<figure><img src="image_1.png" /></figure>

HAL
open science

Implementation and Evaluation of 10X Genomics
Chromium technology

Claire Kuchly, Olivier Bouchez

<figure><img src="image_2.png" /></figure>

To cite this version:

Claire Kuchly, Olivier Bouchez. Implementation and Evaluation of 10X Genomics Chromium technology. Retour d'expérience, GeT-PlaGe, Nov 2017, Castanet Tolosan, France. hal-04100440

HAL Id: hal-04100440

https://hal.science/hal-04100440

Submitted on 17 May 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_3.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_4.png" /></figure>

Genotoul
GeT

<figure><img src="image_5.png" /></figure>

L A B E L
TOULOUSE
EUROPEAN CITY OF SCIENCE
ESOF 2018

<figure><img src="image_6.png" /></figure>

INRA
SCIENCE & IMPACT

# Implementation and Evaluation of 10X Genomics Chromium technology

Claire Kuchly & Olivier Bouchez

28/11/2017

<figure><img src="image_7.png" /></figure>

http://get.genotoul.fr
get@genotoul.fr
@get_genotoul

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /></figure>

Chromium evaluation: pilot phase

* Platform installed in november 2016

* Training: november 15th 2016

* 3 pilot projects: Rabbit, Tomato & Fish

<figure><img src="image_11.png" /></figure>
# Chromium 10XGENOMICS

* Library preparation for Illumina sequencing

* Long range genomics (>50 kb), haplotyping/genome phasing, structural variants detection, de novo sequencing

* Single cell analysis (500->10000)

* Exome sequencing

<figure><img src="image_12.png" /><figcaption>Chromium</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>HiSeq3000</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>NovaSeq</figcaption></figure>

<figure><img src="image_15.png" /></figure>
# Chromium 10XGENOMICS

## How does it work ?

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>
# Chromium 10XGENOMICS

## How does it work ?

## Complete wetlab workflow: 2 days

GEM (Gel Bead in Emulsion)

<figure><img src="image_18.png" /></figure>

Input: ~1 ng
HMW gDNA

<figure><img src="image_19.png" /></figure>

Linked-Reads

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>
# Advantage of linked-reads: genome phasing

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

## Resolve the Genome Into Multi-Megabase Phase Blocks

Phase the full spectrum of variants (SNVs, indels, and large-scale structural rearrangements) into ultra long multi-megabase phase blocks, enabling a full understanding of diploid genome sequence without the need for a reference.
# Haplotyping using Chromium

## Results via Loupe

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

## Linked-reads and structural variant calling

<figure><img src="image_26.png" /></figure>
# Single cell analysis

Estimated Number of Cells
552

Mean Reads per Cell
650,811

Median Genes per Cell
585

## Sequencing

<table><tr><td>Number of Reads</td><td>359,248,007</td></tr><tr><td>Valid Barcodes</td><td>98.2%</td></tr><tr><td>Reads Mapped Confidently to Transcriptome</td><td>60.0%</td></tr><tr><td>Reads Mapped Confidently to Exonic Regions</td><td>63.6%</td></tr><tr><td>Reads Mapped Confidently to Intronic Regions</td><td>13.6%</td></tr><tr><td>Reads Mapped Confidently to Intergenic Regions</td><td>8.8%</td></tr><tr><td>Sequencing Saturation</td><td>98.9%</td></tr><tr><td>Q30 Bases in Barcode</td><td>98.7%</td></tr><tr><td>Q30 Bases in RNA Read</td><td>59.3%</td></tr><tr><td>Q30 Bases in Sample Index</td><td>89.5%</td></tr><tr><td>Q30 Bases in UMI</td><td>98.8%</td></tr></table>

## Cells

<figure><img src="image_27.png" /></figure>

<table><tr><td>Estimated Number of Cells</td><td>552</td></tr><tr><td>Fraction Reads in Cells</td><td>34.8%</td></tr><tr><td>Mean Reads per Cell</td><td>650,811</td></tr><tr><td>Median Genes per Cell</td><td>585</td></tr><tr><td>Total Genes Detected</td><td>14,168</td></tr><tr><td>Median UMI Counts per Cell</td><td>887</td></tr></table>

## Sample

t-SNE projection of Cells Colored by Automated Clustering

<figure><img src="image_28.png" /></figure>
Chromium limits for genome sequencing

* Development on human genome (3 Gb), usefull for other genomes?

