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UnCTC: Unbiased characterisation of single circulating tumor cell transcriptomes [scRNA-seq]

ABSTRACT: The identification and characterisation of Circulating Tumour Cells (CTCs) is important to get insights into the biology of metastatic cancers, monitoring disease progression, and potential use in liquid biopsy-based personalised cancer treatment. The major limitation of CTC isolation is due to their heterogeneous nature, altered phenotype from primary tumour and availability in limited numbers. In the past years, several techniques have been developed to detect CTCs from peripheral blood, based on canonical markers. These methods are, however, prone to miss a larger set of CTCs due to variable or no expression of these markers. Furthermore, CTC enrichment processes are not free from White Blood Cell (WBC) contamination. Single cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate. The first and most important roadblock encountered in CTC scRNA-Seq data analysis is confirmation of CTC capture. We present unCTC, an R software for unsupervised and semi-supervised characterisation of CTC transcriptomes, in contrast with WBCs. unCTC features various standard and novel computational/statistical modules for clustering, Copy Number Variation (CNV) inference, and marker based verification of the malignant phenotypes. Notably, we propose Deep Dictionary Learning using K-means clustering cost (DDLK) that robustly clusters scRNA-Seq profiles of CTCs and WBC contaminates to characterise heterogeneity among the concerned cell population. Interestingly, DDLK performs better as gene expression data is transformed into pathway enrichment profiles. To demonstrate the utility of unCTC, we produce scRNA-Seq profiles of breast CTCs enriched by the integrated ClearCell® FX/PolarisTM workflow that works on the principles of size selection and negative enrichment for CD45, a pan leukocyte marker.

ORGANISM(S): Homo sapiens

PROVIDER: GSE186288 | GEO | 2022/11/09

REPOSITORIES: GEO

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Project description:Cancer cells metastasize through the bloodstream either as single migratory circulating tumor cells (CTCs) or as multicellular groupings (CTC-clusters). Existing technologies for CTC enrichment are designed primarily to isolate single CTCs, and while CTC-clusters are detectable in some cases, their true prevalence and significance remain to be determined. Here, we developed a microchip technology (Cluster-Chip) specifically designed to capture CTC-clusters independent of tumor-specific markers from unprocessed blood. CTC-clusters are isolated through specialized bifurcating traps under low shear-stress conditions that preserve their integrity and even two-cell clusters are captured efficiently. Highly parallel architecture of the chip allows deterministic screening of clinically relevant volumes of blood samples at slow, and hence, non-damaging flow rates. Using the Cluster-Chip, we identify CTC-clusters in 30-40% of patients with metastatic cancers of the breast, prostate and melanoma. RNA sequencing of CTC-clusters confirms their tumor origin and identifies leukocytes within the clusters as being tissue-derived macrophages. Together, the development of a device for efficient capture of CTC-clusters will enable detailed characterization of their biological properties and role in cancer metastasis. We used the Cluster-Chip to capture CTC-clusters from the blood of a breast cancer patient with high CTC counts, released CTC-clusters in solution, stained them with TexasRed-conjugated antibodies against the leukocyte cell surface markers CD45, CD14 and CD16, and then isolated intact CTC-clusters individually using a micromanipulator. From a single time point, we retrieved 15 CTC-clusters, and each of those clusters was individually subjected to RNA-sequencing analysis using a next generation platform (SOLiD 5500). In addition, two leukocytes were isolated from the blood of a healthy donor were individually subjected to RNA-sequencing analysis using the same platform.

Project description:Single-cell RNA sequencing (scRNA-seq) holds great potential to revolutionize our understanding of cancer biology by discovering biomarkers and biological functions from circulating tumor cells (CTCs). However, the contamination from blood cells is one of the main challenges in CTC studies. Through investigation of recently published studies, we found that several previously reported CTC “biomarkers” were apparently contaminated by blood-derived transcripts such as HBB, HBA1/2, ITGA2B, and LCP1. Through re-analysis of several published CTC and cell line datasets, we revealed that CTC “biomarkers” were not only highly expressed in blood, but showed a high correlation between CTC and blood. We sequenced transcriptome of seven blood components from a healthy donor, finding that the platelet exhibited the highest probability to confound CTC data in terms of expression profiling and correlation analysis. Using CTC spike-in and recovery assay and we showed that the platelet induced artifacts could be reproduced in vitro. Surprisingly, the reproduced artifacts were also highly expressed in the platelet data but not in the other six blood components. We performed SNP calling on recovered CTC simulators, finding cross-contamination of exogenous transcripts. Furthermore, we applied a multiple linear regression model to estimate the platelet confounding index, showing that previously published CTC datasets were confounded with 13%~26% of platelet fractions and 7.9%~17% platelet cloaking judged by fluorescence flow cytometry. We devised a red blood cell lysis based protocol that better removed platelet compared to several platelet inhibitory reagents and kept transcriptomic profile undisturbed in CTC scRNA-seq. This work not only demonstrated the potential blood cell confounding factors in CTC studies, validated them in the past published datasets, and reproduced the artifacts in CTC simulation assay, but also devised an easily applicable method to remove the platelet cloaking, guaranteeing the reliability of CTC scRNA-seq research.

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UnCTC: Unbiased characterisation of single circulating tumor cell transcriptomes [scRNA-seq]

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