<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>Abhinav Soni</submitter><organism>Mus musculus</organism><software>R software</software><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-17032</full_dataset_link><description>Tumor-associated macrophages (TAMs) play important roles in cancer progression and resistance to therapy. Recent studies have shown that TAMs include both long-lived resident tissue macrophages (RTMs) and short-lived monocyte-derived macrophages (MDMs) with limited proliferative potential. RTMs and MDMs have been suggested to play divergent roles in tumorigenesis; RTMs are aligned with trophic functions, whereas MDMs are enriched for immune-regulatory pathways. Here we established a specific role for the AP-1 factor JUN in the differentiation and maintenance of MDMs and the specification of pro-tumoral trophic functions during tumor development. Alternatively, the immune-regulatory functions of TAMs remained JUN-independent. JUN was required for the specification and maintenance of pro-tumoral TAMs that support blood vessel maturation and tumor growth. Single-cell transcriptomics analysis uncovered the alternative fates for tumor-infiltrating monocytes and the development of distinct TAM states associated with trophic functions and immune-regulation. These studies demonstrate an important role for JUN in the specification of pro-tumoral monocyte-derived TAMs that could offer opportunities for selective TAM-targeted therapies for cancer.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Library Construction - The libraries for RNA-seq were prepared using ILL DNA Library Prep Tagmentation LV (Pur,FA) and quality of the RNA libraries was confirmed using fragment analyser</sample_protocol><sample_protocol>Sample Collection - 300mg tumour fragments digested with tumour dissociation kit (Miltenyi BioTec) were stained with CD45.2-PE and CD45+ cell isolated with anti-PE beads (StemCell) after enrichment iTAM and mTAM populations were sorted by flow cytometry (10000 cells). Nucleic acid extraction protocol: Arcturus PicoPure.</sample_protocol><sample_protocol>Sequencing - After purification, libraries were quantified on a Fragment Analyzer (Agilent). Libraries were sequenced on an Illumina NovaSeq 6000 in 100 bp paired-end mode to a depth of 50-60 million fragments per library.   NovaSeq S4 v1.5 4XP (200cyc)  Illumina NovaSeq 6000 (sequencer)</sample_protocol><sample_protocol>Nucleic Acid Extraction - Sample prep (RNA-cDNA) \"Smart-Seq2\" - adapted from Picelli et al 2013 https://www.nature.com/articles/nmeth.2639, in-house method on robotics.</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - Post-alignment several RNA-seq QC metrics were calculated. FeatureCounts (RRID:SCR_012919) was used to read count relative to gene biotype. The code used to run the nf-core pipeline for RNA-seq analysis is the following: nextflow run nf-core/rnaseq --input Samplesheet_RNA.csv --genome mm10 --outdir results_mm10 -c config.txt -profile singularity -r 3.14.0  The count matrix generated using nf-core pipeline was extracted and further processed for differential analysis using DEseq2(v1.44.0) (RRID:SCR_015687) in R programming environment (v4.4.1).</data_protocol><data_protocol>Sequence Alignment - To process RNA-seq reads, the nf-core/rnaseq pipeline written in the Nextflow domain-specific language was used. Briefly, the nf-core pipeline for RNA-seq (v3.14.0) was used to perform a quality check on fasta files generated after sequencing, adapter and quality trimming, removal of genomic contaminants and STAR (RRID:SCR_004463) and Salmon based genome alignment and transcript quantification.</data_protocol><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>Miltenyi Biotec (cat no. 130-096-730) ,Fortessa X-20 flow cytometer (BD Biosciences)</instrument_platform><instrument_platform>R software</instrument_platform><instrument_platform>nf-core pipeline</instrument_platform><instrument_platform>Illumina NovaSeq 6000</instrument_platform><instrument_platform>Illumina DNA Tagmentation</instrument_platform><instrument_platform>Arcturus PicoPure</instrument_platform><study_type>RNA-seq of coding RNA</study_type><species>Mus musculus</species><pubmed_authors>Abhinav Soni</pubmed_authors><pubmed_authors>Toby Lawrence</pubmed_authors></additional><is_claimable>false</is_claimable><name>RNAseq of iTAM and mTAMs isolated from tumors in Junf/f and Jun Csf1r KO mice</name><description>Tumor-associated macrophages (TAMs) play important roles in cancer progression and resistance to therapy. Recent studies have shown that TAMs include both long-lived resident tissue macrophages (RTMs) and short-lived monocyte-derived macrophages (MDMs) with limited proliferative potential. RTMs and MDMs have been suggested to play divergent roles in tumorigenesis; RTMs are aligned with trophic functions, whereas MDMs are enriched for immune-regulatory pathways. Here we established a specific role for the AP-1 factor JUN in the differentiation and maintenance of MDMs and the specification of pro-tumoral trophic functions during tumor development. Alternatively, the immune-regulatory functions of TAMs remained JUN-independent. JUN was required for the specification and maintenance of pro-tumoral TAMs that support blood vessel maturation and tumor growth. Single-cell transcriptomics analysis uncovered the alternative fates for tumor-infiltrating monocytes and the development of distinct TAM states associated with trophic functions and immune-regulation. These studies demonstrate an important role for JUN in the specification of pro-tumoral monocyte-derived TAMs that could offer opportunities for selective TAM-targeted therapies for cancer.</description><dates><release>2026-05-24T00:00:00Z</release><modification>2026-05-26T20:15:26.304Z</modification><creation>2026-05-14T10:42:52.153Z</creation></dates><accession>E-MTAB-17032</accession><cross_references><ENA>ERP193383</ENA><Biostudies>E-MTAB-13634</Biostudies><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0004917</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0003738</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>