Accelerate Biological Discovery with Open Source Spatial Genomics Data

Vizgen, a life sciences company dedicated to improving human health by visualizing information from single-celled spatial genomics, recently launched an ongoing data dissemination program, giving the scientific community access to data of open source spatial genomics. The first dataset to be released under the program, the MERFISH Mouse Receptor Map, is the largest public dataset for spatial genomics available to date.

Dr. George Emanuel, Scientific Co-Founder, Director of Technology and Partnerships at Vizgen, spoke with Technological networks to tell us more about the program, its objectives and expected impacts. In this interview, Dr Emanuel also discusses some of the most interesting findings from the mouse receptor map and highlights how the datasets will benefit the Human Cell Atlas project.

Anna MacDonald (AM): Can you tell us more about the origins and objectives of the Data Release Program?

George Emanuel (GE):
Spatial transcriptomic profiling with single-molecule resolution across entire tissues has the potential to provide the next step in our understanding of complex biological systems. We have set up our data dissemination program to introduce the community to the type of data that our MERSCOPE platform measures for several key reasons. The first, as a resource that can complement research already underway to better understand the functional composition of the brain. Second, this data serves as an example for researchers of how these high-resolution spatial transcriptomics data can be applied to their specific research questions. Third, providing a complete dataset can allow the IT community to begin developing tools to extract relevant biological characteristics from this type of data. Spatial genomics is an emerging field, and we want to help educate those who have never worked with this kind of data to understand the types of information and discoveries that the next generation of genomics will enable.

AM: What impact do you see having access to open source spatial genomics data?

High-quality open source data has the potential to accelerate biological discoveries. Most academic data eventually falls into the public domain, but usually a year or two after it is first acquired. Since MERFISH has already been validated by various leading scientific publications, we are comfortable with the quality of the data generated with MERSCOPE and want to put the data in the hands of as many researchers as possible so that they can start tapping them for biological insight. . We expect that access to open source spatial genomics data will help the research community understand the additional level of information available in spatial genomics. Our vision for making this data accessible to the public is to enable researchers to make new discoveries with biological significance or to be inspired to develop new tools for analyzing data.

AM: How will the program fit into the Human Cell Atlas project?

The Human Cell Atlas project is a initiative of create complete reference maps of all human cells. A map by its nature requires spatial information and there is no technologies other than MERSCOPE on the market that can provide single cell information for all cells across large tissues with such high multiplexing capacity. Our MERFISH mouse brain receptor map demonstrates the ability of MERFISH to lead the Human Cell Atlas project. This data could, for example, help the Human Cell Atlas community to build on their measurements of existing single cells to uncover new discoveries, or could fuel bioinformatics development pipelines. We see many opportunities to integrate the data into the Human Cell Atlas project, and we look forward to presenting at the Human Cell Atlas meeting at the end of June.

AM: How are the datasets generated?

The datasets are generated using a prototype of the MERSCOPE platform solution. The data set contains nine measurements of complete coronal slices, three positions in the mouse brain with three biological replicates at each position. Each slice was processed according to our standard MERSCOPE sample preparation protocol. From a block of fresh frozen tissue, a 10 micron thick slice was cut and glued onto a MERSCOPE slide, the MERSCOPE encoding probes were hybridized to print each binary barcode on the targeted transcripts, and the The sample was placed on the MERSCOPE to read the barcode using sequential rounds of imaging. The platform acquired raw images of the entire sample and processed these images to decode barcodes and identify the positions of each of the RNA transcripts in addition to segmenting cell bodies using additional DAPI and PolyT dyes. This resulted in the MERSCOPE output which was published for all nine slices: the list of all detected transcripts and their three-dimensional spatial locations (CSV), mosaic images (TIFF), output from cell segmentation analysis , including cell matrix transcriptions (CSV), cell metadata (CSV), and cell boundaries (HDF5).

AM: Why was the Mouse Receptor Map chosen as the first dataset?

The brain contains a complex arrangement of different cell types, including many variations of inhibitory and excitatory neurons, astrocytes, monocytes, endothelial cells, and oligodendrocytes. We still don’t fully understand how these cells are spatially arranged in the brain and how their spatial organization contributes to the overall emergence of complex cognitive processes, but MERFISH is a tool that allows researchers to directly interrogate these aspects. We chose the MERFISH mouse brain receptor map as our first dataset because it shows the power of spatial transcriptomics to advance biological discovery in one of the most difficult tissues to fully understand.

AM: Have details of particular interest been revealed?

For this first dataset, we constructed a panel of 483 genes, including around 50 canonical markers and around 450 mouse brain receptors, including GPCRs and RTKs. GPCRs mediate signaling and may play a critical role in brain aging and neurodegenerative disorders, but are weakly expressed and difficult to capture with other technologies. Although many of the GPCRs had not been characterized in depth across the brain, almost all exhibited non-trivial spatial organization. Something to highlight from the dataset is that we have demonstrated the ability of MERSCOPE to detect certain particularly low-expressing genes, including OXTR (oxytocin receptor) TSHR (thyroid stimulating hormone receptor), and INSR (insulin receptor). The ability to detect weakly expressed genes such as these GCPRs may aid in drug development as we begin to understand the functional significance of different GPCRs expressed in different regions of the brain, for example.

AM: Can you comment on the future atlases that are in preparation?

We plan to continue publishing datasets highlighting the power of the MERSCOPE platform in different application areas. It is possible that our next data release will be more clinically relevant, such as human cancerous tissue or denser tissue where cell segmentation is not as straightforward as in the brain. We have a number of exciting results from internal projects that could lead to more data releases. We hope to eventually build a library of accessible and well-cataloged MERFISH data for the research community as a whole.

Dr. George Emanuel was speaking to Anna MacDonald, Science Editor for Technology Networks.

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