* Genome size

- 100 Mb minimum

* DNA size

- 50 kb minimum, 100 kb for de novo assemblies

* Improvements to develop for smaller genomes

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>
# Haplotyping using Chromium

*DNA prep, without BluePippin*

<figure><img src="image_31.png" /></figure>

* Young rabbit = low amount of DNA

* Molecular weight assessed by Fragment Analyzer

<figure><img src="image_32.png" /></figure>
# Haplotyping using Chromium

Results via Loupe software

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

## Without BluePippin sizing

### GEM Performance

<figure><img src="image_35.png" /></figure>

<table><tr><td>GEMs Detected</td><td>1,636,253</td></tr><tr><td>N50 Linked-Reads per Molecule (LPM)</td><td>13.0</td></tr><tr><td>Mean DNA per GEM</td><td>600,199 bp</td></tr></table>

### Input DNA

<figure><img src="image_36.png" /></figure>

<table><tr><td>Molecule Length</td><td>μ 44,559 bp</td></tr><tr><td>DNA in Molecules >20kb</td><td>85.5%</td></tr><tr><td>DNA in Molecules >100kb</td><td>11.8%</td></tr><tr><td>Corrected Estimated of DNA Loaded</td><td>1.41 ng</td></tr></table>

### Phasing

<figure><img src="image_37.png" /></figure>

<table><tr><td>SNPs Phased</td><td>97.1%</td></tr><tr><td>Longest Phase Block</td><td>3,241,488 bp</td></tr><tr><td>N50 Phase Block</td><td>632,948 bp</td></tr></table>

Phase Block View

<figure><img src="image_38.png" /></figure>

Phase Block Length (kb)

<figure><img src="image_39.png" /></figure>

Molecule Length (kb)

### Structural Variants

<figure><img src="image_40.png" /></figure>

<table><tr><td>Large Structural Variant Calls</td><td>117</td></tr><tr><td>Short Deletion Calls</td><td>47,585</td></tr></table>
# Haplotyping using Chromium

## DNA prep

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

* Rabbit DNA from 2 parents & 2 babies – Genome ~ 2 Gb

<figure><img src="image_43.png" /></figure>

* Extraction by MagAttract (Qiagen) from blood, molecular weight assessed by Fragment Analyzer

<figure><img src="image_44.png" /></figure>
# Haplotyping using Chromium

With BluePippin sizing : Results via Loupe software

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

## With BluePippin sizing

### GEM Performance

<figure><img src="image_47.png" /></figure>

GEMs Detected 1,593,499

N50 Linked-Reads per Molecule (LPM) 19.0

Mean DNA per GEM 576,954 bp

### Phasing

<figure><img src="image_48.png" /></figure>

SNPs Phased 97.2%

Longest Phase Block 4,417,547 bp

N50 Phase Block 875,494 bp

Phase Block View

<figure><img src="image_49.png" /></figure>

Phase Block Length (kb)

