MAGING DEVELOPMENT IN THE EMBRYONIC HEART OVER … · heart over multiple spatial dimensions,...

IMAGING DEVELOPMENT IN THE EMBRYONIC

HEART OVER MULTIPLE SPATIAL

DIMENSIONS, MODALITIES, AND TIME-SCALES

Michael Liebling

Electrical & Computer Engineering Department

University of California, Santa Barbara

sybil.ece.ucsb.edu

Large Data Sets in Medical Informatics

Institute for Mathematics and its Applications, University of Minnesota

16 November 2011

<< < > >> B F R I

http://sybil.ece.ucsb.edu

Channels

optical properties (absorption,

scattering, refraction index, wavelength)

chemical properties (pH,...)

physical forces,

gene expression level (e.g. via fluorophore)

Space

X, Y, Z

Time

Number of samples

n

Resolution and breadth bottlenecks

• instrument resolution, bandwidth

• available time

• sample integrity

Can we increase imaging resolution in one dimension (space, time,

channels) without a«ecting resolution in the others?

The imaging-space in biology is high-dimensional

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Gra

y’s

Anato

my

Day 22 (Human) Adult (Human)Congenital Heart Defects:

occur in 0.8% of newborn infants,

are the leading cause of birth defect related deaths

Heart is Functional (beating!) while it still develops

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A

V

Advantages:

• zebrafish are vertebrates

• reproduce externally and rapidly

• relatively transparent embryos

• may be genetically engineered to

express fluorescent markers in spe-

cific tissues [e.g. Tg(gata1:GFP)]

48 hpf (hours post fertilization)

mb: midbrain

ot: otocyst

e: eye

h: heart

yolk: yolk mass

Embryonic Zebrafish Heart is (Almost) Perfect Model

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Human umbilical vein endothelial cells exposed to (during 24 h):

Static Conditions (no flow) Laminar Shear Stress

down-regulated, TSS!LSS), thus further indicating that culturedendothelial cells can discriminate between these distinct types offluid mechanical stimulation. These transcriptional profilingdata, analyzed in terms of global patterns of gene regulation,thus confirm and extend our working hypothesis that the endo-thelial cell is a mechanosensitive element in the blood vessel wall,whose phenotype can be modulated at the transcriptional levelby fluid shear stresses (7, 11).

Categorization and Functional Annotation of Differentially ExpressedEndothelial Cell Genes. To begin to comprehend the endothelialphenotypes emerging in this model of biomechanical stimula-tion, we categorized the patterns of expression of known genesby different analytical methods. First, the genes that exhibitedthe greatest degree of change under the experimental conditionsexamined were grouped according to the direction (up or down)of their regulation. In Table 2, the 10 most regulated namedgenes are listed for each condition pair examined (LSS vs. static,TSS vs. static, TSS vs. LSS). Second, an average-linking hierar-chical clustering algorithm (17) was applied to group namedgenes that were similarly regulated by each of the comparisonconditions. As seen in Fig. 2, blocks of genes with similar colorcoding (connoting similar regulation across compared condi-tions) comprised qualitatively different categories. Some cate-gories appear to be enriched for genes associated with particularfunctions. For example, many genes down-regulated by LSS (butless so with TSS) are known to be involved in the process of genetranscription, including Brahma (SWI!SNF matrix associated,subfamily a, member 2) and chromatin assembly factor 1, subunitB (p60). A rigorous analysis of such enrichment awaits asystematic classification of human transcripts into functionalcategories (18). It is notable that Fig. 2 contains examples of allpossible combinations of up- and down-regulation among thethree experimental conditions examined. For example, certaingenes such as connective tissue growth factor were comparablyup-regulated by both LSS and TSS, whereas others, such asCD164 (sialomucin), were comparably down-regulated by bothstimuli, as compared with static control cultures. A subset ofgenes were found to be down-regulated by LSS and up-regulatedby TSS (fibrogenic lymphokine, activating transcription factor5), and vice versa (eukaryotic translation initiation factor 2,serine protease inhibitor Kazal type 2). These latter categoriesare particularly interesting because they may contain pathophysi-ologically relevant genes that would be hypothesized to contrib-ute to biomechanically induced ‘‘atheroprotective’’ or ‘‘athero-prone’’ endothelial phenotypes in the in vivo setting (14). Thisentire data set can be found at a searchable database maintainedby our laboratory at http:!!vessels.bwh.harvard.edu!papers!PNAS2001.

