PSLID,
the
Protein
Subcellular
Loca4on

            Image
Database:

   Subcellular
loca4on
assignments,

  annotated
imag...
Contributors

•    Michael
Boland

•    Mia
Markey
                   •    David
Casasent

•    Gregory
Porreca
          ...
Overview

•  SLIC

   –  Subcellular
Loca.on
Image
Classifica.on,
Clustering,

      Comparison

•  PUnMix

   –  Subcellul...
The
Challenge

 Comparison
of
cell
images
pixel‐by‐pixel

  or
region‐by‐region
matching
does
not

  work
for
cell
paVern...
SLIC
tool
categories

•    Segmenta.on

•    Feature
calcula.on

•    Classifica.on

•    Clustering

•    Comparison

Feature
levels
and
granularity



  Single           Single                  Single
  Object            Cell              ...
ER
                  gian.n
            gpp130



                                                                        ...
SLIC
versions
–
Source
code

•  Matlab

•  Python

•  C++/ITK
(subset;
from
Badri
Roysam’s
group)

Decomposing

              mixture
paVerns


•  Proteins
can
be
in
more
than
one
structure

•  Clustering
or
classifying
w...
PUnMix

•  Learn
unmixing
model
instance

•  Unmix
images
using
model
instance

Examples
of
Object
Types





Learn
the
types
by
clustering
using
object
features
   11

0.5

             0.4



Amt fluor.
             0.3
                                                                     ...
Test
samples

•  How
do
we
test
a
subcellular
paVern
unmixing

   algorithm?

•  Need
images
of
known
mixtures
of
pure

  ...
Tao Peng, Ghislain Bonamy, Estelle
Glory, Sumit Chanda, Dan Rines
(Genome Research Institute of
Novartis Foundation)


   ...
•  Mitotracker

•  Mixture
of
Lysotracker
and
Mitotracker

PaVern
unmixing
results





                           17
PUnMix
versions

•  Open
source
–
Matlab
including
C++

•  Compiled
versions
(not
requiring
Matlab

   license)
for
MacOS,...
SLML
Tools
‐
Genera.ve
models
of

        subcellular
paVerns


•  Build
model
instance
from
image
collec.on

•  Generate
...
LAMP2
paVern





               Cell membrane


               Nucleus




         Protein
Nuclear
Shape
‐
Medial
Axis
Model


                                        width


       Rotate




 Represented by two ...
Synthe.c
Nuclear
Shapes

With
added
nuclear
texture

Cell
Shape

Descrip.on:
Distance
Ra.o



                 d1 + d 2
      d1
             r =
      d2
                   d2
Genera.on

Models
for
protein‐containing
objects


                                                  •  Mixture
of
Gaussian

        ...
Synthesized
Images





                      Lysosomes
                        Endosomes


     Have
XML
design
for
captu...
Model
Distribu.on


•  Genera.ve
models
provide
beVer
way
of

   distribu.ng
what
is
known
about

   “subcellular
loca.on
...
Combining
Models
for
Cell
Simula.ons


           Protein 1
       Cell Shape
      Nuclear Model

               Protein ...
Example
combina.on





 Red
=
nuclear
membrane,
plasma
membrane

 Blue
=
Golgi

 Green
=
Lysosomes

 Cyan
=
Endosomes

SLML
Tools
versions

•  Open
source
–
Matlab
including
C++

•  Compiled
versions
(not
requiring
Matlab

   license)
for
Ma...
PSLID

•  Loading
pipeline
driven
by
script

   –  Calculates
thumbnail
images,
features,
segmenta.on

   –  Creates
datab...
PSLID

•  Open
source

•  Linux
only:
tomcat,
postgres

Annotated
Datasets

•  2D
and
3D
images
of
9
major
subcellular

   paVerns
in
HeLa
cells

•  3D
images
of
~300
proteins
in...
•  hVp://murphylab.web.cmu.edu/sooware

•  hVp://murphylab.web.cmu.edu/data


•  Past
major
support
from
NSF

•  Current
s...
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Transcript of "Murphy_PSLID_BOSC2009"

