Article

From single cells to deep phenotypes in cancer

Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, California, USA.
Nature Biotechnology (Impact Factor: 41.51). 07/2012; 30(7):639-47. DOI: 10.1038/nbt.2283
Source: PubMed
ABSTRACT
In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.

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Available from: Sean C Bendall, Apr 21, 2014
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nature biotechnology VOLUME 30 NUMBER 7 JULY 2012 6 3 9
Much of modern biomedical research starts from a premise that there
is a deeply encrypted code by which cells act together, in pathways
and across networks, to maintain cellular tissue and organ health.
Adjacent cells might appear the same, but even recently divided
daughter cells show variation from their neighbors owing to changes
in the microenvironment, the nature of the cells that are adjacent,
and exposure to subtle shifts in gradients of growth factors, oxygen
and other environmental components. When every cell we look at
is potentially unique, how can we discern the principles of the code
underlying how cells work alone or together? And what do we do
when we come up against a formidable problem that underpins a
devastating class of disease—that of cancer heterogeneity—where
many of the rules so neatly obeyed in normal cells are rewritten at
every cancer cell division?
Traditional biochemical approaches underpinned our initial
comprehension of cellular functions. In the early days, assays were
often based on lysis or dissolution of complex cell mixtures to enable
purification and discernment of their component parts. Using such
biochemical methods, and drawing on an understanding of single-
cell biology, researchers have shown that in normal cells, ordered
signaling events drive cellular responses to internal and external cues
leading to phenotypic outcomes, such as differentiation, proliferation,
apoptosis and secretion of effector molecules.
A growing body of research now indicates that the biology of
single cells is rarely deterministic
1
and that even recently divided cells
in culture are highly variable
1,2
. It has become increasingly clear to
researchers over the past 15 or so years that, by averaging information
across many cells, differences among cells, which may be important in
explaining mechanisms, can be lost. Consequently, the caveats in the
conclusions one draws about how any cell type functions when one uses
bulk biochemical or molecular approaches quickly pile up when aver-
aging cellular information: the system and context and the co-varying
features are muddled. Making an assumption that two cells are
identical—or that a cell population can be declared homogeneous—
is a gross simplification of how biology operates. We now appreciate
with far greater clarity how stem cell hierarchies, transcription start
sites, cell signaling pathways (and more) all function against a back-
drop that assumes that carefully orchestrated single-cell stochastics,
in concert with mass action, is what determines outcome.
One illustration of why this matters can be found in the study of
mechanisms underpinning the development of cancers. Tumors have
long been understood to play by different rules compared with normal
cells. They are not merely collections of malignant cells but rather
are complex, atavistic entities
3
in which a pathologically symbiotic
interplay occurs between cancer cells and adjacent immune, inflam-
matory, vascular and stromal cells. In solid tumors even the epithelial
compartment itself is variable. Our view of tumor heterogeneity has
sharpened to a vision where tumors are viewed as a hierarchy of cells,
with tumor-initiating cells presumed to be at the top of a local hierar-
chy, and cells with lesser, or at least different, potential for unlimited
growth descending from that apex. This has led to the key notion that
all-too-common treatment failures in several cancers are caused by
intrinsically chemoresistant, tumor-initiating cell populations that
can reestablish a complete tumor cell hierarchy after treatment
4–8
.
Cancer starts as one cell, ultimately shields itself in a forest of
genetic and epigenetic diversity
3
and appears capable, after therapy,
of resurrecting itself from just one surviving cell with tumor-initiating
cell activity. The extent, then, to which genetically diverse tumor cell
subpopulations differ with respect to phenotypes, including renewal
capacity and drug resistance, is far from fully characterized. Why are
we so far from a more complete understanding? Because we simply
have not had the tools to characterize the crucial differences, and
just as importantly, sort out which differences are causal and which
From single cells to deep phenotypes
in cancer
Sean C Bendall & Garry P Nolan
In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions
of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that
can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that
allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring
order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches
that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful
outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell
function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the
development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different
sets of mutations predispose patients to relapse or disease progression.
Baxter Laboratory for Stem Cell Biology, Department of Microbiology &
Immunology, Stanford University School of Medicine, Stanford, California, USA.
Correspondence should be addressed to G.P.N. (gnolan@stanford.edu).
Published online 10 July 2012; doi:10.1038/nbt.2283
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mutations are passengers of the diversification process at the heart
of cancer pathology. Finally, it is critical to understand the extent
to which the kinds of heterogeneity drive treatment decisions. For
instance, depending upon where in a gastric cancer specimen one
measures HER2, a different clinical outcome might be predicated for
responsiveness to trastuzumab (Herceptin)
9
. Thus, even an incipient
cancer cannot be assumed to be driven by a common underlying
set of genes. Analysis of one part of a tumor might determine it is
treatable by a different drug compared with analysis of a different
part of a tumor.
At the heart of these inquiries is the extent to which observable
phenotypes in cancer and genomic heterogeneity are linked at the
single-cell level. That is, do various mutations work in concert to
converge on a limited number of cancer cell behaviors and/or pheno-
types? How far away from individual mutations should we step
to understand how they work in concert to drive common cancer
phenotypes and outcomes
3
? It is well understood that gross genetic
abnormalities result in distinct cancer cell behavior. For instance,
mutations resulting in constitutive regulatory kinase expression can
lead to uncontrolled cellular growth but also provide specific thera-
peutic targets
10
. Other mutations can stretch the phenotypic bounda-
ries (CD34
) of the cancer-initiating cell in acute myeloid leukemia
(AML)
11
. But the fact remains that there are not an infinite number
of wholly different biological or clinical behaviors in any given
cancer—meaning that despite the overwhelming number of mutations
we observe in cancers
12,13
, genetic constraints on the system define
the kinds of mutations ‘permitted’, which in turn limit the phenotypes
of a cancer and still allow that cancer to remain viable
14
.
