Mission

Our central task is the management and coordination of the plant phenotyping infrastructure at IPK, in cooperation with internal and external partners we therefore contribute to the elucidation of plant growth and its plasticity under diverse environmental conditions. We work closely with the Research Group Bioinformatics and Information Technology (BIT) to implement a data management system for standardised documentation, sustainable storage and dissemination of phenomics datasets according to FAIR principles.

The focus of our own research is predominantly to study the plant response to abiotic stress, especially drought and heat. As this response is very environment-dependent dynamic, we use precision phenotyping systems that allow non-invasive observation under controlled environmental conditions. Our focus is on genetics and physiology and also on modelling plant growth and water use (Fig. 1). Plant materials are mainly plant genetic resources of cereals and legumes, which represent a valuable source for improving the stress tolerance of modern varieties via breeding approaches for the necessary adaptation to the ongoing climate change.

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Projects

Within the German Plant Phenotyping Network (DPPN), we are expanding the existing infrastructure for plant phenotyping and making it available to users from research and industry within the framework of collaborative projects and transnational access projects (EPPN2020).

Within the scope of STARGATE we will support our partner in Portugal (UCP) to become an established centre of excellence on the use of sensors, multi-omics and plant phenotyping technologies.

In the frame of INCREASE we are contributing to foster agricultural biodiversity in Europe by evaluating growth and drought tolerance of 200 genetic resources of chickpea via precision phenotyping and by conducting a citizen science experiment across Europe with 1,000 genetic resources of common bean.

In BRACE we are studying mechanisms of sustainability under abiotic stresses in the first wild barley multi-parental nested association mapping (NAM) population (HEB-25) using modern automated phenotyping approaches.


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Staff

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Publications

Author
Title
2022

Amitrano C, Junker A, DAgostino N, De Pascale S, De Micco V:

Integration of high-throughput phenotyping with anatomical traits of leaves to help understanding lettuce acclimation to a changing environment. Planta 256 (2022) 68. https://dx.doi.org/10.1007/s00425-022-03984-2

Arend D, Psaroudakis D, Memon J A, Rey-Mazón E, Schüler D, Szymanski J J, Scholz U, Junker A, Lange M:

From data to knowledge - big data needs stewardship, a plant phenomics perspective. Plant J. 111 (2022) 335-347. https://dx.doi.org/10.1111/tpj.15804

Badaeva E D, Konovalov F A, Knüpffer H, Fricano A, Ruban A S, Kehel Z, Zoshchuk S A, Surzhikov S A, Neumann K, Graner A, Hammer K, Filatenko A, Bogaard A, Jones G, Özkan H, Kilian B:

Genetic diversity, distribution and domestication history of the neglected GGAtAt genepool of wheat. Theor. Appl. Genet. 135 (2022) 755–776. https://dx.doi.org/10.1007/s00122-021-03912-0

Deblieck M, Szilagyi G, Andrii F, Saranga Y, Lauterberg M, Neumann K, Krugman T, Perovic D, Pillen K, Ordon F:

Dissection of a grain yield QTL from wild emmer wheat reveals sub-intervals associated with culm length and kernel number. Front. Genet. 13 (2022) 955295. https://dx.doi.org/10.3389/fgene.2022.955295

Langstroff A, Heuermann M C, Stahl A, Junker A:

Opportunities and limits of controlled-environment plant phenotyping for climate response traits. Theor. Appl. Genet. 135 (2022) 1–16. https://dx.doi.org/10.1007/s00122-021-03892-1

Lauterberg M, Saranga Y, Deblieck M, Klukas C, Krugman T, Perovic D, Ordon F, Graner A, Neumann K:

Precision phenotyping across the life cycle to validate and decipher drought-adaptive QTLs of wild emmer wheat (Triticum turgidum ssp. dicoccoides) introduced into elite wheat varieties. Front. Plant Sci. 13 (2022) 965287. https://dx.doi.org/10.3389/fpls.2022.965287

Narisetti N, Henke M, Neumann K, Stolzenburg F, Altmann T, Gladilin E:

Deep learning based greenhouse image segmentation and shoot phenotyping (DeepShoot). Front. Plant Sci. 13 (2022) 906410. https://dx.doi.org/10.3389/fpls.2022.906410

Neumann K, Schulthess A W, Bassi F M, Dhanagond S, Khlestkina E, Börner A, Graner A, Kilian B:

Genomic approaches to using diversity for the adaptation of modern varieties of wheat and barley to climate change. In: Ghamkhar K, Williams W, Brown A H D (Eds.): Plant Genetic Resources for the 21st Century. The OMICS Era. : Apple Academic Press Inc. (2022) ISBN 9781774910825, in press.

