An innovative, microwell-based platform for single-cell RNA sequencing (RNA-seq) combines cost efficiency, scalability and parallelizability, and will enable many new avenues of biological inquiry. See related Research article: http://dx.doi.org/10.1186/s13059-015-0684-

Hughes, Travis K.

Shalek, Alex K.

Wadsworth, Marc H.

Genome Biology

PubMed

Marc H. Wadsworth

Travis K. Hughes

Alex K. Shalek

Springer - Publisher Connector

Marrying microfluidics and microwells for parallel, high-throughput single-cell genomics

Harvard University - DASH 

Crossref

An innovative, microwell-based platform for single-cell RNA sequencing (RNA-seq) combines cost efficiency, scalability and parallelizability, and will enable many new avenues of biological inquiry

Wadsworth, Marc Havens

Shalek, Alex

DSpace@MIT

Wadsworth et al. Genome Biology  (2015) 16:129 DOI 10.1186/s13059-015-0695-0RESEARCH HIGHLIGHT Open AccessMarrying microfluidics and microwells forparallel, high-throughput single-cell genomicsMarc H. Wadsworth II1,2,3,4†, Travis K. Hughes2,3,4,5† and Alex K. Shalek1,2,3,4,5,6*See related Research article: http://dx.doi.org/10.1186/s13059-015-0684-3AbstractAn innovative, microwell-based platform for single-cellRNA sequencing (RNA-seq) combines cost efficiency,scalability and parallelizability, and will enable manynew avenues of biological inquiry.microdevices — including microfluidics [3–5], reverse-emulsion droplets [6, 7] and microwells [8, 9] — haveOver the past several years, it has become increasinglyclear that there can be substantial variability in the be-haviors of cells once deemed to be identical. Thisrealization has brought about a renewed interest in de-fining cellular phenotypes and their variation, and inexamining how these change under different biologicalcontexts. To achieve this, approaches are needed thatcan deeply profile individual cells across diverse experi-mental conditions. Now, by marrying recent advances inmolecular biology with microfluidics and microwells,Bose and colleagues present a new platform for achiev-ing this goal [1], opening up exciting new opportunitiesto explore cells and their heterogeneity.Studying cellular heterogeneity with single-cellRNA-seqSingle-cell RNA-seq represents a powerful new approachfor investigating the causes and consequences of cellularheterogeneity. As has been demonstrated, co-variation ingene expression across single cells can be used to identify,genome-wide, distinct cell states and cellular circuits, aswell as their molecular markers and drivers (‘extrinsic fac-tors’) [2–8]. However, identifying such structure can bedifficult as the utility of any one single-cell genomic profileis limited by technical artifacts in sample preparation* Correspondence: shalek@mit.edu†Equal contributors1Department of Chemistry, Massachusetts Institute of Technology,Cambridge, MA, USA2Institute for Medical Engineering & Science, Massachusetts Institute ofTechnology, Cambridge, MA 02139, USAFull list of author information is available at the end of the article© 2015 Wadsworth et al. This is an Open AcceLicense (http://creativecommons.org/licenses/medium, provided the original work is propercreativecommons.org/publicdomain/zero/1.0/(‘technical noise’) and the stochasticity associated withbiological processes (‘intrinsic noise’) [2].To overcome these limitations, researchers have fo-cused on increasing their power through examining setsof genes [3–8] or developing approaches for profilinglarger numbers of cells [3–8]. In accomplishing the latter,come to play an increasingly important role. These micro-scale platforms can be tailored to enable easy capture andprocessing of single cells, reducing labor and costs, whileimproving efficiency and consistency over conventionalsingle-cell genomic approaches [3–9]. For example, Flui-digm recently released the C1 Auto Prep System whichcouples with an integrated microfluidic chip to enable au-tomated capture and processing of up to 96 single cells inparallel; this facilitated an order-of-magnitude improve-ment in throughput, with a similar reduction in cost. Le-veraging the scale afforded by this platform, researchershave been able to uncover rare immune