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Cell type-specific novel long non-coding RNA and circular RNA in the BLUEPRINT hematopoietic transcriptomes atlas

Transcriptional profiling of hematopoietic cell subpopulations has helped to characterize the developmental stages of the hematopoietic system and the molecular bases of malignant and non-malignant blood diseases. Previously, only the genes targeted by expression microarrays could be profiled genome-wide. High-throughput RNA sequencing, however, encompasses a broader repertoire of RNA molecules, without restriction to previously annotated genes. We analyzed the BLUEPRINT consortium RNA-sequencing data for mature hematopoietic cell types. The data comprised 90 total RNA-sequencing samples, each composed of one of 27 cell types, and 32 small RNA-sequencing samples, each composed of one of 11 cell types. We estimated gene and isoform expression levels for each cell type using existing annotations from Ensembl. We then used guided transcriptome assembly to discover unannotated transcripts. We identified hundreds of novel non-coding RNA genes and showed that the majority have cell type-dependent expression. We also characterized the expression of circular RNA and found that these are also cell type-specific. These analyses refine the active transcriptional landscape of mature hematopoietic cells, highlight abundant genes and transcriptional isoforms for each blood cell type, and provide a valuable resource for researchers of hematologic development and diseases. Finally, we made the data accessible via a web-based interface: https://blueprint.haem.cam.ac.uk/bloodatlas/.


RNA extraction
RNA was extracted from TRIzol according to the manufacturer's instructions, quantified using a Qubit RNA HS kit (Thermofisher) and quality controlled using a Bioanalyzer RNA pico kit (Agilent).

Library construction and sequencing
With the exception of platelets, eosinophils and basophils, libraries were prepared using a TruSeq Stranded Total RNA Kit with Ribo-Zero Gold (Illumina) using 200ng of RNA as input. Platelet, eosinophil and basophils samples were prepared with the Kapa stranded RNA-seq kit with riboerase (Roche) according to the manufacturer's instructions.
Small RNA extraction RNA was extracted using the miRNeasy Mini Kit (Qiagen) from cell pellets provided their RNA Integrity Number (RIN) was between 7.3 and 10, as assessed with an RNA 6000 Nano kit on a 2100 Bioanalyzer (Agilent). Small RNA libraries were prepared using the NEBNext® Multiplex Small RNA Library Prep Set for Illumina (New England Biolabs) and the LongAmp Taq 2x Master Mix. Size selection was performed with 6% polyacrylamide gels, and library quality was verified on a 2100 Bioanalyzer (Agilent). Equimolar (2 nM) amounts of each library, as verified with Picogreen® dsDNA Quantification Reagent (Promega), were pooled and sequenced on an Illumina HiSeq 2000 using 50 bp single end reads.

Backsplice classification
Backsplices were classified into five groups based on their genomic locations relative to Ensembl 75 annotations exonic_known: the backsplice corresponds to known splice sites; exonic_novel: the backsplice overlaps at least one annotated exon and utilises only one known splice site; intronic: the backsplice is internal to an annotated intron; intergenic: the backsplice does not overlap any annotated exons or introns, and antisense: the backsplice is antisense to annotated exons or introns.
Modelling of circRNA expression. The read counts reported by PTESFinder were normalised by dividing them by the total number of splicing reads in each sample and multiplied by 10 6 . For each sample, we computed the abundance proportion (AP) of a gene as the number of backsplice reads in that gene divided by the total number of spliced reads of any kind in the sample. Differential expression analysis was performed using DESeq2 14 . Z-scores for differentially expressed backsplices identified by DESeq2 were computed over samples from the normalised backsplice read counts. Figure S1: Schematic of the bioinformatic pipelines. Paired end reads (75bp and 150bp; left), were trimmed to remove adapter sequences, quality controlled and aligned to the human transcriptome with Bowtie. The alignments were modelled by MMSEQ to estimate gene and transcript level expression. The reads were also aligned to the human genome with STAR and fed to StringTie to identify novel genes. The reads were aligned to the human genome with three aligners (STAR, Bowtie and BWA) to identify circRNA species and model their expression levels. Single end reads (50bp; right) were aligned to the human mirnome with RapMap. The alignments were modelled by MMSEQ to estimate miRNA expression levels. Figure S2: Principal components analysis and hierarchical clustering of gene and miRNA expression. S2A: Proportion and cumulative proportion of variance explained by successive principal components, derived from the log expression estimates of genes with a log expression estimate greater than zero in at least one sample. S2B: Proportion and cumulative proportion of variance explained by successive principal components, derived from the log expression estimates of miRNAs with a unique read count >10 in at least one sample. S2C: Heatmap of the Spearman rank correlation coefficient (rho) of genes with a log expression estimate greater than zero in at least one sample. The rows and columns have been ordered by complete linkage hierarchical clustering using 1-rho as the distance measure. S2D: Heatmap of the Spearman rank correlation coefficient (rho) of miRNAs with unique read count >10 in at least one sample. The rows and columns have been ordered by complete linkage hierarchical clustering using 1-rho as the distance measure.    List Supplementary Tables S1 List of total RNA-sequencing samples used in this manuscript.

S2
List of abbreviations and grouping used in this manuscript.
S3 List of small RNA-sequencing samples used in this manuscript.

S4
Number of genes accounting for 50% and 75% of total expression.

S5
List of GO terms found enriched for the genes in S4.

S6
List of gene constituting the transcriptional signature of each cell type.

S7
List of GO terms found enriched for the genes in S6.

S8
List of miRNAs constituting the transcriptional signature of each cell type.

S9
List of circRNA back splice junctions identified. S10 Classification of circRNA back splice junctions identified. S11 List circRNA abundance rations.

Supplementary files description
Supplementary file 1 lists the protein coding genes accounting for 50% and 75% of the transcriptional output of each cell type.
Supplementary file 2 contains the miRNAs accounting for 75% of the small RNA transcriptome.
Supplementary file 3 contains the genomic coordinates of the novel genes identified by guided transcriptome reconstruction.