Based on my experience, there are several things that might come to one's experimental design and data analysis.
1. How many replicates per time point?
2. How many time points, and how do you distribute the time points along the course? One may allocate denser time points within a very interesting time window, and the time points need not to be evenly distributed.
3. Try to squeeze all your samples onto one chip for sequencing to avoid batch effect. So know your sample size limit. (If can't, then depending on how you want to compare the data, plan the chip so that batch effect will not be a concern.)
4. Try to perform all your replicated experiments at the same time, using the same equipments, and with the same hands, to avoid batch effect.
4.1 When planning the time points, take into account how long does it need to extract the samples. Don't make two time points too close.
5. Sometimes samples drawn from different time points can have very different transcriptome profiles. Be aware when you do normalisation if this is the case, since many of the normalisation methods in those popular pipelines assume not many differentially expressed genes exist between samples.