Data-analysis strategies for image-based cell profiling

Juan C. Caicedo; Sam Cooper; Florian Heigwer; Scott Warchal; Peng Qiu; Csaba Molnar; Aliaksei S. Vasilevich; Joseph D. Barry; Harmanjit Singh Bansal; Oren Kraus; Mathias Wawer; Lassi Paavolainen; Markus D. Herrmann; Mohammad Rohban; Jane Hung; Holger Hennig; John Concannon; Ian Smith; Paul A. Clemons; Shantanu Singh; Paul Rees; Peter Horvath; Roger G. Linington; Anne E. Carpenter

doi:10.1038/NMETH.4397

Data-analysis strategies for image-based cell profiling

Juan C. Caicedo, Sam Cooper, Florian Heigwer, Scott Warchal, Peng Qiu, Csaba Molnar, Aliaksei S. Vasilevich, Joseph D. Barry, Harmanjit Singh Bansal, Oren Kraus, Mathias Wawer, Lassi Paavolainen, Markus D. Herrmann, Mohammad Rohban, Jane Hung, Holger Hennig, John Concannon, Ian Smith, Paul A. Clemons, Shantanu SinghPaul Rees, Peter Horvath, Roger G. Linington, Anne E. Carpenter^*

^*Corresponding author for this work

Research output: Contribution to journal › (Systematic) Review article › peer-review

Abstract

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Original language	English
Pages (from-to)	849-863
Number of pages	15
Journal	Nature Methods
Volume	14
Issue number	9
DOIs	https://doi.org/10.1038/NMETH.4397
Publication status	Published - Sept 2017

Keywords

HIGH-THROUGHPUT MICROSCOPY
HIGH-CONTENT SCREENS
DRUG RESPONSES
ILLUMINATION-CORRECTION
NORMALIZATION METHODS
GENETIC INTERACTIONS
STATISTICAL-METHODS
FUNCTIONAL-ANALYSIS
SOFTWARE TOOLS
ASSAY QUALITY

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10.1038/NMETH.4397Licence: CC BY

Cite this

Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., Wawer, M., Paavolainen, L., Herrmann, M. D., Rohban, M., Hung, J., Hennig, H., Concannon, J., Smith, I., Clemons, P. A., ... Carpenter, A. E. (2017). Data-analysis strategies for image-based cell profiling. Nature Methods, 14(9), 849-863. https://doi.org/10.1038/NMETH.4397

@article{e2672bd10a8440158fb35982c20079b1,

title = "Data-analysis strategies for image-based cell profiling",

abstract = "Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.",

keywords = "HIGH-THROUGHPUT MICROSCOPY, HIGH-CONTENT SCREENS, DRUG RESPONSES, ILLUMINATION-CORRECTION, NORMALIZATION METHODS, GENETIC INTERACTIONS, STATISTICAL-METHODS, FUNCTIONAL-ANALYSIS, SOFTWARE TOOLS, ASSAY QUALITY",

author = "Caicedo, {Juan C.} and Sam Cooper and Florian Heigwer and Scott Warchal and Peng Qiu and Csaba Molnar and Vasilevich, {Aliaksei S.} and Barry, {Joseph D.} and Bansal, {Harmanjit Singh} and Oren Kraus and Mathias Wawer and Lassi Paavolainen and Herrmann, {Markus D.} and Mohammad Rohban and Jane Hung and Holger Hennig and John Concannon and Ian Smith and Clemons, {Paul A.} and Shantanu Singh and Paul Rees and Peter Horvath and Linington, {Roger G.} and Carpenter, {Anne E.}",

year = "2017",

month = sep,

doi = "10.1038/NMETH.4397",

language = "English",

volume = "14",

pages = "849--863",

journal = "Nature Methods",

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Caicedo, JC, Cooper, S, Heigwer, F, Warchal, S, Qiu, P, Molnar, C, Vasilevich, AS, Barry, JD, Bansal, HS, Kraus, O, Wawer, M, Paavolainen, L, Herrmann, MD, Rohban, M, Hung, J, Hennig, H, Concannon, J, Smith, I, Clemons, PA, Singh, S, Rees, P, Horvath, P, Linington, RG & Carpenter, AE 2017, 'Data-analysis strategies for image-based cell profiling', Nature Methods, vol. 14, no. 9, pp. 849-863. https://doi.org/10.1038/NMETH.4397

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T1 - Data-analysis strategies for image-based cell profiling

AU - Caicedo, Juan C.

AU - Cooper, Sam

AU - Heigwer, Florian

AU - Warchal, Scott

AU - Qiu, Peng

AU - Molnar, Csaba

AU - Vasilevich, Aliaksei S.

AU - Barry, Joseph D.

AU - Bansal, Harmanjit Singh

AU - Kraus, Oren

AU - Wawer, Mathias

AU - Paavolainen, Lassi

AU - Herrmann, Markus D.

AU - Rohban, Mohammad

AU - Hung, Jane

AU - Hennig, Holger

AU - Concannon, John

AU - Smith, Ian

AU - Clemons, Paul A.

AU - Singh, Shantanu

AU - Rees, Paul

AU - Horvath, Peter

AU - Linington, Roger G.

AU - Carpenter, Anne E.

PY - 2017/9

Y1 - 2017/9

N2 - Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

AB - Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

KW - HIGH-THROUGHPUT MICROSCOPY

KW - HIGH-CONTENT SCREENS

KW - DRUG RESPONSES

KW - ILLUMINATION-CORRECTION

KW - NORMALIZATION METHODS

KW - GENETIC INTERACTIONS

KW - STATISTICAL-METHODS

KW - FUNCTIONAL-ANALYSIS

KW - SOFTWARE TOOLS

KW - ASSAY QUALITY

U2 - 10.1038/NMETH.4397

DO - 10.1038/NMETH.4397

M3 - (Systematic) Review article

SN - 1548-7091

VL - 14

SP - 849

EP - 863

JO - Nature Methods

JF - Nature Methods

IS - 9

ER -