Category Archives: virology

ASCB15 – part 2

I ended Part 1 after the morning session on pushing the boundaries of imaging.

After the amazing talks on imaging, I browsed the halls, visited some exhibitors, sampled a couple of exhibitor tech-talks. I later went to a mycrosymposium (#2: signaling in health & disease). This was mainly to see how this ePoster thing works, but also I promised Qunxiang Ong – with whom I discussed optogenetics the day before – to be at his presentation. He used a light-induced dimerization of signaling proteins to study the effect on neurite growth. The nice thing in his system was that the cells were plated in wells which were partly dark – so light-induction cannot take place in these regions. This allowed for analysis of neurite growth in lit vs “light-protected” regions of the same cell.

After this session, I attended my first “discussion table”. Continue reading

A new probe may enable researchers to identify drug resistant influenza viruses

Flu has always been a public scare issue, the latest being the “swine flu” (a.k.a H1N1 strain). One of the problems with the influenza virus is that its genome is composed of 7-8 separate pieces of RNA (an unusual case, since most viral genomes are composed of one or two pieces). These pieces can recombine among different strains creating new strains – hence the apparent low effectiveness of flu vaccines.

Other than vaccines, there is also an antiviral drug called Oseltamivir (also known as “Tamiflu”) that slows down the viral infection. It does so by inhibiting a viral enzyme called neuraminidase (NA). NA breaks down sialic acid. NA activity helps the elution and mobility of the virus particles.  However, recent years showed some flu strains that are resistant to Tamiflu.

NA belongs to a large family of sialidases that are found in many other pathogens, as well as in our own cells. Hence, understanding sialidases biology is important.

In a paper published now in PNAS, researches developed a cell permeable sialidase inhibitor. This inhibitor binds to the active site, the same site that also binds Tamiflu. The inhibitor itself is not fluorescent, so the researchers chemically conjugated it to biotin (after it bound the sialidases). They then probed the cells with fluorescently labeled streptavidin (a protein with high affinity for biotin) and could detect flu-infected cells.

Visualization of influenza-infected cells using DFSA labeling. Green: FITC-tagged streptavidin; Red (Alexa fluor 594): immuno-fluorescence with anti influenza nucleoprotein antibody; Blue: DAPI staining of nuclei. Source: Tsai C et al. PNAS 2013;110:2466-2471

Visualization of influenza-infected cells using DFSA labeling. Mock – non-infeced cells. Green: FITC-tagged streptavidin; Red (Alexa fluor 594): immuno-fluorescence with anti influenza nucleoprotein antibody; Blue: DAPI staining of nuclei. Source: Tsai C et al. PNAS 2013;110:2466-2471

They claim (but for some reason don’t show) that adding Tamiflu resulted in the absence of labeling of Tamiflu sensitive strains, but the labeling persisted for resistant strains. Hence, this method may assist in identifying resistant strains.

ResearchBlogging.orgTsai CS, Yen HY, Lin MI, Tsai TI, Wang SY, Huang WI, Hsu TL, Cheng YS, Fang JM, & Wong CH (2013). Cell-permeable probe for identification and imaging of sialidases. Proceedings of the National Academy of Sciences of the United States of America, 110 (7), 2466-2471 PMID: 23359711

Nature methods special issue: focus on bioimage informatics (Part I )

The July issue of Nature Methods is dedicated to microscopy. Actually, to quantitative microscopy. As the editor points out, Microscopy started as a qualitative method to visualize structures and movement of single cells and organelles. With advancement of optics on the one hand, and computer programs on the other hand, microscopy is becoming much more quantitative. We can now accurately measure signal intensity; get to time resolution of milliseconds and spatial resolution of a few nanometers. We have an increasing number of tools for fluorescent microscopy that allow multiple color imaging in both fixed and live cells. However, many of the programs are still in their infancy and there is room for improvement. Another problem the editor points at is the fact that most of “bioimage informatics” tools are not so much user-friendly, especially for biologists with little or no background in physics and programing.

Last, but not least, is the fact we can use only a limited number of colors per experiment, thus limiting the number of genes or proteins that we can measure in each experiment. This is in contrast to the accumulating whole-genome/transcriptome/proteome data that is now prevalent in the literature.

So, in the next several posts I will review the articles in this special issue. I think it would be interesting.

Methods in brief section:

In this section, a brief overview of a recent paper from Erin Schuman’s lab points to a new method to assess mRNA abundance in a sample. This method, Nanostring nCounter, uses mRNAs attached to a surface, and probes that are fluorescently bar-coded (i.e. each probe has a specific color barcode). Following hybridization, one can estimate for each mRNA the signal intensity and assess mRNA level. However, this method was not used in situ to determine mRNA levels of multiple genes in the same cell.

A similar bar-coded approach, but with single cell application, is found in an article in this issue. I will discuss that in a separate post.

