Publication Guidelines for Optimized Multiparameter Immunolabeling Panels (OMIPs)

Abstract

Those with a keen eye will immediately notice a “mistake” in the title. The expected term Optimized Multicolor Immunofluorescence Panel has been replaced with Optimized Multiparameter Immunolabeling Panel, while retaining the familiar acronym OMIP. This change clarifies that OMIPs encompass panels for technologies that use fluorescence, as they have since 2010, other optical emissions such as those used in intravital multiphoton microscopy, and mass cytometry, as they have since 2017. OMIPs may also include other methods of labeling molecules, such as genetically encoded fluorochromes and dyes, and the measured parameters may include endogenous fluorescence, such as that of NAD(P)H. These panels typically include at least a few labeled antibodies (Abs) used to detect their target molecules. The new name is therefore appropriate for all such panels.

When OMIPs were first introduced in 2010, primarily with flow cytometry in mind, the highest multiplexing achievable with conventional flow cytometry (CFC) was 18 distinct fluorochromes [1]. Mass cytometry (MC), which enables multiplexing beyond 30 parameters, was adopted more widely in the years that followed. The field has since advanced to the point where 60+ fluorescent parameters can be measured with spectral flow cytometry (SFC), and higher levels of multiplexing are likely to emerge. This progress has driven a new wave of fluorochrome design aimed at narrowing excitation and emission windows to reduce spillover-spreading between reagents. These tools are crucial for building reliable high-parameter panels, but the task remains far from trivial. OMIPs are therefore increasingly important for sharing optimized panels with the research community and for communicating the advantages and limitations of different procedures, protocols, and reagent combinations. They have also proven relevant in veterinary applications, where even small panels can be challenging because of limited species-specific resources. All existing OMIPs can be queried in a database developed and maintained by Antonio Cosma and colleagues, available via the ISAC website. ¹

OMIPs have been adopted beyond CFC and now include panels for MC, SFC, and imaging mass cytometry (IMC) (Graphic 1). They will hopefully soon also feature panels for imaging flow cytometry (IFC), fluorescence microscopy (FM), and intravital microscopy (IVM), as well as panels used for clinical applications. Although the original OMIP publication guidelines remain valid [1], authors who submit panels for these different technologies and applications require more specific guidance. In addition, the full OMIP will henceforth be published as a single document within the printed journal. OMIPs were initially designed to consist of a brief section presenting the panel in the printed journal and a more elaborate online section presenting the technical details and discussion of optimization procedures. The online material, which represents the core technical content of an OMIP, cannot be indexed and queried in the same manner as regular journal content, which has proven unsatisfactory. The present document therefore provides renewed and expanded guidelines that incorporate all changes and developments since the introduction of OMIPs.

The present document covers requirements specific to OMIP submissions, and elements that have repeatedly raised questions in the past. For general topics not addressed here, including statistical reporting, data analysis presentation, manuscript formatting, ethical compliance, and the peer-review process, consult the general author guidelines for Cytometry Part A. ² Where the general author guidelines and the present OMIP guidelines overlap, the OMIP guidelines take precedence for OMIP-specific elements.

The basic OMIP qualifiers remain largely unchanged and are reproduced here, with some adjustments and additional explanations, from the original OMIP guidelines [1].

The Panel Presentation section should be concise.

The Technical Information section should present all pertinent information required to reproduce the panel and the reported results. This includes the instrumentation, reagents, and protocol used to achieve the reported panel performance. Any tables should support important technical aspects of the panel or instrumentation and be clearly discussed in the manuscript.

It is important to distinguish between panel design and panel optimization. Panel design uses available knowledge, tools, reagent availability, and prior experience to generate a panel expected to perform well. Panel optimization, in contrast, empirically evaluates and improves marker resolution, identifies and eliminates unwanted reagent interactions, and assesses the accuracy of observed staining patterns under the actual staining and acquisition conditions. Mathematical, statistical, and computational tools may support both phases. However, their utility depends on the context in which they are applied and on the type of input data used. For example, models based on theoretical inputs, such as spectra from spectral libraries, can guide initial panel design but do not account for all assay-specific variables, including reagent performance, sample autofluorescence, staining conditions, instrument configuration, biological expression patterns, or spectral modulations exerted by buffers. Models based on measured inputs, such as single-stained controls acquired under the same experimental conditions, provide stronger evidence for panel optimization, but do not replace empirical assessment of the fully stained panel. A panel generated by theoretical or computational design alone, even when it appears suitable for its intended purpose, is therefore insufficient for an OMIP. Rather, such a panel represents only the starting point for the optimization procedures required to establish its empirical performance.

The Panel Design and Optimization section should be the most comprehensive part of the manuscript, reflecting the complex and iterative process required to develop an optimized panel. Balance completeness with focus, and ensure that the description includes all key steps needed to justify the final panel arrangement and optimization decisions.

