Do You Know if Your Antibodies were Validated?

Increase productivity immediately! Get the results faster! Get more funding now! Publish first! These are just a few of the internal and external time pressures that many scientists face every day regardless of geography or lab setting. To meet these demands, a scientist may find he or she must order critical experimental laboratory reagents from vendors promising quick delivery. The reagent arrives quickly, as promised, and the scientist conducts the experiment, presuming that the vendor and/or manufacturer has spent the time and energy needed to test the quality of the reagent and its usefulness for the particular experiment type being done. As it turns out, this is an often wrong presumption to make, resulting in multiple bad outcomes.

The US spends close to $800 million dollars a year on conducting research that use protein-binding reagents. A whopping $350 million of it is wasted due to bad reagents, particularly antibodies that fail to perform as expected (Bradbury & Plückthun, 2015). These are significant wasted resources that could have been used to further the scientist’s research more productively. So, how can the “faulty antibodies” be avoided in the first place?

The simplest answer is to validate the antibodies. But who should validate, and to what degree?

It seems obvious to state that the manufacturer should invest the time and resources to fully validate their antibody products before making them available. Actually, this may not be feasible because the sheer volume of antibodies that would need to be tested in numerous different assay formats that have unlimited number of buffers/conditions. Nevertheless, several companies are taking up the torch to validate some of their antibody products (Baker, 2015a).

Efforts have been made to establish third party groups to help validate antibodies. Several websites and a few antibody companies are gathering/sharing reviews, data or articles using particular antibodies, such as the Antibodypedia, Antibody Validation Channel, Biocompare, St. John’s Laboratory, and CiteAb (Baker, 2015a; Freedman et al., 2016). Some companies, including ThermoFisher Scientific and Abgent, are even offering to validate an antibody for the wary scientist.

But ultimately, the burden of responsibility for validating an antibody for a particular use or uses falls on the end user. According to a survey put out by the Research Antibodies and Standards Task Force (set up by the Global Biological Standards Institute), the vast majority of seasoned researchers (~6 or more years post-training) realize that this is the case (Freedman et al., 2016). But the same survey also revealed that less than 45% of researchers who recently completed their training take the time to validate their antibodies (Freedman et al., 2016).

What would prevent a scientist from making sure the antibody is good and the results are trustworthy? The very same survey revealed that time, money, and delay in research were the primary reasons why a scientist may elect to not validate an antibody (Freedman et al., 2016). Here’s the head-scratcher: If the antibody yields a false-positive result or unreproducible result, the researcher would have already wasted money, time, and experienced a huge set back in their research. It would appear that more seasoned researchers have learned this lesson, but it will take time for those with less experience to come to this realization.

What if the researchers who recently completed their training do not fully know how to validate their antibodies, but want to? Yale University’s David Rimm (having falling victim to dubious antibody performance and now a champion for antibody validation) developed a flowchart of methods that can be used for validating an antibody, including the use of cell-lines that have the expression of the antigen knocked down (Baker, 2015b; Bordeaux et al., 2010). The scientists could also perform labor-intensive pull-downs followed by mass spectrometry to identify the protein(s) that bound to the antibody.

Why are antibodies so difficult? Like life, antibodies can be complicated. They are created in either living animals or in cell-lines, which can lead to variations in antibody composition, post-translational modifications (chemical changes made after the antibody or protein is created) or complete loss of the product if a cell-line dies/fails to grow. This can cause batch-to-batch antibody variability or cause a product to become unavailable.

Aside from variations due to the origination from animals or cells, other factors can affect antibody performance. For example, the purification of the antibodies from animal blood or from cell-lines can vary in quality and in the amount of contaminating proteins. Improper shipping, handling, or storage (wrong conditions or using past the expiration date) of the antibodies may cause them to unfold and lose activity. Another source of performance problems can be attributed to the antigen used in antibody development, which may possess a different set of post-translational modifications compared to its counterpart found in tissue or other biological sample, thus affecting antibody binding. If the experimental conditions are different from those used to create the antibody, the antigen’s antibody binding-site could become obscured by either the antigen adopting a different conformation or the antigen forming different complexes with other biologics. Yet, another source of performance issues could be attributed to the antibody’s specificity: The antibodies may bind to proteins that are similar to the intended target or to extremely abundant proteins. These are by no means the complete set of reasons for the problems seen with antibodies in research (for a more complete list, see a recent review by Michael Weller (Weller, 2016)).

