Enhanced Multiplexed Single-Cell RNA-Sequencing for Accurate Detection of Treatment Effects Without Batch Correction in the Avian Embryonic Model
Bibliographic record
Abstract
Summary Single-cell RNA sequencing has revolutionized our ability to explore cellular heterogeneity and is a powerful tool to study the impact of environmental perturbations on multiple cell states. However, environmental perturbations can be subtle, and the associated biological effects could be masked by experimental noise and bioinformatic processing, especially when the samples are generated separately. Multiplexing strategies have been developed to label each sample and process them together to reduce experimental noise, but existing multiplexing methods often fall short for non-human and non-mouse models in established single-cell RNA sequencing protocols like the BD Rhapsody technology. To address this gap, we combined Lipid Modified Oligonucleotide (LMO) cell tagging with the BD Rhapsody platform to achieve efficient and scalable multiplexing of early embryonic chick cells under different environmental conditions. This species-agnostic LMO approach overcomes limitations of antibody-based multiplexing methods that are often restricted to human and mouse systems, and the only multiplexing option available with the BD Rhapsody system. In our study, we successfully compartmentalized and multiplexed chick embryonic cells under different treatment conditions, analyzing up to 40,000 viable cells per experiment. This strategy minimized experimental noise, eliminating the need for bioinformatics-based batch correction. As a result, we were able to achieve high-quality transcriptomic profiling with minimal loss of critical biological information and identified subtle biological differences that were masked when using data integration pipelines. Our workflow provides an adaptable, robust solution for LMO-tagged single-cell analyses of complex non-human models with the BD Rhapsody technology and opens new avenues for developmental biology research by accurately capturing treatment-induced effects in embryonic tissues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".