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Record W4382794101 · doi:10.1016/j.celrep.2023.112737

Dissecting the spermatogonial stem cell niche using spatial transcriptomics

2023· article· en· W4382794101 on OpenAlexafffund
Shreya Rajachandran, Xin Zhang, Qiqi Cao, Andre L. Caldeira‐Brant, Xiangfan Zhang, Youngmin Song, Melanie Evans, Orhan Bükülmez, Edward J. Grow, Makoto Nagano, Kyle E. Orwig, Haiqi Chen

Bibliographic record

VenueCell Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCecil H. and Ida Green Center for Reproductive Biology SciencesNational Institutes of Health
KeywordsBiologyNicheStem cellCell biologyStem cell nicheTranscriptomeEcological nicheComputational biologyGeneticsGeneProgenitor cellGene expressionEcology

Abstract

fetched live from OpenAlex

Spermatogonial stem cells (SSCs) in the testis support the lifelong production of sperm. SSCs reside within specialized microenvironments called "niches," which are essential for SSC self-renewal and differentiation. However, our understanding of the molecular and cellular interactions between SSCs and niches remains incomplete. Here, we combine spatial transcriptomics, computational analyses, and functional assays to systematically dissect the molecular, cellular, and spatial composition of SSC niches. This allows us to spatially map the ligand-receptor (LR) interaction landscape in both mouse and human testes. Our data demonstrate that pleiotrophin regulates mouse SSC functions through syndecan receptors. We also identify ephrin-A1 as a potential niche factor that influences human SSC functions. Furthermore, we show that the spatial re-distribution of inflammation-related LR interactions underlies diabetes-induced testicular injury. Together, our study demonstrates a systems approach to dissect the complex organization of the stem cell microenvironment in health 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.256
Teacher spread0.227 · 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 designBench or experimental
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

Citations42
Published2023
Admission routes2
Has abstractyes

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