Spatial Transcriptomics - Bridging Histology and RNA Sequencing
KTH Royal Institute of Technology,Science for Life Laboratory,, Sweden
Spatially resolved transcriptomics provides us with new insights into the molecular diversity of heterogeneous tissue structures. Several approaches have been established in order to preserve gene expression information together with its tissue localization. However, existing challenges for many spatial technologies include the extent of existing knowledge about the targets, the labor-intensive nature of the methods or the fact that they are not applicable to clinical samples. Here, we present a method whereby whole intact tissue sections can be studied in a spatial whole-transcriptome manner.
Spatial Analysis Of Transcriptome And Proteome During Early Development
Institute of Biotechnology, Czech Republic
Starting from a single fertilized egg and then followed by various divisional steps, a complex organism is developed that has a distinct head-tail (bottom-up), left-right and dorsal-ventral (back-belly) asymmetrical axes. One of the main challenges in developmental biology is to understand how and when these asymmetries are generated and how they are controlled. Xenopus laevis (African clawed frog) and Acipenser ruthenus (sturgeon) are ideal models for studies of early development because of their large eggs and embryos. We have developed a unique molecular tomography platform based on RT-qPCR, RNA-seq and iTraq UPLC-ESI-MS/MS to measure asymmetric localization of fate determining materials such as mRNAs, non-coding RNAs and proteins within the early developmental stages. Additionally, we have developed new tools and approaches for analyzing data originating from spatial transcriptomics. Herein we present the results from our work.
High-throughput Single-cell Targeted DNA Sequencing from Tumor and Metastatic Samples Reveal Spatial Resolution of Evolutionary Trajectory Routes to Clonal Propagation
Mission Bio, Inc., United States of America
Recent advancements in genomic analysis of tumors have revealed that cancer disease evolves by a reiterative process of somatic variation, clonal expansion and selection. Therefore, intra- and inter-tumor genomic heterogeneity has become a major area of investigation. While bulk NGS has contributed significantly to our understanding of cancer biology and genomics, the genetic heterogeneity of a tumor at the individual cellular level is masked with the average readout provided by a bulk measurement. Very high bulk sequence read depths are required to identify lower prevalence mutations. Even at these high read depths, confidence confirmation in events at the 1% range or less is a formidable challenge. Rare events and mutation co-occurrence within and across select population of cells are obscured with such average signals. Additionally, recent reports highlight some of the crucial issues in whole exome studies for false detection rates. In an effort to explore this biology at higher resolution, we conducted single-cell targeted DNA analysis with the Mission Bio Tapestri™ Platform using sectioned melanoma metastatic tissues. Leveraging proprietary droplet microfluidics, the workflow unlocks access to gDNA, enabling high coverage uniformity of ~90% and low ADO of ~10%. Up to 20,000 cells can be interrogated with catalog or custom amplicon panels for any solid tumor type. Here, we use the Tapestri Single-Cell DNA Tumor Hotspot Panel that targets 59 commonly-mutated genes across 244 amplicons. We report that an analysis of multiple spatially-separated samples obtained from distinct metastatic sites in subjects revealed unique genomic signatures mapping to each solitary sample. These datasets support a number of conclusions including: 1) our recently optimized universal nuclei extraction process removes cellular components that are known to be highly inhibitory to PCR amplification – in this case melanin from melanoma cells, 2) single-cell analysis correlates strongly with bulk sample analysis, enabling confident comparison with previously-acquired results, 3) rare subclones, present at 0.15%, were detected, which is critical when monitoring disease progression, 4) single-cell analysis unambiguously identified the clones in each sample, enabling the reconstruction of clonal phylogeny, 5) the different clonal lineage observed in distant metastatic tissues highlights complex disease progression, and 6) tumor purity was measured at the single-cell genetic level. In summary, single-cell analysis offers to overcome the limitations of bulk NGS and can provide unique insights into the cellular-level complexities of tumor heterogeneity and phylogenesis. Here, the use of the Tapestri Platform demonstrates the power of single-cell DNA sequencing for characterizing solid tumor tissue samples and understanding disease evolution.
Using Single-cell Transcriptomics To Decipher The Formation Of Blood Stem Cells.