As a third approach, we subjectively grouped highly regulatedgenes, which have known or putative functions in mechanosig-naling, vascular response-to-injury reactions, and atherogenesis(Table 3). We validated the regulation of several of these genesat the mRNA level by using quantitative real-time PCR (Taq-Man), and their translation into protein by using Western blotanalysis (Table 3). This list contains 28 genes of diverse func-tions, including genes involved in lipid metabolism (apolipopro-tein E, long chain fatty acid-CoA ligase 3, megalin); the puri-noreceptor, P2X4, which mediates fluid shear stress-dependentactivation of calcium influx in endothelial cells (19); as well asisoforms of !, " and # subunits of G proteins. The coordinatedup-regulation of trimeric G proteins is interesting because thesemolecules have been implicated in mechanosignaling in responseto fluid shear stress (20, 21). Certain genes involved in vascularresponses to injury also showed significant regulation, includingfibrogenic lymphokine (fibrosin), thioredoxin reductase, andmatrix-gla protein. Thioredoxin reductase is recognized to be animportant modulator of the redox state of the cell (22), whereas

matrix-gla protein is a negative regulator of vascular calcification(23). Other genes whose regulated expression also may berelevant to vascular pathophysiology include connective tissuegrowth factor (CTGF), a pleiotropic growth factor, which can acton endothelial cells, smooth muscle cells, and fibroblasts (24);and CYP1B1, a cytochrome P450 enzyme that catalyzes estradiolhydroxylation and activates exogenous chemicals (25). Finally,we observed significant biomechanical regulation of variousgenes encoding extracellular matrix components, matrix recep-tors, and matrix remodeling enzymes, such as fibronectin, se-creted protein acidic and rich in cysteine (SPARC), laminin "1,

Fig. 3. Changes in cytoskeletal elements of endothelial cells exposed to LSS.(a) Cytoskeleton-related genes showing significant up- or down-regulation inresponse to LSS stimulation. (b) Fluorescence confocal micrographs of theapical (supranuclear) region of HUVEC stained green for F-actin (Top), myosinheavy chain (Middle), and plectin (Bottom). Cells were counterstained withSYTOX (red) to identify nuclei. (Left) HUVEC under static (no flow) conditions;(Right), HUVEC exposed to LSS (10 dyn!cm2 for 24 h).

Garcıa-Cardena et al. PNAS " April 10, 2001 " vol. 98 " no. 8 " 4483

CELL

BIO

LOG

Y

Flow

G. García-Cardeña, et al. PNAS 98(8), pp. 4478–4485, 2001

Cells Remodel when Exposed to Flow (in vitro)

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Many Genes Respond to Flow-Induced Shear-Stress (in vitro)

Up-Regulated Genes Down-Regulated GenesTable 1. Genes that increase under shear stress detected by DNA microarray analysis

Group* Gene Ratio (SS!Cont)

Antioxidants Cytochrome P450 1B1 6 h 7.55 ! 1.28AA448157 24 h 9.70 ! 2.71Cytochrome P450 1A1 6 h 3.90 ! 0.48AA418907 24 h 11.15 ! 3.75Heme oxygenase-1 6 h 3.08 ! 0.84T71606 24 h 1.78 ! 0.18NAD(P)H:quinone oxidoreductase (NQO1) 6 h 1.28 ! 0.12AA458634 24 h 2.09 ! 0.32

Proliferation!differentiation Zinc finger protein EZF!GKLF 6 h 4.56 ! 0.69H45668 24 h 2.78 ! 0.54Receptor tyrosine phosphatase 6 h 3.73 ! 0.77AA486403 24 h 1.82 ! 0.05TGF-!-stimulated clone 22 (TSC-22) 6 h 2.18 ! 0.23AA664389 24 h 2.18 ! 0.25Tyrosine kinase receptor precursor 6 h 2.31 ! 0.36TIE-2 H02848 24 h 1.80 ! 0.48Tyrosine kinase HTK 6 h 1.40 ! 0.05T51849 24 h 2.09 ! 0.79E2F transcription factor 5, p130 binding 6 h 2.05 ! 0.71AA455521 24 h 1.56 ! 0.64Human orphaned G protein coupled receptor 6 h 2.39 ! 1.17N53172 24 h 1.13 ! 0.31Glycyl tRNA synthetase 6 h 1.97 ! 0.56AA629909 24 h 1.08 ! 0.19Jagged 1 (Human HJ1) 6 h 2.22 ! 0.37R70685 24 h 1.82 ! 0.51

Vascular tone Argininosuccinate synthetase 6 h 2.41 ! 0.21AA676466 24 h 3.04 ! 0.88"-Galactosidase A precursor 6 h 1.42 ! 0.02AA251784 24 h 2.56 ! 0.23Vasoactive intestinal polypeptide receptor 6 h 1.39 ! 0.20precursor (VIPR1) H73241 24 h 2.13 ! 0.28PGHS-2 6 h 2.20 ! 0.45AA644211 24 h 0.91 ! 0.16

