TY  - JOUR
AU  - Kotowicz, Malwina
AU  - Shumanska, Magdalena
AU  - Fengler, Sven
AU  - Kurkowsky, Birgit
AU  - Meyer-Berhorn, Anja
AU  - Moretti, Elisa
AU  - Blersch, Josephine
AU  - Schmidt, Gisela
AU  - Kreye, Jakob
AU  - van Hoof, Scott
AU  - Sánchez-Sendín, Elisa
AU  - Reincke, S Momsen
AU  - Krüger, Lars
AU  - Prüß, Harald
AU  - Denner, Philip
AU  - Fava, Eugenio
AU  - Stappert, Dominik
TI  - Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production.
JO  - PLOS ONE
VL  - 20
IS  - 7
SN  - 1932-6203
CY  - San Francisco, California, US
PB  - PLOS
M1  - DZNE-2025-00759
SP  - e0326678 -
PY  - 2025
AB  - Data management and sample tracking in complex biological workflows are essential steps to ensure necessary documentation and guarantee reusability of data and metadata. Currently, these steps pose challenges related to correct annotation and labeling, error detection, and safeguarding the quality of documentation. With growing acquisition of biological data and the expanding automatization of laboratory workflows, manual processing of sample data is no longer favorable, as it is time- and resource-consuming, prone to biases and errors, and lacks scalability and standardization. Thus, managing heterogeneous biological data calls for efficient and tailored systems, especially in laboratories run by biologists with limited computational expertise. Here, we showcase how to meet these challenges with a modular pipeline for data processing, facilitating the complex production of monoclonal antibodies from single B-cells. We present best practices for development of data processing pipelines concerned with extensive acquisition of biological data that undergoes continuous manipulation and analysis. Moreover, we assess the versatility of proposed design principles through a proof-of-concept data processing pipeline for automated induced pluripotent stem cell culture and differentiation. We show that our approach streamlines data management operations, speeds up experimental cycles and leads to enhanced reproducibility. Finally, adhering to the presented guidelines will promote compliance with FAIR principles upon publishing.
KW  - Antibodies, Monoclonal: biosynthesis
KW  - Humans
KW  - Animals
KW  - Induced Pluripotent Stem Cells: cytology
KW  - Induced Pluripotent Stem Cells: metabolism
KW  - B-Lymphocytes: immunology
KW  - B-Lymphocytes: cytology
KW  - B-Lymphocytes: metabolism
KW  - Reproducibility of Results
KW  - High-Throughput Screening Assays: methods
KW  - Cell Differentiation
KW  - Workflow
KW  - Automation
KW  - Antibodies, Monoclonal (NLM Chemicals)
LB  - PUB:(DE-HGF)16
C6  - pmid:40591905
DO  - DOI:10.1371/journal.pone.0326678
UR  - https://pub.dzne.de/record/279428
ER  -