Clinical Metagenomics for Rapid Detection of Enteric Pathogens and Characterization of the Intestinal Microbiome
•
2 likes•4,267 views
High-throughput sequencing, combined with high-resolution metagenomic analysis, provides a powerful diagnostic tool for clinical management of enteric disease. Forty-five patient samples of known and unknown disease etiology and 20 samples from health individuals were subjected to next-generation sequencing. Subsequent metagenomic analysis identified all microorganisms (bacteria, viruses, fungi and parasites) in the samples, including the expected pathogens in the samples of known etiology. Multiple pathogens were detected in the individual samples, providing evidence for polymicrobial infection. Patients were clearly differentiated from healthy individuals based on microorganism abundance and diversity. The speed, accuracy and actionable features of CosmosID bioinformatics and curated GenBook® databases, implemented in the QIAGEN Microbial Genomics Pro Suite, and the functional analysis, leveraging the QIAGEN functional metagenomics workflow, provide a powerful tool contributing to the revolution in clinical diagnostics, prophylactics and therapeutics that is now in progress globally.
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Clinical Metagenomics for Rapid Detection of Enteric Pathogens and Characterization of the Intestinal Microbiome
Similar to Clinical Metagenomics for Rapid Detection of Enteric Pathogens and Characterization of the Intestinal Microbiome (20)
More from QIAGEN
More from QIAGEN (20)
Recently uploaded
Recently uploaded (20)
Clinical Metagenomics for Rapid Detection of Enteric Pathogens and Characterization of the Intestinal Microbiome
- 1. Clinical Metagenomics for Rapid Detection of Enteric Pathogens and Characterization of the Intestinal Microbiome Dr. Rita R. Colwell University of Maryland, College Park Johns Hopkins University Bloomberg School of Public Health CosmosID Inc.
- 2. History/Cholera Metagenomics Microbiomes in Health and Disease Demo
- 3. Culture - Numerical Taxonomy Nucleic Acid (Base Composition) Density Gradient Hybridization Fluorescent Antibody Microscopy Polymerase Chain Reaction (PCR) Next Gen Sequencing Metagenomics 1960 1965 1970 1975 1985 1996 2008 2000 A Timeline of Microbiology
- 4. First Numerical Approaches to Microbial Taxonomy Culture - Numerical Taxonomy Nucleic Acid (Base Composition) Density Gradient Hybridization Fluorescent Antibody Microscopy Polymerase Chain Reaction Next Gen Sequencing Metagenomics 1960 1965 1970 1975 1985 1996 2008 2000
- 5. Culture Limitations A curiosity – All the bacteria don’t grow in laboratory culture
- 6. Solving the Mystery of the “Viable But Non-Culturable (VBNC)” Bacteria Culture - Numerical Taxonomy Nucleic Acid (Base Composition) Density Gradient Hybridization Fluorescent Antibody Microscopy Polymerase Chain Reaction (PCR) Next Gen Sequencing Metagenomics 1960 1965 1970 1975 1985 1996 2008 2000
- 7. JD Oliver, Journal of Microbiology, 2005 Feb; 43 Spec No: 93-100 Over 400 papers have appeared on the VBNC phenomenon and over 1000 papers describing the various aspects of it. Bacteria Described to Enter The VBNC state
- 8. Cholera: A Global Disease • Acute water-related diarrheal disease • Seventh pandemic started in 1960s • Occurs in more than 50 countries affecting approximately 7 million people • Bengal Delta is known as “native homeland” of cholera outbreaks • Since cholera bacteria • exist naturally in aquatic habitats • evidence of new biotypes emerging, it is highly unlikely that cholera will be eradicated but clearly can be controlled by provision of safe drinking water.
