Animal models are an integral part of the drug development and evaluation process. However, they are unsurprisingly imperfect reflections of humans, and the extent and nature of many immunological differences are unknown. With the rise of targeted and biological therapeutics, it is increasingly important that we understand the molecular differences in the immunological behavior of humans and model organisms. However, very few antibodies are raised against non-human primate antigens, and databases of cross-reactivity between species are incomplete. Thus, we screened 332 antibodies in five immune cell populations in blood from humans and four non-human primate species generating a comprehensive cross-reactivity catalog that includes cell type-specificity. We used this catalog to create large mass cytometry universal cross-species phenotyping and signaling panels for humans, along with three of the model organisms most similar to humans: rhesus and cynomolgus macaques and African green monkeys; and one of the mammalian models most widely used in drug development: C57BL/6 mice. As a proof-of-principle, we measured immune cell signaling responses across all five species to an array of 15 stimuli using mass cytometry. We found numerous instances of different cellular phenotypes and immune signaling events occurring within and between species, and detailed three examples (double-positive T cell frequency and signaling; granulocyte response to Bacillus anthracis antigen; and B cell subsets). We also explore the correlation of herpes simian B virus serostatus on the immune profile. Antibody panels and the full dataset generated are available online as a resource to enable future studies comparing immune responses across species during the evaluation of therapeutics.

Antibody reagents are the key components of multiparametric flow cytometry analysis. Their quality performance is an absolute requirement for reproducible flow cytometry experiments. While there is an enormous body of antibody reagents available, there is still a lack of consensus about which criteria should be evaluated to select antibody reagents with the proper performance, how to validate antibody reagents for flow cytometry, and how to interpret the validation results. The achievements of cytometry moved the field to a higher number of measured parameters, large data sets, and computational data analysis approaches. These advancements pose an increased demand for antibody reagent performance quality. This review summarizes the codevelopment of cytometry, antibody development, and validation strategies. It discusses the diverse issues of the specificity, cross-reactivity, epitope, titration, and reproducibility features of antibody reagents, and this review discusses the validation principles and methods that are currently available and those that are emerging. We argue that significant efforts should be invested by antibody users, developers, manufacturers, and publishers to increase the quality and reproducibility of published studies. More validation data should be presented by all stakeholders; however, the data should be presented in sufficient experimental detail to foster reproducibility, and community effort shall lead to the public availability of large data sets that can serve as a benchmark for antibody performance.Flow cytometry has developed into an indispensable technique in the research and clinical investigation of immune and hematologic systems, with increasing applications in other cell biology disciplines. There are three main pillars in the practical application of this technology, namely the instrumentation, the analytical methods for large data sets, and the reagents used to design biological experiments. Despite dramatic developments in instrumentation (polychromatic (1), mass (2,3), and spectral (4,5) cytometry) and the significant developments in data analysis techniques for high content analyses (6-8), the last pillar of this trio remains consistent across time in its importance to the application: the fluorescent antibody conjugate.Our and others' experiences indicate that nearly half of antibodies, sold by companies or described by academic groups, do not function for the recommended application. They present staining patterns that conflict with those reported in the literature, show unexpected cross-reactivity, or have even failed the most basic specificity tests (9-11). There is growing alarm about results that cannot be reproduced by other research groups, including data published in high-impact journals. Antibodies are believed to be, in part, responsible for inconsistent experimental results and the publication of inaccurate data in the scientific literature (12,13).During recent years, both industry and academic groups have increased their efforts to increase the quality of t...

IntroductionNon-human primates (NHPs) are critical components of drug development because of their similarity to humans. Many key immunology assays, such as flow cytometry, Western blots, immunohistochemistry and immunofluorescence microscopy, make use of antibodies to demarcate specific cell types and quantify signaling moieties. Very few antibodies are raised against non-human primate antigens; instead, researchers typically use anti-human antibodies that are cross-reactive with the non-human primate species that they are studying. To help researchers find antibodies for NHP research, the National Institutes of Health supports a highly valuable database of the cross-reactivity of commercially available antibodies with 13 NHP species (http://www.nhpreagents.org). The database is derived from manufacturer and investigator reports, and typically provides a simple yes/no statement about whether a clone stains a species, with occasional comments about staining intensity or specificity. While an invaluable resource, the database is limited in its coverage. For example, prior to this study, only 28 CD markers had been evaluated in African green monkeys.Additionally, with few exceptions, the database lacks information about the cell types bound by cross-reactive antibodies, and there are many known instances of antibody clones binding different cell types in different species. For example, granulocyte and monocyte marker expression is known to be substantially different in humans than in non-human primates. Anti-human CD33 clone AC104.3E3 was reported in the NIH database and manufacturer's datasheet as cross-reactive with rhesus and cynomolgus macaques, but our lab and others determined that in those species, it prominently stains granulocytes (1, 2), while in humans it stains monocytes and classical dendritic cells. As another example, the Fcγ receptor CD16 is found on granulocytes in humans and sooty mangabeys, but not in macaques or baboons (3, 4), which will likely confound animal studies evaluating therapeutic antibodies, which may bind, transduce signals through and mediate internalization via this Fcγ receptor. Yet another example is CD56, which is expressed on monocytes in macaques (5), but is a canonical NK cell marker in humans. Thus, researchers must confirm that each clone they use is staining the cell population of interest through literature review or experimental verification.Here we present an expansion of both the breadth and depth of primate cross-reactivity data. We screened 332 monoclonal antibodies in blood from two individuals of each of four NHP species: rhesus macaque (Macaca mulatta), cynomolgus macaque (Macaca fascicularis), African green monkey (Chlorcebus aethiops) and olive/ yellow baboon (Papio hamadryas anubis x Papio hamadryas cynocephalus hybrid); and found more than 120 clones that stained one or more populations in each species. Furthermore, we included counter-stain antibodies that allowed us to determine staining specificity in at least five major immune cell populations. Data from ...

This conference report summarized a full-day workshop, “best practices for the development and fit-for-purpose validation of biomarker methods,” which was held prior to the American Association of Pharmaceutical Scientists (AAPS) PharmSci360 Congress, San Antonio, TX, November 2019. The purpose of the workshop was to bring together thought leaders in biomarker assay development in order to identify which assay parameters and key statistical measures need to be considered when developing a biomarker assay. A diverse group of more than 40 scientists participated in the workshop. The workshop and subsequent working dinner stimulated robust discussion. While a consensus on best practices was not achieved, some common themes and major points to consider for biomarker assay development have been identified and agreed on. The focus of this conference report is to summarize the presentations and discussions which occurred at the workshop. Biomarker assay validation is a complex and an evolving area with discussions ongoing.