Integrating 3D imaging, GWAS, and single-cell transcriptome approaches to elucidate root system architecture in <i>Populus</i>
Bibliographic record
Abstract
Roots are essential for nutrient uptake and structural stability in trees. Despite their critical role, the genetic determinants underlying root system architecture (RSA) remain poorly understood. In this study, we employed an integrated approach combining automated 3-dimensional (3D) spatial imaging, multiomics analyses, genetic transformation, and molecular experiments to investigate the genetic architecture and regulatory networks governing RSA in Simon poplar (Populus simonii). Here, using a panel of 303 P. simonii accessions collected from different geographical regions in China, we performed a genome-wide association study (GWAS) on 96 RSA traits and identified S-phase kinase-associated protein 2B (PsiSKP2B) as a candidate gene colocalized by 6 traits. By integrating the findings from GWAS, transcriptome, and single-cell RNA-seq (scRNA-seq) analyses, we identified PsiSKP2B as a key regulator of meristematic tissue cells involved in lateral root (LR) development. Overexpression of PsiSKP2B in 84k (Populus alba × Populus glandulosa) had a substantial effect on RSA traits, increasing the number and density of LRs by 65.9% and 98.6%, respectively, compared with wild-type plants. Our in vitro and in vivo assays revealed that PsiSKP2B modulates LR development by interacting with WUSCHEL-RELATED HOMEOBOX 4 (PsiWOX4) or ZINC FINGER HOMEODOMAIN 9 (PsiZHD9), both of which are specifically expressed in atrichoblast cells, thereby activating a regulatory feedback loop. These findings highlight an atrichoblast-dependent regulatory mechanism through which PsiSKP2B governs LR development. Our study not only introduces an advanced image recognition methodology for quantifying RSA traits in P. simonii but also provides a comprehensive multiomics framework for elucidating the genetic and molecular basis of RSA.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".