Genetic lineage tracing identifies intermediate mesoderm as a novel contributor to mammalian kidney lymphatics
Bibliographic record
Abstract
ABSTRACT The lymphatic vasculature is essential for fluid homeostasis, immune regulation and possesses diverse organ-specific functions. During development, lymphatic endothelial cells (LEC) arise from multiple progenitor sources that form organ-specific lymphatic networks. While the origins of LECs in the heart, skin, and mesentery have been studied, those in the kidney remain unresolved. Here, we combined genetic lineage tracing in mouse embryos with optical clearing and high-resolution three-dimensional imaging to identify two distinct progenitor sources of kidney lymphatics. The majority of kidney LECs originate from a Tie2⁺ endothelial lineage previously linked to venous or capillary vessels. Approximately 15% derive from Osr1⁺ intermediate mesoderm, a lineage that generates kidney nephrons and stroma. Osr1⁺- derived LECs were absent from the heart, mesentery, and skin, indicating a kidney-specific contribution, and arose independently of nephron and stromal lineages. Both Tie2⁺ and Osr1⁺ lineages contributed to vessel sprouting and de novo formation of lymphatic clusters. Revealing a novel cellular origin of LECs and identifying a dual origin for kidney lymphatics, we demonstrate that de novo lymphatic formation can occur from both shared and organ-specific progenitors. This work advances our understanding of how lymphatics assemble during development and provides a framework for targeting kidney lymphatics in disease.
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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 | 0.000 | 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 | 0.000 | 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 | 0.000 |
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 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".