705681

research-article2017

JLAXXX10.1177/2472630317705681SLAS TECHNOLOGY: Translating Life Sciences InnovationBasu

Review

Digital Assays Part II: Digital Protein
and Cell Assays

SLAS Technology
﻿1­–19© 2017 Society for Laboratory
Automation and Screening
https://doi.org/10.1177/2472630317705681
DOI: 10.1177/2472630317705681
journals.sagepub.com/home/jla

Amar S. Basu1

Abstract
A digital assay is one in which the sample is partitioned into many containers such that each partition contains a discrete numberof biological entities (0, 1, 2, 3, . . .). A powerful technique in the biologist’s toolkit, digital assays bring a new level of precision inquantifying nucleic acids, measuring proteins and their enzymatic activity, and probing single-cell genotype and phenotype. Wherepart I of this review focused on the fundamentals of partitioning and digital PCR, part II turns its attention to digital protein and cellassays. Digital enzyme assays measure the kinetics of single proteins with enzymatic activity. Digital enzyme-linked immunoassays(ELISAs) quantify antigenic proteins with 2 to 3 log lower detection limit than conventional ELISA, making them well suited for
low-abundance biomarkers. Digital cell assays probe single-cell genotype and phenotype, including gene expression, intracellularand surface proteins, metabolic activity, cytotoxicity, and transcriptomes (scRNA-seq). These methods exploit partitioning to 1)
isolate single cells or proteins, 2) detect their activity via enzymatic amplification, and 3) tag them individually by coencapsulatingthem with molecular barcodes. When scaled, digital assays reveal stochastic differences between proteins or cells within apopulation, a key to understanding biological heterogeneity. This review is intended to give a broad perspective to scientists
interested in adopting digital assays into their workflows.
Keywords
HTS, high-throughput screening, molecular biology, nanobiotech, lab-on-a-chip, microfluidics

Introduction
This article is the second part of a two-part review in digitalassays. A digital assay is one in which the sample is partitioned into many small containers such that each partition
contains a discrete number of biological entities (0, 1, 2, 3, . . .).
Part I of this review1 began with the fundamentals and benefits of partitioning, including Poisson statistics and sources ofmeasurement uncertainty. These principles apply equally to
all digital assays; hence, the reader is referred to the introductory sections of part I. The remainder of part I covered digital
PCR (dPCR) for quantifying nucleic acids. We described fivecommercial dPCR instruments that partition samples into
physically isolated chambers (cdPCR) or droplet emulsions(ddPCR). We compared the strengths of dPCR (absolute
quantitation, precision, ability to detect rare/mutant targets)with quantitative real-time PCR (dynamic range, larger sample volumes, and throughput). Last, we detailed several
promising applications of dPCR, including copy number
variation (CNV), quantitation of circulating tumor and viralDNA, quantifying RNA/microRNA (miRNA) with reverse
transcription dPCR, and preparing next-generation sequencing libraries.
Part II of this review focuses on how digital assays canbe extended to proteins and cells. As proteins and cells arehierarchically more complex than nucleic acids, the assays

have a correspondingly broader scope. Where digital
PCR only on quantitation, digital protein assays quantify
proteins (digital enzyme-linked immunosorbent assay[ELISA]) as well as their enzymatic activity. Digital cellassays probe a wide range of properties related to singlecell phenotype (gene expression, metabolic activity,
secreted enzymes, quantitation of surface biomarkers) andgenotype (genome, transcriptome). In this review, the relevant sections on digital PCR, protein, and cell assays are
written so they can be read independent of one another.
Sample partitioning, the key step in digital assays, provides
similar benefits in proteins/cells as it does for nucleic acids.
First and foremost is that partitioning and amplification enable
the detection of single entities (nucleic acid, proteins, or cells),
as required for digital quantitation. In the case of nucleic acids,
PCR amplification is ideal for detecting single-nucleotide
1

Department of Electrical and Computer Engineering, andDepartment of Biomedical Engineering, Wayne State University,
Detroit, MI, USA
Received February 2, 2017.
Corresponding Author:
Amar S. Basu, ECE Department, Wayne State University, 5050 Anthony
Wayne Drive, Detroit, MI 48202, USA.
Email: amar.basu@wayne.edu

2sequences with high specificity and sensitivity. Without theluxury of PCR, digital protein and cell assays rely instead on
enzymatic amplification with fluorogenic substrates, which
generates a linear increase in fluorescence over time rather
than an exponential fluorescence increase at each PCR replication cycle. In both cases, however, small partition volumes
enable single-molecule detection within 30 to 40 cycles (PCR)or within minutes to hours (enzymatic amplification). Digitalassays equally benefit from the sample enrichment that comeswith partitioning. In digital PCR, partitioning helps detect rare
nucleic acid targets in the presence of a wild-type population.
In digital ELISA, partitioning enables the detection of lowabundance proteins in the presence of those at much higher
concentrations. In both cases, increasing the number of partitions proportionally increases dynamic range. One differenceis that while nucleic acids are relatively robust molecules, proteins can denature and lose their enzymatic activity or secondary structure required for antibody binding. Due to the highsurface area to volume ratio in small partitions, care must be
taken to avoid nonspecific binding. In digital cell assays, partitioning enables measurements of phenotype and genotype atsingle-cell resolution. In many cases, cells must remain viable
if the assay requires metabolic screening or protein expression.
In small partitions, nutrients are more rapidly depleted, andwaste products build up more rapidly.
As digital PCR is a mature field, part I emphasized themany commercial instruments and a wide variety of applications being explored by the life sciences community withavailable tools. By comparison, digital protein and cellassays represent a younger but rapidly advancing field. As
such, part II has a more academic focus, emphasizing proof
of concept studies in a diverse set of assays. Nevertheless,two commercial instruments to be discussed include theQuanterix Digital ELISA system (Lexington, MA) and 10×
Genomics Chromium system (Pleasanton, CA) for singlecell transcriptomics.

Digital Protein Assays
While much of the initial efforts in digital assays focused on
nucleic acids, more recent efforts have expanded to quantifying proteins as well as their enzymatic activity, all at thesingle-molecule level. Digital enzyme assays can detect
proteins with enzymatic activity, while digital ELISA
extends this to any protein that can be detected with conventional ELISA. Digital enzymatic assays yield insights
into enzymatic turnover rates, while digital ELISA candetect extremely low-abundance protein biomarkers inblood serum for clinical diagnostics.

Digital Enzyme Assays
Digital enzyme assays (1) count the number of enzymes in asample and (2) measure single-enzyme turnover rates. The
chemistry of digital enzyme assays (Fig. 1) is no different than

SLAS Technology ﻿a conventional fluorogenic assay, but the key difference is partitioning. A dilute solution of enzyme is combined with a fluorogenic substrate and immediately partitioned into separate
containers, such that each container has a small number ofenzymes (on average <1) per partition. As the enzyme reacts
with the substrate, the fluorescence in the partition increases at
a rate proportional to the enzyme turnover rate and inversely
proportional to volume. Unlike digital PCR, where each droplet is assigned a positive or negative fluorescence value, indigital enzyme kinetic assays, the rate of fluorescence increaseis measured in each partition.2,3 When there is a discrete number of enzymes in each partition, the rate of fluorescenceincrease also becomes discrete and follows a Poisson distribution (Fig. 1C,D). There is, of course, some variability (i.e.,noise) in the rate of increase due to variations in droplet size,
small variations in substrate concentration, and stochastic differences between the enzymes’ levels of activity. In the timeplot, this can be seen as a range of slopes in the time-fluorescence plot, while in the histogram, it appears as multiple
Gaussian distributions convoluted with the exponential characteristic of a Poisson distribution. There are two advantages tominiaturizing partition size. First, it increases the effective concentration of the enzyme, so that substrate turnover dominates
over nonspecific hydrolysis of the substrate.4 Second, the small
volume rapidly accumulates fluorescence, enabling singlemolecule detection in minutes.
The first demonstration of a digital enzyme assay dates
back to 1961, when Rotman2 measured the enzymatic activityof single molecules of β-D-galactosidase by atomizing a dilute
solution of the enzyme and a fluorogenic reporter into siliconeoil. The spray method resulted in highly nonuniform droplet
sizes (0.1–40 µm). Nonetheless, by analyzing the rate of fluorescence increase in each drop, Rotman found a Poisson distribution typical of discrete molecule encapsulation.
More recently, Rondelez et al.3 demonstrated digital
enzyme assays in a highly uniform array of 30-fL wells,
micromolded on the surface of a clear elastomer (Fig. 2). A
sample of β-galactosidase, diluted to a concentration of
λ = 1 , was dispensed on the array and then sealed into thewells when the array was pressed against a cover slip. The
fluorescence in 300 chambers was monitored, revealing aquantization of reaction rates as found by Rotman.2
However, due to the much smaller small-partition volume,
single-enzyme fluorescence could be observed within minutes compared to >10 h as required in Rotman’s experiment. Other key points demonstrated were that (1) enzymes
retained activity in the well and did not adsorb to the walls
coated with an excess of bovine serum albumin (BSA)
blocking solution, (2) fluorescence did not leak to surrounding chambers, and (3) in a 1.4-fL chamber, as few as 10
fluorescein molecules (10 nM concentration) could be
detected using a conventional EPI-fluorescence setup witha CCD camera with image intensifier. The same group later
demonstrated digital enzyme assays in surface-immobilized
droplets (4.8 µm) arranged on a hydrophobic substrate

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3

Figure 1. Two categories of digital protein assays. Comparison of (A) digital enzyme activity assay, which measures single-enzyme
kinetics, and (B) digital enzyme-linked immunosorbent assay (ELISA), which quantifies single proteins by a digital form of immunoassay.
(C) In both assay types, the fluorescence in each partition increases over time at a rate dependent on the number of encapsulated
enzymes. The green shaded region illustrates variation which may occur due to differences volume or enzymatic efficiency. (D)Histogram showing rates of fluorescence increase in each partition (for lambda<1). The overlaid line shows the poisson distributions
characteristic of discretized encapsulation.