2021

Bellucci E, Aguilar O M, Alseekh S, Bett K, Brezeanu C, Cook D, De la Rosa L, Delledonne M, Dostatny D F, Ferreira J J, Geffroy V, Ghitarrini S, Kroc M, Kumar Agrawal S, Logozzo G, Marino M, Mary-Huard T, McClean P, Meglič V, Messer T, Muel F, Nanni L, Neumann K, Servalli F, Străjeru S, Varshney R K, Vasconcelos M W, Zaccardelli M, Zavarzin A, Bitocchi E, Frontoni E, Fernie A R, Gioia T, Graner A, Guasch L, Prochnow L, Opperman M, Susek K, Tenaillon M, Papa R:

The INCREASE project: Intelligent collections of food-legume genetic resources for European agrofood systems. Plant J. 108 (2021) 646-660. https://dx.doi.org/10.1111/tpj.15472

Cortinovis G, Oppermann M, Neumann K, Graner A, Gioia T, Marsella M, Alseekh S, Fernie A R, Papa R, Bellucci E, Bitocchi E:

Towards the development, maintenance, and standardized phenotypic characterization of single-seed-descent genetic resources for common bean. Curr. Protoc. 1 (2021) e133. https://dx.doi.org/10.1002/cpz1.133

Dodig D, Božinović S, Nikolić A, Zorić M, Vančetović J, Ignjatović-Micić D, Delić N, Weigelt-Fischer K, Altmann T, Junker A:

Dynamics of maize vegetative growth and drought adaptability using image-based phenotyping under controlled conditions. Front. Plant Sci. 12 (2021) 652116. https://dx.doi.org/10.3389/fpls.2021.652116

Fadoul H E, Martínez Rivas F J, Neumann K, Balazadeh S, Fernie A R, Alseekh S:

Comparative molecular and metabolic profiling of two contrasting wheat cultivars under drought stress. Int. J. Mol. Sci. 22 (2021) 13287. https://dx.doi.org/10.3390/ijms222413287

Henke M, Neumann K, Altmann T, Gladilin E:

Semi-automated ground truth segmentation and phenotyping of plant structures using k-means clustering of eigen-colors (kmSeg). Agriculture 11 (2021) 1098. https://dx.doi.org/10.3390/agriculture11111098

Kroc M, Tomaszewska M, Czepiel K, Bitocchi E, Oppermann M, Neumann K, Guasch L, Bellucci E, Alseekh S, Graner A, Fernie A R, Papa R, Susek K:

Towards development, maintenance, and standardized phenotypic characterization of single-seed-descent genetic resources for lupins. Curr. Protoc. 1 (2021) e191. https://dx.doi.org/10.1002/cpz1.191

Li M, Hensel G, Melzer M, Junker A, Tschiersch H, Ruwe H, Arend D, Kumlehn J, Börner T, Stein N:

Mutation of the ALBOSTRIANS ohnologous gene HvCMF3 impairs chloroplast development and thylakoid architecture in barley. Front. Plant Sci. 12 (2021) 732608. https://dx.doi.org/10.3389/fpls.2021.732608

Li M, Ruwe H, Melzer M, Junker A, Hensel G, Tschiersch H, Schwenkert S, Chamas S, Schmitz-Linneweber C, Börner T, Stein N:

The Arabidopsis AAC proteins CIL and CIA2 are sub-functionalized paralogs involved in chloroplast development. Front. Plant Sci. 12 (2021) 681375. https://dx.doi.org/10.3389/fpls.2021.681375

Mayer G, Müller W, Schork K, Uszkoreit J, Weidemann A, Wittig U, Rey M, Quast C, Felden J, Glöckner F O, Lange M, Arend D, Beier S, Junker A, Scholz U, Schüler D, Kestler H A, Wibberg D, Pühler A, Twardziok S, Eils J, Eils R, Hoffmann S, Eisenacher M, Turewicz M:

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases. Brief. Bioinform. 22 (2021) bbab010. https://dx.doi.org/10.1093/bib/bbab010

Narisetti N, Henke M, Seiler C, Junker A, Ostermann J, Altmann T, Gladilin E:

Fully-automated root image analysis (faRIA). Sci. Rep. 11 (2021) 16047. https://dx.doi.org/10.1038/s41598-021-95480-y

Pommier C, Gruden K, Junker A, Coppens F, Finkers R, Hassani-Pak K, Faria D, Hancock J M, Beier S, Costa B, Miguel C, Chaves I, Davey R, Contreras-Moreira B:

ELIXIR Plant sciences 2020-2023 Roadmap [version 1; not peer reviewed]. F1000Research 10 (2021) 145. https://doi.org/10.7490/f1000research.1118482.1

Rebola-Lichtenberg J, Streit J, Schall P, Ammer C, Seidel D:

From facilitation to competition: the effect of black locust (Robinia pseudoacacia L.) on the growth performance of four poplar-hybrids (Populus spp.) in mixed short rotation coppice. New Forest. 52 (2021) 639-656. https://dx.doi.org/10.1007/s11056-020-09813-2

Sharma S, Schulthess A W, Bassi F M, Badaeva E D, Neumann K, Graner A, Özkan H, Werner P, Knüpffer H, Kilian B:

Introducing beneficial alleles from plant genetic resources into the wheat germplasm. Biology 10 (2021) 982. https://dx.doi.org/10.3390/biology10100982

2020

Arend D, König P, Junker A, Scholz U, Lange M:

The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition. GigaScience 9 (2020) giaa107. https://doi.org/10.1093/gigascience/giaa107

Henke M, Junker A, Neumann K, Altmann T, Gladilin E:

A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping. Plant Methods 16 (2020) 95. https://dx.doi.org/10.1186/s13007-020-00637-x

Mang C:

Characterization of high light acclimation capacity in Arabidopsis mutant candidates. (Master Thesis) Mittweida, Hochschule Mittweida, Fakultät Angewandte Computer- und Biowissenschaften (2020) 81 pp.

