Glycoscience: a tea party no longer

Later this year or early next Richard Cummings plans to launch The Human Glycome Project. It will happen during a workshop that he is currently organizing and which is open to scientists from near and far. The workshop is slated to be held at the Radcliffe Institute for Advanced Study at Harvard University. Also in the works is a Harvard-based center for glycoscience that reaches out to potential collaborators at all Boston-area universities and academic medical centers.

Cummings, who hails from Alabama and who moved from Emory University School of Medicine to Harvard Medical School last fall, loves glycans, which are the ubiquitous carbohydrates made by all cells, and which can be linked to lipids or proteins. Both in humans and in a variety of animal species, the universe of glycolipids and glycoproteins is extraordinary, he says.

In Cummings’ box of plans is the development a human reference glycome so the growing research community committed to these macromolecules can explore the diversity of the human glycome and develop methods and standards with which to do so. He also envisions comparative glycomics, the comparison of human, porcine and bovine glycomics to tease out differences and similarities. “It wasn’t possible before, really,” he says. But dreaming big in glycoscience is now becoming possible.

Glycobiology has been hampered by complicated methods, which his and other labs have been addressing over the years. In his recent work, published the June issue of Nature Methods, the Cummings lab uses household bleach to release glycans from tissue and cells. He started this research at Emory School of Medicine and continued at his new lab at Harvard Medical School. He also directs the Center for Functional Glycomics, a virtual center that he already led at Emory and that is funded by the National Institutes of Health to explore protein-glycan interactions and to develop new tools and technologies to explore glycoconjugate functions.

When people now stop by the Cummings lab they can, for example, leave with four grams of carbohydrates in a 50ml tube full of white powder. “Those are all the carbohydrate structures in the pig lung,” he says. With this material on hand scientists can use nuclear magnetic resonance techniques for glycan analysis.

Cummings and his team want to enable more labs around the world to study glycoscience by shipping material to colleagues upon request.

Hear Rick Cummings talk about the offer here (14 seconds)



Glycans are difficult to synthesize but now it is possible to harvest them from natural sources such as eggs, meat or plants. “We can make them at such large scale now, we‘re going to just give them away,” he says. Once purified, glycans can be archived, printed on microarrays to explore glycan recognition by lectins, antibodies, bacteria or viruses, or sequenced with mass spectrometry, nuclear magnetic resonance techniques or other methods.

As researchers become aware of the role of carbohydrates in health and disease, the field of glycoscience is broadening, says Cummings. Glycans are being recognized as one of the four major classes of macromolecules, alongside nucleic acids, proteins and lipids.

In the 1970s and 1980s, this field was just getting its start and it was considered merely another part of biochemistry. When carbohydrate researchers got together at meetings, it was more like “tea parties” with 50 to 100 attendees, says Cummings. Glycoscience was far from the spotlight. The community began using the term glycobiology, which Raymond Dwek coined in 1985 and which resonated with researchers. And then, he says,  “all of us kind of chose the term glycomics at some point to distinguish ourselves scientifically from proteomics and the other ‘omics.”

Hear Rick Cummings talk about the history of the field here (40 seconds)


Studying glycan function preceded the study of carbohydrate structure, says Cummings, a situation not unlike molecular biology. For example, work by the chemist Linus Pauling on sickle cell disease occurred before the responsible mutation had been identified and before it was possible to sequence DNA. “We really didn’t know the gene until years later,” says Cummings. The molecular biology arena exploded when it became possible to clone and to synthesize oligonucleotides. “We’re at that point now in glyco-science,” he says.

These days it’s increasingly difficult for scientists to overlook glycans, says Cummings. Access and collaboration are what is needed next to grow the field now that researchers are more than willing to, as he says, “dip their little toes in the glycoscience waters.” That being said, he does still hear disparaging comments about glycoscience, but he takes the remarks as a matter of pride. “So you can think of glycans as being like that little awkward kid on the playground who grew up to be a sizable individual whom no one bullies anymore.”

Sharing data to advance structural biology

In our May editorial, we highlight two new archives: for raw X-ray crystallography (the Structural Biology Data Grid, or SBDG) and for cryo-EM (EMPIAR). These archives join the long-established Biological Magnetic Resonance Data Bank, or BMRB (which hosts biomolecular NMR spectral data) as important resources which will facilitate greater transparency and accelerate progress in structural biology.

Note that neither archive is intended as a “data dump”: datasets in the SBDG must be tied to a journal publication and must be sponsored by the principle investigator of the work, and datasets in EMPIAR must be tied to an Electron Microscopy Data Bank (EMDB) EM density map entry.

Though we do not mandate raw X-ray or cryo-EM data deposition at this time, we applaud these efforts and welcome feedback from the structural biology community about how these archives are bolstering community needs.

An archive for raw EM data

Earlier this week we published a Correspondence describing EMPIAR, a public archive for raw 2D electron microscopy (EM) image data.

While the established Electron Microscopy Data Bank (EMDB) hosts the 3D EM map data required by most journals for publication, the EM community has long been calling for an archive to host the raw 2D image data underlying the 3D maps, as highlighted in our Method of the Year 2015 feature. EMPIAR, a pilot project from the Protein Data Bank in Europe (PDBe), now fills this need.

At Nature Methods we support this archive as a welcome development in the rapidly growing 3D EM field that will enhance transparency, reproducibility, and facilitate the development and refinement of data analysis tools. Though we do not require that our authors deposit their 2D EM image data in EMPIAR, we do encourage it. We urge researchers to make use of the archive and provide feedback to the developers in order to ensure that it is meeting the needs of the field.

Any interested readers without a subscription or site license may read the full text of the Correspondence here.