patterned with circular hydrophilic features.5 The droplets
formed spontaneously when the aqueous sample solution
was replaced with fluorocarbon oil, leaving sessile dropletsin the hydrophilic regions. The droplets were less uniform,
but the volumes could be adjusted serially using a pressuredriven micropipette. Although less scalable, the open array
format allows for recovery, addition, or exchange of thesample. This study demonstrated single-enzyme detection
within 2 min and also highlighted a key issue with dropletbased digital assays: fluorescein, as well as other fluorophores containing aromatic backbones, may slowly diffuse
into the oil. This can decrease the observed rates of fluorescence and potentially interfere with neighboring assays.
Arayanarakool et al.6 performed enzyme activity assays
in droplets generated using a nanoscale flow-focusing junction. A λ = 0.9 dilution of β-galactosidase (β-gal) was
encapsulated in droplets of size 2 to 3 µm (10 fL) with thefluorogenic substrate fluorescein-β-D-glucopyranoside(FDGlu), yielding a fluorescent product which was detectedusing an EMCCD camera. The authors successfully measured the turnover rates of individual enzymes and found it
was 25% lower than the bulk rate, consistent with prior literature. Curiously, although the authors used a similar
enzyme concentration and smaller volume compared toRondelez et al.,3 a “delay time” of 4 to 10 h (rather thanminutes) was required before a quantized fluorescence
could be detected in each drop. The delay time could not be
explained by the signal-to-noise ratio of the detector andwas thought to be caused by adsorption of the enzyme or

fluorescent product to the liquid-liquid interface or the leakage of fluorescein into the oil. They determined that the
antioxidant nPG, used to prevent photobleaching, inhibited
enzyme performance. In a subsequent study, Guan et al.7
used a similar approach to quantify the concentration of βgal instead of its activity. This study included nearly 200,000
droplets, resulting in a wide linear dynamic range between
40 and 1200 fM. For more information about the relationship between dynamic range and partition number, thereader is referred to part I of this review.1 Despite usinglarger droplets compared to Arayanarakool et al.6 (20 µm vs2 µm), fluorescence could be detected within 2 to 3 h versus>4 h. Several explanations are possible. One possible cause
is that the study by Guan et al. included BSA in the droplets,
which is known to block nonspecific adsorption (and subsequent deactivation of β-gal) on the water-oil interface.
Second, it is not unreasonable to speculate it might also
block leaking of fluorophores into the oil. Third, enzymatic
efficiency may be inhibited in fL droplet systems due to thehigh surface area to volume ratio. However, in the 20- to
100-µm size range, Guan et al. show that assay time scaleswith volume, as expected: in 20-µm droplets, detectable
fluorescence levels developed in <4 h, while in larger droplets (50 and 80 µm), fluorescence could not be distinguishedeven after 20 h.
Digital enzyme assays may also be carried out in a bead-inemulsion format, which is also used for digital ELISA as
described in next section. The basic concept is that a single
active enzyme is adsorbed to a single functionalized bead,

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Figure 2. Digital enzyme assays. (A–D) Digital enzymatic
assays using microfabricated wells. Adapted by permission from
Macmillan Publishers Ltd: Nature Biotechnology,3 ©2005. (A)Partitioning procedure. (B) Fluorescent images at 0 and 60 s.
(C) Fluorescence intensity vs. time for partitions containing
zero, one, and two enzymes. (D-E) Digital enzymatic assays
implemented in microdroplet emulsions. Adapted from
Arayanarakool et al.6 with permission from The Royal Society ofChemistry. (D) Typical increase in fluorescence vs. time. Inset
shows nanodroplet generator and image of fluorescent drops.
(E) Histogram of fluorescence rate.

which is then encapsulated in a well. Kim et al.8 incubated an
enzyme sample with functionalized beads at a 1:10 ratio,

SLAS Technology ﻿ensuring that >99% of the beads had zero or one enzyme
attached. Then, the bead solution was injected into a hydrophobic cavity etched with thousands of hydrophilic microwells,
each sized to fit only a single bead. An immiscible oil, flown
orthogonally across the wells, encapsulated each microwell/bead. This technique involves two random processes, adsorption and encapsulation, and the probability of single-cell
encapsulation becomes the product of two probabilities.
Therefore, only a small fraction of the wells contains a single
bead with a single enzyme; the majority contain beads with noenzyme or no beads at all. The low efficiency is compensatedby having many wells. With a 1 million microwell array, theauthors measured up to 700,000 beads and demonstrated alimit of detection of 10 zM of streptavidin-β-galactosidase
conjugate (SβG).
At the extreme end of low volume, Christensen et al.9
performed digital assays in small unilamellar lipid vesicles(SUVs). Synthesized using lipid self-assembly, SUVs are
15 to 700 nm in diameter and have volumes as little as 0.1
attoliters. When SUVs containing the substrate fluorescein
diphosphate (FDP) and the enzyme alkaline phosphatasewere merged, the reaction resulted in a step increase in fluorescence. This process could be repeated: if another SUV
containing FDP was added, another step increase in the
fluorescent product was observed. This unique approach
attempts to measure the number of FDP molecules per partition, not by the rate of fluorescence increase but by thediscretized fluorescence increase upon merging. However,
it is not clear whether the method could detect single
enzymes, since the authors used high concentrations (i.e.,λ > 3 for enzyme and λ > 600 for substrate).
In summary, the above studies have collectively shown that
although some level of optimization is required, digital enzyme
assays are a versatile and robust method to count single
enzymes and quantify their activity. They can be performed
with an epi-fluorescence microscope and CCD camera, a relatively simple setup compared to more complex alternatives
such as single-fluorophore event counters, which require a
confocal setup with photobleaching control.10 Although the
majority of studies worked with the well-known enzyme
β-galactosidase, the basic principle could be extended to otherenzymes, since a wide range of fluorogenic substrates is commercially available. When developing a digital enzyme assay,
technical requirements include (1) an imaging system able todetect fluorescence at low levels, (2) blocking proteins7 or biocompatible surfactants to prevent adsorption of proteins to thewalls or liquid interfaces,11–14 and (3) appropriate partition
sizes to accumulate detectable fluorescence in a reasonable
timeframe. Compared to digital PCR, the detection of proteins
is more difficult because it relies on linear enzymatic amplification rather than targeted PCR; however, the time can be
decreased to minutes when the assay is miniaturized to subpicoliter partitions.

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Digital ELISA
Conventional vs. digital ELISA. ELISA is a universal
method for detecting any protein that can be bound to an
antibody, and it is a workhorse in both clinical and research
workflows. Digital ELISA (Fig. 1B), its digital counterpart,can similarly detect any protein that can be labeled with anantibody, whereas a digital enzymatic assay is suitable only
for proteins with enzymatic activity on a fluorogenic substrate. Conventional “analog” ELISA measures the concentration of protein in a sample based on the magnitude ofemitted fluorescence, while digital ELISA essentiallycounts the number of proteins by partitioning the sample
and performing single-molecule ELISA in each partition. A
conventional direct ELISA consists of four steps: (1)adsorption of the antigen to a surface coated with a capture
antibody, (2) binding an enzyme-labeled detection antibody
to the antigen, and (3) measuring the increase in fluorescence as the enzyme reacts with a fluorogenic substrate.
After a specified incubation time, the fluorescence magnitude is measured and correlated with protein concentration.
Conventional ELISA is typically performed in a microplate
well with 10- to 100-µL volumes. The detection limit istypically in the range of 10–13 M but is highly dependent on
the sample and label. Digital ELISA (Fig. 1B) is based on
the same chemical principle, except the sample is partitioned into many small containers, such that there is anaverage of <1 protein/enzyme immunocomplex per chamber. Fluorescence in a single partition indicates the presenceof a single protein. The partition volume is typically small,and thus fluorescence accumulates rapidly such that even asingle protein can be detected. Compared to conventional
ELISA, digital ELISA lowers the detection limits by almost
3 log to 10–16 M.15
Digital ELISA technology. In 2010, the pioneering effort ofRissin et al.15 was the first to demonstrate digital ELISA(Fig. 3A–D). A dilute sample of ~3000 proteins in 100 µL(50 aM concentration) was incubated with 200,000 antibodycoated beads, and proteins bound to the beads probabilistically. Due to the large excess of beads, roughly 1% of thebeads had a single protein, while most were empty. The
beads were then labeled with a detection antibody conjugated with β-galactosidase as a reporter. Then, the sample
was partitioned by passing the beads over a single-molecule
array (Simoa) consisting of 50,000 reaction chambers, eachlarge enough to fit only a single bead (50 fL). Only 40% to
60% of the beads were captured in wells, and therefore,
only 0.4% to 0.6% of the wells contained a single bead withthe enzyme-labeled protein. After adding a fluorogenic substrate, the chambers were sealed against a PDMS gasket,
effectively isolating the chambers from one another. After a
set incubation time, the fluorescence intensity in eachchamber was measured using a digital camera. The

5proportion of empty beads (neglecting the empty wells)
was used to determine the antigen concentration, similar
to digital PCR in part I.1 In digital ELISA, the concentration l is typically kept <0.4 because differentiating
fluorescent versus nonfluorescent drops becomes challenging as l approaches 0.5.16 However, the dynamic
range can be extended by using analog detection at higher
concentrations.17
Droplet-based digital ELISA was demonstrated by Shimet al.4 The workflow is similar, except that partitioning was
performed in a droplet generator rather than a physicalarray. The droplet generator operated at >1.2 million droplets per second, nearly 100 times higher than typical implementations. The authors speculated this was achieved using
microfluidic tip streaming18 in a locally narrow flow-focusing
junction, with a low-viscosity fluorocarbon oil and 5% fluorinated surfactant. Small volumes of 32 fL enabled enzymes
to develop detectable fluorescence in ~10 min. While thechip had capacity to hold 200,000 droplets, like the Simoa
system, the published results analyzed 5000 droplets, andthe limit of detection was 46 fM compared to 0.4 fM for the
Simoa system.
Multiplexed digital ELISA of several protein biomarkers
has also been demonstrated in the Simoa system (Fig. 3E).19
Multiplexing requires one subpopulation of beads assigned
to each protein to be detected. Each subpopulation is conjugated with a unique fluorophore, along with a capture antibody
for the protein it is assigned to. As before, proteins are bound tothe beads and decorated with an enzyme-conjugated detection
antibody. During the imaging step, the fluorescence is measured twice: once on the bead itself to identify the protein
and once in the well to quantify the enzymatic product. The
two measurements must be at nonoverlapping wavelengths,
and care must be taken to avoid false positives due to optical and chemical crosstalk between beads. Using this system, Rissin et al.19 demonstrated the simultaneous detectionof four cytokines (tumor necrosis factor [TNF]–α, interleukin [IL]–6, IL-1α, and IL-1β) in plasma.
Commercial products. Digital ELISA technology using
Simoa has been commercialized by Quanterix (MA). The
flagship product, the Simoa HD-1 Analyzer (Fig. 4A,B),
was covered in a 2016 article in the Journal of Laboratory
Automation, the predecessor of the SLAS Technology Journal.15 The HD-1 uses a workflow similar to work by Rissin
et al.16 described above, except that during partitioning, thecommercial system uses a sealing oil instead of a PDMS
gasket,20 and the commercial system has automation andmultiplexing capabilities. The consumable disc contains 24
flow cells, each with 216,000 microwells with a 40-fL volume (4.25 µm diameter × 3.25 µm height). Quanterix currently offers digital ELISA test kits for 30 different antigens,which are said to have 100 to 1000 times improved sensitivity over conventional ELISA, with limit of detection (LOD)6

SLAS Technology ﻿

Figure 3. Digital enzyme-linked immunosorbent assay (ELISA). (A–D) Single-plex digital ELISA. Reprinted by permission from
Macmillan Publishers Ltd: Nature Biotechnology,16 ©2010. (A) Workflow. Beads with enzyme-labeled target are partitioned ona single molecule array. After sealing and incubation, fluorescence accumulates in chambers containing the labeled target. (B)Brightfield images of beads in wells. (C) Fluorescent images, showing positive wells. (D) Limit of detection with PSA, showing the % offluorescent wells vs. protein concentration. (E) Workflow for multiplexed digital ELISA. Adapted from Rissin et al.19 with permission
from The Royal Society of Chemistry.

