MultiOmyxTM: A novel platform for in situ, multiomic, hyper-plexed analyses of systems biology

Background: We describe a novel multiomic, hyper-plexed tissue analyses platform, MultiOmyxTM, which enables the imaging of protein and nucleic acid biomarkers at subcellular level in the same tissue slice. Unlike grind-and-find methods, imaging preserves the spatial architecture of tissue allowing interrogation of both intra and inter-cellular interactions/communications. Image analysis algorithms enable subcellular quantification of biomarkers in individual cells and enable novel systems-level insights into biological mechanisms. Method: From a single tissue section, cells are molecularly profiled and visualized using MultiOmyxTM. They are clustered into families having similar phenotypes using associated and proprietary analysis software. Individual clusters are color-coded and transposed back to original images to provide a novel in situ visualization of patterns of heterogeneity and cellular interactions. Tissue visualization is combined with existing pathway visualization and analysis tools e.g. Cytoscape. Results: The method was applied to a cohort of 747 colon cancer tissue microarray with particular focus on immune infiltration and mTOR and MAPK signal transduction. Cluster analysis of immune cell features showed robust adaptive and innate immune responses in many subjects and conversely, other patients showed a general lack of immune cell infiltration. Cluster analysis and visualization of mTOR and MAPK signal transduction pathway activation at the single cell level revealed unexpected patterns of coexpresssion and mutual exclusivity of common downstream phosphorylation events. Conclusion: Quantitative tumor immunoprofiling and signal transduction analysis revealed extensive immune cell heterogeneity between subjects and unexpected signal transduction exclusivity and coexpression patterns highlighting the unique capability of this platform.

Background autofluorescent

image

Stain slide with dye-labeled antibodies

.

Remove slide, inactivate

signal

Dye Inactivation >60

Proteins then DNA

FISH

Multiplexing: Stain, Image, Erase and Repeat

Image & Data Analysis Workflow

Image Corrections

• Illumination correction

• Image registration

• Autofluorescence subtraction

QC

• Registration failures

• Poor focus

• Damaged tissue

• Illumination issues

Image Analysis

• Epithelium segmentation

• Stromal segmentation

• Single cell measurements

Data Transforms

• Data transformation (log etc.)

• Normalization

• Data integration with clinical data/other data types

Data Exclusion

Rules

• Invalid cells

• Image periphery

• Image annotation

Statistical Feature

Extraction

• Cell clustering

• Moments

• Proximity

• Thresholds

Outcome and Pathway Analysis

• Population level

• Cell level

• Survival/recurrence

• Classification

Images acquired on scanner

DNAseq mutations

Select •Study •Patient •Tissue •Sample •Multiomic data

Pathway Maps • KEGG • Wikipathways • NCI PID • Reactome • BioCarta

pathway scores

Cell Maps

High Low impact

MultiOmyx measures

Future: Visualize MultiOmyx Data in a Pathway Context using Cytoscape

ERK 1/2 Akt 1/2/3 EPCAM

phospho-ERK1/2 T202/Y204 PI3K p110α CD31

Wnt5a Indian Hedgehog Fibronectin

β-Catenin xCT Vimentin

S6 ribosomal protein GLUT1 β-Actin

phospho-S6 S235/S236 CA9 pan-cytokeratin (1,5,6,8)

HER2 ALDH1 α-Smooth Muscle Actin

4EBP1 TKLP1 NA+K+ATPase

phospho-4EBP1 T37/T46 COX2 Collagen IV

NDRG1 MLH1 Albumin

phospho-NDRG1 T346 MSH2 Cytokeratin 19

phospho-GSK3α S21 Lamin A/C Cytokeratin 15

phospho-GSK3β S9 EZH2 Claudin1

EGFR p21 E-Cadherin

phsopho-EGFR Y1068 FOXO3a CD44v6

PTEN FOXO1 CD20

phospho-MAPKAPK2 T334 Cleaved Caspase 3 CD68

Met Cyclin B1 CD79

phospho-Met Y1349 p53 CD8

phospho-p38 MAPK T180/Y182 PCNA CD3

Christine D. Kuslich, Christopher J. Sevinsky, Michael J. Gerdes, Fiona Ginty, John F. Graf, Vidya Kamath, Qing Li, Lee A. Newberg, Brian Ring, Alberto Santamaria-Pang, Anup Sood, Yunxia Sui, Maria I. Zavodszky, Brion D. Sarachan,

GE Global Research and GE Healthcare

Analysis of a large cohort of colon cancer subjects

DAPI:

nucleus

E-cadherin:

epithelial cells

Na+K+ATPase: membrane RPS6:

cytoplasm Segmented cells

Nucleus

Membrane

Cytoplasm

stage I (n=192) stage II (n=278) stage III (n=252)

age 66 (27,89) 69 (35,94) 66 (32,91)

gender F (96,50%) F (136,49%) F (118,47%)

grade=1 50 (26%) 39 (14%) 23 (9%)

grade=2 125(65%) 216(78%) 175(69%)

grade=3 12(6%) 19(7%) 51(20%)

recurrence n=15 n=51 n=94

avg (range) 3.2 (0.7,6.8) 2 (0,9.5) 1.6(0.1,8.9)

Follow-up n=177 n=227 n=157

avg (range) 5.3 (0.1,12.1) 5 (0,13) 4.5(0,11.8)

death from disease n=11 n=44 n=82

avg (range) 3.6 (0.7,6) 2.2 (0,9.7) 2.1(0.2,7.5)

Follow-up n=181 n=234 n=170

avg (range 5.2 (0.1,12.1) 5.3 (0,13) 4.7(0,11.8)

Subjects: 747 colorectal cancer patients – AJCC stage I-III – for summary statistics see Table. Multiplexed immunofluorescence: directly conjugated Cy2, Cy3 and cy5 labeled primary antibodies to hallmarks of colorectal cancer and tissue microenvironment. Sequential fluorescence microscopy: 38 rounds of imaging; 60 protein targets + baseline autofluorescence imaging Automated image analysis: registration; single cell and subcellular nuclear, cytoplasmic and membrane segmentation; target quantification.

Abstract

Single cell segmentation and signal quantitation

Single cell cluster analysis: mutually exclusive signal transduction

Heterogeneity: cell level signal transduction

Cluster analysis of patient level signaling

Psuedo-colored Clusters IDs

mapped to cells

Immune cell profiling

Poor Prognosis Favorable Prognosis

Single cell map Single cell metrics

p4

E-B

P1

pS

6 N

uc

lei

Low Immune cell infiltrate High Immune cell infiltrate

Monitoring immune cell types

in the tumor microenvironment

Quantification of immune cell types in the tumor microenvironment

Quantification of immune cell types is prognostic