- 9. The Copepod Vector Copepods were found to carry approximately 10,000 to 50,000 CFU of V. cholerae per copepod Culture - Numerical Taxonomy Nucleic Acid (Base Composition) Density Gradient Hybridization Fluorescent Antibody Microscopy Polymerase Chain Reaction (PCR) Next Gen Sequencing Metagenomics 1960 1965 1970 1975 1985 1996 2008 2000
- 10. 10 A Simple Solution for Cholera Prevention : Sari Filtration
- 11. The move to genomics…early sixties to the present
- 12. Source: The Institute for Genomic Research Vibrio cholerae Sequenced and published in 2000 Small Chromosome Large Chromosome Culture - Numerical Taxonomy Nucleic Acid (Base Composition) Density Gradient Hybridization Fluorescent Antibody Microscopy Polymerase Chain Reaction (PCR) Next Gen Sequencing Metagenomics 1960 1965 1970 1975 1985 1996 2008 2000
- 13. Genomic re-assortment not serotype defines epidemic clones
- 14. History/Cholera Metagenomics Microbiomes in Health and Disease Demo
- 15. Role of Microbiome in Health and Wellness Acne Alzheimer’s Disease Antibiotic-associated Diarrhea Atherosclerosis & Arthritis Asthma/Allergies Attention Deficit Hyperactivity Disorder Autism Autoimmune Diseases (Multiple Sclerosis, Lupus, Rheumatoid arthritis) Bipolar Disorder Cancer Chronic Fatigue / Fibromyalgia Coeliac Disease Chron’s Disease Cystic Fibrosis Dental Cavities Depression and Anxiety Diabetes Type 1 & 2 Epilepsy Eczema Irritable Bowel Syndrome Gastric Ulcers Malnutrition Narcolepsy Obesity Parkinson’s Disease Ulcerative Colitis
- 16. Identified Bacteria Raw Sequence Reads Biological specimen Community DNA GenBookⓇ Biomarker Matching GenBookⓇ AR/VF Library TetR CIPR mecA ctxA Microbial Identification & Pathogen Characterization GenBookⓇ Database How It Works DNA Sequencing
- 17. CosmosID for Automated Metagenomics in the 21st Century CosmosID Analyzes Microbial DNA sequences • Growing proprietary database of 65,000 microbial genomes (bacteria, viruses, fungi and parasites) • In minutes, our software solution delivers unrivaled specificity and sensitivity • Microbial Identification at subspecies/strain level • Relative abundance of the microbial community • Presence of antibiotic resistance and virulence factors • Sequencing platform agnostic • Can handle both short read and long read NGS data • Illumina, ThermoFisher, Pacific Biosciences and Oxford Nanopore • Commercial products (software and databases are well maintained and continuously updated)
- 18. Infectious Disease – Rapid Evolution § Previously recognized pathogens are evolving faster. § New, potentially dangerous pathogens are emerging every year. § Nosocomial and mixed microbial infections are dramatically increasing. § Many acute infectious diseases have unknown or poorly known etiology § Resident microflora in health and wellness Source: Clinical Infectious Diseases 2013;57(S3):S139–70
- 19. 0.0 0.0 CosmosID_filtered CosmosID BlastMegan_filtered BlastMegan_filtered_liberal LMAT MetaPhlAn Kraken_filtered PhyloSift_filtered DiamondMegan_filtered Kraken OneCodex NBC PhyloSift OneCodex_filtered 0 20 40 60 80 100 percent subspecies BlastMegan_filtered CosmosID_filtered OneCodex_filtered BlastMegan_filtered_liberal DiamondMegan_filtered CosmosID MetaPhlAn Kraken_filtered LMAT PhyloSift_filtered CLARKS Kraken CLARK NBC OneCodex PhyloSift 0 20 40 60 80 100 percent species BlastMeg Cosmos OneCod BlastMegan_filter DiamondMeg Krak PhyloS B C CosmosID Performance Unpublished Data: • 34 datasets of varying complexity and diversity • 12 tools 0.0 F1 0.0 F1 CosmosID_filtered CosmosID BlastMegan_filtered BlastMegan_filtered_liberal LMAT MetaPhlAn Kraken_filtered PhyloSift_filtered DiamondMegan_filtered Kraken OneCodex NBC PhyloSift OneCodex_filtered 0 20 40 60 80 100 percent subspecies BlastMegan_filtered CosmosID_filtered OneCodex_filtered BlastMegan_filtered_liberal DiamondMegan_filtered CosmosID MetaPhlAn Kraken_filtered LMAT PhyloSift_filtered CLARKS Kraken CLARK NBC OneCodex PhyloSift 0 20 40 60 80 100 percent species BlastMegan_filtered CosmosID_filtered OneCodex_filtered BlastMegan_filtered_liberal DiamondMegan_filtered CosmosID MetaPhlAn Kraken_filtered LMAT PhyloSift_filtered CLARKS Kraken CLARK OneCodex NBC PhyloSift 0 20 40 60 80 100 percent genus F1-score precision recall AUPR B A D C Collaboration with Chris Mason, Weil Cornell Medicine
- 20. Accuracy of Relative Abundance ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● −1000100200300400500600700 ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● BLAST−Megan BLAST−Megan−Liberal CLARK CLARK−S CosmosID−filtered CosmosID Diamond Kraken−filtered Kraken LMAT MetaPalette MetaPalette−Specific Metaphlan2 NBC OneCodex−filtered OneCodex Phylosift−filtered Phylosift Percent Difference of Estimated to True Abundance BioPool & NARG1 samples ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●●● ● ●● ●●● ● ● ●●●● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●●●● ● ● ●●●●● ● ● ● ●●● ● ●●● ●● ●●●●● ●●● ●●●●● ● ● ●●●●●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●●● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ●● ● ●● ●●●●● ● ●●● ●●●●● ● ● ●●●●●●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● −1000100200300400500600700800 ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●●● ● ●● ●●● ● ● ●●●● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●●●● ● ● ●●●●● ● ● ● ●●● ● ●●● ●● ●●●●● ●●● ●●●●● ● ● ●●●●●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●●● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ●● ● ●● ●●●●● ● ●●● ●●●●● ● ● ●●●●●●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● BLAST−Megan BLAST−Megan−Liberal CLARK CLARK−S CosmosID−filtered CosmosID Diamond Kraken−filtered Kraken LMAT MetaPalette MetaPalette−Specific Metaphlan2 NBC OneCodex−filtered OneCodex Phylosift−filtered Phylosift Percent Difference of Estimated to True Abundance HC LC samples Synthetic Datasets Biological Datasets Detection is one problem; abundance is much harder
- 21. Biological Specimens (i.e. Stool, CSF, etc.) Sequencing Further analysis using CLC • Functional • Mapping • Assembly CLC Sample to Analysis with CLC + CosmosID Plugin CosmosID CLC Plugin X Y Z BEST MATCH • Microbial Identification (subspecies & strain) • Antibiotic Resistance • Virulence Factors • Relative Abundance • Bacteria, fungi, protists, viruses WGS fasta or fastq file
- 22. Curated Genome Databases § Most comprehensive and largest curated databases (> 65,000 genomes) § Organized as phylogenetic trees § Two types of biomarkers: § Unique to the organism, and § Shared across the phylogenetic lineage in the tree Protists
- 23. CosmosID Use Cases Some Applications: Microbiome Research Clinical Metagenomics Emerging and Re-emerging Pathogen Discovery Polymicrobial Infection Dynamics Hospital-associated Infections Outbreak Investigation Food Safety Functional Food Human Microbiome Home Microbiome Subway Microbiome Animal Microbiome Pharmaceuticals R&D Oil Metagenome Environmental Screening Cosmetics Clinical Trial Analyzed >30K Biological Samples