in the range of 0.1 fM and coefficients of variation (CVs)<10%. The dynamic range is extended to 4 to 5 log by using
Poisson statistics (digital counting) at low concentrations
and averaging the fluorescence from each partition (“analog
detection”) at higher concentrations.17 The HD-1 system
requires about 15 min for binding the target to the beads, 5
min for binding the antibody, and 5 min to bind the SBG
reporter. Then, the sample is exposed to the fluorogenic
substrate resorufin-β-D-galactopyranoside (RGP), partitioned, and imaged.21 The Quanterix system can accommodate antibody affinities (KD) from 10–11 to 10–9 M andrecommends kon of >105 /M-s.22
With regards to sensitivity, the Quanterix systemachieves a LOD in the sub pg/mL range, 3 log smaller than
traditional ELISA.15 High-sensitivity methods like immunoPCR23 and biobarcode assays24 achieve 2 log better
improvement over ELISA but are less amenable to automation. The closest competitor to digital ELISA is the Erenna
Singulex Immunoassay System (EMD-Millipore, Billerica,MA), which also achieves 3 log smaller LOD than ELISA.25
The Erenna system counts individual fluorescently labeledimmune complex using a confocal single-molecule cytometer. It achieves a 4-log range, <1-fM detection limit, and
7% CV. The Simoa HD-1 achieves similar sensitivity anddynamic range but also provides multiplexing capabilities(up to 6),26 although multiplexing proportionally reduces
the number of beads per target and, consequently, thedynamic range. The original Singulex system was designed
for a single fluorophore, but in 2016, Singulex reported a
triplex system that can detect IL-4, IL-6, and IL-10 with
LOD in the 1-pg/mL range.27
Applications of digital ELISA. The commercial push in digital
ELISA is to detect low-concentration protein biomarkers inblood serum, leveraging the 3-log lower LOD compared to
traditional ELISA.15 Examples include biomarkers forcancer (prostate-specific antigen [PSA] at .1–100 pg/mL),neurological disorders (tau protein at 1–10 pg/mL), inflammation (cytokines TNF-α and IL-6 at <5 pg/mL), earlystage human immunodeficiency virus (HIV) (p24 at .01–10
pg/mL), and cardiovascular disease (troponin at 10–100 pg/mL). For example, digital ELISA has been used to detect
PSA in serum with an LOD as low as .05 pg/mL (0.4 fM).28
With a sensitivity 2 log lower than conventional PSA, digital ELISA enables monitoring PSA levels in patients who
have had PSA-producing tissue removed by prostatectomy.
Schubert et al.21 adapted the same assay for quantifying
PSA expression in single prostate cancer cells and showedthat expression can vary from cell to cell by two orders ofmagnitude (104–106 copies) due to genetic drift. A later
study in a mouse cancer model showed that increased PSA
is correlated with tumor growth and, more important, canpredict tumor growth.29 Mice inoculated with 10,000 to
100,000 cancer cells were monitored weekly for PSA. PSA

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Figure 4. (A–C) Simoa HD-1 digital enzyme-linked immunosorbent assay system from Quanterix. Reprinted with permission fromthe Journal of Laboratory Automation.15 (A) HD system. (B) Simoa Disc, with inset showing fluorescent image of wells after assay. (C)Detection of plasma p24 in early human immunodeficiency virus patients, compared with nucleic acid test.

levels of 10 fg/mL could be detected using digital ELISA
but not with conventional ELISA. The PSA levels grew
exponentially until week 6, when tumors could be visuallyobserved. In some cases, the low LOD can lead to less invasive clinical diagnostics. For example, the neuronal proteintau, a marker for Alzheimer disease and hypoxic brain
injury, is typically detected in the cerebrospinal fluid (CSF)
but is also present in the peripheral circulation at a sub <100
pg/mL concentration where it can be detected by digital
ELISA. Automated systems enable serial monitoring ofthese markers for early diagnostics, monitoring treatment
efficacy, and posttreatment screening. Song et al.30 developed an assay for amyloid-β 1-42, a marker associated withplaque formation in Alzheimer disease (AD) patients, inclinical plasma samples. They demonstrated lower LODs in
the .3-pg/mL range, suitable for detecting rapid changes in
response to AD therapeutics. Song et al. also demonstrated
the first measurement of cytokines TNF-α and IL-6 in theplasma of Crohn disease (CD) patients, with LODs of 8 and
6 fg/mL, respectively.31 The latter was found to be elevatedin CD patients (10.9 pg/mL) and decreased 56% after therapy. Rivnak et al.26 extended this work to a multiplex assay
for six inflammatory markers for CD and diabetes, with a
10- to 30-fg/mL LOD for each marker. Wilson et al.28
reported early detection of p24 in early stage HIV patients,
prior to immune response. The p24 limit of detection was
2.5 fg/mL, and the marker levels correlated with viral RNA
levels measured with a nucleic acid test (Fig. 4C).

Digital Cell Assays
The third type of digital assay involves the encapsulation of
discrete numbers of cells in small-volume partitions. Comparedto the digital assays with nucleic acids and proteins, digital cellassays span a much broader scope, a testament to the complexity of the cell itself. Whereas digital PCR focuses only on
quantitation, and digital protein assays focus on quantitationand single-molecule kinetics, digital cell assays measure many
aspects of cell phenotype and genotype: intracellular and extracellular gene expression, surface biomarkers, kinetic activityof cellular proteins, metabolic activity, cell secretions. It also
includes genome and transcriptome sequencing. Each of these
topics will be covered in turn.
Digital cell assays can be considered a subset of singlecell analysis, a vast area of research that is beyond the scopeof this review but is covered extensively elsewhere.32–41
Here, we focus specifically on the subset of single-cellassays that exploit the partitioning of single cells into isolated containers, as was the case with the other digitalassays. The methods discussed here fall into two categories:(1) encapsulating single cells with fluorogenic substrates
for enzymatically amplified detection of proteins or metabolites and (2) co-encapsulation of single cells with barcoded
beads for sequencing and transcriptomics. Methods of singlecell analysis not covered in this article include super-resolution microscopy,42–45 high-content (image) analysis, single
and multicolor flow cytometry,46–50 imaging flow cytometry,51,52 single-cell mass spectrometry, mass cytometry,micropipette aspiration, and patch clamping.53–56

Opportunities and Challenges of Digital Cell
AssaysFrom the standpoint of the cell biologist, partitioning cells
is motivated by the desire to (1) study cellular processes atthe single cell level and (2) better understand cell-to-cellvariability in biological systems.
In conventional cell culture on a petri dish or well plate,a cell is akin to a fish in an ocean—any secretions arequickly diluted in an infinite extracellular volume and may
even be masked by the secretions of other cells within theculture.57,58 In contrast, if a single cell is encapsulated in a
volume comparable to the cell itself, any secretions anduptake will cause detectable changes in the partition’s

8composition. Examples include the metabolic exchange ofnutrients and waste products or the enzymatic activity ofsecreted or surface proteins. Such changes in the extracellular environment cannot be easily detected with imaging or
flow cytometry; however, with digital encapsulation, thesechanges can be detected with high sensitivity with the helpof fluorogenic substrates and enzymatic amplification.
Enzyme-linked reporters can also be used to quantify cellular proteins. As before, miniaturizing partitions makethem more sensitive to changes in the extracellular space,thus providing high sensitivity. The concepts are demonstrated in many of the assays described in this section andsummarized in Figure 5.
Single-cell assays enable the study of cell heterogeneity inpopulations, an area of growing interest with implications indisease pathology, immune response, development, differentiation, and general organism function.59 High-throughput digital cell assays can help statistically quantify genomic or
phenotypic differences between cells and, based on that, classify cells into subpopulations. For example, natural microbial
populations are composed of many diverse cell types and phenotypes, many of which cannot be cultured individually.60
Bulk metagenomic sequencing is unable to resolve thegenomes of low abundant species61 or their metabolic functions in a microbiome.58 Even in monoclonal cell populations
cultured identically (both prokaryotes and eukaryotes), cell-tocell variability arises due to stochastic differences as well as
defined mechanisms.62 It is known, for example, that bacteria
in an isogenic colony differ greatly in their expression of proteins, referred to as expression “noise,” due to stochastic differences in molecular pathways or their kinetic rates.58,63 Other
phenotypic differences could include variation in metabolic
activity, soluble factors secreted to the extracellular environment, cytoskeletal properties, the number of ribosomes, andcell cycle states.62
Single-cell heterogeneity has practical and clinicalimplications. For example, in antibiotic resistance studies,
adaptive (nonmutative) resistance of bacteria is known tobe correlated with phenotype heterogeneity.64 In infection
pathology, the spatial organization of a cell is linked to phenotypic traits that enhance resistance to viral infection.65 In
immunology, genetically identical cells with diverse phenotypes together mount a host’s immune response.66 In oncology, tumors are known to have a diversity of phenotypes
due to genetic mutations, environmental cues, and otherfactors.67,68 Understanding the mechanisms of tumor heterogeneity can lead to better monitoring and the development of targeted therapies. These examples are illustrative
but are by no means exhaustive.

Chamber and Droplet-Based Implementations
As with digital PCR and digital protein assays, single-cellassays can be implemented in chamber or droplet-based

SLAS Technology ﻿platforms. Both must address technical issues related tosingle-cell assays, including cell encapsulation, obtaining
small partition volumes, maintaining cell viability, anddetection. These issues are briefly outlined here, but for amore in-depth discussion, the reader may consult otherexcellent reviews.69–72
Chamber and droplet-based implementations. Chamber-based
digital cell assay systems use sealed microchambers to encapsulate and isolate single cells. Using the flexible membrane
valve technology developed by Unger, Quake, and colleagues,73 cells can be loaded into chambers and sealed using a
network of on-chip valves. This approach has been commercialized by Fluidigm (South San Francisco, CA), which now
provides a wide variety of tools for single-cell analysis (manyof which are out of the scope of this review). Droplet-based
systems use flow focusing channels74 to encapsulate cells inmonodisperse water-in-oil (W/O) emulsions.70 Historicallyspeaking, the concept of emulsification to compartmentalize
single-cell secretions was established well before microfluidics; cells were encapsulated in water-oil-water (W/O/W) emulsions generated by agitation or vortexing75,76 and subsequently
analyzed by flow cytometry.77 However, these methods produced large drops with wide size deviations, resulting in several issues. Large drops increase the time required to generatesufficient fluorescence and may lead to saturation if enzymes
completely consume the substrate. Large drops are also more
likely to contain multiple cells, so the secretion response isaveraged over the population, making it difficult to identify
high- or low-performing mutants.78 Last, the emulsions are
generated randomly but analyzed serially, making it difficult to
pinpoint the start time of the assay. With microfluidic droplet
generators, size can be tightly controlled, single/double emulsions can be made reproducibly, and cell loading, though still
subject to Poisson statistics, is more predictable due to the uniform droplet size.79 Incubation times can also be well controlled by maintaining the order of droplets in channels. For
short reaction times, droplets allow precise control of reaction time when the detector (usually fluorescence) is placeda fixed distance downstream from the droplet generator. For
long reaction times, droplets are immediately flown into
low-temperature vials to stop the reaction until all droplets
are collected. After a fixed incubation period at a chosentemperature, the temperature is again lowered as the drops
are reinjected into the chip for analysis.80 Multiple reviews
discuss the growing use of droplets for high-throughput single-cell biology.57,70,71,81–83
Single-cell encapsulation efficiency. For single-cell studies, one
would ideally like to have 100% encapsulation efficiency (i.e.,
all partitions contain a single cell). In chamber-based systems,
cells can be directed to partitions using valving, as is done with
the Fluidigm systems. In droplet-based systems, however, cells
are subject to random encapsulation, governed by Poisson

