When it comes to differentiation of neural cell types from iPSCs, the regional identity should be checked carefully. Regardless of the method, different cell lines can display differing propensities to generate a given tissue. While the basis of these cell line differences is still not clear, there is some indication that epigenetic marks, such as DNA methylation patterns, may bias towards

expression of particular patterning genes. Because of these line-specific effects, each cell line-protocol combination should be checked for the desired regional and cell type.

There are well-known markers that can be tested by immunofluorescence staining and/or RT-PCR. These should test for neural versus non-neural identity and identify the germ layer by staining for things like N-Cadherin versus E-Cadherin, T, and Sox17. Expression analysis should also test for brain regional identity to determine if the tissue is telencephalon, diencephalon, midbrain, hindbrain or spinal cord.

And within these, more specific regional identity should be examined. Since neurons are generated slightly differently and with different timing in each region, it is important to be sure of the regional identity particularly when comparing two different conditions. Otherwise, one runs the risk of mis-characterizing a difference in neurogenesis that is actually a result of a difference in patterning. Often, a few key markers are enough since based on the protocol one can infer which regions should be produced, and if the relevant markers are not expressed then further detective work may be necessary.

For these markers, the Lancaster Lab has a growing table of useful markers with an analysis of their regional expression.

For iPSCs used to study neurodegenerative and other diseases, genetic background provides the biggest source of variability between cell lines [3]. This can present a challenge, but there are also opportunities for studying clinical heterogeneity by looking at the impact of genetic modifiers.

Small samples may mean that studies miss important genetic signals. Where iPSCs are used to study genetic disease, it is therefore better to use more donors than more lines from the same donor [4]. Below, Prof Selina Wray from University College London highlights how this can help boost credibility (excerpt from a BNA webinar on reproducible research using stem cell derived neurons and organoids).

There has been a surge of research using organoids in the past decade to study brain development in humans and help model neurological disease and neurodevelopmental disorders, but variability between cell lines and between batches presents reproducibility challenges [5].

Familiarising yourself with the tissue you're attempting to model and how that is structured, based on the available literature and in vivo models, will help you to recognise what represents a good organoid sample to take forward. Checking the tissue identity is also important to ensure that you have organoids of the part of the brain that produces particular neuron types that you're studying. Below, Dr Iva Kelava from the MRC Laboratory of Molecular Biology explains why both are important to improve reproducibility for research using organoids (excerpt from a BNA webinar on how to make 3D models more reproducible).

You can reduce bias and build confidence in the study by preregistering a time-stamped account of your research plans prior to starting the study, clearly and openly stating your experimental rational, hypothesis, and methods, including the intended statistical analysis. This helps to demostrate not only the rigorous planning, but also that these aspects of the study were not shaped during or after data collection and that the results are not selectively reported.

The easiest way to preregister a study is via a registry. There are all-purpose registries for a range of scientific disciplines, such as the Open Science Framework or AsPredicted [6]. 

You can also preregister a study through a journal that offers Registered Reports, such as the BNA's Journal Brain and Neuroscience Advances.

Watch Ulrich Dirnagl from the QUEST Center for Responsible Research explain the preregistration options below (exerpt from a BNA webinar on preregistering in vivo studies).

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Using the correct controls seems obvious but is sometimes accidentally overlooked. It is essential for any experiment, but especially when working with cells that can be very sensitive to external factors. Examples include using the equivalent vehicle control when treating cells (particularly when working with ligands dissolved in solvents - solvents are notorious for having potent non-specific effects on cell signalling-pathways); using an equivalent amount of empty vector during transfections (same principle applies to infections – virus expressing the corresponding amount of empty vector); and similarly using negative/scrambled controls in knock-down experiments. And finally, experiments should always contain untreated control cells (to see if the vehicle/control itself has any effect on the cells). 

There are simple steps that you can take to improve the structure of how you manage data collection throughout your study. This can not only help you further down the line when it comes to remembering the processes you used, but also provides transparency on how your raw data are turned into your final results. Retaining all raw data is also useful in case at a latter point you discover a error in the data processing or analysis that you need to trace back to the original data.

Setting up a lab strategy for data-gathering can ensure everyone in the lab adopts the same approaches, which can improve continuity when you have staff turnover and help keep your data safe. Having a structure where data is managed well throughout the study can also help with making the data shareable after the study is completed.

Electronic Lab Notebooks offer one tool to help keep track of your project and enable better research processes [7]. In the video below, Kaitlyn Hair from CAMARADES at the University of Edinburgh highlights the benefits ELNs can bring to data management in your study (excerpt from a BNA webinar on handling in vivo data).

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