Opto-magnetic Selection and Isolation of Single Cells
Bibliographic record
Abstract
Capturing single cells from large heterogenous populations based solely on observable traits is necessary for many cell biology applications and remains a major technical challenge. The protocol we present allows the isolation of viable and metabolically active cells selected for their shape, migration speed, contact to other cells, or intracellular protein localization. We previously introduced a method termed Cell Labeling via Photobleaching (CLaP) for the efficient tagging of cells chosen for visual criteria. Here we describe a new protocol for capturing such cells using ferromagnetic beads termed single-cell magneto-optical capture (scMOCa). This technology is especially useful when the number of target cells represents an extremely low fraction of the total population (potentially one single cell), a situation in which conventional sorting techniques like fluorescent or magnetic activated cell sorting (F/MACS) cannot provide satisfactory results in terms of capture efficiency and specificity. scMOCa uses the lasers of a confocal microscope to photobleach and crosslink biotin-4-fluorecein molecules to cell membranes. Streptavidin coated magnetic beads then adhere to biotin moieties and a magnet allows the capture of illuminated cells. By precisely controlling liquid volumes and spacing between the different parts of a simple setup, high cell selectivity and capture efficacy can be achieved. scMOCA allows visual selection and isolation of any number of cells in a microscopy field and captured cells remain viable to generate new colonies of chosen phenotypes for downstream analyses.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".