Using Thresholds to Measure and Quantify Cells in Image J

Using Thresholds to Measure and Quantify Cells in Image J.


It takes a lot of work to publish a paper. There’s the research itself, of course. Then there’s the writing and preparation of the figures for the paper, that are submitted to the journal for publication. Typically, most of the people who contributed to the research are involved in that, so the draft is circulated between at least 2 people, usually many more. There is one author (typically the 1st author, or the PI who is leading the research) who also needs to combine all the comments & changes into one coherent text.

The paper then goes through several rounds of  changes, following reviewers’ comments, editor’s comments, final proof-reading etc…

The accepted manuscript (MS) should be “perfect”. But mistakes can happen. Mistakes did happen for three of my recent papers.

For my Cell paper, I have prepared a nice schematic model to summarize the main highlights. It was nice, but we decided it is worth spending money on a professional artist to make a nicer one. Somehow, my scheme was uploaded for the accepted MS, instead of the more artistic one, and this is the one that was eventually  published  (you can see the artistic version in the blog post about the paper). I found that out after the fact, but since it was just a model, and the only difference was the art, we decided that publishing a correction just wasn’t worth the trouble.

Then, we published a paper in PLOS One. Only after it was published, I noticed that something happened to figure 4. Here it is:

Can you find the error?

Can you find the error?

Here, we had no choice but to publish a correction.

Recently we published a review paper, in Nature Reviews Molecular Cell biology. We worked very hard with lots of proof-reading and integrating each other’s changes to make sure it is perfect. Then, a few weeks ago, I was looking for some papers which I knew we referenced in that review. Strangely enough, those papers were mis-quoted in our review. Maybe its silly to publish a corrigendum just for a tiny mistake in references. But I think that it is important to keep science as mistake-free as possible, even with those tiny seemingly unimportant mistakes. So we published a corrigendum.

Here is also the place to thank the editors for handling these corrections pleasantly and efficiently.

I’m looking forward for my next corrigendum. What will that be?

Tracking membranes by imaging – mCLING and surface glycans

Living cells exhibit many types of membranes which participate in most biological precesses, one way or another. Imaging membranes is usually acheived by two types of reagents: chemical dyes or fluorescent proteins that are targeted to the membrane itself or inside an organelle.

The chemical dyes are usually targeted to an organelle based on a specific chemical property of that organelle.

For example:

Rhodamine 123, tetramethylrosamine, and Mitotracker  are dyes that preferentially target mitochondria, due to its membrane potential. Mitotracker has thiol groups that allow it to bind to matrix proteins, thus making it more resistant to disruption of the membrane potential (e.g. by fixation).

Lysotracker are lypophilic, mildly basic dyes, which accumulate in the acidic lysosomes.

ER-tracker is a BODIPY (boron-dipyrromethene; a group of relatively pH insensitive dyes that are almost all water insoluble) based dyes which are linked to glibenclamide – a sulfonylurease – which binds to ATP sensitive Potassium channels exclusively resident in the ER membrane.

Long chain carbocyanines like DiL, DiO and DiD are lipophylic fluorescent molecules, which are weakly fluorescent in water, but highly fluorescent when incorporetaed into membranes, particularly the plasma membrane.

FM lipophylic styryl dyes bind the plasma membranes in a reversible manner and are also incorporated into internal vesicles.

On the other hand, fluorescent proteins (FP) are targeted to membranes or organelles by fusing them to either whole proteins that localize to a specific organelle, or to short peptides that carry a localization signal. Thus, a nuclear localization signal (NLS) targets the to the nucleus, mitochondrial targeting signal (MTS) to the mitochondria and a palmitoylation signal to the plasma membrane and endocytic vesicle.

There are advantages and disadvantages to each system, relating to ease of use, specificity, photostability etc… I do not want to go into that.

Here, I would like to mention two new methods to image the plasma membrane.

