Next-Gen Sequencing in the High School...

April 11, 2017 ▪ BOSLAB ▪ Mark Hartman

Next-Gen Sequencing in the High School Classroom

Outline • Introduce the BioSeq program • Walk through one of our favorite projects as an example

of what we do • Technical details about the experiment • How we run it in schools

• Show examples of some other projects

BioSeq program overview • What is BioSeq? Educational outreach program from

Tufts, Department of Chemistry, PI Prof. David Walt • What do we do? Increase access to next-generation

sequencing technology, promote scientific research in general, target the high school audience

• How are we able to do this? Funded by NIH Science Education Partnership Award (SEPA)

The need for genetics outreach • Genetics information is

increasingly commonplace • In the future we are likely to

encounter sequencing in many contexts

• Sequencing and bioinformatics remain inaccessible to the public

• Disconnect between current science and the science classroom

Bioinformatics Inquiry through Sequencing

Goal: Improve interest in and understanding of bioinformatics and sequencing, and improve attitudes toward scientific research

BioSeq

Educational sequencing

center Classroom modules

Summer course

Support science fair projects, class projects, independent summer

projects, etc.

Run lab experiments in local high school

classrooms

Six-week summer class at Tufts for high school

seniors to introduce sequencing and bioinformatics

Assessment

• Most activities in classroom, some activities in lab at Tufts • We allow two weeks for sample processing and sequencing

Overview of lessons

Classroom experiment

(data collection)

Introduction (research question)

Students analyze data (interpretation

of results)

Sample processing and sequencing at

Tufts

Approach Inquiry-based learning • Inquiry inspires student

interest in science, and improves retention of science content and procedures

Near peer mentoring • Younger students can

envision themselves as future scientists

• Undergrads involved in both design and teaching

MD Pluth, et al. Collaboration and Near-Peer Mentoring as a Platform for Sustainable Science Education Outreach. Journal of Chemical Education. 92 625-630.

Blanchard et al. Is inquiry possible in light of accountability?: A quantitative comparison of the relative effectiveness of guided inquiry and verification laboratory instruction.

Science Education. (2010)

Current and future modules • Personal Microbiome Portrait • Genetics of Race • Mutations Investigation • Food Microbiomes • Microbiomes of Water • Personal Genomics • DNA Sequencing for Forensics

Exploring Inter- and Intra-personal Variation in Microbial Communities

Hartman MR, Harrington KT, Etson CM, Fierman MB, Slonim DK, Walt DR. FEMS Microbiology Letters.

10.1093/femsle/fnw266, (In press, 2016.)

Our bodies, our microbiomes

1. Sender, R., Fuchs, S. & Milo, R. (2016) Revised estimates for the number of human and bacteria cells in the body. PLOS Biology 14(8): e1002533. 2. Qin et al. (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59-65.

Karyome 10 trillion cells 3 Gb per cell

~20,000 genes

Microbiome 1 microbial cell per human cell1

100-1,000 unique species >500,000 genes2

Mitochondria 100 mitochondria per cell, 5 mtDNA per

mitochondria = 5 quadrillion mtDNA 17 Kb per mtDNA

37 genes

Okay, so microbiomes are important… but why are they a good fit for a high school NGS outreach program?

Why are microbiomes a good topic for NGS experiments? • Most microbes cannot be cultured • NGS has revolutionized the study

of microbiomes

Why are microbiomes a good fit for high school outreach? • Personal microbiomes = personal

relevance for students • Novelty of the topic promotes open-

ended exploration • Health implications are accessible

Stewart “Growing unculturable bacteria.” J. Bacteriol. 194 4151-4160. (2012).

Kaeberlein, Lewis, Epstein “Isolating "Uncultivable" Microorganisms in Pure Culture in a Simulated Natural Environment.” Science. 296 1127-1129. (2002).

• Most activities in classroom, some activities in lab at Tufts • We allow two weeks for sample processing and sequencing

Overview of lessons

Classroom experiment

(data collection)

Introduction (research question)

Students analyze data (interpretation

of results)

Sample processing and sequencing at

Tufts

Research question

versus

Sample collection Hard Palate Retroauricular Crease

Swab the entire hard palate for 20 seconds

Swab back and forth along the entire crease 50 times

Sample collection • Swab the entire hard

palate for 20 seconds • Use moderate force • Rotate the swab so all

surfaces come in contact with your palate

• Place the swab in a sterile tube

• CAREFULLY use scissors to cut the swab handle

• Use a marker to label your tube

Hard Palate

Swab the entire hard palate for 20 seconds

Sample processing PCR 1

Amplify desired regions Cell lysis

PCR clean up

PCR clean up Library pooling

Sampling

Quality check

PCR 2 Add unique barcode to each sample

Sequencing

Targeted vs. Total sequencing

From <https://www.neb.com/tools-and-resources/feature-articles/addressing-challenges-in-microbiome-dna-analysis>

Recall our research question: We want to identify species and then compare microbial populations.