cell states [3], sur-vey neuronal diversity [4] and assess cellular hierarchywithin lung epithelia [5]. Nevertheless, further scaling ofthis and related systems, necessary for studying increas-ingly complex cellular ensembles, has been hindered bytheir reliance on separate, bulky microfluidic channelsfor delivering processing reagents to each single cell inparallel.Achieving scale through early cellular barcodingRecently, this experimental barrier has been shattered bya new breed of microdevices that re-envision single-cellprocessing. Collectively, these approaches rely on thefundamental realization that once the genetic material ofeach single cell being assayed is uniquely barcoded, thecells can be processed in parallel during subsequentsteps [6–8]. Significantly, by tagging cellular mRNAswith uniquely barcoded bead-associated primers inreverse-emulsion droplets, studies from Macosko et al.[7] and Klein et al. [6] both increased throughput by upss article distributed under the terms of the Creative Commons Attributionby/4.0), which permits unrestricted use, distribution, and reproduction in anyly credited. The Creative Commons Public Domain Dedication waiver (http://) applies to the data made available in this article, unless otherwise stated.Wadsworth et al. Genome Biology  (2015) 16:129 Page 2 of 3to 100-fold, without a concomitant increase in price.In one of these approaches, Macosko and colleaguesused bead-based RNA capture and tagging (‘Drop-Seq’)to analyze the transcriptomes of 44,808 mouse retinalcells, revealing 39 transcriptionally distinct cell popu-lations and their markers [7]; in the other (‘inDrop’),Klein and coworkers used bead-based delivery of bar-coded oligo-dT primers to probe population structureand cellular heterogeneity in mouse embryonic stemcell differentiation after withdrawal of leukemia inhibi-tory factor (LIF) [6].Importantly, although these exciting systems are cap-able of robust, high-throughput single-cell processing,they still require extensive support structures (e.g. syr-inge pumps and controllers) that might limit widespreadadoption. An alternative, similar in principle but not inpractice, is microwells: here, cells are physically confinedin small chambers after settling by gravity [8, 9] ratherthan through the use of controlled aqueous and oil-phaseflows [6, 7]. As a demonstration of the power of such anapproach, Fan and colleagues recently unveiled a commer-cial system that combines microwells with uniquely bar-coded beads to rapidly prepare targeted mRNA profilesfrom approximately 5000 human peripheral blood mono-nuclear cells (PBMCs) [8]. Nevertheless, their approach,like previous efforts, is serial, and thus best equipped toexamine heterogeneity within a single sample, hinderingcross-comparisons.A parallelizable platform for single-cell genomicsIn this issue of Genome Biology, Bose and colleagues[1] describe an important technical advance that mar-ries the simplicity of microwells and the early tech-nique of barcoding of droplets with the parallelizabilityof microfluidics to enable many single cells from mul-tiple different samples or perturbations to be profiledin parallel. The authors’ system consists of five parallelmicrofluidic channels, each of which contains over2000 microwells for cell capture and processing. Tooperate the system, the authors load single cells intomicrowells by simply flushing in a cell suspension.They then profile single-cell gene expression usingeither a RNA-printing-based approach or a bead-basedcapture modality.The RNA-printing approach is conceptually akin tomicroengraving [9]: lysis buffer is added and the micro-wells are quickly pressed against a slide that containscovalently grafted oligo-dT primers. Mature cellularmRNA hybridizes to these oligos and then, after washing,is reverse-transcribed on-chip by flowing appropriatereagents through the device. Expression can then be quan-tified by hybridizing gene-specific probes to these slide-delimited cDNAs. Importantly, this mode of operationmaintains spatial correspondence between cell and well,potentially enabling additional information collectedbefore lysis (e.g. cytokine secretion) [9] to be used indownstream