Einstein Circle 2016
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Transcript of Einstein Circle 2016
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Identifying mediators of local immunosuppression via single-cell sequencing
Aaron Diaz, PhD
Neurological Surgery, UCSF
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Transcriptomics and genomics pipeline:
• Learn clonal structure and model its evolution via Exome-seq
• Identify the transcriptional signatures of these clones via single-cell RNA-seq
• Measure concomitant compositional changes in the microenvironment
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Transcription and mutation profiles for 3 recent cases:• Single-cell RNA-seq enables the profiling of rare cells
whose signal may be lost in a bulk experiment.• Heterogeneity and stromal infiltration can be
assessed in a way not possible in bulk assays.
SF10282
SF10345 SF10360
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Identifying copy-number changes in individual cells:• Copy number alterations identified in
exome-seq (lower left) are recapitulated in single-cell expression trend-lines (lower right).
• Comparison with a normal control enables presence/absence calls for mutations found in the exome-seq data.
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• Each bar represents the genotype of a set of cells. These are the observed, contemporary clones.
• Two branches join if they possess a common ancestor (perhaps unobserved).
• A NSC-like subpopulation occurs at the apex of this phylogeny.
• An OPC gene signature is progressively up-regulated.
• Pro-angiogenesis and PI3K pathway genes increase concomitantly
SF10282
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• RNA-seq• Growth,
migration,invasionassays
Neural stem cells
Normal human astrocytes
Cultures from mesenchymal, EGF-driven GBM biopsies
Assess the effect of PDGFRA induction in normal cells and mesenchymal GBM.
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360 wt 360 gfp 360 del0.58
0.6
0.62
0.64
0.66
0.68
0.7
0.72
0.74
0.76
0.78Transwell invasion assay SF10360c
PDGFR enhances growth and invasion in vitro
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• PDGFRα deletions occur in approximately 18% of TCGA GBM exome-seq data (n=389).
• The most common, at 16%, is PDGFR• All of these deletions target one of the two I-
set domains, immunoglobulin-like folds involved with dimerization.
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Previous works studying PDGFRA deletions:• I. Clark and P. Dirks. A human brain tumor-derived PDGFR-α deletion mutant is transforming. Oncogene, 2003.• Ozawa et al. PDGFRA gene rearrangements are frequent genetic events in PDGFRA-amplified glioblastomas . Genes &
Devel. 2010.87 GBMs: 17% PDGFRA amplified, 40% of those harbor PDGFRA(6% of total cases).
• Paugh et al. Novel oncogenic PDGFRA mutations in pediatric high-grade gliomas . Cancer Res. 2013.90 pediatric HGGs: 6% in-frame deletions (3% in-frame insertions) in dimerization domain.
• Brennan et al. The somatic genomic landscape of glioblastoma. Cell 2013.164 GBMs: 18% expressed PDGFRA mRNA lacking exons 8 and 9, DNA not interrogated
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Ongoing work:
• Identify the prevalence of PDGFRA deletions in TCGA, pan-cancer
• Assess the function of representative deletions• Trans-well migration and invasion assays• Cell-counting and colorimetry proliferation assays• Mouse tumorgenicity/growth/survival assays
• Identify tumor antigens derived from mutant PDGFRA
• Assess the effect of PDGFR kinase inhibitors on mutant PDGFRA
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K. Menger (ed.) Ergebnisse eines Mathematisthen Kolloquiums 2, Kolloquium 5.11.1930, Teubner Leipzig (1932)
Joachim Giesen. SCG '99 Proceedings of the fifteenth annual symposium on Computational geometry. ACM 1999
• Magwene et al. Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics 2003, 19:842–850.
• Trapnell et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 2014.
Lineage reconstruction problem: reconstruct the sequence of transcriptional events that occur as a progenitor cell and its daughters commit to a particular lineage, from an ensemble of transcriptomics experiments.
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• Cluster the cells and form the Gabriel graph between cluster centroids, edge between cell and cell , if .
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• Cluster the cells and form the Gabriel graph between cluster centroids, edge between cell and cell , if .
• Given a source and sink, connect them with a shortest distance path.
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• Cluster the cells and form the Gabriel graph between cluster centroids, edge between cell and cell , if .
• Given a source and sink, connect them with a shortest distance path.
• The Gabriel graph contains as a subgraph the Euclidean minimum spanning tree, the relative neighborhood graph, and the nearest neighbor graph.
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• Given a gene of interest, (Lowess) regress a surface in PCA space on the z-scores of the counts-per-million.
• Gene expression along a particular path is then estimated by the height of the regression surface
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• A. Diaz*, Siyuan J. Liu, Carmen Sandoval, Alex Pollen, Tom J. Nowakowski, Daniel A. Lim, Arnold Kriegstein. SCell: integrated analysis of single-cell RNA-seq data. Bioinformatics, 2016. 10.1093. * corresponding author
• A. Pollen, J. Chen, H. Retallack, C. Sandoval, C. Nicholas, J. Liu, M. Oldham, A. Diaz, D. Lim, A. Kriegstein. Molecular Identity of Human Outer Radial Glia During Cortical Development. Cell. 2015 Sep 24;163(1).
