Keywords: paralog processing, ascorbate, spatial transcriptomics, quantitative resistance

Latest 12 Papers

A novel pathosystem between Aeschynomene evenia and Aphanomyces euteiches reveals new immune components in quantitative legume root-rot resistance.

Authors: Baker, M., Martinez, Y., Keller, J., Sarrette, B., Pervent, M., Libourel, C., Le Ru, A., Bonhomme, M., Gough, C., Castel, B., ARRIGHI, J.-F., Jacquet, C.

Date: 2026-01-11 · Version: 1

DOI: 10.64898/2026.01.11.698850

Model Organism: Aeschynomene evenia

legume immunity Aphanomyces euteiches quantitative resistance transcriptomics Nod factor-independent symbiosis

Regulation of spikelet number during wheat spike development

Authors: Li, C., Li, K., Zhang, C., Dubcovsky, J.

Date: 2026-01-08 · Version: 1

DOI: 10.64898/2026.01.07.698073

Model Organism: Triticum aestivum

spikelet number per spike wheat inflorescence development florigen (FT1)spatial transcriptomics single‑cell analysis

Spatiotemporal regulation of arbuscular mycorrhizal symbiosis at cellular resolution

Authors: Chancellor, T., Ferreras-Garrucho, G., Akmakjian, G. Z., Montero, H., Bowden, S. L., Hope, M., Wallington, E., Bhattacharya, S., Korfhage, C., Bailey-Serres, J., Paszkowski, U.

Date: 2025-11-02 · Version: 1

DOI: 10.1101/2025.10.31.685811

Model Organism: Oryza sativa

arbuscular mycorrhizal symbiosis spatial transcriptomics TRAP-seq arbuscule development nutrient transport

Phylogenomic challenges in polyploid-rich lineages: Insights from paralog processing and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae)

Authors: Moore-Pollard, E. R., Ellestad, P., Mandel, J.

Date: 2025-10-13 · Version: 2

DOI: 10.1101/2025.09.14.676143

Model Organism: Multi-species

phylogenomic discordance polyploidy paralog processing Packera historical biogeography

Conserved 'late' effector genes from Leptosphaeria maculans inducing gene-for-gene quantitative resistance in Brassica napus semi-winter genotypes

Authors: Rabeau, C., Wagner, A., Lapalu, N., Jiquel, A., Faure, S., Fudal, I.

Date: 2025-09-23 · Version: 1

DOI: 10.1101/2025.09.22.677120

Model Organism: Brassica napus

Leptosphaeria maculans Brassica napus late effector genes quantitative resistance semi‑winter genotypes

Spatial transcriptomics identifies distinct domains regulating yield-related traits of the wheat ear

Authors: Qu, Y., Tan, C., Yang, L., Pasquariello, M., Alabdullah, A. K., Sun, S., Iqbal, M., Salamon, J., Boden, S.

Date: 2025-08-13 · Version: 1

DOI: 10.1101/2025.08.12.670006

Model Organism: Triticum aestivum

spikelet development spatial transcriptomics wheat inflorescence RAMOSA2 ALOG1

Plant-Compatible Xenium In Situ Sequencing: Optimised Protocol for Spatial Transcriptomics in Medicago truncatula Roots and Nodules

Authors: Jhu, M.-Y., Heffer, J., Deamer, A., Moraes, T. A., Piskorz, A. M., Xia, C.

Date: 2025-07-25 · Version: 1

DOI: 10.1101/2025.07.22.663073

Model Organism: Medicago truncatula

spatial transcriptomics Xenium in situ sequencing Medicago truncatula root nodules autofluorescence mitigation

Spatial transcriptomics of developing wheat seed reveals radial expression patterns in endosperm and subgenome biased expression of key genes

Authors: Millsteed, T., Kainer, D., Sullivan, R., Sun, X., Li, K. L., Mao, L., Macdonald, A., Henry, R. J.

Date: 2025-06-08 · Version: 1

DOI: 10.1101/2025.06.08.658529

Model Organism: Triticum aestivum

spatial transcriptomics wheat seed development endosperm gene expression subgenome bias STOmics platform

Spatial and single-cell transcriptomics capture two distinct cell states in plant immunity

Authors: Hu, Y., Schaefer, R., Rendleman, M., Slattery, A., Cramer, A., Nahiyan, A., Breitweiser, L., Shah, M., Kaehler, E., Yao, C., Bowling, A., Crow, J., May, G., Tabor, G., Thatcher, S., Uppalapati, S. R., Muppirala, U., Deschamps, S.

Date: 2025-06-06 · Version: 1

DOI: 10.1101/2025.06.03.657683

Model Organism: Glycine max

spatial transcriptomics single-nuclei RNA-seq plant immunity Phakopsora pachyrhizi cell non‑autonomous defense

Tracking Lesion Growth in the Field: Imaging and Deep Learning Reveal Components of Quantitative Resistance

Authors: Anderegg, J., Roth, L., Zenkl, R., McDonald, B. A.

Date: 2025-05-23 · Version: 1

DOI: 10.1101/2025.05.20.655031

Model Organism: Triticum aestivum

lesion expansion quantitative resistance Zymoseptoria tritici deep learning phenotyping wheat cultivars

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