analyses. In the bead-based capture ap-proach, uniquely barcoded oligo-dT beads are co-loaded into the microwells before performing lysis.After mRNA capture, a modified variant of CEL-Seq[10] is performed in which reverse transcription andsecond-strand synthesis take place on-chip, and othersteps are performed off-chip after bead harvest(although, in principle, other amplification strategiesare possible).To demonstrate the utility of their scalable platform,the authors use it in a bead-based capture format to pro-file approximately 600 cells. In one of their five lanes,they profile U87 human glioma cells; in another, MCF10ahuman breast cancer cells; and, in the remaining three, amixture of both. A second, similar experiment comparesU87 and the human fibroblast cell line WI-38. Throughsequencing the cells prepared during these two runs, theauthors detect an average of 635 and 530 genes, respect-ively, enabling them to clearly distinguish each type fromone another. Clearly, there is room for improvement inthe number of genes detected and in the fraction of high-quality libraries made using the chip (currently 50 to 70 %of cells); with future optimizations, this strategy couldpotentially match other high-throughput, but serial, ap-proaches [6, 7]. Most importantly, this method is inex-pensive ($0.10 to $0.20 per cell) and can easily be scaledto accommodate additional assay channels and furtherreduce costs.Concluding remarksIn summary, when coupled with additional microfluidiccontrols, Bose and colleagues’ marriage of microwellsand microfluidics has the potential to usher in a new erafor high-throughput single-cell genomics, where gene-expression profiles of thousands of single cells, from thesame or different experimental conditions, can be rapidlyprofiled and compared.AbbreviationsLIF: Leukemia inhibitory factor; PBMC: Peripheral blood mononuclear cell;RNA-seq: RNA sequencing.Competing interestsThe authors declare that they have no competing interests.Author details1Department of Chemistry, Massachusetts Institute of Technology,Cambridge, MA, USA. 2Institute for Medical Engineering & Science,Massachusetts Institute of Technology, Cambridge, MA 02139, USA. 3BroadInstitute of MIT and Harvard, Cambridge, MA 02142, USA. 4Ragon Institute ofMGH, MIT and Harvard, Cambridge, MA 02115, USA. 5Division of HealthSciences and Technology, Harvard Medical School, Boston, MA, USA.6Immunology, MGH, Boston, MA 02114, USA.Wadsworth et al. Genome Biology  (2015) 16:129 Page 3 of 3References1. Bose S, Wan Z, Carr A, Rizvi AH, Vieira G, Pe'er D, et al. Scalable microfluidicsfor single cell RNA printing and sequencing. Genome Biol. 2015;16:120.2. Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in asingle cell. Science. 2002;297:1183–6.3. Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, et al. Single-cellRNA-seq reveals dynamic paracrine control of cellular variation. Nature.2014;510:363–9. doi:10.1038/nature13437.4. Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G,Juréus A, et al. Brain structure. Cell types in the mouse cortex andhippocampus revealed by single-cell RNA-seq. Science. 2015;347:1138–42.doi:10.1126/science.aaa1934.5. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, et al.Reconstructing lineage hierarchies of the distal lung epithelium usingsingle-cell RNA-seq. Nature. 2014;509:1–16. doi:10.1038/nature13173.6. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Dropletbarcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015;161:1187–201. doi:10.1016/j.cell.2015.04.044.7. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highlyparallel genome-wide expression profiling of individual cells using nanoliterdroplets. Cell. 2015;161:1202–14. doi:10.1016/j.cell.2015.05.002.8. Fan HC, Fu GK, Fodor SPA. Combinatorial labeling of single cells for geneexpression cytometry. Science. 2015. doi:10.1126/science.1258367.9. Chattopadhyay PK, Gierahn TM, Roederer M, Love JC. Single-celltechnologies for monitoring immune systems. Nat Immunol.2014;15:128–35. doi:10.1038/ni.2796.10. Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq bymultiplexed linear amplification. Cell Rep. 2012;2:666–73. doi:10.1016/j.celrep.2012.08.003.

