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Virtual microfluidics for digital quantification and single-cell sequencing

Abstract

We have developed hydrogel-based virtual microfluidics as a simple and robust alternative to complex engineered microfluidic systems for the compartmentalization of nucleic acid amplification reactions. We applied in-gel digital multiple displacement amplification (dMDA) to purified DNA templates, cultured bacterial cells and human microbiome samples in the virtual microfluidics system, and demonstrated whole-genome sequencing of single-cell MDA products with excellent coverage uniformity and markedly reduced chimerism compared with products of liquid MDA reactions.

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Figure 1: Virtual microfluidics for single-molecule and single-cell analysis.
Figure 2: Single-cell whole-genome sequencing of E. coli and S. aureus cells using hydrogel genome amplification.
Figure 3: Fiji microbiome project (FijiCOMP) single-cell whole-genome sequencing.

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Acknowledgements

The authors thank S. Kim, D. Feldman and A. Kulesa for advice on bioinformatics, microscopy and image analysis; N. Ranu and the Hung lab (Broad Institute) for bacterial samples; L. Griffith, G. Lagoudas, J. Borrajo and L. Morinishi for helpful discussions; and members of the Griffith lab (MIT), especially C. Chopko, J. Valdez and H. Lee, for hydrogel expertise. This work was supported in part by a Lawrence Summers Fellowship from the Broad Institute (L.X.), a Career Award at the Scientific Interface from the Burroughs Welcome Fund (P.C.B.) and grants from the Center for Microbiome Informatics and Therapeutics at MIT; a National Human Genome Research Institute, grant number U54HG003067, to the Broad Institute; the Center for Environmental Health Sciences at MIT and the Fijian Ministry of Health. The Broad Institute and MIT may seek to commercialize aspects of this work, and related applications for intellectual property have been filed.

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Contributions

P.C.B. and L.X. conceived the concept for this study. L.X. designed and implemented experiments and conducted data analysis. I.L.B. and E.J.A. provided Fiji microbiome samples. I.L.B. conducted analysis of gut microbe data. L.X., P.C.B. and I.L.B. wrote and all authors approved the manuscript.

Corresponding author

Correspondence to Paul C Blainey.

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The Broad Institute and MIT may seek to commercialize aspects of this work and related applications for intellectual property have been filed.

Integrated supplementary information

Supplementary Figure 1 Real time dMDA for quantification of cluster growth and count with time

The cluster radius curve shows mean radius ± SEM. The zero time point cluster count and radius data points reflect the properties of fluorescent contaminants.

Supplementary Figure 2 E. coli MDA cluster quantification

E. coli MDA cluster’s DNA content was calibrated against (unamplified) hydrogel-embedded mammalian cells (HEK 293) by comparing SYTOX Orange fluorescence intensities. DNA content in individual product clusters and mammalian cells was approximated by integrating pixel intensities under the same hydrogel and staining conditions. The observed integrated fluorescence was comparable across MDA product clusters and unamplified mammalian cells, leading us to conclude that the average DNA content per MDA product cluster was approximately equal to the average DNA content of an unsynchronized mammalian cell population, or on the order of 10 pg. The centerline represents the mean value.

Supplementary Figure 3 Digital single-molecule PCR in hydrogel (Lambda phage DNA)

Measured cluster number per field of view versus calculated cluster number based on template concentration. Reaction conditions are described in methods (PCR). The dotted line indicates the ideal situation when measured cluster number equals to the theoretical cluster number.

Supplementary Figure 4 MDA cluster size decreases with increasing DNA template concentration with the same gel condition

Data is shown as 5 % - 95 % box plot with outliers scattered and the centerline as the median. (n = 2 fields of view at each concentration, number of clusters for each field of view is: n = 43, 62, 89, 88, 191, 167, 321, 305, 478, 542, 711, 833).

Supplementary Figure 5 Cluster size and location correlation analysis

Five reference clusters with the largest radii and five reference clusters with the smallest radii were chosen among clusters in one field of view (100+ clusters) of one MDA hydrogel sample. a) By zooming out from selected clusters, the number of other clusters encountered at successively greater distances was plotted. b) The total volume of other clusters encountered was plotted. c) and d) zoom-in views of a) and b) at 0 to 32 µm from reference clusters. We hypothesized that large clusters consume local resources and thus, would reduce the number and/or size of surrounding clusters. Although slight enhancements in the number and size of clusters surrounding the set of small reference clusters versus the set of large reference clusters exist, the effect is very small. Error bars are SEM.

Supplementary Figure 6 NGS data analysis schematic for E. coli and S. aureus

The analysis workflow is shown with a combination of bioinformatic tools, python scripts and MATLAB scripts.

Supplementary Figure 7 Mapping single-cell genomes to references

Single-cell samples were mapped to E. coli and S. aureus reference sequences with mean percent pair aligned and standard deviation shown. (n = 5 for E. coli punches, n = 7 for S. aureus punches, n = 7 for negative punches).

Supplementary Figure 8 MDA chimera frequency with different insert sizes, E. coli and S. aureus data

a) 1 – 3 kb. b) 3 kb – 10 kb. c) Larger than 10 kb. Centerlines represent the mean value.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Tables 1–8 and Supplementary Notes 1–5 (PDF 3624 kb)

Supplementary Table 9

Features of 117 FijiCOMP single-cell assemblies. (XLSX 69 kb)

Supplementary Software

Supplementary software for data analysis (ZIP 28 kb)

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Xu, L., Brito, I., Alm, E. et al. Virtual microfluidics for digital quantification and single-cell sequencing. Nat Methods 13, 759–762 (2016). https://doi.org/10.1038/nmeth.3955

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