Cell Interaction by Multiplet sequencing (CIM-seq)

Nathanael Andrews, Jason T. Serviss, Natalie Geyer (Karolinska Institute Stockholm), Agneta B. Andersson, Ewa Dzwonkowska, Iva Šutevski, Rosan Heijboer, Ninib Baryawno (Karolinska Institute Stockholm), Marco Gerling, Martin Enge

Published: 2021-11-24 DOI: 10.17504/protocols.io.b2byqapw

Abstract

Single cell sequencing methods facilitate the study of tissues at high resolution, revealing rare cell types with varying transcriptomes or genomes, but so far have been lacking the capacity to investigate cell-cell interactions. Here, we introduce CIM-seq, an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell type in a given tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution of these into their constituent cell types using machine learning. CIM-seq is broadly applicable to studies that aim to simultaneously investigate the constituent cell types and the global interaction profile in a specific tissue.

Before start

Before preparing cell lysis plates it is recommended to thoroughly clean all equipment with 70% EtOH and RNAse away to prevent contamination and avoid RNA degradation.

Steps

Prepare Lysis buffer

1.

CIM-seq is compatible with both plate based and droplet based single cell RNA-seq methods. The following protocol is for Smart-seq2 based CIM-seq. For droplet based (10x), see the methods section of the manuscript and the vignettes in the Enge lab Github repository.

2.

Prepare Lysis Buffer:

NOTE: Reagents are prepared on ice, working quickly. ERCC is stored in single-use aliquots at -80°C, thawed on ice and added last.

ABC
Reagent:Reagent concentration:μl per reaction:
H20-1.31
Inhibitor1 U/μl0.05
ERCC (1:600 000)-0.05
Triton-X100 (10% solution)0.2%0.04
10mM dNTP2.5mM/each0.5
100uM dT2.5μM0.05
Total-2

Add 2µL lysis buffer mix to each well. Cover with appropriate lids. Spin down.

Snap freeze on dry ice . Store until use at -80°C

Sort cells

3.

Sort single cells and multiplets (aggregates of multiple cells) into 2µL lysis buffer mix.

Multiplets can be discerned from singlets by gating on the basis of FSC-W (Forward scatter - Width) and FSC-H (Forward scatter - Height) (see Figure 1 ).

Figure 1. Gating scheme and result of FSC-W and FSC-H based sort of multiplets (top) and singlets (bottom) using HCT116 cells .
Figure 1. Gating scheme and result of FSC-W and FSC-H based sort of multiplets (top) and singlets (bottom) using HCT116 cells .

Following sort, immediately seal with appropriate seals (approved for -80C > 100C) and centrifuge at 2000x g,4°C.

Snap freeze on dry ice . Store until use at -80°C .

Reverse transcription and cDNA amplification

4.

Primer annealing

Thaw plate. Spin down. Incubate in thermocycler at 72°C for 0h 3m 0s . Place on ice immediately.

5.

Prepare RT master-mix

Made fresh.

ABC
Reagent:Reaction concentration:Reagent volume:
SmartScribe15u/µl0.475
RNase Inhibitor1.66u/µl0.125
5x First Strand buffer1X1
DTT (100mM)8.33mM0.25
Betaine (5M) [fridge]1.66M1
MgCl2 (1M) [bench]10mM0.03
TSO (100uM)1.66µM0.05
H20-0.07
Total-3

Dispense 3µL per well.

Cover plate with new film and spin down.

6.

Incubate in thermocycler

42°C 1h 30m 0s

70°C 0h 5m 0s

4°C hold

7.

cDNA preamplification

Made fresh.

ABC
ReagentsReaction concentration:Reagent volume:
H2O-1.0688
Kapa HiFi HotStart ReadyMix (2x)1X6.25
IS_PCR primer (10uM)0.1µM0.125
Lambda Exonuclease0,045u/µl0.05625
Total-7.5000

Dispense 7.5µL per well . Total reaction volume will be 12.5µl.

Spin down. Cover with new lid.

8.

Incubate in thermocycler with the following program:

ABCD
StepTemperatureTimeCycles
Lambda exonuclease37ºC30 min1x
Initial denaturation95ºC3 min1x
Denaturation98ºC20 sec18-24x
Annealing67ºC15 sec
Elongation72ºC4 min
Final elongation72ºC5 min1x
4CHold
9.

cDNA cleanup

We prepare SPRI-beads in 20% PEG-8000 solution as in: https://openwetware.org/wiki/SPRI_bead_mix#Ingredients_for_50_mL_2

Using 20% SPRI-bead solution:

  1. Add 0.7x the reaction volume of SPRI beads per well. Mix well by pipetting. (i.e 8.75µL SPRI-bead solution for 12.5µL reaction volume)

  2. Incubate 0h 5m 0s Room temperature

  3. Place on magnetic stand for 0h 3m 0s

  4. Carefully remove supernatant

  5. Add 40 µl 80% EtOH and incubate 0h 0m 30s

  6. Remove EtOH (without disturbing the beads)

  7. Wash again with EtOH. Make sure to remove well.

  8. Allow beads to air-dry for 0h 10m 0s -0h 15m 0s

  9. Remove plate from magnetic stand

  10. Elute beads in 15µL EB or TE buffer. Mix well by pipetting

  11. Incubate 0h 5m 0s Room temperature

  12. Place on magnetic plate for 0h 3m 0s

  13. Optional: Carefully remove supernatant to the elution plate

10.

cDNA quantification

We measure concentration of random wells using Qubit HS dsDNA, adapted to a 96-well plate reader.

