high content 2019 annual conference september 17 , boston, ma … · 2019-09-18 · high content...

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

mark.bray@novartis.com

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)

The HCS Laboratory

9/11/2019 SBI2 High Content 2019 3

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)

Outline

• The image as quantitative data

• Identifying the image foreground

• Splitting object clusters

• Identifying cellular compartments

• Measurement extraction

• Statistical analysis

9/11/2019 SBI2 High Content 2019 4

Outline

• The image as quantitative data

• Identifying the image foreground

• Splitting object clusters

• Identifying cellular compartments

• Measurement extraction

• Statistical analysis

9/11/2019 SBI2 High Content 2019 5

Images Contain A Wealth Of Information

9/11/2019 SBI2 High Content 2019 6http://www.microscopyu.com Image: Javier Irazoqui

Fundamental Steps

9/11/2019 SBI2 High Content 2019 7

Making measurements,

feature extractionLENGTH, WIDTH,

CURVATURE, TEXTURE…

Result

Object detection, segmentation

(including 3D and tracking over time)

Preprocessing

Image acquisition

Object

classification,

interpretation,

recognition

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…

9/11/2019 SBI2 High Content 2019 8

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

9/11/2019 SBI2 High Content 2019 9

Not comprehensive!

Outline

• The image as quantitative data

• Identifying the image foreground

• Splitting object clusters

• Identifying cellular compartments

• Measurement extraction

• Statistical analysis

9/11/2019 SBI2 High Content 2019 10

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

9/11/2019 SBI2 High Content 2019 11

Illumination Correction

• Nonuniformities introduced in the optical path of the sample, microscope, and/or camera

9/11/2019 SBI2 High Content 2019 12

• 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

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

Background Subtraction

• Top-hat (“rolling ball”) filtering

9/11/2019 SBI2 High Content 2019 14

Image Thresholding

What is the best threshold value for

dividing the intensity histogram into

foreground and background pixels?

9/11/2019 SBI2 High Content 2019 15

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

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

9/11/2019 SBI2 High Content 2019 16

Sommer and Gerlich, JCS 2013, 126:1

Outline

• The image as quantitative data

• Identifying the image foreground

• Splitting object clusters

• Identifying cellular compartments

• Measurement extraction

• Statistical analysis

9/11/2019 SBI2 High Content 2019 17

Separating Touching Objects

• Once the foreground blobs have been identified, what next?• Thresholding is not sufficient to separate clustered or touching objects

9/11/2019 SBI2 High Content 2019 18

• Step 2: Distinguish multiple objects contained in the same foreground blob

Watershed Segmentation

• Consider the image as a surfacewith basins….

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••

•

••

•

•

•

http://www.svi.nl/watershed

Images from Carolina Wahlby

Separating Touching Objects

• Identifying objects: Some options

9/11/2019 SBI2 High Content 2019 20

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

••

•

••

•

•

•

Outline

• The image as quantitative data

• Identifying the image foreground

• Splitting object clusters

• Identifying cellular compartments

• Measurement extraction

• Statistical analysis

9/11/2019 SBI2 High Content 2019 21

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

9/11/2019 SBI2 High Content 2019 22

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

9/11/2019 SBI2 High Content 2019 24

Pre-processing

Sub-object ID

Sub-object relation

Outline

• The image as quantitative data

• Identifying the image foreground

• Splitting object clusters

• Identifying cellular compartments

• Measurement extraction

• Statistical analysis

9/11/2019 SBI2 High Content 2019 25

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

9/11/2019 SBI2 High Content 2019 26

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

9/11/2019 SBI2 High Content 2019 27

http://www.perkinelmer.co.uk/

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

9/11/2019 SBI2 High Content 2019 28

• Related to the amount of marker at a pixel location

Images courtesy of Ilya Ravkin

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

9/11/2019 SBI2 High Content 2019 29

Virus Texture Dataset, http://www.cb.uu.se/~gustaf/virustexture/

Moffat et al., Cell, 2006, 124:1283

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

9/11/2019 SBI2 High Content 2019 30

Miller et al., PNAS 2003

Battich et al., Nat Meth 2013

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

9/11/2019 SBI2 High Content 2019 31

Schmitz et al. Nat Cell Biol 2010, 12:886

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

9/11/2019 SBI2 High Content 2019 32

http://www.perkinelmer.co.uk/

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

9/11/2019 SBI2 High Content 2019 33

• 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

Outline

• The image as quantitative data

• Identifying the image foreground

• Splitting object clusters

• Identifying cellular compartments

• Measurement extraction

• Statistical analysis

9/11/2019 SBI2 High Content 2019 34

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

9/11/2019 SBI2 High Content 2019 35

• Focus imperfections, incorrect exposures, background problems, artifacts

• Identify, eliminate systematic aberrations

Focal blur Saturation artifact

Sommer and Gerlich, JCS 2013, 126:1

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.

9/11/2019 SBI2 High Content 2019 36

+1

0

-1

Cell #6111617

-.2 .7 -.1 0 .2 -.9

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

9/11/2019 SBI2 High Content 2019 37

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

9/11/2019 SBI2 High Content 2019 38

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

9/11/2019 SBI2 High Content 2019 39

Not comprehensive!

Summary: Fundamental Steps

9/11/2019 SBI2 High Content 2019 40

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

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

9/11/2019 SBI2 High Content 2019 41

Summary: The HCS Laboratory

9/11/2019 SBI2 High Content 2019 42

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)