Highly specific enrichment of rare nucleic acid fractions using Thermus thermophilus argonaute with applications in cancer diagnostics.

Detection of disease-associated, cell-free nucleic acids in body fluids enables early diagnostics, genotyping and personalized therapy, but is challenged by the low concentrations of clinically significant nucleic acids and their sequence homology with abundant wild-type nucleic acids. We describe a novel approach, dubbed NAVIGATER, for increasing the fractions of Nucleic Acids of clinical interest Via DNA-Guided Argonaute from Thermus thermophilus (TtAgo). TtAgo cleaves specifically guide-complementary DNA and RNA with single nucleotide precision, greatly increasing the fractions of rare alleles and, enhancing the sensitivity of downstream detection methods such as ddPCR, sequencing, and clamped enzymatic amplification. We demonstrated 60-fold enrichment of the cancer biomarker KRAS G12D and ∼100-fold increased sensitivity of Peptide Nucleic Acid (PNA) and Xenonucleic Acid (XNA) clamp PCR, enabling detection of low-frequency (<0.01%) mutant alleles (∼1 copy) in blood samples of pancreatic cancer patients. NAVIGATER surpasses Cas9-based assays (e.g. DASH, Depletion of Abundant Sequences by Hybridization), identifying more mutation-positive samples when combined with XNA-PCR. Moreover, TtAgo does not require targets to contain any specific protospacer-adjacent motifs (PAM); is a multi-turnover enzyme; cleaves ssDNA, dsDNA and RNA targets in a single assay; and operates at elevated temperatures, providing high selectivity and compatibility with polymerases.

An integrated enrichment system to facilitate isolation and molecular characterization of single cancer cells from whole blood.

Circulating tumor cells (CTCs) carry valuable biological information. While enumeration of CTCs in peripheral blood is an FDA-approved prognostic indicator of survival in metastatic prostate and other cancers, analysis of CTC phenotypic and genomic markers is needed to identify cancer origin and elucidate pathways that can guide therapeutic selection for personalized medicine. Given the emergence of single-cell mRNA sequencing technologies, a method is needed to isolate CTCs with high sensitivity and specificity as well as compatibility with downstream genomic analysis. Flow cytometry is a powerful tool to analyze and sort single cells, but pre-enrichment is required prior to flow sorting for efficient isolation of CTCs due to the extreme low frequency of CTCs in blood (one in billions of blood cells). While current enrichment technologies often require many steps and result in poor recovery, we demonstrate a magnetic separator and acoustic microfluidic focusing chip integrated system that enriches rare cells in-line with FACS™ (fluorescent activated cell sorting) and single-cell sequencing. This system analyzes, isolates, and index sorts single cells directly into 96-well plates containing reagents for Molecular Indexing (MI) and transcriptional profiling of single cells. With an optimized workflow using the integrated enrichment-FACS system, we performed a proof-of-concept experiment with spiked prostate cancer cells in peripheral blood and achieved: (i) a rapid one-step process to isolate rare cancer cells from lysed whole blood; (ii) an average of 92% post-enrichment cancer cell recovery (R2 = 0.9998) as compared with 55% recovery for a traditional benchtop workflow; and (iii) detection of differentially expressed genes at a single cell level that are consistent with reported cell-type dependent expression signatures for prostate cancer cells. These model system results lay the groundwork for applying our approach to human blood samples from prostate and other cancer patients, and support the enrichment-FACS system as a flexible solution for isolation and characterization of CTCs for cancer diagnosis. © 2018 International Society for Advancement of Cytometry.

Automated analysis of flow cytometry data with minimal training files: Research evaluation of an elastic image registration algorithm for TBNK, stem cell enumeration, and lymphoid screening tube assays.

Automated analysis of flow cytometry data can improve objectivity and reduce analysis time but has generally required work by software and algorithm experts. Here, we investigated the performance of BD ElastiGate™ Software (hereafter ElastiGate), which allows users to automate gating by selecting gated training files, then uses elastic image registration to gate new files. Three assays of increasing complexity were examined: TBNK, stem cell enumeration (SCE), and lymphoid screening tube (LST). For TBNK analysis, 60 peripheral blood (PB) samples from normal, HIV+, and controls were tested with ground truth analysis by an existing automated method. For SCE, 128 samples including bone marrow (BM), cord blood (CB), and apheresis were tested with analysis by multiple manual analysts. For LST, 80 PB and 28 BM samples were tested with manual analysis. For ElastiGate, a minimal number of training files was selected. Results were compared by Bland-Altman or F1 score analysis. For TBNK, ElastiGate using three training files (1 control, 1 normal, 1 HIV+) showed mean %bias across all reported populations between -1.48% and 7.13% (average 2.08%). For SCE, ElastiGate using three BM and two CB training files showed median F1 scores >0.93 in comparison to >0.94 and >0.92 for two other manual analysts. For LST, ElastiGate using four training files for each of PB and BM showed median F1 scores >0.945 for 13 of 14 PB populations and 10 of 14 BM populations, with generally similar or better performance for normal samples compared to abnormal; populations with lower scores were often associated with lower agreement between manual analysts. Based on analysis of three assays with four sample types of increasing complexity, ElastiGate with minimal training files may perform as an automated gating assistant. The results reported here are for research use only, not for use in diagnostic or therapeutic procedures.

Rare Event Phenotyping and Molecular Characterization: Circulating Tumor Cells.

