Fibroblast-derived STC-1 modulates tumour associated macrophages and lung adenocarcinoma development

The tumour microenvironment (TME) consists of different cell types including tumour-associated macrophages (TAMs) and tumour-associated fibroblasts (TAFs). How these cells interact and contribute to lung carcinogenesis remains elusive. Using G12DKRAS- and V600E BRAF-driven mouse lung models, we identify the pleiotropic glycoprotein Stanniocalcin-1 (STC1) as a regulator of TAM-TAF interactions. STC1 is secreted by TAFs and suppresses TAM differentiation at least in part by sequestering the binding of GRP94, an autocrine macrophage differentiation-inducing factor, to its cognate scavenger receptors. The accumulation of mature TAMs in the Stc1 -deficient lung leads to enhanced secretion of TGFb1 and thus TAF accumulation in the TME. Consistent with the mouse data, in human lung adenocarcinoma, STC1b expression is restricted to myofibroblasts and a significant increase of naïve macrophages is detected in STC1 -high compared to STC1 -low cases. This work increases our understanding of lung adenocarcinoma development and suggests new approaches for therapeutic targeting of the TME.</p

Heat ‘n Beat: A Universal High-Throughput End-to-End Proteomics Sample Processing Platform in under an Hour

Proteomic analysis by mass spectrometry of small (≤2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a nearly universal, rapid, and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centered on a one-tube homogenization and digestion method called Heat ‘n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided into separate challenges. The initial sample washing and final peptide cleanup steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin embedded (FFPE). Third, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenization treatment, and reduction, alkylation, and proteolysis were optimized into a single step. The refinements produced near doubled peptide yield when compared to our earlier method ABLE delivered a consistently high digestion efficiency of 85–90%, reported by ProteinPilot, and required only 38 min for core processing in a single tube, with the total processing time being 53–63 min. The robustness of HnB was demonstrated on six organ types, a cell line, and a cancer biopsy. Its suitability for high-throughput applications was demonstrated on a set of 1171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 h, producing highly consistent peptide yield and quality for over 3513 MS runs

MOESM1 of Guidelines for whole genome bisulphite sequencing of intact and FFPET DNA on the Illumina HiSeq X Ten

Additional file 1: Table 1. Comparison of number of SNPs called in both WGBS and spike-in WGS data at 13x and 30x coverage. Table 2. Comparison of number of SNPs concordant in spike-in WGS data with WGS-Gold Standard at 30x coverage. Table 3. Comparison of number of SNPs concordant in WGBS data with WGS-Gold Standard at 30x coverage. Table 4. Percentage of SNPs observed across different genomic contexts for WGS-GS and WGBS

MOESM2 of Guidelines for whole genome bisulphite sequencing of intact and FFPET DNA on the Illumina HiSeq X Ten

Additional file 2. Figure 1: Box plot showing the difference in coverage across CpG islands, CpG shores and other regions of the genome for each of the five library preparation methods compared. Figure 2 a, b Two representative examples of regions showing SNP in both the WGS and WGBS data of the same clinical sample. Figure 3 a Bar plot showing the percentage of SNPs from WGBS concordant in WGS-GS at ~ 26× coverage and the percentage of SNPs from spike-in WGS concordant in WGS-GS at 30× coverage. b A representative Venn diagram for one prostate cancer sample, 2ab showing the number of SNPs concordant at 26× coverage for WGBS and 30× coverage for spike-in WGS when compared with WGS-GS data. Figure 4 a, Plot showing the distribution of normalised frequency of number of SNPs called across regions of the genome with varying levels of GC content for WGBS and WGS-GS. b Plot showing the distribution of normalised frequency of number of SNPs called across regions of the genome with varying levels of methylation ratio of CpG sites nearest to a SNP within 50 bp. Figure 5 a, b Box plot showing the coverage distribution across exons, intergenic regions, introns, promoter regions and repeat regions of the genome for a cell line sequenced on one lane of HiSeq X Ten (a) and HiSeq 2500 (b). Figure 6 a, b Two examples showing the difference in distribution of reads for a FFPET library obtained from the TruMethyl WG method and Accel-NGS Methyl-Seq method across a CpG island. Figure 7 a, b Summary of workflow for achieving optimal coverage on the HiSeq X Ten for intact genomic DNA and FFPET DNA