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Multiplexed Ion Beam Imaging (MIBI)
Abstract
Background: An ability to characterize the cellular composition and
spatial organization of the TME using multiplexed IHC has been limited
by the techniques available. Here we show the applicability of MIBI™
technology for cell phenotype identification and spatial relationships
analysis across multiple tumor types.
Methods: FFPE samples from tumor TMAs were stained with a panel of
15 antibodies, each labeled with a specific metal isotope. Brightfield IHC
served as a comparator to establish that the labeling procedure did not
affect antibody affinity. In MIBI the stained section was scanned via
secondary ion mass spectrometry to image the tissue for target
expression. Leave-one-out (LOO) analyses (a single antibody left out of
the panel, compared to the full MIBI panel) showed negligible loss in
signal intensity of targets indicating an absence of interference between
antibodies. Multi-step processing and segmentation produced images of
the TME and the frequency of different cell subsets. Distances between
cell subsets were calculated using this data.
Results: Panel validation by LOO showed that all markers were
repeatable across 9 panels (R2 0.94 - 1.00). Control samples imaged at
study start and end showed consistent marker quantification (inter-run
R2 > 0.99), indicating MIBI staining and acquisition is robust and
reproducible. A total of 50 tumor specimens from 15 tumor types were
characterized for their immune profile and spatial organization (nearest-
neighbor). Most samples showed infiltrating cytotoxic T cells and
macrophages present amongst tumor cells. Spatial analysis of the TME
in two ovarian serous carcinoma samples showed differences in the
distance between tumor and immune cells, highlighting the degree of
mixing between tumor and immune cells. Identification of admixed
PD-L1+ macrophages and PD-1+ T cells in a urothelial carcinoma
sample allowed for calculation of distances to each other.
Conclusions: MIBI offers high-parameter multiplexing capability, at
sensitivity and resolution uniquely suited to understanding the complex
tumor immune landscape including the spatial relationship of immune
and tumor cells and expression of immunoregulatory proteins.
Multiplexed Ion Beam Imaging (MIBI) For Characterization of the Tumor
Microenvironment Across Tumor TypesJason Ptacek1, Darren Locke2, Rachel Finck1, Mary-Ellen Cvijic2, Zhuyin Li2, Murat Aksoy1, Jay Tarolli1, Yi Zhang1, Jessica Finn1
1Ionpath Inc. Menlo Park, CA 2Bristol-Myers Squibb Princeton, NJ
MIBI™ is a trademark of IONpath INC. For Research Use Only. Not for diagnostic use.
© Copyright 2019, IONpath.
MIBI Data Recapitulates IHC Staining Patterns Across
A Variety of Tissue Types
Single Cell Profiling of Immune Subsets of the TME
Spatial Distribution of Immune Cells and Tumor Cells
Differs Between Ovarian Cancer Samples
CD3
CD8
CD68
Keratin
CD8+PD-1+ T cells (red)
CD4+PD-1+ T cells (blue)CD4 and CD3 colocalized (green)
CD8 and CD3 colocalized (red)
CD45
Keratin
Tumor vs Immune
CD3
CD4
CD8
Immune Cell Subsets
CD4
CD8
PD-1
Immune Checkpoint Proteins
PD-L1 present on:
• CD68+ macrophages (blue)
Urothelial cancer sample shows
presence of TILs (cyan)
CD3
CD-68
PD-L1
Immune Checkpoint Proteins
• Patient 34 shows a compartmentalized organization of the TME versus
Patient 35 that shows mixing of immune cells and tumor cells.
• Calculation of nearest-neighbor distances between tumor cells and the
nearest immune cells further characterizes differences in TMEs.
Patient 34
Conclusions
• MIBI offers many features advantageous for the analysis of
complex tissues including the tumor microenvironment.
• MIBI control data indicates that staining and sample
acquisition is robust and repeatable across tissue types.
• The tissue architecture is defined at sub-cellular resolution
enabling segmentation that further enables single-cell
analysis and phenotypic characterization.
• Sensitivity afforded from the low background of MIBI allows
for markers with low expression such as PD-1 and FOXP3
to be readily detected.
• This study evaluating multiple tumor types shows the
possibilities of MIBI for patient stratification and immune profiling
of tumor samples under therapeutic forces.
Acknowledgements
We thank all patients who have donated samples for this investigation.