In this review, we discuss prospects for new technologies aimed at
studying heterogeneity in single cells in tumors. In particular, we sum-
marize the uses of ligand- or pharmaceutical-based interventions that
act by known signaling mechanisms on particular cell subsets—whether
stromal, nurse cells, tumor-initiating cells or other processes—to under-
stand the role of such cell subsets in tumor maintenance and behavior.
Although major advances have also been made in traditional micro-
scopic techniques for picturing and enumerating cancer diversity, these
are not covered here.
Fluorescence-based flow cytometry
Possibly the first of the high-throughput devices that discerned differ-
ences between cells, by simultaneously measuring multiple events per
cell, was the flow cytometer. Although many flow cytometer designs
have been created over the years, one of the most influential of these
is the fluorescence-activated cell sorter (FACS) invented at Stanford
University in the laboratory of Leonard and Leonore Herzenberg
15,16
.
FACS and its competitors and relatives—even in the early 1980s—was
a synergistic blend of physics, biology, informatics, computation and
efforts in data visualization. Arguably, FACS and its associated com-
puter hardware were the first of the ’omics tools. Very large data sets
(even by today’s standards) comprising millions of cells and multi-
ple events per cell were the norm. Flow cytometry was, in essence,
the technical forebear of systems immunologyin that it provided
the most detailed data sets of as many of the immune compartments
one desired to study at any one time. What was missing until recently
were the computational tools to mine the meaning in the data.
From its inception, the aim of flow cytometry was to measure
multiple events per cell and correlate them with biological mechanism,
or disease processes, in animal models and humans. The device made
an a priori assumption of nonhomogeneity of a target cell population.
The family of instruments grouped under the term flow cytometer’
now extends to microfluidics that miniaturize the components, lasers
and detectors into lab-on-a-chip devices
17,18
. As such, the instru-
ments set a high standard for very quantitative and high-throughput
multiparameter measurements of single-cell attributes. Because
of its strengths, flow cytometry has been a mainstay of immuno-
logy and the study of hematological malignancies for more than
40 years (Table 1).
Leukemias and lymphomas were the earliest cancers studied using
flow cytometry analysis. Panels of cell-surface markers standard for
the characterization of cell subsets in human peripheral blood mono-
nuclear cells or bone marrow revealed unusual expression patterns or
cell features in the hematopoietic malignancy compared with normal,
standard samples. Just as a pathologist can look at a field of cells and
grade a tumor’s stage, in part, by the disorder of the cells relative to
each other and other cellular attributes, so an analysis of FACS data can
identify disrupted expression patterns of proteins on cells in cancer.
For instance, levels of protein expression are distorted compared to
normal, healthy controls, but flow cytometry demonstrated that, in
addition, proteins never normally seen together can be co-expressed
on the same cell—suggesting disarray at the level of normal gene
expression programs driving differentiation and signaling. Whereas
heterogeneity in solid tumors has traditionally been determined by
microscopy, some studies have used flow cytometry, to visualize hetero-
geneity, such as with prostate cancer (and cross-documented by immu-
nohistochemistry) with as many as 15 individual markers
19
.
Flow cytometry has been applied to detecting tumor stem cell sub-
sets in liquid and solid tumors
9,20,21
by cell sorting. Bonnet and Dick
22
demonstrated that a prospectively isolated minor population (lineage
negative, CD38
, CD34
+
) of human AML cells exclusively initiated
the disease in an immune-deficient animal model. This paradigm
of leukemia-initiating cells, more broadly thought of as cancer stem
cells or tumor-initiating cells, has since been observed in several other
solid-tissue human malignancies
23,24
. Being able to target neoplastic
cell populations based on unique surface-marker expression patterns
has provided a new avenue in cancer research to study directly the
cells thought to be at the root of the disease. FACS analysis and sort-
ing provides the utility to both isolate and assay such cells and their
capabilities. The importance of cancer stem cells, the surface markers
used to define them and their rarity in various tumors remains a topic
of great debate
25
.
Another avenue taken by FACS analysis in dissecting tumor het-
erogeneity has focused more on signaling behavior and its relation-
ship to disease outcome. This approach pays less attention to cellular
surface-marker phenotype and underlying sequences of gene muta-
tions and more to the signaling (phosphorylation) networks in the
cells that are often regulators of common chemotherapeutic targets.
The first example of this was demonstrated in human AML where
phosphorylation in response to extracellular factors not only revealed
unique regulatory networks subdividing patients but was also corre-
lated to both patient outcome and mutational status
26
. More recently,
similar studies employing single-cell phosphorylation analysis by
flow cytometry have classified patients suspected of having juvenile
myelomonocytic leukemia
27
and predicted outcome in human B-cell
lymphoma
28
and drug sensitivity in chronic lymphocytic leukemia
29
.
The ability to discern heterogeneity in signaling became possible
in the early 2000s by profiling the activities of kinases and networks
at the single-cell level
26,30,31
through so-called phospho-flow. This
led to new applications of the flow cytometer as an instrument with
proteomics applications capable of probing intracellular circuitry
32
on fixed cells. This technique is primarily used to analyze fixed (dead)
cells, and therefore gave up one of the key utilities of FACS—that of live
cell sorting. It is possible, however, to sort these fixed cells for genomic
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and transcriptomic profiling (Y. Goltsev & G.P.N., unpublished data),
but the live-cell analysis remains out of reach for high-parameter,
single-cell signaling applications with current technologies.
A generic template for the use of phospho-flow (and other
perturbation-response systems) started with the notion that the basal
state of a cellular system is a homeostatic, but flexible, resting state
26
.
