E.coli Electroporation vs Chemical Transformation

E.coli Electroporation vs Chemical Transformation 

This is the first in a three part series on the transformation of E.coli. By the end of this you should be an expert on E.coli transformation and on which strains to choose for different applications. If you’re already an expert, I hope it’ll be an enjoyable refresher for you. In either case, please comment […]

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Troubleshooting Thin Layer Chromatography: Some TLC for Your TLC

Troubleshooting Thin Layer Chromatography:  Some TLC for Your TLC 

The whole TLC technique sounds easy to do, but it can be difficult and tricky during interpretation or give unexpected results, especially when working with biomolecules. For this reason, it is important to be familiar with troubleshooting thin layer chromatography. Some of the common problems faced during TLC and their solutions are listed below: Solvent […]

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Qualifications for Off-the-Bench Science Careers

Qualifications for Off-the-Bench Science Careers 

Scientific research is a demanding and stressful career requiring lots of patience, dedication and hard work. Sadly the monetary benefits are not commensurate with the effort. Moreover, lack of opportunities, uncertainty, instability and the pressure to publish makes a traditional academic science career difficult. Inspiring (forcing?) many scientists and researchers to leave their traditional science […]

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SPUD’s Your Bud When it Comes to Sensitive qPCR

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There’s piloting a brand new technique for the first time. Then, there’s jumping through hoops trying to get an established lab technique to work. The former, in contrast to the latter, is expected to be fraught with hardships. Yet troubleshooting an old lab technique that isn’t working anymore, is frustrating at a whole new level. Find comfort in that it probably happens more than you’d expect. In fact, it happened with me and my (now) good buddy qPCR.

Is it Me or the Transcript?

Years of our lab’s data had been produced using qPCR. So much so that it was assumed there were two outcomes: it either showed clear results or nothing amplified because you messed up. When I joined the lab, I became interested in a gene that I now know is expressed in very low levels in my tissue of interest. None of our old qPCR techniques gave consistent results, but it was clear something was there. It was like the data just wasn’t clean enough to be meaningful. I was terrified it was just me.

Inhibitors Inhibit!

When it comes to highly reliable and sensitive qPCR, many qPCR experts agree that template quality is priority number one.1 Template-lurking inhibitors can greatly decrease your ability to detect small differences between samples or, in my case, lowly expressed genes. These inhibitors could include reagents left over from your extraction (like phenol, chloroform, or detergents) and/or things that tagged along with sample collection (like cell culture media, bile salts, and heparin). This list of things that will inhibit your qPCR reaction is pretty impressive and won’t necessarily show up in your A260/A280.2

Inhibitors will also keep you from achieving the efficiency assumed by the widely used quantification method, delta-delta Ct. Using this method, you assume each template is amplified with 100% efficiency — every DNA template makes two DNA copies each cycle. If amplification efficiency is less than that, you have to get into some nitty gritty mathematical corrections.

Never fear, though, because a clever group of researchers came up with a simple test to see if your qPCR reaction contains inhibitors. It’s called the SPUD assay and here’s how it works.

How to Make SPUD Your Bud

Nolan et. al. 2006 described this delightfully simple technique, thus ending the long reign of chaos and bringing about the uninhibited qPCR golden age.3 To see if your qPCR reaction contains inhibitors, you can toss in a completely separate qPCR reaction with your samples and see how it amplifies. The SPUD assay does this using a potato’s (get it?) photoreceptor gene. Unless you happen to be studying potatoes, then your template won’t contain this gene (which leads to the potato root turning purple). You include the gene’s synthesized amplicon and primers within your sample reaction. Then you compare its crossing point in the presence of your sample to its crossing point when amplified alone. If, for example, you have residual phenol left over from your RNA extraction in amounts too small to be picked up on the A260/A280, but enough to inhibit your qPCR reaction, then the crossing point of the potato gene run with your sample will be larger.

So whether you’re frustrated by poor amplification efficiency or if you’re just trying to increase the sensitivity of your qPCR reactions, the SPUD assay might be just what will get your goals off the ground. In my case, it turns out I wasn’t troubleshooting. I was simply optimizing!


  1. Bustin SA and T Nolan (2004) Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. Journal of Biomolecular Techniques. 15(3): 155–166.
  2. Rossen L, et al. (1992) Inhibition of PCR by components of food samples, microbial diagnostic assays and DNA-extraction solutions. International Journal of Food Microbiology. 17(1): 37–45.
  3. Nolan T et al. (2006) SPUD: a quantitative PCR assay for the detection of inhibitors in nucleic acid preparations. Analytical Biochemistry, 351 (308–310).