Patient 34
Patient 35
Patient 34Nearest-Neighbor Distances
Histogram of Nearest-Neighbor Tumor-Immune Distances
MIBI Data Reproducible For High, Medium and Low
Abundance Targets
Patient 35
A
B
C
D
E
F
Breast Carcinoma,
Triple Negative
B
Colonic
Adenocarcinoma
C D E
Prostatic
Adenocarcinoma
F
Urothelial Carcinoma
H
Cutaneous Melanoma
I
Endometrial
Adenocarcinoma
J
H
I
J
B C D E
F H I JG
G
Lung Adenocarcinoma
Lung Squamous Cell
Carcinoma
G
Non-Hodgkin
Lymphoma, Low Grade
A 100 𝜇m
A
Breast Carcinoma,
ER Positive
250 𝜇m
1 mm
• Example images of 10 tumor types comparing H&E images to MIBI.
• MIBI images showing a subset of markers analyzed by MIBI illustrate the
spatial organization of tumor cells and immune cells.
PD-L1+ cells
PD1+CD8+ T cells
PD1+CD4+ T cells
PD1+FoxP3+ T cells
Nearest-Neighbor Distances
NuclearMarker(s)
MembraneMarker(s)
Hybrid Multiplexed Image Watershed Segmentation
Target Clone Label Cell Expression
dsDNA 35I9 DNA 89Y All cells
beta-tubulin D3U1W 166Er All cells
HLA class 1 A, B, and C EMR8-5 160Gd All cells
Na-K ATPase alpha1 D4Y7E 176Yb All cells
Keratin AE1/AE3 165Ho Tumor cells, epithelial cells
CD3 D7A6E 159Tb T cells
CD4 EPR6855 143Nd T helper cells, some macrophages
CD8 C8/144B 158Gd Cytotoxic T cells
CD45 2B11 & PD7/26 175Lu Immune cells
CD56 MRQ-42 150Nd NK cells, neurons
CD68 D4B9C 156Gd Macrophages
FOXP3 236A/E7 146Nd Regulatory T cells
PD-1 D4W2J 148Nd T cells
PD-L1 E1L3N 149Sm Tumor cells, macrophages, APCs
SOX10 BC34 170Er Melanocytes, nerve cells
CD3
CD8
CD68
PD-1
PD-L1
Acq. Date: 5/10/18
Sample: Tonsil 1706
5/27/18
Tonsil 1707100 𝜇m
Phenotypic Classification
100 𝜇m
Titrate and test
antibody by IHC Conjugate
AntibodyCompare by IHC
Conjugated Ab v.
Unconjugated AbTitrate conjugated
antibody by MIBI
MIBI
Staining Panel For Phenotypic Analysis of the TME
• MIBI preserves spatial information allowing tumor-immune cell boundary and
distance determination.
• Segmentation uses multiplexed data and prior training data as ground truth.
• An ion beam rasters across the tissue liberating ions including the
isotopes bound to the tissue via the antibodies.
• Time-of-flight mass spectrometry separates the labels based on mass for
detection of markers present across the tissue.
• Antibody performance is assessed by IHC before conjugation to an isotope.
• Conjugated antibody performance is measured by MIBI and compared to IHC.
dsDNA
CD45
Keratin or SOX10
LOO Panel R2
Full - FOXP3 1.00
Full - PD-1 1.00
Full - PD-L1 1.00
Full - CD56 0.99
Full - CD68 1.00
Full - CD8 1.00
Full - Keratin 0.99
Full - Sox10 0.99
Maker Intensity Across Tonsil Replicates
Run 1 Ion Counts
Run 2
Ion C
ounts
Slope=0.99
R2=1.00
• Serial sections of lung adenocarcinoma were stained
with either the full panel or panels that lacked one
antibody (LOO panel).
• Regression analysis of signal intensity between Full
and LOO panels are summarized in the table.
• Images of keratin expression are similar between the
full panel and panels lacking FOXP3 or PD-1.
• Keratin signal is absent from the TMA stained with the
panel without keratin demonstrating the lack of
significant signal overlap across channels.
• Replicate serial sections of tonsil (shown), placenta, and melanoma acquired
by MIBI before and after acquisition of sample cohort show equivalent results.
• Segmented cells are assigned
a phenotype based on
combination of markers
• Distances between PD-L1+
cells and PD-1+ cells are
calculated
Single Cell Marker Expression
Cells (Cell Score)
Targ
et
Target expression across cells is
quantified by Cell Score, a metric
determined by the target’s signal
intensity and its distribution across a cell
No FOXP3
No Keratin No PD-1
Full Panel 100𝜇m
Keratin
Full Panel Ion Counts
Full
Pane
l –
FO
XP
3 I
on C
ounts
Slope=0.99
R2=1.00
Maker Intensity Between Panels
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