In adulthood, the continuous generation of blood cells relies on the existence of hematopoietic stem cells (HSC), which have the ability to self-renew and generate all blood cell types. Any pathology affecting these cells could lead to the development of serious diseases such as leukemia and anemia. HSC are formed during embryonic life from endothelial cells, building blocks of the vascular system, which is responsible for blood circulation in the body. This process is called endothelial to hematopoietic transition (EHT). Using single-cell transcriptomics, we are uncovering the different populations involved in the process of HSCs’ formation. We are also using this method to understand how transcription factors regulate this process essential for the continuous oxygenation of the body and the establishment of the immune system protecting us from pathogens.
Single-cell Genomics – Are We There Yet?
EMBL, Germany, Flow Cytometry Core Facility, Genomics Core Facility
The advent of single cell sequencing technologies created a very productive and collaborative environment for genomics and flow cytometric methods in the last five years. Flow Cytometry by nature was in the past the best method to analyse quickly thousands, if not millions, of cells for their expression of proteins, peptides or analysing the metabolic state with single cell resolution. Sequencing technologies have closed the gaps improving the quality and general robustness of sequencing DNA and RNA from single cells. This finally let to the development of multiple different, easy to use approaches in single cell generation and subsequent sequencing methods. Single cell sorting by FACS or Fluidigm C1 capture paired with sensitive library preparation methods paved the way. Nowadays, complete kit-solutions from 10xGenomics and their likes have rendered the initial resistance – or better put the initial need for intense technical skill development - to enter this method field almost to zero. Automation of analysis processes and kit-based scientific methods are probably the biggest driver for the high pace science that we are currently enjoying, yet at the same time it is most probably also responsible for the increased appearance of methodically poorly designed studies. We see a steep increase in studies that involve to a significant level single cell sequencing data. What strikes us is the apparent complacency of how authors set up their experiments. Studies with poor methodology are not rare in scientific literature and sadly, to some extent repeat the qPCR story.
Tailoring scRNA-seq to Meet the Challenges of Primary Cytotoxic T-cells
1Division of Animal Physiology and Immunology, School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany; 2Division of Animal Breeding, School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany; 3BCF, Swiss Institute of Bioinformatics, University of Lausanne 1015 Lausanne, Switzerland
Cytotoxic T-cells (CTLs) are major players in the protective immunity against viral infections and cancer. Given this central role, CTLs are of key interest for the development of novel and improvement of existing immunotherapies. This requires deeper understanding of the factors and molecular programs guiding their differentiation and functionality. This can be obtained through Single-cell RNA sequencing, which allows for unbiased discovery of single-cell resolved gene expression networks, which can be further used to define new cell subpopulations in an unbiased way. To allow for comprehensive single-cell resolved analysis of CTLs, as well as to deal with their minute amounts of messenger RNA, we have systematically optimize a droplet-based method providing breadth of cellular profiling and a plate based method providing depth of cellular profiling. We have achieved significant improvement in their sensitivity, providing tools for the establishment of an atlas of single-cell defined CTL cell states which will contribute to development of better conceptual understanding and new therapeutic opportunities.
A Deep Dive Into Single Cell RNA Sequencing Data
1TUM, Germany; 2SIB, Switzerland
In the past 5 years, the number of single cell RNA-sequencing projects is expanding exponentially. In 2016, The human cell atlas was created with the main goal of creating a map of all human cells in order to help the scientific community in their research. The complexity of cancer research is pushing us to dissect and understand tissue sub-populations as well as their respective immunotherapeutic treatments which are all about cells and which of their mechanisms have to be harnessed. Similarly, the exploration of the human brain is also focusing more and more on specific cells in very specific area of the brain.
This has led the field of single-cell study, more particularly single-cell RNA sequencing, to grow exponentially as well. The number of methods that are available today to produce sequencing data is staggering and choosing which one is best suited to your needs can be tricky.
Being able to compare those protocols is critical to make an informed decision. Most of the commercial products propose their own tools to produce count matrices from the raw data. Those platform dependent tools are specifically designed for their own products and can be difficult to tweak for custom designs or experiments.
In order to compare and improve existing methods, we propose dropSeqPipe. A pipeline specifically designed to provide relevant quality control about most available single-cell platforms. Based on the snakemake workflow, it focuses on reproducebility, ease of use and flexibility.
During this talk we will present the tools and it’s different use as well as some examples of how it has helped our lab to improve our own protocol.