Using CellProfiler for Biological Image Analysis Quantitative - - PDF document

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Mark-Anthony Bray, Ph.D Imaging Platform, Broad Institute Cambridge, Massachusetts, USA

mbray@broadinstitute.org

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Using CellProfiler for Biological Image Analysis

Quantitative Analysis of Large-Scale Biological Image Data

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Summary

• Background on image-based screening
• Introduction to CellProfiler considerations

in image analysis

• Construction and use of a pipeline for

analyzing typical image data

• Measurement export and preparation for

additional analysis

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Images Contain A Wealth Of Information

http://www.microscopyu.com Image: Javier Irazoqui

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Visual Appearance Indicates Biological State

• Automatic image analysis is

– Objective – Quantitative, with statistics – Can measure multiple properties at once for every cell – Distinguishes subtle changes, even those undetectable by eye – Faster, less tedious

• Images contain a wealth of

biological information

• That information can be

quantified

Localization … + hundreds of other features mRNA or protein levels morphology

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Cells or organisms in multiwell plates, each well treated with a gene or chemical perturbant

Automated microscopy (any manufacturer)

High-Content Screening

Data exploration & machine learning

Anne Carpenter Ray Jones

Cell measurements

(size, shape, intensity, texture, etc.)

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Software Overview

• Available from www.cellprofiler.org
• Free, open source (Python)
• Software available for Windows, Mac and Linux

Image Analysis & Quantification Image-centric Data Analysis

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CellProfiler: Overview

• Process large sets of images
• Identifies and measures objects
• Export data for further analysis
• Goal: Provide powerful image analysis methods with a

user-friendly interface

• Philosophy: Measure everything, ask questions later...
• Support data analysis based on individual cells

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Typical CellProfiler Pipeline Workflow

• For image-based assays, the

basic objective is always to

– Identify cells/organisms – Measure feature(s) of interest

• The uniqueness of each

assay comes in

– Deciding what compartments to identify and how to identify them – Determining which measure(s) are most useful to identify interesting samples

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Typical CellProfiler Pipeline Workflow

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The CellProfiler Interface

• Pipeline panel: Displays modules in pipeline

– Modules executed in order from top to bottom

Change module position Add or remove modules Module help

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Load pipeline by double-clicking on it View images by double-clicking on the filename

The CellProfiler Interface

• File panel: Displays files in default image folder

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The CellProfiler Interface

• The figure window

has additional menu options

• Toolbar menu:

Pan, zoom in/out

• CellProfiler Image

Tools

– Image Tool (also displayed by clicking on image) – Interactive zoom – Show pixel data (location, intensity)

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The CellProfiler Interface

• Folder panel: Change default input and output directories

– Usually these should be separate folders

Input folder: Contains images to be analyzed Output folder: Contains the output file plus exported data and images

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The CellProfiler Interface

• Settings panel: View and change settings for each module

– Clicking on a different module updates the settings view

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Module Categories

• File processing: Image

input, file output

• Image processing: Often

used for pre-processing prior to object identification

• Object processing:

Identification, modification

• f objects of interest
• Measurement: Collection
• f measurements from
• bjects of interest
• Data Tools: Measurement

exploration, measurement

• utput

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The First Module: LoadImages

• Related how? Depending on the imaging device, one file

may represent

– One channel at one imaging location – Multiple channels at one imaging location – Multiple channels at multiple locations – Etc…

• Loads an image set

̶ A group of related images to be processed

DNA GFP

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The First Module: LoadImages

• Can use text matching to define the difference between images in a set

All images stained for GFP have the text Channel1- in the name Same for DNA images (Channel2-) Assign each image a meaningful name for downstream reference

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Object Identification

• Once the images are loaded, how do you find objects of

interest?

• Step 1: Distinguish the

foreground from the background by picking a good threshold

• Step 2: Identify objects as

regions brighter than the threshold

• Step 3: Cut and join
• bjects to “improve” their

shape

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Primary Object Identification

• Many options for thresholding, cut and join methods, etc.

