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Oseguera, Y.

Publications and source records attributed to Oseguera, Y..

2 recordsLinked to original sources

Tomato pollen tube integrity is critical for pollen function at high temperature and is controlled by RALF signaling.

Reproductive success at high temperature (HT) depends on the ability of pollen to germinate a pollen tube that delivers sperm for double fertilization. We recently found that Solanum lycopersicum (tomato) cultivars capable of setting fruit at HT produce pollen tubes that continue to grow under heat stress. Our present goal is to define the molecular basis of thermotolerant pollen tube growth using quantitative live imaging, transcriptomic and proteomic profiling, and genetic analysis. We found that pollen tube integrity - the ability to maintain an intact tip during germination and elongation - was the pollen performance parameter that distinguished thermotolerant cultivars. We identified the components of the Rapid Alkalinization Factor (RALF) pollen tube integrity signal transduction pathway in tomato and found that RALF peptides repress germination at control temperature and that signaling is antagonized by HT. Loss of a single RALF peptide changes pollen tube cell wall pectin distribution and enhances pollen tube integrity at HT in thermotolerant Heinz, revealing that RALF signaling regulates thermotolerant pollen tube growth and establishing a molecular framework for improving reproductive heat-resilience in crop plants.

plant biology↗

Semi-automated high content analysis of pollen performance using TubeTracker

Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival. TubeTracker integrates manual and automatic image processing routines and the graphical user interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/624782v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1fc2a63org.highwire.dtl.DTLVardef@42f3a2org.highwire.dtl.DTLVardef@18911d6org.highwire.dtl.DTLVardef@1f236f0_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Graphical user interface of TubeTracker showing all supported functionalities. C_FIG

plant biology↗