A cell at homeostasis reflects the sum of its genetic and environmental
history. The cell might be nominally at rest, but it is poised to adapt to
fresh challenges. The biochemical differences that reflect what a cell
might do are often lost in stochastic noise of homeostatic feedback
networks. Thus, a solution is to grossly perturb the system of cells with
input stimuli, thereby taking advantage of the built-in amplification
processes of signaling systems. In this manner, cells are forced to make
strong choices to reveal major conduits by which information flows
within and between cells. It turns out that this information can be
catalogued (Fig. 1), used to create diagnostic predictors at the single-
cell level
26–29,33
and mathematically mined to elucidate cell subsets
and responses that correlate to orthologous or surrogate phenotypes.
Other correlates include clinical outcomes or drug response
34
, and
reconstruction of cellular networks
32,35,36
. Finally, highly correlated
multiplex measurements can be used to discern whether ‘heteroge-
neous’ cancers have a discoverable recapitulating structure (B. Neel,
J. Stewart, W. Fantl & G.P.N., unpublished data), thereby uncovering
previously unknown intercellular relationships linking the functions
of nonadjacent cells of complex tissues (M. Clausson, D. Koller &
G.P.N., unpublished data).
Probing intracellular and intercellular relationships in this context
is achieved using what would be recognized as a classical medical
perturbation test, but at a cellular scale. The approach is related, for
example, to a stress test for heart functions or to a reflex test on your
knee for nervous system function. Although the mechanism is not
always directly inferred, perturbation and its readouts narrow the pro-
cesses in a cellular system to those on which further inquiries should
focus. In cancer, pathways stray from standard or ‘normal’, but there
remain rules by which cellular systems operate such that we can com-
pare a cancer cell’s biology to that of normal cells and determine the
extent of the differences between them. Single-cell perturbation and
cytometric interrogation reveals cell-by-cell response patterns to per-
turbations (by challenging the regulatory wiring of each cell) and then
determining the extent to which the cancer cell population is divergent
from normal. One key finding is that signaling differences can correlate
significantly with outcomes and therefore the development of diagnostic
tests for cancer could be predicated on such evidence
26,37–39
.
A singular drawback of flow cytometry, or any technique that dis-
rupts tissue structure and function in the process of studying living
cells, is that cell-cell contact is lost and cells therefore lose (among
other things) access to nutrients and become hypo-oxygenated in the
process of preparation, as well as subject to mechanical shear forces
that might initiate or change intracellular signaling. In whole blood–
based assays, this tends not to be a problem, but certain pathways
(stress response), might be sensitive to disruption when the cell is
taken out of context. Although this is often not a problem in deriving
Table 1 Different approaches for analysis of tumors at the single-cell level
Technique Sensitivity Speed Resolution Pros Cons Reference
Flow cytometry ~500–10
7
molecules/cell
~25,000
cells/sec
Single cell Up to 15 parameters/cell
Many fluorescent probes for
cellular biology
Sorting of live cells
Cellular autofluorescence interference
Emission spectra interference as
multiplexing increases
78
Mass cytometry ~1,500–10
7
molecules/cell
~1,000
cells/sec
Single cell Currently 42 parameters per cell;
~100 possible
No autofluorescence or
spectral overlap
For every new parameter, new
chemistries are required to attach
isotopes to reagents
Currently, 70% of the cells ejected from
the nebulizer do not maintain full integrity.
After ionization, such subcellular fragments
do not reach the detector as ionic clouds
representing the constituents of a com-
plete cell. A new cell nebulizer design is
expected to reduce cell loss to only 30%
78–80
Single-cell
sequencing
93% of complete
genome
10 d/cell Single cell Most of genome can be sequenced
Identification of mutations
Sequences prone to possible
mutation during early PCR steps
53,54
Single-cell PCR of
targeted transcripts
96 transcripts/well 96 cells/
plate/4 h
Single cell Possible 1,000 cells/day
Relative quantification,
Absolute quantification with controls
Only 96 transcripts per cell reported 63
Transcriptome 1 cellular exome or
10
7
transcripts
2–3 d/cell Single cell Quantitative sequence
counting of all transcripts
Splice-o-forms quantified
Point mutations identified
Limited by exome selection method 53,54
MALDI-imaging
a
~10
−15
mols/µm
2
~1 sec/pixel ~50 µm
pixel size
Theoretically hundreds of different
molecular species (protein and small
molecule) can be analyzed
Tissue structure intact
Reporter masses <250 Da difficult
to observe due to ‘matrix effects’
Fragmentation and molecular
abducts complicate interpretation
65
SIMS/MIMS
a
~10
12
–10
16
atoms/cm
2
for
trace elements
~1 ms/pixel ~0.05 µm
pixel size
Currently able to determine
biologically labeled isotope ratios,
such as N, C, O
Subcellular resolution
Tissue structure largely intact
Fragmentation and molecular
abducts complicate interpretation
Most instrument configurations
limited to <10 analytes per scan
67,75,76
Laser ablation ICP
mass spectrometry
a
~10
2
ppb for
lanthanides
~1 sec/pixel 4 µm
pixel size
Tissue structure intact
Relatively simple to interpret
Limited to the analysis of only
elemental constituents and reporters
(see mass cytometry)
81,82
SMIS, secondary ion mass spectrometry; MIMS, multi-isotope imaging mass spectrometry.
a
Resolution, speed and sensitivity are interdependent. Sensitivity can be increased by increasing pixel size (lower resolution) and increasing scan dwell time (lower speed).
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mechanism (witness many decades of suc-
cessful research despite the disruptions)—and
we have long found that even cell lines harbor
a decent memory of their origin—it remains a
potent caveat in any single-cell study.
FACS-based single-cell approaches have
contributed immensely in the deciphering
of what kinds of cells a tumor might contain
and has provided some knowledge of their
roles in tumor composition (Table 1). The
question remains as to how we will merge
mutational analysis, phenotypic diversity and
single-cell signaling heterogeneity (or a combination of all three with
other methods) to drive development of more effective therapies for
various cancers as well as develop robust predictors of how and when
such therapies should be applied.
