Designing matrix models for fluorescence energy transfer between moving donors and acceptors

A recipe is given for designing theoretical models for donor-acceptor systems in which fluorescence energy transfer and motion takes place simultaneously. This recipe is based on the idea that a system exhibiting both motion and fluorescence energy transfer can be modeled by specifying a number of "states" and the rates of transitions between them. A state in this context is a set of specific coordinates and conditions that describe the system at a certain moment in time. As time goes on, the coordinates and conditions for the system change, and this evolution can be described as a series of transitions from one state to the next. The recipe is applied to a number of example systems in which the donors and/or acceptors undergo either rotational or translational motion. In each example, fluorescence intensities and anisotropies for the donor and acceptor are calculated from solutions of eigensystems. The proposed method allows for analyzing time-resolved fluorescence energy transfer data without restrictive assumptions for motional averaging regimes and the orientation factor. It is shown that the fluorescence quantities depend on the size of the motional step (i.e., on the number of states), only if fluorescence energy transfer occurs. This finding indicates that fluorescence energy transfer studies may reveal whether the dynamics of a system (e.g., a protein) is better described in terms of transitions between a relatively small number of discrete states (jumping) or a large number of dense states (diffusion)

An information theoretic approach to detecting spatially varying genes.

A key step in spatial transcriptomics is identifying genes with spatially varying expression patterns. We adopt an information theoretic perspective to this problem by equating the degree of spatial coherence with the Jensen-Shannon divergence between pairs of nearby cells and pairs of distant cells. To avoid the notoriously difficult problem of estimating information theoretic divergences, we use modern approximation techniques to implement a computationally efficient algorithm designed to scale with in situ spatial transcriptomics technologies. In addition to being highly scalable, we show that our method, which we call maximization of spatial information (Maxspin), improves accuracy across several spatial transcriptomics platforms and a variety of simulations when compared with a variety of state-of-the-art methods. To further demonstrate the method, we generated in situ spatial transcriptomics data in a renal cell carcinoma sample using the CosMx Spatial Molecular Imager and used Maxspin to reveal novel spatial patterns of tumor cell gene expression

High-plex predictive marker discovery for melanoma immunotherapy-treated patients using digital spatial profiling

Purpose: Protein expression in formalin-fixed, paraffinembedded tissue is routinely measured by IHC or quantitative fluorescence (QIF) on a handful of markers on a single section. Digital spatial profiling (DSP) allows spatially informed simultaneous assessment of multiple biomarkers. Here we demonstrate the DSP technology using a 44-plex antibody cocktail to find protein expression that could potentially be used to predict response to immune therapy in melanoma. Experimental Design: The NanoString GeoMx DSP technology is compared with automated QIF (AQUA) for immune marker compartment-specific measurement and prognostic value in non-small cell lung cancer (NSCLC). Then we use this tool to search for novel predictive markers in a cohort of 60 patients with immunotherapy-treated melanoma on a tissue microarray using a 44-plex immune marker panel measured in three compartments (macrophage, leukocyte, and melanocyte) generating 132 quantitative variables. Results: The spatially informed variable assessment by DSP validates by both regression and variable prognostication compared with QIF for stromal CD3, CD4, CD8, CD20, and PD-L1 in NSCLC. From the 132 variables, 11 and 15 immune markers were associated with prolonged progression- free survival (PFS) and overall survival (OS). Notably, we find PD-L1 expression in CD68-positive cells (macrophages) and not in tumor cells was a predictive marker for PFS, OS, and response. Conclusions: DSP technology shows high concordance with QIF and validates based on both regression and outcome assessment. Using the high-plex capacity, we found a series of expression patterns associated with outcome, including that the expression of PD-L1 in macrophages is associated with response. © 2019 American Association for Cancer Research

Multiplex Detection of Clinically Relevant Mutations in Liquid Biopsies of Cancer Patients Using a Hybridization-Based Platform

BACKGROUND: With the advent of precision oncology, liquid biopsies are quickly gaining acceptance in the clinical setting. However, in some cases, the amount of DNA isolated is insufficient for Next-Generation Sequencing (NGS) analysis. The nCounter platform could be an alternative, but it has never been explored for detection of clinically relevant alterations in fluids. METHODS: Circulating-free DNA (cfDNA) was purified from blood, cerebrospinal fluid, and ascites of patients with cancer and analyzed with the nCounter 3 D Single Nucleotide Variant (SNV) Solid Tumor Panel, which allows for detection of 97 driver mutations in 24 genes. RESULTS: Validation experiments revealed that the nCounter SNV panel could detect mutations at allelic fractions of 0.02-2% in samples with 5 pg mutant DNA/mL. In a retrospective analysis of 70 cfDNAs from patients with cancer, the panel successfully detected EGFR, KRAS, BRAF, PIK3CA, and NRAS mutations when compared with previous genotyping in the same liquid biopsies and paired tumor tissues [Cohen kappa of 0.96 (CI = 0.92-1.00) and 0.90 (CI = 0.74-1.00), respectively]. In a prospective study including 91 liquid biopsies from patients with different malignancies, 90 yielded valid results with the SNV panel and mutations in EGFR, KRAS, BRAF, PIK3CA, TP53, NFE2L2, CTNNB1, ALK, FBXW7, and PTEN were found. Finally, serial liquid biopsies from a patient with NSCLC revealed that the semiquantitative results of the mutation analysis by the SNV panel correlated with the evolution of the disease. CONCLUSIONS: The nCounter platform requires less DNA than NGS and can be employed for routine mutation testing in liquid biopsies of patients with cancer