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2012YTOC
Web-based SPADE for extracting a cellular hierarchy from multidimensional fluorescence and mass cytometry datasets
Jonathan Irish1,2, Zach Bjørnson3, Robert Bruggner3, Michael Linderman4, Chad Rosenberg1, Nikesh Kotecha1,3
1Cytobank, Inc, Mountain View, CA 94040, 2Vanderbilt University, Nashville, TN 372203Stanford University, Stanford, CA 94305, 4Mount Sinai School of Medicine, New York, NY 10029
Introduction and Aims Cloud Computing & SPADE Interpreting SPADE TreesOverall goal: Develop a SPADE algorithm for discovery of cell populations in fluorescence and mass cytometry datasets and deploy it in the Cytobank web-based cloud computing environment.
SPADE = Spanning-tree Progression Analysis of Density-normalized Events, a technique for automated population identification and visualization.
Aims: (1) Automatically identify populations of cells in heterogeneous samples, including rare subsets
(2) Visualize markers measured across subsets of cells and samples & organize populations by phenotype.
(3) Provide an interface that connects investigators to datasets and cloud computing resources
SPADE Interface
SPADE Analysis of Fluorescence Cytometry
Optimizing Scales for Computational Analysis
SPADE Analysis of Mass Cytometry
MountSinai
1) Cloud computing enables analysis on a remote server without monopolizing local resources & links analysis results with raw data files.
2) Channel-specific scaling is critical prior to analysis by SPADE or other computational tools, especially for fluorescence cytometry.
3) These results create a cloud-based framework for integrating flow cytometry analysis algorithms, tools, and visualizations.
Conclusions For More Information
Irish Lab
SPADE
MassCytometry
jonathan.irish@vanderbilt.edumy.vanderbilt.edu/irishlab
SPADE on Cytobank info@cytobank.org
www.cytobank.org
Imagine as a 3D shapethe “OK sign”
SPADE will cluster and thenarrange the clusters in a 2D tree
Some 2D trees may be intuitive, while others may not be
Clustering Minimum Spanning Tree
Tree A
Appropriate channel-specific scaling is essential
Selecting nodes or a bubble displays those events in a 2D plot
Rare populations are automatically identified
Tree B Tree C2D tree “close” to original shape Different branch positioning Different breakpoint
SPADE trees depict multidimensional similarity in two dimensions
SPADE Controlson Cytobank
Pre-gating PBMC for CD45+ single cells
Set target clusters
Min:Cofactor:
Result:
Issue:
-30002500Good
-3000500Poor
-3000150Bad
Under-transformedOff scale
For fluorescent flow cytometry data a biexponential or arcsinh transformation corrects the scale near zero.
A 50:50 mix of + and - events stained only for PerCP-Cy5.5 is shown using different scales.
Over-transformed
-30001
Very Bad
12500Bad
-300010,000Poor
- target number of nodes in the tree
- by percentile or # of cells
- remove dead cells, set scales
- can cluster using all or a subset
- used to set ‘basal’ for foldchange calculations
Set downsampling
Optional: pre-gating
Choose channels
Optional: group samples
Qiu P et al., Nature Biotechnology 2011
1) Get High-DCytometry Data
3) Cluster(group by similarity)
4) Project into 2D(minimum spanning tree)
2) Downsample(preserves rare subsets)
Dataset from Engelhardt BG et al., Blood 2012
5
4
1
2 541
23
3
1) Connect flow cytometry tools and knowledge
2) Communicate results and share data files
3) Provide key details (compensation, scales) to computational collaborators4) Apply new algorithms & visualizations that run best with significant compute resources
Growing needs:
Cloud computing is needed as cytometry experiments increase in power
Sam
ple
Num
ber
Features Measured
100
1000+
50
104 10 20 50+
Future of Flow
Routine
CuttingEdge
BleedingEdge
Marker 1: Nucleic Acid
3.51 Singlet PBMCs
Marker 2: CD45
5.15 CD45+ PBMCs
Marker 3: CD3CD3+ CD45+
CD3- CD45+
3.05
Marker 4: CD4CD3+ CD45+
3.01
Monocytes & DCsCD4lo CD3- CD45+
CD3- CD45+
CD4+ T cellsCD4+ CD3+ CD45+
Marker 5: CD8
Monocytes & DCsCD4lo CD3- CD45+
3.38
CD3- CD45+
CD4+ T cellsCD4+ CD3+ CD45+ CD8-
CD8+ T cellsCD8+ CD3+ CD45+ CD4-
Marker 6: CD16
CD3- CD45+
2.55
MDC Group BCD16+
CD4lo CD3- CD45+
