Revealing global stoichiometry conservation architecture in cells from Raman spectral patterns
Creators
- 1. University of Tokyo
- 2. University of Chicago
- 3. Tokushima University
- 4. New York University
Description
Cells can adapt to various environments by changing their biomolecular profiles while maintaining physiological homeostasis. What organizational principles in cells enable the simultaneous realization of adaptability and homeostasis? To address this question, we measure Raman scattering light from Escherichia coli cells under diverse conditions, whose spectral patterns convey their comprehensive molecular composition. We reveal that dimension-reduced Raman spectra can predict condition-dependent proteome profiles. Quantitative analysis of the Raman-proteome correspondence characterizes a low-dimensional hierarchical stoichiometry-conserving proteome structure. The network centrality of each gene in the stoichiometry conservation relations correlates with its essentiality and evolutionary conservation, and these correlations are preserved from bacteria to human cells. Furthermore, stoichiometry-conserving core components obey growth law and ensure homeostasis across conditions, whereas peripheral stoichiometry-conserving components enable adaptation to specific conditions. Mathematical analysis reveals that the stoichiometrically constrained architecture is reflected in major changes in Raman spectral patterns. These results uncover coordination of global stoichiometric balance in cells and demonstrate that vibrational spectroscopy can decipher such biological constraints beyond statistical or machine-learning inference of cellular states.
Data availability
All data and analysis codes have been deposited in Zenodo and are publicly available at https://doi.org/10.5281/zenodo.17090710.
The following data sets were generated
Kamei KF Kobayashi-Kirschvink KJ Nozoe T Nakaoka H Umetani M Wakamoto Y (2025) Zenodo Code and data for "Revealing global stoichiometry conservation architecture in cells from Raman spectral patterns". https://doi.org/10.5281/zenodo.1709071The following previously published data sets were used
Kobayashi-Kirschvink K Nakaoka H Oda A Kamei KF Nosho K Fukushima H Kanesaki Y Yajima S Masaki H Ohta K Wakamoto Y (2018) Mendeley Data Data for: Linear Regression Links Transcriptomic Data and Cellular Raman Spectra. https://doi.org/10.17632/2fx3h2rx2m.1
Cao J O'Day DR Pliner HA Kingsley P Deng M Daza RM Zager MA Kimberly A Blecher R Zhang F O'Day DR Spielmann M Palis J Doherty D Steemers FJ Glass IA Trapnell C Shendure J (2020) NCBI Gene Expression Omnibus ID GSE156793. A human cell atlas of fetal gene expression. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156793
Replogle J Weissman J (2022) figshare "Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq" Replogle et al. 2022 processed Perturb-seq datasets. https://doi.org/10.25452/figshare.plus.20029387.v1
Keseler IM (2017) The EcoCyc ID Version 24.1. The EcoCyc database. https://ecocyc.org
Additional details
Identifiers
- DOI
- 10.7554/elife.101485.3
- Other
- oai:uchicago.tind.io:16967
Funding
- Japan Science and Technology Agency
- JPMJCR1927
- Japan Science and Technology Agency
- JPMJER1902
- Japan Science and Technology Agency
- 19J22448
- Japan Science and Technology Agency
- 21K20672