Basu

9

Figure 5. Comparison of digital cell assays. (A) Time-resolved measurement of intracellular gene expression.98 (B) Extracellular gene
expression. (C) Quantification of cell surface biomarkers.80 (D) Enzymatic efficiency of surface-display proteins.78,102 (E) Metabolic
consumption of single cells.103 (F) Cytotoxicity assay.92 (G) Single-cell transcriptomics.111,113 (H) Bacterial detection assays using DNAzymes.118

statistics, which leads to poor encapsulation efficiency. To
ensure that <2% of the occupied drops contain multiples, thecell suspension must be diluted to λ = 0.040, in which caseonly 3.8% of drops contain a single cell.84 This problem isexacerbated when co-encapsulating multiple targets. However,
it is possible to manipulate and order cells within the fluid prior
to encapsulation such that the encapsulation efficiency isimproved. Edd et al.84 used inertial ordering to improve efficiency two times while maintaining throughputs of 15 KHz.
Using inertial ordering in a curved microchannel,85 Kemna etal.86 demonstrated encapsulation efficiencies of up to 77%
while still maintaining a throughput of 2700 cells/s. Chabert
et al.87 used hydrodynamic shearing to sort droplet-containing
cells by their larger size, at a throughput of 160 cells/s.70,87

Abate et al.88 used deformable hydrogel beads that could bepacked together at the drop generator. Packing the beads
enables them to enter the junction at fixed time intervals. The
droplet generation rate can be tuned to the same interval,
achieving an impressive 98% bead encapsulation efficiency.
Although the technique was not extended to cells, it was eventually adopted in a single-cell transcriptomics product by 10×
genomics (discussed later). Microdrop generators typically
produce a shear of <10 dyn/cm, leading to better cell viability
than vortexing methods79; however, nanodroplet generators
produce higher shear forces that can potentially damage cells.89
Maintaining cell viability and phenotype. Single-cell culture, by
its very nature, negates the cell’s population

10
context. Therefore, mechanisms of heterogeneity arising fromcell-to-cell contact, signaling, or spatial organization62,65 may
not occur if the cell is cultured in an isolated manner. For
example, differences in insulin secretion among β cells are due
to groups of adjacent cells forming secretory units,90 which
would be missed if cells were cultured separately. Thus, cells
that require a population context to maintain heterogeneity
must be cultured within that context, and a digital assay (ifneeded) may be used postculture for detection.
In situations where population context is not needed,
cells may be cultured in partitions, where the following
issues may arise. Eukaryotic cells may require a solid surface to attach to maintain viability. In chamber-based systems, this can be achieved with adhesion proteins on thechannel surface, and in droplet-based systems, cells may be
cultured in beads that are subsequently encapsulated indroplets. Long-term culture in small partitions is a challenge because cells eventually consume finite levels ofnutrients while accumulating waste products within the partition. Bacteria, algae, and yeast cell proliferation slows
dramatically in response to waste products.57 Mammaliancells similarly slow down, for reasons not clearly understood.57 These issues can potentially be alleviated by cell
media exchange, accomplished either by valve operation inchamber-based systems or droplet merging/splitting operations in droplet-based systems.91 The studies mentioned inthis article demonstrate cell viability up to several days, notwith nutrient exchange but with incubation in gas-permeable storage materials.70,92
Detection methods. For most of the studies described here
(Fig. 5), detection is based on fluorescence generated
by enzymatic amplification with fluorogenic substrates.
Chamber-based systems use fluorescence imaging, while
droplets use either imaging or flow cytometry with laserinduced fluorescence (LIF) detection. LIF provides a highdynamic range but analyzes only a single droplet at a time,leading to longer analysis times. In single-cell sequencing
assays, detection is ultimately carried out by the sequencing
instrument. Although not described in this review, bothchamber and droplet-based platforms can be coupled to awide range of detection techniques, including chemiluminescence, raman spectroscopy, electrochemical detection,
mass spectrometry, mass cytometry, and nuclear magnetic
resonance spectroscopy.93 Droplet-based platforms also
include additional methods such as the change in droplet
size due to metabolism-driven osmotic ripening, which candetect single yeast cells.94–96 These size changes can bemonitored using image processing.97

Examples of Digital Cell Assays
Figure 5 compares the many varieties of digital cell assays
to study gene expression, surface biomarkers, directed

SLAS Technology ﻿evolution, metabolic activity, cytotoxicity, and DNA/RNA
sequencing. A common theme is that all methods exploit
partitioning, fluorescence, and fluorogenic substrates insome manner to enable single-cell measurements withimproved temporal resolution, sensitivity, and differentiation between individual cells. Each method is outlined
below.
Intracellular gene expression. By encapsulating cells in 100pL partitions, Cai et al.98 measured single-cell gene expression at a temporal resolution sufficient to resolve individualtranscription events (Fig. 5A and Fig. 6A). The study targeted β-galactosidase expression in yeast and mouse cells,
which were partitioned using membrane valves. Each partition contained the fluorogenic substrate fluorescein-di-β-Dgalactopyranoside (FDG), which releases fluorescencewhen hydrolyzed by β-gal. The rate of fluorescence increaseis proportional to the number of enzymes present. When βgal expression was repressed, the rate of fluorescenceincrease was found to change in discrete steps, indicatingthat β-gal molecules are synthesized in short bursts corresponding to binding/dissociation of the lac promotor. Singlecell gene expression has also been implemented on droplet
platforms99 and was used to determine dose-response curves
for an injected hormone.
Extracellular gene expression. A similar approach can beapplied to extracellular gene expression (Fig. 5B). Ostafe etal. used droplet partitioning to screen yeast cells with highsecreted cellulose activity.78 A cascade reaction detects cellulase secreted within the droplet. Cellulase hydrolyzes carboxymethylcellulose into mono- and oligosaccharides,
which can be detected by a series of enzymatic reactions
that ultimately cleave the fluorogenic substrate aminophenoxifluorescein (APF), forming a fluorescent productwithin 20 min (simplified in Fig. 5B). In cell populations
where <1% were actively expressing cellulase, more than
300-fold enrichment was achieved by sorting the fluorescent droplets using a dielectrophoretic cell sorter.100,101
Surface biomarkers. Enzymatic amplification in small partitions has been used to detect low-abundance surface biomarkers (Fig. 5C).80 Low-abundance biomarkers likecytokines cannot be easily detected by conventional flow
cytometry, which is better suited to medium-high abundance surface markers (100s to 1000s of copies). By encapsulating cells in droplets, Joensson et al.80 leveraged
enzymatic amplification to detect as few as 2.65 copies per
droplet. The human monocytic cells were first labeledwith biotinylated antibodies and a streptavidin-linked
reporter enzyme (β-gal). They were then encapsulated in
40-µm droplets with the fluorogenic substrate FDG andincubated for 5 h to develop fluorescence. By analyzing
the droplet fluorescence, the authors could distinguish

Basu

11

Figure 6. Examples of digital cell assays. (A) Real-time gene expression. Black line: the rate of fluorescence increase can becorrelated to the real-time expression of β-gal in a dividing yeast cell. Red line: empty control chamber. Inset: microfluidic channel
with valves used to encapsulate the cell. Reprinted by permission from Macmillan Publishers Ltd: Nature Biotechnology,98 ©2006. (B)Directed evolution of surface display proteins,102 which includes the following steps: (i) encoding wild-type HRP gene on an inducible
surface display plasmid, (ii) mutagenesis of libraries, (iii) libraries transformed into yeast, (iv) encapsulation with fluorogenic substrate,(v) enzymatic reaction in HRP-active cells generates fluorescence, (vi-vii) fluorescence-activated droplet sorting chooses those withhigh-activity cells, and (viii) repeat evolution with subsequent generation. ©2010 National Academy of Sciences. (C) High-throughput
cytotoxicity screening.92 (i) Generation of droplet libraries containing drugs and a corresponding optical barcode. (ii) Combining
droplets with drugs and droplets containing cells. (iii) Incubation. (iv) Dual-fluorescent readout to measure cell viability and identify
drug by optical barcode. ©2009 National Academy of Sciences. (D) Single-cell reverse transcriptase (RT)–PCR in sealed chambers.107(i) Cell loading into trap, (ii) wash to remove RT-PCR inhibitors, (iii) isolation and heat lysis, (iv) injection of RT reagents, and (v-vi)
priming and injection of PCR reagent for on-chip PCR. ©2011 National Academy of Sciences. (E) Single-cell RT-PCR in droplets.82(i) Encapsulation of cells in droplets; (ii) lysis, dilution of lysis buffer, addition of RT-PCR reagents; (iii) PCR cycling; and (iv) multiplex
detection of transcripts. Adapted with permission from Eastburn et al.,82 ©2013 American Chemical Society.

populations containing low-abundance CD19 and CCR5
receptors from negative controls.
Directed evolution. Directed evolution refers to an iterative
process where (1) mutations are induced into a cell population, (2) mutants are selected from a population based on adesired phenotype, and (3) the mutants are recultured topropagate the optimized traits. The process can be iterated

several times to obtain mutant populations with highly optimized traits. Discovering and evolving rare mutants requires
screening very large populations. In conventional directed
evolution workflows using microtiter plates, screening 105
clones requires weeks or months, and the cost of capital equipment and reagents is often prohibitive.78 Using droplet-based
microfluidics, Agresti et al.102 demonstrated directed evolution of horseradish peroxidase (HRP) with a 1,000-fold12
increase in speed and a 1,000,000-fold reduction in costcompared to microplates. Yeast cells modified with an HRP
surface-display protein were encapsulated in droplets containing a fluorogenic HRP substrate (Fig. 5D and Fig. 6B).
At each round of evolution, droplets with the highest levelsof fluorescence were selected using fluorescence-activateddroplet sorting (FADS).101 In the first round of mutagenesis,
100 mutants were sorted from an initial population of 107
cells, and by the second round, the authors were able toevolve mutants with 10 times faster catalytic activity than
the initial population. Due to high-speed microfluidic drop
generators and sorters, the workflow could screen 108
mutants in less than 10 h using <150 µL in reagents.
Single-cell metabolic activity. Encapsulating single cells inpartitions enables one to quantify the exchange of nutrients and waste products between the intracellular andextracellular space (Fig. 5E). By contrast, flow cytometryis suitable for measuring only intracellular activity. As
before, small partition volumes allow one to quantify
metabolite concentrations and secretions with exquisite
sensitivity using common fluorogenic reagents.92 Wang etal.103 performed metabolic screening in droplets to identify yeast cells with increased xylose consumption, withthe goal of evolving cells to efficiently digest lignocellulosic feedstocks. The cells were cultured and encapsulated
in droplets and then merged with drops containing xylose
detection reagents consisting of Amplex UltraRed, HRP,
and pyranose oxidase. Xylose is oxidized by pyranose oxidase, generating H2O2, which then reacts with Amplex
UltraRed in the presence of HRP to form the highly fluorescent compound resorufin. The latter reaction generates
O2, which is coupled back to the first. Droplets containing
xylose-consuming cells, which have lower fluorescencelevels, were enriched by fluorescence-activated droplet
sorting.101 The authors demonstrated 420-fold enrichment
in a single round and 42,600-fold enrichment in two
rounds. By replacing pyranose oxidase with lactate oxidase, the same system performed a 5800 times enrichmentof L-lactate–producing cells from a population of primarily d-lactate producers. Fluorogenic substrates can beapplied to a wide variety of metabolites. For example, the
HRP/Amplex Ultra Red system can be used with many
oxidase enzymes.103 The oxidase enzyme, specific to thetarget metabolite, oxidizes the metabolite and H2O2, whichthen reacts with Amplex Ultra Red in the presence of HRP
to generate the fluorescent compound resorufin. The fluorescence is proportional to the concentration of metaboliteof interest.
Cytotoxicity. In conventional toxicity screening, the ratio oflive/dead cells in response to a chemical agent is determinedby fluorescent live/dead staining, followed by imaging or
flow cytometry. Brouzes et al.92 implemented toxicity

