BGI 5032 PDF

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Reproducibility, or a lack thereof, is an increasingly important topic across many research fields. A key 5023 of reproducibility is accurate reporting of both experiments and the resulting data. Herein, we propose a reporting guideline for mass spectrometry imaging MSI. Previous standards have laid out guidelines sufficient to guarantee a certain quality of reporting; however, they set a high bar and as a consequence can be exhaustive and broad, thus limiting uptake. It is intentionally not exhaustive, but is rather designed for extensibility and could 5023 eventually become analogous to existing standards that aim to guarantee reporting quality.

For MSIcheck, we show a snapshot review of a one-month subset of the MSI literature that indicated issues with data provision and the reporting of both data analysis steps and calibration settings for MS systems.

Although our bgj is MSI specific, we believe the underlying approach could be considered as a general strategy for improving scientific reporting. Our specific research field—mass spectrometry imaging MSI [ 1 ]—applies mass spectrometry MS for the raster-based collection of mass spectra from a discrete set of locations on the surface of a two-dimensional sample. This can be achieved for synthetic human-made materials and natural geological surfaces or, as is more common, biological cross-sections of plant and animal tissues.

Depending on sample preservation, preparation, and MS settings, MSI can investigate the spatial distributions for a wide variety of analytes, including small molecules drugs, lipids [ 2 ], N -glycans [ 3 ] or peptides, and proteins [ 4 ].

MSI measures the molecular composition of a sample, simultaneously acquiring information on 50322 to thousands of compounds, in some cases, across multiple compound classes, without prerequisite knowledge of composition and without compound-specific labeling e.

The spatial component of the information obtained by MSI is orthogonal to most omics approaches, which generally do not consider the spatial aspect of global abundance changes [ 6 ]. It is in the MSI context that we address the current lack of standardization in reported research. MSI is arguably one of the most challenging experimental approaches in MS and, similar to other MS-based technologies, is data intensive. Combined with the numerous approaches available for sample preparation and analysis, the care required during sample preparation [ 7 ], as well as the observation that dedicated training is necessary to master it [ 8 ], there is a need for highly detailed reporting.

However, adherence to the available reporting standards is not the norm, and MSI requires a substantial step toward standardized acceptable levels of reporting. This is important for building confidence in the reproducibility of experimental results, both for method development and in primary research. The importance of this issue is exemplified by the so-called reproducibility crisis, which is currently being widely discussed in the wider literature and is not MSI specific [ 9 ].

One bbgi the core concepts affecting reproducibility is insufficient quality checks and reporting at both the experimental and data processing levels [ 10 ]. These are the gaps that we propose to address. Publication remains the primary method for result dissemination in the sciences. Gbi, accuracy, and completeness in these reports form a cornerstone of reproducibility.

We propose to adopt the three-part lexicon of Goodman et al. These three parts are methods, results, and inferential reproducibility. We focus on the first two of these in our discussion. Briefly, methods reproducibility is the ability to reproduce the exact data analysis and arrive at identical end results. For example, a publication would be methods reproducible if, using the same analysis methods on the same raw hgi, one could arrive at the same outputs i.

There is a degree of subjectivity in the choice of what results are presented in a figure. In contrast, results reproducibility is the ability of other groups to follow the reported procedures as closely as practicalgenerate new data, and arrive at similar results. This requires that sufficient detail be reported about these procedures and spans sample preparation, data acquisition, the data itself, as well as its processing and analysis [ 11 ].

To achieve methods and results reproducibility [ 11 ], reporting in bbi must obviously meet a certain quality standard. One might expect a minimum quality based on the scientific method—scientists are driven to methodically replicate, validate, and report their findings—that, combined with journal guidelines [ 12 ] and the availability of alternate publication formats such as protocols Rapid Communications in Mass Spectrometrydata briefs Data-in-Briefand video journals Journal of Visualised Experimentswould suggest that all the pieces needed for high-quality reporting are in place.

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Despite this resource-rich environment, there are remaining issues in reporting. There are several other existing standards that are also MSI relevant and aim to encompass different research communities.

One of the broadest is bggi minimum information for biological and biomedical investigations guidelines [ 14 ], which provide a framework of standards for the integrated reporting i. MIAPE is 5023 further to include, e. These standards specify a minimum level of reporting quality by identifying a set of information that should be included when reporting an experiment and that is sufficient to guarantee that the report will meet that specified level of quality.

One way to determine if research is being reported well would be to evaluate its methods reproducibility. Obviously, evaluating results reproducibility is the ideal, but it is expensive and difficult to justify big studies [ 1920 ].

Conversely, confirming methods reproducibility is not easy in practice but ultimately should at least be possible, if not also straightforward.

While such confirmation does not 50032 evaluate research quality, it does evaluate reporting quality. In effect, an individual could not repeat the study without further input from the authors [ 20 ]. This is despite the existence of well-defined standards and is a key component of the reproducibility crisis. Crucially, this is not a reflection on the quality of existing reporting standards, as they highlight core methods that require reporting and raise valid discussion points.

However, they are either being misinterpreted—the assumption being that all scientists will interpret a reporting requirement similarly—or simply not being used.

We suggest that an underlying cause of this is that standards typically aim to set a bar for reporting quality far above the current norm: In practice they end up being sufficient, but not necessary, conditions for reporting quality. To illustrate the concepts of necessary and sufficient conditions for reporting quality, we describe the two fundamental approaches one could take to construct a standard that is both sufficient and necessary.

