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NGS in clinical diagnostics
Paula MöllingMolecular Biologist, Associate professor
Dept of Laboratory Medicine, Clinical Microbiology,
Örebro University Hospital, Sweden
Content
• Routine diagnostics - Pathogens
• NGS technology - MiSeq Illumina
• Outbreak situation – Neonatal dept
• Speed up to get faster answers - Nanopore
• Sharing data nationally - Surveillance and outbreaks
NGS routine diagnostics at Örebro University Hospital
2013 Neisseria meningitidis
Epidemiological surveillance – 2 / year
2016 N. meningitidis + MRSA
Genetic typing routinely / Epidemiological surveillance – every month
2018 N. meningitidis, MRSA + N. gonorrhoeae
Genetic typing routinely / Epidemiological surveillance – every two week
2019 N. meningitidis, MRSA, N. gonorrhoeae + Clostridioides difficile
Genetic typing routinely / Epidemiological surveillance – every week
MISEQ - ILLUMINA
Library prep
(NexteraXT)
2h hands-on
6-8 h
MiSeq seq
20 min
hands-on
1.5 – 2.5 d
Analysis
Different
pipelines
few min - hours
In total 3-4 days from extracted DNA
Databases for typing of bacteria:
• Ridom SeqSphere+, http://ridom.com/seqsphere/ - MRSA, C. difficile
• 1928D, https://1928diagnostics.com/ - MRSA, C. difficile
• BIGSdb, http://pubmlst.org/software/database/bigsdb/ - N. meningitidis
• CLC Geomic workbench - N. gonorrhoeae
Assembly: Put all sequence reads together to contigs - ”puzzle”
Velvet Optimiser, CLC Genomics Workbench, SPAdes
NGS data analysis
Ridom SeqSphere+
- pipeline automatically
MRSA NGS - all new clinical MRSA + S. aureus with toxins
Analyse NGS data:
Ridom Seqsphere+
1928Diagnostics
Spa-type, SCCmec
PVL, TSST, ETA/ETB
Typing:
MLST, 7 house keeping genes
cgMLST, >1800 genes
S.aur
genome
Accepted runs:
≥ 50x coverage
≥ 97% cgMLST Targets
Neonatal outbreak 2018 at Örebro University Hospital
Neighbour Joining tree, cgMLST of 1861 genes Minimum spanning tree within the same cluster
Takes long time to answer in an outbreak situation
MinION (Nanopore)
• Library prep - Rapid barcoding kit (12 samples)
- <1 hour
• Sequencing – Flowcell on MinION and MinIT
- a few hours per sample or <24 h per 12 sample
• Data analysis
- fast5 to fastq (EPI2ME or Guppy, Nanopore)
- Demultiplexing (Barcoding, Nanopore)
- Cluster analysis (cgMLST, 1928D pipeline)
Ion Realtime Sequencing(Oxford Nanopore Technologies)
https://nanoporetech.com/how-it-works
Ultra-long reads - up to 2 Mb (10-20 Kb)
512 Gb
Sequenced by MinION
Result MinION 1928D pipline
Clustering of MinION data - 1928D
National tracing of outbreaks – share data
1928Diagnostics – “Powerful analysis of your WGS data in minutes”
RapidShare project Aim: Share NGS data in real-time to facilitate
MRSA surveillance
Three regions: Örebro, Gothenburg, Stockholm shared
data during a time-period, October 2017 until May 2018.
No epidemiological connections could be found.
•Epidemiologic typing
•Antibiotic resistance profiling
•Virulence factor profiling
Limited time period – looking into surrounding regions
Genomic Medicine Sweden (GMS)
Aim – implement high-throughput sequencing technologies and
genomics in health care to improving Swedish precision
diagnostics.
This will be run through Genomic Medicine centers at the
seven university hospitals with the aim share data and use the
same pipelines within a common national IT-infrastructure.
GMS will focus on diagnostic areas – rare hereditary diseases,
complex genetic diseases, cancer and infectious diseases.
HCP
Public
cloud
HCP - Hitachi Content Platform, national storage (>700 Pb)
GMS - Microbiology
Goal – to obtain fast and reliable typing of
microorganisms where method and results easily
can be shared between regions for surveillance
and tracing of outbreaks in real time.
Pilot project – since sequence data from
microorganisms do not include human DNA
GMS-Micro can be run as a pilot for the entire
GMS project.
Other activities: Sharing meta data and lab methods, antibiotic resistance monitoring,
fast sequencing, clinical metagenomics
GMS-Micro Pilot
• 7 regions together with PHA performs NGS of a
“Reference dataset” of 20 MRSA (outbreaks):
- MiSeq, HiSeq, NextSeq, Ion Torrent
- SeqSphere, 1928D, CLC, Bactyper, own piplines
Aim: to define outbreak related isolates
• All data will be shared and analyzed together
Aim: to test GMS IT-infrastructure, quality of the generated NGS
data from different platforms and to define a common national
data analysis for tracing outbreaks
Conclusion
Using NGS facilitate surveillance and tracing of outbreaks
especially when it is speeded up and data can be
shared between regions
Martin Sundqvist
Bo Söderquist
Amaya Lagos
Marita Ekström
Anna Fagerström
Fredrik Dyrkell
Oscar Aspelin
Dimitrios Arnellos
Kristina Lagerstedt
Susanne Staaf
Thanks to
Erika Tång Hallbäck
Liselott Svensson Stadler
Åsa Lindgren
Christian Giske
Hong Fang
Lars Engstrand
Per Sikora
Hedvig Engström Jakobsson
GMS reference group