Single-cell transcriptome sequencing enables a more complete view of cellular transcriptomes, distinguishing cell types, states, and dynamics in complex samples. However, until now, spatial information relating single cells to their relative positions within tissues was lost, thereby missing the context in which genes are expressed. New technologies in spatial transcriptomics can address this limitation. Now, a partnership between the UW-Madison Biotechnology Center (UWBC) and the Lab in Translational Research Initiatives in Pathology (TRIP) is bringing technologies in spatial genomics to UW researchers.
What is single-cell sequencing and why does it matter?
Previous technology was limited to analyzing bulk tissue samples to get enough material to sequence. Bulk-sample transcriptomic sequencing produces a single transcriptome that is the aggregate sequence and transcript abundance across all cells and cell types in the sample. As such, bulk sequencing loses distinctions between different cell types and heterogeneously behaving cells, obscuring important information.
Technological advances can now generate sequence information from much smaller starting RNA levels. Together with methods to separate and sort individual cells from a complex mixture or tissue, this can generate separate transcriptomes for single cells in the sample. Thus, one experiment can generate transcriptomes for hundreds to tens of thousands of cells.
With careful computational analysis, researchers can use single-cell RNA sequencing to report on the cell types present in tissue samples, discover new cell types, track developmental programs in which cells differentiate over time, and study heterogeneity in how cells respond to environmental stimuli. Single-cell sequencing can also be used for other applications, including sequencing accessible “open” regions of chromosomes and sequencing DNA barcodes engineered into cells for mutant-library screening.
Spatial transcriptomics relates single-cell transcriptomes to the spatial organization of those cells in organs and tissue slices. Dr. Tyler Duellman, an assistant researcher at UWBC, explains, “Until recently, there has been a technological barrier for the study of transcriptomal dynamics within a spatiotemporal framework”. Now, however, it is possible to investigate the transcriptomic landscape within the morphological context of a tissue section, revealing new information on cell-to-cell signaling, physiological differences even for cells of the same type, and more precise markers for disease.
Dr. Aman Prasad, a Resident working in the UW-Madison Department of Dermatology with Drs. Beth Drolet, Lisa Arkin, Richard Halberg, and Christina Kendziorski, is using spatial transcriptomics to investigate gene expression patterns in human skin to differentiate between healthy and disease-affected tissue. A desired outcome of the study is to identify new molecular targets for therapy, using data and methods that were previously out of reach.
“Spatial transcriptomics is helping us understand gene expression patterns within the various layers of the tissue,” Dr. Prasad elaborates. “Prior to this technique, it was necessary to process bulk tissue for RNA sequencing, and spatial information of where expression changes were happening in the tissue was lost.”
UWBC and TRIP labs offer two approaches to spatial genomics: 10x Genomics Visium and NanoString Geo Mx Digital Spatial Profiling (DSP). Visium technology works by overlaying a thin tissue slice onto a glass slide coated in immobilized oligonucleotides whose sequence corresponds to a position on the slide. mRNA released from permeabilized cells is reverse transcribed with spatially resolving barcodes. Recovered cDNA is sequenced using standard Illumina technology, and spatial information is deconvoluted from the barcodes, enabling the researcher to identify which transcripts correspond to which regions of the tissue slice.
Geo MX DSP instead stains tissues with suites of antibodies or gene probes fused to UV-cleavable DNA barcodes. Illumination of specific tissue regions releases spatially programmed barcodes whose sequencing or counting can be interpreted to produce an expression or protein profile of key targets across the region.
“The technological advance is not simply one more step toward better expression profiling, but rather is a decisive jump that is expected to revolutionize studies of tissue structure and function in health and disease.”
Spatial transcriptomics can enable critical insights, but this biological and technological progress requires new statistical methods. “That is where we come in,” says Christina Kendziorski, faculty in the Department of Biostatistics and Medical Informatics and the Center for Genomic Science Innovation, and a leader in single-cell and spatial sequencing analysis. Prof. Kendziorski explains that commercial platforms use data analysis methods developed for bulk or single-cell RNA sequencing, which, while effective for some types of analysis, fail to account for technical artifacts that come with the technology. One artifact Prof. Kendziorski and her student Zijian Ni are overcoming is bleed-through signal from neighboring cells. “If these artifacts are not accommodated, biological signals are obscured and may be distorted.” explains Prof. Kendziorski, “In addition, the spatial localization of the RNA-seq data within specific tissue regions provides the potential to address novel questions; but to do so, novel statistical methods are required.”
Access to both the technology and cutting-edge statistical methods is providing UW-Madison researchers with a competitive advantage in spatial genomics and its applications. More information on these technologies can be found on the UWBC and Trip Lab web pages.