Mikołajczak K, Ogrodowicz P, Ćwiek-Kupczyńska H, Weigelt-Fischer K, Mothukuri S R, Junker A, Altmann T, Krystkowiak K, Adamski T, Surma M, Kuczyńska A, Krajewski P:

Image phenotyping of spring barley (Hordeum vulgare L.) RIL population under drought: selection of traits and biological interpretation. Front. Plant Sci. 11 (2020) 743. https://dx.doi.org/10.3389/fpls.2020.00743

Papoutsoglou E A, Faria D, Arend D, Arnaud E, Athanasiadis I N, Chaves I, Coppens F, Cornut G, Costa B V, Ćwiek-Kupczyńska H, Droesbeke B, Finkers R, Gruden K, Junker A, King G J, Krajewski P, Lange M, Laporte M-A, Michotey C, Oppermann M, Ostler R, Poorter H, Ramı́rez-Gonzalez R, Ramšak Ž, Reif J C, Rocca-Serra P, Sansone S-A, Scholz U, Tardieu F, Uauy C, Usadel B, Visser R G F, Weise S, Kersey P J, Miguel C M, Adam-Blondon A-F, Pommier C:

Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytol. 227 (2020) 260-273. https://dx.doi.org/10.1111/nph.16544

Psaroudakis D:

A multi-omics perspective on Arabidopsis drought response: discovering novel regulators with a machine learning based gene-to-phene approach. (Master Thesis) Mittweida, Hochschule Mittweida, Fakultät Angewandte Computer- und Biowissenschaften (2020) 57 pp.

Psaroudakis D, Liu F, König P, Scholz U, Junker A, Lange M, Arend D:

isa4j: a scalable Java library for creating ISA-Tab metadata [version 1; peer review: 2 approved]. F1000Research 9(ELIXIR) (2020) 1388. https://doi.org/10.12688/f1000research.27188.1

2019

Dodig D, Bozinovic S, Nikolic A, Zoric M, Vancetovic J, Ignjatovic-Micic D, Delic N, Weigelt-Fischer K, Junker A, Altmann T:

Image-derived traits related to mid-season growth performance of maize under nitrogen and water stress. Front. Plant Sci. 10 (2019) 814. https://dx.doi.org/10.3389/Fpls.2019.00814

Ghaffar M, Schüler D, König P, Arend D, Junker A, Scholz U, Lange M:

Programmatic access to FAIRified digital plant genetic resources. J. Integr. Bioinform. 16 (2019) 20190060. https://dx.doi.org/10.1515/jib-2019-0060

Henke M, Junker A, Neumann K, Altmann T, Gladilin E:

Comparison and extension of three methods for automated registration of multimodal plant images. Plant Methods 15 (2019) 44. https://dx.doi.org/10.1186/s13007-019-0426-8

Henke M, Junker A, Neumann K, Altmann T, Gladilin E:

Comparison of feature point detectors for multimodal image registration in plant phenotyping. PLoS One 14 (2019) e0221203. https://dx.doi.org/10.1371/journal.pone.0221203

Lobet G, Paez-Garcia A, Schneider H, Junker A, Atkinson J A, Tracy S:

Demystifying roots: A need for clarification and extended concepts in root phenotyping. Plant Sci. 282 (2019) 11-13. https://doi.org/10.1016/j.plantsci.2018.09.015

Narisetti N, Henke M, Seiler C, Shi R, Junker A, Altmann T, Gladilin E:

Semi-automated Root Image Analysis (saRIA). Sci. Rep. 9 (2019) 19674. https://dx.doi.org/10.1038/s41598-019-55876-3

2018

Henke M, Junker A, Neumann K, Altmann T, Gladilin E:

Automated alignment of multi-modal plant images using integrative phase correlation approach. Front. Plant Sci. 9 (2018) 1519. https://dx.doi.org/10.3389/fpls.2018.01519

Pommerrenig B, Junker A, Abreu I, Bieber A, Fuge J, Willner E, Bienert M D, Altmann T, Bienert G P:

Identification of rapeseed (Brassica napus) cultivars with a high tolerance to boron-deficient conditions. Front. Plant Sci. 9 (2018) 1142. https://dx.doi.org/10.3389/fpls.2018.01142

Shi R, Junker A, Seiler C, Altmann T:

Phenotyping roots in darkness: disturbance-free root imaging with near infrared illumination. Funct. Plant Biol. 45 (2018) 400-411. https://doi.org/10.1071/FP17262

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