ECM!cytoskeleton Elastin 6 h 2.93 ! 0.42AA459308 24 h 2.26 ! 0.59Connexin 37 6 h 2.45 ! 0.88H44032 24 h 1.48 ! 0.38"-Spectrin 6 h 1.76 ! 0.17T60117 24 h 1.99 ! 0.51Galectin 3 6 h 1.22 ! 0.09AA630328 24 h 2.16 ! 0.56

Immune!inflammation Podocalyxin-like protein 6 h 3.37 ! 0.55N64508 24 h 1.77 ! 0.21CD34 6 h 1.70 ! 0.20AA043438 24 h 2.41 ! 0.22IL-1 receptor, type 1 precursor 6 h 1.51 ! 0.38AA464526 24 h 2.22 ! 0.18Leukocyte elastase inhibitor 6 h 1.06 ! 0.11AA486275 24 h 2.11 ! 0.58

Transcription factor Glucocorticoid-induced leucine zipper protein 6 h 3.35 ! 0.70AA775091 24 h 3.28 ! 0.79

Transport systems Human prostaglandin transporter 6 h 3.33 ! 1.05AA037014 24 h 2.49 ! 0.19Heat shock protein 70 6 h 2.08 ! 0.31AA629567 24 h 0.76 ! 0.09Chromogranin A 6 h 1.05 ! 0.17R36264 24 h 2.07 ! 0.83

Protein modification Paired basic amino acid cleaving system 4 6 h 1.74 ! 0.30AA251457 24 h 2.44 ! 0.21

RNA degradation RNase A family, 1 6 h 0.91 ! 0.18AA485893 24 h 2.01 ! 0.06

Thrombosis S100 calcium binding protein A10 6 h 1.27 ! 0.23AA444051 24 h 1.96 ! 0.32

*Genes were grouped based on function. Many genes can be assigned to more than one group (e.g., transcriptionfactors).

McCormick et al. PNAS " July 31, 2001 " vol. 98 " no. 16 " 8957

ENG

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G

previous studies of PG H S-2 protein levels under similar shearconditions (18) .

T he most dramatically up-regulated gene expression wasobser ved in cytochromes P450 (C Y P) 1A 1 and 1B 1 (F ig. 1) .C lassically, the C Y P gene families have been associated withcellular detoxification mechanisms. O f the 19 C Y P genes presenton the G F 211, only C Y P 1A 1 and 1B 1 were affected by shearstress in H U V E C . C Y P 1A 1 activity can be induced in humanendothelial cells (E C ) by toxic aromatic hydrocarbons, but notC Y P 1B 1 (19–21) . C Y P 1A 1 has been postulated to participatein endogenous signaling of oxidative processes (22) . F urther-more, there is evidence that the production of an endothelial-derived hyperpolarizing factor from arachidonic acid in endo-thelial cells is catalyzed by C Y P 1A 1; and, loss of this gene incultured E C correlates with dedifferentiation (23) . E xpression ofC Y P 1B 1 in E C is much less studied than C Y P 1A 1. Acomparison of senescent fibroblast, epithelial, and H U V E C celllines revealed C Y P 1B 1 to be up-regulated only in senescentH U V E C (24) . T he strong induction of these C Y P genes by shearstress is consistent with the suggestion that physiological levelsof shear stress are protective for endothelium.

C T G F , which was initially purified and identified fromH U V E C -conditioned medium, is elevated in fibrotic lesions, and

may play a role in the development of fibrotic diseases (25) . I thas also been shown to be highly expressed in vascular cells inatherosclerotic lesions, but not in normal arteries (26) . T heselesions are usually found in regions of low wall shear stress, oftenwith complex, recirculating blood f low patterns (1) . T he strikingdown-regulation of C T G F expression that we obser ve in re-sponse to normal arterial levels of shear stress is consistent withthese findings, and with the hypothesis that physiological arterialshear stress protects against fibrotic and atherosclerotic diseaseprocesses.

M any of the other genes in T ables 1 and 2, whose biologicalfunctions are known, are associated with vascular biologicalpathways that are regulated at least in part by shear stress. B yidentifying additional genes in these systems it will be possible tofurther define the complex mechanisms by which shear stresscontrols them. F or example, shear stress is considered by manyto be the most important stimulus for N O production in E C (27) .Several genes identified as shear stress responsive from our D N Amicroarray analysis may play a role in the regulation of N Oproduction by shear stress, and suggest the following pathwaysfor shear stress regulation of N O production in endothelial cells.G enerating hypotheses in the absence of further supporting datamay be considered excessive, but we believe they provide stimulifor further work.

