Systematic Replication Enables Normalization of High-throughput Imaging Assays

Motivation High-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work we consider how an experimental design incorporating multiple levels of replication enables removal of technical artifacts from such image-based platforms. Results We develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application on MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays. Availability Raw data is on synapse (syn2862345), analysis code is on github (gjhunt/mema norm), a Docker image is available on dockerhub (gjhunt/memanorm). online.

[1]  Eliot T. McKinley,et al.  Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images , 2021, bioRxiv.

[2]  J. Kuhn,et al.  Comparison of Multiplexed Immunofluorescence Imaging to Chromogenic Immunohistochemistry of Skin Biomarkers in Response to Monkeypox Virus Infection , 2020, Viruses.

[3]  Gregory J. Hunt,et al.  Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data , 2020, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[4]  T. Lim,et al.  Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy , 2020, Cancer communications.

[5]  Joe W. Gray,et al.  RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging , 2019, Communications Biology.

[6]  M. Merad,et al.  Multiplexed Immunohistochemical Consecutive Staining on Single Slide (MICSSS): Multiplexed Chromogenic IHC Assay for High-Dimensional Tissue Analysis. , 2019, Methods in molecular biology.

[7]  Mark A. Dane,et al.  Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer. , 2019, Journal of visualized experiments : JoVE.

[8]  Joe W. Gray,et al.  Microenvironment-Mediated Mechanisms of Resistance to HER2 Inhibitors Differ between HER2+ Breast Cancer Subtypes , 2018, Cell systems.

[9]  Peter Bankhead,et al.  QuPath: Open source software for digital pathology image analysis , 2017, Scientific Reports.

[10]  P. Sorger,et al.  Cyclic Immunofluorescence (CycIF), A Highly Multiplexed Method for Single‐cell Imaging , 2016, Current protocols in chemical biology.

[11]  Anne E Carpenter,et al.  Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes , 2016, Nature Protocols.

[12]  David B. A. Epstein,et al.  Robust normalization protocols for multiplexed fluorescence bioimage analysis , 2016, BioData Mining.

[13]  Mark A. LaBarge,et al.  Fabrication and use of microenvironment microarrays (MEArrays). , 2012, Journal of visualized experiments : JoVE.

[14]  Ullrich Köthe,et al.  Ilastik: Interactive learning and segmentation toolkit , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[15]  Nicholas Hamilton,et al.  Quantification and its Applications in Fluorescent Microscopy Imaging , 2009, Traffic.

[16]  Takeaki Ishizawa,et al.  Real‐time identification of liver cancers by using indocyanine green fluorescent imaging , 2009, Cancer.

[17]  Tony J Collins,et al.  ImageJ for microscopy. , 2007, BioTechniques.

[18]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[19]  W. Pardridge,et al.  Examination of Blood‐Brain Barrier Transferrin Receptor by Confocal Fluorescent Microscopy of Unfixed Isolated Rat Brain Capillaries , 1998, Journal of neurochemistry.

[20]  R. Frisch,et al.  Partial Time Regressions as Compared with Individual Trends , 1933 .

[21]  Chapter Ten – Immunohistochemistry , 2015 .

[22]  M. Heilemann.  2.4 Super-Resolution Microscopy , 2012 .