Cytometry time of flight (CyTOF)
The strength of traditional flow cytometry—fluorescence—has also
been its Achilles heel. Fluorophores, the workhorses of flow cytom-
etry, in concert with lasers tuned to their excitation spectra, have been
unmatched interrogators of single-cell activities. However, multiple
fluorophores dont always play well with each other in the sandbox of a
single cell passing by four lasers in millisecond time frames. The exci-
tation spectra of multiple fluorophores measured simultaneously can
‘bleedinto each others detection channels—obscuring measurements.
Unfortunately, the careful design of reagent panels, optimized lasers
and filters for excitation, along with compensation for spectral overlap
is no match for the need of scientists wishing to quantify more than a
canonical maximum of 10–15 cell-associated events. After all, when
you are watching the guy down the hall measure tens of thousands
of mRNAs per experiment, it’s hard not to be a little jealous. The
introduction of mass spectrometry now promises to change that.
The mass spectrometer has been one of the signature tools of
‘proteomics over the past decade
40
. Quantitative, robust, versa-
tile and sensitive at the level of the number of ions counted, it has
many applications in biochemical analyses. That is until one wants
to measure low-abundance protein or RNA constituents of single
cells. Simply put, the mass spectrometer most familiar to us does
not (today) have the sensitivity to measure rare proteins or their
isoforms at the number of copies present in single cells. Scott Tanner
at the University of Toronto saw an opportunity to adapt a form of
mass spectrometry termed inductively coupled plasma (ICP) mass
spectrometry to the measurement of events on and within cells
41
.
The idea was to tag antibodies with rare isotopes of elements not nor-
mally found in cells, stain cells with those tagged antibodies and then
pass those cells—a single cell at a time—rapidly through a 7,500 K
argon plasma
41,42
. There, the ions of what were once the individual
cells pass into a time-of-flight (TOF) mass spectrometer tuned to
the elemental weight range of the isotopes used to tag the antibodies
bound to the cells. Every molecule within each individual cell is
completely atomized and ionized—and then those ions falling within
a specific mass range are quantified by Tanner’s newly developed
instrument CyTOF
41
(Fig. 2). The summed levels of all isotopes
from each cell are digitized and associated with that cell to create a
spreadsheet of cell-by-cell information that is completely analogous
to a flow cytometry data file (only much bigger).
Using this ‘mass cytometry approach (Fig. 2), Tanner and
colleagues phenotyped and profiled complex primary human bone
marrow cell populations by cell surface, phosphoprotein and nucleic
acid constituents with 35 simultaneous measurements on millions
of cells per sample
42
. The depth of connectivity within the data sets
collected allowed discovery of some of the principles by which the
system is organized. By collecting enough cell surface events in a
progressive system, such as normal hematopoietic development in
human bone marrow
42,43
, they reconstructed the most likely lineage
relationships between cell types. This led to a computational reca-
pitulation of a familiar representation by means of a human hemato-
poietic progression tree
42–44
. By simultaneously measuring multiple
intracellular responses to perturbations, they created a global immune
posture or profile that reflected a cell-by-cell, subset-by-subset
‘phenotype’ of cell function.
A sweet spot for the application of CyTOF at present is in broad
profiling (Fig. 2) of immune cells, inflammatory cell states and
cancers. For instance, one first takes primary cell materials (blood,
bone marrow or tumor tissue) and dissociates it into single cells. As
noted above for phospho-flow and traditional flow cytometry, you
start by interrogating the cells with a variety of environmental stimuli,
such as cytokines or growth factors. The key is diversity of relevant
stimuli to perturb and provoke the cells—and their intracellular
signaling apparatus—into action. The workflow then proceeds to
staining of the cells with antibody panels that probe the biology under
question. These can include pluripotency panels (e.g., Nanog, c-myc,
SOX2), cell cycle
45
(such as with IdU, cyclin B1, cyclin A, p-histone H3,
Rb, p21 and p27), apoptosis, DNA damage, kinases and cell-surface
CD markers for most major immune subsets from mouse and human,
Phosphorylated STAT5
Unstimulated
G-CSF
stimulated
Patient A Patient B Patient C Patient Z
Started high
(high basal)
Responsive to
stimulation
(potentiated)
G-CSF
p-STAT5 p-STAT3
G-CSF
p-STAT5 p-STAT3
G-CSF
p-STAT5 p-STAT3
Phosphorylated STAT3
Mixed clinical outcomes Poor clinical outcomes Better clinical outcomes
Figure 1 Signaling-responsiveness–dependent
heterogeneity can correlate with signaling
outcomes
26
. Tumors observed to be relatively
homogeneous by surface marker expression,
as well as intracellular signaling states
(unstimulated, top row), can instead be
observed to comprise multiple cell subsets
(granulocyte colony stimulating factor (G-CSF)
activated, bottom row of FACS plots), which,
upon retrospective analysis may correlate
with a clinical outcome
26,37–39
. (Reproduced
from Fig. 2, ref. 77, with permission.)
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transcription factors and epigenetic modifi-
cations. In our laboratory we can interrogate
45 parameters per cell, and with novel statisti-
cal approaches and applications of informa-
tion theory, we should be able to double that number (K. Sachs &
G.P.N., unpublished data). It is even possible to detect mRNA at
the single-cell level using branch-chain amplification techniques
simultaneously with measurement of surface proteins and phospho-
epitopes, for example (Y. Goltsev & G.P.N., unpublished data). Luckily,
CyTOF benefits from three decades of fluorescence-based innovation
that paves the way for adapting probes to using isotopes as tags. To
date, most of the major human and murine surface markers, numer-
ous intracellular phospho-epitopes, as well as many apoptosis, cell
cycle and DNA damage markers have been adapted for CyTOF
42,44–46
(G.P.N., unpublished data). Of course, for at least the near future, cer-
tain cellular features will remain the sole provenance of fluorescence,
but in such cases fluorescence cytometer–mass cytometer hybrids are
already envisioned to step into that breach.