Targeted amplification • Primers flank V3 and V4 hypervariable regions of

16S ribosomal RNA gene • Single amplicon ~460 bp

Kuczynski et al. “Experimental and analytical tools for studying the human microbiome.” Nature Reviews Genetics 13, 47-58 (2012).

Klindworth et al. “Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next‐generation sequencing‐based diversity studies.” Nucleic Acids Res 41(1) (2013).

Forward: TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG(N)0-3CCTACGGGNGGCWGCAG

Reverse: GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG(N)0-3GACTACHVGGGTATCTAATCC Underlined region contains “adapter” (sequencing primer) and “linker” (sequence for subsequent index PCR).

Sample processing PCR 1

Amplify desired regions Cell lysis

PCR clean up

PCR clean up Library pooling

Sampling

Quality check

PCR 2 Add unique barcode to each sample

Sequencing

Sequencing process • Sequencing by synthesis

technology: Sequence DNA by observing the synthesis of a complimentary strand

• Capacity ~10 million clusters per run

Illumina MiSeq sequencer

MiSeq Standard Flow Cell

Compare to the HiSeq X series which generates 6 billion reads per run…

Sequencing by synthesis

K. Voelkerding, et al, Clinical Chemistry, 2009

Sequencing output Read 1: GCCTACGGGTGGCAACAGTGGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGACGGCCTTCGGGTTGTAAAGCTCTGTTAATCGGGACGAAAGGTCTTCTTGCGAATAGTTAGAAGAATTGACGGTACCGGAATAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGA

Read 2: TGGCCTACAGTAGTCACTGTCTCTTATACACATCTCCGAGCCCACGAGACTGCAGCTAATCTCGTATGCCGTCTTCTGCTTGAAAAAAAAAAAAAAAAAAGAAGTAATGCAGGGGTGTGAGTGACTAAGAGGAGAGTGGTATGACATAAAACTAAGAAAACAACTAAAACAAGGGGAGGGCACAATATAACGTATCTCTGAGATGGTACTATGTGTCTGTGTAGCATCTGACATAATAACGTCCATATTCA

Read 31,164: CCTACGGGTGGCTGCAGTGGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGACGGCCTTCGGGTTGTAAAGCTCTGTTAATCGGGACGAAAGGTCCTCTTGCGAATAGTTAGAGGAATTGACGGTACCGGAATAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGAT

…

Raw sequencing data: After base calls, demultiplexing, adapter trimming

Desired output data: Relative abundances of microbes so we can compare populations

Workflow for comparing microbiome samples

Raw reads

Join paired end reads

Pick operational taxonomic units

Diversity analysis

Stratify samples

QIIM

E to

ols

Relative abundances

Per-sample analyses

Cross-sample analyses

QIIME is an open-source bioinformatics pipeline for performing microbiome analysis (developed primarily in the Knight and Caporaso labs)

Relative abundances per sample

Overall ~1,000 taxa identified …

ACCTAC TCTGAT

CGTCAT

Our “raw data” sequences Small database of known sequences

Cross-sample comparisons

Sample 2 Sample 1

Gemella Porphyromonas Actinobacillus Mannheimia Leptotrichia

Rothia Veillonella

Haemophilus Streptococcus

Prevotella

Anaerococcus Propionibacterium

Staphylococcus Peptoniphilus

Finegoldia

Sample 1 Sample 2

Lesson #5: Results and discussion

• Health disclaimers • Results • Common bacteria

Streptococcus: Typically thrives in warm and wet environments. Propionibacterium: Consumes sebum, a type of oil that be produced on your skin. Actinomyces, Clostridium, Fusobacterium, Prevotella, Veillonella: Examples of obligate anaerobes (microbes that are not tolerant of oxygen).

Return to the research question

versus

• Revisit the students’ original hypotheses

• Survey students on their actual data-supported conclusions

• Why? What are the implications?

(Is this experimental outcome supported by more rigorous analysis?)

At the completion of the experiment, students are rewarded with stickers

Calculate Unifrac score for ALL samples Phylogenetic tree Dissimilarity matrix in Excel

(Weighted unifrac scores)

Body site versus individual variation

p < 1x10-15

Higher dissimilarity metric arises from body site-to-site variation (same individual) than from person-to-person variation (same body site)

• Find the distribution of dissimilarity scores for each category of sample comparisons

(Is this really supported by more rigorous analysis?)

Personal Microbiome wrap-up • Probably our most popular outreach activity, reaching

hundreds of students in the local area • Positive assessment results indicate gains in knowledge

and attitudes toward science

Like 23andMe, but for microbiomes

https://ubiome.com/explorer

Next-Gen Sequencing in the High School...

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