BASiCz - Blood Atlas of Single Cells in zebrafish

BASiCz - Blood Atlas of Single Cells in zebrafish

The success of the marker-based approaches for dissecting haematopoiesis in mouse and human is reliant on the presence of cell-surface markers specific for diverse progenitor populations. 

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The antibodies for these cell surface markers are thus used to isolate relatively homogeneous cell populations by flow cytometry. Transcriptional profiling of isolated cell populations and more recently single cells, have further allowed genome-wide identification of cell-type specific genes. However, beyond mouse and human, less is known about the transcriptome of blood cell types, mainly due to the low conservation of cell-surface markers between zebrafish and higher vertebrates and lack of suitable antibodies. To overcome this gap in knowledge, we have generated a user-friendly cloud repository, BASiCz (Blood Atlas of Single Cells in zebrafish) for interactive exploration and visualisation of 31,953 zebrafish genes in 1,442 haematopoietic cells. The generated database allows easy access and retrieval of sequencing data from zebrafish myeloid cells.


Single cell gene expression profiles can be found here: BASiCz

The accession number for the data reported in this paper can be found in ArrayExpress: E-MTAB-3947, E-MTAB-4617 and E-MTAB-5530. Human related data were collected from the Gene Expression Omnibus under accession code GSE75478.

Further information

The study was supported by Cancer Research UK grant number C45041/A14953 (to A.C. and E.A.), European Research Council project 677501 – ZF_Blood (to A.C.) and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute. The authors would like to thank WTSI Cytometry Core Facility for their help with index cell sorting and the Core Sanger Web Team for hosting the cloud web application. The authors would also like to thank the CRUK Cambridge Institute Genomics Core Facility for their contribution in sequencing the data.


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Dr Emmanouil Athanasiadis

Visiting Worker - Research Associate