At the end of a mass cytometry workflow, one has an alarmingly
large data set that requires interpretation. Luckily, the problem is
solvable by going back to basics. Traditional flow cytometry has for
30 years relied on modestly scaled extensions of histograms or dot
plots’ where single cells are plotted on graphs that depict the level of
expression of one or two parameters. As the number of parameters
increases, the number of two-dimensional plots required increases
considerably, leading to a visualization problem—our primate brains
can’t readily ‘see five or more dimensions.
Analytic approaches that tackle this complexity
43
are related to
other dimensionality reduction problems in science and are an area
of considerable history and constant innovation. The utility of under-
standing this high dimensionality rests in the often-unappreciated
power of correlated measurements. To know that a marker is co-
expressed in distinct or discoverable patterns across a set of cell types
(especially minor subpopulations) or that a principle determines such
expression patterns allows one to propose that cells exist as subsets,
or express regulatory modules of signaling proteins (phospho-protein
activation events, for instance).
Now that we can achieve this greater, more wholly connected
picture of a tumor’s proteins and signaling, the challenge remains
to computationally compare this new image of cancer across both
patients and tumor types to determine whether this new level of
single-cell resolution yields an improved understanding of the disease
process or can better inform therapeutic interventions. So, if single-
cell data from 10 to 100 dimensions is exciting, it’s about to get better,
or worse, depending on your perspective.
Single-cell DNA and RNA sequencing
Where cytometry technology has tended to move at a slow pace (until
recently), sequencing technology has leapt forward, with each innova-
tion more creative than the last. Initially, the advent of semiquantitative
DNA microarrays for transcriptome profiling opened up the possibil-
ity of hypothesis-generating experiments in cancer. Sophisticated sta-
tistics allowed us to mine the data for expression patterns that gave
rise to gene expression patterns for prediction of clinical outcome and
classification
47,48
. At the same time, despite lacking single-cell resolu-
tion, gene copy number analysis informed by array-based techniques
has also raised the question of clonal heterogeneity and evolution during
disease progression. For example, in two studies using genome-wide
DNA copy number analysis of pre–B cell acute lymphoblastic leuke-
mia
49,50
, a minor clone (cell population), seemingly ignored during
disease diagnosis, became dominant as the cancer progressed or the
patient relapsed. Studies like these foreshadowed the potential of next-
generation sequencing approaches in providing a higher resolution view
of cancer evolution as well as the recurrent mutations that drive it.
At least three general single-cell sequencing techniques have been
reported: first, whole-genome sequencing (WGS); second, whole
mRNA transcriptome sequencing; and third, targeted sequencing
of DNA regions (that is, exome sequencing) or mRNA transcripts.
Epigenetic modifications and nontranslated RNAs could be included
here, but the former we will treat as a subapplication of WGS, whereas
the latter has been extensively discussed with RNA sequencing else-
where
51–54
(Table 1).
Using earlier incarnations of next-generation sequencing plat-
forms, the first WGS analysis of karyotypically normal human AML
Antibodies
labeled with
elemental isotopes
Cell 1 3,8,9,7...8
Cell 2 1,8,6,5...4
Cell 3 9,9,4,5...7
.
.
.
.
Element
A B C D...J
Mass
Cell 1
Cell 2
Cell 3
ICP
Nebulizer
Quadrupole
Time-of-flight
Integrate per cell
Heavy (>100 Da)
Reporter atomic ions
Light (<100 Da)
Overly abundant ions
.FCS file
N
N
N
O
O
O
O
O
O
O
O
O
O
O
Gd
H H
Analysis
10
4
10
4
10
3
10
3
10
2
10
2
10
10
–10
–10
0
0
Element A
Element B
Figure 2 Most antibodies, either against
surface proteins delineating immune cells or
intracellular targets, can be adapted for use
with the CyTOF instrument. First, a polymeric
chelator is loaded with cations of a stable heavy
metal isotope. The loaded polymer is then
conjugated to the antibody of interest using
chemistry similar to that of fluorophores or
biotin. Free ions and unconjugated polymers
are removed by size-based purification. Panels
of appropriate antibodies are assembled for
staining much like one would for a traditional
fluorescence-based panel. The advantage is
that not only is there no compensation to worry
about from interfering channels, but there is
no autofluorescence in mass cytometry that
might obscure weak signals
41
. Cells are passed
through an ICP TOF mass spectrometer and the
individual ions are counted and then parsed into
a flow cytometry file format. This file is then
capable of being analyzed by traditional flow
cytometry software or more advanced software
as required
42,43
. (Parts of this figure are based
on Fig. 1, ref. 42.)
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6 4 4 VOLUME 30 NUMBER 7 JULY 2012 nature biotechnology
R E V I E W
cells from a single patient revealed mutations associated with the dis-
ease
55
. As next-generation sequencing approaches rapidly advanced
to become cheaper, faster and better, however, so did the depth of
genomic characterization of human leukemia. The following year,
the addition of multiple patient samples revealed recurrent muta-
tions associated with human AML
56
. Two retrospective, longitudinal
studies in the same patient, one from AML diagnosis to subsequent
relapse
57
, the other on the myelodysplastic syndrome to secondary
AML transition
58
revealed—as previous gene copy number experi-
ments had suggested
49,50
—a clonal heterogeneity in the primary dis-
ease that changed as the patient progressed through different stages
of disease. By following the frequencies of recurrent somatic and
germline mutations using high-coverage WGS, these studies dem-
onstrated how an often minor genotypic clone in the initial disease
state acquires at least one new mutation, becoming the dominant
or only clone in the subsequent relapsed or transitioned, second-
ary AML disease. This suggests that the therapy itself could be the
selective pressure for further mutations in clonal evolution to a more
aggressive, resistant state. Thus far, the majority of studies have been
reported as case studies, with a limited number of patients, focusing
mostly on hematopoietic malignancies. As in the aforementioned
case with tumor-initiating cells, until there is more evidence to sup-
port this paradigm (through sampling more patients and tumor types
longitudinally), the question remains as to whether it can be informa-
tive to the disease mechanism or outcome.