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Visualizing translation: insert TRICK pun here

Unlike transcription, it is much harder to image translation at the single molecule level. The reasons are numerous. For starters, transcription sites (TS) are fairly immobile, whereas mRNAs, ribosomes and proteins move freely in the cytoplasm, often very fast. Then there are only a few TS per nucleus, but multiple mRNAs are translating in the cytoplasm. Next, there’s the issue of signal to noise – at the transcription site, the cell often produces multiple RNAs, thus any tagging on the RNA is amplified at the transcription site.  Last, it is fairly easy to detect the transcription product – RNA – at a single-molecule resolution due to multiple tagging on a single molecule (either by FISH or MS2-like systems). However, it is much more difficult to detect a single protein, be it by fluorescent protein tagging, or other ways (e.g. FabLEMs).

The rate of translation is ~5 amino acids  per second, less than 4 minutes to a protein 1000 amino-acids long. This is faster than the folding and maturation rate of most of even the fastest-folding fluorescent proteins. This means that by the time the protein fluoresce, it already left the ribosome. However, attempts were made in the past with some success.

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Transcription caught on camera part 2: Fab-ulous Histones

In eukaryotes, the DNA is packages tightly in nucleosomes, which are composed primarily out of histone proteins. There are four major types of histones (1,2,3 & 4). Extensive work has been done on how histones facilitate and regulate transcription. It turns out that there are multiple post-translational modifications on histones, such as methylation and acetylation that are linked to transcription regulation. The majority of the studies use a method called chromatin immunoprecipitation (ChIP) to study these modifications. In essence, an antibody specific for a certain modification is used to affinity-purify only modified histones, along with any DNA region they are associated with. Thus, one could get a map of the specific modified histone along the chromosomes and correlate this locations with transcription activity, ChIP maps of other transcription related proteins etc…

There are two problems with this approach. The first, since the cells are fixed, the time resolution is limited to several minutes, at best. Second, the results are an average of the entire cell population, and therefore factors considered linked may not actually be present in the same cell, same genomic location at the same time.

So, Timothy Stasevich et al. tried a different approach by using a novel method to image histone modifications in live cells.

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In the right place at the right time: visualizing and understanding mRNA localization

The title of this post is also the title of a review paper that I co-authored  with Adina Buxbaum, a recently graduated PhD student from Rob Singer’s lab. The review was published last week in Nature Reviews Molecular Cell biology.

In this paper we review some of the old and new methods to visualize mRNA. These include mostly FISH and MS2-like systems, which I’ve discussed extensively in this blog. There is also a short section (“box”) on quantitative analysis tools for mRNA localization imaging.

We then discuss the current knowledge on the mechanisms of mRNA localization and how it relates to the biology in two very distinct model systems – unicellular organisms (budding yeast) and the extremely polarized neuronal cell.  We also discuss examples in other organisms from bacteria through fly to frog and mammals.

I’m biased, of course, but I think this turned out to be a balanced, comprehensive, yet not too detailed review paper that will benefit both beginners which are unfamiliar with the RNA localization field, as well as experts which are used to a single method or a single model organism.
ResearchBlogging.orgBuxbaum, A., Haimovich, G., & Singer, R. (2014). In the right place at the right time: visualizing and understanding mRNA localization Nature Reviews Molecular Cell Biology DOI: 10.1038/nrm3918

Transcription caught on camera part 1: Halo transcription factors

Transcription factors (TFs) have a fundamental role is regulating gene expression. The basic model, based on numerous biochemical analyses, has determined where TFs bind (usually at specific sites at or near promoters), when they bind the DNA (at a resolution of minutes/hours) and what do they do there (induce/repress transcription. Duh!).  However, much is yet unknown. One aspect that is fairly unknown is the dynamics of how TFs search for their binding sites, bind them and later dissociate, particularly at the single molecule level. To explore this, the Transcription Imaging Consortium (TIC) at Janelia Research Center (JRC) (it used to be Janelia Farm, but the  “farm” part was removed from the name. oh well) applied sophisticated imaging techniques to measure the dynamics of two TFs, SOX2 and OCT4 in the nuclei of live embryonic stem (ES)cells. Their results were published in Cell almost a year ago.

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