MDC Group ACD16lo
CD4lo CD3- CD45+
CD4+ T cellsCD4+ CD3+ CD45+ CD8-
NK cellsCD16+ CD3- CD45+
CD8+ T cellsCD8+ CD3+ CD45+ CD4-
Marker 7: CD19
B cellsCD19+ CD3- CD16- CD4- CD45+
4.03
MDC Group BCD16+
CD4lo CD3- CD45+
MDC Group ACD16lo
CD4lo CD3- CD45+
NK cellsCD16+ CD3- CD45+
CD4+ T cellsCD4+ CD3+ CD45+ CD8-
CD8+ T cellsCD8+ CD3+ CD45+ CD4-
Marker 8: CD20
4.02
MDC Group BCD16+
CD4lo CD3- CD45+
MDC Group ACD16lo
CD4lo CD3- CD45+
NK cellsCD16+ CD3- CD45+
B cellsCD19+ CD20+ CD3-
CD16- CD4- CD45+
CD4+ T cellsCD4+ CD3+ CD45+ CD8-
CD8+ T cellsCD8+ CD3+ CD45+ CD4-
Marker 9: CD14
2.13
NK cellsCD16+ CD3- CD45+
MonocytesCD14+ CD16lo
CD4lo CD3- CD45+
B cellsCD19+ CD20+ CD3-
CD16- CD4- CD45+
CD4+ T cellsCD4+ CD3+ CD45+ CD8-
CD8+ T cellsCD8+ CD3+ CD45+ CD4-
MDC Group BCD14- CD16+
CD4lo CD3- CD45+
Marker 10: CD33
1.10
NK cellsCD16+ CD3- CD45+
MDC Group BCD33lo CD14- CD16+
CD4lo CD3- CD45+
MonocytesCD33+ CD14+ CD16lo
CD4lo CD3- CD45+
B cellsCD19+ CD20+ CD3-
CD16- CD4- CD45+
CD4+ T cellsCD4+ CD3+ CD45+ CD8-
CD8+ T cellsCD8+ CD3+ CD45+ CD4-
Marker 11: CD123
NK cellsCD16+ CD3- CD45+
1.99
Dendritic CellsCD123+
CD33lo CD14- CD16+
CD4lo CD3- CD45+
MonocytesCD33+ CD14+ CD16lo
CD123lo
CD4lo CD3- CD45+
B cellsCD19+ CD20+ CD3-
CD16- CD4- CD45+
CD4+ T cellsCD4+ CD3+ CD45+ CD8-
CD8+ T cellsCD8+ CD3+ CD45+ CD4-
Summary of Major Cell Types Grouped by SPADE(Human PBMC)
5.15
NK cellsB cells
CD4+ T cellsCD8+ T cells
Dendritic Cells
Dendritic Cells
Monocytes
Mono
DC
DC
NKB
CD8+ TCD4+ T
Merged Nodes
Nucleic Acid (Ir), CD45, CD3, CD4, CD8,CD16, CD19, CD20, CD11b, CD123,CD45RA, CD33, CD11c, CD14, CD56,CD38, CD15, CD10, CD44, & HLA-DR
20 Markers:
All available commercially from DVS Sciences
All Human PBMC Events Singlet PBMCs CD45+ PBMCsNucleic Acid+ CD45+ Singlets from human PBMC were clustered using 18 measured surface markers and arranged into a 400 node SPADE tree. 11 markers are shown below.
Major populations included CD4+ and CD8+ T cells, B cells, NK cells, monocytes, and dendritic cells.
Here, CD8 was under-scaled so that an artifical ‘hole’ in the graph existed around zero. This created the false impression of two CD8 populations in this sample gated as CD8 negative. SPADE treated this as a significant difference.
Comparison of CD8scaling for CD8
on cells gated as CD8-
CD8 was measured on PE-Cy5
Corrected ScaleIncorrect Scale
Since computational analysis techniques compare distance similar to what a person does when looking at a plot, these techniques can identify artificial populations near zero if data are not appropriately transformed prior to analysis.
arcsinh (inverse hyperbolic sine) scalehttp://mathworld.wolfram.com/InverseHyperbolicSine.html
In order to analyze fluorescent datasets with SPADE, Cytobank applies channel-specific scales set during routine analysis (gating, making figures).
Without channel specific scaling, population artifacts can arise (see CD8 example to the right).
Analysis of CD4+ T cell subsets in post-transplant diabetes mellitus patients based on SPADE analysis of Foxp3, CD25, CD127, CD45R0, α4β7 integrin (gut homing), and CLA (skin homing).
Rare gut and skin homing subpopulations of Foxp3+ CD25+ CD4+ T cells (~0.02% of total; 200 cells in 1 million) were identified by SPADE.
After the SPADE analysis completes you can interact with the tree by selecting one or more nodes (populations of cells) and naming them according to phenotype.
The tree to the right shows CD4 expression on CD45+ PBMC and the user creating a population named “CD4 T cells”.
A small analysis of 18 features on <100,000 cells (as with the mass cytometry file below) takes ~3 min. Larger experiments with millions of cells and multiple samples can take 1 hour or more (as with the fluorescent dataset to the right). SPADE runs on a server and you can close the browser window after initiating the analysis. When the run is complete Cytobank sends an email with a link to the results.
arcsinh(x) with cofactor c =
CD45R0
CLA(skin homing)
Foxp3α4β7 integrin(gut homing)
CD25 CD127 CD4User selects Foxp3+ CD45R0+ CD4 Treg nodes
No nodes selected,all cells plotted1
2
1
2
All Human PBMC Events Intact PBMCs CD14- Viable PBMCs
CD3+ CD4+
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