Category Archives: Biology

Introduction to Super-resolution Microscopy

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While microscopic methods with protein and atom scale resolution – that don’t break the diffraction barrier! – exist (e.g. electron microscopy, atomic force microscopy, x-ray crystallography, etc.), they tend to be more practically difficult than fluorescence-based microscopy (see posts on Fluorescent Probes and Fluorescent Antibodies). Therefore, to give fluorescence microscopy nanometer resolution, several super resolution techniques have been developed that combine clever (1) optical/photophysical and (2) computational-processing tricks to “clean up” the “blurred data.”

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Understanding the Diffraction Limit in Microscopy

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Basic light microscopy can only resolve objects that are larger than 100 nm which means that while it can visualize animal cells (~10,000nm), organelles and bacteria (~1,000nm), it cannot visualize viruses (<100nm), proteins (<10nm) or small molecules(~1nm) (see post summarizing Biological Scales). This limitation is known as the “diffraction limit” and is caused by the fact light only interacts differently with objects separated by more than one wavelength (λ). Intuitively, its helpful to think of each of these wavelengths as “a minimum pixel size” for a computer image where: infrared light (λ ~ 10.0μm) has pixels 100 times larger than ultraviolet light (λ ~ 0.1μm).

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Introduction to ELISA

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ELISA or Enzyme-Linked Immunosorbent assay is the most commonly used method for measuring proteins concentrations in solution. It is extensively used both in the laboratory (e.g. culture supernatant) and the clinic (e.g. blood tests) due to its simplicity and adaptability to other protein-based assays such as high throughput screening. The way it works is outlined in the figure above and described in detail below:

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Introduction to Flow Cytometry

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Flow cytometry is a technique that can quantitatively measure (1) protein expression levels per single cell and (2) amounts of different cell-types (based on a protein-maker) for thousands of cells in minutes! Flow cytometers use hydrodynamic focusing to force a mixture of stained cells into a single-file line through a flow cell. Then, each cell, sequentially, passes through a laser beam which excites the fluorophore allowing quantification of protein expression for all proteins that are stained!

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Hybridomas for Large-scale Antibody Production

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Following up on our post introducing antibody-based experiments, we wanted to describe how antibodies are made in large volume: First, an animal is immunized with the protein (or peptide) of interest; Second, the spleen is dissected and the plasma cells producing the antibodies of interested are isolated; Finally, these plasma cells are fused with myeloma cells to “immortalize” them into a hybridoma (an antibody-producing cell-line that can be cultured indefinitely).

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Introduction to Antibody Based Experiments

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Antibodies are immune proteins that can be easily engineered to bind (and “detect”) any given protein in via a simple vaccination method. To “signal” the presence of detected proteins, fluorophores are attached to “stain” the protein (and cell or tissue expressing it) a particular color. These fluorescent probes, have been utilized to identify the location of specific proteins in tissue sections (histology), single cells (microscopy) and quantify the amount of protein in cells (flow cytometry) and in complex mixtures (western blotting).

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Back-crossing mutant mice into a genetic background

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Inbred mouse lines are used for most laboratory mouse work to make sure that all the mice have nearly identical genetic backgrounds (i.e. genetic code). These lines were originally generated by repeatedly mating mice with their siblings which minimized genetic variability and makes these mice as close to clones of each other as is practically possible.

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Introduction to Mouse Breeding

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Careful breeding/husbandry of mice is important when one is asking genetic questions about disease. Such questions include: “Does gene X contribute to cancer?”, “Does my drug treat cancer by targeting gene X?”.

In general, these questions can only be answered by comparing mice that have gene X (WT or +/+) or don’t have gene X (KO or -/-). To make sure the genetic difference is the only difference between the mice, you usually compare siblings of inbred mouse-lines which have the same sex, age, environment and genetic background.

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What is Principal Component Analysis??

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Principal component analysis (PCA) attempts to find true trends hidden in complex data by filtering out noise and redundancy. It does this by treating complex data as a n-dimensional shape (where n is the number of measurements in your study) and fitting that shape to n 1-dimensional lines called: “principal components” and ranking these lines by the percentage of data variation that they capture.

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Estimating Metabolite/Protein Concentrations from RNAseq Data

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Under steady-state conditions, it is possible to estimate the concentration of a metabolite from the amount of protein and the amount of protein from the amount of mRNA (see figure above). In general, the conversion factors used for these calculations are simply the ratio of the “first-order” formation and degredation rate constants for the protein/metabolite of interest. Recently, a paper published in Nature, characterized the distribution per gene of: (1) total mRNA and protein (2) rates of mRNA and Protein synthesis and (3) rates of mRNA and protein degradation (see figure below).

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A “Chemical-Structure Map” of the Metabolome

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I’ve always struggled to connect the structures of natural products with the biosynthetic pathways that generate them. I recently found a great resource in the Kyoto Encyclopedia of Genes and Genomes (KEGG) which helped me address this problem directly. The figure above is an adaptation of several of their pathway charts most especially that pictured here.

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An Idea for Visualizing Receptor/Ligand X-Ray Structures

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As an organic chemist, I have always had difficulty relating the 3D PDB/X-ray structures of receptor/ligand complexes to the 2D chemical drawing chemist build their “chemical insight” off of. In the figure above, I present an idea to bridge the gap by presenting sets of receptor residues as “surfaces” interacting with the two types of 2D representations chemists use to think about chemistry.

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Estimating Metabolite Concentrations at Steady State

Biosynthetic Pathway-Funnel Analogy

In a follow up to our post on sequential biochemical pathways, we next wanted to present an method to approximate the concentration of a metabolic intermediate in a biosynthetic pathway. In general, under steady state conditions, the steady state concentration of a metabolite can be estimated from the ratio of the Vmax for the upstream rate-determining enzyme over the rate of decay of that metabolite. A more complete equation is detailed below and further discussed in our post on Estimating Protein/Metabolite Levels from RNAseq data.

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Kinetics #3: Branch Points in Biochemical Pathways

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In a follow up to our posts on Intuiting Enzyme Kinetics and Sequential Biochemical Pathways, we next wanted to consider the kinetic curves branch points in biochemical pathways (or equivalently kinetic competitions). Luckily exact mathematical models exist for competitive first-order processes (see below) and we can use these to develop intuitive rules (figure above) if we consider these pathways to be approximately pseudo-first order (which is often true in the context of biosynthetic pathways).

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Kinetics #2: Sequential Biochemical Pathways

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In a follow up to our post our post on intuiting enzyme kinetics, we next wanted to consider the kinetic curves for sequential biochemical pathways. Luckily exact mathematical models exist for sequential first-order processes (see below) and we can use these to develop intuitive rules (figure above) if we consider these pathways to be approximately pseudo-first order (which is often true in the context of biosynthetic pathways).

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