Magnetic Ranking Cytometry: Profiling Rare Cells at the Single-Cell Level
Bibliographic record
Abstract
ConspectusCellular heterogeneity in biological systems presents major challenges in the diagnosis and treatment of disease and also complicates the deconvolution of complex cellular phenomena. Single-cell analysis methods provide information that is not masked by the intrinsic heterogeneity of the bulk population and can therefore be applied to gain insights into heterogeneity among different cell subpopulations with fine resolution. Over the last 5 years, an explosion in the number of single-cell measurement methods has occurred. However, most of these methods are applicable to pure populations of cultured cells and are not able to handle high levels of phenotypic heterogeneity or a large background of nontarget cells. Microfluidics is an attractive tool for single cell manipulation as it enables individual encasing of single cells, allowing for high-throughput analysis with precise control of the local environment. Our laboratory has developed a new microfluidics-based analytical strategy to meet this unmet need referred to as magnetic ranking cytometry (MagRC). Cells expressing a biomarker of interest are labeled with receptor-coated magnetic nanoparticles and isolated from nontarget cells using a microfluidic device. The device ranks the cells according to the level of bound magnetic nanoparticles, which corresponds to the expression level of a target biomarker. Over the last several years, two generations of MagRC devices have been developed for different applications. The first-generation MagRC devices are powerful tools for the quantitation and analysis of rare cells present in heterogeneous samples, such as circulating tumor cells, stem cells, and pathogenic bacteria. The second-generation MagRC devices are compatible with the efficient recovery of cells sorted on the basis of protein expression and can be used to analyze large populations of cells and perform phenotypic CRISPR screens. To improve analytical precision, newer iterations of the first-generation and second-generation MagRC devices have been integrated with electrochemical sensors and Hall effect sensors, respectively. Both generations of MagRC devices permit the isolation of viable cells, which sets the stage for a wide range of applications, such as generating cell lines from rare cells and in vitro screening for effective therapeutic interventions in cancer patients to realize the promise of personalized medicine. This Account summarizes the development and application of the MagRC and describes a suite of advances that have enabled single-cell tumor cell analysis and monitoring tumor response to therapy, stem cell analysis, and detection of pathogens.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".