scg_lib_structs

Single Cell Genomics Library Structure

Collections of library structure and sequence of popular single cell genomic methods (mainly scRNA-seq).

For NGS newbies

Most single cell sequencing methods are developed to be sequenced on the Illumina platforms. If you are not familiar with the general structure of Illumina sequencing libraries, click here to check some general information about Illumina library structures and the nature of library preparation.

How to use?

Click the following links to view the methods. Notes:

1. The default alignment font is Monaco. Courier New font will be used if Monaco is not available.
2. Index1 (i7) is always sequenced using the bottom strand as template, regardless of the Illumina machine in use. That is why the index sequences are reverse complementary to the primer sequences.
3. In a dual-index library, how index2 (i5) is sequenced differs from machines to machines. According to the Index Sequencing Guide from Illumina, Miseq, Hiseq2000/2500 and NovaSeq 6000 use the bottom strand as template, which is why the index sequences are the same as the primer sequences in those machines. iSeq 100, MiniSeq, NextSeq, HiSeq X and HiSeq 3000/4000 use the top strand as template, which is why the index sequences are reverse-complementary to the primer sequences in those machines. All methods listed below use Miseq, Hiseq2000/2500 and NovaSeq as examples.

• Gene expression

  • SMART-seq family (including SMART-seq, SMART-seq2 and SMART-seq3)
  • STRT-seq family (including STRT-seq, STRT-seq-C1 and STRT-seq-2i)
  • Quartz-seq family (including Quartz-seq and Quartz-seq2)
  • CEL-seq family (including CEL-seq and CEL-seq2)
  • 10x Chromium Single Cell 3’ V3 FeatureBarcoding
  • 10x Chromium Single Cell 3’ V2 and V3 GE
  • 10x Chromium Single Cell 3’ V1 GE
  • 10x Chromium Single Cell 5’ VDJ
  • 10x Chromium Single Cell 5’ GE
  • SureCell 3’ WTA for ddSEQ
  • MARS-seq / MARS-seq2.0
  • SCRB-seq / mcSCRB-seq
  • Drop-seq / Seq-Well
  • scifi-RNA-seq
  • Microwell-seq
  • BD Rhapsody
  • sci-RNA-seq3
  • sci-RNA-seq
  • Seq-Well S3
  • Tang 2009
  • SPLiT-seq
  • inDrop

• Chromatin accessibility and protein-DNA interactions

  • Plate_scATAC-seq and Pi-ATAC-seq
  • 10x Chromium Single Cell ATAC
  • dscATAC-seq/dsciATAC-seq
  • scDNase-seq/scMNase-seq
  • sci-ATAC-seq
  • snATAC-seq
  • scTHS-seq
  • itChIP-seq
  • Drop-ChIP
  • CoBATCH
  • scDamID

• Genomic DNA or DNA methylation

  • scBS-seq
  • MALBAC
  • scRRBS
  • LIANTI

• Multi-Omics

  • 10x Chromium Single Cell Multiome ATAC + Gene Expression
  • scNOMe-seq/scCOOL-seq
  • scDam&T-seq
  • SHARE-seq
  • SNARE-seq
  • scNMT-seq
  • scM&T-seq
  • Paired-seq
  • scCAT-seq
  • scTrio-seq
  • scMT-seq
  • G&T-seq
  • DR-seq

• TODO list:

  • Trac-looping
  • MATQ-seq
  • ASTAR-seq
  • Drop scChIP-seq
  • sci-Plex
  • sci-CAR-seq
  • snmC2T-seq
  • MALBAC-DT
  • GRID-seq
  • ZipSeq
  • PETRI-seq
  • microSPLiT
  • scCC
  • scSPRITE
  • ICICLE-seq
  • sci-ATAC-seq3
  • s3-ATAC/WGS/GCC
  • hsrChST-seq
  • TIP-seq

Technical comparisons (scRNA-seq only)

The basic chemistry is very similar, the main differences among those scRNA-seq methods are summarised in the table below. For a detailed discussion, check the text boxes from our review: From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

