This study aimed to develop methods to detect rare leukemia stem cells (LSCs) in peripheral blood using immunophenotyping and flow cytometry. The researchers used a panel of 5 fluorescent antibodies targeting cell surface markers to distinguish LSCs from other blood cell types in samples spiked with acute lymphoblastic leukemia cells. Their analysis found CD133 and CD24 were the most specific markers for detecting LSCs, while CD45 was best for excluding non-leukemic cells. Bioinformatics tools including ROC curves, PCA, DFA and spectral clustering were used to analyze the high-dimensional flow cytometry data and develop classification algorithms to detect LSCs for applications in diagnostics and targeted therapies.

1. Strategies for detection of and targeting nanomedical systems to rare circulating
leukemia stem cells in peripheral blood
Christy L. Cooper, M.S.1; James F. Leary, Ph.D.1,2
1College of Veterinary Medicine, Department of Basic Medical Sciences, 2College of Engineering, Weldon School of Biomedical Engineering,
Purdue University, West Lafayette, IN 47907 USA
ABSTRACT
Detection of cells present at rare (<1.0%) frequencies in peripheral
blood, as is the case for leukemia stem cells (LSCs), is challenging due
to the presence of various cell types that are present in high
abundance in peripheral blood. LSC detection is further challenging
because these cells are immunophenotypically similar to hematopoietic
stem/progenitor cells (HSPCs). For this project, we utilized a panel of
five fluorescent antibodies directed toward different cell surface
markers in an attempt to distinguish between LSCs, HSCs, and the
bulk leukemic blast populations present within donor peripheral bloods
spiked with RS4;11 acute lymphoblastic leukemia (ALL) cells. This ex
vivo model system will be used in conjunction with advanced statistical
analyses of defined digital data mixtures to develop
immunophenotyping panels for sensitive, robust minimal residual
disease (MRD) (rare event) detection in whole peripheral blood and for
theragnosis of primitive acute leukemias using nanomedical systems.
Methods
Cell lines were obtained from the American Type Culture Collection
(ATCC) (Manassas, VA). RS4;11 acute lymphoblastic leukemia cells
were cultured in RPMI-1640 medium supplemented to contain
10%fetal bovine serum and were incubated at 37 oC, 5% CO2. Donor
peripheral blood was collected in BD K3/EDTA blood collection tubes
and processed using a Ficoll-Paque gradient to collect peripheral
blood mononuclear cells (PBMNCs). Anti-CD133-PE antibody was
obtained from Miltenyi Biotec (San Diego, CA). Anti-CD 34-BV421,
anti-CD45-APC, anti-CD33-PE-Cy5, and anti-CD24-PerCP-Cy5.5
antibodies were purchased from BD Biosciences (San Jose, CA).
Immunophenotyping
Anti-CD133-PE was used at a dilution of 1:10. All other antibodies
were used at a dilution of 1:4. 105 cells in PBS, 2% FBS were
incubated with antibodies at the indicated dilutions and kept at 4oC for
15 minutes. Flow cytometry was performed using a FACSAria III flow
cytometer/cell sorter (BD Biosciences, San Jose, CA) fitted with a
flow nozzle of 100 micron diameter orifice. . Single color
compensation controls and Fluorescence minus one (FMO) control
samples were also prepared.
Data analysis and statistics
Color compensation and gating of immunophenotyping data were
performed in WinList 6.0 3D (Verity Software House, Topsham, ME)
and further processed in Bioconductor/ R packages flowCore and
flowViz. ROC plots were created using the R package ROCR.
Spectral clustering was performed using Bioconductor / R package
SamSPECTRAL. Other R packages were also utilized for PCA, DFA,
and graphical representations of various data.
Table 1. Lasers and filters used for exciting and collecting the
emission of the various immunophenotyping panel antibodies.