  1. 1. PSLID,
the
Protein
Subcellular
Loca4on
 Image
Database:
 Subcellular
loca4on
assignments,
 annotated
image
collec4ons,
image
 analysis
tools,
and
genera4ve
models
of
 protein
distribu4ons
 Estelle
Glory,
Jus.n
Newberg,
Tao
Peng,
Ivan
Cao‐Berg,
and
 Robert
F.
Murphy
 Departments
of
Biological
Sciences,
Biomedical
Engineering
and
 Machine
Learning
and
 1
  2. 2. Contributors
 •  Michael
Boland
 •  Mia
Markey
 •  David
Casasent
 •  Gregory
Porreca
 •  Simon
Watkins
 •  Meel
Velliste
 •  Jon
Jarvik,
Peter
Berget
 •  Kai
Huang
 •  Jack
Rohrer
 •  Xiang
Chen
 •  Tom
Mitchell
 •  Yanhua
Hu
 •  Christos
Faloutsos
 •  Juchang
Hua
 •  Jelena
Kovacevic
 •  Ting
Zhao
 •  Geoff
Gordon
 •  Shann‐Ching
Chen
 •  B.
S.
Manjunath,
Ambuj
Singh
 •  Elvira
Osuna
Highley
 •  Les
Loew,
Ion
Moraru,
Jim
Schaff
 •  Jus4n
Newberg
 •  Gustavo
Rohde
 •  Estelle
Glory
 •  Ghislain
Bonamy,
Sumit
Chanda,
 •  Tao
Peng
 Dan
Rines
 •  Luis
Coelho
 •  Ivan
Cao‐Berg

  3. 3. Overview
 •  SLIC
 –  Subcellular
Loca.on
Image
Classifica.on,
Clustering,
 Comparison
 •  PUnMix
 –  Subcellular
PaVern
Unmixing

 •  SLML
Tools
 –  Genera.ve
Models
of
Cells
and
Subcellular
Organelles
 •  PSLID
 –  Protein
Subcellular
Loca.on
Image
Database

  4. 4. The
Challenge
  Comparison
of
cell
images
pixel‐by‐pixel
 or
region‐by‐region
matching
does
not
 work
for
cell
paVerns
because
different
 cells
have
different
shapes,
sizes,
 orienta4ons
  Organelles/structures
within
cells
are
not
 found
in
fixed
loca4ons
  Instead,
describe
each
image
 numerically
and
operate
on
the
 descriptors
(“SLF”
‐
Subcellular
Loca=on
 Features)

  5. 5. SLIC
tool
categories
 •  Segmenta.on
 •  Feature
calcula.on
 •  Classifica.on
 •  Clustering
 •  Comparison

  6. 6. Feature
levels
and
granularity
 Single Single Single Object Cell Field Object Cell Field features features features Aggregate/average
operator
 Granularity: 2D, 3D, 2Dt, 3Dt
  7. 7. ER
 gian.n
 gpp130
 2D

 LAMP
 Mito
 Nucleolin
 Images
of
 HeLa
 cells
 Ac.n
 TfR
 Tubulin
 DNA
 100 Subcellular
PaVern
 90 Human Accuracy 80 Classifica.on:
 70 Computer
vs.
Human
 60 50 40 Even
beVer
results
using
mul.resolu.on
methods
 40 50 60 70 80 90 100 Computer Accuracy Even
beVer
results
for
3D
images

  8. 8. SLIC
versions
–
Source
code
 •  Matlab
 •  Python
 •  C++/ITK
(subset;
from
Badri
Roysam’s
group)

  9. 9. Decomposing
 mixture
paVerns
 •  Proteins
can
be
in
more
than
one
structure
 •  Clustering
or
classifying
whole
cell
paVerns
will
 consider
each
combina.on
of
two
or
more
 “basic”
paVerns
as
a
unique
new
paVern
 •  Desirable
to
have
a
way
to
decompose
mixtures
 instead
 •  Our
approach:
assume
that
each
basic
paVern
 has
a
recognizable
combina.on
of
different
 types
of
objects


  10. 10. PUnMix
 •  Learn
unmixing
model
instance
 •  Unmix
images
using
model
instance

  11. 11. Examples
of
Object
Types
 Learn
the
types
by
clustering
using
object
features
 11

  12. 12. 0.5 0.4 Amt fluor. 0.3 Pure Lysosomal Pattern 0.2 0.1 0 Golgi class 1 2 Lysosomal class 3 4 5 Nuclear class 6 7 Object type 8 0.5 0.4 
 
Pure
Golgi
PaRern
 0.3 Amt fluor. 0.2 0.1 0 Golgi class 1 2 Lysosomal class 3 4 5 Nuclear class 6 7 Object type 8 0.25 0.2 0.15 Amt fluor. 0.1 0.05 All 0 Golgi class 1 2 Lysosomal class 3 4 5 Nuclear class 6 7 Object type 8
  13. 13. Test
samples
 •  How
do
we
test
a
subcellular
paVern
unmixing
 algorithm?
 •  Need
images
of
known
mixtures
of
pure
 paVerns
–
difficult
to
obtain
“naturally”
 •  Created
test
set
by
mixing
different
 propor.ons
of
two
probes
that
localize
to
 different
cell
parts
(lysosomes
and
 mitochondria)