Where mathematical, statistical, or computational methods are applied for panel design or optimization, report them in sufficient detail to permit independent evaluation. Specify the objective function, input data, assumptions, constraints, parameter settings, and decision criteria. Use conventional terminology for established mathematical concepts, statistics, or metrics and avoid proprietary or idiosyncratic names when a standard term is available [12]. Use transparent notation that allows readers to reconstruct the underlying equations or calculations. Do not use methods that are undisclosed, constitute a trade secret, or are otherwise insufficiently described as the sole basis for panel design or optimization. If a method is described in a published patent application, patent, software documentation, or other accessible source, cite that source and explain how the method was applied in the OMIP. If a new metric, objective function, or optimization algorithm is used, define and characterize it fully, including its mathematical formulation, rationale, inputs, parameters, interpretation, limitations, and role in the panel-design decision process. Avoid introducing new evaluation metrics in an OMIP unless they are essential to the panel design and can be described completely. Instead, introduce and validate substantial new metrics or optimization strategies in a separate methodological publication.

This section is particularly important for clinical panels but may also be relevant for research panels.

Determine the appropriate level of validation according to the intended application of the panel as outlined in CLSI (Clinical and Laboratory Standards Institute) H62 [19]. Table 22 of CLSI H62 specifically identifies validation scenarios based on intended use, helping researchers formulate an appropriate validation plan for their assay. Detailed practical guidance for defining acceptance criteria, complementary to CLSI H62, is provided in the five-part practice guidelines series published by the International Council for Standardization of Hematology (ICSH) in collaboration with the International Clinical Cytometry Society (ICCS) [20-24].

Explicitly state the chosen validation framework, acceptance criteria, and key readouts used to evaluate panel performance. Demonstrate and discuss the validation procedures and results accordingly. Common validation parameters include, but are not limited to, sample stability, post-staining stability, precision or reproducibility, sensitivity, and specificity.

Where small panel modifications are planned to accommodate rare patient phenotypes [25], provide appropriate information and data in a separate section. If the panel edits are substantial, the editor may recommend their presentation in a separate publication. In either case, optimization and validation [26] need to be demonstrated.

Conclude each manuscript with a Data Availability Statement that provides the digital object identifier (DOI), accession number, or other persistent identifier for a permanent online repository and specifies which data have been deposited. Use an appropriate public data repository consistent with the general author guidelines for Cytometry Part A. ²

Authors should include, where applicable, representative data, single-stained controls, and biological controls. Data files used to generate Figure 1 must be provided. If the manuscript claims that the panel is applicable to other tissues, animal strains or species, or disease states, the supporting data files must also be included. For each dataset, provide raw data, where available, as well as compensated or unmixed versions, together with the corresponding compensation or unmixing matrix. Provide these data for peer review and to allow readers to examine the panel in greater depth, particularly when considering whether to adopt an OMIP for their own studies. A statement that data are available only “upon reasonable request” is not acceptable. OMIP data must be publicly available as a condition of acceptance.

Annotate data files with descriptive file names and correct metadata so that they are easy to identify, and accompany them, where possible, with a data-analysis workspace containing all gating and other pertinent analyses. Where applicable, include associated coding scripts, masks, and feature tables.

If an OMIP is based on, or includes, clinical or patient-derived data, obtain formal verification from the institutional data officer, compliance office, or ethics committee before deposition, confirming that all applicable data-protection, de-identification, and sharing requirements have been met. Fully de-identify all shared datasets in accordance with applicable institutional, national, and international standards, such as HIPAA, GDPR, or equivalent frameworks [27, 28]. Deposit de-identified clinical datasets in repositories that provide controlled-access mechanisms and enforce appropriate data-use agreements, such as the Immunology Database and Analysis Portal (ImmPort [29]), the Database of Genotypes and Phenotypes (dbGaP [30]), or the European Genome-phenome Archive (EGA) [31]. Specify the repository name, accession number, access type (such as controlled, registered, or embargoed), and any applicable data-use conditions in the manuscript. Use open-access repositories for human-derived data only when explicitly approved by the relevant institutional data-governance office.

Include a reference list of relevant literature at the end of the manuscript, including all OMIPs cited in Table 1 and in the Similarity to Published OMIPs section.

Finally, include a completed Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) form with each OMIP submission as supplemental material on the publisher's website [32].

Antonio Cosma: writing – review and editing. Tess M. Brodie: writing – review and editing. Andrew Filby: writing – review and editing. Maria C. Jaimes: writing – review and editing. Raluca A. Niesner: writing – review and editing. Alfonso J. Schmidt: writing – review and editing. Yolanda D. Mahnke: writing – review and editing, visualization, writing – original draft, project administration, conceptualization.

The authors acknowledge the Hugh Green Foundation for their support and funding of AJS at the Hugh Green Technology Centre at the Malaghan Institute of Medical Research, as well as the German Research Foundation for grants CRC1444 and NI1167/9-1 to RN.

A.F. is on the Scientific Advisory Board of Zomp. All other authors declare no conflicts of interest.