CIP Advisory Group Meeting ALA Midwinter Conference Saturday, January 16 th , 2010 Boston, MA
High Content 2019 Annual Conference September 17 , Boston, MA … · 2019-09-18 · High Content...
Transcript of High Content 2019 Annual Conference September 17 , Boston, MA … · 2019-09-18 · High Content...
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High Content 2019
6th Annual Conference
September 17th-19th, Boston, MA
Joseph B. Martin Conference Center
Educational Program: Intro to HCS/HCA
Image and Data AnalysisMark-Anthony Bray, Ph.D
Novartis Institutes of BioMedical Research
Cambridge, Massachusetts, USA
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The Basic Skill Sets for an HCS Laboratory
9/11/2019 SBI2 High Content 2019 2|○○○○ | DDMMYY2MEAN.VarInten.CMFDAMEAN.Dif f IntenDensity .CMFDAMEAN.Av gInten.CMFDAMEAN.FiberLength.CMFDAMEAN.NeighborMinDist.CMFDAMEAN.IntenCoocContrast.ActinMEAN.SpotFiberAv gArea.ActinMEAN.SpotFiberTotalArea.ActinMEAN.TotalInten.CMFDAMEAN.VarInten.TubulinMEAN.Dif f IntenDensity .TubulinMEAN.Av gInten.TubulinMEAN.TotalInten.TubulinMEAN.FiberAlign1.TubulinMEAN.NeighborAv gDist.Actin.TubulinMEAN.NeighborVarDist.Actin.TubulinMEAN.Entropy Inten.TubulinMEAN.IntenCoocEntropy .ActinMEAN.Entropy Inten.ActinMEAN.IntenCoocEntropy .TubulinMEAN.MemberObjectAreaDif f .DAPIMEAN.VarRadialInten.ActinMEAN.MemberAv gTotalInten.DAPIMEAN.TotalInten.DAPIMEAN.MemberAv gAv gInten.DAPIMEAN.NeighborVarDist.TubulinMEAN.Av gInten.DAPIMEAN.MemberCount.DAPIMEAN.MemberAv gConv exHullPerimRatio.DAPIMEAN.Av gRadialInten.TubulinMEAN.EqSphereArea.CMFDAMEAN.Area.CMFDAMEAN.EqEllipseProlateVol.CMFDAMEAN.EqSphereVol.CMFDAMEAN.NeighborMinDist.ActinMEAN.EqCircDiam.CMFDAMEAN.Length.CMFDAMEAN.Width.CMFDAMEAN.EqEllipseOblateVol.CMFDAMEAN.SpotFiberCount.TubulinMEAN.NeighborMinDist.TubulinMEAN.Entropy Inten.CMFDAMEAN.Perim.CMFDAMEAN.NeighborMinDist.Actin.TubulinMEAN.EqEllipseLWR.CMFDAMEAN.SpotFiberTotalArea.TubulinMEAN.ShapeLWR.CMFDAMEAN.NeighborVarDist.CMFDAMEAN.SkewInten.CMFDAMEAN.SkewRadialInten.TubulinMEAN.FiberWidth.CMFDAMEAN.SpotFiberAv gArea.TubulinMEAN.KurtRadialInten.TubulinMEAN.KurtInten.CMFDAMEAN.NeighborAv gDist.TubulinMEAN.ShapeP2A.CMFDAMEAN.Conv exHullAreaRatio.CMFDAMEAN.Av gRadialInten.ActinMEAN.SpotFiberCount.ActinMEAN.FiberAlign1.ActinMEAN.TotalInten.ActinMEAN.VarInten.ActinMEAN.IntenCoocContrast.TubulinMEAN.Angle.CMFDAMEAN.Av gInten.ActinMEAN.Dif f IntenDensity .ActinMEAN.MemberAv gConv exHullAreaRatio.DAPIMEAN.MemberAv gArea.DAPIMEAN.MemberAv gCircleDiam.DAPIMEAN.KurtRadialInten.ActinMEAN.NeighborAv gDist.ActinMEAN.FiberAlign2.TubulinMEAN.Conv exHullPerimRatio.CMFDAMEAN.MemberAv gShapeBFR.DAPIMEAN.ShapeBFR.CMFDAMEAN.NeighborAv gDist.CMFDAMEAN.NeighborVarDist.ActinMEAN.MemberAv gShapeP2A.DAPIMEAN.MemberAv gShapeLWR.DAPIMEAN.MemberAv gEllipseLWR.DAPIMEAN.VarRadialInten.TubulinMEAN.FiberAlign2.ActinMEAN.MemberObjectAreaRatio.DAPIMEAN.KurtInten.TubulinMEAN.SkewInten.TubulinMEAN.IntenCoocASM.TubulinMEAN.IntenCoocMax.TubulinMEAN.SkewInten.ActinMEAN.KurtInten.ActinValidObjectCountMEAN.IntenCoocMax.ActinMEAN.IntenCoocASM.Actin
* An Introduction To High Content Screening: Imaging Technology, Assay
Development and Data Analysis in Biology and Drug Discovery (2015), Haney,
S.A, Bowman, D. Chakravarty, A. Davies, A. and Shamu, C.E. John Wiley
Press, NY, NY (in production)
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The HCS Laboratory
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Plate Handler Robot HCS Imager
Plate Visualization / Image Analysis
Workstations
Image Analysis
Computer Cluster
Data Management
System
Network File Server
Network
Instrument Control
Workstation
The Wet Lab
Reagents, protocols,
assay optimization
Hardware and
Image Acquisition
Assay Types and Assay Development
Image and Data Analysis
* An Introduction To High Content Screening And Analysis Techniques:
Practical Advice and Examples, Haney, S.A, Bowman, D. Chakravarty, A.