SLAS Technology ﻿screening in droplet-encapsulated cells, which detected
live/dead fluorescence in both the intracellular and extracellular space (Fig. 5F and Fig. 6C). Human monocytic cells
were encapsulated in droplets and then merged with droplets containing different concentrations of mitomycin, witheach concentration “barcoded” with a proportional concentration of a fluorescent tag (710 nm). Mammalian cells survived for up to 4 days in droplets in fluorocarbon oil,stabilized with a biocompatible fluorosurfactant. Afterincubation, the droplets were merged with droplets containing Calcein-AM and Sytox-Orange, resulting in 520 nm
fluorescence if live or 617 nm fluorescence if dead. This
resulted in a triplex fluorescence readout, one channel (710nm) coding for drug concentration and the other two coding
for cell viability. The survival rates for >900 cells were
measured as a function of mitomycin concentration, generating a dose-response curve. The curve matched those takenusing a standard microplate, although a 10% to 15% error
was attributed to the shearing of cells during the encapsulation process. The advantage of droplets is that large libraries
can be potentially explored, provided that each sublibraryof a drug can be uniquely barcoded.
Single-cell PCR. Single-cell DNA identification is useful for
identifying genetic diversity in polyclonal cell populations
like bacterial samples. Partitioning cells before PCR maintains single-cell traceability during amplification. For
example, Zeng et al.79 demonstrated DNA analysis of single
cells in droplets, using an approach similar to BEAMing.104
BEAMing (beads, emulsion, amplification, magnetics) is atechnique for high-fidelity multiplex PCR, consisting of thefollowing steps: (1) co-encapsulating primer-functionalized
beads with single DNA molecules in droplets; (2) performing emulsion PCR, forming a forest of identical DNA oneach bead; (3) collecting the beads with magnets or centrifugation; (4) labeling the DNA by hybridization; and (5)analyzing the beads by flow cytometry.105 In Zeng et al., alimiting dilution of cells and an equally dilute solution ofbeads with attached forward primers were co-encapsulated
in droplets with an excess of dye-labeled reverse primers
and PCR reagents.79 When cells were lysed in the drop, theDNA with attached reverse primers adsorbed on the beads,
forming a complex along which PCR amplification could
occur. After several rounds of thermal cycling, each bead
contained a forest of adsorbed, dye-labeled DNA. The
emulsion was then broken and the beads were analyzed
using flow cytometry, relating fluorescence intensity togene expression. The authors implemented the method on a
96-plex droplet generator array to detect a single pathogenic Escherichia coli among a nonpathogenic populationof 105. (Part I discusses the importance of many partitions
when detecting rare mutants.) Leung et al.106 encapsulated
single bacteria into droplets and performed whole-genome
amplification (WGA) and off-chip sequencing. A key

Basutechnological advance was a programmable droplet arraychip, which provided partitioning, addressable storage,
metering, and sample recovery required for off-chipsequencing. Sequencing revealed bacterial diversity inmarine and oral samples.
Single-cell reverse transcriptase PCR. RNA quantification isuseful in determining cell-to-cell and temporal variation ingene expression. Performing reverse transcriptase (RT)–PCR
in nanoliter and picoliter volumes reduces measurement noise,
improves sensitivity, and affords single-nucleotide specificity.107 In RT–quantitative PCR, measurement noise increases
significantly when there is less than one template per 100
nL,108 and cell lysate inhibits the PCR reaction at concentrations >20 cells/100 nL.107 Therefore, partitioning with precise concentrations is important to maintain amplification
efficiency. In 2008, Beer et al.109 first demonstrated RTPCR in picoliter droplets (although not on single cells) byco-encapsulating RNA and RT-PCR mix in a droplet followed by thermal cycling. Later, Zhang et al.110 performed
single-cell RT-PCR in agarose droplets containing RT-PCR
cocktail along with cell lysis agents. The labeled RT-PCR
products were trapped on 5′ acrydite primers grafted to the
agarose matrix. The droplets were later solidified into
beads, allowing them to be subsequently analyzed by conventional flow cytometry. The system was used to differentiate cells based on their expression of EpCAM, acarcinoma-associated antigen. White et al.107 demonstrated RT-PCR in single cells using a chamber-based
device, where cells were (1) captured in a hydrodynamic
trap, (2) isolated by pneumatic valves, and (3) subjected toheat lysis and reverse transcription/amplification (Fig.
6D). Single-pot PCR reagents were sequentially added
after lysis to perform reverse transcription and isolation.
An excess of RT reagent was added after lysing to avoid
any inhibition. Using this system, they demonstrated
quantitative analysis of miRNA expression, single-nucleotide variation in 300 cells per run. The highest throughput
RT-PCR was demonstrated by Eastburn et al.,82 who performed RT-PCR in nearly 50,000 cells by using fast droplet generators and small droplet volumes. To avoid
RT-PCR inhibition, they diluted cell lysates using on-chip
droplet injection, merging, and splitting modules (Fig.
6E). By multiplex analysis, they could identify rare cell
types present in as little as 0.3% of the population on thebasis of their gene expression.
Single-cell RNA sequencing (scRNA-seq). Digital partitioning
enables single-cell transcriptome sequencing, which has
long been considered an epitome for single-cell analysis.
While single-cell RT-PCR (discussed above) is useful for
quantifying the expression of specific genes, a full-sequence
messenger RNA (mRNA) transcriptome provides a denovo, comprehensive analysis of cell heterogeneity.

13
Without digital partitioning, an ensemble of cells can bepooled and their DNA can be sequenced, but this approachyields a metagenome. The two techniques described below
combine digital partitioning with barcoding techniques thatsequence thousands of single-cell transcripts in a single
sequencing run, with each transcript linked to the cell oforigin.
Drop-SEQ. First reported in 2015, Drop-SEQ is a method
to obtain single-cell transcriptomes of tens of thousands ofcells at a time (Fig. 5G and Fig. 7A).111 The experimental technique exploits the massive parallelism offered by
microdroplet-based partitioning. Cells are co-encapsulatedwith microbeads in a droplet containing lysis buffer. In theideal case, a droplet contains exactly one cell and one bead(Fig. 5G)—although this is only true of a small fraction of
the droplets (see below). Each bead is functionalized with>108 attached oligomer strands, which capture the cell’smRNA, amplify it, and trace its origin. Each oligomer
strand has four sections: (1) a PCR handle, which serves
as an attachment point for the replication polymerase; (2)a cell barcode, unique to each bead, which indicates thecell of origin; (3) a universal molecular indicator (UMI),
unique to each strand, which is used to count the number oftranscripts from each cell; and (4) a 30T sequence, whichattaches a single mRNA transcript from the lysed cell. The
cell barcodes are 12-base sequences generated by split-poolsynthesis on the beads.112 This method ensures that eachbead is assigned a unique barcode, and all barcodes on abead are identical. By contrast, the 8-base UMIs are formed
by degenerate synthesis on all beads and strands at the sametime, creating a unique random barcode on each strand.
A microfluidic co-flow focusing drop generator is used
to co-encapsulate the beads and cells in a lysis buffer.
Immediately after encapsulation, the cell is lysed, and its
mRNA adsorbs to the beads along the 30T sequence. At thispoint, the emulsion is no longer needed, so it is broken andthe bulk solution is then subjected to reverse transcription,
forming a synthesized complementary DNA (cDNA) libraryon each bead. Template switching is used to add a second
primer at the far end of each strand. The bead-oligomer
complex is referred to as a STAMP (single-cell transcriptomes attached to a microparticle). Each strand contains thecomplement of a single mRNA sequence, labeled with acell barcode and a UMI. Using the STAMPs as templates,
the DNA is amplified and sequenced in a commercial synthesizer (such as Illumina, San Diego, CA). During data
analysis, the cell barcode and UMI allow each cDNA readto be traced back to its cell of origin. In this way, the transcriptome of each cell can be assembled.
Drop-SEQ has proved to be a powerful method for identifying diversity in complex tissue samples. Macosko
et al.111 analyzed 44,800 retinal cells and could identify 39
distinct subpopulations matching previous studies using

14

SLAS Technology ﻿

Figure 7. Single-cell RNA sequencing. (A) Drop-SEQ workflow, as described in text. Reprinted from Macksko et al.111 ©2015, withpermission from Elsevier. (B) The 10× Genomics Chromium system and workflow. (i) Workflow, which includes co-encapsulation with gel
beads in emulsion (GEMs), reverse transcriptase (RT)–PCR to generate barcoded complementary DNA (cDNA), pooling, and sequencing.
(ii) Chromium instrument. Reprinted with permission from 10× Genomics. (C) (i) Projection of 68,000 unsorted human peripheral blood
monocular cells clustered by t-distributed stochastic neighbor embedding (tSNE), with each cluster colored according to cell type identified
by a gene expression heat map (ii). Reprinted from Zheng et al.113 under a Creative Commons CC-BY license.

manual light microscopy. Moreover, further variation ingene expression could be seen within each subpopulation,suggesting deeper subclassifications in cell type or states.
Drop-SEQ can prepare 10,000 single-cell transcripts in 12 h
at a cost of $.065 per transcript, which is about 100 times
faster and cheaper than conventional methods.
One limitation of Drop-SEQ is the efficiency of coencapsulation. Only droplets that contain a single cell andsingle bead provide useful transcripts. A droplet with multiple cells with a single STAMP loses single-cell resolution, and a droplet with multiple STAMPs but one cellwould report two identical cell types. Assuming wellmixed solutions, the number of cells in a droplet and thenumber of beads in a droplet are both independent Poisson
distributions, and thus the probability of co-encapsulationis the product of the two. In DropSEQ, choosing the concentration becomes a trade-off between purity (avoiding
multiple cells per bead) and throughput (maximizing
encapsulation efficiency). For example, if the concentration of cells and beads is at an average concentration ofl = 0.1 per partition, the probability of a single cell andsingle bead is

15

Basu
p (k ) =λ k e−λ=>k!

