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SINGLE-CELL TECHNOLOGIES
Pick a paper, any paper. If it involves the protein, nucleic
acid, or metabolite content of bacterial or eukaryotic cells,
there’s likely a section detailing how those cells were grown
in culture. Cell culture is how researchers expand cells to
harvest macromolecules or to interrogate their responses
to changing conditions or chemical treatment. Inherent
in such work is the assumption that all the cells in a dish
are identical—by growing them in culture, the researcher
is simply amplifying the signal. But that isn’t always
true. Subtle differences at the molecular level can yield
significant variation in cellular behavior, but until recently
researchers had no way to probe that variability. Today,
they do. By Jeffrey M. Perkel
A
kos Vertes, professor of chemistry at George
Washington University in Washington, DC, uses
mass spectrometry methods to quantify metabolites at the single-cell level. He offers a simple
justification for the work: “Cell populations are heterogeneous. And these differences between individual cells in
large populations sometimes become very important.”
Consider cancer stem cells, for instance. Or drugresistant bacteria. Or the many subtly different neuronal cell
types in the brain. It’s possible to detect those subpopulations in a bulk analysis, but it isn’t easy: Their signals tend
to be swamped by the general population. And in any event,
such analyses blur cell-to-cell distinctions, making it impossible to know which cells contribute what to the population.
The only way to untangle that skein of competing signals is
to make measurements cell by cell.
Other researchers turn to single-cell methods because
they have no other choice. In microbiology, for instance, the
vast majority of microbes cannot be cultivated in the lab,
Of single cells and amplification
The key to single-cell biology, of course, is isolating a
single cell. This typically is accomplished using micromanipulation, microfluidics, or fluorescence-activated cell sorting (FACS), the method Stepanauskas favors.
One alternative, developed in the laboratory of Nancy Allbritton, chair of the joint biomedical engineering department
at the University of North Carolina at Chapel Hill (UNC-CH)
and North Carolina State University (NCSU), is the microraft
array (MRA).
An MRA is an array of tiny transparent, magnetic, polystyrene elements, each small enough to fit into a tiny well
on a plate or slide. Commercialized by Cell Microsystems, MRAs typically contain about 10,000 wells, Allbritton says, though some measure in the millions. Researchers plate their cells such that there are, on average, zero
or one cells per element. They can then image the array
immediately, or allow the cells to grow and develop complex phenotypes. Cells of interest are gently isolated using
a microneedle to pierce the bottom of the array and dislodge the MRA element. Because they are magnetic, these
elements are easily captured, at which point they can be
analyzed or clonally expanded.
According to Allbritton, the system enables isolation
strategies that might otherwise be impossible, such as isolating cytotoxic T lymphocytes based on their ability to kill
target cells. In one study, UNC-CH collaborator Scott Magness, working with Allbritton, used an MRA to study intestinal stem cells. Among other things, the team used the
platform to investigate whether intestinal stem cells must
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Single-cell biology:
The power of one
says Ramunas Stepanauskas, director of the Bigelow Laboratory Single Cell Genomics Center in East Boothbay,
Maine. “We really don’t know much about them, although
they are the drivers of the global biogeochemical cycles;
they inhabit every conceivable environment, including our
own bodies, and they are a largely untapped source of novel natural products, bioenergy, and other applications.” The
best way to tap these organisms’ capabilities, he says, is to
sequence them one at a time, letting their genetic material
tell researchers what Petri dishes cannot.
Fundamentally, the process of single-cell research isn’t
difficult at all. Simply isolate a single cell, lyse it, collect its
molecular innards, and analyze what you get. Of course, it’s
not nearly so straightforward in practice.
“There are all kinds of pitfalls in single-cell analysis,” says
Jim Eberwine, codirector of the Penn Program in Single
Cell Biology at the University of Pennsylvania School of
Medicine, who has taught a course on single-cell analytical techniques at the Cold Spring Harbor Laboratory since
2012 and has been studying single-cell gene expression in
his own lab since the 1990s. According to Eberwine, whether a researcher is interested in a cell’s DNA, RNA, protein,
or metabolites, the fundamental issue is always the same:
“Can you detect your signal over the noise.” Researchers
have devised a number of clever strategies to do just that.
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SINGLE-CELL TECHNOLOGIES
physically contact other cells in
ing suggests that a cell’s capacity
order to reach their full potential.
to express a gene is modulated by
“What he showed quite clearly is
the physical signals it receives from
they really need to be touching a
its neighbors. The local microenviPaneth cell to grow, prosper, and
ronment “act[s] as a brake for gene
divide and get the highest outexpression,” Eberwine says.
growth rates,” Allbritton says.