Table 2. Genes that decrease under shear stress detected by DNA microarray analysis

Group Gene Ratio (SS Cont)

Vascular tone Endothelin-1 6 h 0.21 0.08H11003 24 h 0.20 0.05Caveolin-1 6 h 0.49 0.09AA055835 24 h 0.49 0.13

Extracellular matrix CTGF 6 h 0.13 0.06AA598794 24 h 0.10 0.02Cardiac gap junction (Connexin 43) 6 h 0.58 0.24AA487623 24 h 0.39 0.10Matrilin-2 6 h 0.81 0.11AA071473 24 h 0.52 0.08

Proliferation differentiation Spermidine spermine N1-acetyltransferase 6 h 0.37 0.11R58991 24 h 0.20 0.07Cyr61 6 h 0.35 0.15AA777187 24 h 0.41 0.18S1-5 (fibrillin-like, FBNL) 6 h 0.65 0.06AA875933 24 h 0.21 0.05BMP-4 6 h 0.45 0.03AA463225 24 h 0.58 0.13Aldehyde dehydrogenase 1 6 h 0.50 0.22AA664101 24 h 0.52 0.18Adenylosuccinate synthetase 6 h 0.59 0.29AA431414 24 h 0.74 0.24Gene for H4 histone 6 h 0.80 0.10AA868008 24 h 0.50 0.05

Cytoskeleton Nonmuscle myosin heavy chainB Smemb 6 h 0.48 0.14AA490477 24 h 0.87 0.20

-Tubulin 6 h 0.88 0.14AA865469 24 h 0.50 0.04

GTPases Rho B 6 h 0.63 0.10AA495790 24 h 0.27 0.07Guanylate binding protein 1 (GBP1) 6 h 0.48 0.11AA486850 24 h 0.96 0.24

Transcription factor SL3-3 enhancer factor 2 (SEF2-1A) 6 h 0.40 0.10AA669136 24 h 0.52 0.08

Protein modifier Sialyltransferase 1 6 h 0.54 0.13AA598652 24 h 0.46 0.18

Chemotaxis Monocyte chemotactic protein 1 (MCP-1) 6 h 0.35 0.14AA425102 24 h 0.28 0.07

Unknown Myosin heavy chain homolog (Doc1) 6 h 0.39 0.08W69790 24 h 0.58 0.17

8958 www.pnas.org cgi doi 10.1073 pnas.171259298 McCormick et al.

McCormick et al.

“DNA microarray reveals changes in gene expression of shear stressed human umbilical vein endothelial cells,”

PNAS, 93 (16), 2001

Flow-Sensitive Genes Mediate Cell Remodeling

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Reducing the retrograde flow fraction early in the heart development leads

to severe valve dysmorphologies.

J. Vermot, A. Forouhar, ML, et al., PLOS Biology, vol. 7, iss. 11, e1000246, 2009.

Normal valvulogenesis depends on early flow patterns

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96hpf

Control Reducing retrograde flow fraction

leads to valve dysmorphologies

100µm

A

V

A

VA

V



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96hpf

Control Reducing retrograde flow fraction

leads to valve dysmorphologies

100µm

A

V

A

VA

V

AA

V V



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inhibits atrio-ventricular klf2a expression

Atrium

Ventricle

AtriumVentricle

klf2a, 48hpf klf2a, 48hpf

Control Reduced RFF @36-46hpf


Reducing retrograde flow fraction at 36hpf

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Oscillatory flow −→ klf2a expression −→ Valve formation

J. Vermot, A. Forouhar, ML, et al., PLOS Biology, vol. 7, iss. 11, e1000246, November 2009.

⇒ Bad idea to alter (stop) cardiac function to accommodate

time-lapse imaging

How does flow a«ect heart morphogenesis?

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Heart

Morphogenesis Heart Function

Cellular Regulation

Goal: develop imaging methods to capture multi images withoutperturbing heart function or morphogenesis

Morphogenesis and function are entangled

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Heart


Cellular Regulation

Time-lapse microscopy

3D microscopyHigh-speed imaging

Fluorescence imaging

Goal: co-localized measurements over multiple dimensions, at

multiple spatial and temporal scales, and via multiple imaging

modalities.

