DNA Cloning in E.coli MB7008 Notes PDF

MB7008 Notes DNA cloning in E.coli 1. Cloning Overview Purpose: Cloning allows for the creation of recombinant DNA molecules, gene identification, and the production of specific proteins. Key Steps: o Ligation of foreign DNA into a plasmid vector. o Transf...

MB7008 Notes DNA cloning in E.coli 1. Cloning Overview Purpose: Cloning allows for the creation of recombinant DNA molecules, gene identification, and the production of specific proteins. Key Steps: o Ligation of foreign DNA into a plasmid vector. o Transformation into E. coli. o Selection of recombinant clones (e.g., antibiotic resistance). 2. Restriction Enzymes Function: Cut DNA at specific sequences (palindromic sites). Types: o Type II restriction enzymes cut within recognition sequences, leaving either blunt or sticky ends. o Isoschizomers: Enzymes recognizing the same sequence but cutting differently. Example: EcoRI creates sticky ends at 5'…GAATTC…3'. 3. Ligation and Cloning Ligation: DNA ligase joins DNA fragments, using ATP to seal the phosphodiester bond between 5’ and 3’ ends. Ligation Conditions: Low DNA concentration promotes intramolecular ligation (circle formation), while high concentration promotes intermolecular ligation (recombinants). 4. Vector Considerations Plasmid Vectors: o Example: pBR322 (ampicillin and tetracycline resistance). o pUC19: Contains lacZ for blue-white screening (inactivation leads to white colonies for recombinants).  Other Vectors: o Lambda phage (5-25 kb insert). o Cosmid (35-45 kb insert). o BAC (up to 300 kb). 5. Transformation Methods Chemical Transformation: Involves divalent ions (e.g., Ca²⁺) and heat shock to introduce DNA into cells. Electroporation: Uses an electric pulse to create membrane pores, allowing DNA entry. 6. Screening for Recombinants Blue-White Screening: Recombinants disrupt lacZ, producing white colonies on X- Gal medium. Library Screening: Hybridization, PCR, or protein expression assays identify desired clones. 7. PCR Cloning PCR primers can include restriction sites for easier cloning. TA Cloning: Uses vectors with T residues to ligate PCR products with A overhangs (e.g., Promega TA Cloning vector). 8. Advanced Methods Gibson Assembly: Uses exonucleases and DNA polymerase to assemble DNA fragments without restriction sites. Topo Cloning: Vectors have topoisomerase to ligate blunt-end PCR products efficiently. 9. Practical Considerations Host Strain: E. coli strains used in cloning are often engineered to improve plasmid stability (e.g., recA⁻ to reduce recombination). Plasmid Purification: Methods include alkaline lysis, anion-exchange resins, and CsCl-EtBr gradient centrifugation for high-quality plasmid extraction. 10. MCQs (Key Concepts) Correct cloning order: Ligation → Selection → Transformation. Dephosphorylation of vectors reduces background by preventing self-ligation. Proof-reading polymerases ensure fewer mutations during PCR cloning. cDNA Libraries 1. cDNA and RNA The human genome (~3x10⁹ bp) contains ~21,000 protein-coding genes. Protein-coding sequences (~1% of the genome) are found in mRNA, derived from hnRNA via splicing. Both hnRNA and mRNA have a 5' cap and a 3' poly-A tail for stability. 2. Polyadenylation and mRNA Isolation mRNA is ~1-2% of total RNA, separated from other RNAs using affinity chromatography due to its poly(A) tail. Poly(A) tails (~200 nucleotides) stabilize mRNA, which is not encoded in the genome but added post-transcriptionally. 3. cDNA Production cDNA synthesis starts with reverse transcriptase converting mRNA into a complementary DNA strand. Various methods for second strand synthesis, such as Gubler and Hoffman's, use RNase H and DNA polymerase I. Ligation into plasmid vectors can be inefficient, but adding adapters improves this process. 4. Library Screening cDNA libraries contain all mRNA sequences from the sample; rare mRNAs are also rare in the library. Methods like in situ colony screening and hybridization probes are used to identify the desired cDNA clone. 