Wadsworth et al. Genome Biology  (2015) 16:129 
DOI 10.1186/s13059-015-0695-0RESEARCH HIGHLIGHT Open AccessMarrying microfluidics and microwells for
parallel, high-throughput single-cell genomics
Marc H. Wadsworth II1,2,3,4†, Travis K. Hughes2,3,4,5† and Alex K. Shalek1,2,3,4,5,6*
See related Research article: http://dx.doi.org/10.1186/s13059-015-0684-3Abstract
An innovative, microwell-based platform for single-cell
RNA sequencing (RNA-seq) combines cost efficiency,
scalability and parallelizability, and will enable many
new avenues of biological inquiry.microdevices — including microfluidics [3–5], reverse-
emulsion droplets [6, 7] and microwells [8, 9] — haveOver the past several years, it has become increasingly
clear that there can be substantial variability in the be-
haviors of cells once deemed to be identical. This
realization has brought about a renewed interest in de-
fining cellular phenotypes and their variation, and in
examining how these change under different biological
contexts. To achieve this, approaches are needed that
can deeply profile individual cells across diverse experi-
mental conditions. Now, by marrying recent advances in
molecular biology with microfluidics and microwells,
Bose and colleagues present a new platform for achiev-
ing this goal [1], opening up exciting new opportunities
to explore cells and their heterogeneity.
Studying cellular heterogeneity with single-cell
RNA-seq
Single-cell RNA-seq represents a powerful new approach
for investigating the causes and consequences of cellular
heterogeneity. As has been demonstrated, co-variation in
gene expression across single cells can be used to identify,
genome-wide, distinct cell states and cellular circuits, as
well as their molecular markers and drivers (‘extrinsic fac-
tors’) [2–8]. However, identifying such structure can be
difficult as the utility of any one single-cell genomic profile
is limited by technical artifacts in sample preparation* Correspondence: shalek@mit.edu
†Equal contributors
1Department of Chemistry, Massachusetts Institute of Technology,
Cambridge, MA, USA
2Institute for Medical Engineering & Science, Massachusetts Institute of
Technology, Cambridge, MA 02139, USA
Full list of author information is available at the end of the article
© 2015 Wadsworth et al. This is an Open Acce
License (http://creativecommons.org/licenses/
medium, provided the original work is proper
creativecommons.org/publicdomain/zero/1.0/(‘technical noise’) and the stochasticity associated with
biological processes (‘intrinsic noise’) [2].
To overcome these limitations, researchers have fo-
cused on increasing their power through examining sets
of genes [3–8] or developing approaches for profiling
larger numbers of cells [3–8]. In accomplishing the latter,
come to play an increasingly important role. These micro-
scale platforms can be tailored to enable easy capture and
processing of single cells, reducing labor and costs, while
improving efficiency and consistency over conventional
single-cell genomic approaches [3–9]. For example, Flui-
digm recently released the C1 Auto Prep System which
couples with an integrated microfluidic chip to enable au-
tomated capture and processing of up to 96 single cells in
parallel; this facilitated an order-of-magnitude improve-
ment in throughput, with a similar reduction in cost. Le-
veraging the scale afforded by this platform, researchers
have been able to uncover rare immune cell states [3], sur-
vey neuronal diversity [4] and assess cellular hierarchy
within lung epithelia [5]. Nevertheless, further scaling of
this and related systems, necessary for studying increas-
ingly complex cellular ensembles, has been hindered by
their reliance on separate, bulky microfluidic channels
for delivering processing reagents to each single cell in
parallel.
Achieving scale through early cellular barcoding
Recently, this experimental barrier has been shattered by
a new breed of microdevices that re-envision single-cell
processing. Collectively, these approaches rely on the
fundamental realization that once the genetic material of
each single cell being assayed is uniquely barcoded, the
cells can be processed in parallel during subsequent
steps [6–8]. Significantly, by tagging cellular mRNAs
with uniquely barcoded bead-associated primers in
reverse-emulsion droplets, studies from Macosko et al.
[7] and Klein et al. [6] both increased throughput by upss article distributed under the terms of the Creative Commons Attribution
by/4.0), which permits unrestricted use, distribution, and reproduction in any
ly credited. The Creative Commons Public Domain Dedication waiver (http://
) applies to the data made available in this article, unless otherwise stated.
Wadsworth et al. Genome Biology  (2015) 16:129 Page 2 of 3to 100-fold, without a concomitant increase in price.
In one of these approaches, Macosko and colleagues
used bead-based RNA capture and tagging (‘Drop-Seq’)
to analyze the transcriptomes of 44,808 mouse retinal
cells, revealing 39 transcriptionally distinct cell popu-
lations and their markers [7]; in the other (‘inDrop’),
Klein and coworkers used bead-based delivery of bar-
coded oligo-dT primers to probe population structure
and cellular heterogeneity in mouse embryonic stem
cell differentiation after withdrawal of leukemia inhibi-
tory factor (LIF) [6].
Importantly, although these exciting systems are cap-
able of robust, high-throughput single-cell processing,
they still require extensive support structures (e.g. syr-
inge pumps and controllers) that might limit widespread
adoption. An alternative, similar in principle but not in
practice, is microwells: here, cells are physically confined
in small chambers after settling by gravity [8, 9] rather
than through the use of controlled aqueous and oil-phase
flows [6, 7]. As a demonstration of the power of such an
approach, Fan and colleagues recently unveiled a commer-
cial system that combines microwells with uniquely bar-
coded beads to rapidly prepare targeted mRNA profiles
from approximately 5000 human peripheral blood mono-
