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Record W4396991326 · doi:10.1681/asn.20223311s139b

Deciphering the Origins of Kidney Lymphatics

2022· article· en· W4396991326 on OpenAlexaff
Daniyal J. Jafree, Christopher J. Rowan, Maria K. Joannou, Lauren G. Russell, Julie A. Siegenthaler, Adrian S. Woolf, Norman D. Rosenblum, Peter Scambler, David A. Long

Bibliographic record

VenueJournal of the American Society of Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsSickKids Foundation
Fundersnot available
KeywordsLymphatic systemKidneyMedicinePathologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Lymphatics clear excess tissue fluid, cells and macromolecules from organs, and are emerging as players in kidney diseases and transplant rejection. Lymphatics were thought to solely originate by sprouting from veins. More recently, we showed that kidney lymphatics can arise using a distinct cellular mechanism featuring the formation of lymphatic ‘clusters’, akin to a de novo vasculogenic process. In this study, we designed experiments to decipher where kidney lymphatics come from. Methods: To identify kidney lymphatics origins, we utilised Cre recombinase-dependent expression of tdTomato for lineage tracing in mouse embryos. We used the following Cre lines to determine the contribution of different cell lineages to lymphatics: (i) Tie2-Cre for endothelial precursors; (ii) Osr1-CreERTR for posterior intermediate mesoderm and (iii) Six2-Cre, Tbx18-CreERT2 and Foxd1-Cre for nephron epithelial, ureteral mesenchyme and renal stroma, respectively. Intact, lineage traced kidneys were subject to wholemount immunolabelling for tdTomato and the lymphatic markers, PROX1 and PDPN, before tissue clearing and confocal microscopy. From the resulting high-resolution 3D images, we quantitatively assessed contributions from each origin to kidney lymphatics. Results: We found 85% of kidney lymphatics to derive from endothelial precursors, as assessed by labelling Tie2+ cells and their progeny. Conversely, 15% of kidney lymphatics arose from an Osr1+ cell lineage, whereas lymphatics in the heart, skin and lung did not originate from Osr1+ progenitors. We found that kidney lymphatics did not originate from Six2+ cells, or from Tbx18+ ureteral mesenchyme, both which derive from Osr1+ cells. However, we provide evidence that the Foxd1+ renal stroma is a source of progenitor cells giving rise to kidney lymphatics. No single lineage was exclusive for lymphatic clusters. Conclusions: Our study emphasises the early colonisation of the mammalian kidney by lymphatics. It also points to an unexpected variety of lineages giving rise to kidney lymphatics, including a novel non-endothelial origin specific to the kidney. These insights challenge the paradigm that lymphatics from different origins form through distinct cellular mechanisms. Further, they critically inform future studies of the roles of lymphatics in kidney development and disease.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.282
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicLymphatic System and DiseasesFrench-language works237,207