Nanoparticle synthesis
HGC nanoparticles were synthesized by reacting glycol chitosan with
5-beta cholanic acid in 3:1 (v/v) methanol:water overnight. (Key 2012)
HGC-Alexa Fluor 488 was prepared by combining 40mg of
lyophilized HGC with 0.2mg NHS-Alexa Fluor 488 in anhydrous
DMSO. Iron oxide nanocubes, synthesized by thermal decomposition
of iron (III) acetylacetonate in oleic acid/benzyl ether, were loaded into
HGC-Alexa Fluor 488 by probe sonication. HGC-Alexa Fluor 488-
SPIO-anti-CD133 was prepared by adding 25uL of a 1mg/mL HGC-
Alexa Fluor 488 -SPIO solution in water to 5ug of anti-CD133
antibody in 100mM NaH2PO4, 2mM EDTA, pH 8.0. This reaction
proceeded overnight. The product was used as-is in place of or in
addition to anti-CD133-PE antibody in immunophenotyping
experiments.
Figure 3. Schematic
representation of HGC-
SPIO-anti-CD133
nanoconjugate
Dark blue: Glycol
chitosan shell
Green/black spheres
:SPIO in 5β cholanic acid
Green outer spheres
:Alexa Fluor 488
References
1. Leary, J.F, Hokanson, J.A., McLaughlin, S.R. “High Speed Cell Classification Systems for Real-Time Data
Classification and Cell Sorting,” SPIE 2982, 342-352 (1997).
2. Coustan-Smith, E., Campana, D. “Immunologic minimal residual disease detection in acute lymphoblastic
leukemia: A comparative approach to molecular testing,” Best Practice & Research Clinical Haematology
23(3), 347-358 (2010). .
3. Denys, B., van der Sluijs-Gelling, A.J., Homburg, C., van der Schoot, C.E., de Haas, V., Philippe, J.,
Pieters, R. van Dongen, J. J. M. van der Velden, V. H. J. “Improved flow cytometric detection of minimal
residual disease in childhood acute lymphoblastic leukemia,” Leukemia 27(3), 635-641 (2013).
4. Key, Jaehong. Cooper, Christy, Kim, Ah Young , Dhawan, Deepika,. Knapp, Deborah W, Kim,
Kwangmeyung, Park, Jae Hyung, Choi, Kuiwon, Kwon, Ick Chan , Park, Kinam, Leary, James F.,In vivo
NIRF and MR dual-modality imaging using glycol chitosan nanoparticles, Journal of Controlled Release,
163: 249–255 (2012)
5. Zare, H. and Shooshtari, P. and Gupta, A. and Brinkman R.B: Data Reduction for Spectral Clustering to
Analyse High Throughput Flow Cytometry Data. BMC Bioinformatics, 2010, 11:403.
Acknowledgements
The authors would like to acknowledge Jeffrey Woodliff, Ph.D. and Jill Hutchcroft, Ph.D. in the Purdue Cancer Research
Shared Flow Cytometry and Cell Separation Facility supported by grant 5P30-CA023168-33 for their assistance with
scheduling time on the FACSAria III flow cytometer/cell sorter. The authors would also like to thank Lisa Reece for
drawing blood for this work in accordance with IRB protocol 0604003812.
Conclusions
RS4;11 leukemic blasts and the LSC subpopulation thereof could be distinguished from PBMNCs including hematopoietic
stem/progenitors using a combination of fluorescent antibodies toward five cell surface markers. ROC curves indicated that
the most selective positive distinguishing marker was CD133. CD24 was also suitable positive selection markers while
CD45 proved to be a suitable negative selection marker. The ROC curves, shown here for individual markers, will be
repeated using statistical algorithms for constructing multiparametric ROC plots. These multiparametric plots will inform the
selection of new diagnostic markers using an iterative process. Cluster IDs output from spectral clustering algorithms
including SamSPECTRAL will be used to develop a classification scheme for improved diagnostics of acute leukemias and
to inform the development of targeted therapies that will eradicate leukemia cell subpopulations including rare LSC
populations while sparing normal, healthy hematopoietic stem / progenitor cells.
Figure 2. Schematic of the immunophenotypes of RS4;11 pro-B acute
lymphoblastic leukemia cell line and PBMNC populations.