5. Screening Techniques Oligonucleotide probes: Designed from protein sequences, suitable for cDNA but not genomic libraries. Antibody screening: Detects expressed cDNA in expression vectors by binding to specific proteins. 6. Confirmation and Conclusion Sequencing the cDNA clone confirms its correctness, typically using the Sanger method. cDNA lacks non-coding regions, and improved methods focus on efficiency and yield in cDNA cloning. Antibody Production: 1. Antibody Basics Structure: Antibodies have two identical heavy chains and two identical light chains, forming antigen-binding sites (variable regions) and effector sites (constant regions). Types: IgA, IgD, IgE, IgG, IgM. Functions: o Effector Mechanism: Interacts with cellular receptors (e.g., Fc receptors on phagocytic cells). o Antigen Binding: Determined by variability in complementarity-determining regions (CDRs). 2. Monoclonal Antibodies Definition: Produced by a single B cell clone, binding to one specific epitope. Creation: B cells fused with myeloma cells form hybridomas that produce antibodies indefinitely. Advantages over Polyclonal Antibodies: o High epitope specificity. o Genetically manipulable (e.g., humanized antibodies). o Almost infinite production once established. 3. Production Process Clonal Selection: B cells specific to an antigen are selected, immortalized by fusion with myeloma cells. HAT Selection: Used to select hybridomas by killing unwanted cells. Screening: Supernatants from cultured cells are tested for antibody production. Stable clones are expanded and preserved. 4. Applications Affinity Purification: McAbs are used to purify specific antigens. Immunohistochemistry: Locating specific cells within tissues using antibodies. ELISA: Quantifying specific proteins using enzyme-linked antibodies.  Western Blotting: Detecting specific proteins in a sample. 5. Historical Context Nobel Prize (1984): Awarded for the development of monoclonal antibodies based on immune specificity and clonal selection. Researchers: Milstein and Köhler pioneered hybridoma technology in the 1970s. Sequence Similarity Searching 1. Introduction to Sequence Similarity Searching Definition: A method used to compare a query sequence (DNA, RNA, or protein) against a database of known sequences to find regions of similarity. Purpose: To identify homologous sequences, infer functional relationships, detect evolutionary connections, and annotate genes or proteins. 2. Key Concepts Homology: Similarity due to shared ancestry. It can be: o Orthologs: Genes in different species from a common ancestor, typically performing the same function. o Paralogs: Genes within the same species that arose by duplication and may have new functions. Similarity vs. Identity: o Similarity: The extent of matching between sequences, considering conservative substitutions (e.g., lysine to arginine in proteins). o Identity: Exact matching of residues between sequences (no substitutions). 3. Types of Searches Global Alignment: Compares sequences across their entire length (e.g., Needleman-Wunsch algorithm). Local Alignment: Finds regions of highest similarity within parts of sequences (e.g., Smith-Waterman algorithm). Database Searches: Look for matches between a query sequence and a database using tools like BLAST or FASTA. 4. Similarity Searching Tools BLAST (Basic Local Alignment Search Tool): o The most commonly used tool for sequence comparison. o Types of BLAST: ▪ BLASTn: DNA vs. DNA sequences. ▪ BLASTp: Protein vs. protein sequences. ▪ BLASTx: DNA query translated into protein, compared to protein database. ▪ tBLASTn: Protein query against a translated nucleotide database. o E-value (Expect Value): Represents the number of hits expected by chance. The lower the E-value, the more significant the match. FASTA: Another tool similar to BLAST but can handle longer query sequences better. 