Davies, A. and Shamu, C.E. John Wiley Press, NY, NY (in production)
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Outline
• The image as quantitative data
• Identifying the image foreground
• Splitting object clusters
• Identifying cellular compartments
• Measurement extraction
• Statistical analysis
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Outline
• The image as quantitative data
• Identifying the image foreground
• Splitting object clusters
• Identifying cellular compartments
• Measurement extraction
• Statistical analysis
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Images Contain A Wealth Of Information
9/11/2019 SBI2 High Content 2019 6http://www.microscopyu.com Image: Javier Irazoqui
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Fundamental Steps
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Making measurements,
feature extractionLENGTH, WIDTH,
CURVATURE, TEXTURE…
Result
Object detection, segmentation
(including 3D and tracking over time)
Preprocessing
Image acquisition
Object
classification,
interpretation,
recognition
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Image Analysis Software Solutions
• Application modules
• Good for someone new to HCS, or just needs turn-key solution
• Polished user interfaces, fast
• Often integrated with microscope hardware
• Validated, standard assays
• Canned approach: No detailed knowledge re: image analysis needed
• Development environment
• Good for new assay development, more flexible approach
• Customizable assay design instead of pre-built solution
• Typically, combine modules into a workflow
• Higher “cost-of-entry”: Time involved to understand image analysis
details, language, scripting…
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Image Analysis Software Solutions
• Commercial• PerkinElmer Acapella• Definiens Tissue Studio• Molecular Devices Metamorph• GE InCell Analyzer• Media Cybernetics ImagePro+• Mathworks MATLAB• Adobe Photoshop• Etc
• Open-source• ImageJ/FIJI• CellProfiler• BioImageXD• Icy• Vaa3D• ITK/VTK• KNIME• Etc
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Not comprehensive!
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Outline
• The image as quantitative data
• Identifying the image foreground
• Splitting object clusters
• Identifying cellular compartments
• Measurement extraction
• Statistical analysis
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Object Identification
• Also known as segmentation: Partitioning an image into regions of interest
• Step 1: Distinguish the foreground from the background by picking a good threshold
• Foreground: Regions where I(x,y) > threshold T
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Illumination Correction
• Nonuniformities introduced in the optical path of the sample, microscope, and/or camera
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• Example: Uneven illumination from left to right – Can lead to inaccurate segmentation and measurements
– Cell at (a) is brighter than (b) even if cells have same amount of fluorescent material
(a) (b)
Carpenter et al, Genome Biology 2006, 7:R100
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Illumination Correction
• Recommendations
• Create new illumination correction if switching microscopes
• Perform per-plate correction
• Perform per-channel correction, as absolute illumination intensities may differ between channels9/11/2019 SBI2 High Content 2019 13
Images from Carolina Wahlby
Input image Output image
=÷
Approximation of
backgroundAverage many images
Fit continuous function to result
or smooth heavily
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Background Subtraction
• Top-hat (“rolling ball”) filtering
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Image Thresholding
What is the best threshold value for
dividing the intensity histogram into
foreground and background pixels?
