Developmental Biology Trace tissue development at
ultra-resolution

Single-cell sequencing in developmental biology empowers the discovery of molecular mechanisms underlying development, congenital disease, and tissue regeneration. We have experience in over 40 different organisms and are happy to add to our “tree of life” with your species of interest.

Single Cell Discoveries oncology single-cell sequencing
Applications

Understanding developmental biology is vital in unravelling our physiology and provides valuable insights for genetic therapies and regenerative medicine. During development and regeneration, progenitor cells alter their gene expression and differentiate into lineage-restricted cell types.

Single-cell RNA sequencing can provide deeper insights into cells’ differentiation states and developmental trajectories.

Reconstruct differentiation pathways

With single-cell RNA sequencing, it is possible to monitor cells as they differentiate and identify their individual developmental pathways and underlying mechanisms.

While single-cell sequencing captures a static snapshot of differentiating cells, we can disambiguate the data with trajectory inference algorithms and trace cells along their paths, enabling the mapping of an entire tissue’s developmental process. These algorithms reveal that differentiating cells often follow multiple branching pathways, resulting in tree-like models highlighting potential cell fate decision points and important marker genes.

Resolve the mechanisms behind tissue regeneration

Single-cell RNA sequencing helps you identify the molecular mechanisms driving regeneration, dissect heterogeneous stem cell populations, and reconstruct differentiation trajectories. Translational studies employ single-cell technologies to guide cell and tissue engineering strategies and advance treatment for organ and tissue damage due to ageing, trauma, or disease.

Study genetic diseases

Understanding the mechanisms leading to congenital diseases and genetic disorders is crucial for developing treatments and preventive measures. In developmental biology, single-cell RNA sequencing is a powerful means for studying developmental errors underlying such diseases. By analyzing embryonic processes at a single-cell resolution, you can identify the transcriptional changes and faulty pathways that lead to major developmental disorders.

Develop organoid models

Organoids are three-dimensional, self-organizing, and multicellular structures that are derived from stem cells and can recapitulate the architecture and function of native tissues or organs. Based on single-cell sequencing data, you can optimize organoid generation protocols by adjusting culture conditions or differentiation factors to improve the cellular composition, differentiation status, or functional characteristics of the organoids. These optimizations can help to make the organoid models more accurate and representative of the native tissues.

Case studies

Snake Venom Gland Organoids

Three PhD students, under the supervision of Prof. Dr Hans Clevers in molecular genetics and snake expert Prof. Dr Freek Vonk, conducted this remarkable research in which they developed snake venom-producing organoids. The organoids were characterized using SORT-seq. Here, we explain how the project was done.

Developmental Biology
SORT-seq

Colorectal Zebrafish Xenograft Model

This research, conducted at the Champalimaud Foundation in Lisbon, is focused on the immune response after implantation of human cancer cells in zebrafish. The cells in the implanted tumors were characterized using SORT-seq. Here, we explain how SORT-seq contributed to this remarkable study.

Immunology
Oncology
SORT-seq

Single-Cell Atlas of the Human Cornea

Read about the eye-opening collaboration of Single Cell Discoveries and MERLN Institute for Technology-Inspired Regenerative Medicine.

10x
Regenerative Medicine

Recent publications

How can we help?

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Connect with our PhD-level scientists to discuss your biological question, timeline, sample types, and other customizations for your single-cell analysis.