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Analysis of Gene Expression
at the Single-Cell Level
Guo-Cheng Yuan
Department of Biostatistics and Computational Biology
Dana-Farber Cancer Institute
Harvard School of Public Health
Bioconductor, July 31st, 2014
bioconductor
Methods to sequence the
DNA and RNA of single cells
are poised to transform
many areas of biology and
medicine.
--- Nature Methods
• “Recent technical advances have enabled RNA sequencing
(RNA-seq) in single cells. Exploratory studies have already
led to insights into the dynamics of differentiation, cellular
responses to stimulation and the stochastic nature of
transcription. We are entering an era of single-cell
transcriptomics that holds promise to substantially impact
biology and medicine.”
– R. Sandberg, 2014. Nature Methods
Cell Division
Cell-type A
Cell-type C
Cell-type B
Cell-type E
Cell-type D
Cell-type F
R. Sandberg, 2014. Nature Methods
Challenges in single-cell data analysis
• Characterize and distinguish technical/biological
variability
• Identify new and meaningful cell clusters.
• Identify the lineage relationship between
different cell clusters.
• Characterize the dynamic process during cellstate transitions.
• Elucidate the transition of regulatory networks.
• Distinguish stochastic vs real variation
CMP
GMP
MEP
MEP
CLP
Guoji Guo, Eugenio Marco
SPADE: a density-normalized, spanning tree model
Down-sample
Clustering,
Spanning-tree
Visualization
Qiu et al. 2011
Nat Biotech, p886
Log2(CMP1/CMP2)
CD55
7.87
ICAM4
3.98
CD274
3.32
MPL
3.19
TEK
2.83
Cancer Stem Cells
• Each cancer contains a highly heterogeneous cell
population.
• Clonal evolution contributes to cancer heterogeneity
• Cancer cells are hierarchically organized and
maintained by cancer stem cells
• How are the leukemia stem cells related to normal
blood cell lineage? How do they differ?
Single cell analysis of the mouse MLL-AF9
acute myeloid leukemia cells
Compilation of
mouse cell surface
antigens (Lai et al.,
1998; eBioscience
website)
Primer design for 300
multiplexed PCR
(collaboration with
Helen Skaletsky)
Micro-fluidic highthroughput realtime
PCR (96.96 Array)
Guoji Guo, Assieh Saadatpour
t-SNE analysis identifies similarities between cell-types
• t-SNE is a nonlinear
dimension reduction
method, and can
identify patterns
undetectable by PCA
• t-SNE minimizes the
divergence between
distributions over pairs
of points.
• Leukemia cells are
more similar to GMPs
than to HSCs
• Leukemia cells are
highly heterogeneous.
Mapping leukemia cells to normal hematopoietic cell
hierarchy
• Use 33 common
genes to map cell
hierarchy.
• Mapping identifies
two subtypes of
leukemia cells.
• These cells are similar
but not identical to
their corresponding
normal lineages.
Coexpression networks are different among subtypes
All
Leukemia
Leukemia 1
GMP
Leukemia 2
Surani and Tischler, Nature 2012
Guo et al. Dev Cell 2010
Dynamic clustering
T=1
T=2
T=3
T=4
Maximizing the penalized log-likelihood.
log P(x |  )   c  a(c)
2
c
Eugenio Marco, Bobby Karp, Lorenzo Trippa, Guoji Guo
Identifying bifurcation points and directions
EPI
ICM
PE
TE
>80% variance increase during bifurcation is attributed to a
single (bifurcation) direction.
Modeling dynamics by bifurcation analysis
dx  U ( x) dt
I)
U(x)
4a 3  27b 2  0
II)
U(x)
4a 3  27b 2  0
x 4 ax 2
U ( x)  
 bx
4
2
Modeling dynamics by bifurcation analysis
dx  U ( x) dt   dW (t )
I)
U(x)
4a 3  27b 2  0
II)
U(x)
4a 3  27b 2  0
Noise level  has large impact on lineage biases
=1
 = 0.5
=2
Lineage bias due to perturbation of TF activity
Control
U(x)
Perturbation
U(x)
Predicted lineage bias due to 2 fold decrease of TF level
Experimental validation using Nanog mutant
PE
EPI
Nanog
How do we infer dynamics without
temporal information?