### Input DNA

<figure><img src="image_50.png" /></figure>

Molecule Length µ 63,172 bp

DNA in Molecules >20kb 93.2%

DNA in Molecules >100kb 20.8%

Corrected Estimated of DNA Loaded 1.35 ng

<figure><img src="image_51.png" /></figure>

Input DNA size improvement
=
longer phase block

### Structural Variants

<figure><img src="image_52.png" /></figure>

Large Structural Variant Calls 162

Short Deletion Calls 49,846
DNA QC: New Femto Pulse

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>

Same DNA QC on Fragment Analyzer and Femto Pulse => better resolution

<figure><img src="image_57.png" /></figure>
10X Applications

10X GENOMICS®

<figure><img src="image_58.png" /></figure>

Genome &
Exome

Long-range analysis and phasing of
SNVs, indels, and structural variants

Get support →

<figure><img src="image_59.png" /></figure>

Single Cell
Gene Expression

Gene expression profiling at scale
with single cell resolution

Get support →

<figure><img src="image_60.png" /></figure>

Single Cell
V(D)J

Paired V(D)J profiling at scale
with single lymphocyte resolution

Get support →

<figure><img src="image_61.png" /></figure>

De Novo
Assembly

Everyday de novo assemblies for
reference-free genomic analysis

Get support →

Gene Expression + V(D)J

Combined single-cell gene expression and immune
repertoire data from the same sample

Learn more →

<figure><img src="image_62.png" /></figure>
Our 10X Applications

10X GENOMICS®

<figure><img src="image_63.png" /></figure>

Genome &
Exome

Long-range analysis and phasing of
SNVs, indels, and structural variants

Get support →

<figure><img src="image_64.png" /></figure>

Single Cell
Gene Expression

Gene expression profiling at scale
with single cell resolution

Get support →

<figure><img src="image_65.png" /></figure>

Single Cell
V(D)J

Paired V(D)J profiling at scale
with single lymphocyte resolution

Get support →

<figure><img src="image_66.png" /></figure>

De Novo
Assembly

Everyday de novo assemblies for
reference-free genomic analysis

Get support →

Gene Expression + V(D)J

Combined single-cell gene expression and immune
repertoire data from the same sample

Learn more →

<figure><img src="image_67.png" /></figure>
# Genome & Exome solution

<figure><img src="image_68.png" /></figure>

## Genome & Exome

Long-range analysis and phasing of SNVs, indels, and structural variants

Get support →

Reference-Based Workflow

<figure><img src="image_69.png" /></figure>

**Long Ranger**
Analysis Pipelines

<figure><img src="image_70.png" /></figure>

**Loupe**
Genome Browser

## Linked-Reads analysis software
# LongRanger software

Analysis pipelines that perform :

- Sample demultiplexing ( with Illumina software )

- Barcode processing

- Alignment ( Lariat )

- Quality control

- Variant calling

- Phasing

- Structural variant calling

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /></figure>

Standard Pipeline Stages

<figure><img src="image_73.png" /></figure>

10X Genomics Stages

<figure><img src="image_74.png" /></figure>

Software with few parameters for the analysis
Not easy to be run on our cluster

<figure><img src="image_75.png" /></figure>
# Sequencing Quality Control

Longest Phase Block

7,677,566

GEMs Detected

1,306,663

## GEM Performance

<table><tr><td>GEMs Detected</td><td>1,306,663</td></tr><tr><td>N50 Linked-Reads per Molecule (LPM)</td><td>19.0</td></tr><tr><td>Mean DNA per GEM</td><td>644,628 bp</td></tr></table>

## Phasing

<table><tr><td>SNPs Phased</td><td>93.2%</td></tr><tr><td>Longest Phase Block</td><td>7,677,566 bp</td></tr><tr><td>N50 Phase Block</td><td>483,337 bp</td></tr><tr><td>Phase Block View</td><td></td></tr></table>

<figure><img src="image_76.png" /></figure>

## Input DNA

<table><tr><td>Molecule Length</td><td>μ 58,066 bp</td></tr><tr><td>DNA in Molecules >20kb</td><td>89.9%</td></tr><tr><td>DNA in Molecules >100kb</td><td>23.5%</td></tr><tr><td>Corrected Estimated of DNA Loaded</td><td>1.51 ng</td></tr></table>