- 24. History/Cholera Metagenomics Microbiomes in Health and Disease: Microbiome Analysis of Acute Diarrheal Patients Compared with Healthy Individuals
- 25. Total # of NICED samples: 74 Indian Healthy Control (HC): 20 Sick with Unknown Etiology (UE): 28 Sick with Known Etiology (KE): 26 Healthy Human Microbiome Project (HMP): 20 Bacteria Vibrio cholerae Vibrio parahaemolyticus Vibrio fluvialis Aeromonas spp. Campylobacter jejuni Campylobacter coli Shigella Salmonella Escherichia coli Viruses Rotavirus Adenovirus Norovirus Sapovirus Astrovirus Parasites Giardia lamblia Cryptosporidium parvum Entamoeba histolytica Blastocystis hominis Microbiome of Acute Diarrheal Patients Compared with Healthy Individuals In collaboration with the National Institute of Cholera and Enteric Disease (NICED), Calcutta, India Enteric Pathogens Monitored By NICED
- 26. DIARRHEAL PATIENTS HEALTHY INDIVIDUALS A Subpopulation is Overrepresented in Diarrheal Patients Compared to Healthy Individuals γ- Proteobacteria – Firmicutes - Bacteroidetes
- 27. N2_GY16 N2_GY31 N2_GY26 N2_GY09 N2_GY23 N2_GY29 N2_GY30 N2_GY14 N2_GY19 N3_IDH_20 N3_IDH_33 N3_IDH_38 N2_GY22 N2_GY28 N2_GY11 N2_GY12 N3_IDH_37 HMP_SRS023583 HMP_SRS043001 HMP_SRS017433 HMP_SRS019601 HMP_SRS058770 HMP_SRS064557 HMP_SRS015854 HMP_SRS017701 HMP_SRS017307 HMP_SRS015190 HMP_SRS016335 HMP_SRS022609 HMP_SRS056259 HMP_SRS019968 HMP_SRS011586 HMP_SRS013476 HMP_SRS020233 HMP_SRS019161 N3_IDH_10A N3_IDH_2 N3_NICED_27 N2_CSN10 N3_NICED_26 N2_GY20 N2_GY13 HMP_SRS012902 N3_NICED_24 N3_IDH_40 N3_NICED_28 N3_IDH_32 N3_IDH_17 N2_CSN1 N2_CSN6 N2_CSN8 N3_NICED_30 N3_IDH_16 N3_IDH_6A N3_IDH_18 N3_IDH_12 N2_GY17 N3_IDH_9 N3_IDH_4 N3_NICED_22 N3_NICED_25 N2_CSN9 HMP_SRS013215 N2_GY21 N2_GY25 N2_GY18 N2_CSN5 N3_IDH_7 N3_IDH_36 N2_GY27 N2_CSN3 N2_CSN2 N2_CSN7 N2_GY15 Acinetobacter Spirochaetaceae Sphingomonadaceae Shigella Flavobacteriaceae Neisseria Burkholderiaceae Lactobacillus Synergistaceae Salmonella Actinomycetales Actinomycetaceae Lactococcus lactis Moraxellaceae Citrobacter Carnobacteriaceae Micrococcaceae Neisseriaceae Fusobacterium Peptoniphilaceae Enterobacter cloacae complex Enterobacteriales Klebsiella Acidaminococcaceae Campylobacterales Campylobacteraceae Aeromonadaceae Tenericutes Bacillales Family XI. Incertae Sedis Vibrio Coriobacteriales Oxalobacteraceae Methanobacteriaceae Peptostreptococcaceae Burkholderiales Erysipelotrichales Verrucomicrobiaceae Helicobacteraceae Mycoplasmataceae Campylobacter jejuni Actinobacteria Bacteria Brachyspiraceae Selenomonadales Enterococcaceae Bifidobacteriales Bifidobacterium Streptococcus Lactobacillaceae Leuconostocaceae Enterobacteriaceae Escherichia coli Coriobacterineae Desulfovibrionales Fusobacteriaceae Desulfovibrionaceae Coriobacteriaceae Clostridiaceae Rikenellaceae Clostridia Erysipelotrichaceae Eubacteriaceae unclassified Clostridiales Clostridiales Sutterellaceae Bacteroidales Porphyromonadaceae Bifidobacteriaceae Pasteurellaceae Streptococcaceae Prevotellaceae Veillonellaceae Ruminococcaceae Lachnospiraceae Bacteroidaceae Group HC HMP KE UE FamilyLevel Many pathogens can readily be identified from disease patients : Known Etiology : Unknown Etiology
- 29. NICED PCA Bray-Curtis distance HMP HC UE KE Treatment Group -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 Microbiome of Healthy People in India Different From That of Western Europeans Heatlhy Control Unknown Etiology Known Etiology HMP
