You need to analyze gene expression, but your lab has a tight budget. What are your options?

1. Prioritize cost-effective techniques like RT-qPCR with SYBR Green. 2. Utilize open-source bioinformatics tools for data analysis. 3. Consider bulk RNA sequencing for comprehensive analysis, seeking affordable services. 4. Access public gene expression databases for comparative studies. 5. Adopt DIY lab techniques and collaborate to share resources. 6. Focus on essential gene targets and optimize protocols to minimize reagent usage. 7. Explore alternative platforms like digital PCR or LAMP for specific applications. 8. Strategically allocate funds to essential reagents and equipment. 9. Seek collaborative opportunities to access shared facilities or equipment from neighboring labs.

It depends on the number of genes of interest and the number of samples. If you have a handful of genes of interest qPCR is going to be the most cost effective method. If you don't know your genes of interest, sequencing could be the way to go, but you might be limited by the number of samples you can afford to run.

You can always use primers designed using technologies that incorporate fluorophores into DNA, such as Sybr. However, since this test requires efficiency validation, dissociation curve, and often involves primer molarity adjustments, in the medium to long term, I recommend that the laboratory invest a higher initial amount in purchasing probes with a quencher/reporter system (such as Taqman). This will result in substantial cost savings and the generation of much more reproducible and stable results for research or laboratory services.

I isolated mesenchymal cells initially and, before proceeding to PCR, employed immunostaining with specific markers to validate the cell identity. After confirming the identity, I utilized the qPCR method to verify the gene expression of the cells. This approach is employed because immunostaining detects protein levels, and according to the central dogma theory, gene expression is responsible for protein production.

Do you really need to use microarrays? Microarrays are assays where you want to investigate and don't have much idea where to start. Often, utilizing a smaller array with specific genes for the pathway you want to examine, or not using them at all, can bring significant cost savings to your laboratory.

I have developed 10 cell lines derived from immortalized mesenchymal cells originating from the mouse stomach. Through confirmation, I verified that all these cells express gastric mesenchymal cell markers. In order to assess the heterogeneity among these established cell lines, I conducted a microarray analysis to examine their global gene expression. This analysis led to the classification of the cell lines into three clusters. Subsequently, through wet lab experiments, I observed distinct properties in terms of cell proliferation among these three clusters.

Although PCR based methods are much more affordable, unless you know exactly what specific genes you are looking into, it is better to go broader and do the RNA sequencing. That way you can identify all the genes that are differentially expressed and maybe further validate the finding by performing the PCR.

If you just want to look at a certain gene or genes, maybe you can start with RT-qPCR? But if you want to look at the global changes, then you will have to do RNA-sequencing.

Sequencing serves as a more comprehensive method for analyzing global gene expression. Currently, an abundance of single-cell data, particularly in my study using Mus musculus as a model, is accessible online. Due to its costliness, I opted to utilize existing databases for my analysis. Given the substantial size of the data, proficiency in programming is essential. Therefore, it is imperative to have a command of both R and Python programming languages before delving into this aspect of the study.

One other alternative method if looking into the proteins is doing the multi analyte arrays colorimetrically. This way you can analyze many different analyte expression levels (most likely all the proteins from a specific signal pathway) with minimal amount of protein lysate and also stay within budget.

In my experience, laboratories often use gene expression assays because they already have primers from another project or as an alternative to "fill in the gaps" when data is lacking. However, when it comes to assays truly relevant to your experimental question and to science in general, one must question their significance. We know that changes in mRNA levels do not necessarily equate to changes in protein levels.

An alternative method worth considering is the Line Probe Assay (LiPA). LiPA is a strip-based technique wherein specific single-strand DNA sequences corresponding to particular genes are immobilized onto a nitrocellulose membrane strip. The DNA sample, denatured into single strands, is then hybridized onto this strip. Detection can be visually observed through the naked eye, as the appearance of colorimetric bands on the strip indicates the presence of specific genetic sequences.

Fully consider the cost of the entire workflow and the risk of failed experiments. When labs are on a tight budget, it's easy to cut corners too much, which often leads to failed experiments, and you end up spending more time and money to repeat it. You usually get what you pay for. If you really need to reduce financial costs as much as possible, be prepared to spend more time and effort, and understand you might be sacrificing the quality of data. There's always a trade off between money, time, and quality. You can have 2, but not all 3. Make your choice wisely.

It may sound counterintuitive, but spending the money upfront to run high quality data analysis pipelines (rather than trying to force fit open access ones) saves more time and money in the long run. Budget SPENT ensuring quality from the very beginning is imperative to make sure that budget is not WASTED by structuring projects off of faulty data.

Immunostaining emerges as a compelling alternative method for gene expression analysis owing to its unique advantages. Unlike traditional molecular techniques, immunostaining operates at the protein level, allowing for the direct visualization and localization of specific proteins within cells or tissues. This method employs antibodies labeled with fluorophores or enzyme-linked markers, enabling precise detection of target proteins. Furthermore, the compatibility of immunostaining with various microscopy techniques enhances its versatility in diverse experimental settings, making it a worthwhile consideration for researchers exploring alternative avenues in gene expression analysis.

Adding to @JeffBissen’s comment, I would never skimp on making sure the RNA extraction protocols are top notch, reproducible and work well with your specific sample type(s). Also include all necessary controls throughout. Having properly stored high quality starting material is one way to guarantee that regardless of the platform you settle on, that you will obtain quality results.