Rapid Isolation of Glomeruli Coupled with Gene Expression Profiling Identifies Downstream Targets in Pod1 Knockout Mice
Bibliographic record
Abstract
Mouse mutations have provided tremendous insights into the molecular basis of renal and glomerular development. However, genes often play important roles during multiple stages of nephrogenesis, making it difficult to determine the role of a gene in a specific cell lineage such as the podocyte. Conditional gene targeting and chimeric analysis are two possible approaches to dissect the function of genes in specific cell populations. However, these are labor-intensive and costly and require the generation, validation, and analysis of additional transgenic lines. For overcoming these shortcomings and, specifically, for studying the role of gene function in developing glomeruli, a technique to isolate and purify glomeruli from murine embryos was developed. Combined with gene expression profiling, this method was used to identify differentially expressed genes in glomeruli from Pod1 knockout (KO) mice that die in the perinatal period with multiple renal defects. Glomeruli from early developing stages (late S-shape/early capillary loop) onward can be isolated successfully from wild-type and KO kidneys at 18.5 d postcoitus, and RNA can readily be obtained and used for genome-wide microarray analysis. With this approach, 3986 genes that are differently expressed between glomeruli from Pod1 KO and wild-type mice were identified, including a four-fold reduction of alpha 8 integrin mRNA in glomeruli from Pod1 KO mice that was confirmed by immunostaining. This procedure may be adapted to any transgenic strain, providing a rapid and efficient method to dissect the function of specific genes in glomerular development.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".