  14. 14. Tao Peng, Ghislain Bonamy, Estelle Glory, Sumit Chanda, Dan Rines (Genome Research Institute of Novartis Foundation) •  Lysotracker

  15. 15. •  Mitotracker

  16. 16. •  Mixture
of
Lysotracker
and
Mitotracker

  17. 17. PaVern
unmixing
results
 17
  18. 18. PUnMix
versions
 •  Open
source
–
Matlab
including
C++
 •  Compiled
versions
(not
requiring
Matlab
 license)
for
MacOS,
Windows,
Linux

  19. 19. SLML
Tools
‐
Genera.ve
models
of
 subcellular
paVerns

 •  Build
model
instance
from
image
collec.on
 •  Generate
images
from
model
instance
 •  View
mul.‐paVern
images

  20. 20. LAMP2
paVern
 Cell membrane Nucleus Protein
  21. 21. Nuclear
Shape
‐
Medial
Axis
Model
 width Rotate Represented by two curves Medial axis width along the the medial axis medial axis
  22. 22. Synthe.c
Nuclear
Shapes

  23. 23. With
added
nuclear
texture

  24. 24. Cell
Shape
 Descrip.on:
Distance
Ra.o
 d1 + d 2 d1 r = d2 d2
  25. 25. Genera.on

  26. 26. Models
for
protein‐containing
objects
 •  Mixture
of
Gaussian
 objects
 •  Learn
distribu.ons
for
 number
of
objects
and
 r:
normalized
distance,
a:
angle
to
major
axis
 object
size
 •  Learn
probability
 density
func.on
for
 objects
rela.ve
to
 nucleus
and
cell

  27. 27. Synthesized
Images
 Lysosomes
 Endosomes
 Have
XML
design
for
capturing
model
parameters
   SLML
toolbox
‐
Ivan
Cao‐Berg,
Tao
Peng,
Ting
Zhao
   Have
portable
tool
for
genera.ng
images
from
model
 27
  28. 28. Model
Distribu.on
 •  Genera.ve
models
provide
beVer
way
of
 distribu.ng
what
is
known
about
 “subcellular
loca.on
families”
(or
other
 imaging
results,
such
as
illustra.ng
change
 due
to
drug
addi.on)
 •  Have
XML
design
for
capturing
the
models
 for
distribu.on
 •  Have
portable
tool
for
genera.ng
 images
from
the
model

  29. 29. Combining
Models
for
Cell
Simula.ons
 Protein 1 Cell Shape Nuclear Model Protein 2 Cell Shape Simulation Nuclear Model Protein 3 Cell Shape Shared Nuclear Model Nuclear and Cell XML Shape
  30. 30. Example
combina.on
 Red
=
nuclear
membrane,
plasma
membrane
 Blue
=
Golgi
 Green
=
Lysosomes
 Cyan
=
Endosomes

  31. 31. SLML
Tools
versions
 •  Open
source
–
Matlab
including
C++
 •  Compiled
versions
(not
requiring
Matlab
 license)
for
MacOS,
Windows,
Linux

  32. 32. PSLID
 •  Loading
pipeline
driven
by
script
 –  Calculates
thumbnail
images,
features,
segmenta.on
 –  Creates
database
records
and
links
 –  Creates
predefined
sets
 •  Web
applica.on
 –  Create
sets
by
searching
on
context
or
content
 –  Analyze
sets
with
any
SLIC
tool
 –  Full
display
or
summary
 –  SOAP/XML
interface

  33. 33. PSLID
 •  Open
source
 •  Linux
only:
tomcat,
postgres

  34. 34. Annotated
Datasets
 •  2D
and
3D
images
of
9
major
subcellular
 paVerns
in
HeLa
cells
 •  3D
images
of
~300
proteins
in
3T3
cells
 •  2D
images
of
~3000
proteins
in
3T3
cells
 •  2D
and
3D
images
for
paVern
unmixing
 •  Datasets
from
other
inves.gators

  35. 35. •  hVp://murphylab.web.cmu.edu/sooware
 •  hVp://murphylab.web.cmu.edu/data
 •  Past
major
support
from
NSF
 •  Current
support
from
NIH
NIGMS
and
NCRR
 –  Na.onal
Center
for
Networks
and
Pathways:
 Molecular
Biosensors
and
Imaging
Center
(Alan
 Waggoner)

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