TIVA employs a caged,
Vertes takes a lower-throughphotoactivatable double-stranded
put approach to metabolomics.
oligonucleotide coupled to a cellUsing a sharpened capillary and
penetrating peptide and biotin
a micromanipulator—a tool typimoiety. When this “TIVA tag”
cally used in patch clamping and
crosses the cell membrane, the
embryonic manipulation—his
peptide is removed, locking the
team aspirates a fraction of a
molecule within the cell. Laser
picoliter of material from adherirradiation then causes the two
FACS-isolated neural crest cells
ent cells stuck to the bottom of a
strands to separate (“uncaging”),
culture dish and injects it directly
revealing a sequence for capturing
into a mass spectrometer.
polyadenylated messenger RNAs
The key to single-cell biology, of
In that way, Vertes says, his
(mRNAs) in the targeted cells.
course, is isolating a single cell.
team can quantify some 22 meFinally, the biotinylated RNA-RNA
tabolites and 54 lipids from each
hybrids are isolated on streptavidin
of about 30 cultured hepatocytes—not the whole metabobeads, amplified, and analyzed via RNA sequencing
lome, of course, but enough to reveal for instance, the cells’
(RNA-seq), allowing the team to quantify gene expression
adenylate energy charge—a measure of cellular health. It
from locales ranging from specific cells to subcellular
was always possible to measure such variables at the popcompartments. “We can activate it in the cell soma, we can
ulation level, Vertes notes. But by going to the single-cell
activate it in the dendrites or in the nucleus of the cell, and
level, he can observe the distribution of energy states, and
we can do it in one or multiple cells,” Eberwine says.
correlate those values with such parameters as morphology
At Harvard University, Xiaowei Zhuang, a Howard
or metabolic activity. “For every cell, we can tell [if it] was a
Hughes Medical Institute investigator and David B. Arnold
healthy cell, a cell half-dead, or a cell programming itself to
Professor of Science, who invented the superresolution
die,” he says.
method of stochastic optical reconstruction microscopy
(STORM), has also developed a technique for in situ sinSingle-cell transcriptomics
gle-cell transcriptome analysis. But unlike TIVA, her imagJonathan Sweedler, the James R. Eiszner Family Chair
ing-based method retains information on precisely where
in Chemistry at the University of Illinois at Urbanain the cell the transcripts were located. “We know that
Champaign, has developed another novel approach to
RNA are not uniformly distributed inside the cell,” Zhuang
cellular isolation and analysis. His team disperses some
explains. “The local distributions of RNA are actually quite
10,000 cells onto a microscope slide. They then stain
important for the establishment and maintenance of local
the nuclei of those cells, localize them with a fluorescent
cellular structures.”
microscope, and pass those coordinates to a laser for mass
Multiplexed error-robust fluorescence in situ hybridizaspectral analysis. Because the cells are widely dispersed,
tion (MERFISH) is an in situ hybridization-based approach
there’s never a question of whether a laser is hitting one cell
for quantifying thousands of transcripts simultaneously. In
or another, as can occur in traditional mass spec imaging.
one specific implementation, each transcript is assigned a
“We can actually do 100,000 cells this way in an afternoon
unique binary code. The trick is to read that code bit by bit.
if we wanted.”
To do that, oligonucleotides complementary to each
Sweedler’s approach thus trades cellular context and
transcript are flanked by readout sequences corresponding
interaction for throughput. He can quantify many more disto the different bits. This pool, which may comprise 100,000
crete cells than he otherwise might, but he loses any sense
oligos or more, is hybridized to the mRNAs in the fixed
of where those cells were in relation to one another. That’s
cells, “convert[ing] each cellular RNA into a combination
a common problem with single-cell approaches, Eberwine
of readout sequences,” Zhuang explains. Then, the
says, because it also potentially alters cell biology. “You’re
cell is interrogated bit by bit using oligonucleotides
removing the cells from their natural microenvironment,
complementary to the readout sequences. After each
where they have the adjacent cell connections and the influ- hybridization round, the cells are imaged, the fluorescent
ence of the cellular environment.”