Applicability of traditional imaging is limited

<< < > >> B F R I Page 12

Experimental

System

Microscopy

Image

Processing

Image

Analysis

Modeling

Studying

Heart Development

Integrated approach to imaging allows for feedback

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1. Acquire multi-dimensional data

(3D+Time+λ+. . . ) at high frame-rate

2. Merge and segment multi-modal data

3. Joint imaging and untangling

of morphogenesis and function

Steps towards integrated imaging of cardiogenesis

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Framerate (frames per second)

2.5 5 10 20 35 68 104

Integration Time (ms)

400 200 100 50 25 12 6

Optimal framerate is a compromise between:

• minimal motion blur

• su»cient photon count

Vermot, Fraser, ML, HFSP Journal 2008, http://hfspj.aip.org/doi/10.2976/1.2907579.

Spatial, Temporal Resolutions and SNR are Linked

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Point

Source

~Poisson(n)

Point Spread Function

(Spatial Probability

Density Function ~Airy)

Optical

System

Imm

ob

ile

So

urc

eM

ob

ile S

ourc

e

(velo

city v

)

v

Tv

Integration Time: T

PSF width:

ÉxNew PSF width:

Éx′ = T v + Éxwith T : integration time

v : velocity

Example:

fs = 25 Hz

T = 1/fs = 0.04 s

v = 1 mm/s

T v = 40 µm

Vermot, Fraser, ML, HFSP Journal 2008, http://hfspj.aip.org/doi/10.2976/1.2907579.

Poisson meets Airy

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Motion modifies point spread function (PSF)

PSF width: ÉxNew PSF width: Éx′ = T v + Éx with T : integration time, v : velocity

Example: fs = 25 Hz, T = 1/fs = 0.04 s, v = 1 mm/s, T v = 40 µm

Balancing Spatial and Temporal Resolution

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Required Spatial

Resolution ∆x (µm)

Velocity of

of Structure

(µm/s)

0.001

0.001

0.01

0.01

0.1

0.1

1

1

10

10

100

100

1000

1000

1f

sp

1fp

01s

f 1p

001

s

f 1p

51m

tuni

se

f 1p

3h

sruo

f 1

d pay

01f

sp001

f sp

001

f 0

sp00,01

f 0

sp

001

f 000,

sp

Cell Migration

Cardio-vascular

cell dynamics

Calcium

Sparks

VesicleTrafficking

Microtubule

Growth

Chromosome

Dynamics

J. Vermot, S.E. Fraser, ML, HFSP Journal (2008), http://hfspj.aip.org/doi/10.2976/1.2907579.

Framerate Requirements Depend on Target Resolution, Structure Velocity

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1 2 3 4 5 6 7

1 2 3 4 5 6 7

8 9 10 11

5 6 7 8 9 10 11

12 13 14 15

9 10 11 12 13 14 15

1 2 3

1’ 2’ 3’

4 5 6 7

5 6 7 8 9 10 11

9 10 11 12 13 14 15

Time

Time

Time

Multiple Acquisitions

3) Synchronization

1) Acquisition

2) Cutting

4) Noise Reduction

(e.g. Mean or Median, etc.)

S.K. Bhat, I. Larina, K. Larin, M.E. Dickinson, ML, Optics Letters, 34 (23), 2009.

Multi-Cycle Noise Reduction

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Optical Coherence Tomography (50 frames per second) to image beating

heart in E9.5 mouse embryo.

Signal to noise ratio:

57.12 dB 60.71 dB 58.77 dB 61.67 dB

256×512 pixels per frame

S.K. Bhat, I. Larina, K. Larin, M.E. Dickinson, ML, Optics Letters, 34 (23), 3704–06,

2009.

Fast Optical Coherence Tomography at High SNR

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2D slice of zebrafish heart (60 fps)

ML, J. Vermot

[38 hpf wildtype zebrafish, BODIPY FL C5-ceramide]

Zebrafish heart @ 2–3 s/volume

Heart beats ∼5 times per scan.

Imaging may be fast in 2D but too slow for 3D

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Non-gated acquisition. . .

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. . . requires synchronization

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4D Imaging via Post-Acquisition Synchronization

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Before synchronization After synchronization

A.S. Forouhar, ML

[Tg(cmlc2:GFP) 38 hpf zebrafish, Huai-Jen Tsai, National Taiwan University ]

in “Imaging: a Laboratory Manual”, CSHL Press (NY), Chap. 53, 2011, 823–830.

4D in vivo, Fast Confocal Microscopy is Possible

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ML, A.S. Forouhar, M. Gharib, S.E. Fraser, M.E. Dickinson, J. Biomedical Optics, 10 (5), 2005.

Functional Cardiac Imaging at Microscopic Scales

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Raw 2D OCT Sequence Denoised and Synchronized

K.V. Larin, I.V. Larina, ML, M. E. Dickinson "Live imaging of early developmental processes in mammalian

embryos with optical coherence tomography " Journal of Innovative Optical Health Sciences, vol. 2, no. 3, pp.