Tools in brief section:

Super resolution FlAsH – Fluorescein-arsenical helix binder (FlAsH) is a small fluorescent molecule that binds to tetra-cysteine structures in proteins. However, this dye proved to be both toxic to certain cell types and have a high fluorescent background. Here, they mention a work from the lab of Christophe Zimmer, which used FIAsH and super-resolution microscopy to look at HIV intracellular complexes at ~30nm resolution. Using UV light (405nm) in short pulses, instead of the standard blue (488nm) excitation light for FIAsH, they were able to show that the FIAsH-tetracysteine complex is photostable and can fluctuate from dark to bright states, leading to low background and high resolution.

FIAsH image of HIV particles. Source: Lelek, M., et al. Proc. Natl. Acad. Sci. USA 109, 8564–8569 (2012).

News & Views section

Faster and more versatile tools for super-resolution fluorescence microscopy. In this short review, Alex Small discusses two computational methods, published in this issue, to calculate fluorophore position in single molecule or super resolution microscopy.

Omnidirectional microscopy. Here, Weber & Huisken discuss two papers published in this issue that deal with in toto imaging of tissues, organs or embryos.

Selective-plane illumination microscopy (SPIM) with four objectives. The cool way to look at fly embryos! Source: Weber & Huisken. Nature Methods 9,656–657(2012)

The commentary section contains three articles which discuss why bioimage informatics is important; what are the current challenges in creating open-source software for image analysis; and a call to make such software more users friendly.

I know that bioimage analysis software is important to advance the field; I get why open-source would be a better, but more challenging choice for software development; and I am truly and completely agree that these programs should be user friendly, to biologists like me. I mean, I don’t even know how to use all of the capabilities of PowerPoint, let alone programs like ImageJ or Metamorph, which are still relatively friendly compared to other stuff. I certainly do not know how to write plugins or applications and such.

Speaking of ImageJ, there is a historical commentary: 25 years from NIH Image to ImageJ software. This would probably interests programmers more than biologists.

Following that, there are three Perspective articles about other image analysis software: Fiji, BioImageXD, and Icy. I tried to read some of this stuff, but it’s really more for people with programming experience, I guess.

A review paper summarizes the available bioimage tools for acquisition, storage and analysis. Unlike the commentary and perspective sections, this review is readable by a biologist like me. In fact, I would even recommend it, particularly if you are new to the field, and planning to acquire such software.

Well, that’s about it for this post. In the next post I will review the “Brief communications” section, which contains seven short papers. A third post will be devoted to reviewing the “Articles” section, with four interesting papers.

Unrelated to this special issue, here’s an interesting post from the blog It takes 30: Fluorescent protein labeling – a cautionary tale.

ResearchBlogging.orgLelek M, Di Nunzio F, Henriques R, Charneau P, Arhel N, & Zimmer C (2012). Superresolution imaging of HIV in infected cells with FlAsH-PALM. Proceedings of the National Academy of Sciences of the United States of America, 109 (22), 8564-9 PMID: 22586087
Small A (2012). Faster and more versatile tools for super-resolution fluorescence microscopy. Nature methods, 9 (7), 655-6 PMID: 22743767
Weber M, & Huisken J (2012). Omnidirectional microscopy. Nature methods, 9 (7), 656-7 PMID: 22743768
Myers G (2012). Why bioimage informatics matters. Nature methods, 9 (7), 659-60 PMID: 22743769
Cardona A, & Tomancak P (2012). Current challenges in open-source bioimage informatics. Nature methods, 9 (7), 661-5 PMID: 22743770
Carpenter AE, Kamentsky L, & Eliceiri KW (2012). A call for bioimaging software usability. Nature methods, 9 (7), 666-70 PMID: 22743771
Caroline A Schneider, Wayne S Rasband, & Kevin W Eliceiri (2012). NIH Image to ImageJ: 25 years of image analysis Nature Methods, 9, 671-675 DOI: 10.1038/nmeth.2089
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, & Cardona A (2012). Fiji: an open-source platform for biological-image analysis. Nature methods, 9 (7), 676-82 PMID: 22743772
Kankaanpää P, Paavolainen L, Tiitta S, Karjalainen M, Päivärinne J, Nieminen J, Marjomäki V, Heino J, & White DJ (2012). BioImageXD: an open, general-purpose and high-throughput image-processing platform. Nature methods, 9 (7), 683-9 PMID: 22743773
de Chaumont F, Dallongeville S, Chenouard N, Hervé N, Pop S, Provoost T, Meas-Yedid V, Pankajakshan P, Lecomte T, Le Montagner Y, Lagache T, Dufour A, & Olivo-Marin JC (2012). Icy: an open bioimage informatics platform for extended reproducible research. Nature methods, 9 (7), 690-6 PMID: 22743774
Eliceiri KW, Berthold MR, Goldberg IG, Ibáñez L, Manjunath BS, Martone ME, Murphy RF, Peng H, Plant AL, Roysam B, Stuurmann N, Swedlow JR, Tomancak P, & Carpenter AE (2012). Biological imaging software tools. Nature methods, 9 (7), 697-710 PMID: 22743775