Summary The goal of the HuBMAP program is to appreciate the unique contextual role of individual cells within the 3D structure of a tissue at its most basic level of transcriptional activity, cellular signaling and cellular response. To date the subject tissues have not included the mineralized skeletal system due to technical issues that preclude the requirements of the HuBMAP program. We have solved those issues with a protocol that is capable of performing multimodal histology that includes methods for advanced and repetitive in situ hybridization for both RNA and protein targets. In this software engineering supplement project, we aim to revamp one important software tool, called mGEA (Make GEO Accessible), that we have been using internally to identify candidate MERFISH probes for human knee and bone tissues so that it can benefit a larger user base who needs to examine population- based reference gene expression data sets readily available from NCBI GEO. Examining GEO deposited data can be very beneficial for HuBMAP users since one can acquire a cohort of gene expression data sets from which tissue/organ specific reference gene expression patterns could be mined. The importance of using population-based signals for probe design has been hotly discussed during the recent HuBMAP FISH-assay meeting (March 15, 2021 organized by Dr. Ajay Pillai). Unfortunately, GEO has been mostly optimized for “data archiving” and as such, using deposited data by ordinary biologists and even for computational scientists has been severely limited. Our tool mGEA could dramatically lower that barrier. Difficulties of using GEO deposited data include (i) associating experimental platform IDs (e.g., Affymetrix, Illumina, Agilent, etc.) with gene symbols that biologists are mostly familiar with, and (ii) organizing which populations of samples (biological and technical replicates) can be grouped together and compared (e.g., treatment vs. control, KO population vs. WT, etc.). Using mGEA, scientists should be able to convert the archived data into biologists-friendly formats (e.g., Excel spreadsheet with gene symbols, fold change and statistical sample-wise and gene-wise z-scores precomputed) within a few clicks over any web browser. If everything goes well, users should be able to convert a GEO deposited data set into a format amenable to their local exploration in less than 10 minutes using the tool’s user-friendly visual GUI although problematic cases may take longer as manual intervention is needed. Making mGEA cloud-ready would not only benefits the members of the HuBMAP consortium but also the constituents far beyond the HuBMAP. With mGEA, the majority of wet-bench biologists should be able to explore GEO deposited data, thus facilitating GEO to unleash its intended power as an important community resource.