To avoid situations where therapy unwittingly potentiates recur-
rence, we must understand the functional consequence of clones har-
boring different combinations of mutations predisposing the patient
to relapse or progression; this is one of the next great problems of
cancer research. Do multiple combinations of mutations present in
different clones converge or diverge in terms of cellular behavior (that
is, in terms of transcriptional program, cellular phenotype, response
to regulatory environment or therapeutics)? To answer this, we will
require a novel combination of mutational analysis with more targeted
single-cell approaches. One attempt to do this was with fluorescence
in situ hybridization (FISH) where known driver’ mutations in acute
lymphoblastic leukemia were followed at the single-cell level for the
purpose of modeling the clonal architecture of this disease
59
. To go
beyond tracking obvious mutations in a given tumor, advanced cyto-
metric approaches could be married with WGS studies for a more
comprehensive analysis of other single-cell features. Doing this, one
could finally determine whether phenotypic, functional and muta-
tional diversity are linked at the single-cell level in cancer.
Although WGS and FISH can probe clonality(an implication
of single-cell resolution) in cancer, their results still rely on bulk
material and only infer clonal information statistically from the
sequencing information. Thus, these studies have a sensitivity limit
of about 10% of the population for detecting sequence-based clones
from the bulk cell mixture data—potentially hiding an unappre-
ciated level of genomic complexity represented in the individual
cells of most tumors. Two recent studies demonstrated single-cell,
whole exome sequencing (WGS targeted at DNA coding regions) in
cancer
60,61
. One tracked mutations in myelodysplastic syndrome
60
using whole genome exome sequencing on 90 cells to individu-
ally identify novel mutation patterns. The second study focused
on clear cell renal carcinoma and revealed that there was actually
no predominant clonal subpopulation at the single-cell level, sug-
gesting the underlying genotype of this cancer (and others) is more
complicated than previously thought
60,61
. Both of these studies were
hampered only by the fact that the cells had not been previously
selected by FACS or phenotyped to relate genotypes back to physical
cellular features—certainly an obvious next step that might coalesce
certain mutation patterns with cellular phenotypes.
Mapping transcriptional profiles by sequencing at the single-cell
level can be more challenging than WGS. With the exception of a
qualitative profiling of transcripts in a single human blastomere
62
,
currently there is a paucity of quantitative, single-cell, RNA-Seq data
published, largely because of the inability to quantify such low levels
of RNA through existing sequencing approaches. Clarke, Quake
and colleagues
63
undertook an accounting of normal and cancerous
colon cells using semiquantitative, microfluidics-based, single-cell
amplification of 53 unique genes. Using FACS to isolate cells with
defined phenotypes and comparing tumor-initiating cells to those
with a similar phenotype yet inability to engraft as cancer, this study
revealed a close correlation of cellular phenotype to transcriptional
profile. Not only could cancerous cells be mapped back to equivalent
ones in healthy tissue, but transcriptional profiles also provided a
two-gene classifier that could outperform existing pathological prac-
tices in predicting disease outcome. Interestingly, both this study and
another investigation of a mouse leukemia model
64
demonstrated that
(genetically) clonal tumor-initiating cells regenerated the original
cellular hierarchy of the primary tumor. In the case of the mouse
leukemia study, cytometric analysis of the phenotypically distinct
tumor-initiating cells revealed divergent regulatory signaling that
when targeted with the appropriate kinase inhibitor uniquely per-
turbed the activity of that tumor-initiating cell population alone.
Altogether, these observations highlight that mutations alone are not
driving tumor heterogeneity; therefore, understanding other cellular
characteristics (e.g., gene expression) at the single-cell and/or clonal
level will be required.
A near-term goal for single-cell gene expression in cancer is the
ability to undertake a fully quantitative RNA-Seq experiment on
hundreds of individual cells. Besides gene expression itself, quanti-
fication of mRNA splice forms would be highly complementary to
genetic abnormalities identified by WGS. Although efforts towards
this are underway in a variety of settings, sequencing technology
remains sufficiently expensive and time consuming, thus limiting the
absolute numbers of cells measured to only a few hundred per sample
with single samples taking a week or more. Flow cytometry, and the
powerful statistics that can be brought to bear, processes millions of
cells at a time. But a future is coming where RNA-Seq and associated
methods will begin to rival traditional cytometric approaches. Until
then, applications in the immediate future will most likely have to
leverage FACS to isolate a limited number of the most compelling
tumor cell types for sequencing analysis.
New mass spectrometry–based, single-cell applications
Mass spectrometry in its varied forms has not ceded the task of
single-cell measurements to nucleic acids and flow cytometry quite
yet. Tandem and high-resolution biological mass spectrometry, as
noted earlier, has had an immense presence in cellular analysis.