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LncRNA are highly cell-type specific and should be included, when performing single-cell clustering/classification.
S. Liu, T. Nowakowski, A. Pollen, J. Lui, M. Horlbeck, F. Attenello, D. He, J. Weissman, A. Kriegstein, A. Diaz*, D. Lim*. Single cell analysis of long non-coding RNAs in the developing human neocortex . Genome Biology. 2016, 17:67. *corresponding author
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Background:• Macrophages mediate inflammation, dead-cell clearance and
presentation of antigens to T-cells. But, can become co-opted by the tumor into a polarized, immunosuppressive state.
• Tumor associated macrophages (TAMs) contribute to invasiveness, angiogenesis and can suppress T-cell function.
Goals:• Map the spectrum of macrophage polarization
states achievable in GBM.
• Identify mutations in the tumor that mediate TAM polarization.
• Identify targets to reprogram polarized TAMs.
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The GBM microenvironment: a critical gap in our knowledge
• Most approaches thus far have focused on blocking the recruitment of TAMs to the tumor, or depleting TAMs altogether:
a. CSF1R inhibition. (Pyonteck and Joyce, 2013)b. Monocyte depletion: Trabectedin (Germano et al.
2013), amphotericin B (Sarzhkar et al. 2014)c. Periostin inhibition. (Zhou et al. 2015)
• My preliminary data show that TAMs exhibit both anti-tumor and tumor-supportive transcriptional profiles in vivo.
• Approaches which specifically target tumor-supportive TAMs are needed.
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Ivy Glioblastoma Atlas: • 39 primary, 3 recurrent GBMs• RNA-seq: 270 RNA samples micro-dissected from 5 histologically defined structures
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A
B
C
D
Principle components and expression analysis analysis of TAMs identifies a M1/M2 gradient, and sequence analysis of M2 specific genes identifies potential upstream regulators. A) TAM principle components analysis. B) TLR2 and TGFB3 expression heatmaps. C) Tumor-supportive specific genes are enriched for co-localizing SP1 and NFATC3 recognition motifs. SP1 and NFATC3 themselves are enriched in the tumor-supportive TAMs. D) Tumor-supportive TAMs express extracellular and matricellular proteins associated with Glioma growth and invasion.
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TGF-
Fibronectin
ADAMTS4
ADAMTS4
CD68
A
B C
D
E
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An in vitro model of bone-marrow TAM polarization. THP-1 are widely used as a model of monocytes, which we induce to differentiate via tumor conditioned media (TCM). Following exposure to TCM, we see a change in morphology, a relative induction of the M2 cytokine IL10, arginase and other markers. We see an attenuation of M1 compared to media alone, indicating a realistic model of polarization.
We have transduced THP-1 cells to express dCas9-KRAB for use with the CRISPR system.
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Original tubes from
BTRC
Region 1 a.k.a
SF10679A
Region 2 a.k.a
SF10679B
Region 3, beforesplitting
Removednecrotictissue
and splitin half
Discarded necrotic tissue
• SF10679 – oligodendroglioma G3
• Received regions 1 (enhancing edge), 2 (infiltrated white matter), 3 (tumor core) from Tissue Core.
• Region 3 was large and heterogeneous.
• Removed necrotic tissue from Region 3, then split in half to separate vascularized from avascular regions.
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Acknowledgements
Lim Lab:• Daniel Lim• John Liu
Aghi Lab:• Manish Aghi• Brandyn Castro• Ruby Kuang
Okada Lab:• Hideho Okada• Gary Kohanbash
Diaz Lab:• Sören Müller• Tom Bartlett• Beatriz Alvarado
Funding:• The Shurl and Kay Curci
Foundation Research Scholar grant
• SPORE Career Development grant
• UCSF RAP grant
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• Fluidigm C1 microfluidic cell capture, 96 cells with full transcript coverage or ~800 cells using a 3’ tag
• RNA extractions from single cells contain fewer distinct molecules and saturate at a lower sequencing depth
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• = raw read-counts for the sample , gene • reference sample• Sort to compute its order-statistics, • Reorder to , concomitant stats.• Compare to via score-test for binomial
proportions
• BH corrected p-values for this stat correlate with diversity and coverage estimates
• Stat correlates with live-dead staining, Pearson 0.7 in this example.
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• Index of dispersion, , is a test statistic for the null hypothesis that the gene’s read-counts are equal across samples (cells). It has a closed form power function (Selby, 1965).
• Test for zero-inflation: score-test derived from a generalized Poisson null model (Yang et al., 2010)