Seq-Well: portable, low-cost RNA
sequencing of single cells at high throughput
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CitationGierahn, Todd M et al. “Seq-Well: Portable, Low-Cost RNA
Sequencing of Single Cells at High Throughput.” Nature Methods 14,
4 (February 2017): 395–398 © 2017 KSBMB
As Publishedhttp://dx.doi.org/10.1038/NMETH.4179
PublisherSpringer Nature
VersionAuthor's final manuscript
Citable linkhttp://hdl.handle.net/1721.1/113430
Terms of UseCreative Commons Attribution-Noncommercial-Share Alike
Detailed Termshttp://creativecommons.org/licenses/by-nc-sa/4.0/
Seq-Well: A Portable, Low-Cost Platform for High-Throughput
Single-Cell RNA-Seq of Low-Input Samples
Todd M. Gierahn
1,#
, Marc H. Wadsworth II
2,3,4,#
, Travis K. Hughes
2,3,4,#
, Bryan D. Bryson
4,5
,
Andrew Butler
6,7
, Rahul Satija
6,7
, Sarah Fortune
4,5
, J. Christopher Love
1,3,4,*
, and Alex K.
Shalek
2,3,4,*
1
Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts, USA
2
Institute for Medical Engineering & Science (IMES) and Department of Chemistry, MIT,
Cambridge, Massachusetts, USA
3
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
4
Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
5
Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston,
Massachusetts, USA
6
Center for Genomics and Systems Biology, Department of Biology, New York University, New
York City, New York, USA
7
New York Genome Center, New York City, New York, USA
Abstract
Single-cell RNA-Seq can precisely resolve cellular states but application to sparse samples is
challenging. Here, we present Seq-Well, a portable, low-cost platform for massively-parallel
single-cell RNA-Seq. Barcoded mRNA capture beads and single cells are sealed in an array of
subnanoliter wells using a semi-permeable membrane, enabling efficient cell lysis and transcript
capture. We characterize Seq-Well using species-mixing experiments and PBMCs, and profile
thousands of primary human macrophages exposed to tuberculosis.
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*
To whom correspondence should be addressed: shalek@mit.edu (AKS), clove@mit.edu (JCL).
#
These authors contributed equally to this work
*
These senior authors contributed equally to this work
DATA AVAILABILITY.
All RNA-Seq data are available in GEO under Accession Number GSE92495.
AUTHOR CONTRIBUTIONS.
TMG, MHW, TKH, JCL, and AKS developed the concepts and designed the study. TG, MHW, TKH, and BDB performed the
experiments. All authors analyzed and interpreted the data. TMG, MHW, TKH, JCL, and AKS wrote the manuscript with feedback
from all authors.
COMPETING FINANCIAL INTERESTS.
T.M. Gierahn, M.H. Wadsworth II, T.K. Hughes, J.C. Love, A.K. Shalek, and Institutions The Broad Institute and the Massachusetts
Institute of Technology have filed a patent application that relates to Seq-Well, compositions of matter, the outlined experimental and
computational methods, and uses thereof.
HHS Public Access
Author manuscript
Nat Methods
. Author manuscript; available in PMC 2017 August 13.
Published in final edited form as:
Nat Methods
. 2017 April ; 14(4): 395–398. doi:10.1038/nmeth.4179.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
MAIN
The emergence of single-cell genomics has empowered new strategies for identifying the
cellular and subcellular drivers of biological phenomena
1–19
. Patterns in genome-wide
mRNA expression measured by single-cell RNA-Seq (scRNA-Seq) can be leveraged to
uncover distinct cell types, states and circuits within cell populations and tissues
1–5,9–13
. The
unprecedented view of cellular phenotypes scRNA-Seq affords could help transform our
understanding of healthy and diseased behaviors, and guide the rational selection of
precision diagnostics and therapies, if it could be broadly and easily applied to low-input
(≤10
4
cells) clinical specimens.
Typically, scRNA-Seq has involved isolating and lysing individual cells, then independently
reverse transcribing and amplifying their mRNA before generating barcoded libraries that
are pooled for sequencing. Although manual picking
2,5,8
, FACS-sorting
1,3,4
or integrated
microfluidic circuits
7,9,10
can isolate single cells, one-cell-one-sample approaches are
constrained fundamentally in scale by costs, time, and labor. Recently, massively-parallel
methods have emerged that assign unique barcodes to each cell’s mRNAs during reverse
transcription, enabling ensemble processing while retaining single-cell resolution. These
methods typically yield single-cell libraries of lower complexity, but higher throughput
reduces the impact of the technical and intrinsic noise associated with each cell in
analyses
11,12
. The most common variant is microfluidic devices that generate reverse-
emulsion droplets to serially couple single cells with uniquely-barcoded mRNA capture
beads
11,12
. Droplet-based techniques, however, can have inefficiencies in encapsulation,
introduce technical noise through differences in cell lysis time, and require specialized
equipment, limiting where, when, and with what scale scRNA-Seq can be performed.
