Identifying mediators of local immunosuppression via single-cell sequencing

Aaron Diaz, PhD

Neurological Surgery, UCSF

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

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.

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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.

• 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

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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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PDGFR enhances growth and invasion in vitro

• 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.

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

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

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.

• Cluster the cells and form the Gabriel graph between cluster centroids, edge between cell and cell , if .

• 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.

• 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.

• 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

• 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).

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

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.

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.

Ivy Glioblastoma Atlas: • 39 primary, 3 recurrent GBMs• RNA-seq: 270 RNA samples micro-dissected from 5 histologically defined structures

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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.

TGF- 

Fibronectin 

ADAMTS4 

ADAMTS4 

CD68 

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B C

D

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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.

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.

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

• 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

• = 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.

• 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)