253-259, July 2009.

Optical Coherence Tomography Image Denoising

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t

k = 1

k = 2

x

y

s2

Before Synchronization

After Synchronization

ML, H. Ranganathan, Proc. SPIE Wavelets XIII, Vol. 7446, 744602, 2009

Image synchronization problem

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0 10 20 30 40 50 60 70 80−1

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Reference

Test

And what if the heartbeat is irregular?

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0 10 20 30 40 50 60 70 80−1

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Reference

Test

Rigid synchronization is not adapted

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ML, A.S. Forouhar, et al., Dev. Dyn, 2006

[Tg(gata1:GFP) 54 hpf zebrafish]

Method is limited by aperiodicities in data

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0 10 20 30 40 50 60 70 80−1

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Reference

Test

Can we compensate for (slight) irregularities?

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0 10 20 30 40 50 60 70 80−1

−0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Reference

Test

Warped

Nonuniform Registration Warps Test Sequence

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Acquisition Model

Imeasured(x, zk , t) =ZZZI(x′, z, w−1

k (t))h(x − x′, zk − z) dx′dz,

wk(t): unknown warping functions; h(x, z): microscope point spread function

Hypothesis

|I(x, z, t) − I(x, z, t + T )| ≈ 0, ∀tObjective criterion (minimize to synchronize slice-sequence k ′ to k):

Qk,k′,wk {wk′} =Z L0

ZZR2

|Im(x, zk , wk(t)) − Im(x, zk ′ , wk ′ (t))|dx dt

+λZ T0

ββββ d

dtwk ′ (t) − 1

ββββdt.

Goal:

Recover warping functions wk ′ (t)Additional assumptions:

Warping functions are continuous, non-negative, and monotonically increasing

Initialization: w1(t) = t

Nonuniform temporal registration

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’

Goal: Match test sequence to reference sequence

Dynamic Programming Nonuniform Registration

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’

Matrix to contain dissimilarity measure between pairs of frames


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’

Similar Frames: low dissimilarity value

Absolute di«erence or

Mean squared di«erence or

Normalized Mutual Information

−SNMI(A,B) = −H(A) + H(B)

H(A,B)


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’

Dissimilar Frames: higher dissimilarity value





H(A,B)


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’





H(A,B)


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’

Dissimilarity matrix as a graph


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’

Synchronization problem equivalent to finding minimal-cost path

through vertices (extra cost for stretching or shrinking)

Rigid registration:

constrain to paths that are diagonal (135◦)

Non-rigid temporal registration:

Dynamic programming approach,

ML, et al, Proc. ISBI (2006), pp. 1156-1159


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’

Given a fit in column c, find best fit in column c − 1

Stretching or shrinking sequence yields additional cost


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’

Mark best fit with arrow and store total cost


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’

Proceeding recursively from left to right


<< < > >> B F R I Page 31

’

Find minimal cost on bottom row and follow graph from right to left


<< < > >> B F R I Page 31

ML, A.S. Forouhar, et al., Dev. Dyn, 2006

[Tg(gata1:GFP) 54 hpf zebrafish]

Comparison of Reconstruction Techniques

<< < > >> B F R I Page 32

. . . without a«ecting frame rate!

(Swept Source) Optical Coherence Tomography

J. Yoo, I.V. Larina, K.V. Larin, M.E. Dickinson, and ML, Biomedical Optics Express, 2(9),

2614–2622, 2011

Mosaicing extends field of view. . .

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Bright-field (BF)

+ High frame rates (≥1000 fps)

- Images lack specificity

Fluorescence

+ Excellent specificity

- Low frame rates (≤ 50 fps)

JungHo Ohn

Brightfield microscopy: fast but lacks specificity

<< < > >> B F R I Page 35

J.O

hn

Acquiring Multiple Modalities

<< < > >> B F R I Page 36

a

b

c

d

e

f

g

h

time

timetime

time

Parallel Multi-Channel Imaging Sequential Multi-Channel Imaging (slow)

Sequential Multi-Channel Imaging (fast version, before temporal alignment) Multi-Modal Temporal Alignment

Key idea: Fast sequential acquisition, post-acquisition synchronization

J. Ohn et al. genesis, vol. 49, no. 7, pp. 514–521, 2011.

Multi-channel imaging, revisited for speed

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Brightfield Fluorescence (44 fps), Tg(cmlc2:GFP)

Multi-modal images can be synchronized by choosing a mutual

information-based criterion as an image-similarity merit function


Sequentially-acquired (nongated) sequences require synchronization

<< < > >> B F R I Page 38

Brightfield Fluorescence, Tg(cmlc2:GFP)