Clearly, antibodies can be variable not only in their composition, but also in their performance. It would be ideal if a scientist could use affinity reagents that were less prone to variability, such as SomaLogic’s SOMAmer® reagents. SOMAmers, which are made of chemically synthesized modified single-stranded DNAs, can be used in most laboratory assays in place of antibodies. Their chemical origin greatly reduces batch-to-batch variability and the other issues that arise when using antibodies derived from animals or cell-lines. The methodology used to create SOMAmer reagents also includes measures to improve the specificity and enhance affinity. The methodology can be adjusted to generate SOMAmer reagents better suited for binding the desired target in the experimental conditions. SomaLogic researchers characterize what each SOMAmer reagent binds to by using mass spectrometry for high abundance targets, pull downs, and binding assays using very similar proteins to check for specificity. Although this level of characterization gives the user some confidence, it is still up to the researcher to confirm that the specific SOMAmer reagent will work for their specific need.

The old saying “slow and steady wins the race” can apply when it comes to research. Time and money should always be invested to validate binding reagents – or other critical assay components – that will be used for an intended experiment. The external/internal pressures may never go away, but at least fewer resources will have been wasted and more meaningful and reproducible research can happen.

References

Baker, M. (2015a). Antibody anarchy: A call to order. Nature, 527(7579), 545-551. doi:10.1038/527545a

Baker, M. (2015b). Reproducibility crisis: Blame it on the antibodies. Nature, 521(7552), 274-276. doi:10.1038/521274a

Bordeaux, J., Welsh, A., Agarwal, S., Killiam, E., Baquero, M., Hanna, J., . . . Rimm, D. (2010). Antibody validation. Biotechniques, 48(3), 197-209. doi:10.2144/000113382

Bradbury, A., & Plückthun, A. (2015). Reproducibility: Standardize antibodies used in research. Nature, 518(7537), 27-29. doi:10.1038/518027a

Freedman, L. P., Gibson, M. C., Bradbury, A. R., Buchberg, A. M., Davis, D., Dolled-Filhart, M. P., . . . Rimm, D. L. (2016). [Letter to the Editor] The need for improved education and training in research antibody usage and validation practices. Biotechniques, 61(1), 16-18. doi:10.2144/000114431

Weller, M. G. (2016). Quality Issues of Research Antibodies. Anal Chem Insights, 11, 21-27. doi:10.4137/ACI.S31614



Time. Is it on My Side?

Time. Is it on My Side?

Time. There never seems to be enough of it. With our hectic lives, even the simplest of inconveniences, such as a car breaking down or a heart attack, can totally sour the afternoon and derail our well-laid plans. Wouldn’t it be nice to have advance warning for when we might expect to encounter an interruption to those plans? In part to gain us such a portent, several groups recently assessed a new technology for determining an individual’s cardiovascular disease risk, and the development of a warning test. Their combined efforts may indeed allow us to better plan our lives, or at least serve as a wake-up call that we need to change something in order to have more time to live.

The new technology that could play a crucial role in granting us more time is the SOMAscan® assay, which currently measures changes in over 1,130 proteins. To assess the practicality of SOMAscan in cardiovascular disease research, a research group led by Rob Gerszten at Beth Israel Deaconess Medical Center looked at “controlled heart attacks” to identify protein differences between pre- and post-heart attack in patients’ blood. Aside from identifying biomarkers that are well-established for heart injuries, the researchers also found several not previously seen. They also looked for biomarkers related to other traits that are known to elevate a person’s risk for cardiovascular disease (e.g. age, smoking, cholesterol levels, etc.). They noted the candidate biomarkers they discovered using SOMAscan may shed light into novel pathways that could in some way relate back to the development of cardiovascular disease. The researchers also noted that the SOMAscan assay was faster compared to mass spectrometry, an important consideration when assaying a large number of participants.

In addition to evaluating the SOMAscan assay for biomarker discovery, the research group also evaluated the accuracy of the individual SOMAmer® reagents in identifying their intended target proteins. Using mass spectrometry, the researchers found that all the SOMAmers tested did indeed hit the right targets.

In related work, a research group led by Peter Ganz at University of California San Francisco used the SOMAscan assay to identify a potential prognostic test for true cardiovascular risk in patients with stable coronary heart disease (CHD). The researchers initially analyzed plasma samples from CHD patients who took part in the Heart and Soul study (a study initially intended to assess how mental health affects heart disease patients) for biomarkers that could stratify risk, a measurement that has proved challenging when using traditional or even genetic methods. From the initial phase, the group identified nine proteins that passed the statistical rigors.