2

 ( 0.1)1 e −0.1  = .82%p ( coencapsulation ) = p (1) * p (1) = 


1!


while the probability of a doublet is p ( 2 ) * p (1) = .04%. If
l is increased to 1 to improve encapsulation efficiency, theprobability of a single bead and cell increases to 13.5%, but
the double probability increases to 6.7%. Drop-SEQ advocates a high purity mode or a high-speed mode,111 depending on the application.
10× Genomics Chromium System. A recently available,commercial variant of Drop-SEQ improves on the encapsulation efficiency while providing an off-the-shelf solution to single-cell RNA sequencing. Sold by 10× Genomics,
the Chromium system is similar to Drop-SEQ in that it coencapsulates single cells with barcoded beads; however,
Chromium achieves 10 to 50 times better bead encapsulation efficiency by using soft beads made from a hydrogel
instead of a hard polymer (Fig. 7B). As shown by Abate etal.,88 deformable beads can be tightly packed upstream of adrop generator without clogging the chip. The close-packed,
ordered beads flow single file into the drop generator with
fixed spacing, resulting in nearly >100% encapsulation efficiency.113 10× Genomics refers to this technology as gel beadin emulsion (GEM). The cell suspension, added at a second
inlet, still has randomized encapsulation; therefore, the combined encapsulation efficiency is only a single Poisson distribution instead of a product of two. The Chromium system
obtains 80% encapsulation efficiency of the GEMs and a combined 50% efficiency. The beads are collected and subjected to
RT-PCR to create a cDNA library which is then removed andpooled for sequencing. The throughput of the system can be as
many as ~50,000 single cells per 6-min run.113
Like Drop-SEQ,111 the Chromium system provides highthroughput single-cell gene expression data, which wereused to identify subpopulations in immune samples.113
Zheng et al. profiled 68,000 unsorted human peripheral
blood mononuclear cells (PBMCs) using Chromium
together with Illumina sequencing. Based on the expressionof multiple genes, subpopulations could be clustered using
a 2D t-distributed stochastic neighbor embedding (tSNE)
projection (Fig. 7C). Cell types could then be inferred, as
illustrated in the heatmap. Also described in this article was
the detection of donor and host cells in bone marrow samples from acute myeloid leukemia (AML) patients. Notable
was the ability to obtain transcriptomes from nearly 17,000
single bone marrow mononuclear cells (BMMCs), which
are known to be fragile and difficult to measure after
chemotherapy.

Linked-read genome sequencing. In addition to studies in cell
heterogeneity, the 10× Genomics system enables a newtechnique called linked-read sequencing, which has shownpromise in whole-genome sequence assembly.114 Wholegenome shotgun sequencing today is based primarily on
short reads (e.g., Illumina), which are assembled andaligned to a reference genome. This approach has some
limitations: (1) although it can detect short nucleotide variants, it has difficulty in assembling structural variants >1
kbp, including long repeating sequences; (2) it is well suited
for aligning a sample sequence to a reference genome but
less suited for de novo assembly; and (3) it is difficult toinfer chromosome location or phasing. Long-read sequencers (e.g., PacBio, Menlo Park, CA) can address some ofthese limitations in principle but still have limitations withthroughput, cost, and accuracy.
The Chromium system provides so-called linked reads,
where long genomic DNA sequences are encapsulated in
GEMs. From the long parent molecule, multiple short reads
are generated, each with a unique barcode linked to the parent. During assembly, the barcodes help to assemble theparent sequence from the corresponding small reads andinfer connections between parents. Linked reads thus provide long-range information for assembling haplotypes,
identifying complex structural variants like segmental
duplications, covering low-accessibility regions, and performing de novo assembly.115 Together, the three data types(short, long, and linked reads) provide complementary setsof sequences and mapping information: long reads provide
scaffold information, linked reads are used to extend contigs and fill gaps between adjacent contigs generated bylong reads, and short reads provide the accuracy for identifying single-nucleotide variations (SNVs). Neighboring
scaffolds tend to appear in the same barcoded pools, whichhelps to identify and orient linked scaffolds.
Combining 10× linked reads together with Illumina shortreads, Zheng et al.113 reconstructed a genome-wide haplotype of a known family sample, demonstrating the ability toidentify SNVs, structural changes (deletions, insertions,and inversions), and phasing. Seo et al.116 performed denovo assembly and haplotype phasing of a Korean genome,
using the PacBio long reads, the Illumina short reads, the
10× Genomics linked reads, and bacterial artificial chromosome (BAC) to represent a single haplotype. Mostovoy etal.114 performed de novo assembly and phasing of a human
HapMap sample by combining 10× linked-reads, shortreads from Illumina sequencing, and long reads from the
Bionano Genomics (BNG, San Diego, CA) mapping system. These studies suggest that a hybrid approach achieves
results similar to ideal long reads but at an affordable cost.
Digital detection of pathogens in blood serum. As a final application, digital cell assays also demonstrate strong clinical potential in detecting rare cells in blood. Kang et al.117 introduced

16“comprehensive digital droplet detection” (IC 3D), which candetect single bacteria in milliliters of diluted blood. Detecting
such low levels is difficult with conventional cell culture or
PCR because the high levels of background lead to false positives or inhibition of the PCR reaction (see part I). Droplet partitioning circumvents this issue by increasing the effective
concentration of a single bacterium and enriching it from background (see part I of this review). Detection was accomplishedusing enzymatic amplification by DNAzymes, RNA/DNA
hybrid probes, which release fluorescence upon binding with
crude extracellular matrix (CEM) secreted by E. coli cells (Fig.
5H). Droplet fluorescence was detected using a rotationalscanning confocal microscope118 in a cuvette, achieving ratesof 100,000 droplets per second. As a result of the high throughput, the authors were able to analyze mL-scale clinical samples, whereas other droplet systems are typically limited to<100 µL. The system could detect <10 bacteria to as many as
10,000 per sample in ~4 h. A modified version of the system
has also been used for miRNA detection in purified bloodplasma,119 replacing the DNAzyme sensor with reagents for
isothermal exponential amplification (EXPAR) to amplify anddetect specific miRNAs. The IC 3D system is being commercialized by Velox Biosystems (Irvine, CA), a startup company
spun out of UC Irvine.

Conclusions
It has been said that significant advances in biology andmedicine are accompanied by enabling advances in measurement technology, and this is certainly true of digitalassays. Like many scientific advances, the concept of adigital assay is relatively simple but has proved to be powerful in many contexts. In a relatively short time, digital
PCR has been commercialized and used for detecting viraland tumor DNA in serum at levels that enable better patientmonitoring. A similar story is true for digital ELISA and its
ability to detect more than 50 low-abundance serum protein
biomarkers with unprecedented levels of sensitivity. Digitalcell assays have yielded fundamental insights in the transcription rates within single cells, enabled rapid directed
evolution to create high-efficiency enzymes, and helped
quantify single-cell metabolic activity. Perhaps the most
exciting recent development is single-cell RNA sequencing,which probes the single-cell transcriptomes of thousands ofcells in a single run, providing a powerful tool for life scientists to understand cell diversity and their role in disease.
Like digital PCR and digital ELISA, single-cell RNA-seq
and linked-read genomic sequencing are being commercialized. Extrapolating their rapid adoption, it is likely thesetools will soon become commonplace in research and clinical labs, providing a powerful combination of discovery
tools (scRNA-seq, linked-read genomics) with clinicaldiagnostics (digital ELISA, digital PCR). Together withother expected innovations, these tools will accelerate the

SLAS Technology ﻿ever-present push for bench-to-bedside translation of basic
knowledge, leading to improved patient care in diseases
like cancer, HIV, and neurological disorders.
Acknowledgment
The author thanks Priyan Weerappuli for manuscript review.

Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.

Funding
The author disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: The
author gratefully acknowledges support from the National Science
Foundation Division of Chemical, Bioengineering, Environmental,
and Transport Systems (CBET) award numbers 1236764 and
1512544, and from the Division of Electrical, Communications
and Cyber Systems (ECCS) award number 1232226.

References
1. Basu, A. S. Digital Assays, Part I: Partitioning Statistics andDigital PCR. SLAS Technol. J. 2017, in press.
2. Rotman, B. Measurement of Activity of Single Molecules of
β-D-Galactosidase. Proc. Natl. Acad. Sci. U. S. A. 1961, 47,
1981–1991.
3. Rondelez, Y.; Tresset, G.; Tabata, K. V.; et al. MicrofabricatedArrays of Femtoliter Chambers Allow Single Molecule
Enzymology. Nat. Biotechnol. 2005, 23, 361–365.
4. Shim, J.; Ranasinghe, R. T.; Smith, C. A.; et al. Ultrarapid
Generation of Femtoliter Microfluidic Droplets for SingleMolecule-Counting Immunoassays. ACS Nano 2013, 7,
5955–5964.
5. Sakakihara, S.; Araki, S.; Iino, R.; et al. A Single-Molecule
Enzymatic Assay in a Directly Accessible Femtoliter Droplet
Array. Lab Chip 2010, 10, 3355.
6. Arayanarakool, R.; Shui, L.; Kengen, S. W. M.; et al. SingleEnzyme Analysis in a Droplet-Based Micro- and Nanofluidic
System. Lab Chip 2013, 13, 1955.
7. Guan, Z.; Zou, Y.; Zhang, M.; et al. A Highly Parallel
Microfluidic Droplet Method Enabling Single-Molecule
Counting for Digital Enzyme Detection. Biomicrofluidics
2014, 8, 014110.
8. Kim, S. H.; Iwai, S.; Araki, S.; et al. Large-Scale Femtoliter
Droplet Array for Digital Counting of Single Biomolecules.
Lab Chip 2012, 12, 4986.
9. Christensen, S. M.; Bolinger, P.-Y.; Hatzakis, N. S.;
et al. Mixing Subattolitre Volumes in a Quantitative andHighly Parallel Manner with Soft Matter Nanofluidics. Nat.
Nanotechnol. 2011, 7, 51–55.
10. English, B. P.; Min, W.; van Oijen, A. M.; et al. EverFluctuating Single Enzyme Molecules: Michaelis-Menten
Equation Revisited. Nat. Chem. Biol. 2006, 2, 87–94.
11. Song, H.; Chen, D. L.; Ismagilov, R. F. Reactions in Droplets in
Microfluidic Channels. Angew. Chem. Int. Ed. 2006, 45, 7336.