	Single cell isolation/capture	Where RT happens	2nd strand synthesis	Full-length cDNA synthesis	Barcode addition	Pooling before library	Library amplification	Gene coverage
10x Chromium Single Cell 3’	Droplet	In droplets	TSO	Yes	Barcoded RT primers	Yes	PCR	3’
10x Chromium Single Cell 5’	Droplet	In droplets	TSO	Yes	Barcoded TSO primers	Yes	PCR	5’
BD Rhapsody	Nanowells	In collection tubes	Random priming and primer extension	No	Barcoded RT primers	Yes	PCR	3’
CEL-seq/CEL-seq2	FACS	In 96w/384w wells	RNase H and DNA pol I	No	Barcoded RT primers	Yes	In vitro transcription	3’
Drop-seq	Droplet	In collection tubes	TSO	Yes	Barcoded RT primers	Yes	PCR	3’
Illumina Bio-Rad SureCell 3’ WTA	Droplet	In droplets	RNase H and DNA pol I	No	Barcoded RT primers	Yes	PCR	3’
inDrop	Droplet	In droplets	RNase H and DNA pol I	No	Barcoded RT primers	Yes	In vitro transcription	3’
MARS-seq/MARS-seq2.0	FACS	In 96w/384w wells	RNase H and DNA pol I	No	Barcoded RT primers	Yes	In vitro transcription	3’
Microwell-seq	Nanowells	In collection tubes	TSO	Yes	Barcoded RT primers	Yes	PCR	3’
Quartz-seq	FACS	In 96w/384w wells	PolyA tailing and primer ligation	Yes in principle	Ligation of barcoded Truseq adapters	No	PCR	3’
Quartz-seq2	FACS	In 96w/384w wells	PolyA tailing and primer ligation	Yes in principle	Barcoded RT primers	Yes	PCR	3’
sci-RNA-seq	Not needed	In situ	RNase H and DNA pol I	No	Barcoded RT primers and library PCR with barcoded primers	Yes	PCR	3’
sci-RNA-seq3	Not needed	In situ	RNase H and DNA pol I	No	Barcoded RT primers and hairpin adapters	Yes	PCR	3’
scifi-RNA-seq	Droplet multiple cells	In situ	TSO	Yes	Barcoded RT primers and gel bead barcodes	Yes	PCR	3’
SCRB-seq/mcSCRB-seq	FACS	In 96w/384w wells	TSO	Yes	Barcoded RT primers	Yes	PCR	3’
Seq-Well	Nanowells	In collection tubes	TSO	Yes	Barcoded RT primers	Yes	PCR	3’
Seq-Well S3	Nanowells	In collection tubes	Random priming and primer extension	No	Barcoded RT primers	Yes	PCR	3’
SMART-seq/SMART-seq2/SMART-seq3	FACS or Fluidigm C1	In 96w/384w wells	TSO	Yes	Library PCR with barcoded primers	No	PCR	full-length
SPLiT-seq	Not needed	In situ	TSO	Yes	Ligation of barcoded RT primers	Yes	PCR	3’
STRT-seq	FACS	In 96w/384w wells	TSO	Yes	Barcoded TSO primers	Yes	PCR	5’
STRT-seq-C1	Fluidigm C1	In microfluidic chambers	TSO	Yes	Barcoded Tn5 transposase	No	PCR	5’
STRT-seq-2i	FACS or dilution	In 9600w wells	TSO	Yes	Barcoded PCR primers and Tn5 transposase	Yes	PCR	5’
Tang 2009	FACS or manual	In 96w/384w wells	PolyA tailing and primer extension	Yes in principle	Ligation of barcoded adaptors	No	PCR	Biased to 3’

Motivation

I was a little bit bombarded with all the single cell methods and got completely lost. To help myself understand all of them and future troubleshooting, I start to perform an on-paper library preparation whenever I see a new single cell method.

Why bother?

Here I borrow from Feyman:

What I cannot create, I do not understand.

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Feedback

I would be very happy if you go through them and let me know what you think. If you spot some errors/mistakes, or I’ve missed some key methods. Feel free to contact me:

Xi Chen
chenx9@sustech.edu.cn