Top-down and bottom-up analysis have interrogated and provided
information on the protein composition and in some respects the
proteomic relational dependence that reflects cell translation. Even
so, in almost all instances, these insights are global, reporting on the
ensemble average of cell lysates. Maximal information from limited
samples, whether chromatographically separated or discrete spatial
images of biologic state, benefit from ‘simultaneousanalysis. Among
the technologies that offer this capability, TOF mass spectrometry
continues to hold an important position. Perhaps the impact of mass
spectrometric technologies will be first felt in the imaging of tissues,
as complementary or even competitive with microscopy. Imaging by
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nature biotechnology VOLUME 30 NUMBER 7 JULY 2012 6 4 5
R E V I E W
matrix-assisted laser desorption ionization (MALDI)-TOF has the
advantage of being able to measure hundreds of cellular constitu-
ents at a time
65
in a raster format, wherein individual subregions of
a tumor tissue section are successively analyzed and then stitched
together into a larger format image (Table 1). Although MALDI-
TOF is not yet readily applicable to the resolution of multiple single
cells, it is certainly technically feasible with today’s techniques to place
cells in an organized raster to achieve single-cell measures of highly
expressed metabolites. Thus, although sensitivity remains an issue
for MALDI in this application, the field is making progress and we
await further developments in the near future. Laser ablation ICP
mass spectrometry offers the opportunity to interrogate less-abundant
proteins that have been immunologically stained with metal-
conjugated antibodies
41,66
; here again, the spatial resolution is
generally insufficient to allow analysis at the cellular level, although
technology advances should be anticipated in this direction
67
.
One mass spectrometry technology that might be competitive
with TOF is magnetic sector
68–70
with array detection technology.
With an atomic mass spectrometry history of nearly a decade, multi-
collector mass spectrometry
71
offers the potential to measure up to
9 mass channels simultaneously; indeed, the instrumentation has
found important use in isotope ratio analysis. The extension to allow
many simultaneous channels was further developed by Hieftje and
colleagues
72–74
. Assuming that technological developments (not yet
in sight) will allow analyses on the time scale of single-cell ioniza-
tion events, we believe this could be a technology to eventually rival
CyTOFs foreseen applications.
With a history that both precedes and is contemporary with the above
technologies, secondary ion mass spectrometry and multi-isotope
imaging mass spectrometry (MIMS) have been resurgent
67,75
. In
MIMS a sample is sputtered’ with high-intensity ions, such as Cs
+
.
Ion bombardment generates secondary ions from the surface and
immediate substructure of the sample, which are manipulated and
gathered by ion optics and measured. An advantage is the ability to
track isotopically labeled atoms as they are metabolized inside cells.
To date, as many as six isotopes (parameters) have been measured at
a time (Table 1).
The sensitivity and resolution of MIMS imaging was illustrated in
recent work that provided novel insight into the kinetic and spatial
properties of actin turnover within the inner ear
67,75
. Before this, actin
filaments in stereocilia were thought to be replaced every 1–2 days in a
treadmilling’ process that proceeded from tip to base. MIMS imaging
of stereocilia from frogs fed an N
15
-enriched diet was used to visual-
ize incorporation of new actin filaments. The results of this work
revealed the process is much slower than previously thought—10%
per day—and occurred only at the tips. Similar strategies have been
used to analyze fatty acid transport in adipocytes, metabolic activity in
bacteria
76
, nuclear colocalization of DNA and RNA, and interactions
between membrane proteins and lipids.
Conclusions
The individuality of the single cell, and the information it contains, is
likely to be the key to therapeutically targeting every cell in a tumor.
Sadly, we are currently treating each patient as if they had an average
cancer, the clinical phenotype and subsequent treatment of which
has been determined by averaging cellular characteristics across
thousands of cells and millions of patients. Some of the single-cell
techniques described in this review should help us discern how to
treat every cancer cell in every patient according to its unique biology.
A range of approaches are being developed that measure more, with
less effort, on a per-cell basis helping to sort out the heterogeneous
tumors into understandable subsets of cells. With the exception of
surface marker analysis, in the near term, we must make do with tech-
niques that kill or destroy cells to study them. Although not optimal,
there is still much to be gained by such methods.
Of course, it is hardly productive to spend too much time arguing
about which technology is the most informative overall—because in
most cases that depends upon the question. If the goal is to understand
the ‘systemof cancer, then we should look for ways to merge technol-
ogies measuring different cellular attributes. Would it be unrealistic to
develop a technology that reads multiple cellular attributes through
a common tag? Fluorescence certainly provided an abstract proof
that a single measurement platform (emitted light) could be bent to
multiple purposes for measuring a variety of cellular phenotypes. We
have yet to figure out how multiple types of a single cell’s constituents
(DNA, mRNA, noncoding RNA, metabolites and proteins) can be
separated quantitatively and then passed to the micro-platform best
enabled to measure them. Certainly there have been attempts from
single-cell PCR and microfluidic devices of dizzying variety, but effec-
tive simultaneous measures or tandem measures are not in practice
to date. Already, though, cells can be individually sorted using FACS
by surface markers (and other attributes) at medium throughput and
sequenced
63
. Similarly, laser microdissection and sequencing is fairly
common. How soon until we see more all-in-one analyzers that are
not separate machines, but effective tandem devices?
The inherent heterogeneity in cancer, coupled with the environmen-
tal diversity due to unpredictable tissue structures, means that for now
we have to ignore the nonuniformity of cancer when it is considered as
a system. But lurking in the notion that cancer is a system is an implica-
tion of order (no matter how chaotic cancer seems at first, second or
third glances). If such order exists, then our answer both diagnostically
and therapeutically lies in multiparameter measurements in which we
consider the attributes of a single cell (and its interactions with other
single cells and their attributes) as a key determinant of a complex
interplay wherein a cancers weakness might hide.
The days of the single, one-size-fits-all diagnostic is gone. The
future is in complexity—or at least a level of complex simplicity that
serves as a surrogate of cancer systems we can use to go after the
moving target of cancer pathology. Certainly single-cell sequencing
will be at the forefront in this new approach. Flow cytometry and
its variants are still useful for diagnostic purposes, but the various
mass spectrometry technologies will enter the fray as soon as they are
sufficiently reproducible for clinical validation.