Multi-modal images can be synchronized by choosing a mutual

information-based criterion as an image-similarity merit function


. . . fast imaging in multiple channels

<< < > >> B F R I Page 38


Sequentially-acquired (nongated) sequences require synchronization

<< < > >> B F R I Page 39


Fast imaging in multiple channels

<< < > >> B F R I Page 40

Transgenic zebrafish expressing green fluorescent protein in the heart

ava

v v

av

a

v a

A B C D

ET33-1a ET33-mi3a ET31 GW64C

A myocardium of both the atrium (a) and ventricle (v)

B ventricular myocardium

C atrioventricular canal (av)

D myocardium and endocardium of the ventricle.

K.-L. Poon, ML, et al., “Zebrafish Cardiac Enhancer Trap Lines: New Tools for in vivo Studies of Cardiovascular

Development and Disease,” Dev. Dyn, 239:914–926, 2010. Korzh Lab (a*STAR Singapore)

Problem:

Many structures/cell types can be labeled in di«erent fish, but only a few

simultaneously in single fish!

Transgenic lines reveal di«erent tissue types

<< < > >> B F R I Page 41

High-speed camera

Controller- XY stage- Focus drive- Incubator temperature- Camera trigger- Data storage/process- etc

Incubation chamber

XY stage

Focus drive

Temperature

Objective

sensor

V A

J. Ohn, ML, ISBI’2011, 1549–1552

High-throughput functional imaging

<< < > >> B F R I Page 42

AV

ROI

0.5 1

−0.3

0

0.3

time

(sec)

velocity(mm/s)

D

S

θ

28°C

fish #1

fish #2

#10

29°C

38.1°C

0

1

2

time

(sec)

temperature(°C)

# of samples(n)

velocity

J.

Oh

n,M

L,

ISB

I’2

01

1,

15

49

–1

55

2

Functional signature depends on sample,temperature

<< < > >> B F R I Page 43

−0.3

−0.15

0

0.15

0.3

Velocity(m

m/s)

28°C

0 100 200 Time(ms)

29°C

0 100 200 Time(ms)

30.2°C

0 100 200 Time(ms)

32°C

0 100 200 Time(ms)

33.4°C

0 100 200 Time(ms) 0 100 200 Time(ms)

34.5°C

35.2°C

0 100 200 Time(ms)

36.2°C

0 100 200 Time(ms)

37.3°C

0 100 200 Time(ms)

−0.3

−0.15

0

0.15

0.3

Velocity(m

m/s)

−0.3

−0.15

0

0.15

0.3

Velocity(m

m/s)

J.

Oh

n,M

L,

ISB

I’2

01

1,

15

49

–1

55

2

Increase in heart rate with temperature (28◦C to 38.1

◦C (n = 10)): 47± 5%

Intra-population variability: 18%–30% (velocity amplitude), 4%–8% (heartbeat length).

Functional signature conserved within population

<< < > >> B F R I Page 44







<< < > >> B F R I Page 45

ML, A.S. Forouhar, et al., Dev. Dyn, 2006 | A. Forouhar, ML et al., Science, 2006.

Fast 3D Imaging Reveals Functional Changes

<< < > >> B F R I Page 46

Required Spatial

Resolution ∆x (µm)

Velocity of

of Structure

(µm/s)

0.001

0.001

0.01

0.01

0.1

0.1

1

1

10

10

100

100

1000

1000

1 fp

s

1 fp

10s

1 fp

100

s

1 fp

15

minut

es

1 fp

3 h

ours

1 fp

day

10 fp

s

100

fps

1000

fps

10,0

00 fp

s

100,

000

fps

Cell Migration

Embryonic

Heart

Calcium

Sparks

Cell

Traffick

Microtubule

Growth

Chromosome

Dynamics

J. Vermot, S.E. Fraser, ML, HFSP Journal (2008), http://hfspj.aip.org/doi/10.2976/1.2907579.

Framerate Requirements Depend on Target Resolution, Structure Velocity

<< < > >> B F R I Page 47

Time point 1

90 frames(30 fps)

3 seconds3 seconds

∆z: 5µm

Nz: 6 steps

90 frames(30 fps)

3 seconds3 seconds

∆z: 5µm

Nz: 6 steps

Time point 2 Time point 3

~~

Before synchronization

After synchronization

Time development

2 minutes 2 minutes

Time point 1

A

B

C

D

zy

x

t fast

t slow

t fast t fast

z

t fast

z

t slow

^ t fast^

Time point 2

zy

x

t fast

J.O

hn,H

.-J.T

sai,

ML

,O

rganogenesis

,5(4

),2009.