Basu
12. Roach, L. S.; Song, H.; Ismagilov, R. F. Controlling
Nonspecific Protein Adsorption in a Plug-Based Microfluidic
System by Controlling Interfacial Chemistry Using FluorousPhase Surfactants. Anal. Chem. 2005, 77, 785–796.
13. Baret, J.-C. Surfactants in Droplet-Based Microfluidics. Lab
Chip 2012, 12, 422.
14. Holtze, C.; Rowat, A. C.; Agresti, J. J.; et al. Biocompatible
Surfactants for Water-in-Fluorocarbon Emulsions. Lab Chip
2008, 8, 1632–1639.
15. Wilson, D. H.; Rissin, D. M.; Kan, C. W.; et al. The
Simoa HD-1 Analyzer: A Novel Fully Automated Digital
Immunoassay Analyzer with Single-Molecule Sensitivity andMultiplexing. J. Lab. Autom. 2016, 21, 533–547.
16. Rissin, D. M.; Kan, C. W.; Campbell, T. G.; et al. SingleMolecule Enzyme-Linked Immunosorbent Assay Detects
Serum Proteins at Subfemtomolar Concentrations. Nat.
Biotechnol. 2010, 28, 595–599.
17. Rissin, D. M.; Fournier, D. R.; Piech, T.; et al. Simultaneous
Detection of Single Molecules and Singulated Ensemblesof Molecules Enables Immunoassays with Broad Dynamic
Range. Anal. Chem. 2011, 83, 2279–2285.
18. Anna, S. L.; Mayer, H. C. Microscale Tipstreaming in a
Microfluidic Flow Focusing Device. Phys. Fluids 2006, 18,
121512.
19. Rissin, D. M.; Kan, C. W.; Song, L.; et al. Multiplexed Single
Molecule Immunoassays. Lab Chip 2013, 13, 2902.
20. Kan, C. W.; Rivnak, A. J.; Campbell, T. G.; et al. Isolation
and Detection of Single Molecules on Paramagnetic Beads
Using Sequential Fluid Flows in Microfabricated Polymer
Array Assemblies. Lab Chip 2012, 12, 977–985.
21. Schubert, S. M.; Walter, S. R.; Manesse, M.; et al. Protein
Counting in Single Cancer Cells. Anal. Chem. 2016, 88,2952–2957.
22. Chang, L.; Rissin, D. M.; Fournier, D. R.; et al. Single
Molecule Enzyme-Linked Immunosorbent Assays: Theoretical
Considerations. J. Immunol. Methods 2012, 378, 102–115.
23. Niemeyer, C. M.; Adler, M.; Wacker, R. Immuno-PCR:
High Sensitivity Detection of Proteins by Nucleic Acid
Amplification. Trends Biotechnol. 2005, 23, 208–216.
24. Nam, J.-M.; Thaxton, C. S.; Mirkin, C. A. Nanoparticle-Based
Bio-Bar Codes for the Ultrasensitive Detection of Proteins.
Science 2003, 301, 1884–1886.
25. Todd, J.; Freese, B.; Lu, A.; et al. Ultrasensitive Flow-Based
Immunoassays Using Single-Molecule Counting. Clin. Chem.
2007, 53, 1990–1995.
26. Rivnak, A. J.; Rissin, D. M.; Kan, C. W.; et al. A FullyAutomated, Six-Plex Single Molecule Immunoassay for
Measuring Cytokines in Blood. J. Immunol. Methods 2015,
424, 20–27.
27. Gilbert, M.; Livingston, R.; Felberg, J.; et al. Multiplex Single
Molecule Counting Technology Used to Generate Interleukin
4, Interleukin 6, and Interleukin 10 Reference Limits. Anal.
Biochem. 2016, 503, 11–20.
28. Wilson, D. H.; Hanlon, D. W.; Provuncher, G. K.; et al.
Fifth-Generation Digital Immunoassay for Prostate-Specific
Antigen by Single Molecule Array Technology. Clin. Chem.
2011, 57, 1712–1721.
29. Schubert, S. M.; Arendt, L. M.; Zhou, W.; et al. Ultra-Sensitive
Protein Detection via Single Molecule Arrays towards Early
Stage Cancer Monitoring. Sci. Rep. 2015, 5, 11034.

17
30. Song, L.; Lachno, D. R.; Hanlon, D.; et al. A Digital
Enzyme-Linked Immunosorbent Assay for Ultrasensitive
Measurement of Amyloid-β 1-42 Peptide in Human Plasma
with Utility for Studies of Alzheimer’s Disease Therapeutics.
Alzheimers Res. Ther. 2016, 8, 58.
31. Song, L.; Hanlon, D. W.; Chang, L.; et al. Single Molecule
Measurements of Tumor Necrosis Factor α and Interleukin-6
in the Plasma of Patients with Crohn’s Disease. J. Immunol.
Methods 2011, 372, 177–186.
32. Andersson, H.; van den Berg, A. Microtechnologies andNanotechnologies for Single-Cell Analysis. Curr. Opin.
Biotechnol. 2004, 15, 44–49.
33. Andersson, H.; van den Berg, A. Microfluidic Devices for
Cellomics: A Review. Sens. Actuators B Chem. 2003, 92,
315–325.
34. Voldman, J. Engineered Systems for the Physical Manipulationof Single Cells. Curr. Opin. Biotechnol. 2006, 17, 532–537.
35. Sims, C. E.; Allbritton, N. L. Analysis of Single Mammalian
Cells On-Chip. Lab Chip 2007, 7, 423–440.
36. Anselmetti, D.: ed. Single Cell Analysis: Technologies andApplications. Wiley, Hoboken, NJ; 2009; pp V-XV. In Single
Cell Analysis: Technologies and Applications. 2009.
37. Lin, Y.; Trouillon, R.; Safina, G.; et al. Chemical Analysis ofSingle Cells. Anal. Chem. 2011, 83, 4369–4392.
38. DiCarlo, D.; Lee, L. P. Dynamic Single-Cell Analysis
for Quantitative Biology. Anal. Chem. 2006, 78, 7918–7925.
39. Wheeler, A. R.; Throndset, W. R.; Whelan, R. J.; et al.
Microfluidic Device for Single-Cell Analysis. Anal. Chem.
2003, 75, 3581–3586.
40. Kortmann, H.; Blank, L.; Schmid, A. Single Cell Analytics:
An Overview. Müller Thomas Bley, S.; ed. In Advances in
Biochemical Engineering/Biotechnology; 2010; Springer
Berlin Heidelberg, Berlin, Germany; pp 1–24.
41. Zhang, L.; Cui, X.; Schmitt, K.; et al. Whole Genome
Amplification from a Single Cell: Implications for Genetic
Analysis. Proc. Natl. Acad. Sci. 1992, 89, 5847.
42. Sengupta, P.; Van Engelenburg, S.; Lippincott-Schwartz,
J. Visualizing Cell Structure and Function with PointLocalization Superresolution Imaging. Dev. Cell 2012, 23,1092–1102.
43. Betzig, E.; Patterson, G. H.; Sougrat, R.; et al. Imaging
Intracellular Fluorescent Proteins at Nanometer Resolution.
Science 2006, 313, 1642–1645.
44. Hess, S. T.; Girirajan, T. P. K.; Mason, M. D. Ultra-High
Resolution Imaging by Fluorescence Photoactivation
Localization Microscopy. Biophys. J. 2006, 91, 4258–4272.
45. Rust, M. J.; Bates, M.; Zhuang, X. Sub-Diffraction-Limit
Imaging by Stochastic Optical Reconstruction Microscopy(STORM). Nat. Methods 2006, 3, 793–796.
46. Huh, D.; Gu, W.; Kamotani, Y.; et al. Microfluidics for Flow
Cytometric Analysis of Cells and Particles. Physiol. Meas.
2005, 26, R73.
47. Hur, S. C.; Tse, H. T. K.; Carlo, D. D. Sheathless Inertial Cell
Ordering for Extreme Throughput Flow Cytometry. Lab Chip
2010, 10, 274–280.
48. Perfetto, S. P.; Chattopadhyay, P. K.; Roederer, M. SeventeenColour Flow Cytometry: Unravelling the Immune System.
Am. J. Epidemiol. 2003, 158, 207–213.
49. De Rosa, S. C.; Herzenberg, L.; Herzenberg, L.; et al. 11Color, 13-Parameter Flow Cytometry: Identification of

18
Human Naive T Cells by Phenotype, Function, and T-Cell
Receptor Diversity. Nat. Med. 2001, 7, 245.
50. Ateya, D. A.; Erickson, J. S.; Howell, P. B.; et al. The Good,the Bad, and the Tiny: A Review of Microflow Cytometry.
Anal. Bioanal. Chem. 2008, 391, 1485–1498.
51. Han, Y.; Gu, Y.; Zhang, A. C.; et al. Review: Imaging
Technologies for Flow Cytometry. Lab Chip 2016, 16, 4639–
4647.
52. Basiji, D. A.; Ortyn, W. E.; Liang, L.; et al. Cellular Image
Analysis and Imaging by Flow Cytometry. Clin. Lab. Med.
2007, 27, 653–670.
53. Dunlop, J.; Bowlby, M.; Peri, R.; et al. High-Throughput
Electrophysiology: An Emerging Paradigm for Ion-Channel
Screening and Physiology. Nat. Rev. Drug Discov. 2008, 7,
358–368.
54. Seo, J.; Ionescu-Zanetti, C.; Diamond, J.; et al. Integrated
Multiple Patch-Clamp Array Chip via Lateral Cell Trapping
Junctions. Appl. Phys. Lett. 2004, 84, 1973.
55. Brüggemann, A.; Stoelzle, S.; George, M.; et al. Microchip
Technology for Automated and Parallel Patch-Clamp
Recording. Small 2006, 2, 840–846.
56. Mathes, C. QPatch: The Past, Present and Future of AutomatedPatch Clamp. Expert Opin. Ther. Targets 2006, 10, 319–327.
57. Joensson, H. N.; Andersson Svahn, H. Droplet Microfluidics—ATool for Single-Cell Analysis. Angew. Chem. Int. Ed. 2012, 51,
12176–12192.
58. Single-Cell Microbiology. Nat. Biotechnol. 2016, 34, 1077–
1077.
59. Gough, A.; Stern, A. M.; Maier, J.; et al. Biologically Relevant
Heterogeneity: Metrics and Practical Insights. SLAS Discov.
2017, 22, 213–237.
60. Brehm-Stecher, B. F.; Johnson, E. A. Single-Cell
Microbiology: Tools, Technologies, and Applications.
Microbiol. Mol. Biol. Rev. 2004, 68, 538–559.
61. Lasken, R. S. Genomic Sequencing of Uncultured
Microorganisms from Single Cells. Nat. Rev. Microbiol.
2012, 10, 631–640.
62. Pelkmans, L. Using Cell-to-Cell Variability—A New Era in
Molecular Biology. Science 2012, 336, 425–426.
63. Kalisky, T.; Quake, S. R. Single-Cell Genomics. Nat. Methods
2011, 8, 311–314.
64. Sanchez-Romero, M. A.; Casadesus, J. Contribution of
Phenotypic Heterogeneity to Adaptive Antibiotic Resistance.
Proc. Natl. Acad. Sci. 2014, 111, 355–360.
65. Snijder, B.; Sacher, R.; Rämö, P.; et al. Population Context
Determines Cell-to-Cell Variability in Endocytosis and Virus
Infection. Nature 2009, 461, 520–523.
66. Herderschee, J.; Fenwick, C.; Pantaleo, G.; et al. Emerging
Single-Cell Technologies in Immunology. J. Leukoc. Biol.
2015, 98, 23–32.
67. Meacham, C. E.; Morrison, S. J. Tumour Heterogeneity andCancer Cell Plasticity. Nature 2013, 501, 328–337.
68. Lawrence, M. S.; Stojanov, P.; Polak, P.; et al. Mutational
Heterogeneity in Cancer and the Search for New CancerAssociated Genes. Nature 2013, 499, 214–218.
69. Hümmer, D.; Kurth, F.; Naredi-Rainer, N.; et al. Single Cells
in Confined Volumes: Microchambers and Microdroplets.
Lab Chip 2016, 16, 447–458.