Receiver operator characteristic (ROC) curves measure the true positive
vs. false positive rate for a given diagnostic marker or test.
Principal components analysis (PCA) and linear discriminant analysis
(LDA) are used for both unsupervised dimensional reduction and for
distinguishing between multiple groups of data via selection of the
appropriate number of variables (or components) to retain for
classification (PCA) and for measuring classifier performance while
maximizing separation between the studied data groupings (DFA). For
this research, we employed a density-based spectral clustering method
with faithful sampling and clustering via a modified Markov clustering
algorithm. This method is implemented in the Bioconductor/R package
SamSPECTRAL (Zare 2010) and was selected because it saves time
computationally with its faithful sampling method and because it is
capable of finding clusters of rare (<2%) numbers of events.
Figure 4. Two parameter histograms (“dot plots”) of RS4;11 blasts (left) and PBMNCs (right).
CD133 v. CD33
CD133 v. CD34
CD133 v. CD24
CD133 v. CD45
ROC analysis on digital defined data mixtures of PBMNC and RS4;11 cells’
immunophenotyping data indicated that the order of specificity of the markers
(antibodies) was, from best to worst performance: CD133 > CD24 > CD34
>CD33 > CD45.
Marker(Ab) AUC
(Area under curve)
CD24 0.9188
CD33 0.4885
CD34 0.5020
CD45 0.1933
CD133 0.9994
Bioinformatics Approach
Experiment
Design
Data collection
Gate populations
Color compensate
Arcsinh transform
Combine/subset
Statistical
Analysis
Data
transformation
Cluster analysis
Data mining
ROC curves
Flow cytometry
Cytotoxicity and
cell targeting
studies
Purple: CD133
Green: CD24
Orange: CD34
Blue: CD33
Red: CD45
RS4;11 PBMNCs (“normal”)
Fig 7 Representative 3D PCA plots colored according to cell type (A,C) and by SamSPECTRAL cluster
assignment (B,D). PCA appears to separate clusters from each other, but lacks ability to clearly define rare
populations when cell types are used for PCA analysis..
Figure 1. Workflow for disease
classification using flow
cytometry and bioinformatics
tools with therapeutic targeting
to diseased cells.
Results: Immunophenotyping
Results: Marker Sensitivity and Specificity
Figure 5. ROC curves
and AUC values for
immunophenotyping
panel antibodies
PBMNCs: black
RS4;11: red
A B
C D
Fig 8 Representative 3D DFA plots colored according to SamSPECTRAL cluster assignment (A-C) and by cell
type (D). DFA identifies PBMNC clusters and RS4;11 clusters well but does not well resolve clusters in digital
data mixtures of the combined PBMNC and RS4;11 data as would be observed clinically in leukemia patients..
PBMNCs: black
RS4;11: red
PBMNC
only
RS4;11
only
PBMNC and RS4;11
combined
Results: Dimensionality Reduction and Classification
PBMNCs: black
RS4;11: red
Cell Type Mean LD1 Standard deviation LD1 Sample size
PBMNC -5.795301 0.9863204 89752
RS4;11 6.160970 1.0143405 84425
Allocated to PBMNC Allocated to RS4;11
Is PBMNC 89591 161*
Is RS4;11 376* 84049
Classification Rules
1. If MeanLD1 for a given cell < the average mean for the two
cell types, assign that cell to be a normal PBMNC.
2. If MeanLD1 for a given cell is > the average mean for the two
cell types, assign that cell to be an RS4;11 lymphoblast.
Misclassification rate
(total number of misclassified cells/ total number of cells) * 100 = 0.31%
The overall misclassification rate is 0.31%.
:* : Misclassified cells
RS4;11
PBMNC
Figure 9. Histograms of LD1 values corresponding to
PBMNCs (group 1) and RS4;11 blasts (group 2).
Decision boundary= 0.185
Populations overlap in this
region at low frequencies.
Truepositiverate(Sensitivity)
False positive rate (1-Specificity)