5. How BLAST Works Seeding: Identifies short matching sequences (words) between the query and the database. Extension: Extends these initial matches to find larger regions of similarity. Scoring: o Substitution Matrices (for protein sequences): Used to score alignments based on evolutionary likelihood. ▪ PAM (Point Accepted Mutation): Based on evolutionary divergence. ▪ BLOSUM (Blocks Substitution Matrix): Based on conserved blocks in protein families. o Gap Penalties: Penalize gaps (insertions or deletions) introduced during alignment to optimize score. 6. Evaluating Search Results Bit Score: Reflects the quality of the alignment; higher bit scores indicate better matches. E-value: Measures statistical significance; the smaller the E-value, the less likely the match is due to random chance. Percent Identity: Shows the percentage of exact matches between the query and the subject sequence. Alignment Length: The length of the aligned region in the search. 7. Applications of Sequence Similarity Searching Gene and Protein Annotation: Identifying unknown genes/proteins by comparing them to annotated sequences. Evolutionary Studies: Identifying orthologous and paralogous genes to study evolutionary relationships. Function Prediction: Inferring the function of a sequence based on similarity to known functional genes or proteins. Medical Applications: Identifying disease-related genes or proteins by comparing patient sequences with known sequences. 8. Advanced Techniques PSI-BLAST (Position-Specific Iterated BLAST): o A variation of BLAST that identifies more distant homologs by using multiple iterations to build a position-specific scoring matrix. HMMER: o Uses hidden Markov models (HMMs) to find more sensitive alignments, especially useful for detecting remote homologs. CLUSTAL Omega and MUSCLE: o For multiple sequence alignment (MSA), used to align more than two sequences simultaneously. 9. Challenges and Considerations False Positives: Matches that appear significant but are not biologically relevant. Database Size: Larger databases increase search time and the probability of random matches, making E-values more critical.  Query Length: Very short or highly repetitive queries may yield unreliable results. Quality of Reference Databases: Reliable results depend on the completeness and accuracy of the database being searched. Proteomics 1. Introduction to Proteomics Definition: Study of the entire set of proteins, their modifications, expression patterns, and interactions. Key Questions: o What proteins are present? o What are their quantities and modification states? o How do they interact with other proteins or molecules? 2. Proteomics Approaches Top-Down Proteomics: Studies intact proteins to determine their structure and modifications. Bottom-Up Proteomics: Breaks down proteins into peptides, identifies them using mass spectrometry, and reassembles the data to deduce protein identities. 3. Key Proteomics Techniques Protein Digestion: Proteins are digested into peptides using enzymes like trypsin, which cleaves at lysine and arginine residues. Reduction & Alkylation: Disulphide bonds are broken using DTT, and cysteine residues are alkylated with iodoacetamide to prevent reformation. Mass Spectrometry (MS): o Measures the mass-to-charge ratio (m/z) of peptides. o Tandem MS/MS: Further fragments peptides to deduce their sequence. o LC-MS: Separates peptides using liquid chromatography before MS detection. 4. Protein Identification Peptide Mass Fingerprinting (PMF): Identifies proteins by matching peptide masses to known databases. In-Silico Digestion: Computationally predicts peptide fragments from protein sequences for comparison with experimental MS data. 5. Quantification in Proteomics Relative Quantification: Compares protein abundance between samples using techniques like 2D gel electrophoresis or chemical labeling (e.g., iTRAQ, TMT). Absolute Quantification: Determines exact protein quantities using internal standards or label-free approaches. 6. Post-Translational Modifications (PTMs) Common PTMs: Phosphorylation, glycosylation, ubiquitination, acetylation, and methylation. PTMs regulate protein function, interactions, and localization. 7. Applications of Proteomics Clinical Diagnostics: Identifying disease biomarkers. Drug Development: Discovering protein targets for therapeutic interventions. Molecular Network Discovery: Understanding protein-protein interactions and signaling pathways. 