- 30. Number of Individuals with AMR genes present in microbiome beta.lactamase tetracycline sulphonamide rifampicin quinolone fosfomycin nitroimidazole phenicol macrolide trimethoprim aminoglycoside Unknown Etiology Known Etiology Healthy or Asymptomatic Control 0 4.8 9.6 15 Genes which match at > 50% coverage HMP samples had no genes present which matched at this level of coverage
- 31. Predominance of genes related to carbohydrate metabolism Amino Acids and Derivatives Carbohydrates Cell Wall and Capsule Cofactors, Vitamins, Prosthetic Groups, Pigments DNA Metabolism Membrane Transport Protein Metabolism unclassified Alanine, serine, and glycine Arginine; urea cycle, polyamines Aromatic amino acids and derivatives Branchedunclassifiedchain amino acids Glutamine, glutamate, aspartate, asparagine; ammonia assimilation Histidine Metabolism Lysine, threonine, methionine, and cysteine Proline and 4unclassifiedhydroxyprolineunclassified_1 Aminosugars Central carbohydrate metabolism CO2 fixation Diunclassified and oligosaccharides Fermentation Monosaccharides Oneunclassifiedcarbon Metabolism Organic acids Polysaccharides Sugar alcohols unclassified_2 Capsular and extracellular polysacchrides GramunclassifiedNegative cell wall components GramunclassifiedPositive cell wall components unclassified_3 CRISPsDNA recombination DNA repair DNA replicationDNA uptake, competence unclassified_4 ABC transporters Protein and nucleoprotein secretion system, Type IV Protein secretion system, ChaperoneunclassifiedUsher pathway (CU) Protein secretion system, Type II Protein secretion system, Type III Protein secretion system, Type VI Protein secretion system, Type VII Protein secretion system, Type VIII (Extracellular nucleation/precipitation pathway, ENP) Protein translocation across cytoplasmic membrane Sugar Phosphotransferase Systems, PTS TRAP transporters Uniunclassified Symunclassified and Antiporters Protein biosynthesis Protein degradation Protein folding Protein processing and modification Secretion Selenoproteins Arabinose Sensor and transport moduleCell Division and Cell Cycle Central metabolism Clusteringunclassified based subsystemsDormancy and Sporulation Fatty Acids, Lipids, and Isoprenoids Iron acquisition and metabolism Metabolism of Aromatic Compounds Miscellaneous Motility and Chemotaxis Nitrogen Metabolism Nucleosides and Nucleotides Phages, Prophages, Transposable elements Phages, Prophages, Transposable elements, Plasmids Phosphorus Metabolism PhotosynthesisPotassium metabolism Predictions based on plantunclassified prokaryote comparative analysis Regulation and Cell signaling Respiration RNA Metabolism Secondary Metabolism Stress Response Sulfur Metabolism Transcriptional regulation Virulence Virulence, Disease and Defense unclassified Alanine, serine, and glycine Arginine; urea cycle, polyamines Aromatic amino acids and derivatives Branchedunclassifiedchain amino acids Glutamine, glutamate, aspartate, asparagine; ammonia assimilation Histidine Metabolism Lysine, threonine, methionine, and cysteine Proline and 4unclassifiedhydroxyprolineunclassified_1 Aminosugars Central carbohydrate metabolism CO2 fixation Diunclassified and oligosaccharides Fermentation Monosaccharides Oneunclassifiedcarbon Metabolism Organic acids Polysaccharides Sugar alcohols unclassified_2 Capsular and extracellular polysacchrides GramunclassifiedNegative cell wall components GramunclassifiedPositive cell wall components unclassified_3 CRISPsDNA recombination DNA repair DNA replicationDNA uptake, competence unclassified_4 