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Raw input
image
Thresholded
binary image
0: Background
1: Objects
Labeled objects
Colored ROI:
Connected
pixels
Here?
Or here?
Pixel values
Fre
qu
en
cy
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Pixel-Based Image Classification
• For images where a threshold cannot be found…
• Machine-learning tools can be helpful, e.g., ilastik• User manually labels regions of image• Suite of features are used to distinguish regions and create a classifier
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Sommer and Gerlich, JCS 2013, 126:1
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Outline
• The image as quantitative data
• Identifying the image foreground
• Splitting object clusters
• Identifying cellular compartments
• Measurement extraction
• Statistical analysis
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Separating Touching Objects
• Once the foreground blobs have been identified, what next?• Thresholding is not sufficient to separate clustered or touching objects
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• Step 2: Distinguish multiple objects contained in the same foreground blob
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Watershed Segmentation
• Consider the image as a surfacewith basins….
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••
•
••
•
•
•
http://www.svi.nl/watershed
Images from Carolina Wahlby
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Separating Touching Objects
• Identifying objects: Some options
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Peaks
2
1 2
Indentations
1
• Intensity-based: Works best if objects are brighter at center, dimmer at edges
• Shape-based: Works best if objects have indentations where objects touch (esp. if objects are round)
1
••
•
••
•
•
•
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Outline
• The image as quantitative data
• Identifying the image foreground
• Splitting object clusters
• Identifying cellular compartments
• Measurement extraction
• Statistical analysis
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Identifying Cell Objects
• Nuclei more easily separated than cells
• DNA markers are specific
• Yield good foreground/background contrast
• Uniform shape
• Identifying cells is more difficult• Available markers often lower contrast
• Unclear boundaries between cells, depending on the cell type and culture conditions
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Secondary Object Identification
• “Growing” the primary objects to identify cell boundaries
• Use segmented nuclei as “seeds” by using a cell stain channel
• Some assays do not require precise cell ID
• E.g, is a protein located in nucleus or cytoplasm?
• Produce proxy cells by growing nuclei by Npixels if no cell stain available
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Identifying Subcellular Structures
• With appropriate markers, other
subcellular compartments can be labeled
• These can be identified using the same
methods already mentioned
• Consider using enclosing object as mask
for better pre-processing, thresholding
• Make sure to assign subfeatures to
enclosing objects
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Pre-processing
Sub-object ID
Sub-object relation
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Outline
• The image as quantitative data
• Identifying the image foreground
• Splitting object clusters
• Identifying cellular compartments
• Measurement extraction
• Statistical analysis
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Measuring Object Counts
• Most common readout• # of cells per image/well
• # of organelles per image/well
• # of organelles per cell
• Number of objects per image/well is often a useful readout for QC purposes
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Measuring Object Morphology
• Reduce an aspect of object shape to a single value
• Example features
• Area: Pixel coverage of object
• Perimeter: Length of object boundary
• Eccentricity: Object “oblongness”
• Major, minor axis length: Object elongation
• Form factor: Measure of compactness
• Zernike features
• Objects touching the image border should be excluded if shape is important
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http://www.perkinelmer.co.uk/
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Measuring Object Intensity
• Example features• Integrated (total) intensity: Sum of the object
pixel ∝ amount of substance labeled
• Mean, median, standard deviation intensities
• Lower/upper intensity quartiles
• Correlation coefficients between channels: Colocalization
• Make sure to illumination correct beforehand
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• Related to the amount of marker at a pixel location
Images courtesy of Ilya Ravkin
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Measuring Object Texture
• Determine whether the staining pattern is smooth or coarse at a particular scale
• Selecting the appropriate texture scale
• Higher scale: Larger patterns of texture
• Smaller scale: More localized (finer) patterns of texture
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Virus Texture Dataset, http://www.cb.uu.se/~gustaf/virustexture/
Moffat et al., Cell, 2006, 124:1283
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Measuring Location
• Cell or organelle location within image may be meaningful
• Example features• Distance from organelle to nucleus, cell
membrane
• Change in position often important in time-lapse imaging
