Optimal adjustment sets for causal query estimation in partially observed biomolecular networks.

Causal query estimation in biomolecular networks commonly selects a 'valid adjustment set', i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving 'optimal adjustment sets' that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

Magnetic resonance imaging of tumor-associated-macrophages (TAMs) with a nanoparticle contrast agent.

In the tumor micro-environment, tumor associated macrophages (TAMs) represent a predominant component of the total tumor mass, and TAMs play a complex and diverse role in cancer pathogenesis with potential for either tumor suppressive, or tumor promoting biology. Thus, understanding macrophage localization and function are essential for cancer diagnosis and treatment. Typically, tissue biopsy is used to evaluate the density and polarization of TAMs, but provides a limited "snapshot" in time of a dynamic and potentially heterogeneous tumor immune microenvironment. Imaging has the potential for three-dimensional mapping; however, there is a paucity of macrophage-targeted contrast agents to specifically detect TAM subtypes. We have previously found that sulfated-dextran coated iron oxide nanoparticles (SDIO) can target macrophage scavenger receptor A (SR-A, also known as CD204). Since CD204 (SR-A) is considered a biomarker for the M2 macrophage polarization, these SDIO might provide M2-specific imaging probes for MRI. In this work, we investigate whether SDIO can label M2-polarized cells in vitro. We evaluate the effect of degree of sulfation on uptake by primary cultured bone marrow derived macrophages (BMDM) and found that a higher degree of sulfation led to higher uptake, but there were no differences across the subtypes. Further analysis of the BMDM showed similar SR-A expression across stimulation conditions, suggesting that this classic model for macrophage subtypes may not be ideal for definitive M2 subtype marker expression, especially SR-A. We further examine the localization of SDIO in TAMs in vivo, in the mammary fat pad mouse model of breast cancer. We demonstrate that uptake by TAMs expressing SR-A scales with degree of sulfation, consistent with the in vitro studies. The TAMs demonstrate M2-like function and secrete Arg-1 but not iNOS. Uptake by these M2-like TAMs is validated by immunohistochemistry. SDIO show promise as a valuable addition to the toolkit of imaging probes targeted to different biomarkers for TAMs.

Highly Sensitive and Selective Spiropyran-Based Sensor for Copper(II) Quantification.

The metal-binding capabilities of the spiropyran family of molecular switches have been explored for several purposes from sensing to optical circuits. Metal-selective sensing has been of great interest for applications ranging from environmental assays to industrial quality control, but sensitive metal detection for field-based assays has been elusive. In this work, we demonstrate colorimetric copper sensing at low micromolar levels. Dimethylamine-functionalized spiropyran (SP1) was synthesized and its metal-sensing properties were investigated using UV-vis spectrophotometry. The formation of a metal complex between SP1 and Cu2+ was associated with a color change that can be observed by the naked eye as low as ≈6 µM and the limit of detection was found to be 0.11 µM via UV-vis spectrometry. Colorimetric data showed linearity of response in a physiologically relevant range (0-20 µM Cu2+) with high selectivity for Cu2+ ions over biologically and environmentally relevant metals such as Na+, K+, Mn2+, Ca2+, Zn2+, Co2+, Mg2+, Ni2+, Fe3+, Cd2+, and Pb2+. Since the color change accompanying SP1-Cu2+ complex formation could be detected at low micromolar concentrations, SP1 could be viable for field testing of trace Cu2+ ions.

Synthesis and Comparative Evaluation of Photoswitchable Magnetic Resonance Imaging Contrast Agents.

A series of spiropyran (SP)-based magnetic resonance imaging (MRI) contrast agents have been synthesized and evaluated for changes in relaxivity resulting from irradiation with visible light. Both electron-donating and electron-withdrawing substituents were appended to the SP ring in order to study the electronic effects on the photochromic and relaxivity properties of these photoswitchable MRI contrast agents. Photoswitches lacking an electron-withdrawing substituent isomerize readily between the merocyanine and SP forms, while the addition of a nitro group prevents this process. Complexes capable of isomerizing were demonstrated to effect a change in the relaxivity of the appended gadolinium complex.

Effect of Structure and Intramolecular Distances on Photoswitchable Magnetic Resonance Imaging Contrast Agents.

Light-activated sensors are of great interest for biological applications but are limited by the depth of penetration of light. We have been interested in transducing light activation to a magnetic signal that can be detected through noninvasive imaging by magnetic resonance imaging (MRI). We have previously developed agents incorporating spiropyran derivatives as the sensing moiety and characterized features that influence photoswitching; however, we found the MRI response to be unpredictable. In this work, we delve deeper into the potential mechanisms for the observed MRI responses in an effort to better understand the structural effects on controlling magnetic properties. A series of light-activatable MRI contrast agents were synthesized and characterized to assess the effect of spiropyran positioning on contrast agent functions and properties. These compounds are based on the same spiropyran skeleton, also named 1',3',3'-trimethyl-6-nitrospiro[chromene-2,2-indoline], which is linked with an MRI contrast agent, gadolinium-1,4,7,10-tetraazacyclododecane-1,4,7-triacetate (DO3A). We investigated the photo-to-magnetic conversion properties of these novel compounds by adjusting linker lengths over a range from three to seven methylene groups. The primary results indicated that the contrast agent with a five-carbon linker (25) showed the highest light-sensing ability after irradiation with visible light. The results will aid in the design of future spiropyran-based MRI sensors.

A novel gamma GLM approach to MRI relaxometry comparisons.

PURPOSE: To demonstrate that constant coefficient of variation (CV), but nonconstant absolute variance in MRI relaxometry (T1 , T2 , R1 , R2 ) data leads to erroneous conclusions based on standard linear models such as ordinary least squares (OLS). We propose a gamma generalized linear model identity link (GGLM-ID) framework that factors the inherent CV into parameter estimates. We first examined the effects on calculations of contrast agent relaxivity before broadening to other applications such as analysis of variance (ANOVA) and liver iron content (LIC). METHODS: Eight models including OLS and GGLM-ID were initially fit to data obtained on sulfated dextran iron oxide (SDIO) nanoparticles. Both a resampling simulation on the data as well as two separate Monte Carlo simulations (with and without concentration error) were performed to determine mean square error (MSE) and type I error rate. We then evaluated the performance of OLS/GGLM-ID on R1 repeatability and LIC data sets. RESULTS: OLS had an MSE of 4-5× that of GGLM-ID as well as a type I error rate of 20-30%, whereas GGLM-ID was near the nominal 5% level in the relaxivity study. Only OLS found statistically significant effects of MRI facility on relaxivity in an R1 repeatability study, but no significant differences were found in a resampling, whereas GGLM was more consistent. GGLM-ID was also superior to OLS for modeling LIC. CONCLUSIONS: OLS leads to erroneous conclusions when analyzing MRI relaxometry data. GGLM-ID factors in the inherent CV of an MRI experiment, leading to more reproducible conclusions.