To further assess the accuracy of the nine-protein panel, Ganz and his group conducted another round of SOMAscan testing on samples from a completely different set of individuals (participants in the HUNT3 study whose medical data and samples were collected and could be used for further medical or social science research), they verified their findings from the Heart and Soul samples. The researches also evaluated paired samples from the Heart and Soul study participants to determine if the individuals’ risk change as a cardiovascular event approached. They found that indeed that the closer an individual came to a cardiovascular event, the greater the change for the nine-protein panel results when compared to baseline values. These changes were, in turn, shown to be a more reliable and accurate measure than the current clinical standards for assessing cardiovascular risk.

The related findings of these two research groups underline the ability of the SOMAscan assay to benefit cardiovascular disease research, and suggest that we are inching closer to being able to fine-tune our prediction of when a cardiovascular event may happen. Which in turn, may grant us the time we need to accomplish all our well-laid plans.

References:

Ngo, D., Sinha, S., Shen, D., Kuhn, E. W., Keyes, M. J., Shi, X., . . . Gerszten, R. E. (2016) Aptamer-Based Proteomic Profiling Reveals Novel Candidate Biomarkers and Pathways in Cardiovascular Disease. Circulation, 134(4), 270-285. doi:10.1161/CIRCULATIONAHA.116.021803

Ganz, P., Heidecker, B., Hveem, K., Jonasson, C., Kato, S., Segal, M. R., . . . Williams, S. A. (2016) Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients With Stable Coronary Heart Disease. JAMA, 315(23), 2532-2541. doi:10.1001/jama.2016.5951



The Toll of Aging on the Quest for Gold

Although the summer Olympics are over for another four years, the world is again amazed at the amount of training these Herculean athletes endure in order to capture gold. A young body usually recovers quite easily after an intense training session; thus, permitting young athletes to continue their quest. However, age inevitably starts to take its toll on the body. Repairing damaged muscles becomes increasingly more difficult with advancing age, though “why” the ability to regenerate muscle is lost is largely unknown. To try to figure it out, a research group led by Jerome Feige and C. Florian Bentzinger at the Nestlé Institute of Health Sciences decided to look the effects of age on muscle stem cells, in the context of their normal contribution to muscle regeneration.

The research group started with comparing gene expression in muscle stem cells from young and old mice. They found that the cells from the injured old mice showed lower expression of genes involved in the cell cycle regulation, higher expression of JAK-STAT and MAP kinase pathway-associated genes (major cell signaling pathways for a variety of functions), and multiple changes in the gene expression for proteins associated with the extracellular matrix (ECM) receptor pathway. The ECM findings were of particular interest, suggesting a role for ECM in muscle regeneration.

To confirm the ECM’s contribution to the muscle regeneration, the group used the SOMAscan® assay to directly measure and compare changes in protein levels in homogenized muscles from injured or uninjured, and young or old mice. They found that many ECM proteins showed higher levels in the old uninjured mice, an observation that is consistent with the usual increase in fibrosis seen in aging muscles. However, when looking at the injured mice, they saw that, in young mice, elevated levels of fibronectin occurred quickly after injury. However, in the old injured mice there was significantly less fibronectin present.

Upon further investigation, the overall importance of fibronectin in regenerating injured muscles solidified. Originating predominantly from lineage-positive cells (stem cells expressing markers seen in mature cells) near the sites of injury, fibronectin serves as an ideal substrate for muscle stem cells to adhere. The researchers found that loss of this ideal substrate led to alterations in several signaling cascades, in line with previous observations from other groups. These alterations may contribute to the aged muscle cells’ increased susceptibility to anoikis (cell-death induced by failure to anchor onto matrix).

Digging even deeper, the investigators looked into the role of a protein called focal adhesion kinase (FAK), which they noted is a known inhibitor of anoikis and dependent on fibronectin for activation. Like fibronectin, they observed the FAK levels to decrease with age in muscle stem cells, which may account for the increased susceptibility of the aged muscle stem cells to anoikis.

Is fibronectin the key to the fountain of youth for aged muscle stem cells? The researchers saw that aged muscle stem cells showed improved adhesion to matrices that included fibronectin, which reduced the cells’ susceptibility to anoikis and slightly improved their proliferation. The inclusion of fibronectin in the matrix also restored the FAK activity and subcellular localization in the aged muscle stem cells. When the researchers injected injured old mice with fibronectin, they saw more FAK in cells undergoing myogenesis, and the localization of the FAK within the cells was comparable to that of cells from young mice. In addition to the improved signaling, the researchers saw improved proliferation of the cells that would go on to become muscle cells. The resulting muscle fibers indeed showed fewer immature muscle fibers than seen in control mice, suggesting that the effects of age on muscle regeneration were mitigated by injection of fibronectin.