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Thresholding

• Definition: Division of the

image into background and foreground

• Method: Pick the method that provides the best results

– Otsu: Default - Good for readily identifiable foreground / background – Background, RobustBackground: Good for images in which most of the image is comprised of background

• What is the best threshold

value for dividing the intensity into foreground and background pixels?

Pixel values Frequency

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Thresholding

• Correction factor

– Multiplication factor applied to threshold – Adjusts threshold stringency/leniency – Setting this factor is empirical

• Upper/lower bounds

– Set safety limits on automatic threshold to guards against false positives – Helpful for unexpected images: Empty wells, images with dramatic artifacts, etc

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Object Separation

• We need to distinguish multiple objects

contained in the same “clump”

Images from Carolina Wahlby

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• Once the foreground objects have been

identified, what next?

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Object Separation

• Two step process in “de-clumping”
• 1. Identification of the objects in a clump
• 2. Drawing boundaries between the clumped objects

Adjust settings to “de-clump” objects

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Object Separation

– Intensity: Works best if

• bjects are brighter at

center, dimmer at edges – Shape: Works best if

• bjects have

indentations where clumps touch (esp. if

• bjects are round)

Peaks

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Indentations

• Clump identification: Two options

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• •

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Object Separation

– Distance: Draws boundary lines midway between

• bject centers

– Intensity: Draws boundary lines at dimmest line between

• bjects
• Test Mode allows users to view results of all

setting combinations

• Drawing boundaries: Two options

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• •

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Object Separation

• Additional separation settings: Adjust these

settings if objects are being incorrectly split into pieces or merged together

Original image Smoothing filter size = 4 Smoothing filter size = 8

• Smoothing: Increase to reduce intensity

irregularities which produce over-segmentation

• f objects

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Object Separation

• Suppress Local Maxima

– Smallest distance allowed between object intensity peaks to be considered one object rather than a clump – Decrease to reduce improper merging of objects in clumps

Original image Maxima distance = 4 Maxima distance = 8

Maxima

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Object Separation

• Adjusting can produce more improper segmentation than

it solves

• The proper settings are usually a matter of trial and error

– The automatic settings are a good starting point, though

• However….

Original image Smoothing filter size = 4 Smoothing filter size = 8

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Filtering Invalid Objects

• See FilterObjects module for more advanced filtering options

Discard objects that fail size criterion or touch the image border

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Primary Object Identification

• Segmented objects are

colored

– Shows if each object has been identified and separated properly

• Outlines: Valid objects

– Green: Valid – Yellow: Invalid – Touching border – Red: Invalid – Size criterion

• Also outputs object count

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Secondary Object Identification

• Goal: Identify cell boundaries by “growing” primary objects

– Nuclei typically more uniform in shape, more easily separated than cells

• Approach: Segment nuclei → Seeds for cell segmentation by using a

cell stain channel

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Secondary Object Identification

• Methods

– Distance-N: Ignores image information

• Useful in cases where no cell

stain is present

– Watershed, propagate, Distance-B: Uses image information

• Finds dividing lines between
• bjects and background /

neighbors

• Test mode allows user to

view results of all methods

Propagation Distance-N

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Tertiary Object Identification

• Goal: Identify tertiary objects by removing

the primary objects from secondary objects

– “Subtract” the nuclei objects from cell objects to obtain cytoplasm

Cells Nuclei Cytoplasm — ═

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Pixel-Based Image Classification

• For images where a threshold cannot be found…
• CellProfiler is packaged with ilastik, a pixel-based

classification tool

– User manually labels regions of image – ilastik uses features to distinguish regions and create a classifier – Classifier used as input into ClassifyPixels module – Currently, Windows only

DIC ilastik Foreground/background mask

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Measurement Modules: Object Morphology

Select the objects to measure

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Module: MeasureObjectAreaShape

• Goal: Measure morphological features such as

– Area – Perimeter – Eccentricity – MajorAxisLength – MinorAxisLength – Orientation – FormFactor: Compactness measure, circle = 1, line = 0

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Measurement Modules: Object Intensity

Select the image to measure from Select the objects to measure

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Module: MeasureObjectIntensity