8. Challenges and Considerations Data Complexity: High volume of data requires advanced analysis tools. FDR (False Discovery Rate): Used to assess the accuracy of protein identifications from MS data. Lecture Notes: Manipulation of Gene Expression (Dr. Veryan Codd) 1. Why Manipulate Gene Expression? Investigating Gene Function: Reducing or increasing gene expression to understand its role in biological processes. o Loss of Function (LOF): Reducing or knocking down gene expression (e.g., RNA interference). o Gain of Function (GOF): Overexpressing genes to observe phenotypic changes. Disease Models: Creating animal or cell models (e.g., FTO gene for obesity) to study disease mechanisms. Gene Therapy: Modifying gene expression as potential treatment. 2. Methods of Manipulation Overexpression: o Transient Overexpression: Using vectors with constitutive promoters, resulting in high but temporary gene expression. Useful for short-term studies. o Stable Overexpression: Inserting a gene into the genome for long-term expression. This can involve viral delivery (e.g., retroviral, lentiviral), but there is a risk of disrupting other genes. Gene Knockdown: o RNA Interference (RNAi): Widely used for post-transcriptional gene silencing. It involves using siRNA or shRNA to degrade target mRNA, reducing gene expression but not achieving a complete knockout. o Pros and Cons: RNAi is cost-effective and commercially available but can lead to off-target effects. 3. CRISPR-Cas9 for Gene Editing CRISPR-Cas9: A bacterial system adapted for genome editing. It uses the Cas9 enzyme and a guide RNA (gRNA) to create double-strand breaks (DSB) at specific genome sites. Applications: o Gene Knockout (KO): Achieved by introducing indel mutations through non- homologous end joining (NHEJ), leading to gene inactivation.  o Gene Knock-In (KI): Specific modifications through homologous recombination (HDR), though this method is less efficient than NHEJ. CRISPR Variants: o CRISPRi: Inhibition of gene expression using a deactivated Cas9 (dCas9) fused with repressor domains. o CRISPRa: Activation of gene transcription by using dCas9 fused to activators. 4. Experimental Considerations Which Method to Choose?: Depends on the time frame (short vs long-term), phenotypic outcome (quick changes vs gradual), and the cell type used (primary cells, immortalized cell lines, or stem cells). Primary vs. Immortalized Cells: o Primary Cells: More biologically relevant but harder to grow and expensive. o Immortalized Cells: Easier to culture but often derived from cancer cells, so they may not be physiologically normal. o Stem Cells: Can differentiate into multiple cell types but are difficult to culture and require specialized media. 5. Challenges in Gene Manipulation Off-Target Effects: Can occur with RNAi or CRISPR, making it essential to verify gene manipulation with multiple approaches (e.g., sequencing, protein quantification). Ethical Concerns: Particularly relevant for embryonic stem cell research. Clonal Variability: In stable overexpression, different integration sites can produce varying phenotypes, so multiple clones must be analyzed. 6. Summary There are several techniques for manipulating gene expression, including overexpression, RNAi, and CRISPR. The choice of technique depends on the research goals, the biological system, and logistical factors like cost and time. No single method works for all scenarios, and older, established techniques are still commonly used Lecture Notes: Identifying Genetic Causes of Human Disease (Dr. Veryan Codd) 1. Why Study Genetic Causes of Disease? Understanding Inheritance: Helps in genetic counseling and predicting disease risk. Elucidating Molecular Mechanisms: Identifying mutations helps in understanding the disease at a molecular level. Applications: o Drug Targets: Identifying genes involved in disease can lead to the development of targeted therapies. o Precision Medicine: Personalizing treatments based on genetic information. o Gene Therapy: Correcting genetic mutations to treat diseases. 