Radek Sindelka *, Pavel Abaffy, Silvie Tomankova, Ravindra Naraine, Mikael Kubista
Institute of Biotechnology-Biocev, Czech Republic;
Starting from a single fertilized oocyte, through manifold of divisions a complex organism is developed that has distinct head-tail (bottom-up), left-right and dorsal-ventral (back-belly) asymmetries. One of the main challenges in developmental biology is to understand how and when these asymmetries are generated and how they are controlled. The African clawed frog (Xenopus laevis) is an ideal model for studies of early development thanks to their very large oocytes. We have developed a unique molecular tomography platform based on qRT-PCR, RNA-seq and UPLC-ESI- MS/MS to measure asymmetric localization of fate determining mRNAs, non-coding RNAs and proteins within the oocyte and among the early stage blastomeres. The first axis called animal- vegetal, is formed during oogenesis and we found mRNA and microRNA gradients determining its formation. First cell division following fertilization producing 2-cell stage embryo forms the left-right, and second cleavage generating 4-cell embryos specifies the dorsal-ventral axis.
Afif M. Abdel Nour 1,*, Georges Nemer2, Lara Hanna Wakim1, Esam Azhar3,4
1Faculty of Agricultural and Food Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
2 Department of Biochemistry and Molecular Genetics, American University of Beirut-Medical Center, Beirut, Lebanon
3 Special Infectious Agents Unit-Biosafety Level 3, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia;
4Medical Laboratory Technology Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
In the past few years Single cell research had tremendous inter- est from public and private researchers and as a result many institutions were striving in publishing single cell related papers. Over the course of the last 10 years the growth rate of published papers was not far from double digits. Although, most of the pro- tocols are based on qPCR, we are presenting today our “one stop full solution” for needs in single cell analysis this including the qPCR as well as the ddPCR. This workflow includes rapid, ultra- sensitive analysis of 2-10 genes in as low as single cell. We have developed a cost effective and streamlined gene expression pro- filing workflow that enables monitoring of hESC/iPSC culture for quality control, reprogramming, and lineage determination with minimal sample consumption. The 4-step integrated workflow couples column-free total RNA isolation (gDNA-free) with lysate- compatible reverse transcription and target specific (up to 100) tandem pre-amplification followed by SYBR-based qPCR analysis. The entire workflow can be completed from sample to quantita- tion of up to 100 targets in less than 1 day. The STDEV from three independent pre-amplifications reactions is only 0.09, and 97% of expressed targets show less than 0.75 deviation from predicted values. Other tools are also presented including single cell sequenc- ing. The reductionist approach to study organisms using single cell analysis proved to be efficient specially in oncology.
Pascal Barbry 1,2, Marie-Jeanne Arguel1,2, Kévin Lebrigand1,2, Agnès Paquet1,2, Sandra Ruiz-Garcia1,2,Laure-Emmanuelle Zaragosi1,2,Rainer Waldmann1,2
1Université Côte d’Azur, France
2 CNRS, France;
Single cell RNA sequencing approaches are instrumental in stud- ies of cell-to-cell variability. 5′ selective transcriptome profiling approaches allow simultaneous definition of the transcription start size and have advantages over 3′ selective approaches which just provide internal sequences close to the 3′ end. The only currently existing 5′ selective approach requires costly and labor intensive fragmentation and cell barcoding after cDNA amplification. We developed an optimized 5′ selective workflow where all the cell indexing is done prior to fragmentation. With our protocol, cell indexing can be performed in the Fluidigm C1 microfluidic device, resulting in a significant reduction of cost and labor. We also designed optimized unique molecular identifiers that show less sequence bias and vulnerability towards sequencing errors result- ing in an improved accuracy of molecule counting. We provide comprehensive experimental workflows for Illumina and Ion Pro- ton sequencers that allow single cell sequencing in a cost range comparable to qPCR assays.
Stefan Günther ,Michail Yekelchyk, Isabelle Salwig, Jens Preussner, Thomas Braun
MPI for Heart and Lung Research, Germany
Molecular analysis of complex tissues in adult model organisms is a common tool for deciphering processes and networks during development, aging and disease. The majority of such “omic” anal- yses were done using whole tissue/organs or in macro or micro dissected anatomical regions. However, the complexity and cellu- lar composition of such samples prevent high-resolution analysis. Identification of small subpopulations or specific changes within small cell populations are usually not detected due to massive overrepresentation of signals/data points from bulk cells. To over- come these problems and to gain insights into subpopulation of cells many attempts are being made to switch from bulk anal- yses to identification of molecular changes in single cells. This approach allows a very high-resolution analysis, but is associ- ated with numerous other problems that have to be resolved to achieve meaningful insights. Our main focus is on the transcrip- tomics of individual cells and subpopulations. Many techniques and tools were developed during last years to obtain transcrip- tomics data from hundreds or thousands of cells from different model organisms, organs and tissues. Nonetheless, many exper- iments require special tissue-dependent conditions limiting the use of standard methods. Here, we show data from 2 projects, which faces special challenges regarding cell size and number of cellular subpopulations within individual samples. In collabora- tion with Wafergen/Takara Bio we demonstrate that the ICELL8TM Single-Cell System can be used to obtain high-resolution single cell data for particularly large cells or complex cell populations, which are difficult to analyze with other techniques. Our results provide new insights into the heterogeneity of cell populations and the transcriptional networks that regulate biological processes in the corresponding tissues.
Single-cell technologies have gained popularity in developmen- tal biology because they allow resolving potential heterogeneities due to asynchronicity of differentiating cells. Common data analy- sis encompasses normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellu- lar differentiation, we may not expect clear clusters to be present – instead cells tend to follow continuous branching lineages.
We show that modeling the high-dimensional state space as a diffusion process, where cells move to close-by cells with a distance-dependent probability well reflects the differentiating characteristics. Based on the underlying diffusion map transition kernel, we then order cells according to a diffusion pseudo- time (DPT), which allows for a robust identification of branching decisions and corresponding trajectories of single cells. We demon- strate the method on scRNA-seq data of myeloid differentiation. DPT identifies a dominant branching into different myeloid lin- eages and a minor subpopulation of lymphoid outliers. Moreover, a graded transition reflecting erythroid differentiation is identified that dissent from previously stated cluster sequences. We finally identify driver genes and propose how to include additional data sets for integrative analysis across multiple downstream lineages.
Altogether, this illustrates that the concept of discrete transi- tions of progenitors to developed cells may need to be adapted.