One alternative is to use arrays of subnanoliter wells loaded by gravity. Operational
simplicity reduces the need for peripheral equipment, decreases dead volumes, and
facilitates parallelization. As proof-of-principle, cells and beads have been co-confined in
unsealed nanowell arrays to perform targeted single-cell transcriptional profiling
13
, yet the
use of an open-array format significantly limits capture efficiency and increases cross-
contamination (Supplementary Fig. 1). To avoid these issues, nanowells have also been
combined with microfluidic channels that facilitate oil-based single-cell isolation via fluid
exchange
14
. Nevertheless, this design limits buffer exchange and necessitated integrated
temperature and pressure controllers, impacting ease-of-use and portability
15
. Semi-porous-
membrane-covered nanowells have been used to link pairs of specific transcripts from single
cells
16
; however, transcript capture and sealing efficiency were not addressed, and unique
single-cell libraries were not achieved using many beads per well.
To overcome these assorted challenges, we have developed Seq-Well, a portable, simple
platform for massively-parallel scRNA-Seq (Supplementary Fig. 2). Similar to previous
nanowell-based implementations, Seq-Well confines single cells and barcoded poly(dT)
mRNA capture beads in a PDMS array of ~86,000 subnanoliter wells. Designing well
dimensions to accommodate only one bead enables single-bead loading efficiencies of ~95%
(Figure 1a, Supplementary Fig. 3a; Supplementary Video 1). A simplified cell-loading
scheme, in turn, enables capture efficiencies around 80% (Methods; Supplementary Fig.
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3b), with a rate of dual occupancy that can be tuned by adjusting the number of cells loaded
and visualized prior to processing (Supplementary Fig. 3c).
Importantly, Seq-Well uniquely leverages selective chemical functionalization to facilitate
reversible attachment of a semi-permeable polycarbonate membrane (10 nm pore size) in
physiologic buffers. This trait enables rapid solution exchange for efficient cell lysis but
traps biological macromolecules, increasing transcript capture during hybridization and
reducing cross-contamination (Supplementary Fig. 4a; Supplementary Protocol;
Supplementary Video 2). The array’s unique three-layer surface functionalization comprises
an amino-silane base
20
crosslinked to bifunctional poly(glutamate)/chitosan top via a
p-
Phenylene diisothiocyante intermediate (Methods; Supplementary Fig. 4); this bifunctional
top, with poly(glutamate) coating the inner surfaces of the nanowells (where cells are lysed)
and chitosan the array’s top surface (where the membrane binds), prevents non-specific
binding of RNA to the array and efficient sealing, respectively (Methods; Supplementary
Protocol; Supplementary Fig. 4b,c). To test sealing and buffer exchange, we monitored the
fluorescence of dye-labeled, cell-bound antibodies before and after adding a guanidinium-
based lysis buffer. We observed rapid diffusion of the antibodies throughout the wells within
five minutes of buffer addition and, unlike unsealed or previously-described, membrane-
covered BSA-blocked arrays
16
, little change in fluorescent signal over 30 minutes,
suggesting robust retention of biological macromolecules despite use of a strong chaotrope
(Methods; Supplementary Fig. 5).
After lysis, cellular mRNAs are captured by bead-bound poly(dT) oligonucleotides that also
contain a universal primer sequence, a cell barcode, and a unique molecular identifier (UMI)
(Methods; Supplementary Table 1). Next, the membrane is peeled off and the beads are
removed for subsequent bulk reverse transcription, amplification, library preparation and
paired-end sequencing, as previously described
12
(Methods). Critically, beyond a disposable
array and membrane, Seq-Well only requires a pipette, a manual clamp, an oven, and a tube
rotator to achieve stable, barcoded single-cell cDNAs (Fig. 1a), enabling it to be performed
almost anywhere.