Debates on genomic footprinting

Our March editorial is on the value of scientific disagreement and can be found here

The papers we refer to in this editorial are:

Genomic footprinting

Genome-wide footprinting: ready for prime time?

Analysis of computational footprinting methods for DNase sequencing experiments

Methods and probes for cleared tissue: an imperfect table

TF table methagora post

Digital Vision/Punchstock

In the March issue of Nature Methods, the technology feature explores some ways that labs are optimizing probes to image cleared tissue. As we interviewed scientists, we learned about published work and ongoing unpublished experiences. Here is a snapshot of how some probes work with some clearing methods. It’s an imperfect table and is likely to evolve as research continues. We welcome your comments. We know there are different viewpoints and varying experiences and we hope it will be helpful to others to hear about them.

We wish this could be a wiki page, then again that might deprive you of your nighttime rest, as you edit one another’s entries. This way, your comments can be seen by all.

 

An imperfect table about some labels that have been used in cleared tissue
Probe Labeling shown in large samplesb Labeling shown in small samples Does not work well?
Genetically encoded fluorescent proteins 3DISCO (signal quenched after a few days), CLARITY, CUBIC, ExM (variant)a, PACT/PARS (signal retained 6 months and longer)a, sDISCOa BABB (signal quenched after a few hours), ClearT2, ExM (variant)a , ExM/ePACTa , ScaleA2, ScaleS, SeeDB,
Sucrose, TDE
Immunolabels 3DISCO, CLARITY, iDISCO, iDISCO+a, iSeeDB, PACT/PARS, Sucrose, SWITCH BABB, ClearT, ClearT2, CUBIC, SeeDB, SeeDB2a (in press) Sucrose, TDE, ExM
Specific nucleic acid detection EDC-CLARITY CLARITY, ExM (variant)a, PACT/PARS
Dyes and stains
Congo Red 3DISCO
Lipophilic dyes such as DiI, Sudan Black PACT/PARS (Sudan Black), SWITCH (DiI) ClearT, ScaleS, SeeDB
Nuclear stains such as DAPI, DRAQ5, SYTO and PI CUBIC, ExM (variant)a, PACT/PARS, SWITCH SeeDB, SeeDB2a
SNAP-tags with SiR probes PACT/PARS, BABB, Scale SeeDB

Clearing method: Solvent-based; simple immersion; hyperhydration; hydrogel-based.

aUnpublished information

bIndicates larger samples such as whole organs

Sources: E. Boyden, MIT; K. Chung, MIT, K. Deisseroth, Stanford University; H-U. Dodt, Vienna University of Technology/Medical University of Vienna; V. Gradinaru, Caltech; P. Heppenstall, EMBL; Takeshi Imai, RIKEN; ­­K. Johnsson, EPFL; J. Lichtman, D. Richardson, Harvard University; A. Miyawaki, RIKEN; M. Tessier-Lavigne, N. Renier, Rockefeller University.

*

Glossary of some tissue clearing agent acronyms

3DISCO :  Three-dimensional imaging of solvent-cleared organs

sDISCO: Stabilized three-dimensional imaging of solvent-cleared organs

BABB :  Benzyl alcohol and benzyl-benzoate

CLARITY : Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging/Immunostaining/In situ hybridization-compatible Tissue-hYdrogel

CUBIC : Clear unobstructed brain imaging cocktails and computational analysis

EDC-CLARITY : 1-Ethyl-3-3-dimethyl-aminopropyl carbodiimide-CLARITY

ePACT: PACT-based expansion clearing.

iDISCO : Immunolabeling-enabled 3D imaging of solvent-cleared organs

iDISCO+ : Immunolabeling-enabled 3D imaging of solvent-cleared organs plus

PACT :  Passive Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging/Immunostaining/In situ hybridization-compatible Tissue-hYdrogel

PARS : Perfusion-assisted agent release in situ

SeeDB : See Deep Brain

Spalteholz’s preparation :  Benzylbanzoate/methylsalicate

TDE  : 2,2′-thiodiethanol

 

Some lab resource pages

Chung lab resources – Literature, protocols, videos and discussion pages from the MIT lab of Kwanghun Chung related to SWITCH, electrostochastic transport and CLARITY.

CLARITY Resources – Protocols, literature, data and videos related to CLARITY, developed in the lab of Karl Deisseroth at Stanford University. Links to CLARITY Wiki and CLARITY Forum

Expansion microscopy resources – Literature and protocols related to expansion microscopy developed in the MIT lab of Edward Boyden.

iDISCO resources – Literature, protocols and information about validated antibodies related to iDISCO

SeeDB Resources – Protocols, literature videos related to SeeDB developed in the RIKEN lab of Takeshi Imai.

Understanding and documenting variation in human genomes

To understand disease one needs to understand the genetic variations that underlie it. Many tools exist that predict the deleteriousness of variants in the human genome; PolyPhen2, SIFT or CADD (combined annotation dependent depletion), to name only a few examples.  On page 109 of our March issue Yuval Itan et al. present the mutation significance cutoff (MSC) to replace a global threshold for calling variants deleterious, often used for CADD scores, with a gene-level threshold. For MSC, as for any other variant prediction tool, it was important to validate the quality of the predictions with variants known to be deleterious. Established mutation databases are often used as ground truth to test the quality of prediction tools.  MSC, for example, was validated against variants found in two large databases, HGMD and ClinVar.

The February editorial discusses the strength and limitations of large human variation databases and emphasizes the importance of sharing variant data in publicly accessible databases. We encourage our readers to share their experience with these databases and to recommend their favorite ones.

Reviewing computational papers

Reviewing papers reporting algorithmic developments and/or new software in the biological literature is no easy task. In this month’s editorial we discuss some of our experiences with this type of peer review over the past years. You can read the editorial here.

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