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Los Angeles, CA September 2, 2016: Here at DeciBio we’ve been following the field of single cell genomics (SCG). Exciting scientific developments continue to emerge driven by advances in single cell technologies. To characterize the field, we used Network Analysis Interface for Literature Studies (NAILS) to analyze ~1,500 single cell genomics papers published since 2007 and generated a list of power researchers, network collaborations and most important papers published. Metrics used include number of papers published, first authorships, co-authorships and number of citations. Additional details on the methods used to generate these lists can be found here1.
Large installed base,
~2M (up to 5M in
Large installed base,
Large installed base,
Rapid turnaround time
Note: Not comprehensive. Represents forecasted
Key insights and analysis methods have come from the above authors, many of which moved on to establish their own labs focused on developing and using single cell approaches to understand cellular heterogeneity. Fuchou Tang was the first author of one of the earliest eukaryotic single cell sequencing papers in 2009, 4 years before this technique became Nature’s “Method of the year”.
Many of the above authors are pioneers in methods to access and or analyze the limited genetic material available in a single cell. Roger Lasken developed Multiple Displacement Amplification (MDA), a method to amplify DNA that is sold under the brand names Repli-g (Qiagen) and TempliPhi and GenomiPhi (GE Healthcare). Iain C. Macaulay is the first author of a 2015 paper that uses G&T-seq, a method for separating and sequencing genomic DNA and full-length mRNA from the same single cell. Thomas Kroneis has developed protocols to capture rare target cells based on specific staining. Navin and colleagues developed methods to isolate and sequence individual cells from tumors resulting in the identification of five distinct sub-populations of cells in a breast cancer tumor sample. Alex Shalek has developed strategies that use single cell RNA-Seq to identify distinct cell states and circuits from the natural variation that exists between seemingly identical cells. Tomer Kalisky uses microfluidic platforms invented by Stephen Quake, his post-doctoral advisor, that allow tens of thousands of PCR reactions in parallel.
~30% of the top researchers are focused on microbiology, one of the earliest areas in which single cell gained traction as it enabled characterization of unculturable microbes. One of the earliest single cell genomics centers (SCGC), Bigelow Laboratory for Ocean Sciences is focused on microbial species and has produced ~14 papers in 2016 alone, including publications from by Ramunas Stepanauskas and colleagues. In 2015, three SCGC were opened, in Sweden, the U.S. and Australia. These centers and others (e.g., Wellcome Trust Sanger Institute’s SCGC) have accelerated the development of methods, applications and discoveries, as well as made SCG tools available to the scientific community at large.
Single cell sequencing of RNA and DNA dominates the landscape and the above authors and others continue to break technical barriers including generating higher throughput methods to analyze many more cells (from hundreds to thousands) in a given experiment. The fields’ pioneers continue to develop methods to generate insights from complex single cell sequencing data, which is often plagued by technical noise, obscuring the interpretation and thus functional relevance of expression differences. The bottleneck in this field will be rate of the advancements in the data analysis, visualization and interpretation of single cell sequencing. UC Berkeley’s FastProject, described in the recently published paper, takes a step towards improved visualization and interpretation of single cell RNA-Seq data. We’ll be keeping an eye out for further developments in this area.
The above analysis however is heavily weighted towards first authorships, and as a result fails to identify key labs from which these papers have been published. We took the raw data and used Tableau to generate a broader list to identify key single cell labs where many of the above researchers were trained (e.g., Quake S., Voet T., Linnarsson S., Eberwine J). Below is the analysis. Each segment represents one author. The relative size of each segment corresponds to the number of publications and the shading corresponds to the number of citations. These 36 authors are associated with 273 unique publications. We used a cut off of 8 papers (99th percentile). For the complete data set of 8,000 authors, the median number of publications is 1. Scroll over each segment for more information.
We took the same raw data and used VOSviewer to display collaboration networks, to highlight groups working together and how frequently (red = highest frequency).
Important papers were identified and ranked using NAILS, which uses following criteria:
1) In-degree in the citation network, 2) citation count provided by Web of Science (only for papers included in the dataset), and 3) PageRank score in the citation network.[table “10” not found /]
We will continue to follow advancements in the single cell genomics field. For more information on the commercial side of single cell genomics, take a look at our other blog posts where we discuss companies such as Fluidigm, Illumina, Bio-Rad, 10X Genomics, BD, Cellular Research (acquired by BD) and more. For an in-depth analysis on the SCG market see DeciBio’s Single Cell Genomics market report, with information on market size, segmentation, growth, competition and trends.
1 Knutas, A., Hajikhani, A., Salminen, J., Ikonen, J., Porras, J., 2015. Cloud-Based Bibliometric Analysis Service for Systematic Mapping Studies. CompSysTech 2015
Author: Miguel Edwards, Associate at DeciBio Consulting, LLC
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Disclaimer: Companies listed above may be DeciBio clients and/or customers