For the aging Olympian athletes, fibronectin injections would probably be seen as a new form of doping. For the rest of the aging populace, however, increasing fibronectin may be the winner as a way to maintain a more active lifestyle well into “old” age.

Link to paper: http://rdcu.be/i9ib (Lukjanenko et al., 2016)

Resources

Lukjanenko, L., Jung, M. J., Hegde, N., Perruisseau-Carrier, C., Migliavacca, E., Rozo, M., . . . Bentzinger, C. F. (2016) Loss of fibronectin from the aged stem cell niche affects the regenerative capacity of skeletal muscle in mice. Nat Med. doi:10.1038/nm.4126



Warding off the Theft of Independence and Memories by Alzheimer’s Disease

Alzheimer’s disease (AD) robs people of their memories and their independence. Determining those at greatest risk for the disease as early as possible may prove key for warding off AD for as long as possible. Although changes in the brain can show up between 4 and 17 years prior to manifestation of AD symptoms, these are only seen by using magnetic resonance imaging (MRI); not ideal for wide-spread population screening.1 What is needed is a non-invasive, readily accessible and inexpensive diagnostic test that would help clinicians manage patient health through early intervention.

As a first step towards such a diagnostic test, Steven Kiddle and his colleagues set out to identify biomarkers that associated with thought-based tests predictive of AD.2 Using relevant blood samples from the TwinsUK study, they acquired the subjects’ proteomic profiles using the SOMAscan® assay, and compared those findings with those obtained from traditional cognitive tests to see if they could identify potential AD biomarkers. The researchers also performed MRI scans to check for AD-related physical changes in the brain.

After collecting and processing data from the numerous tests, the research group analyzed the data to identify biomarker candidates. From the 1,129 proteins measured in samples from the TwinsUK group, the researchers found associations between the levels of three different proteins and brain volume changes as seen by the MRI scans. However, when the researchers compared these volume-associated proteins with measures of cognitive function, only one of them (called MAP2K4) showed an association, and only with the 10-year change in cognitive function testing. In looking at cognitive function scores only, the researchers also found an association between the 10-year change and MAPKAPK5. This protein, however, showed a nominal association with brain volume changes.

To confirm the findings, the researchers turned to a different set of samples that came from the AddNeuroMed study, again performing MRI scans and SOMAscan. From these data, the researchers did find an association between MAP2K4 plasma protein levels and brain volume. However, the smaller sample numbers requires additional studies to be done to potentially nail down a predictive biomarker for early intervention therapy in patients showing increased risk of developing AD. Though preliminary, the findings could eventually lead to making a huge difference in how long an at-risk person can live independently and still reminisce about the past.

References

1 Villemagne VL et al. (2013) Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurology 12 (pp. 357–67)

2 Kiddle SJ et al. (2015) Plasma protein biomarkers of Alzheimer’s disease endophenotypes in asymptomatic older twins: early cognitive decline and regional brain volumes. Translational Psychiatry 5(6): e584. doi: 10.1038/tp.2015.78.



SOMAmer Sandwich Assays: A New Diagnostic Flavor

The famous chef, Emeril Lagasse, delights his audience with his approach to cooking. As he cooks, he theatrically takes the dish to the “next level” by adding another ingredient to the flavor. Bam! The food is even better.

In many ways, laboratory work mirrors cooking. Researchers routinely look to bring out the best “flavors” in any particular experiment. Recently, a group of SomaLogic scientists figured out how to take the already impressive utility of SOMAmer® reagents (small pieces of synthetic DNA with modified nucleotides) up a notch. They recognized that the very properties that make SOMAmer reagents special could be harnessed for traditional antibody-based applications, such as diagnostic sandwich assays (where one binding reagent captures a protein and other one is used to detect the protein).1, 2  Because of ongoing problems with antibody consistency and availability, the use of SOMAmer reagents (easily made by synthetic means) in these assays could be significantly beneficial for both research and clinical applications.

The group devised a way to make a pair of SOMAmer reagents that bind to different places on the same protein. Using Clostridium difficile binary toxin (CdtA) as the target protein, Urs Ochsner and his colleagues modified the “recipe” for whipping up SOMAmer reagents to create tens of thousands of candidates.2, 3 The significant change to the process was including a new ingredient, a previously identified CdtA SOMAmer reagent 4758-6. For comparison, they performed the same procedure, but left out 4758-6.