nuclear cells (PBMCs) [8]. Nevertheless, their approach,
like previous efforts, is serial, and thus best equipped to
examine heterogeneity within a single sample, hindering
cross-comparisons.
A parallelizable platform for single-cell genomics
In this issue of Genome Biology, Bose and colleagues
[1] describe an important technical advance that mar-
ries the simplicity of microwells and the early tech-
nique of barcoding of droplets with the parallelizability
of microfluidics to enable many single cells from mul-
tiple different samples or perturbations to be profiled
in parallel. The authors’ system consists of five parallel
microfluidic channels, each of which contains over
2000 microwells for cell capture and processing. To
operate the system, the authors load single cells into
microwells by simply flushing in a cell suspension.
They then profile single-cell gene expression using
either a RNA-printing-based approach or a bead-based
capture modality.
The RNA-printing approach is conceptually akin to
microengraving [9]: lysis buffer is added and the micro-
wells are quickly pressed against a slide that contains
covalently grafted oligo-dT primers. Mature cellular
mRNA hybridizes to these oligos and then, after washing,
is reverse-transcribed on-chip by flowing appropriate
reagents through the device. Expression can then be quan-
tified by hybridizing gene-specific probes to these slide-
delimited cDNAs. Importantly, this mode of operation
maintains spatial correspondence between cell and well,potentially enabling additional information collected
before lysis (e.g. cytokine secretion) [9] to be used in
downstream analyses. In the bead-based capture ap-
proach, uniquely barcoded oligo-dT beads are co-
loaded into the microwells before performing lysis.
After mRNA capture, a modified variant of CEL-Seq
[10] is performed in which reverse transcription and
second-strand synthesis take place on-chip, and other
steps are performed off-chip after bead harvest
(although, in principle, other amplification strategies
are possible).
To demonstrate the utility of their scalable platform,
the authors use it in a bead-based capture format to pro-
file approximately 600 cells. In one of their five lanes,
they profile U87 human glioma cells; in another, MCF10a
human breast cancer cells; and, in the remaining three, a
mixture of both. A second, similar experiment compares
U87 and the human fibroblast cell line WI-38. Through
sequencing the cells prepared during these two runs, the
authors detect an average of 635 and 530 genes, respect-
ively, enabling them to clearly distinguish each type from
one another. Clearly, there is room for improvement in
the number of genes detected and in the fraction of high-
quality libraries made using the chip (currently 50 to 70 %
of cells); with future optimizations, this strategy could
potentially match other high-throughput, but serial, ap-
proaches [6, 7]. Most importantly, this method is inex-
pensive ($0.10 to $0.20 per cell) and can easily be scaled
to accommodate additional assay channels and further
reduce costs.
Concluding remarks
In summary, when coupled with additional microfluidic
controls, Bose and colleagues’ marriage of microwells
and microfluidics has the potential to usher in a new era
for high-throughput single-cell genomics, where gene-
expression profiles of thousands of single cells, from the
same or different experimental conditions, can be rapidly
profiled and compared.
Abbreviations
LIF: Leukemia inhibitory factor; PBMC: Peripheral blood mononuclear cell;
RNA-seq: RNA sequencing.
Competing interests
The authors declare that they have no competing interests.
Author details
1Department of Chemistry, Massachusetts Institute of Technology,
Cambridge, MA, USA. 2Institute for Medical Engineering & Science,
Massachusetts Institute of Technology, Cambridge, MA 02139, USA. 3Broad
Institute of MIT and Harvard, Cambridge, MA 02142, USA. 4Ragon Institute of
MGH, MIT and Harvard, Cambridge, MA 02115, USA. 5Division of Health
Sciences and Technology, Harvard Medical School, Boston, MA, USA.
6Immunology, MGH, Boston, MA 02114, USA.
Wadsworth et al. Genome Biology  (2015) 16:129 Page 3 of 3References
1. Bose S, Wan Z, Carr A, Rizvi AH, Vieira G, Pe'er D, et al. Scalable microfluidics
for single cell RNA printing and sequencing. Genome Biol. 2015;16:120.
2. Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a
single cell. Science. 2002;297:1183–6.
3. Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, et al. Single-cell
RNA-seq reveals dynamic paracrine control of cellular variation. Nature.
2014;510:363–9. doi:10.1038/nature13437.
4. Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G,
Juréus A, et al. Brain structure. Cell types in the mouse cortex and
hippocampus revealed by single-cell RNA-seq. Science. 2015;347:1138–42.
doi:10.1126/science.aaa1934.
5. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, et al.
Reconstructing lineage hierarchies of the distal lung epithelium using
single-cell RNA-seq. Nature. 2014;509:1–16. doi:10.1038/nature13173.
6. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet
barcoding for single-cell transcriptomics applied to embryonic stem cells.
Cell. 2015;161:1187–201. doi:10.1016/j.cell.2015.04.044.
7. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly
parallel genome-wide expression profiling of individual cells using nanoliter
droplets. Cell. 2015;161:1202–14. doi:10.1016/j.cell.2015.05.002.
8. Fan HC, Fu GK, Fodor SPA. Combinatorial labeling of single cells for gene
expression cytometry. Science. 2015. doi:10.1126/science.1258367.
9. Chattopadhyay PK, Gierahn TM, Roederer M, Love JC. Single-cell
technologies for monitoring immune systems. Nat Immunol.
2014;15:128–35. doi:10.1038/ni.2796.
10. Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by
multiplexed linear amplification. Cell Rep. 2012;2:666–73. doi:10.1016/
j.celrep.2012.08.003.


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Marrying microfluidics and microwells for parallel, high-throughput single-cell genomics

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