Rapid Analysis and Exploration of Fluorescence Microscopy Images

Summary

Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

Abstract

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.

Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Introduction

Over the past decade, rapid advances in imaging technology have given many labs the ability to perform high-throughput microscopy. The challenge has now shifted from one of imaging to one of analysis. How can we characterize, compare, and explore the torrent of complex yet subtle phenotypes generated by a typical high-throughput imaging screen?

A number of image-analysis and informatics platforms have given users sophisticated toolboxes^1-3 for extracting biological information from large collections of images. Yet many significant obstacles remain in analyzing data from high-throughput image-based screens. Difficulties involved w....

Protocol

1. Preparing Samples and Imaging

1. Cell culture
   1. Split cultures routinely and maintain at 20-70% confluence. One day prior to the experiment, grow HeLa cells in DMEM (Dulbecco's Modified Eagle Medium, see Table of Materials) and supplemented with 10% FBS (Fetal Bovine Serum) and 1x penicillin/streptomycin.
   2. On day of experiment, split cells growing in culture (about 50% confluent).
   3. Wash cells with PBS (Phosphate Buffered Saline), expose to approximately 2 ml of trypsin/EDTA (Ethylenediaminetetraacetic acid) for a few seconds. Aspirate excess trypsin/EDTA and keep cells bathed in a thin film of trypsin/EDTA in the hood at R....

Results

Here we tested the ability of this workflow to group drugs based on their mechanism of action. HeLa cells were seeded in 30 wells of a 384-well plate and stained for DNA/actin/α-tubulin. The specific primary antibodies, fluorescently-labeled secondary antibodies, and other fluorescent stains that were used are listed in Table of Materials. Wells were treated with 15 drugs belonging to three mechanistic classes (histone deacetylase inhibiting, microtubule targeting and DNA damaging) for 24 hr and imaged using an epifluore.......

Discussion

The workflow described here allows fast and easy characterization and comparison of microscopy images. We first demonstrated how this workflow can help experiment optimization, for example by quickly performing quality control on microscopy images. Next, we demonstrated its potential for analyzing high-throughput screening data: we were able to group drugs based on their mechanism of action. The grouping of drugs was comparable to that found using more complex methods⁶, even though PhenoRipper was orders of ma.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
DMEM, High Glucose	Invitrogen	11965	High Glucose, L-Glutamine
Marker Hoechst 33342	Invitrogen	H1399	DNA stain, dilution 1:2,000 of 5 mg/ml stock
Marker phalloidin-Alexa Fluor 488	Invitrogen	A12379	Conjugated phallotoxin, dilution 1:200
Primary Antibody α-tubulin	Sigma	T9026	Primary Ab, mouse monoclonal, dilution 1:200
Secondary Antivody anti-Mouse TRITC	Jackson	115-025-166	Conjugated secondary Ab, 0.5 mg in 1 ml PBS + 1 ml glycerol
Aldosterone			concentration used: 0.2 μM
Apicidin			concentration used: 20 μM
Colchicine			concentration used: 0.16 μM
Cortisol			concentration used: 0.2 μM
Dexamethasone			concentration used: 0.2 μM
Docetaxel			concentration used: 0.2 μM
M344			concentration used: 20 μM
MS-275			concentration used: 5 μM
Nocodazole			concentration used: 0.3 μM
Prednisolone			concentration used: 0.2 μM
RU486			concentration used: 0.2 μM
Scriptaid			concentration used: 70 μM
Taxol			concentration used: 0.3 μM
Trichostatin A			concentration used: 0.2 μM
Vinorelbine			concentration used: 0.3 μM