Using CellProfiler for Biological Image Analysis Quantitative Analysis of Large-Scale Biological Image Data

Transcription

1 Using CellProfiler for Biological Image Analysis Quantitative Analysis of Large-Scale Biological Image Data Mark-Anthony Bray, Ph.D Imaging Platform, Broad Institute Cambridge, Massachusetts, USA ,

2 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 2 2

3 Images Contain A Wealth Of Information Image: Javier Irazoqui 3 3

4 Visual Appearance Indicates Biological State Localization mrna or protein levels morphology + hundreds of other features Images contain a wealth of biological information That information can be quantified 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 4 4

5 High-Content Screening Cells or organisms in multiwell plates, each well treated with a gene or chemical perturbant Automated microscopy (any manufacturer) Cell measurements (size, shape, intensity, texture, etc.) Data exploration & machine learning Ray Jones Anne Carpenter 5 5

6 Software Overview Image Analysis & Quantification Image-centric Data Analysis Available from Free, open source (Python) Software available for Windows, Mac and Linux 6 6

7 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 7 7

8 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 8 8

9 Typical CellProfiler Pipeline Workflow 9 9

10 The CellProfiler Interface Module help Add or remove modules Change module position Pipeline panel: Displays modules in pipeline Modules executed in order from top to bottom 10 10

11 The CellProfiler Interface Load pipeline by double-clicking on it View images by double-clicking on the filename File panel: Displays files in default image folder 11 11

12 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) The CellProfiler Interface 12 12

13 The CellProfiler Interface Input folder: Contains images to be analyzed Output folder: Contains the output file plus exported data and images Folder panel: Change default input and output directories Usually these should be separate folders 13 13

14 The CellProfiler Interface Settings panel: View and change settings for each module Clicking on a different module updates the settings view 14 14

15 File processing: Image input, file output Image processing: Often used for pre-processing prior to object identification Object processing: Identification, modification of objects of interest Measurement: Collection of measurements from objects of interest Data Tools: Measurement exploration, measurement output Module Categories 15 15

16 The First Module: LoadImages Loads an image set A group of related images to be processed DNA GFP 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 16 16

17 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 Assign each image a meaningful name for downstream reference Same for DNA images (Channel2-) 17 17

18 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 objects to improve their shape 18 18

19 Primary Object Identification Many options for thresholding, cut and join methods, etc

20 Definition: Division of the image into background and foreground Thresholding What is the best threshold value for dividing the intensity into foreground and background pixels? Frequency Pixel values 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 20 20

21 Correction factor Thresholding 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 21 21

22 Object Separation Once the foreground objects have been identified, what next? We need to distinguish multiple objects contained in the same clump Images from Carolina Wahlby 22 22

23 Object Separation Adjust settings to de-clump objects Two step process in de-clumping 1. Identification of the objects in a clump 2. Drawing boundaries between the clumped objects 23 23

24 Object Separation Clump identification: Two options Intensity: Works best if objects are brighter at center, dimmer at edges 1 Shape: Works best if objects have indentations where clumps touch (esp. if objects are round) 1 2 Indentations 1 2 Peaks 24 24

25 Object Separation Drawing boundaries: Two options 1 Distance: Draws boundary lines midway between object centers Intensity: Draws boundary lines at dimmest line between objects Test Mode allows users to view results of all setting combinations 25 25

26 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 of objects 26 26

27 Object Separation Maxima Original image Maxima distance = 4 Maxima distance = 8 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 27 27

28 Object Separation However. Original image Smoothing filter size = 4 Smoothing filter size = 8 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 28 28

29 Filtering Invalid Objects Discard objects that fail size criterion or touch the image border See FilterObjects module for more advanced filtering options 29 29

30 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 30 30

31 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 31 31

32 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 objects and background / neighbors Distance-N Test mode allows user to view results of all methods Propagation 32 32

33 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 33 33

34 Pixel-Based Image Classification For images where a threshold cannot be found DIC ilastik Foreground/background mask 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 34 34

35 Measurement Modules: Object Morphology Select the objects to measure 35 35

36 Module: MeasureObjectAreaShape Goal: Measure morphological features such as Area Perimeter Eccentricity MajorAxisLength MinorAxisLength Orientation FormFactor: Compactness measure, circle = 1, line =

37 Measurement Modules: Object Intensity Select the image to measure from Select the objects to measure 37 37

38 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 object Example: Ph3 intensity may be measured using the nuclei objects 38 38

39 Measurement Modules: Object Texture Select the image to measure from Select the objects to measure Select the spatial scale 39 39

40 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 40 40

41 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 41 41

42 Data Export Modules Select the objects to export User may output images or image measurements 42 42

43 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 Measurement Display 43 43

44 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 perobject and per-table plus configuration file for a MySQL or SQLite database 44 44

45 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 of 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

46 Megakaryocyte Polyploidization: Leukemia DMSO SU6656 (negative control) (positive control) DNA stain, with outlines identifying the nuclei proportion of cells DMSO SU6656 Status: Identified 206 polyploidization regulators from 10k compound screen Martha Vokes Mark Bray per-cell DNA content (log2) Project in progress John Crispino, Northwestern University Jeremy Wen, postdoc 46 46

47 Measuring Morphology Images from BioImage SBS image analysis comparison. Thanks to Ilya Ravkin Carpenter, et al., Genome Biology,

48 Major changes Upcoming: CellProfiler 2.1 Streamlined loading of images and associated data Takes advantage of multiple CPU cores, so very large sets of images can now be processed on a regular desktop computer Release scheduled for early

49 Final Notes Where to get help Access help from the CellProfiler main window Ask for help on the CellProfiler.org forum 49 49

50 Annual Support Training Plan Contact for more details 50 50

51 Carpenter Lab / Broad Institute Imaging Platform Director Image assay development Apply image analysis methods to biological questions IT/Administration Anne Carpenter David Logan Mark Bray Matthew Veneskey Algorithm development & software engineering Develop & test new image analysis and data mining methods and create open-source software tools Lee Kamentsky Carolina Wählby Vebjørn Ljoså Shantanu Singh Holger Hennig 51 51

52 Acknowledgments Free, at Contact: Many thanks to our many biology collaborators who provide images Recent funding for this work provided by: NIH NIGMS (Carpenter: R01 GM and Wahlby: R01 GM095672) The Broad Institute of Harvard and MIT S.D.G