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Droplet microfluidics: a tool for massively parallel single-cell analysis Juozas Nainys Vilnius University, Lithuania Evolution in electronics • Miniaturization Enables Fast Processing Speeds • Integration of Multiple Functions on One Device • Scaleable 2 Evolution of liquid handling ❍ Each chemical in a separate tube Labels ❍ Hand manipulation 1970 ❍ Handwritten labeling Glass wear ❍ 1000s of assays per week ❍ Each chemical in well of a plate Barcodes ❍ Robotic handling 1990 ❍ Barcode labeling ❍ 1000s of assays per hour Fluorescent ❍ Each chemical in microscopic droplet Labels ❍ Microfluidic handling on chip 2005 ❍ Fluorescent labels (“Barcodes”) ❍ 1,000’s of assays per second Microfluidic droplets 3 Microfluidic systems 96 reactions (latest chip: ~ 800 reactions) Valve based Well based ~10,000 reactions 1 well = 10 µm Droplet based >1,000,000 reactions 4 in vitro compartmentalization Each droplet can be considered as a single tube or a 96-well Oil 50 µm Reagents Reaction volume: 1pL – 1nL Oil Reaction volume: 1µL – 200µL Reaction volume: >10 ml 5 Reduced volumes improve reaction sensitivity 1 µL 1 nL 1 pL 1 fM 1 pM 1 nM 6 Encapsulation of biological samples ❍ Emulsification of different liquids ❍ Each droplet can have a single molecule, bead or cell ❍ Precise and uniform ❍ Reproducible ❍ Up to 20,000 per second ❍ 1 to 200 μm size drops Reagents 50 µm Beads Cells 7 Droplet microfluidics for single-cell biology Cells Oil Speed 0.01x Loading module Reagent A 200µm Reagent B Oil • Small reaction volumes (pL to nL) • Access to >10.000 single-cells per experiment • one cell = one droplet = one well 8 Single-cell profiling Population Single-cell average measurement 9 Single-cell barcoding & sequencing DNA barcoding beads Cells Main disadvantage: cell + bead co-encapsulation events are rare 10 11 How do we make hydrogel beads? H N O O P O O- O N H NO2 O Acrylic phosphoroamidite Photo-cleavable moiety spacer PE1 primer site T7 RNAP promoter O O P O CGATGACGTAATACGACTCACTATAGGGATACCACCATGGCTCTTTCCCTACACGACGCTCTTC -3' O- Acrylamide Ac-DNA primers 100 µm 3’ 3’ 3’ 3’ 3’ Hydrogel beads Hydrogel bead collection ssDNA primers Hydrogel bead with ssDNA 12 Barcode synthesis (split & pool) 3’ 3’ Photo-cleavable spacer 3’ 3’ T7 RNAP promoter PE1 site 3’ 3’ Hydrogel beads PE1* site barcode1* W1* site PE1* site barcode1* W1* site PE1 site barcode1 W1 site ssDNA primers 5’ 3’ PE1 site ssDNA “stubs” 5’ 3’ 3’ W1* site barcode2* UMI* poly-A 5’ ssDNA “barcodes” W1* site barcode2* UMI* poly-A 5’ 3’ 3’ W1 site barcode2 UMI poly-T W1 site barcode2 UMI poly-T 3’ 3’ 3’ 3’ Hydrogel beads Photo-cleavable spacer T7 RNAP PE1 site promoter 3’ barcode1 barcoded ssDNA primers [SPC]-[T7RNApromoter]-[SEQSITE]-[CELL-BCD1]-[ADAPTER]-[CELL-BCD2]-[UMI]-[polyT] 13 Bead encapsulation real-time Reagents Cells ~100 drops s-1 Hydrogel beads 14 Co-encapsulation: 1 cell + 1 barcode RT/lysis Encapsulation Cells Hydrogel beads Collection 15 Lysis reagents + RT mix T7-promoter barcode UMI polyT 5’-Acryd-PC3’ Cells Barcoding beads 3’ 3’ 3’ 3’ 3’ Hydrogel beads Synthesize barcode beads Encapsulate cells+beads Photo-release barcodes barcoded ssDNA primers RT/ barcoding reaction Break droplets Library prep / enrich targets Sequence 16 Lysis reagents + RT mix T7-promoter barcode UMI polyT 5’-Acryd-PC3’ cell lysis Cells Barcoding beads 3’ 3’ 3’ 3’ 3’ Hydrogel beads Synthesize barcode beads Encapsulate cells+beads Photo-release barcodes barcoded ssDNA primers RT/ barcoding reaction Break droplets Library prep / enrich targets Sequence 17 Lysis reagents + RT mix T7-promoter barcode UMI polyT 5’-Acryd-PC3’ DNA barcode release Cells Barcoding beads 3’ 3’ 3’ 3’ 3’ Hydrogel beads Synthesize barcode beads Encapsulate cells+beads Photo-release barcodes barcoded ssDNA primers RT/ barcoding reaction Break droplets Library prep / enrich targets Sequence 18 Emulsion RT reaction Lysis reagents + RT mix Droplet T7-promoter barcode UMI polyT Emulsion 5’-Acryd-PC3’ mRNA primer TT TT TTT Cells T TT TT TTTT Barcoding beads sequencing adaptor T7 RNAP promoter mRNA Synthesize barcode beads Encapsulate cells+beads Photo-release barcodes RT/ barcoding reaction Break droplets UMI cell barcode TTTTT AAAAA cDNA Library prep / enrich targets Sequence 19 RNA quantity (BioAnalyzer [FU]) Encapsulate cells Reverse transcription Break droplets Library prep Sequence aRNA Library Primer-dimer UV pre-RT mRNA capture with released DNA barcodes UV post-RT MW marker mRNA capture on the beads aRNA length post-IVT (nt) 20 Breaking and pooling Lysis reagents + RT mix mRNA T7-promoter barcode UMI polyT 5’-Acryd-PC3’ primer TT TT TTT T TT TT Cells TTTT Barcoding beads sequencing adaptor T7 RNAP promoter UMI cell barcode mRNA Synthesize barcode beads Encapsulate cells+beads Photo-release barcodes RT/ barcoding reaction Break droplets TTTTT AAAAA cDNA Library prep / enrich targets Sequence 21 Library prep RT Second strand synthesis Lysis reagents + RT mix IVT KAPA PCR Second RT step Illumina seq T7-promoter barcode UMI polyT 5’-Acryd-PC3’ Library prep. Cells T7 Pol linear amplification } cell 1 Barcoding beads } cell 2 } cell 3 } cell n Synthesize barcode beads Encapsulate cells+beads Photo-release barcodes RT/ barcoding reaction Break droplets Library prep / enrich targets Sequence 22 Lysis reagents + RT mix T7-promoter barcode UMI polyT 5’-Acryd-PC3’ Data analysis Cells Barcoding beads Synthesize barcode beads Encapsulate cells+beads Photo-release barcodes RT/ barcoding reaction Break droplets Library prep / enrich targets Sequence 23 Zilionis, Rapolas, Nainys, Juozas et al. "Single-cell barcoding and sequencing using droplet microfluidics.” Nature Protocols 12.1 (2017): 44-73. 24 Control: can we distinguish different cells? oil mouse ES oil human lymphocytes x1 x2 multi- 4% multi-cell droplets Mapped to mouse (x12) Mapped to human Klein, Allon M., Mazutis, Linas, et al. "Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells." Cell 161.5 (2015): 1187-1201. 25 Low technical noise Reduced reaction volume reduces technical noise Klein, Allon M., Mazutis, Linas, et al. "Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells." Cell 161.5 (2015): 1187-1201. 26 COMPUTATIONAL ALGORITHMS 27 Computational analyses Low level • • • • Demultiplex Align transcripts Create cell x count matrix Resource intensive High level • • • • • Dimensionality reduction Clustering Imputation Visualization Done locally 28 Dirty secret of scRNAseq 29 Data dropout problem Francesconi,M.,&Lehner,B.(2014).Theeffectsofgeneticvariationongeneexpressiondynamicsduringdevelopment. Nature, 505(7482),208-211. 30 Benefits of imputation MAGIC (Markov Affinity-based Graphical Imputation of Cells) Paul,F.,etal.(2015).Transcriptionalheterogeneityandlineagecommitmentinmyeloidprogenitors. Cell, 163(7),1663-1677. 31 Benefits of imputation van Dijk, David, Nainys Juozas et al. "MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNAsequencing data." bioRxiv (2017): 111591. 32 What’s next in the field? • • • • Whole transcriptome Single-cell epigenomics (ATAC-Seq) Targeted transcriptomics Non-coding RNA Hydrogel beads with customized primers 3’ Assay reagents 3’ 3’ 3’ Cells 3’ Hydrogel beads ssDNA primers Hydrogel beads 33 Fast-track options Acknowledgements Linas Mazutis Dana Pe’er David van Dijk Ambrose Carr Vaidotas Kiseliovas Rapolas Zilionis Microfluidic modules control the flow of droplets Formulate Load Combine Mix Store Detect Re-Loading Split Direct 36 Integrates microfluidics platform Library (genes, cells, RNA, etc) Biochemical Reagents Mixing Negatives Incubation Microfluidics chip Detection Droplet fusion Sorting Data collection Substrate Positives 37 Surfactants Surfactants used to stabilize droplets are the key Microfluidic channel Water Poly(ethylene) glycol Na+ SDS Droplet Oil Fluorinated polypropylene tails Surfactant Poor surfactant Efficient surfactant 100 µm 100 µm Slowed down ~ 100-times 38 Thank you!
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