Characterization of early bipotential progeny of
Lgr5+ intestinal stem cells
Crosnier 2006. Nature Review
Tae-Hee Kim, Assieh Saadatpour
Principal Curve Analysis Reconstruct
Temporal Information
t-SNE plot
indicates two
distinct clusters,
linked a small
number of
transitional cells
Principal Curve Analysis Reconstruct
Temporal Information
t-SNE plot
indicates two
distinct clusters,
linked a small
number of
transitional cells
Principal curve
analysis captures
the overall trend
of cell-state
transition
Olfm4
Rnf43
apdh
dca7
Actb
Pcna
Taf1d
oc2-2
Cd24
Itgav
Vil1
rom1
CD44
Gcnt3
t1h1e
Axin2
oc2-1
ox9-1
Cnn3
Cdx2
Ccl9
csm3
Kitl
Gpld1
Vdr
Hopx
es1-2
Fgfr4
dkn1b
Nr4a2
Vegfc
Tex9
Znrf3
Lrp1
Cd9
Dpp4
Farp1
Ccnl1
Ascl2
ox9-2
c14a1
Lrig1
Nop2
cnd1
Lcp1
Hprt
Ly75
Il6st
obtb3
Cdx1
Tyms
Bmi1
Fos
Nfat5
Atf3
nase1
Dll1
Cd14
Zfp36
Cd82
emin4
Sycn
Hig2
Il6
Rfc4
Cdk1
Defa5
cnb1
Rrm2
Plk1
Ang4
asp12
Tlr1
Lct
Kit
Apoa1
Lifr
Tdgf1
Dct
Muc2
Spdef
Nupr1
Dll4
Tert-1
urod1
Tert-2
Clca3
Lipe
toh1-1
Tbx3
usp1
JunB
Psrc1
dkn2b
Bmp4
Hes3
Treh
ep15
Chga
Alpi
Pax4
toh1-2
Cck
dkn1a
urog3
cna2
Mki67
Defa3
achd1
Ephb3
Fstl1
enpe
hisa2
Nfatc3
Jun
Prelp
otch1
es1-1
Fgfr2
es1d
Apex1
Abcg2
Igf1r
Lgr5
Agr3
Arg2
ap1l1
Clca1
Heph
ma4d
otch2
dr90
Ppif
Tac1
Egr2
cne1
Selm
Gas6
Sox4
Hbegf
Cd38
Fosb
Dbd2
Lyz1
Clca2
Igfbp4
asa3
Msi
Cd55
Aspa
Ifnar1
Tcof1
usp4
Mmp7
Kcnq1
Syp
Dclk1
Chgb
d51l1
GFP
Hes5
E2F1
Egr1
Hey1
Insm1
m614
Hey2
Sp5
Btla
Lyz2
Heyl
Ccl6
Reg4
Myc
Hes7
reb3l4
Cd83
Fcgbp
dkn1c
Zic2
cam2
Gfi1
kx2.2
md1a
Gfi1b
dkn2a
Tff3
Diap3
Cdkn3
Inferred dynamic gene expression profile
Use the principal curve coordinate
as a proxy for temporal evolution.
10
9
8
7
6
5
4
3
2
1
Conclusions
• Single-cell genomics is a powerful technology
for understanding cellular heterogeneity and
hierarchy.
• Single-cell gene expression data analysis
present many new methodological challenges.
• It is a great time to develop algorithms and
software for single cell data analysis.
Acknowledgement
Eugenio Marco
Assieh Saadatpour
Stuart Orkin
Guoji Guo
Bobby Karp
Ramesh Shivdasani
Tae-Hee Kim
Lorenzo Trippa
Paul Robson
Funding from
NIH, HSCI