🌍 Introduction: Why Study Microbial Communities?

Microbes are everywhere — in air, soil, water, humans, and even in extreme environments. They drive:

• Natural cycles (carbon, nitrogen, etc.)
• Human health and biotechnology 🧫
• Biogas, water treatment, nutrient recovery, and even bioplastics production ♻️

Understanding who’s there (identification) and what they do (function) is key. But bacteria are invisible 👀 — so we need methods to identify them!

🔬 Methods to Identify Microbes

1. Cultivation / Isolation

Grow bacteria on plates. ✅ Pros: allows study of metabolism and traits. ❌ Cons: only a small fraction can be cultured, slow, and selective. Metaphor: studying wild wolves 🐺 by raising a poodle 🐶 — not the same!

2. FISH (Fluorescent In Situ Hybridization)

You’ve probably used this before! ✅ Visualizes bacteria in situ, no need to culture. ❌ Slow, limited by probe design (you only see what you look for). Good for morphology and spatial distribution.

3. DNA Sequencing (16S rRNA gene)

The core of modern microbiome analysis.

• Targets the 16S ribosomal RNA gene, about 1500 bp long.
• Contains:
  • Conserved regions 🧱 → for universal primer binding
  • Hypervariable regions 🎨 → unique bacterial “fingerprints”

✅ Fast, affordable, and cultivation-independent. ❌ You get names, not functions directly, and biases exist (primer mismatch, copy number variation).

🧬 The 16S Workflow: From Sample to Data Table

1. Sampling

Critical first step. Must represent the environment (soil, wastewater, etc.). Bad sample = bad data.

2. DNA Extraction

Goal: isolate pure DNA.

1. Break cells open 🔨
2. Remove proteins, RNA, and contaminants
3. Store clean DNA (−20 °C or −80 °C)

3. Library Preparation

Prepare DNA for sequencing:

• Amplify specific 16S regions via PCR
• Add adapters (for sequencing) and barcodes (for identifying samples)
• Clean up PCR products (remove junk)
• Check quality via gel or TapeStation

Samples are pooled → reduces cost.

🧫 Sequencing Technologies

🧩 Illumina

• Short-read sequencing (e.g., 2×250 bp)
• Uses bridge amplification and sequencing-by-synthesis (detects fluorescent signals per base). Widely used, high accuracy.

🔄 PacBio

• Long-read sequencing.
• Reads a circularized DNA template multiple times for accuracy. Also detects light, like Illumina.

⚡ Nanopore

• Long-read sequencing using a tiny pore 🕳️
• DNA passes through → changes in electrical current reveal bases.
• Portable and real-time sequencing (common in many labs today).

🧠 Bioinformatics Pipeline (after sequencing)

1. Raw reads → remove primers, barcodes, adapters.
2. Merge forward/reverse reads.
3. Detect unique sequences (ASVs) or cluster (OTUs).
   • OTU: 97 % similarity threshold.
   • ASV: 100 % identical sequences (higher resolution).
4. Remove chimeras (artifacts).
5. Demultiplex (assign reads to samples).
6. Taxonomic assignment → map sequences to a database. Output: a feature table (ASV × sample matrix).

🧮 Databases for 16S Classification

👉 Use ecosystem-specific DBs when possible for better functional inference.

📊 The Output: The ASV Table

A matrix like this:

This connects identity (taxonomy) with abundance (how much is there).

🌿 Diversity Metrics

🧩 Alpha Diversity (within-sample)

Measures how rich and even a single community is.

• Richness = number of species 🍎🍌🍇
• Evenness = balance between them

High diversity → more stable ecosystems.

🔁 Beta Diversity (between-samples)

Compares similarity or dissimilarity between samples. Example: microbiomes in healthy vs sick individuals 🧍‍♂️🧍‍♀️ Similar composition → similar state.

💎 Core Communities

Bacterial groups found consistently across many samples. Helps identify “key players” and environmental drivers in ecosystems.

🧩 Key Takeaways

• Microbes are fundamental in environmental processes 🌎
• 16S rRNA sequencing is the gold standard for community profiling
• Each step (sampling → extraction → sequencing → analysis) matters
• ASVs offer fine taxonomic resolution
• Interpretation relies on diversity, taxonomy, and ecology metrics