One long-term goal is to move toward tagless, or near-tagless,
measurement in which viable cells are prospectively isolated based on
function, gene expression and genotypes and then assayed directly for
tumor-initiating capacity. Techniques that probe the three-dimensional
composition (architecture and cell types) of tumors, while minimally
disrupting cell biology, are also required to gain more information on
cell-type location and its relationship to disease. Currently, the only
approaches we have readily available (e.g., green fluorescent proteins
or other gene expression–based reporters) require genetic modifica-
tion of the target cell. Although the tools we have available and on the
horizon should keep us busy for the immediate future, its up to the next
generation of single-cell converts to devise solutions that make what
we use today look primitive by comparison.
ACKNOWLEDGMENTS
G.P.N. is supported by the Rachford and Carlota A. Harris Endowed Professorship
and grants from U19 AI057229, P01 CA034233, HHSN272200700038C,
1R01CA130826, CIRM DR1-01477 and RB2-01592, NCI RFA CA 09-011,
NHLBI-HV-10-05(2), European Commission HEALTH.2010.1.2-1, and the
Bill and Melinda Gates Foundation (GF12141-137101). S.C.B. is supported by
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6 4 6 VOLUME 30 NUMBER 7 JULY 2012 nature biotechnology
R E V I E W
the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09).
The authors would also like to thank M. Angelo for useful discussions
pertaining to the information in Table 1.
COMPETING FINANCIAL INTERESTS
The authors declare competing financial interests: details are available in the online
version of the paper.
Published online at http://www.nature.com/doifinder/10.1038/nbt.2283.
Reprints and permissions information is available online at http://www.nature.com/
reprints/index.html.
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    • "The analysis of rare and biologically important cell subtypes presents a common challenge in the study of cancer , immunology, development and infectious disease. Subtypes within a sample are often not observable through bulk molecular measurements performed on the entire population [1][2][3][4]. Consequently, tools that can individually analyze single cells within a population are essential for uncovering critical biological information on subtypes. Fluorescence Activated Cell Sorting (FACS) is one such single-cell analysis method that has been widely employed to characterize heterogeneous populations of cells [5, 6]. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Rare cell subtypes can profoundly impact the course of human health and disease, yet their presence within a sample is often missed with bulk molecular analysis. Single-cell analysis tools such as FACS, FISH-FC and single-cell barcode-based sequencing can investigate cellular heterogeneity; however, they have significant limitations that impede their ability to identify and transcriptionally characterize many rare cell subpopulations. Results PCR-activated cell sorting (PACS) is a novel cytometry method that uses single-cell TaqMan PCR reactions performed in microfluidic droplets to identify and isolate cell subtypes with high-throughput. Here, we extend this method and demonstrate that PACS enables high-dimensional molecular profiling on TaqMan-targeted cells. Using a random priming RNA-Seq strategy, we obtained high-fidelity transcriptome measurements following PACS sorting of prostate cancer cells from a heterogeneous population. The sequencing data revealed prostate cancer gene expression profiles that were obscured in the unsorted populations. Single-cell expression analysis with PACS was subsequently used to confirm a number of the differentially expressed genes identified with RNA sequencing. Conclusions PACS requires minimal sample processing, uses readily available TaqMan assays and can isolate cell subtypes with high sensitivity. We have now validated a method for performing next-generation sequencing on mRNA obtained from PACS isolated cells. This capability makes PACS well suited for transcriptional profiling of rare cells from complex populations to obtain maximal biological insight into cell states and behaviors. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2694-2) contains supplementary material, which is available to authorized users.
    Full-text · Article · Dec 2016 · BMC Genomics
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    • "An example to illustrate the importance of somatic heterogeneity is cancer. Here, analysis may reveal the existence of rare transformed individual cells, differences in driver mutations, and diverse resistance mechanisms in response to treatment [4][5][6][7][8][9]. There is also a great interest in the identification of somatic structural variations in normal tissues—in particular, in the brain [10]. "
    [Show abstract] [Hide abstract] ABSTRACT: Single-cell sequencing technologies, i.e., single cell analysis followed by deep sequencing investigate cellular heterogeneity in many biological settings. It was only in the past year that single-cell sequencing analyses has been applied in the field of virology, providing new ways to explore viral diversity and cell response to viral infection, which are summarized in the present review.
    Preview · Article · May 2016 · Viruses
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    • "Of great interest to cancer biology is the merging of technologies to enable a snapshot of individual cell function in time and space. The advancement in techniques that measure mRNA, miRNA, and protein levels and their interactions and modifications simultaneously in single cells will give birth to integrated disciplines based on in silico disease modeling, drug discovery, and precision medicine [12,13]. In flow cytometry, single-cell analysis is merging with other technologies such as chip cytometry, CONTACT Bjørn T. Gjertsen bjorn.gjertsen@uib.no "
    [Show abstract] [Hide abstract] ABSTRACT: Single-cell proteomics in cancer is evolving and promises to provide more accurate diagnoses based on detailed molecular features of cells within tumors. This review focuses on technologies that allow for collection of complex data from single cells, but also highlights methods that are adaptable to routine cancer diagnostics. Current diagnostics rely on histopathological analysis, complemented by mutational detection and clinical imaging. Though crucial, the information gained is often not directly transferable to defined therapeutic strategies, and predicting therapy response in a patient is difficult. In cancer, cellular states revealed through perturbed intracellular signaling pathways can identify functional mutations recurrent in cancer subsets. Single-cell proteomics remains to be validated in clinical trials where serial samples before and during treatment can reveal excessive clonal evolution and therapy failure; its use in clinical trials is anticipated to ignite a diagnostic revolution that will better align diagnostics with the current biological understanding of cancer.
    Full-text · Article · Feb 2016 · Expert Review of Molecular Diagnostics
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