2. Types of Genetic Variation Numerical Variations: o Gain or loss of entire chromosomes (e.g., trisomy 21 in Down syndrome). o Loss of autosomes is usually lethal; gain of chromosomes 13, 18, or 21 is viable. Structural Variations: o Deletions, duplications, inversions due to replication errors or faulty DNA repair mechanisms. Detection Methods: o Karyotyping: Visualizing chromosome number and structure. o Array CGH: Detecting deletions or duplications by comparing test and reference DNA. o Whole Genome Sequencing: Detects both numerical and structural variations, though it's more expensive. 3. Types of Inheritance Autosomal: Trait carried on non-sex chromosomes (e.g., cystic fibrosis).  Sex-linked: Trait carried on X or Y chromosomes (e.g., hemophilia). Mitochondrial: Traits carried on mitochondrial DNA, inherited maternally. 4. Monogenic vs. Complex Disorders Monogenic Disorders: Caused by mutations in a single gene (e.g., sickle cell anemia). They follow Mendelian inheritance patterns and are relatively rare. Complex Disorders: Involve multiple genes and often have environmental factors (e.g., diabetes, coronary artery disease). 5. Identifying Mutations in Monogenic Disorders Candidate Gene Approach: Focuses on sequencing genes where biology suggests a role in the disease. This method is biased and may miss mutations in unknown genes. Whole Exome/Genome Sequencing: Unbiased and comprehensive, though expensive. It identifies large numbers of variants, which need filtering based on frequency, predicted effect, and presence in healthy individuals. 6. Complex Traits and Genome-Wide Association Studies (GWAS) Complex diseases do not follow simple inheritance patterns and involve multiple genes. GWAS: Compares the DNA of affected and unaffected individuals to identify single nucleotide polymorphisms (SNPs) associated with disease. o Bonferroni Correction: Used to set a threshold for significance (p < 5 x 10⁻⁸) to reduce false positives. o False Discovery Rate (FDR): Helps control for multiple testing errors, with acceptable rates at 1% or 5%. 7. Identifying Causal Genes and SNPs Once an associated SNP is found, researchers must determine which gene in the region is involved in the disease. Bioinformatics Tools: Used to predict SNP function, such as whether a missense mutation changes protein function or alters gene expression. 8. Genetic Risk Scores Genetic risk scores aggregate the effects of multiple genetic variants to predict an individual's risk of developing a disease. Application: Particularly useful for diseases like coronary artery disease (CAD) where large-scale data (e.g., UK Biobank) have been analyzed. 9. Genetic Testing for Cancer Some cancers are linked to inherited mutations (e.g., BRCA1/BRCA2 in breast and ovarian cancer). Testing helps in early detection and personalized prevention strategies (e.g., elective surgery). Targeted Therapies: Some cancer mutations (e.g., in EGFR or PDL1 genes) inform the use of specific drugs (e.g., tyrosine kinase inhibitors or immunotherapy). 10. Summary There are many types of genetic variation contributing to disease, and different methods are used to identify them. Advances in large-scale genetic databases and bioinformatics are improving the understanding of disease mechanisms and enabling better diagnostics and treatments. Lecture Notes: CRISPR and Genome Editing (Dr. Tom Webb) 1. Overview of Genome Editing Definition: Genome editing refers to the precise modification of the nucleotide sequence in a genome. Techniques: o Homologous Recombination: Targets specific genome sites but has low efficiency and is labor-intensive. o Nuclease-Based Methods: Utilizes enzymes to introduce DNA breaks at targeted sites, including:  ▪ Zinc Finger Nucleases (ZFNs). ▪ TALENs (Transcription Activator-Like Effector Nucleases). ▪ CRISPR/Cas9 System. 