Sanja Vickovic

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Spatial structure of RNA expression RNA-seq and similar methods can record gene expression within and among cells. Current methods typically lose positional information and many require arduous single-cell isolation and sequencing. Ståhl et al. have developed a way of measuring the spatial distribution of transcripts by annealing fixed brain or cancer tissue samples directly to bar-coded reverse transcriptase primers, performing reverse transcription followed by sequencing and computational reconstruction, and they can do so for multiple genes. Science, this issue p. 78 A new technique allows visualization and quantitative analysis of the spatially resolved transcriptome across individual tissue sections. Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call “spatial transcriptomics,” that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

Massive and parallel expression profiling using microarrayed single-cell sequencing

Single-cell transcriptome analysis overcomes problems inherently associated with averaging gene expression measurements in bulk analysis. However, single-cell analysis is currently challenging in terms of cost, throughput and robustness. Here, we present a method enabling massive microarray-based barcoding of expression patterns in single cells, termed MASC-seq. This technology enables both imaging and high-throughput single-cell analysis, characterizing thousands of single-cell transcriptomes per day at a low cost (0.13 USD/cell), which is two orders of magnitude less than commercially available systems. Our novel approach provides data in a rapid and simple way. Therefore, MASC-seq has the potential to accelerate the study of subtle clonal dynamics and help provide critical insights into disease development and other biological processes.

DNA methylation: the future of crime scene investigation?

Proper detection and subsequent analysis of biological evidence is crucial for crime scene reconstruction. The number of different criminal acts is increasing rapidly. Therefore, forensic geneticists are constantly on the battlefield, trying hard to find solutions how to solve them. One of the essential defensive lines in the fight against the invasion of crime is relying on DNA methylation. In this review, the role of DNA methylation in body fluid identification and other DNA methylation applications are discussed. Among other applications of DNA methylation, age determination of the donor of biological evidence, analysis of the parent-of-origin specific DNA methylation markers at imprinted loci for parentage testing and personal identification, differentiation between monozygotic twins due to their different DNA methylation patterns, artificial DNA detection and analyses of DNA methylation patterns in the promoter regions of circadian clock genes are the most important ones. Nevertheless, there are still a lot of open chapters in DNA methylation research that need to be closed before its final implementation in routine forensic casework.

Magnetic bead assisted labeling of antibodies at nanogram scale

There are currently several initiatives that aim to produce binding reagents for proteome‐wide analysis. To enable protein detection, visualization, and target quantification, covalent coupling of reporter molecules to antibodies is essential. However, current labeling protocols recommend considerable amount of antibodies, require antibody purity and are not designed for automation. Given that small amounts of antibodies are often sufficient for downstream analysis, we developed a labeling protocol that combines purification and modification of antibodies at submicrogram quantities. With the support of magnetic microspheres, automated labeling of antibodies in parallel using biotin or fluorescent dyes was achieved.

Spatially resolved transcriptome profiling in model plant species

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study high-resolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes high-throughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.

Parallel barcoding of antibodies for DNA‐assisted proteomics

DNA‐assisted proteomics technologies enable ultra‐sensitive measurements in multiplex format using DNA‐barcoded affinity reagents. Although numerous antibodies are available, nowadays targeting nearly the complete human proteome, the majority is not accessible at the quantity, concentration, or purity recommended for most bio‐conjugation protocols. Here, we introduce a magnetic bead‐assisted DNA‐barcoding approach, applicable for several antibodies in parallel, as well as reducing required reagents quantities up to a thousand‐fold. The success of DNA‐barcoding and retained functionality of antibodies were demonstrated in sandwich immunoassays and standard quantitative Immuno‐PCR assays. Specific DNA‐barcoding of antibodies for multiplex applications was presented on suspension bead arrays with read‐out on a massively parallel sequencing platform in a procedure denoted Immuno‐Sequencing. Conclusively, human plasma samples were analyzed to indicate the functionality of barcoded antibodies in intended proteomics applications.

