | Collisional Cross Section |
TOF | Time-of-Flight |
MS1 / MS2 | First / Second stage MS run |
SCoPE2 | Single-Cell Proteomics by Mass Spectrometry 2 |
TMT-pro | Tandem-Mass-Tag (18-plex) |
AQUA | Absolute QUAntification heavy peptide standard |
MARQUIS | Multiplex Absolute Re-QUantification Using Internal Standards |
iBAQ | Intensity-Based Absolute Quantification |
Bayesian iBAQ | iBAQ weighted with Bayesian priors |
RNA-velocity | Spliced/unspliced RNA to predict future states |
Slingshot | Trajectory inference (pseudotime) |
t-SNE | t-distributed Stochastic Neighbour Embedding |
d::pPop | DL model for peptide detectability |
DeepMass | DL model for ionisation efficiency |
PASEF | Parallel Accumulation–Serial Fragmentation |
Live-Seq | Force-microscopy cytoplasmic sampling for scRNA-seq |
DropMap | Droplet assay for secreted proteins |
MEFISTO | Method for Function Integration of Spatial & Temporal Omics |
ODE | Ordinary Differential Equation |
CRL | Causal Representation Learning |
PTM | Post-Translational Modification |
GSEA | Gene-Set Enrichment Analysis |
Summary
Goal: To develop in-silico single-cell proteomic simulations to facilitate virtual drug testing and hypothesis generation.
Problem: No technology currently measures thousands of protein copy numbers in the same cell at multiple time-points, yet such data are needed to train a real-time simulator.
Solution: A modified SCoPE2 single-cell mass-spectrometry protocol paired with Live-Seq and Raman imaging. Machine learning is split into (1) a translational model converting Raman/Live-Seq to proteome snapshots, and (2) a dynamics model predicting proteome evolution through time.
Real-Time Proteomic Simulation
With a database comprising thousands of quantitative protein measurements at the single-cell level across timepoints, it may be possible to use machine learning to predict how a cell’s proteome evolves (Fig. 1). …
Brief Description of Mass Spectrometry for Proteomics
First, the sample is homogenised … peptides are separated by HPLC, ionised by ESI, analysed by MS1/MS2, etc. (Fig. 2, Fig. 3).



Using Mass Spectrometry to Generate the Database
For the required database, SCoPE2 currently offers quantification of ~1,000 proteins across thousands of cells (Fig. 4). …

Ionisation Bias
Most MS proteomics is sample-to-sample relative … AQUA vs label-free approaches, proteomic ruler, DeepMass, MARQUIS ladder, d::pPop detectability, instrument choices, and a mixed strategy for absolute quantification. …
Redundant Proteins
Bottom-up ambiguity for protein groups; add targeted top-down on carrier material (~10%) to resolve isoforms/PTMs and estimate proportions. …
Sample Destruction
Proteomic MS destroys cells; propose a hybrid secondary domain for longitudinal signals: Live-Seq anchors + continuous Raman imaging; optional DropMap. (Fig. 5) …

Applying Machine Learning to Generate a Real-Time Simulation
Use a shared-encoder translational model (Raman + Live-Seq → latent Z → proteome P), then a dynamics model (Pt → Pt+Δ) augmented with CRL and mechanistic priors (turnover ODEs, stoichiometry). Validate on synthetic data first (sparsity, dropout, ionisation bias). …
Equipment
- Mass Spec: UCD Conway Proteomics Core (Orbitrap Exploris 480; timsTOF Pro). Orbitrap Eclipse Astral available at University of Birmingham (UK).
- Raman: UCD Spectral Imaging Research Group (Renishaw inVia).
- Live-Seq: FluidFM setup (would need local installation).
- Compute: ICHEC GPU partitions for SFI-funded projects; dedicated storage for Raman cubes.
Figure References
- Figure 2: A [7]; B [8]; C [9]; D [10]; E [11]; F [12]; G [13]
- Figure 3: A [14]; B [15]; C [15]; D [14]
- Figure 4: [16]
- Figure 5: A [17]; B [17]; C [18]; D [18]; E [19]
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