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The ability to assign cellular origin to low-abundance secreted factors in extracellular vesicles (EVs) would greatly facilitate the analysis of paracrine-mediated signaling. Here, we report a method, named selective isolation of extracellular vesicles (SIEVE), which uses cell type-specific proteome labeling via stochastic orthogonal recoding of translation (SORT) to install bioorthogonal reactive groups into the proteins derived from the cells targeted for labeling. We establish the native purification of intact EVs from a target cell, via a bioorthogonal tetrazine ligation, leading to copurification of the largely unlabeled EV proteome from the same cell. SIEVE enables capture of EV proteins at levels comparable with those obtained by antibody-based methods, which capture all EVs regardless of cellular origin, and at levels 20× higher than direct capture of SORT-labeled proteins. Using proteomic analysis, we analyze nonlabeled cargo proteins of EVs and show that the enhanced sensitivity of SIEVE allows for unbiased and comprehensive analysis of EV proteins from subpopulations of cells as well as for cell-specific EV proteomics in complex coculture systems. SIEVE can be applied with high efficiency in a diverse range of existing model systems for cell–cell communication and has direct applications for cell-of-origin EV analysis and for protein biomarker discovery.

Various computational approaches have been developed for estimating the relative abundance of different cell types in the tumor microenvironment (TME) using bulk tumor RNA data. However, a comprehensive comparison across diverse datasets that objectively evaluates the performance of these approaches has not been conducted. Here, we benchmarked seven widely used tools and gene sets and introduced ConsensusTME, a method that integrates gene sets from all the other methods for relative TME cell estimation of 18 cell types. We collected a comprehensive benchmark dataset consisting of pan-cancer data (DNA-derived purity, leukocyte methylation, and hematoxylin and eosin–derived lymphocyte counts) and cell-specific benchmark datasets (peripheral blood cells and tumor tissues). Although none of the methods outperformed others in every benchmark, ConsensusTME ranked top three in all cancer-related benchmarks and was the best performing tool overall. We provide a Web resource to interactively explore the benchmark results and an objective evaluation to help researchers select the most robust and accurate method to further investigate the role of the TME in cancer (www.consensusTME.org).Significance:This work shows an independent and comprehensive benchmarking of recently developed and widely used tumor microenvironment cell estimation methods based on bulk expression data and integrates the tools into a consensus approach.

Figure S1: Additional Analyses: A) Statistical framework benchmark results, each point represents the rank of that statistical framework when compared against the rest for each benchmarking experiment. ConsensusTME gene sets were used for all frameworks. B) Historical benchmark, each consecutive version of consensus contains signature/approach from an additional method, added chronologically. A) Kendall's correlation coefficients (Ï„) of xCell PBMC data based on CyTOF (top left: SDY311 n = 61, top right: SDY420 n = 104) and CIBERSORT PBMC data based on flow cytometry (bottom left, n = 20) vs RNA-derived cell type-specific estimates for the tested methods. The grey box represents correlation coefficients that have a q-value > 0.05 (B-H method). Box plots are sorted according to median correlation coefficient (left to right: decreasing performance).

Supplementary Table 1: A) TME cell estimation method information. B) Benchmarking dataset information. C) Cell type inclusion for multiple linear regression analyses. D) TME cell estimation method to ground truth cell type matching for cell type-specific benchmarking experiments.

Various computational approaches have been developed for estimating the relative abundance of different cell types in the tumor microenvironment (TME) using bulk tumor RNA data. However, a comprehensive comparison across diverse datasets that objectively evaluates the performance of these approaches has not been conducted. Here, we benchmarked seven widely used tools and gene sets and introduced ConsensusTME, a method that integrates gene sets from all the other methods for relative TME cell estimation of 18 cell types. We collected a comprehensive benchmark dataset consisting of pan-cancer data (DNA-derived purity, leukocyte methylation, and hematoxylin and eosin–derived lymphocyte counts) and cell-specific benchmark datasets (peripheral blood cells and tumor tissues). Although none of the methods outperformed others in every benchmark, ConsensusTME ranked top three in all cancer-related benchmarks and was the best performing tool overall. We provide a Web resource to interactively explore the benchmark results and an objective evaluation to help researchers select the most robust and accurate method to further investigate the role of the TME in cancer (www.consensusTME.org). Significance: This work shows an independent and comprehensive benchmarking of recently developed and widely used tumor microenvironment cell estimation methods based on bulk expression data and integrates the tools into a consensus approach.

Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naïve patient and by leveraging the analytical power of multi-sample analyses, we reveal evidence of diversification among metastatic lineages. UV-induced mutations dominate the trunk, whereas APOBEC-associated mutations are found in the branches of the evolutionary tree. Multi-sample analyses from a further seven patients confirmed that lineage diversification was pervasive, representing an important mode of melanoma dissemination. Our analyses demonstrate that joint analysis of cancer cell fraction estimates across multiple metastases can uncover previously unrecognised levels of tumour heterogeneity and highlight the limitations of inferring heterogeneity from a single biopsy., Metastatic melanoma is associated with a poor prognosis and understanding the genetic features of metastases may enable better treatment strategies. Here, the authors analyse multiple metastases from individual patients finding high levels of heterogeneity in metastases from different organs.

Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naïve patient and by leveraging the analytical power of multi-sample analyses, we reveal that metastatic cells may depart the primary tumour very early in the disease course and follow a branched pattern of evolution. Truncal UV-induced mutations that often swamp downstream analyses of heterogeneity, were found to be replaced by APOBEC-associated mutations in the branches of the evolutionary tree. Multi-sample analyses from a further 7 patients confirmed that branched evolution was pervasive, representing an important mode of melanoma dissemination. Our analyses illustrate that combining cancer cell fraction estimates across multiple metastases provides higher resolution phylogenetic reconstructions relative to single sample analyses and highlights the limitations of accurately inferring inter-tumoural heterogeneity from a single biopsy.