Capturing morphogenesis & function: combine high-speed with timelapse

<< < > >> B F R I Page 48

A

C

D

B

av

82:52:00

82:54:00

82:56:00

82:58:00J. Ohn, H.-J. Tsai, ML, Organogenesis, 5(4), 2009.

see movie: http://dx.doi.org/10.4161/org.5.4.10568

<< < > >> B F R I Page 49

24 36 48Development Time (Hours)

Heartbeat Time (ms)

0

0

300

600

Atrium

Ventricle

J.O

hn,H

.-J.T

sai,

ML

,O

rganogenesis

,5(4

),2009.

Heart morphogenesis & function—untangled at last

<< < > >> B F R I Page 50

Heart


Cellular Regulation

Time-lapse microscopy

3D microscopyHigh-speed imaging

Fluorescence imaging

Multi-cycle

image denoising

microscopy Joint

time-lapse/function

imaging

Multi-modal

imaging,merging,

and segmentation

Summary: integrated imaging system allows co-localized measurements in

multi-dimensions and scales while preserving normal heart function and

morphogenesis

Summary

<< < > >> B F R I Page 51

Heart


Cellular Regulation

Cell fate

Tissue dynamics

Organ morphometry

Cell shape

Cell behavior (division, differentiation, migration, death)

Gene expression patterns

Fluid forces map

Cardiac function

(pumping efficiency,

temporal & spatial

flow patterns)

Multi-cycle

image denoising

Cardiac function

(pumping efficiency, (pumping efficiency,

temporal & spatial temporal & spatial temporal & spatial

Joint

time-lapse/function

imaging

Multi-modal

imaging,merging,

and segmentation

Summary: integrated imaging system allows co-localized measurements in

multi-dimensions and scales while preserving normal heart function and

morphogenesis

Summary

<< < > >> B F R I Page 51

Contact: [email protected], http://sybil.ece.ucsb.eduUCSB

Sandeep Bhat

JungHo Ohn

Cooper Thomas

Apolonio Arias

Kyle Stewart

Chieh Lee

JeaBuem You

Hiranmayi Ranganathan

Dmitry Fedorov

B.S. Manjunath

Caltech

Arian S. Forouhar

Julien Vermot (@IGBMC)

Le Trinh

Mory Gharib

Scott E. Fraser

Caltech (Quail)

Jen Yang

Rusty Lansford

BCM Houston (Mouse)

Irina Larina

Mary E. Dickinson

U. Houston (OCT)

Kirill Larin

a*star, Singapore

(Heart transgenics)

Kar Lai Poon, Vlad Korzh

Hard- and Software

Imaris (Bitplane AG)

µManager (Nico Stuurman)

Leica Microsystems

Olympus

Carl Zeiss, AIM

Zebrafish

Shuo Lin, UCLA

H.-J. Tsai, Nat. Taiwan U.

Le Trinh, Caltech

ZIRC

Funding

NIH (NHLBI R01 HL078694)

NSF (DMR 0960331)

Hellman Family Fellowship

Santa Barbara Cottage Hospital

Regent’s Junior Faculty Fellowship

Acknowledgments

<< < > >> B F R I Page 52

High-dimensional....................... 2

Human heart.............................. 3

Zebrafish embryos..................... 4

EC and flow ............................... 5

Fraction Retrograde................... 7

klf2a in situ................................. 10

Flow and klf2a model................. 11

Morpho, function, cell resp. ....... 12

Integrated imaging..................... 13

Speed, resolution, SNR............. 15

Poisson Meets Airy.................... 16

Shinkansen blur......................... 17

Velocity vs Resolution Plot ........ 18

OCT Denoising.......................... 20

Gated imaging animation........... 22

cmlc2 raw/regist......................... 23

48hpf heart and hair .................. 24

4D OCT ..................................... 25

Visual Problem explanation ....... 26

What if irregular? ....................... 27

Nonuniform reg. eqs.................. 30

Uniform vs nonuniform .............. 32

Cardiac Mosaicing..................... 33

Brightfield vs fluorescence ........ 35

Microscope Setup...................... 36

Multi-Modal Registration............ 38

ET Transgenic Zebrafish............ 41

Development gata1.................... 46

Velocity vs Resolution (2) .......... 47

Zebrafish cell division ................ 49

Fast and slow cartoon ............... 50

— — — — — — — — — — — 53

Index

<< < > >> B F R I Page 53

MAGING DEVELOPMENT IN THE EMBRYONIC HEART OVER … · heart over multiple spatial dimensions,...

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