Molecular diagnostics using the microbiome in animal health. Designed, executed, and launched a Direct To Consumer (DTC) genomic test--a microbiome test with a clinically actionable reporting goal. Responsible for bringing the NGS laboratory in-house, evaluating and sourcing the appropriate technology (16S rRNA sequencing - Illumina NextSeq & MiniSeq-150PE), designing and executing the appropriate assay to measure the microbiome. I reduced the turnaround time from 6-8 weeks to <2 weeks with a ten-fold increased processing volume while hiring and training a laboratory team.

1. PRODUCT DEVELOPMENT:
DIRECT TO CONSUMER (DTC)
MICROBIOME SERVICE
KIRSTEN A COPREN, PHD

2. GOAL
Design and execute an affordable, clinically actionable
microbiome test to aid consumers and veterinarians in the
treatment of digestive illnesses and nutritional planning for dogs
and cats.

3. WHAT IS CLINICALLY ACTIONABLE?
Treatment for digestive
illnesses
Timeline of days Validated laboratory
developed test (LDT)

4. DEVELOPMENT ROADMAP
Project Initiation
Research
Design
Cost Analysis
01
Early Runs
Installation
Training
Optimization
02
Later Runs
Validation
Standardization
Training
03
Throughput
Retrospective
Planning
Adjustments
04

5. PHASE 1: PROJECT INITIATION
RESEARCH & DESIGN
COST ANALYSIS

7. NGS TECHNOLOGY CONSIDERATIONS
Cost Capacity Scalability
Feasibility Ability to
innovate
Data storage and
processing needs

8. TECHNOLOGY
CHOICE: MINISEQ
Two color chemistry
Affordable
Fast
Compare to MiSeq—Legacy technology.
30% more expensive but longer reads.
Illumina.com

9. THE CHALLENGE
• Clinically actionable TAT
(turnaround time) for digestive
illnesses is days.
• NGS tests can take weeks.
• Starting TAT was 6-8 weeks.
• Final TAT goal was 1-2 weeks.
Comeau et al, 2017. mSystems. ASM.

10. GOALS FOR A
DIAGNOSTIC
GENETIC ASSAY
SENSITIVE ACCURATE PRECISE
(REPEATABLE,
REPRODUCIBLE)
TRAINABLE (HANDS
ON TIME,
DIFFICULTY)
ROBUST (HANDS-
ON TIME, MINIMIZE
SOURCES OF ERROR)
CONNECTS TO
ANALYSIS STEPS
EFFICIENTLY

11. TECHNOLOGIES OF INCREASING SENSITIVITY
1
Sanger
Sequencing
—25%
2
QPCR –
10%
160,000
rxns/year
3
Sequenom
MassARRAY
– 3%
30,000
rxns/year
4
Digital PCR—
5% in
synthetic
control
5
CAST-PCR—
0.1 % in
synthetic
control, 3% in
cell lines
6
NGS – 0.1%-
0.05% (2000X)
ION TORRENT
7
Single Cell
Analysis
Technologies

12. PHASE 2: EARLY RUNS
INSTALLATION
TRAINING
OPTIMIZATION

13. DNA PROCESSING WORKFLOW
Sample
Preparation
DNA
Extraction
Library Prep
(PCR)
Purify &
Normalize
Libraries,
Pool
Denature &
Dilute
Library Pool
Sequence FASTQ

14. SEQUENCING
AMPLICON
PRIMERS
• Fusion primers
• V4 Target = 254 bp
• Dual barcoding (aka indices)
allows multiplexing of up to
384 samples in a single run
• Custom design for Miniseq
Comeau et al, 2017. mSystems. ASM.