Source Article: http://bitesizebio.com/28936/spuds-your-bud-when-it-comes-to-sensitive-qpcr/

A Link to Leukemic Tumor Growth Has Been Revealed By Quantifying Autophagy by Flow Cytometry

Flow cytometry is a powerful technology for investigating many aspects of cell biology and for isolating cells of interest. Flow cytometry utilizes highly focused, extremely bright beams of light (usually from lasers) to directly reveal aspects of cells (e.g. size and granularity) by the way light is scattered, or indirectly by introducing fluorescent probes to cell compartments (e.g. through DNA binding dyes that stain nuclei or fluorescently-labeled antibodies that specifically detect cellular proteins). The power of flow cytometry derives from the fact that it quantitatively analyzes individual cells, thus permitting the identification of subpopulations within a sample. The power of single cell analysis is compounded by the ability to measure multiple parameters simultaneously on each individual cell, to do this very fast (in excess of 20,000 cells/second), and to isolate/purify/sort desired subpopulations (up to 4 simultaneously). One of the many uses of flow cytometry today is the analysis of autophagy by measuring autophagic flux and quantifying the content of autophagic vesicles in cells. Autophagy is a catabolic delivery pathway for excess or damaged cytoplasmic constituents to the lysosomes where macromolecules are broken down and their components freed for anabolic activities. Upon induction following metabolic stress, autophagy maintains mitochondrial health and metabolic pathways being induced following metabolic stress under the control of the mTOR complex 1 (mTORC1). Under favorable conditions, activated mTORC1 signals for cell growth, promotion of translation, cell cycle progression, and glycolysis while inhibiting autophagy. To maximize cell mass during proliferation, suppression of self-catabolism may be vital for growth activities and indeed, it was found that induction of autophagy prolongs cell survival at the cost of cell size and growth.

Activation of the Akt/mTOR pathway is a common feature of cancers, including leukemias and is required for proliferation in acute myeloid leukemia (AML) models. Knockout of autophagy genes in mice is associated with hyper-proliferation in some tissues and eventual tumor development. Previous studies indicated that mice without the autophagy gene Atg7 in the hematopoietic system develop pre-leukemic myeloproliferation. However, it remains unclear how Atg7 promotes cell proliferation and whether this is an Atg7-specific function. With the literature demonstrating both tumor-promoting and -inhibiting roles for autophagy in leukemia, its involvement in the biology of cancer cells is still controversial. It is, however, well accepted that transformation events leading to AML may occur at the stem or progenitor cell stage. Hematopoietic stem cells (HSCs) strike a fine balance between quiescence, self-renewal, and differentiation. When this balance is perturbed, the consequences may include biased differentiation and/or hematopoietic malignancies. In steady-state hematopoiesis, the majority of HSCs are quiescent. Quiescent cells are particularly hardy and able to survive long periods of metabolic stress. HSCs downregulate protein synthesis and activate pathways that sustain them during periods of non-division. Therefore, autophagy may be required for maintenance of the long-lived HSC, as their slow turnover prevents the dilution of damaged macromolecules to daughter cells, similar to a post-mitotic neuron or cardiomyocyte. Moreover, autophagy controls mitochondrial quality.

Using Enzo’s CYTO-ID® Autophagy detection kit and flow cytometry analysis, Dr. Watson and colleagues from John Radcliffe Hospital Oxford found that autophagy levels were highest in the most immature human and mouse hematopoietic stem and progenitor cells (HSPCs). They also demonstrated that loss of Atg5 results in an identical HSPC phenotype as loss of Atg7, confirming a general role for autophagy in HSPC regulation. Compared to more committed/mature hematopoietic cells, healthy human and mouse HSCs displayed enhanced basal autophagic flux, limiting mitochondrial damage and reactive oxygen species in this long-lived population. Moreover, human AML blasts typically only displaying heterozygous Atg deletions readily showed reduced expression of autophagy genes and displayed decreased autophagic flux with accumulation of unhealthy mitochondria. Also, heterozygous loss of autophagy in an MLL-ENL model of AML led to increased proliferation in vitro, a glycolytic shift, and more aggressive leukemias in vivo. Taken together these data are compatible with autophagy limiting leukemic transformation. With autophagy gene losses also identified in multiple other malignancies, these findings point to low autophagy providing a general advantage for tumor growth.

Article Source: http://www.enzolifesciences.com/science-center/technotes/2016/january/quantifying-autophagy-by-flow-cytometry-revealed-a-link-to-leukemic-tumor-growth/