Selection of surfactants for cell lysis in chemical cytometry to study protein‐DNA interactions
Bibliographic record
Abstract
Protein-DNA interactions play a defining role in many cellular processes. Studying such interactions at the single-cell level is important and challenging. Here we make the first step toward achieving this goal with chemical cytometry. Chemical cytometry utilizes capillary separation for detailed chemical analyses of single cells. The cell is injected into a capillary, lysed, and its components are analyzed by CE or capillary chromatography with highly sensitive detection. In order to apply chemical cytometry to studies of protein-DNA interactions, cell lysis must not destroy protein-DNA complexes. Surfactants represent the most practical means of cell lysis inside the capillary. This work aimed at finding surfactants and lysis conditions that do not destroy protein-DNA complexes. We studied three groups of surfactants--ionic, zwitterionic, and nonionic--with respect to their ability to lyse the cell membrane without significantly influencing the stability of protein-DNA complexes. Nonequilibrium CE of equilibrium mixtures with surfactants in the equilibrium mixtures and in the run buffer was used to measure the equilibrium constant, K(d), and rate constant, k(off), of protein-DNA complex dissociation. We found that nonionic surfactants worked best: they lyse the plasma membrane without significantly influencing K(d), k(off), or the EOF. This work creates the foundation for studies of protein-DNA interactions in single cells by chemical cytometry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".