2. CRISPR-Cas System Origin: Derived from a bacterial immune system that uses RNA-guided enzymes to cleave invading DNA. Components: o Cas9: The CRISPR-associated protein, an RNA-guided endonuclease with two nuclease domains (HNH and RuvC). o crRNA (CRISPR RNA): Guides Cas9 to specific DNA targets. o tracrRNA (Trans-activating CRISPR RNA): Combines with crRNA to form a guide RNA (gRNA). o PAM (Protospacer Adjacent Motif): A short sequence next to the target DNA required for Cas9 to bind and cleave. Mechanism: o Acquisition: Bacteria acquire viral DNA and integrate it into their CRISPR loci. o Processing: crRNAs are produced from the integrated sequences. o Interference: Cas9, guided by crRNA, cleaves the foreign DNA. 3. Genome Editing with CRISPR/Cas9 Advantages: o Easy to design and produce. o Quick and cost-effective. o High-throughput with high efficiency. Limitations: o Potential off-target effects (i.e., unwanted edits in non-targeted regions). o Requires careful gRNA design to minimize off-target cleavage. Applications: o Gene knockout by introducing double-strand breaks (DSBs), leading to frameshift mutations. o Gene knock-in via Homology-Directed Repair (HDR) using a template to introduce specific changes. 4. Alternative Genome Editing Techniques Zinc Finger Nucleases (ZFNs): o Recognize specific DNA sequences using zinc finger motifs. o Cleave DNA through the dimerization of FokI nuclease. TALENs: o Use TALE proteins from Xanthomonas bacteria to recognize DNA sequences. o Similar to ZFNs but offer higher precision and efficiency. 5. Base Editing and Prime Editing Base Editing: o Introduces precise nucleotide changes without creating DSBs. o Converts cytosine (C) to thymine (T) or adenine (A) to guanine (G) using deaminases. Prime Editing: o Uses a Cas9 nickase fused to reverse transcriptase. o More precise than base editing and can make all 12 possible nucleotide changes. 6. Examples of CRISPR Applications Cell Models: For studying diseases like Parkinson’s by introducing specific mutations into cells. Mouse Models: To model human diseases by introducing known mutations via CRISPR. Gene Therapy: o Ex vivo: Engineering patient cells outside the body (e.g., CAR-T therapy). o In vivo: Directly editing genes inside the patient’s body (e.g., for treating Leber congenital amaurosis). 7. Ethical and Regulatory Considerations Human Embryo Editing: Ethical debates, particularly following the controversial CRISPR editing of human embryos (e.g., the Lulu and Nana case). Regulatory Recommendations: Current guidelines focus on ensuring that genome editing is used for therapeutic purposes, with a ban on human germline enhancements. Introduction to Liquid Biopsy (Dr. David Guttery) 1. What is Liquid Biopsy? A minimally invasive method to detect cancer biomarkers in bodily fluids (blood, urine, saliva, cerebrospinal fluid). Replaces traditional, invasive tissue biopsies, enabling longitudinal sampling to monitor cancer over time. 2. Key Biomarkers in Liquid Biopsy Circulating-Free DNA (cfDNA): Fragments of DNA found in the bloodstream, released by both healthy and tumor cells. o Diagnostic Biomarker: Indicates the likely presence of cancer. o Prognostic Biomarker: Predicts disease progression in untreated individuals. o Predictive Biomarker: Identifies patients who are likely to respond to treatment. Circulating Tumor DNA (ctDNA): Specific to tumor cells, used for detecting cancer and monitoring minimal residual disease (MRD). Circulating Tumor Cells (CTCs): Shed from primary tumors into the bloodstream, important for studying metastasis. 3. Applications of Liquid Biopsy Cancer Diagnosis: Early detection of cancer through biomarkers like cfDNA and ctDNA. Monitoring Treatment: ctDNA can be used to track response to therapy and detect relapses earlier than imaging. Prognosis: Biomarker levels help predict patient outcomes and guide treatment decisions. 4. Technologies for Liquid Biopsy Analysis PCR-based Methods: For detecting point mutations (e.g., ddPCR). Next-Generation Sequencing (NGS): Used to identify mutations, copy number alterations (CNAs), and structural rearrangements. Digital Droplet PCR (ddPCR): Highly sensitive method for detecting low levels of ctDNA. 