  1. Add 97µL of 1X Qubit HS dsDNA solution to a flat-bottom, black plate

  2. Add 3µL of cDNA sample

  3. Add Standards (NOTE: We make a 8-step ladder from 0ng/µl --> 10ng/µl Qubit Standard DNA in TE buffer)

  4. Read in plate reader using 485nM excitation/528nm emission

  5. Calculate cDNA concentration

11.

(optional) cDNA quality control

Using Agilent HS 5000 DNA chips (or equivalent)

Figure 2. cDNA profile of single cell run on HS D5000 Agilent tapestation
Figure 2. cDNA profile of single cell run on HS D5000 Agilent tapestation
12.

Make cDNA dilution plate

Dilute cDNA in water based on average concentration from Qubit measurements.

Target concentration 150pg per µl in 15µL.

cDNA tagmentation

13.

Tn5 digestion

Tn5 is produced from psfTn5 (Addgene #79107), purified to ~3mg/ml and assembled with Illumina Tn5 adapters (see oligos) as in Picelli et al, 2014.

Citation
Picelli S, Björklund AK, Reinius B, Sagasser S, Winberg G, Sandberg R 2014 Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome research https://doi.org/10.1101/gr.177881.114

14.

Prepare Tn5 master mix

Note
NOTE: TAPS-PEG Buffer contains PEG, which is viscous. Buffer should equilibrate to room temperature before use to allow proper mixing.

ABC
ReagentReaction conc.µl per reaction
Nuclease free H2O-1.05
TAPS-PEG (50mM TAPS, 25mM MgCl2, 40% PEG-8000)10mM TAPS, 5mM MgCl2, 8% PEG-80000.5
psfTn5, loaded with 50µM MEDS-A/B0.25
Total1.8

Dispense 1.8µL per well in a new plate(tagmentation plate)

15.

Add 0.7µL cDNA (normalized to 150pg/µl)

Mix well by vortexing plate. Cover with new lid and spin down.

16.

Incubate in thermocycler at 55°C``0h 10m 0s

Remove immediately and stop reaction by adding 1µL per well of 0.1% SDS.

Vortex, spin down and incubate 0h 7m 0s at 55Room temperature

cDNA library PCR and barcoding

17.

Make PCR master-mix

AB
Reagentsµl per reaction
H2O13.25
5x buffer5
dNTPs0.75
KAPA0.5
Total19.50

Dispense 19.5µL per well to tagmentation plate (containing 3.5µL sample after step 14)

18.

Add primers/barcodes

2µL per well (from 384-well index plates, with 3.75µM/each forward/reverse primers; see oligos in materials ).

Total reaction volume is 25µL (3.5µL sample + 19.5µL PCR mix and 2µL primers).

19.

Vortex. Spin down and cover. Incubate in thermocycler as following:

ABCD
StepTemperatureTimeCycles
Gap fill72ºC3 min1x
First denature95ºC30 sec1x
Denature95ºC15 sec12x
Denature67ºC30 sec
Denature72ºC45 sec
Final extension72ºC4 min1x
4-10ºChold
20.

Pool 2.5µL from each well to an 1.5ml Eppendorf tube.

21.

Library cleanup

  1. Add 0.9x pooled library volume of SPRI-bead solution. Incubate for 0h 5m 0s at Room temperature .

  2. Place on magnetic rack for 0h 3m 0s.

  3. Remove supernatant without disturbing magnetic beads.

  4. Add at least 1mL 80% EtOH (fresh). Incubate for 0h 0m 30s.

  5. Remove supernatant.

  6. Repeat EtOH wash.

  7. Air dry for 0h 10m 0s - 0h 15m 0s.

  8. Re-suspend beads thoroughly in 100 µl EB or TE buffer.

  9. Place eppendorf on magnetic rack for 0h 3m 0s.

  10. Transfer supernatant to new 1.5ml Eppendorf tube.

  11. Repeat cleanup (from step 1-7) and elute in 30 µl EB or TE buffer.

12.(Optional) Place eppendorf on magnetic rack for 0h 3m 0s and transfer supernatant to new tube.

22.

Pooled library QC

Figure 3. cDNA profile of a library of 784 cells (both single cells and multiplets) on HS D5000 Agilent tapestation.
Figure 3. cDNA profile of a library of 784 cells (both single cells and multiplets) on HS D5000 Agilent tapestation.

Data pre-processing

23.

A series of pre-processing steps must be performed in order to generate a counts file:

  1. Trim reads, remove adapter sequences and align RNAseq data to reference genome using STAR:

Citation
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR 2013 STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England) https://doi.org/10.1093/bioinformatics/bts635

  1. Remove duplicate reads using Picard:

Citation
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA 2010 The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome research https://doi.org/10.1101/gr.107524.110
3. Generate transcript counts file using HTSeq:

Citation
Anders S, Pyl PT, Huber W 2015 HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England) https://doi.org/10.1093/bioinformatics/btu638

Dimensionality reduction and classification

24.

Once a counts file has been generated the data can be analyzed. CIM-seq requires four arguments in order to run:

  1. The raw counts data with gene IDs as rownames and sample IDs as

colnames.

  1. The ERCC spike-in counts data with gene IDs as rownames and sample

IDs as colnames.

  1. The dimensionality reduced representation of the data.

  2. A class for each of the individual singlets.

In order to generate the last two of these we recommend using the Seurat package in R, as CIM-seq is implemented in R as well. A number of tutorials for Seurat can be found on the Satijalab website:

https://satijalab.org/seurat/vignettes.html

CIM-seq

25.

CIM-seq can be downloaded from:

https://github.com/EngeLab/CIMseq

Or installed directly in R using the devtools package:

devtools::install_github("EngeLab/CIMseq")

The CIM-seq vignette can be found at:

https://github.com/EngeLab/CIMseq/tree/master/vignettes

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