<figure><img src="image_77.png" /></figure>

Quality of
input DNA

## Sequencing

<table><tr><td>Number of Reads</td><td>633,906,276</td></tr><tr><td>Median Insert Size</td><td>377 bp</td></tr><tr><td>Mean Depth</td><td>38.4 X</td></tr><tr><td>Zero Coverage</td><td>6.15%</td></tr><tr><td>Mapped Reads</td><td>95.8%</td></tr><tr><td>PCR Duplication</td><td>4.95%</td></tr><tr><td>Q30 bases, Read 1</td><td>92.4%</td></tr><tr><td>Q30 bases, Read 2</td><td>72.7%</td></tr></table>

<figure><img src="image_78.png" /></figure>
<figure><img src="image_79.png" /></figure>

## Differents views

<figure><img src="image_80.png" /><figcaption>### Plotype 2</figcaption></figure>

Phasing view – Indels, SNVs, SVs (50bp to up 30Kb)

Structural variant view – deletion, insertion
Our 10X Applications

10X GENOMICS®

<figure><img src="image_81.png" /></figure>

Genome &
Exome

Long-range analysis and phasing of
SNVs, indels, and structural variants

Get support →

<figure><img src="image_82.png" /></figure>

Single Cell
Gene Expression

Gene expression profiling at scale
with single cell resolution

Get support →

<figure><img src="image_83.png" /></figure>

Single Cell
V(D)J

Paired V(D)J profiling at scale
with single lymphocyte resolution

Get support →

<figure><img src="image_84.png" /></figure>

De Novo
Assembly

Everyday de novo assemblies for
reference-free genomic analysis

Get support →

Gene Expression + V(D)J

Combined single-cell gene expression and immune
repertoire data from the same sample

Learn more →
<figure><img src="image_85.png" /></figure>

# De Novo Assembly solution

<figure><img src="image_86.png" /></figure>

## De Novo Assembly

Everyday de novo assemblies for reference-free genomic analysis

Get support →

Reference-Free
Workflow

<figure><img src="image_87.png" /></figure>

Supernova
De novo Assembly

<figure><img src="image_88.png" /></figure>

File Formats
Genomes supported for assembly

<table><thead><tr><th>Genomes</th><th>Supported</th></tr></thead><tbody><tr><td>Human germline genomes</td><td>Smiley</td></tr><tr><td>Mamalian genomes</td><td>Smiley</td></tr><tr><td>High repeat content</td><td>Confused</td></tr><tr><td>Small genomes (100 Mb or greater)</td><td>Confused</td></tr><tr><td>Genomes having ploidy > 2</td><td>Confused</td></tr><tr><td>Microbes (&lt;100 Mb)</td><td>Confused</td></tr><tr><td>Human Non Germline (e.g Cancer)</td><td>Confused</td></tr><tr><td>Large genomes (&gt;3.2 Gb)</td><td>Confused</td></tr></tbody></table>

<figure><img src="image_89.png" /></figure>

<figure><img src="image_90.png" /></figure>

<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

<figure><img src="image_93.png" /></figure>

Work well

<figure><img src="image_94.png" /></figure>

May work but may increase run time

<figure><img src="image_95.png" /></figure>

Unlikely to work well or risky

<figure><img src="image_96.png" /></figure>
# DNA Size recommendation

DNA size is the **key point** for a successful analysis

<figure><img src="image_97.png" /></figure>
<figure><img src="image_98.png" /></figure>

Genotoul
GeT

<figure><img src="image_99.png" /></figure>

INRA
SCIENCE & IMPACT

Examples

Olivier Bouchez & Claire Kuchly

28/11/2017

<figure><img src="image_100.png" /></figure>

http://get.genotoul.fr
get@genotoul.fr
@get_genotoul

<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /></figure>

<figure><img src="image_103.png" /></figure>
# 10X for Small Genomes ?

## Genome size 400Mb

<figure><img src="image_104.png" /></figure>

<figure><img src="image_105.png" /></figure>
PacBio RSII + 10X Chromium = ?

For assembly purposes, are PacBio RSII and 10X Chromium results similar or is there a benefit to use them together?

