Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production.
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 o...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326678 |
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| author | Malwina Kotowicz Magdalena Shumanska Sven Fengler Birgit Kurkowsky Anja Meyer-Berhorn Elisa Moretti Josephine Blersch Gisela Schmidt Jakob Kreye Scott van Hoof Elisa Sánchez-Sendín S Momsen Reincke Lars Krüger Harald Prüß Philip Denner Eugenio Fava Dominik Stappert |
| author_facet | Malwina Kotowicz Magdalena Shumanska Sven Fengler Birgit Kurkowsky Anja Meyer-Berhorn Elisa Moretti Josephine Blersch Gisela Schmidt Jakob Kreye Scott van Hoof Elisa Sánchez-Sendín S Momsen Reincke Lars Krüger Harald Prüß Philip Denner Eugenio Fava Dominik Stappert |
| author_sort | Malwina Kotowicz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e4f79910a64b43a08c50a298087a7029 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e4f79910a64b43a08c50a298087a70292025-08-20T03:29:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032667810.1371/journal.pone.0326678Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production.Malwina KotowiczMagdalena ShumanskaSven FenglerBirgit KurkowskyAnja Meyer-BerhornElisa MorettiJosephine BlerschGisela SchmidtJakob KreyeScott van HoofElisa Sánchez-SendínS Momsen ReinckeLars KrügerHarald PrüßPhilip DennerEugenio FavaDominik StappertData 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.https://doi.org/10.1371/journal.pone.0326678 |
| spellingShingle | Malwina Kotowicz Magdalena Shumanska Sven Fengler Birgit Kurkowsky Anja Meyer-Berhorn Elisa Moretti Josephine Blersch Gisela Schmidt Jakob Kreye Scott van Hoof Elisa Sánchez-Sendín S Momsen Reincke Lars Krüger Harald Prüß Philip Denner Eugenio Fava Dominik Stappert Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production. PLoS ONE |
| title | Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production. |
| title_full | Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production. |
| title_fullStr | Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production. |
| title_full_unstemmed | Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production. |
| title_short | Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production. |
| title_sort | gain efficiency with streamlined and automated data processing examples from high throughput monoclonal antibody production |
| url | https://doi.org/10.1371/journal.pone.0326678 |
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