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Every Cell Matters: New, Advanced Approaches in High-Throughput Single Cell Sequencing

About this event

Leave no cell behind

The adoption of single cell analysis approaches is accelerating, leading to rapid progress in our understanding of complex biological systems. However, severe limitations in single cell approaches remain, requiring the rapid development of new approaches and technologies.

Using two ground-breaking case studies presented by eminent researchers in their field, this webinar will outline several brand-new innovative approaches in single cell analysis that will remove some of these limitations – including the capturing and preservation of the widest range of cells (including those that are sensitive to degradation), and the use of time-resolved transcriptomics.

Following a short introduction, the first webinar talk – delivered by Professor Hao Chen of the Medical School of Nantong University – will present a comprehensive spatio-temporal atlas of mouse epididymal cells using innovative single-cell RNAseq methods. This will include a novel method to detect time-resolved RNA dynamics, using case studies from both recently published work (see the reference below) and unpublished work.

The second webinar talk – delivered by Dr. Julie Laliberte, Head of R&D at Singleron Biotechnologies – will outline a new, cutting edge (yet simple) approach to ensure that you truly ‘leave no cell behind’, capturing the most cell types, including those that are prone to degradation, as well as reduce bias for species and cell types.

What you will learn from this webinar

  1. Leave no cell behind: How to overcome the limitations of sample multiplexing, to remove species and cell-type bias, using a completely, antibody-independent new method based on “click chemistry”. This ground-breaking method will ensure that you capture the most cells types, including those that are prone to degradation.
  2. Take your single cell analysis from ‘photo’ to ‘video’: Move beyond a taking a ‘snapshot’ to obtain time-resolved transcription dynamics, allowing you to observe ‘new’ versus ‘old transcripts across time. This will be illustrated using a specific case study from a mouse model used to study reproductive system development.

Talks and Speakers

Talk 1: Spatio-temporal landscape of mouse epididymal cells defined by large-scale single-cell RNA-seq

Prof Hao Chen, Institute of Reproductive Medicine, Medical School of Nantong University


Talk 2: Leave no cell behind: Cutting-edge chemical engineering for innovative single cell applications

Dr. Julie Laliberte, Head of R&D, Singleron Biotechnologies


Q&A with all speakers


Reference: As well as presenting unpublished research, a portion of the work presented in this webinar has just been published in the following paper:

Shi, J., Fok, E., Dai, P., Qiao, F., Zhang, M., Liu, H., Sang, M., Ye, M., Liu, Y., Zhou, Y., Wang, C., Sun, F., Xie, G. and Chen, H. (2021). Spatio-temporal landscape of mouse epididymal cells and specific mitochondria-rich segments defined by large-scale single-cell RNA-seq. Cell Discovery. 7. 34. 10.1038/s41421-021-00260-7.


This webinar has been produced with the kind support of Singleron Biotechnologies. You can find out more about the technologies outlined in this webinar by visiting the Singleron Biotechnologies website.

Hosted by

  • Guest speaker
    JL G
    Julie Laliberte Singleron Biotechnologies

  • Team member
    RL T
    Richard Lumb Front Line Genomics

  • Guest speaker
    HC G
    Hao Chen1

  • Guest speaker
    YC G
    Yuan Chen

  • Guest speaker
    NF G
    Nan Fang Singleron Biotechnologies

  • Guest speaker
    HC G
    Hao Chen2

  • Guest speaker
    YC G
    Yuanyuan Chen Singleron Biotechnologies

Front Line Genomics

Delivering the Benefits of Genomics to Patients Faster

Front Line Genomics is a genomics-focused media company, with a social mission to deliver the benefits of genomics to patients faster. We organise the Festival of Genomics, digital events and webinars. We also produce reports and operate a content-rich website.