Upon completion, Ochsner and colleagues looked at the results. In comparing the sequences generated by the method lacking 4758-6 to those from the method that included the older SOMAmer reagent, they saw clear differences in sequence patterns and sequence abundance, as well as some similar sequences. The differences observed suggest that addition of 4758-6 may have worked. Several sequences were chosen for testing in sandwich assays, which revealed that new sequence 5579–12 paired the best with 4758-6. The researchers also conducted additional experiments to confirm that these two SOMAmers did indeed bind to different sites on the protein: a SOMAmer reagent sandwich pair had been created.

Based on the success of their trial run, the investigators decided to make SOMAmer reagent pairs for eight more proteins. In the first phase, the researchers sifted through existing SOMAmer reagents for suitable pairings, finding existing candidates for three of the proteins. In the second phase, they performed the modified procedure to identify the best pairings for the five remaining proteins, successfully identifying SOMAmer reagent pairs for four of them (in line with the usual success rate in generating new SOMAmer reagents).

Compared to antibodies (the traditional protein-binding reagents used in most sandwich assays), SOMAmer reagents are chemically synthesized resulting in better consistency between batches. They also have tremendous multiplexing capabilities, with the possibility of combining thousands of SOMAmer reagents into same experiment. The use of diagnostic sandwich assays incorporating SOMAmer reagent sandwich pairs rather than antibodies may indeed take disease research and diagnostic proteomics up a notch. Bam!

References

Davies DR et al. (2012) Unique motifs and hydrophobic interactions shape the binding of modified DNA ligands to protein targets. Proc. Natl. Acad. Sci. 109: 19971–19976.

Ochsner UA et al. (2014) Systematic selection of modified aptamer pairs for diagnostic sandwich assays. BioTechniques 56(3): 125–133.

Gold LD et al. (2010) Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5: e15004.



Attention to Details

During the 2016 Olympics, an oversight occurred. At first, it might have seemed minor, but it really ruffled some feathers and caused major embarrassment for the Olympic committee. So, what happened? The Olympic committee approved and used incorrect Chinese flags with the smaller yellow stars pointing in the wrong direction.1 The mistake was caught and deep apologies were made. If greater attention had been paid to even these seemingly tiny details, then this situation would not have happened.

In science, minute changes that may seem subtle or inconsequential can also have a huge impact. For example, a single-nucleotide polymorphism (SNP) is a change of one nucleotide that can occur anywhere in a genome. Many times, the single change is no big deal. Other times, the change can happen in a spot that affects how a resulting protein is made, perhaps leading to disease, changing a patient’s response to medication, or making a person more vulnerable to toxins.2 Negative outcomes from these seemingly innocuous scenarios are not inconsequential.

Detecting SNPs at the DNA level is the conventional way for finding them. However, SNPs can also be detected at the protein level by SomaLogic’s SOMAscan assay. SomaLogic researchers have documented that certain SOMAmer reagents (a modified single-stranded DNA that is seminal to the SOMAscan assay) can distinguish between proteins resulting from SNPs and normal proteins in human plasma.3 For example, a SNP causes an amino acid change (histidine to arginine) at position 167 in the low affinity immunoglobulin gamma Fc region receptor II-a protein (FCGR2A for short) in about 44% of the population. The protein version of FCGR2A used to create the SOMAmer reagent carried the SNP. When the SOMAscan assay was performed using samples from healthy individuals, a “bimodal distribution” was observed in both plasma and serum, suggesting that some of the individuals did not possess the SNP version of the protein. In other words, the SOMAmer reagent in the assay could not bind to the “non-SNP” version of the protein.

To confirm this hypothesis, the scientists performed a series of binding experiments. Using purified normal FCGR2A and mutant FCGR2A (i.e., the one that contains the SNP), they measured the affinity of the SOMAmer reagent to these two proteins. While they observed very good affinity to the mutant FCGR2A, the binding to the normal protein was significantly weaker. An additional experiment using a closely related protein to dissect the binding pattern of the SOMAmer reagent ultimately supported the hypothesis.

This work demonstrates how the subtlest change in a protein can affect experimental results. Paying attention to the bimodal data of the SOMAscan assay revealed marvelous insight into the SOMAmer reagent’s specificity for that particular protein. This should not be the cause for any embarrassment, and no apologies are needed.

References

Brocchetto, M. (2016). Rio 2016 officials apologize to China for using wrong flag. Retrieved from http://www.cnn.com/2016/08/08/americas/rio-2016-china-wrong-flag-trnd/

What are single nucleotide polymorphisms (SNPs)? Genetics Home Reference Website. https://ghr.nlm.nih.gov/primer/genomicresearch/snp Published August 9, 2016.

Wilcox S. (2015). SOMAscan Assay: A Proteomic Platform that Can Also Detect SNPs. Poster presentation shown at Human Proteome Organziation (HUPO).