15. BIOINFORMATIC PROCESSING WORKFLOW
De-
Multiplex,
Generate
FASTQ
Sequence
QC
Stitch
Paired-End
Reads
Remove Low
Quality and
Chimeric
Reads
Cluster
sequences
into OTUs
(Database)
QIIME
Diversity
Analysis
Create
report

16. GOOD LABORATORY PRACTICE, REGULATORY CONCERNS
(FOR DVM AND DIAGNOSTIC MARKETING)
• Pre and post-pcr processes are in physically separate rooms to control for contamination.
• Properly controlled experiments – 16S vs 18S rRNA controls
• Written protocol with version control – Bio-protocol or Protocol.io.
• Positive controls
• Negative controls

17. PHASE 3: LATE RUNS
VALIDATION
STANDARDIZATION
TRAINING

18. ILLUMINA RECOMMENDED OPTIMAL METRICS
INPUT: LIBRARY & PHIX
SPIKE-IN (1.8 pM)
OPTIMAL CLUSTERING
(170 – 220 K/MM2)
%PF ≥ 85%
(% Reads Passing
Filter)
%Q30 ≥ 85%
(Quality score per base call.
Error probability = 0.001)
POSITIVE AND
NEGATIVE CONTROLS

19. EXPERIMENTAL RESULTS (N = 28 RUNS)
AVE PHIX SPIKE-IN: 26.5%
AVE CLUSTERING:
160 K/MM2
AVE %PF = 90.3%
AVE %Q30 = 91%
Total reads
sequenced =
221,784,120
Raw reads per
sample = 108,392

20. SEQUENCING QC METRICS
0
0.2
0.4
0.6
0.8
1
1.2
1.4
60
65
70
75
80
85
90
95
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
AVE
SEQ
ERROR
PERCENT
SEQ RUN /FLOW CHIP
% PF AVE %Q30 R1 Error
Optimal ≥85%

21. ACCURACY
1. Composition
2. Taxonomic specificity
3. Sequencing chemistry limitations
Greater specificity and accuracy decrease false positives.

22. THE MICROBIOME
REPORT
• Curated, proprietary database
• Measurements of diversity,
richness, evenness
• Added taxonomic detail
• Actionable insights
Animalbiome ®

23. ADDED TAXONOMIC
DETAIL AND
ACTIONABLE
INSIGHTS
Animalbiome ®

24. ACCURACY AND COMPOSITION - POSITIVE CONTROLS
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
H11 E12 E10b E10 MC* G10 H11_27 H11A F05B
PERCENT
COMPOSITION
SEQ RUN POSITIVE CONTROLS
1. Bacillus 2. Listeria 3. Staphylococcus 4. Lactobacillus 5. Enterococcus
6. Escherichia 7. Pseudomonas 8. Salmonella 9. Other
ZymoBIOMICS Microbial Community DNA Standard

25. KNOWN BIASES IN
16S V4
• Consistent with previous
publications
• Mock community (positive control)
• 16S gene regions show different
taxonomic biases
• Amplicon size is 254 bp
• Accurate to genus
Comeau et al, 2017. mSystems. ASM.

26. NON-TEMPLATE
(NEGATIVE)
CONTROLS-
(NTCS) 0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
9a 11a 13a 15a 16a 16c 17b 17c 17d 18a 19b 19d 19f 21a 23a 26a
Number
of
Reads
Seq Run
Bacteroides Megamonas Fusobacterium Prevotella
Blautia Collinsella Clostridium Lactobacillus
Cetobacterium Ruminococcus Propionibacterium Streptococcus
Escherichia Janthinobacterium Sutterella Stenotrophomonas
Enterococcus Bifidobacterium Pseudomonas Bacillus

27. ACCURACY & SENSITIVITY CONCLUSIONS OF
16S V4 MICROBIOME ASSAY
Less sensitive with
fewer amplicon
regions.
Accurate to genus
level only. Not
species.
Solutions to improve
accuracy and
sensitivity:
Full 16S Gene - Long
Read technology
(PacBio)
Full 16S gene with
short read technology
using LoopSeq
Whole Genome
Shotgun Sequencing