To assess transcript capture efficiency and single-cell resolution, we profiled a mixture of
5×10
3
human (HEK293) and 5×10
3
mouse (3T3) cells using Seq-Well. The average fraction
of reads mapping to exonic regions was 77.5% (Supplementary Fig. 6), demonstrating high
quality libraries. Shallow sequencing from a fraction of an array revealed highly organism-
specific libraries, suggesting single-cell resolution and minimal cross-contamination (Fig.
1b; Supplementary Fig. 7a–c). In the absence of membrane sealing, by comparison, we
obtained poor transcript and gene detection, and substantial cross-contamination
(Supplementary Fig. 1). From deeper sequencing of a fraction of a second array, we detected
an average of 37,878 mRNA transcripts from 6,927 genes in HEK cells and 33,586 mRNA
transcripts from 6,113 genes in 3T3 cells, comparable to a droplet-based approach using the
same mRNA capture beads (Drop-Seq)
12
(Fig. 1c,d & Supplementary Fig. 7&8). Upon
matched-read downsampling, we also observed levels of transcript and gene detection
consistent with other massively-parallel bead-based scRNA-Seq methods (Methods;
Supplementary Fig. 7d–g). Moreover, we found strong correlations between bulk RNA-seq
data and populations constructed
in silico
from individual HEK cells (R=0.751±0.073–
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0.983±0.0001 for populations of 1–1,000 single cells, respectively), suggesting
representative cell and transcript sampling (Methods; Supplementary Fig. 9).
Next, to examine the ability of Seq-Well to resolve populations of cells in complex primary
samples, we loaded human peripheral blood mononuclear cells (PBMCs) into arrays in
triplicate prior to beads, allowing us to perform on-array multi-color imaging cytometry
(Methods; Fig. 2a,b, Supplementary Tables 2&3). Sequencing one-third of the beads
recovered from each array yielded 3,694 high-quality single-cell libraries (Methods).
Unsupervised graph-based clustering revealed unique subpopulations corresponding to
major PBMC cell types (Methods; Fig. 2b, Supplementary Fig. 10–12; Supplementary
Table 4). Each array yielded similar subpopulation frequencies (Fig. 2c), with detection
efficiencies comparable to other massively-parallel technologies (Supplementary Fig. 13).
The proportion of each subpopulation determined by sequencing also matched on-array
immunophenotyping results (Fig. 2a,b). Critically, sequencing provides additional
information: in addition to resolving dendritic cells from monocytes (Fig. 2b), we found
significant variation among the monocytes (captured in PC3) due to differential expression
of inflammatory and anti-viral gene programs (Fig. 2d)
1,3
. Overall, characterizing a sample
in two ways using a single platform increases the amount of the information that can be
extracted from a precious specimen, while also allowing analysis of one measurement in
light of the other.
Finally, to test the portability of Seq-Well, we profiled primary human macrophages exposed
to
M. tuberculosis
(H37Rv) in a BSL3 facility (Methods). In total, we recovered 14,218 (of
40,000 possible) macrophages with greater than 1,000 mapped transcripts from a TB-
exposed and an unexposed array. Unsupervised analysis of 4,638 cells with greater than
5,000 transcripts per cell revealed five distinct clusters (Fig. 3a,b & Supplementary Fig.
14a,b; Supplementary Table 5). Two had lower transcript capture and high mitochondrial
gene expression (suggestive of low quality libraries)
17
, and were removed; the remaining
three (2,560 cells) were identified in both the exposed and unexposed samples (Fig. 3a,
Supplementary Fig. 14c,d&15), and likely represent distinct sub-phenotypes present in the
initial culture.
We next examined common and cluster-specific gene enrichments (Methods). Although
Clusters 1 and 3 did not present strong stimulation-independent enrichments, Cluster 2
uniquely expressed several genes associated with metabolism (Supplementary Tables 6&7).
Intriguingly, within each cluster, we observed pronounced shifts in gene expression in
response to
M. tuberculosis
(Methods; Fig. 3c & Supplementary Table 8), with common
enrichments for gene sets previously observed in response to intracellular infection, LPS
stimulation, and activation of TLR7/8 (Supplementary Tables 9&10). Cluster 1 uniquely
displayed stimulation-induced shifts in several genes associated with cell growth, Cluster 3
in transcripts associated with hypoxia, and Cluster 2, again, in genes linked to metabolism.
Overall, these data suggest that basal cellular heterogeneity may influence ensemble
tuberculosis responses. Equally importantly, they demonstrate the ability of Seq-Well to
acquire large numbers of single-cell transcriptomes in challenging experimental
environments.
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