High-Throughput Imaging Workflows for the 

Systematic Dissection of Cellular Pathways

Gianluca Pegoraro 

High-Throughput Imaging Facility (HiTIF) 

National Cancer Institute/NIH 

Interoperability of Web Computational Plugins, NIST, Dec 5 2019
1. HiTIF and High-throughput Imaging (HTI)

2. Example image analysis workflow

3. Informatics infrastructure roadmap 

Outline
High-Throughput Imaging (HTI) 

Experimental 

perturbation

Phenotypic 

change 

Imaging-based 

cellular assay

HiTIF

Automated 

liquid handling

Janus

EL406

High-content 

image analysis

Columbus

KNIME/R

Highthroughput 

microscopy

CV7000

Investigator

Up to: 

• 104 Wells

• 104 Cells/Well

• 102 Feat./Cell 
HTI Data Generation

log10(Channels)

log10(Cells/Well)

1

0

2

3

4

1 2 3 4 5

1

2

3

4

GW 

Screens

log10(Wells)

5

Deep Imaging

Multiplexing

Focused 

Screens

n wells

2D images/day = n * m * λ * z * t ≈ up to 2*105

m fields λ

R

G

B

t

0 time t

1

t...

t

t

z0 z

z1

z...

zz

Typical Dataset Size: 5 - 500 GB
HTI Assay Formats 

Deep Imaging

(Stavreva, 2019; Finn, 2019; Jowhar 2018; 

Zane, 2017; Burman, 2015a; Burman, 2015b )

Focused Screens

(Jevtic, 2019; Liskovykh, 2019; Baranes-Bachar, 

2018; Veschi, 2017; Kubben, 2016; Shachar et al., 

2015)
2. Imaging of Gene Expression in Live Cells

Bertrand, Mol. Cell, 1998 

Femino, Science, 1998 

Fusco, Curr. Biol., 2003 

Shav-Tal, Science, 2004 

Chubb, Curr. Biol., 2006 

Larson, Science, 2011

GFP-MS2

PP7 

stem loop

RNA Fluo. Prot.

MS2 

stem loop

MS2-PP7

Yihan Wan 

Larson Lab, NCI

Position of Pol II

Fluo.

MS2 coat

protein

GFP

MS2

stem-loop
Position of Pol II

Fluo.

Imaging of Single Molecule mRNA Particles
Position of Pol II

Fluo.

MS2 loops inserted in 

endogenous gene

Time

Fluo.

12 hr

RNA Tagging Measures Transcription and 

Splicing
Time

Fluo.

“ON” time

“OFF” time

T

ON

 = T

3’SS

+ T

splicing

Position of Pol II

Fluo.

“ON”Time

3’ SS

Splicing time

RNA Tagging Measures Transcription and 

Splicing

Throughput: 1 gene per year
Design of a Live HT Single Cell Assay 

AIM: Increase the Throughput of 

Transcription Dynamics Measurements

Live HT 

Microscopy 

Platform

HT Tracking of Nuclei and RNAbinding-protein Spots
High-Throughput Live Cell Imaging Acquisition

Yokogawa CV7000S

• 96-well plates

• 40X air or 60X water obj.

• Projected 5 -15 slices Z-stack

• Frame Interval: 100 s

• Up to 6 Wells, 6 Fields

• Up to 680 frames (~ 19 hrs)

• ~20,000 images

• 1276x1076 pixels 
Diana Stavreva 

Hager Lab, NCI

SiR-DNA PP7-GFP

HT Live Imaging of GR Transcription at Single Sites

Responsive to Glucocorticoid 

Receptor (GR) binding hormones

SiR-DNA PP7-GFP

Integrated MMTV 

promoter with PP7 cassette
2D-t Images

Input

Nucleus 

Segmentation 

Nucleus 

Tracking

RNA Spot 

Segmentation 

Nucleus 

Registration

RNA Spot 

Registration

RNA Spot 

Tracking Spot Int. Tracks

Output

RNA Spot 

Filtering

HCA Pipeline Workflow

Reddy Gudla & George Zaki

(DL/RFPredict)

(RFPredict)
KNIME for High-Content Image Analysis

• Open source

• GUI / Web* / Headless

• Platform agnostic:

• Desktop (Mac/PC)

• Windows Server

• Linux HPC

• Reusable image processing code:

• KNIP

• ImageJ

• Matlab/Python/Etc 

Adapted from: Jan Eglinger, KNIME Summit, Spring 2017
Segmentation

Seeded Watershed + 

Random Forest Classifier

Tracking

LAPTracker

Nucleus Segmentation and Tracking

SiR-DNA
1: Calculate 

Rigid Body 

Transformation

Nucleus Registration

2: Apply Rigid 

Body 

Transformation

SiR-DNA PP7-GFP
Temporal Coding Interpolated Tracks

Spot Detection 

+ 

LAPTracker

2

3

1

4

5

Track1

Track2

Track3

Track4

Track5

Spot Detection and Tracking

Registered Nucleus

PP7-GFP
Interpolated Tracks

2

3

1

4

5

Two-state HMM Modeling

0

400

800

1200

0

1300

2600

3900

0

1300

2600

3900

0

2000

4000

6000

0

800

1600

2400

18

15222936435057647178859299

106113120127134141148155162169176183190197204211218225232239

Intensity (A.U.)

HMM States

Frame Number (100s)

PP7-GFP
Workflow Applications

• Analysis of GR transcription dynamics

(Stavreva et al., Mol Cell, 2019) 

• Semi-genome wide Imaging of endogenous gene

expression (Wan et al., submitted) 

• Secondary validation of RNA-FISH based

chemical- and functional-genomics screens. 
3. HiTIF Image Bioinformatics Infrastructure

# Cores

Staging

~ 64 TB

~ 1.3 TB/Day Max. ~ 35 TB

CV7000S Omero DB Columbus

32

KNIME 1

KNIME-HPC 

(GPU)

40 64*

CV8000

~ 1.3 TB/Day Max.

Analysis

HTM

Storage

On Site

Rockville

Bethesda

Up to 10 Gbps

Up to 1 Gbps

Legend

KNIME 2

40

Archive

~ PB
• Lack of an open-source, web-based interface to manage,

visualize, manually annotate, and analyze images

• Need to triage different analysis tools and computational

hardware for different tasks

• Duplication data and moving data from data center to

data center 

Image Analysis Infrastructure Bottlenecks
• Image visualization

• Reusable image analysis modules for basic tasks

• Secondary data analysis and plotting

• Handles datasets in the 10 GB - 1 TB range 

Image Analysis Platform Wish List (1)
• Usable by cell biologists

• Programmable, open source state-of-the art 

algorithms

• Brings together data storage and computation

• Takes advantage of deep-learning and GPU’s

• Provides an interface to generate, store and organize

manual annotations 

Image Analysis Platform Wish List (2)
Acknowledgements

HiTIF

P. Gudla

L. Ozbun

Others

G. Zaki, HPC, CBIIT, NCI

CBIIT Server Team, NCI

Biowulf, HPC, CIT, NIH

ABCC, FNLRC, NCI 

C. Dietz, KNIME/KNIP

Larson Lab

Y. Wan

J. Rodriguez

M. Palangat

D. Larson

Hager Lab

D. Stavreva

G. Hager

This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, 

Project Num. 1ZICBC011567-01. The content of this presentation does not necessarily reflect the views or policies of 

the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations 

imply endorsement by the U.S. Government.