Data processing workflow for large-scale immune monitoring studies by mass cytometry

Paulina Rybakowska; Sofie Van Gassen; Katrien Quintelier; Yvan Saeys; Marta E. Alarcón-Riquelme; Concepción Marañón

doi:10.1016/j.csbj.2021.05.032

Data processing workflow for large-scale immune monitoring studies by mass cytometry

Paulina Rybakowska, Sofie Van Gassen, Katrien Quintelier, Yvan Saeys, Marta E. Alarcón-Riquelme^*, Concepción Marañón^*

^*Corresponding author for this work

Pulmonary Medicine

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies.

Original language	English
Pages (from-to)	3160-3175
Number of pages	16
Journal	Computational and Structural Biotechnology Journal
Volume	19
DOIs	https://doi.org/10.1016/j.csbj.2021.05.032
Publication status	Published - Jan 2021

Bibliographical note

Funding Information:
PR, MA, CM acknowledge support from the IMI2-JU project GA No 831434 (3TR) and IMI-JU project GA No 115565 (PRECISESADS). The JU receives support from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA. PR received support from EMBO (7966) short-term fellowships and from Consejería de Salud y Familias de Junta de Andalucía (EF-0091-2018) to perform 3 and 2 month internships, respectively, at the VIB-UGhent. The authors also acknowledge funding from Consejería de la Salud y Familias de la Junta de Andalucía (PIER-0118-2019) and Instituto de Salud Carlos III (PI18/00082), partly supported by European FEDER funds. These results are part of the the PhD thesis in Biomedicine at the University of Granada of PR. We are grateful to Olivia Santiago and Jose Díaz Cuéllar for technical support as a Cytometry Core Facility in Genyo research center.

Funding Information:
PR, MA, CM acknowledge support from the IMI2-JU project GA No 831434 (3TR) and IMI-JU project GA No 115565 (PRECISESADS). The JU receives support from the European Union's Horizon 2020 Research and Innovation Programme and EFPIA. PR received support from EMBO (7966) short-term fellowships and from Consejer?a de Salud y Familias de Junta de Andaluc?a (EF-0091-2018) to perform 3 and 2 month internships, respectively, at the VIB-UGhent. The authors also acknowledge funding from Consejer?a de la Salud y Familias de la Junta de Andaluc?a (PIER-0118-2019) and Instituto de Salud Carlos III (PI18/00082), partly supported by European FEDER funds. These results are part of the the PhD thesis in Biomedicine at the University of Granada of PR. We are grateful to Olivia Santiago and Jose D?az Cu?llar for technical support as a Cytometry Core Facility in Genyo research center.

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© 2021 The Authors

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Data processing workflow for large-scale immune monitoring studies by mass cytometryFinal published version, 3.95 MBLicence: CC BY-NC-ND

Cite this

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title = "Data processing workflow for large-scale immune monitoring studies by mass cytometry",

abstract = "Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies.",

author = "Paulina Rybakowska and {Van Gassen}, Sofie and Katrien Quintelier and Yvan Saeys and Alarc{\'o}n-Riquelme, {Marta E.} and Concepci{\'o}n Mara{\~n}{\'o}n",

note = "Funding Information: PR, MA, CM acknowledge support from the IMI2-JU project GA No 831434 (3TR) and IMI-JU project GA No 115565 (PRECISESADS). The JU receives support from the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme and EFPIA. PR received support from EMBO (7966) short-term fellowships and from Consejer{\'i}a de Salud y Familias de Junta de Andaluc{\'i}a (EF-0091-2018) to perform 3 and 2 month internships, respectively, at the VIB-UGhent. The authors also acknowledge funding from Consejer{\'i}a de la Salud y Familias de la Junta de Andaluc{\'i}a (PIER-0118-2019) and Instituto de Salud Carlos III (PI18/00082), partly supported by European FEDER funds. These results are part of the the PhD thesis in Biomedicine at the University of Granada of PR. We are grateful to Olivia Santiago and Jose D{\'i}az Cu{\'e}llar for technical support as a Cytometry Core Facility in Genyo research center. Funding Information: PR, MA, CM acknowledge support from the IMI2-JU project GA No 831434 (3TR) and IMI-JU project GA No 115565 (PRECISESADS). The JU receives support from the European Union's Horizon 2020 Research and Innovation Programme and EFPIA. PR received support from EMBO (7966) short-term fellowships and from Consejer?a de Salud y Familias de Junta de Andaluc?a (EF-0091-2018) to perform 3 and 2 month internships, respectively, at the VIB-UGhent. The authors also acknowledge funding from Consejer?a de la Salud y Familias de la Junta de Andaluc?a (PIER-0118-2019) and Instituto de Salud Carlos III (PI18/00082), partly supported by European FEDER funds. These results are part of the the PhD thesis in Biomedicine at the University of Granada of PR. We are grateful to Olivia Santiago and Jose D?az Cu?llar for technical support as a Cytometry Core Facility in Genyo research center. Publisher Copyright: {\textcopyright} 2021 The Authors",

year = "2021",

month = jan,

doi = "10.1016/j.csbj.2021.05.032",

language = "English",

volume = "19",

pages = "3160--3175",

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AU - Rybakowska, Paulina

AU - Van Gassen, Sofie

AU - Quintelier, Katrien

AU - Saeys, Yvan

AU - Alarcón-Riquelme, Marta E.

AU - Marañón, Concepción

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PY - 2021/1

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N2 - Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies.

AB - Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies.

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DO - 10.1016/j.csbj.2021.05.032

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Data processing workflow for large-scale immune monitoring studies by mass cytometry

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