Annual Scientific Report 2015

John Marioni
PhD in Applied Mathematics, University of
Cambridge, 2008. Postdoctoral research in the
Department of Human Genetics, University of
Chicago.
At EMBL since 2010.
this source of heterogeneity in gene expression. Finally,
motivated by developments in scRNA-seq technology,
we are developing more appropriate methods for
normalising scRNA-seq data, which are robust to the
presence of ‘stochastic’ zeros in the data due to technical
drop-outs.
We developed and validated a high-throughput method
to identify the precise spatial origin of cells assayed
using scRNA-seq within a tissue of interest (Achim,
Pettit et al., 2015). This approach compares complete,
specificity-weighted mRNA fingerprints of a cell with
positional gene-expression profiles derived from a geneexpression
atlas (e.g. generated via in situ
hybridization experiments).
We applied our novel computational approaches to
the study of heterogeneity in populations of mouse
Embryonic Stem Cells in collaboration with the
Teichmann group (Kolodziejczyk, Kim et al., 2015),
and demonstrated that globally, gene-expression
noise levels in stem cells cultured in different media
show no difference, yet specific sets of genes vary
systematically. Additionally, in collaboration with the
Zernicka-Goetz group at the University of Cambridge,
we applied scRNA-seq to study symmetry breaking in
pre-implantation mammalian development. We showed
that, at the four-cell stage, there exists substantial
heterogeneity in gene expression levels between cells,
which can bias cells towards particular cell fates in a
non-deterministic fashion (Goolam et al., in press). This
provides important insights into when cell fate decisions
are first determined.
Our group was strengthened by the appointment of
John Marioni as a Senior Group Leader at the Cancer
Research UK Cambridge Institute at the University
of Cambridge. This position will enable more direct
interactions between members of the group and
empirical researchers, especially in the area of cancer
biology, an exciting future direction. In addition, the
group received funding through two Wellcome Trust
Strategic Awards that will enable the appointment
of two fully funded three-year postdoctoral
researcher positions.
Future plans
Our group will continue to develop computational tools
for understanding the regulation of gene-expression
levels. We will focus on methods for analysing single-cell
RNA-sequencing data, which has the potential to
reveal novel insights into cell fate decisions, cell-type
identity and tumourigenesis. We will actively develop
new computational approaches for handling single-cell
RNA-sequencing data, providing robust methods for
finding differentially used highly variable genes, as well
as assessing the direct impact of various normalisation
strategies and the efficacy of extrinsic,
spike-in molecules for this purpose. We
will also generate methodology for handling dropSeq
data, focusing particularly on how to model errors in the
cellular and molecular barcodes, which can impede data
interpretation. Our group also plans to build on spatiallyresolved
single-cell transcriptomic data, gained by using
novel scRNA-seq analysis methods (Achim, Pettit et
al., 2015), using these data to identify cell types and
examine heterogeneity in expression at the spatial
level. This project will require the development of new
computational approaches than can cluster multiple
data modalities simultaneously.
From the biological perspective, we will use our new
methods to obtain insights into cell fate decisions
during gastrulation – arguably the most important
time in our lives. Preliminary results are extremely
promising, suggesting that we can define at the highest
possible resolution cell fate decisions during this key
developmental period. Moreover, we will continue to
apply our models in numerous biological contexts, such
as the study of heterogeneity in different populations of
neurons, cancer biology and non-model systems to study
evolution and selection.
Selected publications
Achim K, Pettit JB, Saraiva LR, et al. (2015) Single-cell
expression profiling and spatial mapping into tissue of
origin. Nature Biotechnol. 33:503-09
Goolam M, Scialdone A, Graham SJL, Heterogeneity in
Oct4 and Sox2 targets biases cell fate in four-cell mouse
embryos. Cell (in press)
Kim JK, Kolodziejczyk AA, Tsang JCH et al. (2015)
Single cell RNA-sequencing of pluripotent states
unlocks modular transcriptional variation. Cell Stem Cell
17(4):471-85
Kolodziejcyzk AA, Kim JK, Ilicic T et al. (2015)
Characterizing noise structure in single-cell RNA-seq
distinguishes genuine from technical stochastic allelic
expression. Nature Commun. 6:8687
Vallejos CA, Marioni JC, Richardson S (2015) BASiCS:
Bayesian Analysis of single-cell sequencing data. PLoS
Comput Biol 11:e1004333
2015 EMBL-EBI Annual Scientific Report 140