6 Ways to Improve the Quality of Quantitative Histopathology Data

The production of robust tissue-based quantitative histopathology data is completely dependent on high quality sample preparation and staining. Failure to ensure that samples are of a high quality can hinder subsequent image analysis processes, negatively impact on data quality, and in some cases, prevent an image analysis study from proceeding. These issues can result in additional unjustifiable costs and extended timelines for a project.

A number of issues can arise during the analysis of poor quality prepared sections. In this blog, we outline 6 of the most commonly encountered issues that hinder or negatively impact upon histopathology data quality.

1. Artefacts

Artefacts are extraneous objects or a defects in a section such as folds or tears in the tissue, hair, dust particles, staining deposits or air bubbles.  Artefacts can adversely impact on data generation due to image analysis detection of false positive staining. It is important to minimise the introduction or inclusion of potential artefacts during microscope slide preparation and to perform stringent reviews of sections during quality control procedures.

Figure 1 (right). Fold in a stained tissue section.

Improving the Quality of Histopathology Data: An example of a fold artefact present on a scanned tissue section

2. Edge Effect

The ‘edge effect’ is the term used for the staining pattern observed when the outside of a tissue preferentially takes up staining more than the inside, resulting in a ring of non-specific differential staining around the edge of the tissue. This may result in false positive or inaccurate data readouts.

Edge effects can be caused by inconsistent fixation, the tissue drying out or lifting around the edges.  Repeat staining may not eliminate this problem and care with respect to storage time and environment, for cut slides, should be considered.

Figure 2 (Right). A stained tissue section showing the ‘edge effect’, with outside areas more intensely stained than the inside of the tissue.

Improving the Quality of Histopathology Data: An example of 'edge effect' staining

3. Intra- and Inter-run IHC Staining Variability

Intra-run variability occurs when IHC-stained slides, from the same processing run, exhibit notably different staining intensities, which are not deemed to be due to biological variance. This can be caused by incorrectly diluted reagents or machine failure resulting in inconsistent application of reagents.

Inter-run variability occurs when runs performed at different times or on different days result in variable staining across a series of slides or different studies. This variability can be caused by many factors such as issues with the automated IHC staining platform or not using identical batches of antibody or reagents.

When inter- and intra-run variability occurs, it can be challenging for image analysist scientists to make accurate comparisons between samples within or across studies.

Automated IHC staining platforms that include environmental controls and utilise standardised reagents can reduce potential intra- and inter-run variability.

Figure 3 (Right). An example of inter-run staining variability.

Improving the Quality of Histopathology Data: An example of interrun staining variability

4. Staining gradient across a slide

Another issue commonly observed, with IHC slides, is the appearance of a staining gradient across a slide i.e. strong staining at one side of the slide fading to weak staining at the opposite side. Staining gradients can be caused by a number of errors such as uneven tissue fixation, application of reagents, issues with the automated stainer or by failing to keep the slides flat until they have dried.

To avoid the rejection of sections during pre-analysis quality control reviews, sections exhibiting extreme variations in staining gradients should be excluded and repeat staining performed.

Figure 4 (right). An example of a staining gradient across a slide. 

Improving the Quality of Histopathology Data: An example of a staining gradient across a slide

5. Understaining or Overstaining

Under or overstaining is a common problem associated with IHC, especially when staining is not performed on an environmentally controlled platform. Staining levels can be influenced by a number of factors including incubation time, incubation temperature or antibody concentration.

Staining that is too weak or too strong can produce false negative or false positive results. For example, a dark blue overstained haematoxylin nucleus may be difficult to differentiate from a dark brown DAB labelled positive nucleus, or even mask weakly DAB labelled positive nuclei. In addition, morphological characteristics can be difficult to discriminate by inappropriate stain levels.

Validation of IHC assays should be performed using dilution and precision evaluations and should include testing of counterstain application levels. When processes have been verified these should be adhered to and regulated for all staining runs performed.

Figure 5 (right). Overstained tissue section.

Improving the Quality of Histopathology Data: An example of an overstained scanned tissue section

6. Scanning Errors

Potential errors arising from the digitisation of stained slides during scanning processes include: uneven illumination lines caused by a worn scanner stage, deterioration in the scanner lamp or, an uncalibrated scanner. A major issue of scanning is the production of poorly focused regions, which are primarily caused by artefacts in/on the slide, impacting on the focal plane for a particular field.

Regular preventative maintenance of a slide scanner and system calibrations can reduce scanning errors.

Figure 6 (Right). Scanned tissue section with an out of focus area on the left. 

Improving the Quality of Histopathology Data: An example of an out of focus scanned slide

OracleBio offers project management of IHC studies that require quantitative endpoints, to support our clients in minimising the impact of histology, IHC and scanning issues on studies. Occasionally, random artefacts are unavoidable and OracleBio’s dedicated image analysis scientists can manually interact with the acquired images to exclude undesirable features. Taking time to digitally remove artefacts does result in improved image analysis and provides better quality histopathology data, however, manual interventions with images can significantly extend the time required to complete a study. In studies where a low number of artefacts are present algorithms may be developed and utilised to exclude such features in order to obtain reliable results.

It should also be emphasised that the inclusion of appropriate controls within each study or batch of staining should be part of the study remit e.g. to establish the impact of stain-related variances a known positive control should be stained with each run/batch and analysed to compare the coefficient of variation (CofV), based on the positive stain measures being evaluated and/or intensity or optical density. A study defined CofV should be pre-established to justify the acceptance level for variability.

OracleBio are experts in image analysis and are highly experienced in quality checking IHC. We are able to project manage studies to ensure IHC slides are of a high quality required for image analysis, facilitating rapid production of good quality robust data. If you would like more information on OracleBio’s image analysis services or to find out more about how we can project manage your combined IHC and image analysis study, please contact us.

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