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Miller et al., PNAS 2003
Battich et al., Nat Meth 2013
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Time-Lapse Analysis
• Very sensitive to problems in object identification
• GIGO: Assay development, image acquisition must be optimized for tracking success
• Take note of mis-segmentations especially for cell cycle, lineage studies
• Software• Bitplane Imaris, Perkin-Elmer Volocity,
Molecular Devices Metamorph
• CellProfiler, FIJI, etc
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Schmitz et al. Nat Cell Biol 2010, 12:886
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Measuring Clustering
• Characterization of spatial relationships between objects
• Example features• Number of neighboring objects
• Percent of the perimeter touching neighbor objects
• Distance to the nearest neighbor
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http://www.perkinelmer.co.uk/
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Combinations of Measurements
• Phenotype identification may be difficult if hand-
selecting from a limited measurement set
• Machine learning (ML) approaches can identify
phenotypes from a combination of measurements
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• Some measurements (e.g., texture) are hard to interpret as readouts but are
excellent fodder for ML approaches to downstream analysis
Sommer and Gerlich, JCS 2013, 126:1
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Outline
• The image as quantitative data
• Identifying the image foreground
• Splitting object clusters
• Identifying cellular compartments
• Measurement extraction
• Statistical analysis
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Quality Control
• Ideally, QC should be performed at beginning of workflow
• Use automated measures, with option of manual vetting• Machine learning approaches can be useful here
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• Focus imperfections, incorrect exposures, background problems, artifacts
• Identify, eliminate systematic aberrations
Focal blur Saturation artifact
Sommer and Gerlich, JCS 2013, 126:1
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Data Analysis
• What does this data set look like?
• Cytological profile, or Cytoprofile
• Shows all the measurements acquired• For each individual cell • In every image • In the entire experiment.
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+1
0
-1
Cell #6111617
-.2 .7 -.1 0 .2 -.9
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Data Normalization
• Used to remove systematic errors from the data
• Allows comparison of screening runs from different plates, acquisition times, etc.
• Ideally, results in:• Similar measurement ranges observed across different wells with the same treatment• Similar measurement distributions of the controls (positive or negative)• Keep in mind the recommendations from Assay Development section!
• Common approaches• % of control: Divide by mean of corresponding measurement from control• % of samples: Divide by mean of corresponding measurement from all samples• Z-score, robust Z-score: Transform to zero mean/median, unit variance/MAD
• Alternative approach: Normalized value = percentile within rank-ordered data
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Statistical Analysis Software
• Spreadsheets (e.g., Microsoft Excel)• Widely used because of familiarity,
• Unable to handle large screening datasets
• Lack sophisticated analysis methods
• HCS/HTS microscope vendors often bundle data-analysis functionality with hardware, image-analysis software
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Statistical Analysis Software
• Specialized commercial tools• Wide variety of products• Often bundled with hardware• Talk to vendors for more details
• Open-source tools• KNIME• CellProfiler Analyst• Weka• Bioconductor
• Custom scripts• MATLAB• R• Python
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Not comprehensive!
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Summary: Fundamental Steps
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Knowledge about
the application!
Making measurements,
feature extractionLENGTH, WIDTH,
CURVATURE, TEXTURE…
Result
Object detection, segmentation
(including 3D and tracking over time)
Preprocessing
Image acquisition
Object
classification,
interpretation,
recognition
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Additional Resources
• Introduction to the Quantitative Analysis of Two-Dimensional Fluorescence Microscopy Images for Cell-Based Screening
• Ljosa and Carpenter, PLoS Computational Biology, 5(12), 2009
• DOI: 10.1371/journal.pcbi.1000603
• Biological imaging software tools• Eliceiri et al, Nat Meth, 9(7), 2012
• DOI: 10.1038/nmeth.2084
• Assay Guidance Manual• Introduction: http://www.ncbi.nlm.nih.gov/books/NBK100913
• Advanced methods: http://www.ncbi.nlm.nih.gov/books/NBK126174
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Summary: The HCS Laboratory
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Plate Handler Robot HCS Imager
Plate Visualization / Image Analysis
Workstations
Image Analysis
Computer Cluster
Data Management
System
Network File Server
Network
Instrument Control
Workstation
The Wet Lab
Reagents, protocols,
assay optimization
Hardware and
Image Acquisition
Assay Types and Assay Development
Image and Data Analysis
* An Introduction To High Content Screening And Analysis Techniques:
Practical Advice and Examples, Haney, S.A, Bowman, D. Chakravarty, A.
Davies, A. and Shamu, C.E. John Wiley Press, NY, NY (in production)