Arabidopsis Field Phenomics and Transcriptomics
Plants continuously adapt to their surroundings, adjusting growth and development in response to seasonal changes. Our project explores how Arabidopsis thaliana responds to natural climatic conditions and investigates the underlying molecular mechanisms.
In field phenotyping - 35000 phenotypic datapoints
We observed significant phenotypic differences across multiple years at a Arabidopsis collection site, despite minimal genetic variation. Detailed phenotyping revealed seasonal shifts in key traits, suggesting that environmental factors play a major role in shaping plant morphology. To further investigate these effects, we conducted comparative studies across different locations, uncovering site-specific differences in plasticity. We conducted an extensive field survey of over 2,500 naturally occurring
Arabidopsis thaliana plants, spanning two main habitats during winter and summer (2021–2024). These plants were phenotyped for 15 traits, resulting in a dataset of more than 35,000 phenotypic data points. This comprehensive dataset allows us to detect subtle trait variations, identify patterns of environmental adaptation, and provides an exceptionally rich resource for statistical and machine learning approaches.
Transcriptomes of more than 1000 individual plants
To uncover the genetic and molecular drivers of these changes, we performed transcriptome sequencing on a subset of these field-grown plants, generating over 1,000 transcriptomes from the 2,500 plants analyzed. This enabled us to study gene expression under natural conditions and investigate how environmental factors shape plant phenotypes.
Our lab is particularly interested in how gene regulation shapes plant phenotypes, and this dataset allows us to study how environmental factors influence transcriptional programs in natural conditions. Leveraging machine learning approaches, we integrated transcriptome and trait associations to identify genes regulating specific phenotypic traits. The size and depth of our dataset significantly enhance the predictive power of computational models, helping to pinpoint key regulatory genes.
Validation experiment in semi-field and lab setting
These predictions are validated in laboratory and semi-field conditions, bridging the gap between computational predictions and experimental verification. This research provides new insights into how plants integrate seasonal cues to optimize growth, highlighting the importance of studying plants in their natural habitat. By combining field studies, molecular analyses, and computational models, we aim to refine our understanding of plant plasticity, with implications for agriculture, climate adaptation, and plant resilience in changing environments.
The People Behind the Data
This project would not have been possible without the dedication and effort of many individuals. We extend our heartfelt thanks to everyone who contributed to assembling this extensive dataset—field assistants, students, and colleagues who braved all weather conditions to collect samples, meticulously recorded phenotypic traits, and ensured the careful processing of plant material. We also appreciate the invaluable support from our collaborators in data analysis, genomics and transcriptomics, whose expertise helped transform raw data into meaningful insights.
A special thank you to the entire team for their commitment, hard work, and enthusiasm in uncovering the complexities of plant plasticity in natural environments. Your contributions are the foundation of this research
Plant Collection and Phenotyping (in alphabetical order)
Akash Shivhare, Bianca Rosinsky, Carola Kretschmer, Carolin Apel, Cornelius Schmidtke, Deike Stoffers, Dirk Albach, Eneza Mjema, Helge Bruelheide, Joachim Weber, Mandy Koller, Marcel Quint, Michael Kleyer, Nadja Janzen, Rebecca Schwab, Sandra Schüler, Sascha Laubinger, Sylvia Haider, Tamara Schneider, Ute Jandt
Genome & Transcriptome Analysis (From Sample Preparation to Analysis) (in alphabetical order)
Annemarie Weise, Bart Verwaaijen, Christa Lanz, Deike Stoffers, Detlef Weigel, Eneza Mjema, Matthias Jordan, Maria Bonatelli, Rebecca Schwab, Svea Küper
Students in Courses and Theses (in alphabetical order)
Anica Schmauch, Brit Bömer, David Wessendorf, David Würpel, Elisabeth Leutemann, Fynn Schröder, Friedrich Schönauer, Hannah Reich, Jennifer Prautsch, Johannes Köther, Kevin Kinder, Kevin Krawetzke, Maike Cirksena, Phil Weikert, Robert Helm, Svea Männel, Tessa Rieger, Vanessa Brückner, Vincent Munzer, Wiebke Dammann