5. Examples of Biomarkers ER/PR in Breast Cancer: Oestrogen and progesterone receptors are prognostic biomarkers for breast cancer; ER+ patients have better survival rates. ESR1 Mutations: Associated with resistance to therapy in metastatic breast cancer, reducing progression-free survival. 6. Challenges in Liquid Biopsy Blood Processing: Requires careful handling to prevent contamination by white blood cells, which could interfere with ctDNA detection. Limit of Detection (LOD): Varies by technique; more sensitive methods are required to detect low ctDNA levels. 7. Emerging Technologies New developments in ctDNA analysis are improving the detection and monitoring of cancer, especially in early stages and post-treatment surveillance. Protein Purification (Dr. Cyril Dominguez) 1. What Are Proteins? Proteins are polypeptides made of amino acid chains that fold into specific structures. Crucial for cellular functions, proteins are encoded by genes (~22,000 genes, ~100,000 proteins in humans). 2. Why Purify Proteins? To study the function of specific proteins and understand biological processes. Necessary for in vitro experiments to: o Identify the roles of proteins in pathways. o Investigate diseases caused by mutations or overexpression. 3. Sources of Proteins Natural sources: Tissue, bacteria, or plants. Recombinant expression: Cloning and overexpressing proteins in bacteria, yeast, insect, or mammalian cells. 4. Protein Purification Methods Size Exclusion Chromatography: Separates proteins based on size using porous beads. Smaller proteins move slower through the column. Ion Exchange Chromatography: Separates based on charge; positively charged proteins bind to negatively charged beads and are eluted using a salt gradient. Affinity Chromatography: Utilizes tags (e.g., His-tag, GST) that bind to specific ligands (e.g., Nickel for His-tag) for protein purification. 5. Detecting Proteins Absorbance: Measures protein concentration based on light absorption at specific wavelengths (e.g., 280 nm). SDS-PAGE: Separates proteins by molecular weight using gel electrophoresis. Western Blot: Uses specific antibodies to detect proteins after gel separation. Mass Spectrometry: Identifies proteins based on mass. 6. Applications of Purified Proteins Structural studies: X-ray crystallography, Cryo-EM, or NMR to determine protein structure. Functional assays: Investigating enzymatic activity, binding assays, and protein- protein interactions. Short Lecture Notes: Electron Microscopy (Dr. Natalie Allcock) 1. Introduction to Electron Microscopy (EM) Electron Microscopy: Used to visualize structures beyond the resolution of light microscopy. Provides higher resolution by using electrons instead of light. Two main types: o Transmission Electron Microscope (TEM): For viewing internal structures of thin samples. o Scanning Electron Microscope (SEM): For examining surface details. 2. Resolution and Magnification Resolution: Ability to distinguish two points as separate. Electron microscopes achieve higher resolution (down to 0.1 nm for TEM) compared to light microscopes (200 nm limit). Magnification does not necessarily improve image quality; resolution is key for detailed imaging. 3. Units of Measurement 1 mm = 10⁻³ meters. 1 µm = 10⁻⁶ meters. 1 nm = 10⁻⁹ meters. 1 Å = 10⁻¹⁰ meters. 4. Advantages of Electron Microscopy High-resolution imaging of sub-cellular structures. Reveals ultrastructures like organelles, bacteria, viruses, and even macromolecules (e.g., proteins, nanoparticles). Provides structural context for biological processes. 5. TEM vs. SEM TEM:  o Transmits electrons through the sample. o Produces 2D images of internal structures. o Samples must be thin (90% particle loss in some cases. Current technology limits resolution for structures smaller than ~60 kDa. 4. Cryo-EM Workflow Protein Production and Purification: Proteins are prepared using recombinant methods or purified from natural sources. Sample Freezing (Plunge Freezing): o Samples are plunge-frozen in liquid ethane at ~10⁵°C/sec, preventing ice crystal formation. o Thin specimens (