SLAS Technology ﻿
70. Lagus, T. P.; Edd, J. F. A Review of the Theory, Methods andRecent Applications of High-Throughput Single-Cell Droplet
Microfluidics. J. Phys. Appl. Phys. 2013, 46, 114005.
71. Zagnoni, M.; Cooper, J. M. Droplet Microfluidics for Highthroughput Analysis of Cells and Particles. Darzynkiewicz, Z.;
Holden, E.; Orfao, A.; Telford; W.; Wlodkowic, D., eds. In
Methods in Cell Biology; Elsevier, 2011; Vol. 102, pp 23–48.
72. Köster, S.; Angile, F. E.; Duan, H.; et al. Drop-Based
Microfluidic Devices for Encapsulation of Single Cells. Lab
Chip 2008, 8, 1110–1115.
73. Unger, M. A.; Chou, H.-P.; Thorsen, T.; et al. Monolithic
Microfabricated Valves and Pumps by Multilayer Soft
Lithography. Science 2000, 288, 113–116.
74. Anna, S. L.; Bontoux, N.; Stone, H. A. Formation of
Dispersions Using “Flow Focusing” in Microchannels. Appl.
Phys. Lett. 2003, 82, 364.
75. Tawfik, D. S.; Griffiths, A. D. Man-Made Cell-Like
Compartments for Molecular Evolution. Nat. Biotechnol.
1998, 16, 652–656.
76. Miller, O. J.; Bernath, K.; Agresti, J. J.; et al. Directed Evolution
by In Vitro Compartmentalization. Nat. Methods 2006, 3, 561.
77. Hai, M.; Bernath, K.; Tawfik, D.; et al. Flow Cytometry: A
New Method to Investigate the Properties of Water-in-Oil-inWater Emulsions. Langmuir 2004, 20, 2081–2085.
78. Ostafe, R.; Prodanovic, R.; Lloyd Ung, W.; et al. A HighThroughput Cellulase Screening System Based on Droplet
Microfluidics. Biomicrofluidics 2014, 8, 041102.
79. Zeng, Y.; Novak, R.; Shuga, J.; et al. High-Performance
Single Cell Genetic Analysis Using Microfluidic Emulsion
Generator Arrays. Anal. Chem. 2010, 82, 3183–3190.
80. Joensson, H. N.; Samuels, M. L.; Brouzes, E. R.; et al.
Detection and Analysis of Low-Abundance Cell-Surface
Biomarkers Using Enzymatic Amplification in Microfluidic
Droplets. Angew. Chem. Int. Ed. 2009, 48, 2518–2521.
81. Shapiro, E.; Biezuner, T.; Linnarsson, S. Single-Cell
Sequencing-Based Technologies Will Revolutionize WholeOrganism Science. Nat. Rev. Genet. 2013, 14, 618–630.
82. Eastburn, D. J.; Sciambi, A.; Abate, A. R. UltrahighThroughput Mammalian Single-Cell Reverse-Transcriptase
Polymerase Chain Reaction in Microfluidic Drops. Anal.
Chem. 2013, 85, 8016–8021.
83. Mazutis, L.; Gilbert, J.; Ung, W. L.; et al. Single-Cell Analysis
and Sorting Using Droplet-Based Microfluidics. Nat. Protoc.
2013, 8, 870–891.
84. Edd, J. F.; Di Carlo, D.; Humphry, K. J.; et al. Controlled
Encapsulation of Single-Cells into Monodisperse Picolitre
Drops. Lab Chip 2008, 8, 1262.
85. Bhagat, A. A. S.; Kuntaegowdanahalli, S. S.; Papautsky, I.
Continuous Particle Separation in Spiral Microchannels Using
Dean Flows and Differential Migration. Lab Chip 2008, 8, 1906–
1914.
86. Kemna, E. W. M.; Schoeman, R. M.; Wolbers, F.; et al.
High-Yield Cell Ordering and Deterministic Cell-in-Droplet
Encapsulation Using Dean Flow in a Curved Microchannel.
Lab Chip 2012, 12, 2881–2887.
87. Chabert, M.; Viovy, J.-L. Microfluidic High-Throughput
Encapsulation and Hydrodynamic Self-Sorting of Single
Cells. Proc. Natl. Acad. Sci. 2008, 105, 3191–3196.

Basu 	88. Abate, A. R.; Chen, C.-H.; Agresti, J. J.; et al. Beating
Poisson Encapsulation Statistics Using Close-Packed
Ordering. Lab Chip 2009, 9, 2628–2631.
	 89. Moon, S.; Ceyhan, E.; Gurkan, U. A.; et al. Statistical
Modeling of Single Target Cell Encapsulation. PLoS ONE
2011, 6, e21580.
	  90. Li, D.; Chen, S.; Bellomo, E. A.; et al. Imaging Dynamic
Insulin Release Using a Fluorescent Zinc Indicator for
Monitoring Induced Exocytotic Release (ZIMIR). Proc.
Natl. Acad. Sci. 2011, 108, 21063–21068.
	  91. Tran, T. M.; Lan, F.; Thompson, C. S.; et al. From Tubes
to Drops: Droplet-Based Microfluidics for UltrahighThroughput Biology. J. Phys. Appl. Phys. 2013, 46, 114004.
	 92. Brouzes, E.; Medkova, M.; Savenelli, N.; et al. Droplet
Microfluidic Technology for Single-Cell High-Throughput
Screening. Proc. Natl. Acad. Sci. 2009, 106, 14195–14200.
	 93. Zhu, Y.; Fang, Q. Analytical Detection Techniques for
Droplet Microfluidics—A Review. Anal. Chim. Acta 2013,
787, 24–35.
	 94. Joensson, H. N.; Uhlén, M.; Svahn, H. A. Droplet Size
Based Separation by Deterministic Lateral Displacement—
Separating Droplets by Cell-Induced Shrinking. Lab Chip
2011, 11, 1305.
	  95. Boitard, L.; Cottinet, D.; Kleinschmitt, C.; et al. Monitoring
Single-Cell Bioenergetics via the Coarsening of Emulsion
Droplets. Proc. Natl. Acad. Sci. 2012, 109, 7181–7186.
	 96. Hofmann, T. W.; Hänselmann, S.; Janiesch, J.-W.; et al.
Applying Microdroplets as Sensors for Label-Free Detectionof Chemical Reactions. Lab Chip 2012, 12, 916.
	  97. Basu, A. S. Droplet Morphometry and Velocimetry (DMV):
A Video Processing Software for Time-Resolved, LabelFree Tracking of Droplet Parameters. Lab Chip 2013, 13,1892.
	 98. Cai, L.; Friedman, N.; Xie, X. S. Stochastic Protein
Expression in Individual Cells at the Single Molecule Level.
Nature 2006, 440, 358–362.
	  99. Baret, J.-C.; Beck, Y.; Billas-Massobrio, I.; et al. Quantitative
Cell-Based Reporter Gene Assays Using Droplet-Based
Microfluidics. Chem. Biol. 2010, 17, 528–536.
100. Ahn, K.; Kerbage, C.; Hunt, T. P.; et al. Dielectrophoretic
Manipulation of Drops for High-Speed Microfluidic Sorting
Devices. Appl. Phys. Lett. 2006, 88, 024104.
101. Baret, J. C.; Miller, O. J.; Taly, V.; et al. FluorescenceActivated Droplet Sorting (FADS): Efficient Microfluidic
Cell Sorting Based on Enzymatic Activity. Lab Chip 2009,
9, 1850–1858.
102. Agresti, J. J.; Antipov, E.; Abate, A. R.; et al. UltrahighThroughput Screening in Drop-Based Microfluidics for
Directed Evolution. Proc. Natl. Acad. Sci. 2010, 107, 4004–
4009.
103. Wang, B. L.; Ghaderi, A.; Zhou, H.; et al. Microfluidic HighThroughput Culturing of Single Cells for Selection Basedon Extracellular Metabolite Production or Consumption.
Nat. Biotechnol. 2014, 32, 473–478.

19104. Dressman, D.; Yan, H.; Traverso, G.; et al. Transforming
Single DNA Molecules into Fluorescent Magnetic Particles
for Detection and Enumeration of Genetic Variations. Proc.
Natl. Acad. Sci. 2003, 100, 8817–8822.
105. Diehl, F.; Li, M.; He, Y.; et al. BEAMing: Single-Molecule
PCR on Microparticles in Water-in-Oil Emulsions. Nat.
Methods 2006, 3, 551–559.
106. Leung, K.; Zahn, H.; Leaver, T.; et al. A Programmable
Droplet-Based
Microfluidic
DeviceAppliedtoMultiparameter Analysis of Single Microbes and Microbial
Communities. Proc. Natl. Acad. Sci. 2012, 109, 7665–7670.
107. White, A. K.; VanInsberghe, M.; Petriv, O. I.; et al. HighThroughput Microfluidic Single-Cell RT-qPCR. Proc. Natl.
Acad. Sci. 2011, 108, 13999–14004.
108. Bengtsson, M.; Hemberg, M.; Rorsman, P.; et al.
Quantification of mRNA in Single Cells and Modelling of
RT-qPCR Induced Noise. BMC Mol. Biol. 2008, 9, 63.
109. Beer, N. R.; Wheeler, E. K.; Lee-Houghton, L.; et al.
On-Chip Single-Copy Real-Time Reverse-Transcription
PCR in Isolated Picoliter Droplets. Anal. Chem. 2008, 80,
1854–1858.
110. Zhang, H.; Jenkins, G.; Zou, Y.; et al. Massively Parallel
Single-Molecule and Single-Cell Emulsion Reverse
Transcription Polymerase Chain Reaction Using Agarose
Droplet Microfluidics. Anal. Chem. 2012, 84, 3599–3606.
111. Macosko, E. Z.; Basu, A.; Satija, R.; et al. Highly Parallel
Genome-Wide Expression Profiling of Individual Cells
Using Nanoliter Droplets. Cell 2015, 161, 1202–1214.
112. Klein, A. M.; Mazutis, L.; Akartuna, I.; et al. Droplet
Barcoding for Single-Cell Transcriptomics Applied to
Embryonic Stem Cells. Cell 2015, 161, 1187–1201.
113. Zheng, G. X. Y.; Terry, J. M.; Belgrader, P.; et al. Massively
Parallel Digital Transcriptional Profiling of Single Cells.
Nat. Commun. 2017, 8, 14049.
114. Mostovoy, Y.; Levy-Sakin, M.; Lam, J.; et al. A Hybrid
Approach for De Novo Human Genome Sequence Assemblyand Phasing. Nat. Methods 2016, 13, 587–590.
115. Kitzman, J. O. Haplotypes Drop by Drop. Nat. Biotechnol.
2016, 34, 296–298.
116. Seo, J.-S.; Rhie, A.; Kim, J.; et al. De Novo Assembly andPhasing of a Korean Human Genome. Nature 2016, 538,
243–247.
117. Kang, D.-K.; Ali, M. M.; Zhang, K.; et al. Rapid Detectionof Single Bacteria in Unprocessed Blood Using Integrated
Comprehensive Droplet Digital Detection. Nat. Commun.
2014, 5, 5427.
118. Altamore, I.; Lanzano, L.; Gratton, E. Dual Channel
Detection of Ultra Low Concentration of Bacteria in RealTime by Scanning Fluorescence Correlation Spectroscopy.
Meas. Sci. Technol. 2013, 24, 065702.
119. Zhang, K.; Kang, D.-K.; Ali, M. M.; et al. Digital
Quantification of miRNA Directly in Plasma Using
Integrated Comprehensive Droplet Digital Detection. Lab
Chip 2015, 15, 4217–4226.