Deciphering the Relationship between Cell Growth and Cell Cycle in Individual Escherichia coli Cells by Flow Cytometry.
Lina WuYuzhen ZhangXinyi HongMingkai WuLiangan WangXiaomei YanPublished in: Analytical chemistry (2024)
Accurate coordination of chromosome replication and cell division is essential for cellular processes, yet the regulatory mechanisms governing the bacterial cell cycle remain contentious. The lack of quantitative data connecting key cell cycle players at the single-cell level across large samples hinders consensus. Employing high-throughput flow cytometry, we quantitatively correlated the expression levels of key cell cycle proteins (FtsZ, MreB, and DnaA) with DNA content in individual bacteria. Our findings reveal distinct correlations depending on the chromosome number (CN), specifically whether CN ≤2 or ≥4, unveiling a mixed regulatory scenario in populations where CN of 2 or 4 coexist. We observed function-dependent regulations for these key proteins across nonoverlapping division cycles and various nutrient conditions. Notably, a logarithmic relationship between total protein content and replication origin number across nutrient conditions suggests a unified mechanism governing cell cycle progression, confirming the applicability of Schaechter's growth law to cells with CN ≥4. For the first time, we established a proportional relationship between the synthesis rates of key cell cycle proteins and chromosome dynamics in cells with CN ≥4. Drug experiments highlighted CN 2 and 4 as pivotal turning points influencing cellular resource allocation. This high-throughput, single-cell analysis provides interconnected quantitative insights into key molecular events, facilitating a predictive understanding of the relationship between cell growth and cell cycle.
Keyphrases
- cell cycle
- single cell
- high throughput
- flow cytometry
- cell proliferation
- induced apoptosis
- lymph node metastasis
- rna seq
- escherichia coli
- cell cycle arrest
- high resolution
- endoplasmic reticulum stress
- poor prognosis
- oxidative stress
- stem cells
- gene expression
- emergency department
- machine learning
- signaling pathway
- single molecule
- electronic health record
- long non coding rna
- pi k akt
- pseudomonas aeruginosa
- nucleic acid