High resolution 3D mapping of cellular heterogeneity within multiple types of mineralized tissues

摘要 HuBMAP计划的目标是了解3D中各个单元格的独特上下文角色 处于转录活动、细胞信号和细胞反应的最基本水平的组织结构。 到目前为止，由于技术问题，主题组织还没有包括矿化的骨骼系统 排除HuBMAP计划的要求。我们已经通过一项协议解决了这些问题 能够进行多模式组织学，包括先进的和重复的原位方法 RNA和蛋白质靶标的杂交。 在这个软件工程补充项目中，我们的目标是修改一个重要的软件工具，称为 MGEA(使GEO可访问)，我们一直在内部使用它来识别候选MerFish探测器 人类的膝盖和骨组织，以便它可以造福于需要检查人口的更大的用户群- 基于可从NCBI GEO获得的参考基因表达数据集。检查GEO存放的数据 对HuBMAP用户非常有益，因为人们可以从以下位置获取一组基因表达数据集 可以挖掘哪些组织/器官特异的参考基因表达模式。使用的重要性 在最近的HuBMAP FISH分析中，用于探针设计的基于群体的信号已经被热烈讨论 会议(2021年3月15日，由阿贾伊·皮莱博士组织)。不幸的是，GEO在很大程度上已经针对数据进行了优化 因此，使用普通生物学家甚至计算科学家存储的数据 受到了严格的限制。我们的工具mGEA可以显著降低这一门槛。使用土力工程处存放的困难 数据包括(I)关联实验平台ID(例如，Affymetrix、Illumina、Agilent等)。带基因 生物学家最熟悉的符号，以及(Ii)组织哪些样本群体(生物和 技术重复)可以组合在一起并进行比较(例如，处理与对照、KO种群与 WT等)。使用mGEA，科学家应该能够将存档的数据转换成对生物学家友好的格式 (例如，带有基因符号、折叠变化和统计样本式和基因式z分数的Excel电子表格 预计算)，只需在任何网络浏览器上点击几下即可。如果一切顺利，用户应该能够 在不到10分钟的时间内将GEO存放的数据集转换为适合当地勘探的格式 使用该工具的用户友好的可视化图形用户界面，尽管有问题的情况可能需要更长的时间，因为人工干预 需要的。让mGEA为云做好准备不仅会让HuBMAP联盟的成员受益，而且 远远超出HuBMAP的成分。有了mGEA，大多数湿凳生物学家应该能够 探索GEO存放的数据，从而促进GEO释放其作为重要社区的预期力量 资源。

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.