ABC transporters Protein and nucleoprotein secretion system, Type IV Protein secretion system, ChaperoneunclassifiedUsher pathway (CU) Protein secretion system, Type II Protein secretion system, Type III Protein secretion system, Type VI Protein secretion system, Type VII Protein secretion system, Type VIII (Extracellular nucleation/precipitation pathway, ENP) Protein translocation across cytoplasmic membrane Sugar Phosphotransferase Systems, PTS TRAP transporters Uniunclassified Symunclassified and Antiporters Protein biosynthesis Protein degradation Protein folding Protein processing and modification Secretion Selenoproteins 5 10 15 Average Abundance (%) Functional Groups Level 1 Abundance < 5% Functional Groups Level 1 Abundance > 5% Functional analysis
- 32. Qiagen Functional analysis – Reinforces the Community Composition Beta-diversity analysis Permutation analysis (significance of clustering) Differential abundance analysis Evaluation of differentially abundant function HMP Indian healthy HMP Indian healthy Ontology (GO) Clustering based on Pfam Clustering based on Gene
- 33. Summary • Microbial communities found in the healthy volunteers suggest that the microbiome of healthy humans of Indian descent is markedly different than those of Western European descent. • Indian population may tolerate low number of pathogenic microorganisms that may indicate a “disease state” for Western European descent • Metadata revealed that patients who exhibited profound watery diarrhea contained in their microbiome pathogens primarily of the Escherichia coli complex, namely pathogenic E. coli and Shigella species. • Multiple pathogens can readily be identified from disease patients • Microbial community of Indian population encodes alarming rate of antibiotic resistance genes • Functional analysis of the Indian microbiome indicates predominance of carbohydrate metabolism genes • Over abundance of cyto-/hemolysis genes observed in unknown etiology help explain diseased state
- 34. Pilot Studies Underway: NGS based (culture free) direct detection Study Area Sample Type Wound and surgical Infections Tissues, aspirates, swabs Infective Endocarditis cardiac valves Broad range pathogen detection CSF and Biopsies HCAP BAL specimens; Necrotizing Fasciitis Tissues: Muscle, Lung, Liver Neonatal Sepsis blood, CSF, urine Orthopedic Infection Pure isolates Cystic Fibrosis and UTI Stool and Urine Study Area Sample Type Healthcare Associated Infections: Strain subtyping and molecular epidemiology Hospital Isolates HAI: infection control and source tracking Isolates and biofilms Broad Range Pathogen detection Blood; Ulcer, isolates Empyema Plural effusion Neutropenic Infection & Aseptic Meningitis Blood, CSF, Throat swab etc Strain ID and sub-typing Clinical Isolates Prosthetic Joint Infection Tissue, swab Lyme Disease Blood, CSF CosmosID is working with the top research hospitals in the United States and Europe
- 35. Enabling End to End Microbiome Research with Great Partners Sample Collection & Biobanking DNA Isolation Sequencing High Resolution Microbial Characterization and Identification Functional Analysis Sequencing Partners Sample Action
- 36. History/Cholera Metagenomics Microbiomes in Health and Disease Demo
- 37. How CosmosID Works Reference database Metagenomic SampleUnique and Shared Regions Identified Sample Matched with Database Identification Staphylococcus aureus subsp aureus USA300 TCH959 Propionibacterium acnes KPA171202 Enterococcus faecalis OG1RF