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Heart failure is a major health problem linked to poor quality of life and high mortality rates. Hence, novel biomarkers, such as fetal marker genes with low expression levels, could potentially differentiate disease states in order to improve therapy. In many studies on heart failure, cardiac biopsies have been analyzed as uniform pieces of tissue with bulk techniques, but this homogenization approach can mask medically relevant phenotypes occurring only in isolated parts of the tissue. This study examines such spatial variations within and between regions of cardiac biopsies. In contrast to standard RNA sequencing, this approach provides a spatially resolved transcriptome- and tissue-wide perspective of the adult human heart, and enables detection of fetal marker genes expressed by minor subpopulations of cells within the tissue. Analysis of patients with heart failure, with preserved ejection fraction, demonstrated spatially divergent expression of fetal genes in cardiac biopsies.

Multidimensional transcriptomics provides detailed information about immune cell distribution and identity in HER2+ breast tumors

The comprehensive analysis of tumor tissue heterogeneity is crucial for determining specific disease states and establishing suitable treatment regimes. Here, we analyze tumor tissue sections from ten patients diagnosed with HER2+ breast cancer. We obtain and analyze multidimensional, genome-wide transcriptomics data to resolve spatial immune cell distribution and identity within the tissue sections. Furthermore, we determine the extent of immune cell infiltration in different regions of the tumor tissue, including invasive cancer regions. We combine cross-sectioning and computational alignment to build three-dimensional images of the transcriptional landscape of the tumor and its microenvironment. The three-dimensional data clearly demonstrates the heterogeneous nature of tumor-immune interactions and reveal interpatient differences in immune cell infiltration patterns. Our study shows the potential for an improved stratification and description of the tumor-immune interplay, which is likely to be essential in treatment decisions.

Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity

Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor microenvironment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.Heterogeneity within tumors presents a challenge to cancer treatment. Here, the authors investigate transcriptional heterogeneity in prostate cancer, examining expression profiles of different tissue components and highlighting expression gradients in the tumor microenvironment.

05.16 Transcriptome visualisation of the inflamed rheumatoid arthritis joint

Background The Rheumatoid Arthritis (RA) synovial tissue is heterogenous with a mix of stromal and immune cells. Macrophages and T cells are the most abundant immune cells, while B cells are more rare and often found within ectopic lymphoid structures. Much of our understanding of the synovial inflammation is based on different immunostainings approaches. Here we have utilised the recently described Spatial Transcriptomics (ST) method to explore the RNA profile of tissue sections from RA synovial biopsies.1 Materials and methods Two snap frozen synovial biopsies from ACPA+ HLA shared epitope+ RA patients undergoing joint replacement surgery was used. Sections of 7 µm representing a single layer of cells were cut and placed on a barcoded ST slide, fixated and stained using Hematoxylin and Eosin. Thereafter permeabilization of the cells and cDNA synthesis of the captured mRNA were conducted on chip, removal of the tissue and the DNA from the surface was released for library preparation. Sequencing was performed using Next-generation sequencing. The RNA-Seq data was de-convoluted back to its original position in the section based on the barcoded information, using the ST pipeline (https://github.com/jfnavarro/st_pipeline). Data analysis was performed using the R packages DESeq and EdgeR.2–3 Results Extracted RNA from the synovial biopsies had RIN values of 8.6 and 9.2 respectively. On average 1 M reads per sample was generated with 17 800 numbers of detected genes from each tissue section. When focusing on the lymphocyte aggregates within the tissue, some displayed features of fully developed ectopic lymphnode stuctures including expression of T cell, B cell and APC specific and related genes such as CD2, CD52, CD20 and CXCL13. Differential expression analysis revealed clusters corresponding to fibrotic areas with high expression of genes involved in protein synthesis and protein-protein interactions, areas of infiltrates with high numbers of inflammation markers and areas surrounding infiltrates with genes involved in wound repair, tissue remodelling, motility and invasion. Conclusion The spatial transcriptomic method allows for both unbiased analysis of the transcriptional activity in tissue biopsies as well as hypothesis driven investigation of cell subsets defined by combinations of markers not easily captured by 2–3 parameters. References 1. Ståhl PL, et al.:Visualisation and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78–82 2. Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15:550. 3. Robinson MD, McCarthy DJ, Smith GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–140.