<figure><img src="image_106.png" /></figure>

~ 1Gb)

<figure><img src="image_107.png" /></figure>

VS

<figure><img src="image_108.png" /></figure>

<table><thead><tr><th>Technology</th><th>Assembly analysis</th><th>Cov (X)</th><th>Tot bases (Mb)</th><th># contigs</th><th>N50</th><th>L50</th><th>Completeness (BUSCO V2)</th></tr></thead><tbody><tr><td>10X</td><td>Supernova</td><td>78</td><td>818</td><td>45 319</td><td>1,1 Mb</td><td>157</td><td>82,4 %</td></tr><tr><td>PacBio</td><td>(Pre-correction Canu)<br>Smartdenovo</td><td>71</td><td>808</td><td>701</td><td>4,1 Mb</td><td>55</td><td>88,1 %</td></tr><tr><td>PacBio</td><td>Canu</td><td>71</td><td>1 015</td><td>4 062</td><td>1,3 Mb</td><td>126</td><td>91 %</td></tr></tbody></table>

<figure><img src="image_109.png" /></figure>
PacBio RSII + 10X Chromium = ?

For assembly purposes, are PacBio RSII and 10X Chromium results similar or is there a
benefit to use them together?

<figure><img src="image_110.png" /></figure>

~ 1Gb)

<figure><img src="image_111.png" /></figure>

+

<figure><img src="image_112.png" /></figure>

<table><thead><tr><th>Technology</th><th>Assembly analysis</th><th>Cov (X)</th><th>Tot bases (Mb)</th><th># contigs</th><th>N50</th><th>L50</th><th>Completeness (BUSCO V2)</th></tr></thead><tbody><tr><td>10X</td><td>Supernova</td><td>78</td><td>818</td><td>45 319</td><td>1,1 Mb</td><td>157</td><td>82,4 %</td></tr><tr><td>PacBio</td><td>(Pre-correction Canu)<br>Smartdenovo</td><td>71</td><td>808</td><td>701</td><td>4,1 Mb</td><td>55</td><td>88,1 %</td></tr><tr><td>PacBio + 10X</td><td>ARCS<br>(PacBio Smartdenovo assembly + 10X)</td><td></td><td>808</td><td>534</td><td>5,4 Mb</td><td>44</td><td>88,1 %</td></tr><tr><td>PacBio</td><td>Canu</td><td>71</td><td>1 015</td><td>4 062</td><td>1,3 Mb</td><td>126</td><td>91 %</td></tr><tr><td>PacBio + 10X</td><td>ARCS<br>(PacBio Canu assembly + 10X)</td><td></td><td>1015</td><td>3 583</td><td>1,6 Mb</td><td>102</td><td>91 %</td></tr></tbody></table>

With the information of the linked reads, we can **improve the primary assembly** and make
an **scaffold assembly**

<figure><img src="image_113.png" /></figure>
<figure><img src="image_114.png" /></figure>

<figure><img src="image_115.png" /><figcaption>Jerôme Gouzy</figcaption></figure>

<figure><img src="image_116.png" /></figure>

GenOTOUl
Bioinfo

<caption>Christophe Klopp</caption>

Thanks!

**Laboratoire de Physiologie et génomique des poissons**

Yann Guiguen

**NGS team:**

Olivier Bouchez

**PacBio team:**

Alain Roulet

Céline Roques

**10X Genomics team:**

Adeline Chaubet

Sophie Valière

Pauline Heuillard

Frédéric Martins

**Bioinfo team:**

Maxime Manno

Anaïs Poiraudeau

Claire Kuchly

Céline Vandecasteele

<figure><img src="image_117.png" /></figure>

GenOTOUl
GeT

<figure><img src="image_118.png" /></figure>
# Stats assemblage 10X supernova

<figure><img src="image_119.png" /></figure>

N50/L50

Total assembly size: 1000 Mb

N50

50 % assembly: 500 Mb

Sequences order by descending size (Mb)

<figure><img src="image_120.png" /></figure>

N50 = 7 sequences

L50 = 50 Mb

<figure><img src="image_121.png" /></figure>