One approach would be to start with a list of experimental details that, together, are sufficient but not necessary. As many details as possible would then be removed while maintaining sufficiency. Eventually, if the details are granular enough, the last superfluous detail would be removed, and no other details could be removed while still maintaining sufficiency. At this point, the standard would not only be sufficient but also necessary to achieve the specified level of quality.

This is the approach the existing standards take. Conversely, one could start from a blank slate—no experimental details—and add a necessary detail, such as provision of raw data. This would now be necessary, but not sufficient, for achieving the given level of reporting quality.

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One could then continue to add necessary experimental details until sufficiency is reached. We are not setting a fixed bar, but introducing a bar that can be steadily lifted as the field adopts it. Note that although having a standard that bfi both sufficient and necessary would be the best-case scenario, we are not claiming that this is realistic.

Rather, we claim that the approach of starting with a sufficient standard and iterating from there has not produced the intended improvements in the field. Taking a different approach, such as starting from a necessary standard and iterating in the other direction, could potentially produce more tangible improvements in reporting quality as it would produce intermediate steps that are easier to implement. The requirement for sufficiency in existing standards necessitates broad, all-encompassing, and consequently ambiguous phrasing.

This can result in the unintentional omission of experimental detail s. The only way to conclusively determine that the methods described are insufficient to reproduce an analysis would be to apply the methods to the raw data and demonstrate different results.

This is not standard practice during peer review and, as we will demonstrate, is not realistic given the bggi of raw data provision in MSI.

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One goal of reporting should be to convince reviewers and the community that reproduction would be possible bgk time and resources, not to complicate design or data such that reproduction is deemed impractical. In the absence of automated data and meta-data harvesting [ 22 ], a human-driven reporting standard could compensate for human error by directly prompting the inclusion of as many necessary experimental details as possible [ 15 ].

Furthermore, such granular standards that directly prompt inclusion of very specific information also allow for auditing of existing reports with relative ease in comparison to the existing broad standards for which this would be relatively difficult. Easy auditing could be of interest to authors, reviewers, journals, or any other individual or organization for whom systematic reporting quality control is key.

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Standards that allow auditing with relative ease could also be translated to in-house record-keeping processes, thereby not only reducing the time required for translation of research notes to publication format but also making inter-laboratory comparisons, quality control, and standardization consistent and practical.

Misinterpretation of standards due to broad and vague phrasing can be minimized by creating more granular standards, in which the required information is defined more explicitly [ 23 ]. As a side effect, an increase in specificity may also lead to greater ease of use and therefore greater reporting compliance [ 24 ]. However, granular and explicit terms make it unrealistic to be broad and all-encompassing, at least initially before such standards have had time to evolve as the research community interacts with them.

Because of this, any initial proposed standard will preferentially favor a certain subset of methods and experimental designs. It should be noted that we, and others before us, do not intend for granularity to subsequently imply the preferred use of particular methods or experimental designs; we aim to address structured reporting only [ 1525 ].

We also claim that progressive standards iteration and collaboration should produce improvements that will incrementally remove any such researcher-driven bias, as the standards gradually evolve to become more broad and all-encompassing while also reflecting the true method and design biases present in the MSI field. Granular items of necessary detail are exemplified by the recent suggestion to include negative controls for analyte delocalization in MSI [ 26 ].

This would involve the inclusion of off-tissue spectra in every MSI data acquisition region. The suggestion is a good one and necessary to evaluate sample preparation quality. However, it is narrow in scope and, alone, it is far from sufficiency.

At the other end of the spectrum, the MIRAGE guidelines are a good example of a complete granular standard—to the point of having an explicit list of fields to be filled in. This typifies the prompting concept and is a consequence of the specific requirements of glycomics, as related to the complexity of glycans and their fragility during MS analysis [ 15 ].

Unfortunately, the MSI community did not seem to engage with this initiative. The last contribution was in June and is currently not accessible.

Although potentially less powerful, a practical and, therefore, minimalist standard makes implementation easier. This approach may be the driver needed for tangible reporting improvements. Since then, there has been a need to continue the reporting standards discussion initiated by the MSIS and prompt the community to engage effectively with a standard.

In this context, we bvi a template of reporting fields, MIAMSIE, that directly prompts the collection of very specific granular information, reminiscent of a methods section that lists materials and processes, rather than providing this as prose.

This allows not only straightforward completion but also rapid quantitative evaluation of adherence to the standard when collected in a standardized format. Collating this information is a Herculean task for published studies that follow a sufficient guideline but provide this information as unstructured prose. Given that current sufficient standards suffer from low uptake rates, we also propose that MIAMSIE 50332 initially be abbreviated to prioritize uptake over completeness: The abbreviated reporting template was named MSIcheck.

As is to be expected, both the full and abbreviated versions are suited for different tasks. MIAMSIE is an author-driven reporting aid intended to prompt the user into providing a number of key pieces of granular information [ 15 ], represented as a list of fields that are important for reporting an MSI experiment.

However, the intent is for community engagement to drive expansion to encompass the entirety of the field. The key is to ensure that the ultimate aim of sufficiency in a mature MSI standard is not lost. MSIcheck makes implementation as easy as possible.

It is important to note that it is not a separate standard but rather a subset of MIAMSIE that is designed to be very easy to use in order to achieve more rapid impact. Instead of setting the bar for reporting quality, MSIcheck focuses on absolute key aspects of a report in order to evaluate the current 532 of the field and identify problem points that need to be prioritized.

Note that the intent of MIAMSIE is not to provide a complete, field-wide, reporting template and mechanism that ngi gold-plate adherence to a standard.