Quantitative large scale gene expression profiling from human stem cell culture micro samples using multiplex pre-amplification
Why this work is in the frame
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Bibliographic record
Abstract
Transcriptional profiling is a powerful tool to study biological mechanisms during stem cell differentiation and reprogramming. Genome-wide methods like microarrays or next generation sequencing are expensive, time consuming, and require special equipment and bioinformatics expertise. Quantitative RT-PCR remains one of today's most widely accepted and used methods for analyzing gene expression in biological samples. However, limitations in the amount of starting materials often hinder the quantity and quality of information that could be obtained from a given sample. Here, we present a fast 4-step workflow allowing direct, column-free RNA isolation from limited human pluripotent stem cell (hPSC) cultures that is directly compatible with subsequent reverse transcription, target specific multiplex pre-amplification, and standard SYBR-Green quantitative PCR (qPCR) analysis. The workflow delivers excellent correlations in normalized gene-expression data obtained from different samples of hPSCs over a wide range of cell numbers (500-50,000 cells). We demonstrate accurate and unbiased target gene quantification in limiting stem cell cultures which allows for monitoring embryoid body differentiation and induced pluripotent stem cell (iPSC) reprogramming. This method highlights a rapid and cost effective screening process, allowing reduction of culture formats and increase of processing throughputs for various stem cell applications.
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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 | 0.001 | 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 | 0.000 | 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 it