Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a chemical imaging method that can visualize spatial distributions of particular molecules. Plant tissue imaging has so far mostly used cryosectioning, which can be impractical for the preparation of large-area imaging samples, such as full flower petals. Imaging unsectioned plant tissue presents its own difficulties in extracting metabolites to the surface due to the waxy cuticle.
We address this by using established delipidation techniques combined with a solvent vapor extraction prior to applying the matrix with many low-concentration sprays.
Using this procedure, we imaged tissue from three different plant species (two flowers and one carnivorous plant leaf). Material factorization analysis of the resulting data reveals a wide range of plant-specific small molecules with varying degrees of localization to specific portions of the tissue samples, while facilitating detection and removal of signal from background sources.
This work demonstrates applicability of MALDI-MSI to press-dried plant samples without freezing or cryosectioning, setting the stage for spatially resolved molecule identification. Increased mass resolution and inclusion of tandem mass spectrometry are necessary next steps to allow more specific and reliable compound identification.
A comparison of methods using different materials to exclude light from stems to prevent stem CO2 exchange (i.e., photosynthesis), without affecting stem conductance to water vapor, surface temperature, and relative humidity, was conducted on stems of avocado trees in California.
The experiment featured three materials: aluminum foil, paper-based wrap, and mineral-based paint. We examined stem CO2 exchange with and without the light exclusion treatments. We also examined stem surface temperature, relative humidity, and photosynthetic active radiation (PAR) under the cover materials. All materials reduced PAR and stem CO2 exchange. However, aluminum foil reduced stem surface temperature and increased relative humidity.
Methods used to study stem CO2 exchange through light exclusion have historically relied on methods that may induce experimental artifacts. Among the methods tested here, mineral-based paint effectively reduced PAR without affecting stem surface temperature and relative humidity around the stem.
Sessa, E. B., R. R. Masalia, N. Arrigo, M. S. Barker, and J. A. Pelosi. 2023. GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data. Applications in Plant Sciences 11(4): e11536.
In the Acknowledgments, a grant number was left out of the sentence “Funding was provided by the National Science Foundation (DEB #1844930 to E.B.S.).” This should have read “Funding was provided by the National Science Foundation (DEB #1844930 and IOS #2310485 to E.B.S.).”
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Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil.
RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data.
The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.
McAssey, E. V., Downs, C., Yorkston, M., Morden, C., and Heyduk, K. 2023. A comparison of freezer-stored DNA and herbarium tissue samples for chloroplast assembly and genome skimming. Applications in Plant Sciences 11(3): e11527
A statistical error was found after article publication. The relevant text from the Results section is provided below, with the corrected values shown in bold text. The error does not affect the findings of the study.
“Herbarium tissue library samples had significantly smaller insert sizes of mapped chloroplast reads compared to their freezer-stored DNA paired samples, taking into account covariates of read numbers and year (F1,25 = 229.243, P < 0.001). There was also a significant interaction effect between library size and sampling year (F1,25 = 9.753, P < 0.01). Similarly, herbarium tissue samples also had higher amounts of adapter sequences in the reads (F1,25 = 85.009, P < 0.001), with sampling year a significant covariate in the model (F1,25 = 6.378, P < 0.05).”
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Many plant communities across the world are undergoing changes due to climate change, human disturbance, and other threats. These community-level changes are often tracked with the use of permanent vegetative plots, but this approach is not always feasible. As an alternative, we propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities where the use of permanent structures may not be possible.
In 2021 and 2022, we took iPhone photographs to record species presence in 1-m2 plots distributed across alpine communities in the northeastern United States. We then compared field estimates of percent coverage with coverage estimated using DSMs.
Digital surface models can provide effective, minimally invasive, and permanent records of plant species presence and percent coverage, while also allowing managers to mark survey locations virtually for long-term monitoring. We found that percent coverage estimated from DSMs did not differ from field estimates for most species and substrates.
In order to continue surveying efforts in areas where permanent structures or other surveying methods are not feasible, photogrammetry and structure-from-motion methods can provide a low-cost approach that allows agencies to accurately survey and record sensitive plant communities through time.
The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre-defined, higher-order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually.
The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s).
GO annotations are a widely used “universal language” for describing gene functions and products. GOgetter is a fast and easy-to-implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.
Recent technological advances in long-read high-throughput sequencing and assembly methods have facilitated the generation of annotated chromosome-scale whole-genome sequence data for evolutionary studies; however, generating such data can still be difficult for many plant species. For example, obtaining high-molecular-weight DNA is typically impossible for samples in historical herbarium collections, which often have degraded DNA. The need to fast-freeze newly collected living samples to conserve high-quality DNA can be complicated when plants are only found in remote areas. Therefore, short-read reduced-genome representations, such as target capture and genome skimming, remain important for evolutionary studies. Here, we review the pros and cons of each technique for non-model plant taxa. We provide guidance related to logistics, budget, the genomic resources previously available for the target clade, and the nature of the study. Furthermore, we assess the available bioinformatic analyses, detailing best practices and pitfalls, and suggest pathways to combine newly generated data with legacy data. Finally, we explore the possible downstream analyses allowed by the type of data generated using each technique. We provide a practical guide to help researchers make the best-informed choice regarding reduced genome representation for evolutionary studies of non-model plants in cases where whole-genome sequencing remains impractical.
Coined by Dutch theoretical biologists in the 1970s, the term bioinformatics originally denoted a broad concept relating to the study of information processing in biological systems, such as ecosystem interaction, neuronal messaging, and transfer of genetic information (Hogeweg, 2011). Subsequently co-opted to describe the sequencing and analysis of molecules (from nucleic acids to proteins), bioinformatics has diverse applications including the analysis, visualization, storage, and generation of data relating to living organisms and the molecular information they carry. Plant biology has reaped dividends from the development and maturation of bioinformatics; it has not only extended our understanding of model plant species such as Arabidopsis thaliana (Cantó-Pastor et al., 2021) but also driven innovative solutions to characterize non-model species (Nevado et al., 2014). Both avenues of discovery contribute to key objectives in improving food security, conservation, and biotechnology.
The size and complexity of many plant genomes has historically made their analysis financially and computationally difficult. Frequent polyploidy and repeat element expansion make the elucidation of plant genome sequences challenging (Soltis et al., 2015). Furthermore, high heterozygosity in wild populations, pervasive hybridization, and a lack of inbred lines present roadblocks to analyses such as read mapping and assembly (Kajitani et al., 2019). Long-read technologies have become ever more accessible in recent years, and algorithmic advances have accommodated sequential updates to error models, read lengths, and library types (Michael and VanBuren, 2020). Moreover, novel methods to scaffold contigs and obtain long-range interaction information have driven impressive improvements in genome assembly quality, making telomere-to-telomere genome sequencing projects an achievable goal for many labs (Kress et al., 2022).
Long-read technologies paired with novel mapping algorithms have fueled discovery of new transposable element (TE) dynamics, and there has been an associated resurgence of interest in their role in adaptive trait evolution and phenotypic variation (Schrader and Schmitz, 2019; Pimpinelli and Piacentini, 2020). Bioinformatics developments in this field have led to vast improvements in our ability to detect complex TE mobilization patterns such as nested insertions and structural variants (Bree et al., 2022; Lemay et al., 2022). Despite these advancements, characterization and annotation of genomic features such as genes and repetitive elements remain challenging due to species-specific genomic configurations, taxonomically patchy reference databases, and a lack of robust benchmarking and quality control. While structural and functional annotation methods still have significant obstacles to ov
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