• Goal: Measure object intensity features such as

– Integrated intensity: Sum of the pixel intensities within an object – Mean, median, standard deviation intensities – Maximal and minimal pixel intensities – Lower/Upper quartile

• The object intensity may be obtained from any

image, not just the image used to identify the

• bject

– Example: Ph3 intensity may be measured using the nuclei objects

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Measurement Modules: Object Texture

Select the image to measure from Select the objects to measure Select the spatial scale

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MeasureObjectTexture

• Goal: Determine whether the staining pattern is

smooth on a particular scale

• Selection of the appropriate texture scale is

essentially empirical

– A higher number measures larger patterns of texture – Smaller numbers measure more localized (finer) patterns of texture

• Can also add several texture modules to the

pipeline, each measuring a different texture scale

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Other Measurement Modules

• CalculateMath: Arithmetic operations for measurements
• CalculateStatistics: Assay quality (V and Z' factors) and

dose response data (EC50) for all measurements

• Image-based measures

– MeasureImageAreaOccupied – MeasureImageGranularity – MessureImageIntensity

• Object-based measures

– MeasureCorrelation – MeasureObjectNeighbors – MeasureRadialDistribution

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Data Export Modules

• User may output images or image measurements

Select the objects to export

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Measurement Display

• The average

measurements for all objects in the image are displayed in the figure window

• However, the

individual measurements for each object are stored in the output file

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Data Export Modules

• Goal: Retain images of intermediate image processing

steps for quality control or save measurements for later analysis and exploration

• SaveImages: Writes an image to a file

– Intermediate images in the pipeline are not saved unless requested – Choice of many image formats to write → module can be used as an image format converter

• ExportToSpreadsheet: Export measurements as a

comma-separated file readable by spreadsheet programs

• ExportToDatabase: Export measurements as a per-
• bject and per-table plus configuration file for a MySQL
• r SQLite database

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Cluster Computing

• If processing time is too great on a single

computer, then run the pipeline on a cluster

– Install CellProfiler on a computing cluster – Add the ExportToDatabase module – Add/configure the CreateBatchFiles module to the end

• f the pipeline

– Run the pipeline to create a batch file – Submit the batches to your cluster for processing – Check the progress of processing

• For really big screens, it is necessary to process

images in batches on a computing cluster.

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Megakaryocyte Polyploidization: Leukemia

DNA stain, with

• utlines

identifying the nuclei

Martha Vokes Mark Bray

SU6656 (positive control)

Project in progress

per-cell DNA content (log2) proportion of cells

SU6656 DMSO DMSO (negative control)

John Crispino, Northwestern University Jeremy Wen, postdoc

Status: Identified 206 polyploidization regulators from 10k compound screen

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47 Images from BioImage SBS image analysis comparison. Thanks to Ilya Ravkin

Carpenter, et al., Genome Biology, 2006

Measuring Morphology

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Upcoming: CellProfiler 2.1

• Major changes

– Streamlined loading of images and associated data – Takes advantage of multiple CPU cores, so very large sets of images can now be processed

• n a regular desktop

computer

• Release scheduled for early 2014

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Final Notes

• Where to get help

– Access help from the CellProfiler main window – Ask for help on the CellProfiler.org forum

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Annual Support Training Plan

• Contact imagingadmin@broadinstitute.org for more details

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Image assay development

Apply image analysis methods to biological questions Mark Bray Anne Carpenter David Logan

Algorithm development & software engineering

Develop & test new image analysis and data mining methods and create open-source software tools

IT/Administration

Matthew Veneskey Vebjørn Ljoså Carolina Wählby

Carpenter Lab / Broad Institute Imaging Platform

Lee Kamentsky Shantanu Singh

Director

Holger Hennig

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Acknowledgments

• Free, at www.cellprofiler.org:

Recent funding for this work provided by: NIH NIGMS (Carpenter: R01 GM089652 and Wahlby: R01 GM095672) The Broad Institute of Harvard and MIT

Many thanks to our many biology collaborators who provide images Contact:

imagingadmin@broadinstitute.org