probes quenched, and the process repeats. Each
Indeed, using a method called “transcriptome in vivo
fluorescent signal in any given round represents an RNA
analysis” (TIVA), Eberwine and his colleagues compared
whose code reads “1” at that position in the corresponding
the gene-expression patterns in pyramidal neurons in intact
binary code. By mapping these points over 16 rounds
mouse brain slices and dissociated cells. They found that
of hybridization and quenching, the system can quantify
neurons in intact tissues expressed 12,000 transcripts on
numerous RNAs in the cell, and map their subcellular
average, compared to 16,000 when dissociated. That findlocation. continued>
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SINGLE-CELL TECHNOLOGIES
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But according to Zhuang, multiplexing is only half the
story. FISH has a low but measurable error rate, Zhuang
says. Over 16 rounds of hybridization, that can add up. So,
taking a cue from telecommunications, her team designed
its codes such that each code differs from every other one
by up to four bits. “That means you have to make four errors
in one RNA in order to convert it to a different RNA,” she
explains. Thus, even if a particular transcript is read out with
a single error, the software can determine its correct identity,
although RNAs containing two or more errors can be detected but no longer corrected, as it is no longer possible to
unambiguously identify them. “We can actually do both error detection and error correction,” Zhuang says, “and that’s
how we get very highly accurate measurements.”
Using this strategy, Zhuang’s team quantified as many
as 1,000 transcripts in individual cells. But there’s no reason that number of transcripts cannot go higher, she adds.
A 32-bit code, for instance, could easily encode 30,000
genes. Now she hopes to apply the technology to map the
different cell types in the human brain and in cancer, as well
as the subcellular distribution of RNAs within them.
Single-cell proteomics
Bernd Bodenmiller, assistant professor for quantitative
biology at the University of Zürich, Switzerland, and Sean
Bendall, assistant professor of pathology and immunology
at the Stanford University School of Medicine, are both
former postdocs of Garry Nolan, also at Stanford, who
champions mass cytometry, a cross between flow cytometry and mass spectrometry that overcomes one of the key
shortcomings of the former technique.
Traditional flow cytometry, constrained by overlapping
fluorescent channels, is limited to about 18 simultaneous
signals, or markers, though in practice, the real limit is even
lower. Mass cytometry can theoretically resolve more than
100 channels, but is currently limited to about 44—the
result of conjugation chemistry needing to catch up with the
instrumentation itself.
Like flow cytometry, mass cytometry, commercialized
698
by DVS Sciences as the CyTOF (recently acquired by
Fluidigm), is a flow-based method that uses heavy metalconjugated antibodies rather than fluorescent dyes to
achieve its high information content. But in a pair of papers
published in 2014, Bodenmiller and Bendall simultaneously
demonstrated methods to turn a CyTOF analyzer into a
mass spec imager, enabling a kind of high-throughput
single-cell proteomics spatial analysis. “We built what
you could call a mass cytometry-based microscope,”
Bodenmiller explains, enabling “spatial systems biology or
spatial proteomics.”
As with advanced mass spec imaging systems, this
approach allows researchers to measure protein content
cell by cell in situ. But, unlike traditional mass spec
imaging, which typically detects the most abundant
proteins, mass cytometry allows researchers to actually
preselect the proteins they want to image.
Bodenmiller used his mass cytometry microscope
to study the impact of tumor microenvironment on
metastasis—for instance, the impact of macrophage
infiltration. “I think here is the sweet spot of our imaging
mass cytometry approach, because it’s antibody-based,
so we can exploit prior knowledge to detect different
immune cells or stromal cells, and we can use many
antibodies to learn what these cells are actually doing.”
Bendall and Nolan used their multiplexed ion beam
imaging (MIBI) system to probe the histology of breast
cancer sections.
Separately, Bendall and Nolan used traditional CyTOF
mass cytometry to map the single-cell trajectories of
B-cell development, using a set of 42-marker molecular
profiles—everything from extracellular markers associated
with B-cell development to intracellular signaling factors—
and a novel algorithm called “Wanderlust” to trace how
molecular signatures change as a lymphocytic stem cell
differentiates into a mature B cell.
Though 40-plus parameters are impressive, researchers
like Bendall and Bodenmiller hope to ultimately push
that number even higher. All that’s required is efficient
chemistry to conjugate the ions to the antibodies.
Bendall jokes of a creeping condition among flow and
mass cytometrists that he calls “PDS,” or “parameter
dependency syndrome.” “At first you ask yourself, ‘Well,
what the heck am I going to do with 40 parameters?’ And
then after you’ve done this for a while, you’re like, ‘How
am I [going to] fit everything I want to measure into 40
parameters?’” To put it another way, researchers never say
no to more data.
Of course, those data describe just one particular aspect
of single-cell biology. And therein lies one of the field’s primary challenges, says Eberwine. What’s needed now, he
says, are methods for integrating these different datasets to
gain a comprehensive, quantitative, and reproducible view
of the cell. Work on that front is ongoing. “I think the information will be incredible,” he says.
Jeffrey M. Perkel is a freelance science writer based in Pocatello, Idaho.
DOI: 10.1126/science.opms.p1500099
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