Noninvasive isolation of circulating tumor cells (CTCs) from patient blood samples allows for interrogation of valuable molecular and phenotypic information useful for disease diagnosis and monitoring response to therapy. However, CTCs are extremely rare relative to red and white blood cells (R/WBC), thus making CTC isolation from unmanipulated whole blood very time-consuming. Moreover, single CTC analysis often requires hand-picking, a step that can result in more CTC loss and compromised cell integrity. Here we describe an automated flow cytometry-based approach for isolation and analysis of single, viable CTCs that combines gentle RBC lysis and magnetic, no-wash negative-depletion of WBCs, followed by a highly adaptable sorting protocol for rare cells of interest. Multiparametric flow-cytometric panels allow probing of numerous extracellular markers for immunophenotyping, while whole transcriptome analysis contributes to molecular characterization of individual CTCs. Index sorting links single CTC proteogenomics information.

Measurement and immunophenotyping of pleural fluid EpCAM-positive cells and clusters for the management of non-small cell lung cancer patients.

OBJECTIVES: A malignant pleural effusion (MPE) is a common complication in non-small cell lung cancer (NSCLC) with important staging and prognostic information. Patients with MPEs are often candidates for advanced therapies, however, the current gold standard, cytological analysis of pleural fluid samples, has limited sensitivity. We aimed to demonstrate the feasibility of non-invasive enumeration and immunophenotyping of EpCAM-positive cells in pleural fluid samples for the diagnosis of a MPE in NSCLC patients. MATERIALS AND METHODS: Pleural fluid specimens were prospectively collected from patients with NSCLC and the CellSearch® technology was utilized for the enumeration of pleural EpCAM-positive cells (PECs) and determination of PD-L1 expression on PECs from pleural fluid samples. The diagnostic performance of the enumeration of single PECs and PEC clusters was assessed using receiver operating characteristic (ROC) curves. The Kaplan-Meier method and Cox proportional hazards model was used to assess the impact of PECs and PEC clusters on overall survival (OS). RESULTS: 101 NSCLC patients were enrolled. The median number of PECs was significantly greater in the malignant (n = 84) versus non-malignant group (n = 17) (730 PECs/mL vs 1.0 PEC/mL, p < 0.001). The area under the ROC curve was 0.91. A cutoff value of 105 PECs/mL had a sensitivity and specificity of 73% and 100% for the diagnosis of a MPE, respectively. Among 69 patients with a pathology-confirmed MPE and tissue immunohistochemistry (IHC) results, 15 (22%) had greater than 50% PD-L1+ PECs. Overall concordance between tissue and PEC PD-L1 expression was 76%. Higher numbers of pleural effusion single PECs were associated with inferior overall survival (Cox adjusted HR 1.8, 95% CI: 1.02-3.05 p = 0.043). CONCLUSION: Non-invasive measurement of PECs in NSCLC patients, using an automated, clinically available approach, may improve the diagnostic accuracy of a MPE, allow for immunophenotyping of PECs, and provide prognostic information.

Flow based single cell analysis of the immune landscape distinguishes Barrett's esophagus from adjacent normal tissue.

Barrett's esophagus (BE) is metaplasia of the squamous epithelium to a specialized columnar epithelium. BE progresses through low- and high-grade dysplasia before developing into esophageal adenocarcinoma. The BE microenvironment is not well defined. We compare 12 human clinical BE and adjacent normal squamous epithelium biopsies using single cell immunophenotyping by flow cytometry. A cassette of 19 epithelial and immune cell markers was used to detect differences between cellular compartments in normal and BE tissues. We found that the BE microenvironment has an immunological landscape distinct from adjacent normal epithelium. BE has an increased percentage of epithelial cells with a concomitant decrease in the percentage of immune cells, accompanied by a shift in the immune landscape from a predominantly T cell rich microenvironment in normal tissue to a B cell rich landscape in BE tissue. Hierarchical clustering separates BE and normal samples into two discrete groups based upon our 19-marker panel, but also reveals unexpected, shared phenotypes for three patients. Our results suggest that flow based single cell analysis may have the potential for revealing clinically relevant differences between BE and normal adjacent tissue, and that surface immunophenotypes could identify specific subpopulations from dysplastic tissue for further investigation.

Correction: Flow based single cell analysis of the immune landscape distinguishes Barrett's esophagus from adjacent normal tissue.

[This corrects the article DOI: 10.18632/oncotarget.26911.].

An integrated flow cytometry-based platform for isolation and molecular characterization of circulating tumor single cells and clusters.

Comprehensive molecular analysis of rare circulating tumor cells (CTCs) and cell clusters is often hampered by low throughput and purity, as well as cell loss. To address this, we developed a fully integrated platform for flow cytometry-based isolation of CTCs and clusters from blood that can be combined with whole transcriptome analysis or targeted RNA transcript quantification. Downstream molecular signature can be linked to cell phenotype through index sorting. This newly developed platform utilizes in-line magnetic particle-based leukocyte depletion, and acoustic cell focusing and washing to achieve >98% reduction of blood cells and non-cellular debris, along with >1.5 log-fold enrichment of spiked tumor cells. We could also detect 1 spiked-in tumor cell in 1 million WBCs in 4/7 replicates. Importantly, the use of a large 200µm nozzle and low sheath pressure (3.5 psi) minimized shear forces, thereby maintaining cell viability and integrity while allowing for simultaneous recovery of single cells and clusters from blood. As proof of principle, we isolated and transcriptionally characterized 63 single CTCs from a genetically engineered pancreatic cancer mouse model (n = 12 mice) and, using index sorting, were able to identify distinct epithelial and mesenchymal sub-populations based on linked single cell protein and gene expression.