In situ imaging and isolation of proteins using dsDNA oligonucleotides
Bibliographic record
Abstract
As proteomics initiatives mature, the need will arise for the multiple visualization of proteins and supramolecular complexes within their true context, in situ. Single-stranded DNA and RNA aptamers can be used for low resolution imaging of cellular receptors and cytoplasmic proteins by light microscopy (LM). These techniques, however, cannot be applied to the imaging of nuclear antigens as these single-stranded aptamers bind endogenous RNA and DNA with high affinity. To overcome this problem, we have developed a novel method for the in situ detection of proteins using double-stranded DNA oligonucleotides. To demonstrate this system we have utilized the prokaryotic DNA-binding proteins LacI and TetR as peptide tags to image fusion proteins in situ using dsDNA oligonucleotides encoding either the Lac or Tet operator. Using fluorescent and fluorogold dsDNA oligonucleotides, we localized within the nucleus a TetR-PML fusion protein within promyelocytic leukaemia protein (PML) bodies by LM and a LacI-SC35 fusion protein within nuclear speckles by correlative light and electron microscopy (LM/EM). Isolation of LacI-SC35 was also accomplished by using biotinylated dsDNA and streptavidin sepharose. The use of dsDNA oligonucleotides should complement existing aptamer in situ detection techniques by allowing the multiple detection and localization of nuclear proteins in situ and at high resolution.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".